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TREES II Pre-feasibility Study 
Final Report 



Digitized by the Internet Archive 

in 2010 with funding from 

UNEP-WCIVIC, Cambridge 



http://www.archive.org/details/treesiiprefeasib93eart 






Final Report 
Contents 



Executive Summary 

1. Introduction 1 

2. The TREES I Project 2 

3. Capabilities of Remote Sensing in Monitoring Tropical Forests 4 

4. The User Community 5 

5. Analysis of User Requirements 6 

6. Implementation of Requirements through Products 7 

7. System Development and Research Requirements 10 

8. Management and Work Plan 11 

9. Recommendations 16 

Acronyms 18 



Technical Notes: 

1. Assessment of the Current TREES Project (TN-001) 

2. A Review of the Capabilities of Remote Sensing Techniques and Technologies 
used for Monitoring Tropical Forests (TN-002) 

3. Analysis of Users and Requirements (TN-003) 

4. Problems and Recommendations for Solutions (TN-004) 

5. Work Plan (TN-005) 



TREES II Pre-feasibility Study Executive Summary 

1. 

Introduction 



In early 1993, Earth Observation Sciences Ltd and the World Conservation Morutoring 
Centre were contracted by the CEC to undertake a 'TREES II Pre-feasibility Study'. 
TREES II is proposed to follow on from the current TREES I project at JRC Ispra. The TREES 
I project is investigating the potential of remote sensing in tropical forest inventory and 
monitoring. It has been underway for three years at JRC in research and development form 
and is scheduled for completion by the end of 1993. TREES II is to be a semi-operational 
system supplying useful information concerning tropical forests. 

The purpose of the EOS/WCMC study is to analyse, assess and review the current and 
potential capabilities of TREES I, collect user requirements and prepare a work plan for 
TREES II. The project has been undertaken in two stages. The first stage covered an 
assessment of TREES I and a review of current capabilities. The output from this stage was 
an interim report which was presented at a review meeting in Brussels on 19th April 1993. 
The second stage covers user requirements analysis and the development of a work plan for 
TREES II. This report contains the output from both stages of the work. Five technical notes 
have been prepared, the first two being from stage one of the work. The five are found in 
the appendices to this report. The sections of this report summarise the technical notes. 



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Executive Summary TREES II Pre-feasibility Study 



2. 
The TREES I Project 



The two major objectives of the TREES project were to address deforestation issues at a 
global scale and to develop new satellite remote sensing techiuques for the monitoring of 
forests. A 'wall-to-wall' baseline inventory has been pursued by the TREES 1 project to meet 
these goals. The production of a baseline inventory at 1km resolution and the identification, 
if not solution, of problems in its production can be considered as meeting the first goal. 
The development of new techniques is well imderway. Progress in the inventory is 
gradually establishing suitable techniques, and new methods are being developed in the 
area of segmentation of SAR data. 

TREES I rims imtil the end of 1993. It would appear that there is a good chance that the 
baseline inventory will be completed by this time, although much work remains to be done. 
The majority of the sateUite data has been collected, and about one-third of the data have 
been classified. The S.E. Asian classification is about 50% complete; about 50% of the 
African work has been completed by other sources including NASA, but needs revisiting in 
the light of the S.E. Asian work; the analysis of the South American data has not yet begun. 
The remaining two-thirds could be completed by the end of the TREES 1 project since the 
data are now available and pre-processed, and classification procedures used by the JRC 
team have been developed. 

The original time-table called for the completion of the data collection during 1991-2 and for 
analysis to proceed during 1992-3. This sUppage is due almost solely to the inability of ESA 
to supply the pre-processed data to JRC as originally required. JRC staff have therefore had 
to invest considerable time and effort in what should have been routine work, and have 
been able to spend much less time on data analysis than was intended. 

The current position of the TREES project in summary is: 

1. The collection of raw 1km AVHRR data for the production of the wall-to-wall 
inventory is largely achieved. 

2. Routes and procedures have been established by the project routinely to receive 
AVHRR data. 

3. The project team has shown that it is possible to use AVHRR data at 1 km to 
provide classification into non-forest, evergreen forest and seasonal forest types, 
at least over S.E. Asia, and probably over Africa, although actual figures for 
accuracy are not yet available. 

4. Different regiorml deforestation patterris have been identified. The spatial nature 
of these patterns is such that different techniques are required to identify 
deforestation in each region 



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TREES II Pre-feasibility Study Executive Summary 



5. The potential of TFIS, the Tropical Forest Information System, while not fully 
demonstrated, is high in two respects. Firstly that of adding ancillary data to the 
AVHRR composites to aid classification and secondly in presenting results to 
users in a comprehensible manner and with contextual information. 

6. The use of ERS-1 SAR data for global monitoring is uiJikely, but it is possible 
that it may prove useful in monitoring small selected areas. Techniques for 
segmentation and classification are imder development. 

7. The monitoring and modelling aspects of TREES require considerable further 
effort. 



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Executive Summary TREES II Pre-feasibility Study 



3. 



Capabilities of Remote Sensing in Monitoring 

Tropical Forests 



There is a considerable body of literature relating to the use of remotely sensed data for 
vegetation mapping and monitoring. There is relatively much less concerning tropical 
forestry. The data sources represented by the literature survey in Technical Note No. 2 were 
AVHRR LAC, GAC and GVl, Landsat TM, SPOT, various radar programmes and future 
sensors. These were examined in terms of data acquisition and management programmes 
and the subsequent analysis. 

Data Acquisition Progranunes 

There have been several major initiatives for acquiring and managing these types of data 
either indirectly (IGBP) or directly (FAO, NASA) for general resource management, research 
and tropical forests usage. The use of AVHRR LAC/HRPT data is demanding in terms of 
management and the problem is far from solved, although recent work on catalogue 
interoperabUity and the establishment and consolidation of HRPT ground stations in 
tropical areas gives cause for hope. Due to the spatial and temporal resolution of LAC, 
AVHRR is seen by many workers as a viable mapping and monitoring source. However, 
other groups espouse the use of Landsat images for mapping (NASA, FAO) and possibly for 
monitoring (FAO sample strategy). There is therefore no consensus on the optimxim data 
source, although there is synergy between the FAO and TREES programmes. 

Data Analysis 

Discriminants of parameters and classification of AVHRR and Landsat data is well 
established for vegetation in general in the form of the NDVl (GVl) data product. For 
tropical forests, the identification of discriminants is less weU developed and the subsequent 
classification is almost totally imdeveloped with only a handful of papers in the literature. 
Work with Radar data is somewhat separate from work with other sensors. This is partly 
because of the local scale of results and partly as a result of the different techniques required 
for information extraction (e.g. segmentation). 

In relation to TREES I, the wall-to-waU inventory cannot easily be created from techniques 
and technology existing in early 1993. In particular AVHRR data in a consistent processed 
state are not readily available for all tropical areas. Discriminants of tropical forest 
parameters for some of the many data sources which have been discussed are known and 
documented. However, classification on an operatiorml basis (which implies the ability 
systematically to extract the same classes across time and space) is in its infancy. The 
TREE S 1 project has therefore had to break a considerable amount of new ground and can 
be seen to be addressing a significant gap in capabilities. 



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TREES II Pre-feasibility Study Executive Summary 

4. 
The User Community 



The community involved in tropical forest issues is large and varied and the availabUity and 
quality of information on tropical forests is critical to their work. The research and 
information base on the state and extent of tropical forests remains generally weak, and the 
general proposition in the scientific community is that remote sensing is able to provide 
useful inputs to information needs. In order to ascertain the requirements of this 
community for such information a set of potential users for TREES II outputs has been 
identified. The user community was segmented into a set of eight groups: 

The Global Change Commvmity (A) 
Natiorial Forestry Departments (B) 
United Nations Agencies (C) 
Intergovernmental Agencies and Programmes (D) 
lnterr\ational non-Government Agencies (E) 
National Government Agencies (F) 
National non-Government Agencies (G) 
The Forest Research Commimity (H) 
Timber Traders (I) 

Each of these loser groups is characterised by assessing the activities of individual agencies 
in several senses: 

• mandate 

• funding position 

• political affiliations 

• written objectives 

• track record 

• information gathering activities 

This characterisation is a means of understanding the commvmity so that it can be 
approached and analysed for its requirements. Section 2 of Technical Note No. 3 outlines 
the forest activities and profiles of selected users. Because of the European basis to the 
TREES project this review of selected users concentrates to a certain extent on the European 
perspective on tropical deforestation. This analysis is also important in aiding CEC's 
appreciation of the political context of the various organisations. Such an appreciation is 
critical to the effort fully to utilise the technical solutioris offered by remote ser\sing to the 
information requirements. 



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Executive Summary TREES II Pre-feasibility Study 



5. 
Analysis of User Requirements 



A questionnaire was specifically designed for the study and sent to 655 potential users of 
forest information identified from the analysis of the user community described above. 

There was a relatively good response to the survey with 140 replies (21%). Using the 'user 
groups' defined above, the responses to the questionnaires were analysed. The best level of 
response came from iiitemational groups (UN, NGOs) and the research community with 
less response from national forestry departments and timber traders. 

Potential users were able to make valuable, positive comments about the future 
development of TREES. Some specified their interest in becoming involved with the project, 
mainly via data validation and exchange. It would therefore be highly advantageous to set 
up collaborative links with these users. Products outlined below, which are based on the 
results of the user survey, cotild also be tested by users and their comments integrated into 
future planning of TREES. 

A full analysis was made of the requirements for each user group. Four main conclusions 
arose in terms of information requirements arid the form of ii\formation delivery which are 
listed below: 

• high and medivim resolution information is requested more frequently than low 
resolution information 

• rational level coverage was of principal interest followed by local scale and then smaller 
scale coverage 

• yearly information updates were of particular interest with 3 and 5 year updates being 
less frequently requested 

• information concerning forest boundaries, protected areas and forest types was 
frequently requested. 



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TREES II Pre-feasibility Study Executive Summary 



6. 
Implementation of Requirements through Products 



The responses gathered by the questionnaire described above were analysed into a number 
of requirements by user group. The requirements by user group were then analysed with 
respect to the following questions: 

• Is the required information potentially extractable from remotely sensed data? 

• Can the information be presented at the required spatial scale? 

• Can the information be extracted with the required frequency? 

Thus a set of parameters required by one or more user groups were extracted. There are 24 
such information requirements. In addition, three non-technical requirements are described 
resulting from more general considerations and survey responses. All 27 requirements were 
then considered with respect to the capabilities, both current and potential, of remote 
sensing technologies. 

The capabilities of TREES technology was summarised by proposing a set of outputs or 
products which encapsulate the maximvim number of requirements in a minimiim number 
of realisable outputs. They are summarised below. In addition to the products, two reports 
have been proposed which aim to interpret and place in context the information provided 
by the products. 

Tropical Monitoring Product (TMP) 

This is a yearly, pan-tropical product based on 1 km AVHRR data and principally providing 
estimates of the forest cover of each tropical nation in a statistical framework. These area 
statistics are aimed towards monitoring trends in gross coverage in each nation. The 
estimate of forest cover is a surrogate for boundary location. While boundary location itself 
is favoured by many potential users, it is not a meaningful piece of information at medium 
and coarser resolutior\s. 

Boreal Monitoring Product (BMP) 

This is a yearly, regional product for Boreal forest regions. It is suggested that this 
principally provides an estimate of the forest cover of sub-regions of Siberia in a statistical 
framework. The data source and uses are similar to those of the TMP. 

Tropical Inventory Product (TIP) 

This is a pan-tropical product output every 3 to 5 years, based on 1 km AVHRR data and 
principally providing cartographic output showing the location, area and boundary of a 
number of forest types and relationships with other legaUy gazetted boundaries (including 
protected and reserved areas). 

Tropical Local Product (TLP) 

This is local scale product with single outputs covering small sub-national areas (eg. 10,000 
km2) It utilises the strengths of TM and SAR to provide highly detailed graphical outputs 



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Executive Summary TREES II Pre-feasibility Study 



for areas of interest. It is not designed to be pan-tropical in extent. 

Active Areas Report (AAR) 

This report details forest boundary changes detected when comparing consecutive TMP, 
BMP or TIP products. Using auxiliary data and specific change analysis algorithms, text as 
well as graphical information is output. 

Expert Analysis Report (EAR) 

This is a means of capturing the results of human interaction with the products and a range 
of other data sources (which may be more important than the products) in a mixed 
graphical and text report. 

The matrix in table 1 details the 24 requirements and shows how each is satisfied or not by 
the products. Where requirements are not satisfied, one of 3 reasons is given. 

1 Data handling constraints (especially excessive volumes of data and costs resulting from 
the use of TM) 

2 Techrucal difficulties (the information required cannot be extracted directly from remote 

sensing data) 

3 Requirements are covered by other programmes, notably the JRC FIRE project and the 
NOAAGVI product) 

The BMP product is not shown on the matrix. It satisfies the same requirements as the TMP 
and additionally the non-technical requirement (25) of covering a non-tropical area. The 
two reports (AAR and EAR) are not shown since they are derivatives of the products, and 
while they may meet additional requirements not met by the products, this is as yet 
imdetermined. The user groups satisfied by each product can be seen in the column headed 
'user groups' which refers to the user group categories (A-1) described in section 4 above. 

Of the 24 requirements, 13 can be met by the products, while 11 carmot be met. Discussion 
in Technical Note No. 4 covers the justification for meeting or not meeting each 
requirement. 



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Executi\e Summa 



ry 



Requirein'l; Ret) ui icment | 


j Requirement* Met User Requiran'ls ,Sot Mei 


Number | Description 1 Comment* 


ITMPItIP TLP Group*; l| l] ~3 




1 fine Kale, arniuaJ 
boundary- data 


Ilus requirement would need TM data ic achieve sufficieni accuiar>'. TM has a )(^ay 
repeal cj-cle, st> that obtaiiung sufficieni cloud free data could be a problem Areas 
would have io be labelled as 'unclassified' if cloud free data were unavailable. 








B.E,G, 
H 


.' 




1 




I mediujn &ca}e, annual 
boundary' data 


It would be possible lo achieve this with 1 km AX'HRR data at a mapping scale of abou 
1:1,000,000. provided that it is possible lo mark areai as 'unclassified' if cloud free data 
are not obtained d uring the year. Pro\*is)Qr. of area! statisbct is feasible 


(V 






B.D.E 
G 










coarse scale, 3 yearly 
boundary' data 


This IS certainly possible with 1 km AVHRR data, and cloud free data should be 
available during this time frair^ 




V 




.^C 








< 


medium scale, 3 yearly 
boundary- data 


The 3-year time period would mean that greater atientjon could be paid lo the accuracy 
of the classification than for Requirement 1 so that the results would be more 
meaningful. While use of TM data would be desirable, 1 km AVHRR data would be 
adequate. 


V 




B.D.F 










fine scale, 3 yearly 
boundary data 


As with requirement 1, this would require TM data with correspondingly high data 
volumes and costs. The 3-year time-frame would however reduce these considerably 
from those of requirement 1. 






v 


B 








€ 


medium scale, annual fire 
data 


This is provided by ano^er prograxnji^ 








D.G.E 






V 


7 


medium scale, annually, 
roads 


This would require TM data txcept in areas with clearly delineated roads where 1 km 
AVHRR data might be adequate. 








C,E- 




v"~ 




6 


fine scale, eve^y 3 yean, 
protected area monitoring 


Td derive these data would require the use of TM. possibly si^leir^nted by airborne 
data (both optical and SAR). This would be possible, although a five-year repeat period 
migjit be more realistic. 






V 


B,CJ34 
,GJ^ 






9 


medium scale, every 3 
yezji, protected area 
monitoring 


This would require 1 tan AVHRR data at a minimum 




V 












10 


fine scale, annually, 
protected area monitoring 


This would require TM data on an annual basis with correspondingly hj^ dau 
volumes ai\d costs. 








.CM 


•V 






11 


medium scale, annually, 
protected area monitoring 


This would require 1 km AVHRR data at a minimum. 


V 






B,CD4 
.CM 








12 


fine scale, every 3 years or 
more,biomass 


At the required scale of less than 1:100,000 it would be necessary' to use TM data, or 
even airborne data. It would be difiicult to cover ti^e whole tropical area at this scale; 
but it should be feasible to provide data for selected areas. 






V 


AJ 








13 


medium scale, every 3 
years, biomass 


This is provided by another programme. 








AS 






■J 


14 


fine scale; annually, 
Momass 


Similar comments apply as for Requirement IZ To achieve this annua&y wou]d be an 
extremely heavy processing load. 








AJ= 


V 






15 


Pine scale, annually, forest 
types 


TM data would be required, probably supplemented by airborne SAR and airborne 
optical data. The data volume and costs would be very high. 








AH 


V 






16 


medium scale, annually, 
forest types 


See discussion for requirement IS. 








AH 


V 






17 


4ne scale every 3 years, 
'crest types 


This would be possible using TM data, but possibly only for selected areas (see 
discussion on protected areas). 






V 


All 








16 


medium scale every 3 
years, forest types 


Provisian of ftiese data would be achievable wift 1 km AVHRR. 




yl 




AH 








19 


ine scale, every 3 years or 
more, timber volume 


Provided fte relationships exist as discussed above in Section 2.3 (TN-04) this would be 
achievable using TM data. It would be difficult to cover ihe whole tropical area at this 
scale, but data could be provided for selected areas. 






</ 


CD 








20 


medium scale, every 3 
fehn or more, timber 
raliime 


1 km AVHRR data would be a minimum requirenttnt 




J 




CD 








21 


ine scale, annually, timber 
kfolume 


"yui would require TM data with corre^onding high data volumes and costs, see also 
•omments for requirement 19. 








CD 1 








22 


nedium scale, annually, 
iirtoer volume 


nils would be possible using 1 tan AVHRR data, but see comments under requirement ■< 
9. 


i 




( 


CD 








23 


ine scale, every 3 years, 
Jiodtversi^ I 


"M or SPOT data supplemented by airborne optical arwl SAR data would be required, 
providing the restrictions outlined in Secticn 13 (TN-04) are overcome. 






i 
i 


.cx>. 

i 


> 






24 I 


nedium, every 3 years, 
riodtversity c 


fM data should provide adequate spatial resolution providing ttut the restrictions 
mtlined in Section 2J (TN-W) arc overcome. 






I 


,CD, 

i 


> 







Table 1 Summary of Requirements and Recommendations 



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ExecuHve Summary TREES II Pre-feasibility Study 



7. 

System Development and Research Requirements 



The preceding analysis of users and requirements concluded by outlining a set of products 
and reports as a focus for TREES II development. In order to produce these outputs, a 
manageable system must be developed. Elements of this systems were of course developed 
during TREES 1. However, there are several new aspects which should be introduced into 
TREES II. 

The first of these is a more rigorous engineering approach towards the specification and 
development of the various components. The components identified by the study corisist of 
software, data, hiiman interaction and various institutional considerations. In order to 
develop these components into a semi-operational system, there is a clear need for a number 
of trade-off studies between optior^s as well a detailed requirements specification for new 
software plus possible re-engineering of prototype software developed in TREES I. Two 
components not represented in TREES I are required; namely a data management 
component and a user component. The first can be developed from current prototype 
systems in the JRC MTV unit (IBIS) or via the CEO, while the second encapsulates the 
requirement for ongoing interaction with the user commimity. Included here are a number 
of suggestions for the dissemination of products and means of continuing to develop an 
understanding of the users and their requirements. The latter point is critical to maintaining 
as dear direction for TREES II. 

Probably the greatest initial challenge to TREES 11 is the component covering the 
operationalisation of the supply of AVHRR data to the project. This task requires 
considerable effort to tap the appropriate data source and to prepare correctly the raw data 
for classification. The route used during TREES I was direct contact by the team with HRPT 
stations supplying raw data which was processed at JRC. For a number of reasons, this task 
is probably better now done by ingesting data from the IGBP project via ESA. Pre- 
processing requires specification so that appropriate software can be developed and 
applied. Some TREES 1 data may require reprocessing. Several trade-off studies are 
required before a firm commitment is made. 

Two components given particular attention during TREES I will continue to be the focus of 
activity during TREES II. These are the manipulation and classification of images and the 
extraction of information products from the images. The latter component was set up 
during TREES I as TFIS. Apart from further software development, several research topics 
are directed at this component. These concentrate on determining the accuracy of the 
information derived from classified images and various auxiliary data. The work on images 
drives a number of further research topics, particularly the classification techniques and the 
selection of training sites. Eleven research topics are identified in all, of which eight are seen 
as critical in terms of the need to address and solve the issues whUe the remainder should be 
addressed but are not critical. There is some dependence here on the funding scenarios; in 
that the size of TREES II will allow or not the non<ritical topics to be addressed. The 
research topics are: 



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TREES II Pre-feasibility Study Executive Summary 



Research topic 

Decision on the classes to be contained in TIP 

Derivation of classification technique and rules for TMP 

Derivation of classification techniques and rules for TIP 

Derivation of classification techniques and rules for BMP 

Selection and use of training and test sites for all products 

Methods for improving registration accuracy between images, and between 

image and coastline /national boundary data 

Determination of appropriate projections for production of areal statistics 

Methods for determining accuracy and precision of areal statistics 

Methods for determining accuracy of boundary locations 

Methods for determining change detection 

Methods for segmenting SAR data 



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Executive Summary TREES II Pre-feasibility Study 

8. 
Management and Work Plan 



The original TREES project focused strongly on research. Consequently the techniques and 
methodologies required were not apparent at the beginning of the project. It was therefore 
necessary to approach the problem by allowing scientists freedom to devise creative 
solutior\s to the various problems involved. With so many uncertainties in both approach 
and anticipated results a flexible management structure was employed. During TREES II, 
the nature of the project will change, moving away from research and toward operational 
product generation, though there will always be strong elements of research to support the 
operations. This change in focus must be accompanied by a change in management 
structiires which will continue to encourage innovative research while supporting and 
controlling the operational aspects of the project. The key areas for attention in terms of 
project management and team development are planning and control, scheduling and 
deliverables, and human resources. 

Planning and Control 

A planning and reporting cycle should be maintained which allows the project leader to 
maintain control of the direction of the project without constraining the scientific innovation 
of the project staff. The cycle should begin with the generation of a project plan by the 
project team Monitoring of progress against the plan should be undertaken, preferably on a 
monthly basis. 

Scheduling and Deliverables 

A schedule for the accomplishment of key tasks in the project is proposed below, with the 
associated deliverables at each stage. The setting of milestones and deliverables within the 
project schedvile fulfils a number of purposes of value both to the external perception of the 
project, and the internal planning. Many of the deliverables are demonstration or interim 
products, which demonstrate the progress of the project to the outside world. The 
remaining deliverables are research reports which are either necessary for the successful 
undertaking of other project tasks, or provide for the dissemination of project results or 
research findings to the wider scientific community. 

The value of these self-imposed deadlines for the production of research reports is in 
maximising the personal research productivity of each of the team members. It is proposed 
that each of the research reports could become self-standing papers in appropriate journals. 
This will raise the profile of the staff involved, enhancing their research careers, and 
maintaining maximum motivation through the project. 

Human Resources 

The success of the TREES 1 project was partly inhibited by the difficulty and slowness of 
recruiting permanent scientific staff through The Commission. This made the planning of 
staff resources difficult, and left shortages in manpower which caused bottlenecks in the 
project process leading to unrealistic demands on the available staff. 



12 EOS-93/090-RP-O02 



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The success of the TREES II project, is criticaUy dependant upon the timely availability and 
motivation of staff with the correct experience and expertise. Accepting that delays and 
difficulties in recruiting high calibre staff are inevitable, it is necessary to pay close attention 
to staff planning to ensure that the key posts are filled by permanent staff (research) and 
contractors (operational tasks). 

It is proposed that a proportion of the posts to be filled in order to execute the TREES 11 
project be defined as the "core team", and be assigned to permanent members of staff. The 
core team should comprise 30-50% of the total team and be assigned roles of key scientific or 
organisatior\al importance. Many of the key roles require less than a full-time person, and 
so could be combined within one post. The key roles are Project Leader, Project 
Administrator, Science and Research leader. Data Gather Leader, Data Manager, Software 
and Systems Engineer, External Relations Coordinator and Project Librarian. 



Work Plan 

The work plan is presented as three scenarios. These scenarios are based on three cost levels 
to the CEC; namely one, two and three million ecu per year. Within each scenario, different 
functionality and levels of output are achievable by the TREES II project. 

Full Ftmctionality Scenario 

The full functionality scenario provides for all research topics to be investigated and 
reported and several products of each type to be produced. The cost table (Table 2) gives an 
estimate for the size of team and other costs incurred to cover the work and the timeline for 
the various elements is shown in Schedule 1. 

The discussion of management requirements indicates the need for a 
management /administration team of eight staff and a research and operations team of 
seven staff. One of the operators could work at ESA/ESRIN where pre-processing might be 
sited. The second operator will become important later during the project as image 
classification becomes more routine. The cost of airborne campaigns is speculative in the 
sense that information is not easily available and the need for TREES 11 to fund such 
campaigns is undecided (collaboration with other programmes may provide input). The 
expert reports are related to research topics where the TREES team requires input from 
outside consultants. This occurred in TREES I and it is wise to continue to take such advice. 
The conferences and travel expenses are driven by the need to consult widely with other 
projects and to develop user contacts. 

Reduced Functionality Scenario 

In this option the management/administration team has been reduced by combining roles 
and the researcPi and operatiorxs team reduced. The BMP product has been removed along 
with the research and data collection required for its production. The number of TMP and 
TIP products is also reduced, along with field and airborne campaigns. Other costs reduce 
to match the team size and needs for software development. There is of course a major 
political disadvantage in this option in that monitoring of a non-tropical area is a stated user 
requirement (see Technical Note No. 4, Section 3). The possibility of extending TREES 
beyond the tropical scope is considerably reduced without this preliminary 'pathfinder' 
effort and once TREES III is operational it becomes much more difficult to add functior\ality. 



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Executive Summary TREES II Pre-feasibility Study 



Basic Functionality Scenario 

In the basic functionality scenario, the TLP product is removed altogether reducing data, 
research and several other costs substantially. In particular, airborne SAR campaigns and 
SAR research would not be required. There is a major disadvantage in this option is in that 
several important requirements are not met. The TLP products are likely to be highly 
valued by users who are substantially in favour of obtaining information at fine scales. 
They also provide information which can be used in production of the coarser scale 
products, which if lacking could put these products at risk. 





Full 


Reduced 


Basic 


Item 


Scenario 


Scenario 


Scenario 




1 


2 


3 


Management and Administration 


2400 


1860 


960 


Research and Operations 


1490 


990 


530 


Data Acquisition/Processing 


690 


480 


270 


Hardware and Software 


350 


200 


100 


Field Campaigns 


300 


150 


150 


Airborne Campaigns 


1000 


500 




Expert Studies 


300 


300 


150 


Software Developments 


500 


400 


310 


Travel/Meetings and Materials 


430 


420 


295 


TOTAL (Kecu) 


7500 


5300 


2800 



Table 2 Summary Scenario Costs 



14 EOS-93/090-RP-002 



TREES II Pre-feasibility Study 



Executive Summar)' 







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Schedule 1 Summaiy for Three Scenarios 



EOS-93/090-RP-002 



15 



Executive Summary TREES II Pre-feasibility Study 

9. 
Recommendations 



In starting TREES II, a major objective is to move from a phase v^hich has been predominantly 
research-oriented to one w^hich is pre- operational. The system level recommendations which 
seek to drive this move are: 

1 Development and specification of a set of outputs or products which meet user 
requirements. 

This task has been started in this report by specifying a set of products. These are not full 
specifications and a number of questions are outstanding which should be answered during ' 
TREES II. It may become necessary significantly to alter product specificatioris, delete products 
altogether or add new ones. The important points are that a clear link exists between user 
requirements and the output from TREES, and that the output from TREES II can be 
characterised. 

2 To engineer a set of system components which will enable products to be 
output 

The system outlined in the work plan is derived from elements many of which were developed 
during TREES I. Additional components have been added in order to create a system which 
leads towards operationalisation. 

3 To study options and determine the best means of providing a routine supply 
of processed 1 km data to the project 

The ingestion of these data during TREES 1 was a major operation which must be contained and 
if at aU possible removed from the JRC team. This will enable the TREES II team to concentrate < 
on the specialist areas of research and product development. 

TREES I demonstrated that valuable information can be derived from the data sources used. 
However, a nimiber of areas remain outstanding where additional research is required: 

4 A set of critical research topics must be addressed 

5 A set of desirable research topics should be addressed 



16 EOS-93/090-RP-002 



TREES II Pre-feasibility Study Executive Summary 

The management and team development aspects are critical: 

6 Clearly identify roles and responsibilities for all team members including 
special positions such as data management, data gathering, systems 
development, science leader etc.. 

The necessity to meet schedules and deliver value for money means that responsibilities must 
be dearly understood at technical and non-technical levels. This is not easy in an environment 
of permanent and contracted staff. 

7 To involve a mix of people with skills in research, operation, engineering, 
management, negotiation and liaison. 

The user component of the system discussion emphasised the need to communicate v^ith users. 
The contact v^ith potential users reported in this study must not be seen as a one-off. It is vital 
to develop a clear imderstanding of the user commimity and its requirements. 

8 Establish links with users, distribute products and monitor feedback. 



Lastly, it is necessary to estimate the financial cost of the TREES II project. There are 
however a large number of research topics and trade-off studies involved in its 
development. This means that the final form of a system in terms of necessary functior^ality 
cannot be determined at this stage. In turn this means that the costs of development, 
research, software and data acquisition can be estimated only very generally. 

However, the discussions and recommendations above have been brought together into the 
the three optior\s discussed in the section above. It is not possible here to recommend which 
of these options is taken. It is a matter for CEC to determine the value of meeting or at least 
probably meeting the requirements in each option. 

In summary, it can be said that with the full functionality scenario, a TREES II project 
following the suggestions and recommendations in the technical note will almost certainly 
be able to meet around half of the requirements identified in Technical Note No. 4. Thus 
TREES will be able to make a significant contribution to the management of tropical forests 
and associated activities. In the reduced functionality scermrio the boreal monitoring 
requirement is not met and research and products generation elements are much reduced. 
Boreal monitoring is a non-technical requirement whose value must be weighed by the CEC. 
In the basic functionality scenario a significant number of requirements are not met, 
especially those associated with SAR. 

In conclusion it can be said that TREES II presents a valuable opportimity to utilise remote 
sensing technology in tropical forest monitoring. With clear reqvurements, the project can 
make a considerable contribution to the knowledge and imderstanding of this vital issue of 
global significance. 



EOS-93/090-RP-(X)2 17 



Executive Summary 



TREES II Pre-feasibility Study 



Acronyms 



AAR Active Areas Report 

AATSR Advanced Along Track Scanning Radiometer 

ACP African, Caribbean and Pacific 

AML Arc Macro Language 

ATSR Along Track Scanning Radiometer 

AVHRR Advanced Very High Resolution Radiometer 

CDC Conservation Data Centre 

CEC Commission of the Eixropean Communities 

CEO Centre for Earth Observation 

CEOS Committee on Earth Observing Satellites 

CGIAR Consultative Group on International Agricultural Research 

CIESIN Consortium for International Earth Science Information Network 

CIFOR Centre for International Forestry Research 

CIRAD Centre de cooperation intemationale en recherche agronomique pour le 

developement (formerly knovvTi as CTFT) 

CORINE Coordination of Information on the Environment 

CPD Centres of Plant Diversity 

CSIRO Central Scientific & Industrial Research Organisation 

DAAC Distributed Active Archive Center 

DCW Digital Chart of the World 

DEM Digital Elevation Model 

DGL Data Gather Leader 

DIS Data and Information System 

EAR Expert Assessment Report 

EARSEC European Airborne Radar Remote Sensing Capability 

EBA Endemic Bird Area 

EC European Community 

ENVISAT Environmental Satellite 

EPA Environmental Protection Agency 

EOS Earth Observation Sciences (UK) 

EOS Earth Observing System (US) 

EROS Earth Resources Observation System (of U.S. Dept. of Interior) 

EROT European Communities' Regional Fund 

ERS-1 European Remote sensing Satellite 

ESA European Space Agency 

ETFRN European Tropical Forest Research Network 

FAO Food and Agriculture Orgaiusation 

FNOC Federal National Oceanographic Center 

FOE Friends of the Earth 

FRA Forest Resources Assessment 

GAG Global Area Coverage (AVHRR 4km daily data) 

GAIM Global Analysis, Interpretation and Modelling 

GCTE Global Change and Terrestrial Ecosystems 

GEMS Global Environment Monitoring System 

GHz Gigahertz 



18 



EOS-93/090-RP-002 



TREES II Pre-feasibility Study 



Executive Summary 



GIMMS Global Inventory Monitoring and Modelling System 

GIS Geographic Information System 

GOES Geostationary Orbiting Environmental Satellite 

GRID Global Resource Information Database 

GSCF Goddard Space Flight Center 

GVI Global Vegetation Index 

G7 Group of 7 leading industrial countries 

HTFI Humid Tropics Forest Inventory 

HRPT High Resolution Picture Transmission 

HRV High Resolution Visible 

IBIS Image Browse and Information System 

ICBP Birdlife International (formerly International council for Bird Preservation) 

ICSU International Council of Scientific Unions 

IGBP International Geosphere / Biosphere Programme 

IGBP-DIS IGBP Data and Information System 

nED International Institute for Environmental Development 

ILWIS Integrated Land and Water Information System 

INPE Institute Nacional de Pesquisas Espacias 

IPCC International Panel on Climate Change 

IRSA Institute for Remote Sensing Applications 

ISY International Space Year 

rrC International Institute for Aerospace Survey & Earth Sciences 

ITTA International Tropical Timber Agreement 

iriO International Tropical Timber Organisation 

lUCN International Union for Conservation of Nature and Natural Resources 

lUFRO International Union of Forestry Research Organisatior\s 

JERS-1 Japanese Environment Resources Satellite 

JRC Joint Research Centre 

LAC Local Area Coverage (AVHRR 1km data) 

MAB UNESCO Man and the Biosphere Programme 

MARS Monitoring Agriculture using Remote Sensing 

MERIS The MEdium Resolution Imaging Spectrometer 

MODIS The MOderate Resolution Imaging Spectrometer 

MTV Monitoring Tropical Vegetation Unit ORC) 

MSS MultiSpectral Scanner 

NASA National Aeronautics and Space Administration 

NASDA National Space Development Agency of Japan 

NDVI Normalised Difference Vegetation Index 

NESDIS NASA Earth Sciences Data and Information System 

NGDC National Geophysical Data Center 

NGO Non-Governmental Organisation 

NIR Near Infra-red 

NOAA National Oceanic and Atmospheric Administration 

OO Object Oriented 

PAF Processing and Archiving Facility 

RAMP Radar Mapping of Panama 

SAR Synthetic Aperture Radar 

SAREX South American Radar Experiment 

SASIFY Space Agency Forum on the International Space Year 

SHARK Station HPJ'T Archiving & Reprocessing Kernel 

SHARP Standard HRPT Archive Request Product 

SIR Shuttle Imaging Radar 



EOS-93/090-RP-002 



19 



Executive Summary 



TREES n Pre-feasibility Study 



SLAR Side Looking Airborne Radar 

SPACE Software for Pre-processing AHVRR data for the Communities of Europe 

SPANS Spatial Analysis System 

SPOT Systeme Pour I'Observation de la Terre 

TFAP Tropical Forest Action Plan 

TFIS Tropical Forest Information System 

TIP Tropical Inventory Product 

TLP Tropical Local Product 

TM Thematic Mapper 

TMP Tropical Monitoring Product 

TREES Tropical Ecosystem Environment observation by Satellites 

TREIS Tropical Radar Environment Information System 

UN United Nations 

UNCED United Nations Conference on Environment and Development 

UNESCO United Nations Education and Science Organisation 

UNEP United Nations Environment Programme 

WCMC World Conservation Monitoring Centre 

WDC World Data Center 



20 



EOS-93/090-RP-002 



TREES II Pre-feasibility Study Assessment of the Current TREES Project 



Technical Note No. 1 



Assessment of the Current TREES Project 



EOS-93/090-TN-OOl 



Assessment of the Current TREES Project TREES II Pre-feasibility Study 



Contents 



1. Introduction and Discussion 1 

1.1 Overall Objectives for Tropical Forest Monitoring 1 

1.2 Overall Objectives of TREES in Relation to the 3 
Key Issues of Tropical Forest Assessment 

1.3 TREES I Technical Objectives 4 

2. Tropical Forest Baseline Inventory 6 

2.1 Data Collection 6 

2.2 Data Pre-processing 7 

2.3 Data Analysis 9 

2.4 Validation 11 

2.5 The Tropical Forest Information System 11 

2.6 Research Developments 12 

2.7 Other Issues 

3. Monitoring Active Deforestation 14 

3.1 Identification of Active Areas 14 

3.2 Monitoring Active Areas 15 

4. Modelling Tropical Deforestation Dynamics 16 

5. SAR ERS-1 Data Collection and Analysis 17 

6. Conclusions 19 
References 



EOS-93/090-TN-(X)l 



TREES II Pre-feasibility Study Assessment of the Current TREES Project 



1. 

Introduction and Discussion 



The purpose of this report is to describe the current status of the TREES I project arising 
from a visit by the TREES II Pre-feasibility Study project team to JRC, Ispra in March 1993. 
It is intended to be an assessment of the project agaiast its stated objectives and not a critical 
study. 

This chapter introduces the background to the assessment with an overview of the more 
general objectives for tropical forest monitoring within which context the TREES project is 
set. 



1.1 Overall Objectives for Tropical Forest Monitoring 

The key issues in tropical forest monitoring can be reduced to four interlinked questions, 
what to assess, how to assess, why assess and for whom to assess. These questions provide 
a background in which the TREES project objectives can be addressed, and the extent to 
which they have been met can be assessed. 

1.1.1 What to Assess 

Is tropical forest changing in quantity (extent) or quality, or both? To assess the extent of 
tropical forest requires that it be defined. Closed forest can be distinguished from open 
woodland on satellite imagery (e.g. the FAO 1980 assessment), but problems in defining die 
position of the boundary arise where the forest is thinned out by selecttve tree removal or 
broken into a mosaic by patches of farmland - such problems get worse as resolution of 
study increases. In those places where forest runs continuously from the tropics to the non 
tropics (central Africa, South America, IndoChina, AustraUa) there is no sharp ecological 
boimdary, but a gradual transition to subtropical and then temperate forest. For this 
transition the Tropics of Cancer and Capricorn provide logical limits for h-opical forest. 

Within tropical forests there are many different forest formations defined on structure and 
physiognomy. These occupy distinct habitats and are sharply boimded where the habitats 
are, for example, peat swamp forest, forests on limestone, or upper montane rain forest. 
However, some transitions are gradual, e.g. between lower montane and upper montane 
rain forest on wet tropical moimtains. Sometimes two formations exist ir\ a mosaic at their 
boundary, for example evergreen rain forest extends into semi-evergreen rain forest on 
well-watered or fertile soils, and a similar mosaic occurs between semi-evergreen and 
deciduous forest formations. These boimdaries and mosaics are usually complicated by 
human intervention. 

It has not so far proved possible to detect from satellite images these different forest 
formations, despite much effort. In recent years radiometric boundaries have been 



EOS-93/090-TN-OOl 



Assessment of the Current TKEES Project TREES II Pre-feasibility Study 



discovered, e.g. in southern Yunnan and Africa, however the biological significance of this 
has not yet been ascertained. Seasonal changes can be detected and used to map semi- 
evergreen versus evergreen forests. Differences between years have been detected m 
evergreen forest, but its biological meaning is not understood. 

Tropical rain forest which is logged for timber loses some of its big trees, so the canopy top 
becomes very uneven. The forest is further broken up by tractor tracks and lorry roads. 
However, from a low-flying aeroplane it is difficult, within a year or two, to detect that 
logging has taken place, except for the presence of the lorry roads. If not used, these too wiU 
become re-vegetated within a few years. The amount of timber removed per hectare (and 
hence the amount of disturbance created) varies from 5-10 cum (cubic metres) to 100-200 
cum in parts of Sabah, with 20-30 cum a common figure. The number of trees represented 
by these volumes clearly depends on their size, the range is about 1-10 per hectare. It is not 
easy for all these various reasons to distinguish logged from pristine forest. A decision must 
be made on the scales and frequencies (e.g. annual seasons) of assessment. 

1.1.2 How to Assess 

Ground survey, aerial photography and satellite imagery all have roles to play. Satellite 
imagery is available at various scales, and are able to discriminate different environmental 
parameters. Radar imagery penetrates the cloud which commonly lies over much of the 
world's rain forests. Amongst the satellite images low resolution (1 km) AVHRR is repeated 
daily. Landsat and SPOT have higher resolution (10-30 m) but are repeated less frequently 
(typically 20-30 days in fixed configuration). Operating in the visible part of the spectrum, 
cloud is a serious problem. A more detailed analysis of current capabilities for the remote 
sensing of tropical forests is presented in Technical Note No. 2. 

1.1.3 Why Assess 

There are various reasons why tropical forests should be assessed: 

• Tropical deforestation is a matter of widespread concern. Agenda 21 of the 1992 
UNCED Rio meeting set out the aim to have an operational decentralised global 
monitoring programme working in ten years. 

• Forest quality is of interest - but the definition of measures of quality present 
problems. Also its observation from satellites has constraints, in that it has 
proved impossible so far to recognise the different tropical rain forest formations 
recognised by ecologists, and as mentioned above forest alteration by logging is a 
complex phenomenon, with the forest subsequently and progressively growing 
back to something dose to its original state. 

1.1.4 For Whom to Assess 

The three groups of questions what, how and why to assess wUl have different answers for 
different users. Various groups can be identified: 

• global change community 

• government agencies of tropical countries 

• United Nations agencies 

• intergovernmental agencies and programjnes 

• international non-governmental organisations 



EOS-93/090-TN-OOl 



TREES II Pre-feasibility Study Assessment of the Current TREES Project 



• governments of non-tropical forest countries 

• national non-governmental agencies 

• forest research organisations 

• timber traders. 

These are further elaborated in Technical Note No. 3. 



1.2 Overall Objectives of TREES in Relation to the Key Issues of Tropical 
Forest Assessment 

With the overall objectives established, and the key issues described the objectives of the 
TREES project itself may be addressed. 

One objective of TREES II is pan-tropical monitoring of the extent of tropical forests. This is 
currently based on low resolution (1 km) AVHRR data for which collection has been 
arranged. Steps are in hand to have preprocessing (e.g. geometric and atmospheric 
correction) done at a number of centres aroimd the tropics. AVHRR images are obtained 
daily so the capacity exists for continual monitoring. No other body is making such a pan- 
tropical survey. UNCED Agenda 21 requires such a survey and there are other users with 
pan-tropical or regional interests. AVHRR imagery may be replaced by superior products 
from a French SPOT satellite in a year or two, making frequent images at the same low 
resolution, but (imlike AVHRR) designed explicitly for vegetation survey, and able to store 
data on board so that it can be downloaded for the whole of the tropics at a single station, 
e.g. Kiruna. TREES needs to be designed with the flexibility to keep up with this and other 
advances in technology. It would be useful for TREES to be consulted over the design of 
what this satellite system aims to deliver. This draws attention to the fact that it is the 
information system that is different about TREES - i.e. the determination to combine and 
analyse data fi-om a range of sources, and disseminate it to many users. 

The concern of UNCED with forests was not confined to the tropics. A case can be made for 
global forest monitoring as opposed to tropical monitoring solely. 

TREES also aims to use high resolution satellite images to monitor locations of active 
deforestation more closely, using Landsat, SPOT or ERS-1 SAR images. This presents 
several problems: 

• The procedures for collection and handling the data are different at different 
resolutions. 

• For whom is such monitoring being conducted? The precise and frequent 
monitoring of forest extent and forest degradation of an individual nation is a 
politically sensitive issue, exacerbated if loci of active deforestation are referred 
to by the emotive Tiot spot' and the procedures for their identification are 
referred to as an 'alarm system', when in practice the activity might in fact be 
part of a land-use management plan. However, frequent monitoring at a scale 
that AVHRR imagery is able to deliver, may be of great advantage to the national 
user by indicating unexpected change in forest cover (provided the forest loss is 
large enough). This loss may be imperceptible from less frequently gathered 
higher resolution imagery such as Landsat or SPOT. 



EOS-93/090-TN-OOl 



Assessment of the Current TREES Project TREES II Pre-feasibility Study 



• The problems of defining forest boundaries become more difficult as the 
resolution increases. 

• The necessity to define and measure degraded forest becomes more important at 
high resolution. 

• The identification of loci of active deforestation needs to be done objectively, 
based on explicitly stated criteria. There are various approaches. These are 
discussed in more detail in Section 3.1. 

• The final difficulty with high resolution detailed descriptions of loci of active 
deforestation is what one does with this politically sensitive information once it 
has been collected. How does one translate information into action? Who does 
this? Nations are sovereign states and will take umbrage at data perceived to 
criticise their freedom of action unless it is very carefully targeted and presented. 
One general issue of tropical forest assessment is whether data are collected and 
armlysed at regional stations or a single station. This issue has both practical and 
poUtical aspects. For low resolution imagery AVHRR, and even more so the 
French data which may soon supplement it, there is a good case for a single 
global station. For high resolution imagery, which is more poUtically sensitive, 
natiorml stations may be preferable. 

TREES needs to be designed in relation to other projects monitoring tropical forests. There 
is no one else aiming at pan-tropical coverage at 1 km resolution which has the potential to 
be repeated frequently. NASA Pathfinder has higher resolution, FAO aims to have higher 
resolution still, based on individual nations. The three projects are thus complementary. 
They need to maintain a dialogue. FAO hopes to get funds from CEC as does the French 
vegetation satellite project that would produce data similar to AVHRR. This gives TREES 
the chance to influence these two projects. It wiU also be very important to keep in touch 
with the Japanese monitoring project as it develops. 



1,3 TREES I Technical Objectives 

This section deals with more specific technical objectives of the TREES project, and sets out 
those specific, written objectives for the project against which the assessment in this report is 
made. In order to achieve the overall objectives, the project was grouped into three general 
items, these are described in detail in TREES Series A: Technical Document No 1, Part 1. The 
first item is the production of a global tropical forest baseline inventory, a 'wall-to-wall' 
coverage, using AVHRR 1 km data as the main data source, supplemented by ERS-1 SAR, 
SPOT and Landsat TM or MSS data where appropriate. Following from this are two 
additional items: the detection and monitoring of active deforestation areas, and the 
modelling of tropical deforestation dynamics. 

The production of the baseline inventory was subdivided into six fundamental activities: 

• data collection 

• data pre-processing 

• data analysis 

• validation 



EOS-93/090-TN-OOl 



TREES II Pre-feasibility Study Assessment of the Current TREES Project 



• development of a Tropical Forest Information System (TFIS) 

• research developments. 

The item to monitor areas of active deforestation was also subdivided into several activities: 

• development of monitoring procedtires 

• identification of active deforestation areas 

• monitoring of these areas via high resolution satellite data 

• development of an 'alarm system'. 

This document has been sub-divided using the the Item and Activity headings outlined 
above (where work has been performed), since these formed the basis of the work activity as 
originally specified. In the following chapters each activity is examined separately. The 
current status of the work is described and related to the stated objectives. 

The use of ERS-1 SAR data is a further important component of the TREES I project. Since 
these data have only very recently become available and methods of data analysis are still 
being actively researched, their use can be considered separately from the thrust of the 
production of the baseline inventory, which relies on established data sources and 
processing techniques. The use of ERS-1 SAR data for tropical forest monitoring is therefore 
described in a separate section. 



EOS-93/090-TN-OOl 



Assessment of the Current TREES Project TREES II Pre-feasibility Study 



2. 
The Tropical Forest Baseline Inventory 

The production of the baseline inventory was subdivided into six fundamental activities: 

• data collection 

• data pre-processing 

• data analysis 

• validation 

• development of a Tropical Forest Information System (TFIS) 

• research developments. 

In addition, during the study a number of issues of a more administrative nature were 
noted, which are outlined at the end of this section. 

2.1 Data Collection 

The data involved in the programme is: 

• AVHRR data - low resolution 

• high resolution data - Landsat TM and MSS, SPOT and ERS-1 SAR. 



2.1.1 AVHRR Data 

The origii\al intention of the TREES project was that the 1 km AVHRR data (LAC) would be 
provided by ESA using their existing receiving station network and technology, kideed this 
was a major rationale for joint project fimding by CEC and ESA. However, it became dear 
at an early stage that ESA were imable to collect the required data routinely and that no 
money could be specifically allocated for data collection using a non-European serisor. 
Considerable time and effort has therefore had to be spent by TREES persormel in routine 
data collection. Data are obtained both directly by JRC personnel, and by contractors 
identified by JRC. 

There is a number of specific data requirements: 

• low cloud cover over the area of interest 

• the area of interest must be as close to nadir as possible in the image swath 

• aU five channels should preferably be available in 10-bit resolution; faiLtng this a 
minimum of Channels 2 and 3 

• afternoon passes are preferable 

• images obtained during the dry season are preferable 

• where archived data are available the most recent data are preferable. 

These requirements were taken from the specifications to data collection contractors listed in 



EOS-93/090-TN-OOl 



I 



TREES II Pre-feasibility Study Assessment of the Current TREES Project 



TREES Se-ries A; Technical Document No 3. 

The aLm was therefore to collect as many suitable images as were available over the analysis 
period (1990 to present, with the exception of some African data from 1988-1991). Collection 
requires a detailed examination of each possible image and is highly labour intensive. This 
would be hard to achieve using a fully automated system, since the precise location of 
cloud-free areas is as important as the total amount of cloud in an image. 

There are separate problems for data acquisition for different continents and these are 
described below. 

Africa 

The receiving stations at Niamey, Nairobi, Maspalomas and LaReunion provide Sharp lA 
products which are routinely purchased by JRC for the MTV (Monitoring Tropical 
Vegetation) project. These tapes have the raw five channel data plus sets of control points 
for image rectification. In order to select an image as a candidate for further processing it 
must be examined either by displaying the full image or using a quick-look product. ESA 
quick-look SHARK products are also available, but are of limited value for scene selection 
because haze and smoke are not clearly distinguishable. 

Southeast Asia 

There has been a major data collection problem in this region. Stations are operational in 
Jakarta and Bangkok, but these do not routinely archive any data since their primary 
purpose is to provide data for meteorological forecasting. The TREES group have therefore 
either to buy a tape sight unseen, or view the image during collection and request it. For the 
Bangkok station they have an on-site contractor who views the images during collection and 
purchases those wbdch are suitable. The data product from these stations is similar to the 
NOAA IB product i.e. it is thermally calibrated, but has no orbital model. There is therefore 
a considerable reqtiirement for pre-processing (see Section 2.2). Recently the TREES team 
has reached an agreement with CSIRO and are now routinely receiving data from the 
Townsville station in Australia; some data have also been piorchased from the Philippines 
station in Mardla via ESA. 

South America 

Data have only recently become available for tliis area via NOAA in Washington, USA 
where data are now routinely downloaded from the satellite. Previously, no data were 
available from Brazil despite the existence of two receiving stations; further, the 
commissioning of the Cotopaxi station has been significantly delayed and no data have been 
available. Qtiick-look colour composites can be examined in Washington, and data can be 
provided as a full NOAA IB product. The coUecfion activity has been contracted out by JRC 
to a team at the University of Reading, UK. 

2.1.2 High Resolution Data 

High resolution data must also be collected for validation; this mainly consists of Landsat 
TM imagery, although research work into the use of ERS-1 SAR data is being performed by 
JRC staff (see Section 5). The high resolution (Landsat TM) data are provided by JRC 
personnel to contractors imdertaking the validation. There is also an intention to collect 4 
km GAC data for use in analysing seasonal variability of the forest coverage. At present no 
routine system exists for this collection. 



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In summary, despite considerable initial problems in data collection the TREES team have 
managed to establish a reasonably routine data coUection system which relies on a mixture 
of using local personnel at receiving stations, coUection via archiving facilities in the USA, 
and use of data routinely collected for other projects. Now that this network has been 
established, future problems in basic data collection are likely to be much reduced. 



2.2 Data Pre-processing 

The initial intention of the project was that, as in the case of data collection, routine pre- 
processing of the data would be carried out by ESA who would then deliver a final product 
to JRC for analysis. In practice however, all the pre-processing has had to be carried out 
either at JRC or by using contractors identified and monitored by JRC. This has inevitably 
led to a considerable delay in data pre-processing and hence in the production of the 
complete inventory. 

The pre-processing consists of: 

• correction of the five raw spectral channels to top-of-atmosphere reflectance 
(Channels 1 and 2) , radiance (Channel 3), and brightness temperahire (Channels 
4 and 5) retaining 10-bit precision 

• atmospheric correction for Rayleigh scattering 

• geometric correction to a Plate Carree representation at a resolution of 100 
pixels/degree and with one pixel image-to-image registration accxiracy 

• generation of cloud and land/sea masks, and sim and viewing angles, where 
possible. 

However, it has emerged from discussion with JRC personnel (JRC, VO4.3400.93) that 
atmospheric correction is not routinely performed on data at JRC, although it is performed 
by at least one contractor (A. MiUington, pers comm). It should be noted that without 
atmospheric correction any interpretation of seasonal effects in the data must be made with 
caution. 

This phase of the data processing is relatively routine since the radiometric and geometric 
correction processes are well known. The radiometric calibration is carried out using the 
detailed procedures laid down for SHARP-2A product generation, and using calibration 
coefficients provided in the international journals, as opposed to prefUght calibration 
coefficients for channels 1 and 2. Similarly, the atmospheric correction uses reference values 
described in specified literature. Tbie geometric correction consists of two stages: 

• orbit model correction 

• interactive selection of ground control points. 

Resampling is carried out using nearest neighbour resampling which is considered by JRC 
persormel to be superior in terms of processing simplicity and speed over the use of cubic 
convolution. 

The TREES team have developed their own software for orbit modelling and geometric 
correction. The former was necessary because the S.E. Asian data did not contain the 
appended geographic information found in NOAA IB data (see Section 2.1). Where pre- 



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processing is carried out under contract, the contractor is expected to provide the software 
and to use methods clearly defined in the contract. This process is apparently well 
monitored at JRC (although the comments made above with respect to atmospheric 
correction should be noted here also). 

The TREES team reported that there have been difficulties with geometric correction due to 
inaccuracies in the coastline maps, particularly along the Ivory Coast. In the S.E. Asian data 
the preliminary geometric correction has a 2 to 3 km error which must then be further 
reduced by manual techniques. The coastline maps are obtained from the WDB-II dataset 
held in TFIS (see Section 2.5); it is possible that when WDB-Il is replaced by the newer DCW 
data this problem may be reduced. 

The TREES team reported that the pre-processing stage for approximately 20 images would 
take about 2 man-weeks for the S.E. Asian data. 

In summary, data pre-processing is a routine but time-consuming component of the data 
assembly. Methods appear to be clearly specified, and where the work is performed by a 
contractor it is monitored by JRC. Inaccuracies in geometric correction could become a 
potential problem, particularly where multi-temporal data sets are used, since this has an 
impact on the potential accuracy of any change detection process. 



2.3 Data Analysis 

The production of a classified data set is at the heart of the baseline inventory. This stage of 
the analysis was originally intended as the starting point of the JRC work, however, due to 
the circumstances described in the preceding two sections work on the data analysis has not 
been imdertaken for the length of time originally intended. 

The actual procedure varies to some extent with image quality and quantity. In general the 
focus has been on image 'windows' which are derived to indicate areas with a range of 
forest types. Within S.E. Asia windows have been specified for Thailand, Burma and 
Sumatra. The Thailand window comprises the ground area covered by three AVHRR 
scenes containing a total of 12 images from different dates. The Burma window is formed 
from the groimd area covered by three AVHRR scenes and contains eight images. For 
Sumatra the window was formed from the ground area covered by five AVHRR scenes and 
contains ten images. For each 'window' the ideal seems to be five or six images obtained at 
the start of the dry season while the vegetation is drying out. However, for some areas, 
restrictions on data availability, particularly with respect to cloud cover, mean that orily a 
single image, or a restricted set of images, might be available. 

The classification procedure consists of a preliminary unsupervised classification of both a 
single AVHRR image and of whatever time-series of images is available. This preliminary 
classification can produce up to about twenty separate classes. These are then grouped 
using data from Landsat TM or SPOT imagery. The use of data from TFIS (see Section 2.5), 
such as elevation, or eco-floristic zone is possible at this stage, but has not yet been fully 
implemented. This classification procedure has been fotmd by JRC to produce better results 
than the use of supervised classifications or the use of an unsupervised classifier where the 
number of classes is pre-determined. However, it is a time-consuming process which 
requires considerable familiarity with each individual image, and is not readily 
standardised. The avaUabiLity of supporting information, such as TM data or field visits, is 



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necessary Jor the accurate grouping of classes. Currently it is estimated that the fine tuning 
of the initial rapid unsupervised classification can take about a month per image window. 

So far, the majority of the work has been carried out on images of continental S.E. Asia 
which is a region mainly of seasonal forests. Results show that in addition to the forest:non- 
forest classes additional classes corresponding to various types of seasonal forest can also be 
identified. There have, however, been significant problems associated with transitions 
across image boundaries where classes in one image do not match those in another. 

The classifications of west and central Africa done by NASA, a region mainly of rain forests, 
show a marked difference from those for S.E. Asia in terms of the numbers and types of 
classes attainable. The differentiation between types of seasonal forest is less clear, and the 
major demarcation seems to be between evergreen forest and 'degraded' forest. The 
appearance on AVHRR of degraded forest is often similar to that of certain agricultural 
types, so that acciirate identification of degraded forest is difficult. This does not appear to 
pose a problem in S.E. Asia. The classifications in the African data have been carried out 
using both visual interpretation and digital classifications similar, but not identical, to those 
employed in S.E. Asia. It is the intention of the TREES team to revisit these data using the 
experience gained in the S.E. Asian work 

No resxilts are yet available for S.America. 

To be useful for global monitoring the classes obtained from the data should be both 
meaningful and reaUstic. Ideally, the same classes should be obtained across the globe 
using the same methodology. However, these prehminary results indicate that due to 
differences in vegetation types and agriculttrral/ social practices on the different continents 
it may not be possible to develop such a standardised procedure. The scale dependence of 
the classification schemes is also a problem in the analysis of satellite data, as it is in any 
traditional classification method. Decisions must be made as to the percentage cover within 
a pixel that results in its assignment to a particular class. Decisions on how classes 
identifiable in high resolution data are represented at low resolutions must also be 
formalised. Despite a long history of similar problems in other fields of remote sensing, this 
work is stUl in its early stages within TREES, but the TREES team are confident that the 
AVHRR data can be used globally to provide three basic classes: non-forest, evergreen 
forest, and seasonal forest. 

The TREES team have carried out some interesting work on the spatial patterns of 
deforestation, their relationship to seasonal variability in the forest and the image 
resolutions required for their detection. They have identified four basic patterns of 
deforestation which are applicable in different parts of the tropics: 

• a broad moving front of deforestation 

• small patches cleared within a forested area (typical of Laos) 

• deforestation along major roads/rivers forming broad belts (central Africa) 

• large patches of deforestation containing dense road networks (Rondonia). 

Each type might require detection with both different classification procedures and different 
sensor resolutions. For the broad moving front a low resolution sensor would be adequate. 
The patchy deforestation in Laos can be detected with a high resolution sensor, but also can 
cause changes to the appearance of the AVHRR data which mimic the appearance of 
seasonal forest and so might be identifiable by seasor\aI monitoring. The use of ancillary 
information in the form of road/river/settlenient data provided via TFIS (see section 2.5) 



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might provide important information whic±i could be used in a contextual classification of 
the African data. 

In summary, the preliminary classification results indicate that it may not be possible to 
apply a standardised procedure across the globe, and that any local scheme is likely to 
require lengthy fine tuning using a variety of ancillary information. It is one of the 
sigriificant challenges to the TREES problem to reconcile the problems inherent in applying 
a standardised procedure with the need to account for regional differences in forest 
characteristics. However, it is probable that three classes can be distinguished globally at 
the 1 km resolution: non-forest, evergreen forest and degraded/seasonal forest. 



2.4 Validation 

The validation process relies on the use of high resolution TM, MSS or SPOT data, 
supplemented by field visits where possible. It is intended eventually to include SAR data 
from ERS-1, but this has not yet been achieved. 

The Thailand area has been validated using five TM scenes; three in the north, one in the 
south, and one in the east. These scenes were chosen to encompass particular patterns of 
forest and of deforestation. Within each TM scene 25 regularly spaced samples are chosen, 
with each sample corresponding to an area of 15 by 15 AVHRR LAC pixels. The TM scene 
is classified by field experts using whatever procedure they consider appropriate, and using 
ground data as much as possible. These field experts are either contractors identified by the 
TREES team, or members of the team. Contractors are supplied with TM data by the TREES 
team. There appears to have been some difficvdty in identifying sufficient experts to cover 
all the regions of interest. The AVHRR classification is compared with the TM classification, 
and statistical comparisons are made. Visual inspection indicates a striking correlation 
between the two data sets. Statistical results show that the AVHRR classification 
consistently overestimates percentage forest cover in areas of high cover, and 
imderestimates coverage in areas of low coverage, but that the AVHRR results follow the 
TM classifications very closely in terms of location of the boundaries. 

There is interest in the research team in developing a 'correction factor' which could be 
routinely appUed to an AVHRR classification to enable it to correspond more closely with 
the TM classification. A difficulty with this is that the correction factor has, to date, been 
found to be highly image-specific, so that extrapolation to different times/locations is not 
possible. 

As in the classification procedure described in Section 2.3 there is a problem in making 
consistent decisions across the two scales of classification. Further, it is assumed in the 
vaUdation analyses that all the images used are 'perfectly' registered so that differences in 
classification due to mis-registration can be ignored. In the light of the mis-registration 
problems reported earher, this assumption will clearly lead to problems in interpreting 
some of the classification results. 



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2.5 Tropical Forest Information System 

The Tropical Forest Information System (TFIS) is an Arc/ Info based GIS which is intended 
both to provide data for use in the monitoring procedures and as a user 'front-end'. In this 
phase of TREES the focus has been on providing data to assist in classification, and its use as 
a 'front-end' has not yet been fuUy developed. 

TFIS allows the user to display data at a variety of scales from the global, through regional 
'windows' and down to the local scale. Currently data is included covering: 

ArcWorld 

Digital Chart of the World (in progress) 

Mundocart 

World Database II 

ETOPO-5 Global Elevation 

Major Ecosystem Complexes 

Eco-FIoristic Zones for the tropics 

WCMC Managed Areas 

WCMC Forest Maps 

Various local data sets. 

It is intended that at the local level many more data sets could be included such as scanned 
photographs, press clippings etc.. Additionally, both colour composites of raw AVHRR 
data, and classified AVHRR data can be included, although this has currently only been 
done for part of S.E. Asia, and it has not yet been determined how to deal with image 
overlap areas. Satellite data from other sensors could also be included at the local level. 

TFIS includes data from many different sources, some in raster format and some vector. At 
present no attempt has been made to register these data, and it is apparent that registration 
is not adequate in some areas. This may however be less of a problem when the DCW data 
set becomes available. 

Ultimately it should be possible to use TFIS data for comparison of the AVHRR 
classifications with those from other sources (e.g. WCMC). This has not yet been attempted, 
and it will be necessary to re-project all the Lat/Long data in order to derive areal statistics. 

The use of TFIS during the AVHRR classification is restricted at present to input of the 
coastline data into the geometric correction procedure. It is not yet widely used by TREES 
staff, partly because it is a single-user system and partly because the user-interface has orily 
recently been developed sufficiently to be accessible to non-GIS specialists. There is clearly 
considerable potential for the development of contextual classification procedures which 
could use, for example, the elevation maps, eco-floristic zones, or road and river maps as 
inputs to the classifier. 



2.6 Research Developments 

The seasonal nature of some parts of the tropical rainforest has already been noted as an 
important component of the data analysis (see Section 2.3). It would appear that in some 



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circumstances degradation of evergreen forest gives rise to a spectral signature in the 
AVHRR data which is similar to that of seasonal forest. Whether this is an actual 
seasonality, or an effect which looks the same on AVHRR data is yet to be determined, but 
in any event its detection could form an important component of an operational monitoring 
system. 

It is possible that the use of low resolution (4 km) GAC data which is collected on a daily 
basis could provide important seasonal information, and be used to identify forest areas 
whose apparent seasonality characteristics were changing. This was included in the original 
TREES 1 proposal as a specific activity area. However, it would appear that, because of the 
difficulties experienced in obtaining the baseUne coverage little further work has yet been 
done in this area. 

Research into the operationalisation of the data collection, pre-processing, and analysis, and 
the inclusion of ERS-1 data were aU originally cited as areas requiring research during 
TREES I. It would seem that very little progress has been made in these areas. 



2.7 Other Issues 

A number of other issues were noted, which may have had an effect on awareness of the 
project and its progress, namely: 

• the Hmited amount of technical and internal project doctmientation 

• the lack of promotion of the project to the user commimity 

• the difficulties experienced in establishing the project team, due to the protracted 
recrxiitment procedures 

• project control and scheduling procedures relevant to a research envirorunent. 

Along with the technical issues, the above will be addressed in the Work Plan for TREES II 
(Technical Note. No. 4). 



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Monitoring Active Deforestation 



The item to monitor areas of active deforestation was subdivided into several activities: 

• development of monitoring procedures 

• identification of active deforestation areas 

• monitoring of these areas via high resolution satellite data 

• development of an 'alarm system'. 

There has been little progress or the task has been subsumed into other work, for the first 
and last activity area Usted above. 



3.1 Identification of Active Areas 

There is a two-way flow between the identification of active areas of deforestation following 
routine monitoring, and the selection of active areas for intensive study from non-sateUite 
sources. The TFIS can be considered as the interface between the two flows, with data 
entering the system from both the AVHRR classifications and from ancillary sources. 

Using satellite data, active areas can be identified from routine monitoring, although it is not 
yet clear how frequent such monitoring should be. Very frequent updating of the baseline 
would be costly, but if the frequency is too low the results would only be useful for a post- 
mortem analysis rather than as part of an active system. 

Ancillary satellite-based iiiformation to identify active areas can come from morutoring of 
such deforestation indicators as fires and road construction. Work done during TREES I has 
indicated that the optimal timing of AVHRR passes for data analysis is in the early 
afternoon, however the burning times tend to be later than this and so are missed. In any 
case after 1995 the required data wiU no longer be routinely available. The use of road 
indicators is also problematic in that in some areas of S.E Asia the logging roads are not 
actually open to the sky and so cannot be detected via satellite data. 

Within the current TREES project little specific work has been undertaken in this area due to 
the emphasis on production of the baseline inventory. The identification of active areas has 
been pursued through a contract to Dr. N. Myers (Oxford), followed up by a second contract 
to Myers and Dr R. Lucas (Swansea) and on which so far only Lucas has reported. Myers' 
approach is summarised in his paper to a 1992 conference in BrazU (Myers 1992). In that 
paper he Usts 14 'tropical deforestation hot spot areas' but examination of this raises 
questions concerning the criteria for their inclusion. For example, logging pressures are 
equally heavy over all Simiatra and Kalimantan not just North and East and South 
respectively, and Eastern Malaysian forests whilst being vigorously exploited for timber are 
not being deforested, and this region has presumably been included because Myers chooses 



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to define logging of dlpterocarp forests as deforestation (Myers 1990: see Whitmore and" 
Sayer 1992). Myers (1992, P. 13) lists criteria whereby new 'hot spots' can be identified, but 
all of these are based on ground observations. The question then arises as to how TREES 
can help pinpoint deforestation areas that are not already known. 

TREES 1 has begun to explore means whereby loci of active deforestation can be identified 
from low resolution images, i.e. on low resolution images a retreating forest boundary or 
change in radiometric signal as forests change near to a boundary, and on high resolution 
images fire, increased seasonality detected as altered radiometric signal, roads or mosaics. 
These signs present difficulties in definition and detection. They have the merit of being 
objective. 

Another way to locate forest areas where loss would be serious is to consider known 
patterns of species richness. Enough is known about faunal and floristic richness in general, 
and of groups such as birds, mammals and butterflies in particular to identify areas of high 
richness and/or endemism. This has been done by Bibby et al. (1992) and also WCMC 
(1992) contains much basic data. For plants there are now sample plots scattered across the 
tropics on which number of tree species per hectare are recorded and these plots identify 
where tree-species rich forests occur although there are gaps in coverage (e.g. ntuch of 
Amazonia and the Guyanas). 



3.2 Monitoring Active Areas 

Once an active area has been identified it must be monitored. This is seen as a separate 
issue from the routine monitoring reqtiired to identify the areas themselves. Active area 
monitoring should be based upon the analysis of high resolution TM or possibly ERS-1 data. 
This activity has not received direct attention in TREES 1, however experience with analysis 
of the AVHRR and TM data indicates that the incorporation of seasonal data wUl be 
extremely important to distinguish long term from seasonal effects. 



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Modelling Tropical Deforestation Dynamics 



The original project plan called for activity on producing spatio-temporal models of forest 
change, biosphere-atmosphere interactions and ecological modelling. 

Due to the length of time taken to produce the baseline inventory little work has been done 
on the modelling programme. The categories of spatial patterns of deforestation identified 
by the team and described in section 2.3 are a preliminary step in producing a spatial model 
of forest change. A separate research programme (FIRE) is investigating the role of fire as 
an agent of transformation. 



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SAR ERS-1 Data Collection and Analysis 



The use of SAR data from the ERS-1 satellite is still in the very early stages of 
implementation so that it is considered separately here from the routine use of AVHRR. 
Although eventually it is intended that SAR data be included in the monitoring and 
validation procedures, this has not yet been attempted. 

The advantage of a microwave sensor is that imaging capability is independent of both 
cloud cover and day/night operation. Thus data can be obtained routinely over areas 
which are normally cloudy for much of the time. However the disadvantage from the point 
of view of global monitoring is that only a relatively small area is imaged at one time, so 
that enormous quantities of data would be required for any large area analysis. Further, the 
images are severely distorted in areas of rapid change in relief and require both an accurate 
DEM for generation of a geocoded image and further analysis to investigate the effects of 
layover and shadow on the received signal. Since accurate DEMs are not generally available 
at sufficient resolution over the tropical forest areas the data analysis is effectively restricted 
to regions of vmiform relief. Research into the modification of the received signal according 
to variations in slope is still in its early stages, as is research into the generation of DEMs 
from the SAR data themselves using interferometric and stereo techniques. 

Currently the TREES SAR effort focuses on the development of a set of tools for information 
extraction. Segmentation of the image proceeds in several stages: 

1. The raw SAR image is block averaged to 100 m by 100 m. 

2. Within each block a gamma filter is applied to reduce speckle, and edges and 
lines in the block are detected. 

3. Certain patterns of edges/lines are taken to represent mountainous terrain and 
blocks so identified are removed from the analysis. 

4. The remaining data are then resampled at about 40 m resolution using the mean 
value for each region. 

5. These data are then classified with either an unsupervised (abf or afb procedure) 
classifier, or a contextual supervised classifier. This produces forest:non-forest 
classes. 

6. The resulting product can be geocoded for inclusion in TFIS. 

7. Multi-temporal data are considered, but since the images have not been 
geocoded these must be co-registered using sampling techniques. The results of 
co-registration are not considered to be very accurate, this leads to problems in 
interpretation of multi-temporal data. 

Identification of seasonal effects has not yet been very successful and still requires 
considerable research. It is not clear whether this is due to multi- temp oral registration 
problems, or to some other effect. 

Currently several sample sites in Brazil and central Africa are being studied in detail. These 
are Acre, Rio Tapajos, Manaos, Bayla and Sassandra. Not all of these sites correspond with 



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the detailed test sites used in the AVHRR analysis, although this is planned for the near 
future. Results have been encouraging, but the differentiation of shifting cultivation from 
degraded forest is a problem (see also Section 2.3). 

Obtaining ERS-1 data has been a major problem, and data are only recently becoming 
available. It is felt that the data may be limited because of the steep look and the short 
wavelength (5 cm). A longer waveband might provide 3-dimensional information since 
there is more penetration of the forest canopy. For future work the focus might have to be 
on the use of airborne data. 



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6 
Conclusions 



The introductory discussion shows that the monitoring or tropical forests is a complex task. 
The requirements for assessment are confused by the variations in agency information 
needs and the simple fact that what can be provided by remote sensing is not yet fully clear. 

The two major objectives of the TREES project were to address deforestation issues at a 
global scale and to develop new satellite remote sensing techniques (see Section 1). The 
production of a baseline inventory and the identification, if not solution, of problems in its 
production can be considered as meeting the first goal. The development of new techniques 
is well underway. Progress in the inventory is gradually establishing suitable techniques, 
and in the area of segmentation of SAR data new methods are being developed. 

TREES I runs imtil the end of 1993. It would appear that there is a good chance that the 
baseline inventory will be completed by this time, although much work remains to be done. 
The S.E. Asian classification is arouTid 50% complete; about 50% of the African work has 
been completed by NASA, but needs revisiting in the Ught of the S.E. Asian work; the 
analysis of the South American data has not yet begun. Therefore, overall about one-third 
of the spatial content of the wall-to-wall classification is complete. The remaining two- 
thirds could be completed by the end of the TREES 1 project since the data are now available 
and pre-processed; and classification procedures used by the JRC team have been 
developed. 

The original time table called for the completion of the data collection during 1991-2 and for 
analysis to proceed during 1992-3. This slippage is due almost solely to the inability of ESA 
to supply the pre-processed data to JRC as originally required. JRC staff have therefore had 
to invest considerable time and effort in what should have been routine work, and have 
been able to spend much less time on data analysis than was intended. 

The current position of the TREES project in summary is: 

1. The collection of data for the production of the waU-to-wall inventory is largely 
achieved. 

2. Routes and procedures have been established by the project routinely to receive 
AVHRR data. The effort in collecting similar data for future inventory or 
monitoring exercises should be much reduced. 

3. The project team has shown that it is possible to use AVHRR data at 1 km to 
provide classification into non-forest, evergreen forest and seasonal forest types, 
at least over S.E. Asia, and probably over Africa, although actual figures for 
accuracy are not yet available. 

4. Different regional deforestation patterns have been identified, leading to the 
possibility of using different techniques in particular areas. 



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The potential of the TFIS system, while not fully demonstrated, is high in two 
respects. Firstly that of adding ancillary data to the AVHRR composites to aid 
classification and secondly in presenting results to users in a comprehensible 
manner and with contextural information. 

The use of ERS-1 SAR data for global monitoring is unlikely, but it is possible 
that it may prove useful in monitoring small selected areas. Techniques for 
segmentation and classification are under development. 

The monitoring and modelling aspects of TREES require considerable further 
effort. 



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References 



Bibby, C. J., et al, 1992. Putting Biodiversity on the Map. Priority Areas for Global Conservation, 
ICBP, Cambridge. 

Earth Observation Sciences, 1993. A Review of the Capabilities of Remote Sensing Techniques 
and Technologies used for Monitoring Tropical Forests, EOS-93/090-TN-002. 

FAO, 1992. The Forest Resources of the Tropical Zone by Main Ecological Regions, UNCED, Rio 
de Janeiro. 

Joint Research Centre - fax ref: VO4.3400.93. 

Joint Research Centre, 1990. TREES : Tropical Ecosystem Environment Observations by 
Satellites - A Joint Project of the Commission of the Etaropean Communities and the European 
Space Agency. 

Joint Research Centre, 1991. TREES Series A: Technical Document No. 1, Strategy Proposal 
1991-1993, Part 1: AVHRR Data Collection and Analysis. 

Joint Research Centre, 1992. TREES Series A: Technical Document No. 1, Strategy Proposal 
1991-1993, Part 2: ERS-1 SAR Data CoUection and Analysis. 

Joint Research Centre, 1992. TREES Series A: Technical Document No. 3, Specifications for 
Collection and Preprocessing of AVHRR HRPT or LAC Data (1 km resolution) for Forest 
Resource Assessment in the Tropical Belt 

Myers, N., 1992, Future operational monitoring of tropical forests: an alert strategy. In 
Malingreau, J. P., da Cimha, R. and Justice, C. (eds). World Forest Watch Conference, JRC, Ispra, 
Italy. 

Whitmore, T.C. and Sayer, J. A., 1992. Deforestation and species extinction in tropical moist 
forests, in Whitmore, T.C. and Sayer, J. A. (eds) Tropical Deforestation and Species Extinction, 
Chapman and Hall, London. 

World Conservation Monitoring Centre, 1992, Global Biodiversity. Chapman and Hall, London. 

World Conservation Monitoring Centre, 1993. Analysis of the Users and their Requirements, 
WCMC-93/TN-003. 



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Technical Note No. 2 



A Review of the Capabilities of Remote 

Sensing Techniques and Technologies 

used for Monitoring Tropical Forests 



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Contents 



1. Introduction 


2. DataS 


ources 


2.1 


AVHRR Data 


2.2 


Landsat Data 


2.3 


SPOT Data 


2.4 


Radar Data 


2.5 


Future Satellite Sensors 



2 
2 
3 
4 
4 
5 

3. Data Acquisition and Management Progranimes 7 

3.1 International Geosphere-Biosphere Programme 

- Data Information System 7 

3.2 FAO Forest Resource Assessment 9 

3.3 NASA Pathfinder 11 

3.4 United Nations Environment Programme 11 

3.5 Radar Data Initiatives 12 

3.6 Conclusions 13 

4. Data Analysis and Classification 14 

4.1 Introduction 14 

4.2 Vegetation Classification Systems 14 

4.3 Classification of Tropical Forests using Remotely Serised Data 16 

4.3.1 Data Discriminants 16 

4.3.2 Classification Methods 19 

4.3.3 Validation of Classifications 22 

4.4 Conclusions 23 

5. Summary and Conclusions 27 

5.1 Summary 27 

5.2 Current Initiatives in Relation to TREES 27 

6. Bibliography 28 



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1. 

Introduction 



Over the past decade, there have been a number of initiatives by research and development 
groups, both national and international, in using remotely sensed data for the inventory and 
monitoring of tropical forests. There have been high level discussions in this area in a 
number of fora including the International Space Year (ISY), the United Nations Conference 
on Environment and Development (UNCED) in Rio de Janeiro, the International Geosphere 
- Biosphere Programme (IGBP), the Committee on Earth Observation Satellites (CEOS), the 
United Nations, the Commission of European Commimities (CEC), the G7 and many others. 
The resulting initiatives have followed several routes both technically and in relation to 
perceived requirements. 

Much of this work has been technology driven, particularly by the existence of data from 
instruments such as the NOAA AVHRR, airborne and spacebome radar, Landsat TM and 
MSS and the French SPOT HRV instument. These data have been investigated to ascertain 
what information can be extracted from them in terms of forest type, distribution and 
condition. Where such ir\formation can be extracted, the question of monitoring arises; i.e. 
can this information be extracted repeatedly and consistently in order to provide time series 
of the values of each parameter. If the use of such remotely sensed data could be 
established and made operatior\al then it is hoped that the use of the resulting uiformation 
could be beneficial to the management of the world's tropical forests. 

These initiatives are outlined in the following chapters. The structure of the report is based 
on the three steps necessary for all of the initiatives; namely identifying sources, data 
acquisition and management and data classification and analysis which leads to the 
production of information concerning tropical forests. Activities at the pan-tropical scale are 
emphasised although regional and non-tropical work is reviewed where it is relevant. 

The review material and discussion in this report is oriented towards comparisons with the 
current TREES I work and the overall TREES objectives (see Technical Note No. 1). 



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2 
Data Sources 



This section outlines, very briefly, the major sources of remotely sensed data which are or 
wlU be available and have been used for observing tropical forests. 



2.1 AVHRR Data 

The Advanced Very High Resolution Radiometer (AVHRR) of the NOAA series of satellites 
has produced data in five channels since 1978: 



Channel 


Wavelength (micrms) 


1 


0.58 - 0.68 


2 


0.72-1.10 


3 


3.55-3.93 


4 


10.3-11.3 


5 


11.5-12.5 



One daylight pass per day is obtained, with a swath width of 3000 km. Since there are 
always two satellites in operation there is potential for twice daily coverage (Hill, 1991), 
although the spatial distortion introduced by off-nadir viewing Limits this in practice. Data 
are available in various forms as described below. 



HRPT Data 

The High Resolution Picture Transmission (HRPT) data provides reflectance and brightness 
temperature data to 10 bit precision in all five channels. The measurement scale of the raw 
data is not constant across the image, changing from a pixel aspect ratio of almost 1:1 at 
nadir to 1:2.9 at the scene edges. The raw data are generally transformed to a specified map 
projection for analysis which usually involves resampling of the data to a ground resolution 
of 1 km. Data coverage is linuted to those areas where the satellite is in direct line of sight of 
a ground receiving station anterma. Until recently this has not included the whole of the 
tropical forest belt. The length of archive available depends upon the individual stations, 
and is highly variable. 



LAC Data 

Local Area Coverage (LAC) data are at fuU resolution of 1.1 km, 10 bits and are the same as 
the HRPT data but are recorded on-board the satellite rather than transmitted to a receiving 
station. Limitations in the on-board recording capacity means that only partial coverage of 
the Earth's surface is possible. Priority is given to emergencies, U.S. security, military and 
commercial use, such that scientific acquisition has a low priority (see Section 3.1), and 
coverage is inconsistent in both time and space. 



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GAC Data 

Global Area Coverage (GAC) data are at reduced spatial resolution of 4 km, but still at 10 
bits. They are generated by on-board sampling of the full resolution data such that for a 
given scan line the first four pixels out of every five are averaged, and only every third scan 
line is processed. Thus, although the data are generally treated as 4 km resolution, this is 
not strictly the case. Hov^ever, the reduction in data volume means that on-board recording 
is possible for the whole globe daily. 

The GAC data set is archived back to June 1981 and is available from NOAA. These data 
are not produced to a standard map projection, and hence require geometric rectification. 
This is complicated by the fact that the groimd size of the pixels varies considerably across 
the sv^ath (Townshend, et al, in press). 



GVI Data 

The Global Vegetation Index (GVI) product consists of the Normalised Difference 
Vegetation Index (NDVI) produced from Channels 1 and 2; Channels 1 and 2 truncated to 8 
bit precision (for 1982 - 1984); and Channels 1, 2, 4 and 5 v^here 4 and 5 are converted to 8- 
bit GOES coimts (1985 onwards). 

The NDVI is calculated from the daytime passes of all 14 daily orbits of GAC data, mapped 
from 1985 to the present to a Plate Carree projection (16 km at the equator) or a Mercator 
projection (19.5 km at the equator). Only one GAC pixel is selected for each GVI pixel 
according to a set of well established rules, such that the maximimx NDVI is retained for 
each pixel. This is to ensure that pixels least contaminated by atmospheric effects are 
obtained. The final product is a result of compositing the daily arrays over a 7-day period 
such that the maximum NDVI value is retained for each location. 

The large undersampling of the GVI data, and the compositing into a maximum over seven 
days limits these data for use in global monitoring (Townshend and Justice, 1986) however, 
their value lies in the high frequency of coverage. Goward, et al (1990) provide a detailed 
description of the data limitations. 



2,2 Landsat Data 

MSS Data 

Landsat MSS scanner data have been available since the launch of the first Landsat satellite 
in 1972 - the current satellite is Landsat 5. They provide data at 79 m spatial resolution in 
four spectral bands. The data are quantised to 6 bits. 



Channel 


Wavelength (microns) 


1 


0.5-0.6 


2 


0.6-0.7 


3 


0.7-0.8 


4 


0.8-1.1 



The orbital coverage is such that the entire globe can be covered with an 18 day repeat cycle 
(Landsats 1, 2 and 3 had a 16 day cycle), with each image covering a ground area of 185 by 
185 km. 



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TM Data 

Landsat TM data have been available since the launch of Landsat 4 in 1982. Data are 
produced with the same repeat cycle and ground coverage as MSS, although the spatial 
resolution is 30 m, and the data quantisation is to 8 bits. The spatial resolution of band 6 is 
120 m. 

Channel Wavelength (microns) 

1 0.45-0.52 

2 0.52-0.60 

3 0.63-0.69 

4 0.76-0.90 

5 1.57-1.78 

6 2.08-2.35 

7 10.4-12.5 



2.3 SPOT Data 

The French SPOT satellite was laimched in 1986, with a follow on in 1990. It provides 
imagery with a spatial resolution of 10 metres in panchromatic mode, and 20 metres in 
multispectral mode. Each ground swath covers 60 km, and there is a repeat period every 26 
days. 

The satellite has a pointing capability such that the detectors can point up to 27° either side 
of the ground track, providing the possibility both for stereoscopic imagery and more 
frequent coverage of a particular area. 



Channel 


Wavelength (microns) 


XSl 


0.50-0.59 


XS2 


0.61-0.68 


XS3 


0.79-0.89 


Panchromatic 


0.51-0.73 



2.4 Radar Data 

Satellite-borne microwave sensors are a very recent addition to the set of available satellite 
data which could be used for environmental modelling. The primary advantage, especially 
for tropical areas is that cloud cover is not a restriction. Data availability is considerably 
more restricted than for the other longer established sensors. 



ERS-1 

The ERS-1 satellite was laxmched in July 1991 carrying a C-band SAR which has a frequency 
of 5.3 GHz, W polarisation, and a nominal ground resolution of 30 metres. The mission is 
divided into different phases differentiated by alterations in the orbital characteristics. For 
environmental monitoring the 35-day repeat phase is the most important. However, due to 
the high power consumption of the SAR instrument and the limitations of on-board storage 
the actual receipt of data is severely limited to aii operation of 10 to 12 minutes per orbit and 



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to operation within the reception area of a ground station. Insofar as tropical forest 
monitoring is concerned, data acquisition is limited to the period from April 1992 to 
December 1993 because of demands for sea ice observation during the 3-day repeat periods. 

Data products can be divided into three categories: 

• raw annotated data available at the PAFs a few days after acquisition 

• fast delivery products generated and distributed by the ground stations 

• off-hne products available via the PAFs at varying levels of processing. 

Apart from the SAR on ERS-1, there is an Along Track Scanning Radiometer (ATSR) which 
has some channels similar to the AVHRR. However, ATSR is optimised for sea surface 
temperature and there are only two visible/NIR channels, of which one failed during 1992. 



JERS-1 

JERS-1 was launched by Japan in 1992 carrjdng an L-band SAR which has a frequency of 1.3 
GHz, HH polarisation, and a nominal ground resolution of ISmetres. The repeat period is 
41 days. 



Airborne Sensors 

There have been many missions to map tropical forests using airborne SLAR. These are 
discussed in Section 3.5. 



2.5 Future Satellite Sensors 

'VegetaHon' for SPOT 4 

There is a proposal to mount a specific vegetation monitoring instrument on the next 
generation SPOT satellite. This would be similar to AVHRR, with the important difference 
that on-board recording capabUity would be sufficient to allow downloading of data once 
per day to a ground station. This would allow daily global coverage at 1 km resolution. A 
further advantage is that data would be exactly contemporaneous with the higher resolution 
data on the same satellite allowing for easier comparisons between the data. 

Since this proposed system would overcome many of the inherent problems in using 
AVHRR data it is likely to be of considerable use for global tropical forest monitoring. 

Radarsat 

This is a primarily Canadian initiative with U.S. partnership, and is scheduled for launch in 
1994. The satellite will carry a C-band SAR capable of an extensive range of operating 
modes. There will be a 16 day repeat cycle. 



ERS-2 

The Advanced or AATSR will be flown on ERS-2 and ENVISAT and wiU include more 

channels in wavebands suitable for land applications. 



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MODIS 

The Moderate Resolution Imaging Spectrometer (MODIS) is a part of the U.S. Earth 
Observing System (EOS). In terms of its field of view and frequency of imaging it will be 
comparable to the AVHRR, but will have improved spectral, radiometric and geometric 
properties specifically optimised for land applications. However the swath width will be 
less than AVHRR, which means that the return period and hence the ability of MODIS to 
gain cloud free coverage is reduced by comparison. The spatial resolution for the land 
application spectral bands will be 250 m and 500 m (Townshend, et al, 1993). Launch is 
planned for 1998. 



MERIS 

The Medium Resolution Imaging Spectrometer (MERIS) is a European instnjment which 
will have 15 spectral bands in the range 0.4 - 1.1 microns with a ground resolution of 250 m 
for land applications. Recording on-board of all data in each pass is plarmed ensuring 
security of data supply. A number of vegetation products are currently under investigation. 
Launch is planned for 1998. 



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3. 
Data Acquisition and Management 



To enable information extraction, data from the various sources requires collation, 
processing into standard formats and managing. Management includes cataloguing, 
archiving and preparation for particular applications. This section outlines the major 
programmes concerned with such activities in relation either directly or indirectly to 
tropical forests. 



3.1 International Geosphere-Biosphere Programme - Data Information 
System (IGBP-DIS) 

The 'Land Cover Pilot Project' (IGBP 1992) is a major initiative by the IGBP to gather global 
scale remotely sensed data sets for use in a range of projects researching global change. This 
obviously includes tropical forests. A series of trade-offs between data availability, 
coverage and temporal and spatial resolution led the IGBP to the conclusion that the NOAA 
AVHR]R data should be the first of these sets to be gathered. This data set was regarded as 
the best available for the investigation of land processes for a number of reasons including: 

• availability at different resolutioris including the relatively high resolution 1 km 
LAC 

• relatively broad range of the spectrum across which scanning occurs 

• temporal resolution being relatively frequent for the whole globe. 

In order to prepare suitable data sets, LAC data is required to be collected continuously. 
This is because the frequent cloud contamination of images necessitates the use of mvdtiple 
images of the same area to build up complete surface observation records. There are several 
barriers to such a goal: 

• instnoment acqmsition and on-board data storage capacity 

• receiving station operation and location. 

LAC data cannot be recorded on-board for the whole globe and are obtained largely by 
direct downlLrLk of the HRPT to receiving statior\s in view of the satellite at the time of 
acquisition. HRPT is broadcast continuously to any station able to receive it. 

There are more than 50 such stations scattered around the globe. The primary motivation 
for the national organisations receiving these data is for weather forecasting rather than land 
process studies. Forecast use is often satisfied by immediate interpretation of images and 
data are subsequently not always archived, still less catalogued. The exact status of the 
various stations is difficult to ascertain (IGBP 1992, Appendix 2). There are however notable 
gaps in national HRPT coverage in northern South America, the Middle East and large parts 
of Asia. 



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There is some on-board storage capability enabling LAC to be obtained for selected areas 
and downlinked, but this is relatively small (aroimd 10-12 minutes per orbit). However, 
acquisition is assured for the instrument operators. Acquisition is controlled by the 
'Interactive Processing Branch' of NESDIS and the coverage is prioritised, with U.S. 
emergency, military and commercial interests having first priority. This leaves scientific 
requirements as a low priority. The recorded LAC data are downlinked to one of two sites 
in the U.S. and re-transmitted (via satellite) to centres in Washington D.C. and the EROS 
Data Center. There is a further centre at Lannion in France but this is not operational for 
receipt of recorded LAC data. The recorded LAC data provides some 40 scenes per day for 
the archives. 

The IGBP initiative envisages the coordination of selected HRPT stations and the recorded 
LAC coverage to provide continuous monitoring of land areas. This coordination would be 
under IGBP-DIS, CEOS or the ISY and be centred at the EROS Data Center. The selected 
stations, many of which are in developing countries, may require assistance to perform the 
necessary activities, a task which is already in hand in several cases with ESA support. The 
Lannion centre could also be upgraded to provide increased acquisition flexibility. By 1993, 
the EROS Data Center (also known as the Land Process Distributed Active Archive Centre - 
DAAC) had acquired 10,000 AVHRR scenes by this type of coordination of recorded and 
HRPT stations, hi addition, browse and metadata products have been produced. 

It is dear from the distributed nature of data acquisition, that management of incoming data 
is a major task. For this and other global data sets, the management task can be broken 
down into three areas (IGBP, 1992, P. 53): 

• disseminate catalogue information 

• assure suitable archive techniques 

• enable access to the data. 

The IGBP has encouraged the development of a directory based on the NASA Master 
Directory. This directory aims to provide a comprehensive listing and description of data 
sets useful for global change studies. The current status is that there are directories at a 
large number of data centres including NASA, NASD A, ESA, UNEP/GRID, EROS etc.. This 
system allows users to ascertain where data are, but not actually to obtain them. This is a 
matter of distribution policy. 

Archiving can be centralised or distributed and carried out using a range of media. The key 
to efficient archiving across sites as seen by IGBP is the networking of data centres with a 
central node and larger regional centres as outlined above. A wider institutional framework 
is provided by the ICSU World Data Centre (WDC) network which has collected a large 
amoimt of data on a discipline basis since the 1960s. Utilisation of WDCs, with perhaps a 
central archive at EROS is possibly a suitable means to archive the 1 km data set. 

There is currently no internationally accepted long term archive media other than hard 
copy. There are a number of options each with its own advantages and disadvantages. 
Capacity, life expectancy, transfer rate, cost, availability and automated handling all vary. 
For example, exabyte is inexpensive and capacious in comparison with 5.25-inch optical 
disc; although the latter has greater robustness and longevity. The 1 km data set itself 
comprises approximately 1 terabyte annually which gives an indication of the volume of 
data from such global data sets. 



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For tropical forest monitoring using AVHRR (and future data sets) these issues are clearly 
critical. If the management of these data can be organised efficiently on a global scale in 
terms of acquisition, cataloguing, archiving and distribution then monitoring using them 
becomes a realisable goal. The applicability of the IGBP data set to tropical forest 
monitoring is clear in the light of the TREES 1 data acquisition activities reported in 
Technical Note No. 1. This was also recognised by the ISY World Forest Watch corvference 
(Rasool, 1992). 



3.2 FAO Forest Resources Assessment 

A quite different data acquisition and management activity, but one aimed specifically at 
tropical forests is the FAO Forest Resources Assessment (FRA) project. The first project was 
undertaken in 1980 and was largely an effort to collect and collate statistics on forest cover 
from national sources. Many of the statistics were gained from non-remote sensing sources. 
Two mapping imits were used, namely forest and woodland, the definition being closed 
woody canopy versus tree cover <40%. 

The 1990 project has relied more heavily on remote sensing techniques with a survey using 
Landsat 4/5 scenes for a sample of 10% of the forest area. The 1990 survey has recently been 
completed using eco-floristic zones produced in the 1980s in cooperation with the French 
Institute for the International Vegetation Map (FAO, 1993). It is not yet clear how these 
zones relate to the 1980 mapping imits (FAO, 1992). Data acquisition is simple compared to 
AVHRR given the repeat period of 10 years and the 10% area coverage, in fact 117 scenes 
were interpreted by selected national forest agencies. Landsat is of course prone to the same 
cloud coverage problem as AVHRR necessitating the checking of many scenes before cloud 
free images can be found. Quicklooks are available from commercial vendors which enables 
cloud free scene selection to be made relatively easily. 

The FRA project has also suggested an annual monitoring exercise based on the 10% sample 
which would necessitate a somewhat larger acquisition exercise. A different 10% is sampled 
each year resulting in fuU coverage every 10 years. This programme for continuous 
assessment of tropical forest resoxirces using high resolution satellite data is currently 
seeking funds. The work would be decentralised and done in close collaboration with 
individual cotmtries. The results would be collected by FAO who would compUe global 
statistics. The results from both the 1980 and 1990 FRA exercises are controversial. The 
table below gives tropical moist forest areas and deforestation rates for the preceding years 
(for fuU details see Grainger, 1993, Tables 2, 3, 5 and 6). These estimates suffer from several 
of the problems noted below and should be viewed very cautiously. 



Source 


Date 


Area 


D efo Testation 






(million hectares) 


(mha/annum) 


Perssont 


1974 


979 


n/a 


Sommer 


1976 


935 


11-15 (for FAO) 


FAO 


1980 


1201 


7.3 (closed inc moist forest) 


Myerst 


1980 


972 


7.5-20 


Grainger 


1983 


1081 


6.1 


Myers 


1989 


800 


14.2 


FAO 


1992 


1282 


12.2 (preliminary) 


t derived by Grainger (1984) 






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Grainger (1993) also made a number of points about the accuracy of figures which apply to 
FAO and other inventory work: 

• Who made the estimate? There are only two primary estimates of deforestation 
rates at a global level; that of the FAO and that from Myers. Most other estimates 
are secondary; i.e. derived from these primary estimates. 

• To what type of change in forest cover does the estimate refer? Grainger defines 
deforestation as the 'temporary or permanent clearance of forest for agriculture 
or other purposes' which should be distinct from degradation caused by selective 
logging. FAO also make this distinction. The implications for inventory where 
definitions are not consistently applied are dear. 

• What type of tropical forest is referred to in the estimate? This relates to the 
question of classification (see Section 4.2). Estimates for closed forest, open 
forest, moist forest etc., obviously cannot be compared either between nations or 
through time for different classifications. 

• Was the estimate based on specified measurements or on the subjective 
judgement of an expert? If a measurement system was used then a reliability 
estimate should be available. 

• K measurements were made, what remote sensing technique was employed? 

• If measurements were made, was the whole forest area surveyed or just part? 

• What were the dates of the measurements? 

• Were estimates on which deforestation rates were based, both derived from the 
same source? 

• Does the change refer to an historic or a projected change? It appears that 
projections have sometimes been taken as historic fact. 

The FAO is aware of these problems; their 1980 figiire of 7.3 is for forest clearance only. 
Further figures relate to vmdisturbed closed forests logged, but not cleared (4.4 million ha) 
and clearance of open trees formatioris (3.8 million ha). Adding both closed forest figures 
gives a figure closer to Myers' figure which are less explicit in definition. 

Whitmore and Sayers (1992) indicate similar difficulties caused by problems in varying 
definifioris of deforestation and classification criterion in their review of specific countries. 
Estimates of tropical closed forest area in Asia shows some countries with large decreases, 
while others such as Laos show increases. Deforestation rates show general increases at a 
national level which, despite definition problems, supports the general conclusioris that 
rates have increased during the 1980s. 

Final figures given by the 1990 FAO FRA report (FAO, 1993) estimate annual deforestation 
1980-1990 at 15.4 million ha. This figure includes a wide range of forest types broken down 
into lowland formations (rainforest, moist deciduous, dry and very dry), upland formatioris 
and non-forest zone (< 0.5% contribution). The main division between forest and non-forest 



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is at 10% crown cover with deforestation defined as any land use change causing depletion 
of cover to below this figure. Degradation is not reflected in the FAO figvires. 

It is clear that FAO have made a significant contribution to information on tropical forests. 
However, many problems remain to be solved. 



3.3 NASA Pathfinder 

The Pathfinder activity is closely related to the IGBP-DIS initiative reported in Section 3.1. 
The NASA research group based at Goddard Space Flight Center has worked extensively 
with GAC (Global Area Coverage) to study vegetation in general. The advantage of GAC is 
that it is recorded globally and downlinked at one point making acquisition a simple task. 

The Pathfinder (GIMMS) group is currently engaged in two data acquisition and 
management activities linked to tropical forestry. 

The first activity is involved with the reprocessing of GAC data from 1981 to 1991. 
Channels 1 and 2 (visible) are being recalibrated and new NDVI are being output for 
fortnightly intervals for the 10 year period (Townshend, et al, in press). 

Secondly, the group is using Landsat data to compile multi-date pan-tropical (wall-to-wall) 
inventory of tropical forests using manual interpretation techniques. In comparison with 
the FAO FRA effort, the acquisition of pan-tropical (i.e. covering the tropics) Landsat scenes 
is large (500 minimum). The results, known as the Humid Tropics Forest Inventory (HTFI), 
wiU produce complete forest cover maps of selected regions of S.E.Asia, central Africa and 
Amazonia for the mid-1970s, mid 1980s and mid 1990s. The objective is to monitor changes 
in cover. 

There are several other acquisition and management problems faced by the NASA group 
(Justice, 1992) : 

• The availability of scenes from the tropics. After the commercialisation of the 
supply, the number of requests for tropical areas dropped considerably which 
means that the historical record is sparse. 

• Local receiving stations in the tropics do provide coverage missing from the 
central archive, but the metadata from and contact points at these stations is 
often uncertain. 

• The quality of and access to MSS data from the 1970s is problematic since much 
of the data is in photographic format and there are dropouts and detector 
banding in the scenes. 



3.4 United Nations Environment Programme 

The United Nations Environment Programme (UNEP) coordinates the Global Environment 
Monitoring System (GEMS) as an element of the United Nations Earthwatch programme. 



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The Global Resource Information Database (GRID) is part of the GEMS. Its mandate is to 
collect, store and disseminate geo-referenced data of all types. In relation to tropical forests, 
GRID aims to collect and disseminate AVHRR data for tropical regions and assist the FRA 
(see Section 3.2). 



3.5 Radar Data Initiatives 

Radar covers both airborne and spacebome (Synthetic Aperture Radar - SAR) technology. 
There is a relatively long history of forest research and attempts at inventory using radar in 
both forms. 

Early work using Side Looking Airborne Radars (SLARs) in the 1950s and 1960s quickly 
became conunerdal in the RAMP (Radar Mapping of Panama) project. This led to a number 
of similar projects in south and central America such as the Brazilian RAD AM project. The 
scale of the RAD AM mosaics was 1:250,000 and covered an area of 8.5 million square 
kilometres. The SLAR images were interpreted visually to provide thematic maps geared 
towards terrain analysis for oil and mining sites evaluation. In addition, the images 
provided information on broad forest type, land use for agriculture, soil type etc. (Churchill 
and Sieber, 1991 and van der Sanden, 1990). 

Current work by a Dutch group under the Netherlands Remote Sensing Board consisting of 
various workers from ITC and the Wageningen Agricultural University (van de Burg, 1992) 
focuses on the investigation of airborne radar backscatter. The research uses local sites in 
Columbia and Guyana for which substantial amounts of local survey work has been done 
(including optical remotely sensed data). Correlating backscatter with known forest types 
enables radar signatures to be characterised. This should allow spacebome SAR to be 
uHlised in forest inventory; in particular it is hoped that the ERS-1 SAR can be utilised. 
Similar work is being undertaken as part of the 'South American Radar Experiment' 
(SAREX-92) and planned for the CEC-ESA EARSEC project. 

For this recent work the key data acquisition effort is in synchronising local field and 
airborne stirvey with spacebome (i.e. ERS-1) overpasses such that SAR images can be 
obtained. This is more difficult than for NOAA- AVHRR and Landsat since the ERS-1 SAR 
is only able to acquire data for 10% of any orbit. In addition, only a limited quantity of 
acquired data can be produced at the required product level in the ground segment due to 
the very heavy processing requirements. Thus, a precise request for the location of the 
required image must be submitted to ESA in advance of the appropriate orbit. 

In space, the first SAR to be flown was on Seasat in 1978. This was an L-band instrument as 
were the two Shuttle Imaging Radars (SIR-A/B) flown in the early 1980s. Currently the 
ERS-1 mission provides C-band coverage. This is to be followed by ERS-2 and then 
Radarsat, all C-band. The recently launched JERS-1 has an L-band instrument. There are 
several problems which Umit potential use of spacebome SAR by tropical users: 

• the very high data production rate requiring sophisticated ground receiving 
equipment 

• the heavy processing requirement requiring extensive computing facilities. 

In the context of the TREES project, acquisition of such data is planned to be an important 



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input for local validation and calibration of globally acquired data (such as AVHRR) or in 
giving detail to areas of active deforestation indicated by global scale monitoring. The 
ability of radar to provide global or regional scale information in itself will depend on the 
scale of airborne missions and on the representativeness of the relatively very small areas 
covered by SAR. 

Further discussion of the use of both space and airborne instruments in found in Section 
4.3.1. 



3.6 Conclusions 

The issues in the acquisition and management of data for tropical forest monitoring from 
ctirrent remote sensing instruments are many, both technical and institutional/political and 
include: 

• instrument acquisition modes are not optimised for tropical areas 

• data volumes are large 

• data holdings are neither centralised nor standardised. 

Section 4 of this report gives more detail about the subsequent analysis of these data and 
highlights further issues. 



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4. 
Data Analysis and Classification 



4.1 Introduction 

In order to map ar\d monitor tropical forests, a classification system is required. This 
classification must answer the following questions: 

• what is the appropriate spatial scale of measurement for tropical forest 
monitoring? 

• what temporal scale is required for monitoring (i.e. to identify seasonal effects)? 

• what temporal scale is required for change detection? 

• what classes should be extracted from the data? 

• what classes can be extracted from the data? 

• should the classification be based on established (non-remote sensing) scheme? 

• should the same classes be extracted globally, or should the emphasis be on 
classification appropriate to local conditions? 

• what classification procedure best identifies the classes of interest? 

• is there a set of classes which can be extracted operationally, even though these 
may not represent the full potential of the data? 

• how should the results be validated? 

Section 4 cor\siders current work with the exception of the TREES work itself which is 
considered in Technical Note No. 1 and attempts to establish how far these questions are 
being addressed. Section 4.2 outlines some issues in vegetation classification in general and 
in relation to remotely sensed data and Section 4.3 outlines the information potentially 
available from sateUite data, describes current methods used for information extraction and 
considers validation methods. 



4.2 Vegetation Classification Systems 

Despite a long history of attempts stretching back over a century, there is currently no one 
agreed system for classifying the world's vegetation This is sometimes a problem in fields 
associated with agriculture and the environment where information about the distribution 
and condition of vegetation communities is required. There have been a number of 
attempts to produce schemes; and these are three types (Adams, 1992): 

• Structural-physiognomic 

These classifications are based on the visible features of structure (i.e. height), 
cover and physionomy; for example leaf form, deciduousness and spininess. 
Broad-scale schemes such as those of Kuchler (1967), Kuchler and Zonnerveld 
(1988) and UNESCO (1973) fall into this category. For tropical forests, the 



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scheme widely used in India and Malesia (Champion, van Steenis and 
Whitmore) and for Africa (White) also fall into this catagory. Matthews (1983) 
used the UNESCO system to produce a 1° raster grid for the world containing 32 
classes such as temperate/subpolar evergreen forest, cold-deciduous forest 
without evergreens and tropical/sub-tropical evergreen drought-deciduous 
forest. There are in addition a large number of smaller schemes relating to 
regional, national and sub-national levels. 

• Floristic 

Key plant types characteri.se the particular class. The plant is seen as typifying 
class character and ecological relationships. Takhtajan's (1973) world 
biogeographical provinces is one example while at a regional level, Braun- 
Blanquet's (1965) classification of Europe is well known. 

• Bioclimatic 

Such classifications use climatic criteria such as rainfall, temperature and frost 
occiu-rence to classify vegetation regions. Holdridge (1967), Koppen (1936) and 
Walter (1973) are major examples. The Holdridge classification contains 39 
classes such as: boreal moist forest, warm temperate dry forest and subtropical 
wet forest. 

With this range of systems, the spatial extent of different types of vegetation can become a 
point of dispute; partly because of variations in the meaning of classes between systems and 
partly as a result of different measurement techniques even where classes do coincide 
(Townshend, et al, 1991). 

Adams (1992) examined the various criteria used for classification systems and attempted to 
achieve the best compromise solution for each: 

Criteria 'Solution' 

structure, floristics or dimate structural / climatic 

defirution of limits quantitative 

field mapping factors use existing classes as far as possible 

number of categories 20 

transitions use existing methods but narrow options for divisions 

anthropogenic influence use time limited disturbance criteria 

The resulting forest categories are for example: tropical broadleaved woodland, tropical 
deciduous broadleaved forest and cool woodland. The major discriminants at the highest 
level are the coverage of woody vegetation of certain heights (e.g. >6m, <0.6m); the 
broadleaved coverage and the average temperature of the warmest/coldest month. This 
leads to the production of 22 classes divided into natural/intensively cultivated and non- 
inundated/wetland groups. For spatial resolution, 1 km squares are recommended. Adams 
floated this idea in 1992 and is looking for response and discussion to try and move towards 
corisensus. 

Adams' classification attempts to meet the needs of a wide range of researchers, planners 
and developers. It attempts to take into account various requirements and reconcile them in 
a single system. The system is not systematic in terms of language or quantitative criteria 
neither is it novel, but is grounded in the real experience of workers in the field. Such an 



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attempt is therefore very valuable in giving an insight into requirements to be met by 
remote sensing. It is not as yet an accepted system, but represents an important viewpoint 
and one endorsed by WCMC. 

The key discussion point raised by Adams is that of the need to use classification factors 
which are "easy to map from field vegetation" (Adams, 1992, P. 6). The challenge is to 
reconcile classes realisable from remotely sensed data to the classes defined by "real 
information which a vegetation category must carry in terms of its character as a habitat for 
animals, a fuel wood resource of whatever" (Adams, 1992, P. 6). Obvious parameters 
quoted by Adams (and which form a vital part of his proposed scheme) are canopy cover in 
relation to vegetation height. These can be directly related to biological realities which then 
enable management activities to be undertaken. 

Taking Adams' proposed classification as a statement of requirement, the question arises as 
to whether remote sensing can supply some or all parameters necessary to define the 
classes. The obvious first answer is that no single spaceborne system can currently supply 
values for all of his parameters. However, some can be supplied and the following 
discussion outlines what they are. 



4.3 Classification of Tropical Forests Using Remotely Sensed Data 

The goal of the data analysis is to produce a classification of tropical forests which is useful 
for assessment of forested areas and for subsequent monitoring. Therefore for remotely 
sensed data to be used in forest classification there must exist a relationship between the 
parameters measurable from the image data and the required classes. This procedure can 
be data-driven, i.e. researchers can attempt to discover what classes could be extracted from 
the data. Alternatively it can be user-driven, i.e. given a set of required classes (e.g. Adams') 
a method must be established to extract these from the satellite data. 

Section 4.3.1 describes the parameters extractable from the data. Section 4.3.2 describes 
methods which have been employed to use these parameters to produce a classification 
scheme. 



4.3.1 Data Discriminants 

This section outlines the parameters available from remotely sensed data which can be used 
to discriminate between tropical evergreen forest and other land cover categories. 



Data Discriminants for Optical/Thermal Sensors 
Spectral 

Spectral discrimination between vegetation classes in remotely sensed data depends upon 
varying responses in the red, near infra-red and thermal portions of the spectrum. As a 
broad rule photosynthetically active vegetation produces a low spectral response in the red 
channel and a high spectral response in the near infra-red. This has led to the extensive use 
of various ratios between these bands in studies of vegetation, the most widely used of 
which is the normalised difference vegetation index (NDVI) where the index is derived from 
the ratio of near infra-red minus red to red plus near infra-red (Townshend and Justice, 
1986). This index can be produced from Landsat, SPOT and AVHRR data. However, 



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despite the production of a Global Vegetation Index (GVI) data set from AVHRR data on a 
routine basis, the index has been found to be unsatisfactory for tropical forest monitoring 
(Malingreau, et al, 1989). This may be because of the poor quality of the red channel data 
(AVHRR Channel 1) which is greatly influenced by haze, smoke and other atmospheric 
effects, and exhibits an overall low contrast. Also, AVHRR Channel 2 includes a major 
water absorption band, and variations in water vapour can cause changes in the appearance 
of NDVI values unrelated to any change in groimd conditions (Townshend, et al, in press). 

However, the thermal infra-red channel of AVHRR can on its own provide discrimination 
between tropical forest and other vegetation types, since there is a clear drop in signal over 
closed forest canopies (Malingreau, et al, 1989). Thermal contrasts between forest and non- 
forest are at their greatest at the end of the dry season and the thermal Channel 3 of AVHRR 
has been successfully used for forest:non-forest discrimination (GasteUu-Etchegorry, et al, 
1991). Some forests of course have no dry season. Channel 4 and Channel 5 can also be 
used satisfactorily. 

Temporal 

The use of time-series of remotely sensed data to discriminate between various forms of 
'seasonal' forest has not been fully exploited. It is known from analysis of GVI and GAG 
data that the periodicity in the photosynthetic activity of the tropical forests can be traced. It 
is also known that as the seasonal character of the vegetation increases so does the 
amplitude of the NDVI; this appears to be related to rainfall patterns (Malingreau, 1991). In 
order to exploit this variation it would be necessary to have sufficient seasonal data 
available at a resolution at which the seasonal variations can be identified. This represents a 
major problem, since the high resolution data such as Landsat TM or SPOT may not be 
available sufficiently frequently, and would require in any case immense amovmts of data 
processing, while the more readily available GAC data may not be of siifficient resolution to 
be suitable for spectral discrimination of seasonal change. 

The use of time series of data for monitoring tropical forests has also not so far received 
much attention, due in large part to the absence of suitable long term data sets. A further 
problem in interpretation lies in the necessity to atmospherically correct the data so that any 
spatial or temporal changes may be attributed to real differences rather than to changes in 
radiometric calibration of the sensor, or atmospheric or illxunination differences (Ahem, et 
al, 1987, Singh, 1988). The utility of both GAC and GVI data is limited by the absence of on- 
board calibration of Channels 1 and 2 and the effects of instrument degradation over time 
(Holben, et al, 1990). Inter-annual comparisons must therefore be made with care 
(Townshend, et al, in press). Accurate registration is also a problem when analysing such 
data sets (see also Section 4.3.3). Additionally, the problem of cloud cover can limit the 
availability of suitable data. 

Circumstantial Evidence 

The major circumstantial indicators that deforestation may be occxirring are the presence of 

fires, roads, bum scars and smoke (Malingreau, 1991). 

Active burning can be detected using thermal data, for example Channel 3 of AVHRR. 
Although the actual area of fire cannot be measured, the total nimiber of fires can be used as 
an indicator of activity. There is however a problem in detection, since the times of peak 
burning do not necessarily coincide with the times of a satellite overpass. Also, AVHRR 
Chaimel 3 can saturate quickly, leading to confusion between fire and merely hot ground. 
Additionally, the imdersampling of AVHRR GAC and GVI data is such that potential fire 



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pixels may be completely missing from the data (Belward and Lambin, 1990). High 
resolution 1 km data are therefore necessary for adequate detection of fires. However, bum 
scars from recent fires can be detected with AVHRR (Malingreau and Belward, 1992), 
(Langaas and Kane, 1991). 

Road building can also be a major indicator of active deforestation (Feamside, 1985) and has 
been used in monitoring studies in the Amazon Basin (Malingreau and Tucker, 1988). 
However, roads may not always be identifiable at the scale of the satellite data, particularly 
AVHRR LAC and GAC, and logging roads may not always be open to the sky, for example 
in Guyana (Whitmore, pers. comm.). The potential of road indicators for global monitoring 
is therefore limited. 

Radar Data Discriminants 

Cloud cover is a major limitation in the use of the optical portion of the electromagnetic 
spectrum, and the problem is particularly severe in the humid tropics where cloud cover 
can be an almost perrrmnent feature. This is a major reason for the interest in the use of 
radar for tropical forest monitoring. The image derived from radar is a measure of the 
backscatter of energy from each element or pixel of that image. Backscatter varies according 
to a number of parameters (van der Sanden, 1990): 

• radar system configuration in terms of frequency/wavelength, polarisation, 
incidence angle and viewing geometry 

• terrain and surface cover properties including soil moisture. 

System Configuration 

Radar wavelengths/frequencies are traditionally one of several lettered bands ranging from 
Ka-band at around 8 mm (-36 GHz) to P-band at around 1 m (-420 MHz). Longer 
wavelengths penetrate the forest canopy to a greater extent and thus provide information on 
parameters such as stem volvtme (including the volume of branches enhanced by coriiferous 
needles from which long wavelengths give a strong backscatter). On the other hand, shorter 
wavelengths are scattered by the canopy, especially if the canopy is of deciduous leaves. 
This means that the band chosen can help to distingxiish forest type (coniferous and 
deciduous). In addition, the longer wavelengths can discriminate between primary and 
secondary forest due to the differences in woody biomass and surface clutter. In terms of 
workable applicatior\s, Werle (1986) lists discriminants which cotild be extracted from X- 
and C-band: 

• deciduous/coniferous forest 

• age/height differences 

• forest boundary. 

Polarisation is usually H(orizontal) or V(ertical) both for transmission and reception; i.e. 
HH, HV, VH or W. Cross polarised (HV or VH) images tend to emphasise volume 
scattering (i.e. from canopies) in either mode. Whereas, like polarisation (HH and W) 
produce differing results depending on the mode due to the interaction with the orientation 
of branches (Sieber, 1985). Sader (1987) Lists the discriminants which may be discernible: 

• canopy characteristics 

• species composition/ structure 

• wet sou exposure. 



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Incidence angle can have a significant effect on backscatter. A number of studies show how 
angles ranging from 20° to 60° are related to ability to distinguish forest parameters. An 
optimum angle of about 30-40° is agreed by several since at this angle, differentiation 
between forest classes is maximised. 

The relationship between the sensor and object orientation affects backscatter directly; 
notably the enhanced backscatter at forest edges. 

Terrain and Surface Cover Properties 

Topography has well known effects on radar images in terms of reUef displacement 
(foreshortening and layover) and shadow. The removal of such effects is the subject of 
much work with DEMs and precise geocoding. 

Within the forest, there are no simple relationships between backscatter and forest structure 
parameters; except perhaps for biomass and backscatter although response tends to saturate 
early with rising biomass. Using multiple regression techniques improves relationships by 
combining parameters. Both soil and canopy moisture plays a part in affecting backscatter. 
Soil effects in particular have been quantified. As an example, although a non-tropical one, 
of the ability of SAR to provide quantitative soil moisture data; Dobson, et al, (1992) worked 
on a recently acquired ERS-1 scene from the U.S. Canadian border area. They foimd that 
"backscattering from forested area is dominated by properties of the crown layer consisting 
of foliage and branches. Furthermore, ERS-1 SAR is sensitive to near-surface soil moisture 
from grass covered soil and soil moisture retrievals are possible" (P. 210). 

Models 

In order to answer the many questions raised by the experimental field work, attempts have 
been m.ade to build models. These models attempt to include known physical properties 
which contribute to backscatter and to be configured in such a way as : 

• to assess backscatter sensitivity to parameter changes 

• to allow the identification of target parameters 

• to allow for extrapolation of results. 

A generally applicable model has yet to be developed due to the complexities of the subject. 



4.3.2 Classification Methods 

The data discriminants described in the preceding section must be mapped into a set of 
forest and non-forest land cover classes, i.e. a procedure must be found which maps a pixel 
with spectral/temporal characteristics X to a class C. Some references are made here to 
forest surveys outside the tropical regions where these are considered relevant to the 
tropical monitoring problem. 



Visual Interpretation 

Despite the availability of remotely sensed data in digital form and the development of 
many classification procedures for remotely sensed data (see next section) many of the 
surveys carried out to date have employed visual interpretation of photographic products, 
for example, (Kummer, 1992, Tardin and da Cunha, 1992). In fact, the vegetation 
monitoring study of the Landsat Pathfinder programme (see Section 3) uses visual 
interpretation as a major input (Townshend, pers. comm.). The procedure developed by 



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INPE and NASA for production of deforestation statistics relies on visual interpretation of 
imagery, digitisation of the boundaries, and entry of the results into a GIS (Justice, 1992). 
The advantages are that it is a relatively cheap procedure which can be conducted by people 
with local knowledge. Results may be more accurate than those obtained from computer- 
based methods, but this has not been proven. Kummer (1992) pointed to the failure of five 
different surveys of deforestation in the Philippines, four of which were visual 
interpretations of Landsat or SPOT data. He concluded that visual interpretations of photo- 
mosaics were more accurate with respect to forest cover than the computer-assisted 
classifications, however there were considerable local discrepancies. In particular there were 
differences in categorisation of different forest types such that the classification schemes 
were not strictly comparable. 

Much of the work done to date using radar data has used visual interpretation as the major 
tool for analysis. This is a response to the complexity of computer-based classification work 
and the power of the human eye to discriniinate textural classes. 



Computer Based Classifications 
Thresholding 

The application of a simple threshold to the digital data, either to a single channel, or to 
some composite of channels such as the NDVl is widely used in studies of tropical forest 
cover, e.g. (Stone and Woodwell, 1988, Mahngreau, et al, 1989, and Mahngreau, 1991). This 
approach is relatively simple to apply, since it consists of establishing a threshold level with 
all digital values above the threshold assigned to, say, forest, and all those below to, say, 
non-forest (MaUngreau and Tucker, 1990, Sawada, et al, 1991). The disadvantage of the 
method when using the AVHRR thermal channel is that it can only be applied locally 
because of spatial variability of surface thermal patterns, and that the same thresholds 
cannot be used from day-to-day because of intervening meteorological events (Malingreau, 
1991). The same restrictions would also apply to other spectral bands, and the problems of 
sensor calibration and atmospheric effects outlined in Section 4,3.1 make temporal 
comparisoT\s problematic. 

Supervised Classification 

Supervised classifications rely on the identification of 'training areas' of known cover type. 
Pixels are then assigned to the training class to which they most closely correspond using a 
set of statistical rules, for example Bayes maximum-Hkelihood (e.g. GasteUu-Etchegorry, et 
al, 1991). Recentiy, there has been a trend towards using a contextual component in this sort 
of classification in that the classes of the surrounding pixels are also considered (Gastellu- 
Etchegorry, et al, 1991, Tomppo 1989, Jaakkola, et al, 1988). The advantages of using these 
procedures are that they are repHcable and statistically robust. The disadvantage is that 
they require considerable effort and local knowledge in selection of suitable training sites. 
Also, it is often necessary to establish training sites for classes which may not be of 
particular interest, but which, since they are present in the data, would have to be 
differentiated from the forest classes. Supervised methods are useful where the user knows 
exactly which classes are required, i.e. in a user-driven as opposed to a data-driven scheme 
(see Section 4.1). 

Unsupervised Classification 

An vmsupervised classification procedure will segment the image into areas with similar 

spectral/temporal properties producing either a pre-determined number of classes, or a 



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data-determined number depending upon the exact type of procedure used. In all cases a 
statistical rule is used to establish the boundaries between classes in feature space. Once the 
classification has been made, it is necessary to assign labels to the classes produced. This 
requires considerable user-intervention and local knowledge, analogous to the selection of 
training sites described in the description of supervised classification above. It is possible to 
obtain classes for which no corresponding land cover type can be identified. If the 
procedure is restricted to producing a set nvimber of classes, statistical anomalies can result 
in very inaccurate results. Despite its wide use in other applications of remote sensing (e.g. 
Belward, et al, 1990) this method has not been widely used for tropical forest appHcations 
outside of the TREES project (see Technical Note No. 1), although Sawada (1991) provides 
an example. 

The imsupervised method is basically data-driven in that the 'intrinsic' classes in the data 
are determined, and then these must be assigned to 'actual' classes which may or may not 
be of interest to the user. 

Mixture Modelling and the Problem of Scale 

The problem of scale is inherent in any remote sensing classification. As described above 
and in preceding sections the basic assumption of a classification is that a spectral/temporal 
property can be uniquely related to a land cover class. In practice a pixel represents a sum 
of spectral responses from a variety of land cover types present in the pixel area. As spatial 
resolution increases the variation and complexity of this sum will increase. This implies that 
as resolution increases it will be increasingly difficult to make a unique assignment of a 
pixel to a land cover class. 

Ormsby, et al, (1987), simulated AVHRR pixels from composites of MSS pixels in an attempt 
to model the internal variability of vegetation in an AVHRR pbcel. Settle and Drake (in 
press) derived a scheme to model the potential mixture of cover classes within a pbcel. 
TowT\shend and Justice (1990) have examined the effect of variations in scale on vegetation 
monitoring. The only research that has looked at mixttire modelling, specifically for tropical 
forests, is Cross, et al (1991). _ _ , 

Classification of Radar Images 

Relatively little work has been done on the classification of radar images in comparison with 
the work on backscatter response. Radar images are different from optical images in one 
important respect; the existence of speckle. Speckle means that per pixel classifiers used in 
optical/ thermal images cannot be used to define regions of similar response. Instead, 
various techniques based arovmd segmentation have been devised. While per pixel 
grouping usually relies on aggregating individual pixels with similar values in a bottom up 
ser\se; segmentation starts at a higher level (note however that optical images can also be 
segmented). For example, Nooren, et al, (1985) took the whole image as the starting point 
breaking it into a number of squares. Using a 'split-and-merge' algorithm the mean and 
variance of each square is calculated and a decision taken whether to merge the squares. If 
the square is not merged, then it is subjected to splitting and a further test. A comparable 
technique was used by Conway, et al, (1991) in applying a clustering algorithm. This 
algorithm operates by analysing clusters of pixels based around points in the image and 
iteratively rrunimises variances within clusters. 

Once segments have been defined in the radar image, the second stage in the classification 
requires that they are labelled. This differs little in principle to the optical unsupervised 
case; i.e. the classes produced are compared to ground survey data in some way. However, 



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Conway, et al, (1991) use a labelling system which relies on comparing model-based cluster 
characteristics with those from the segmented image. The model is of backscatter from the 
land type, in this case arable crops, and is produced by modelling crop growth and 
predicting backscatter from the crop. The predicted backscatter from various crops is then 
compared to the backscatter of the segments. 

Ancillary Data 

The incorporation of ancillary information into segmentation schemes is becoming 
increasingly important (Gastellu-Etchegorry, et al, 1991, Tomppo, 1989, Itten, et al, 1991). 
For example, Gastellu-Etchegorry, et al include digitised elevation data as part of their 
iterative contextual classification of Sumatran forests and conclude significant 
improvements are obtained. Senoo, et al (1990) combine DEM data with SPOT data to 
improve classification of forests in Japan. Sawada, et al (1991) incorporate data on 
temperatiire and evapo-transpiration into their classification of Malaysian forests. 
Topographic data is also important in the analysis of radar images due to the relief effect. 

Ancillary data is naturally incorporated into many visual analyses, since the human 
interpreter does not classijfy on a per-pixel basis, but will naturally group the data into 
regions and can take account of local variatioris in the data, while also including contextual 
knowledge. 



4.3.3 Validation of Classifications 

In order to be useful, a classification of tropical forests should have some indication of its 
accuracy. This requires a combination of field survey and comparison with other data 
sources, for example with satellite data at a higher resolution. 

Itten, et al. (1991) compared the classified AVHRR data for Swiss forests with digitised map 
data and obtain 80.6% correct classification. GasteUu-Etchegorry, et al. (1991) used digitised 
vegetation maps in combination with local regional knowledge in validation of their 
Sumatran tropical forest classification using GAG data. Contingency tables of 'ground 
truth' versus sateUite-based classification were produced. They found that extrapolation of 
their results was difficult, especially where there was extensive cloud cover. Tomppo (1989) 
uses a similar procedure to analyse the accuracy of classifications of Finnish forests. 
Kummer (1992) compares the results of a computer-based classification of tropical forest 
cover in the Philippines with four other visually based classifications and found 
considerable discrepancies between the results, (see also Section 4.3.2). 

One problem with most validation schemes is that they require comparison of data 
classified at one scale with that classified at another (Kummer, 1992). Different sensors 
produce different sets of spectral/temporal measures, and different mixtures in each pixel at 
different spatial resolutions which leads to problems in class assignments. Additionally, if 
comparison is to be done accurately the data sets must be perfectly registered so that any 
changes between them are due to mis-classification rather than mis-registration. Work by 
Townshend, et al. (1993) has shown that even discrepancies of less than one pixel can lead to 
severe potential mis-interpretation of the data. Atmospheric effects, if not adequately 
corrected, can also lead to problems in comparison between results from different dates and 
different sensors (Ahem, et al, 1987). Accuracy figures must therefore be interpreted with 
this in mind. Further, comparison between, for example, AVHRR GAG, LAC and GVl data 
has been shown by Malingreau and Belward (1992) and Townshend and Justice (1986) to be 
problematic because the pixels used to obtain tiie data are not necessarily congruent. 



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4.4 Conclusions 

Consideration of the analytical methods outlined in the preceding sections suggest that the 
answers to the questions posed in Section 4.1 are being data-driven; the emphasis has been 
on 'how much information can be extracted from such and such a data set?' as opposed to 
an analysis of the monitoring requirements which might then lead to an identification of 
appropriate classification parameters (e.g. as required by Adams 1992). This is in large part 
due to the fact that most work is at the research rather than an operational level, so that the 
emphasis has not been on establishing operational criteria. Also, there has been a 
considerable research emphasis on the use of satellite data for vegetation monitoring in 
general, not just on the classification of tropical forests. The use of NDVI to identify areas of 
high green leaf biomass has different requirements from the classification of forest types 
which relate to the criteria described in Section 4.2. 

Spatial and temporal scales of analysis 

In considering global monitoring of tropical forests it is important to determine an 
appropriate temporal and spatial scale for such monitoring. Townshend and Justice (1988) 
identified the major factors controlling change detection to be: 

• spatial properties (registration and resolution) 

• radiometric properties (radiometric resolution, bandwidth, and calibration) 

• temporal properties (imaging frequency and repeat coverage). 

The thrust of their work is the identification of a globally applicable spatial resolution. 
Further work remains to be carried out on the temporal requirements. 

Empirical studies using different spatial scales have varied from the use of SPOT data 
(Ktmm\er, 1992, Senoo, et al. 1990) with high resolution but relatively infrequent coverage 
through to the use of GAC (Gastellu-Etchegorry, 1991) with low resolution but relatively 
frequent coverage. The cor\sensus seems to be that AVHRR LAC data provide a reasonable 
compromise between spatial and temporal resolution, and these data have been most 
widely used in tropical forest studies. However, there is also considerable use of Landsat 
TM data (e.g. in the NASA Pathfinder studies, see Section 3) and an apparent feeling in 
certain sectioris of the research corrunimity that it may be necessary to use data at this 
resolution to obtain sufficiently accurate results (Townshend, et al, in press, Jiistice, 1992). 

Little empirical work appears to have been done on routine morutoring of the same area 
using data sets obtained over an extended period, so that the requirements of a system of 
change detection have not been fuUy identified. 

Classification 

A major problem is to establish which classes are of interest, for example, whether the data 
are to be divided into forest.non-forest, or whether a further sub-division of the forest into 
different types such as degraded forest, seasonal forest etc., is needed. The result must be 
integratable into classifications carried out using groimd based methods (see Section 4.2). 

In considering work done on global, or even regional, monitoring of tropical forest cover it 
is surprising how little systematic work has been done. Very few actual classifications of the 
tropical forests have been carried out using automated techniques over more than a very 
localised area. There seems to be no broad corisensus as to which classes are appropriate 
even within a region. 



EOS-93/090-TN-002 23 



Review of the Capabilities of Remote Sensing TREES II Pre-feasibility Study 



There is general agreement that thermal and near infra-red images provide the best spectral 
discrimination of forest cover, but there is no agreement as to the optimum classification 
method. Visual interpretation is probably used most widely, but this seems to be for 
pragmatic rather than scientific reasons. Classifications using a supervised method and 
employing ancillary data such as elevation seem to meet with the greatest success (e.g. 
Gastellu-Etchegorry, et al, 1991). Classifications produced by this method should be able to 
correspond to non-remote sensing schemes which incorporate climatic data (e.g. Adams, 
1992, and the bioclimatic classification schemes described in Section 4.2). 

Validation 

Determination of the levels of classification accuracy attainable has received little attention, 
either in determining the accuracy attainable at a given spatial scale, or in considering 
methods for detecting levels of change in forest coverage over time. Problems of data 
registration with respect to monitoring accuracy have also received scant attention. It is not 
clear how the scale differences in classifications from different resolutions should be 
reconciled in accuracy determination. 

Radar Potential 

Radar can be seen to have significant potential in forest resource management. The ability 
of radar to distinguish forest structure, forest type etc., has been demonstrated. However, 
the concliision of most workers, at this time, is that considerably more research work needs 
to be done if radar is to become an operational tool. This is because of inadequate 
understanding of factors influencing variation of backscatter responses. A shortage of 
image data is one major reason why this has occurred. 

In comparison with optical /thermal systems, a great deal of research has gone into 
investigating the physical relationship between measured backscatter and the surface object. 
This is because the relationship is based not only on the immediate surface characteristics of 
the surface object, but also on the underlying structure of, for example, the crop canopy, 
which radar has the ability to penetrate. In addition, radar resolution, being generally 
greater than other sensors leads to the investigation of small scale individual phenomena 
such as tree trunks. AVHRR by contrast gives a response aggregated from a 1 km square. 

In conclusion, radar would appear to have great potential and may ultimately prove more 
powerful than optical systems for forest monitoring Unfortunately, the complexity and 
relatively poor data availability (at least at present) means that operational use in the near 
future, other than by using local scale airborne sensors, is unlikely. 

One initiative which should be mentioned here is that of Raney and Specter (1991), who 
proposed a system consisting of a space segment (i.e. a SAR instrument) optimised for 
tropical forest use and a ground segment optimised for the user. The characteristics of the 
proposed system (TREIS - Tropical Radar Environmental Information System) are: 

• a P-band SAR (long wavelength) 

• a low bit rate telemetry stream based around 25 kbits (as AVHRR) 

• on-board processing to reduce bit rate 

• 50 m resolution, 20 km swath width, continuous operation 

• twice yearly repeat of tropical areas 

• minimal processing requirements 

• PC class hardware distributed to many regional centres. 



24 EOS-93/090-TN-002 



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These characteristics constitute a response to many of the difficulties encountered with 
radar to date. The proposal has received support from the remote sensing community but 
has yet to be realised. 

Operationalisation 

A major attraction of monitoring with satellites is the potential application of automated 
operational procedures. However, although much research has been carried out on 
interpreting satellite data in tropical forest areas, this has not generally been with a view to 
producing an operational monitoring system for forests. 

In Section 4.1 a set of questions was posed covering the requirements of a classification of 
tropical forests for monitoring purposes. An attempt is made below to answer these 
questions in the light of the research work described in the preceding section. 

• What is the appropriate spatial scale of measurement for tropical forest monitoring? 
The generally accepted compromise between detail and coverage is 1 km2. 
Adams (1992 P. 21) believes that this scale 'avoids the problems of becoming 
distracted by small scale heterogeneity'. AVHRR LAC provides such coverage. 

• What temporal scale is required for monitoring (i.e. to identify seasonal effects)? 
Depends on the requirement and the forest type being monitored. For example, 
images from both wet and dry seasons each year are required to enable effects 
caused by deciduousness, soil moisture etc., to be accounted for. However, cloud 
cover problems mean that, in practice, aU available data are used to form cloud 
free composites. GAC data are sufficient in quantity to provide relatively 
frequent composites, but work using GAC has to date been with NDVI only. 
LAC data have not yet been collected in sufficient quanfities to enable frequent 
composites to be formed. 

• What temporal scale is required for change detection? 

No single answer is possible. FAO are aiming at 10 yearly inventory. Given the 
known rate of change in certain areas, more frequent coverage would be 
preferable. An ideal would be yearly with account taken of seasonal changes; 
but in practise, the frequency will be determined by data availability and 
technical considerations. 

• What classes should be extracted from the data ? 

This depends on the purpose of the analysis. Since there is no agreement 
concerning vegetation classification in general, stiU less for tropical forests; 
remote sensing work cannot be expected to follow any one scheme; even if this 
was possible. The requirements are for useful classes which can be extracted 
automatically and consistently across images and through time. It is not clear 
what these should be. 

• What classes can be extracted from the data? 

Unknown. The classes are currently data- driven (i.e. what can be extracted from 
the image); and there is no clear agreement as to what classes can be extracted 
from either AVHRR LAC, GAC or Landsat images. 



EOS-93/090-TN-002 25 



Review of the Capabilities of Remote Sensing TREES II Pre-feasibility Study 



Should the classification be based on established (non-remote sensing) schemes? 

Yes. Ideally, an established (if not universally agreed) system should be used. 

Adams' scheme takes some account of remote sensing capability. 

Should the same classes be extracted globally, or should the emplmsis be on classification 
appropriate to local conditions? 

Depends on requirements. For a global monitoring scheme returning aggregated 
statistics, classes must be consistent across the globe. However, for alternate (or 
additional) requirements where, for example, national concerns are paramount, 
then a regional (e.g. S.E. Asian) classification, tailored to particiilar surface 
conditions or data avaUabUity may prove more appropriate. 

VV?wf classification procedure best identifies the classes of interest? 

All work to date has been data-driven. The best of the results comes from 

supervised classifications using ancillary data (especially DEMs) or visual 

interpretation. 

Is there a set of classes which can be extracted operationally, even though these may not 
represent the full potential of the data? 

From work to date, the minimum classification which might serve operatiormUy 
is forest:non-forest. 

How should the results be validated? 

Validation and ground survey for classification purposes are easily confused. 
Schemes for validation in the sense of 'after the event' checking of classification 
results are established, but issues are outstanding (e.g. the effects of mis- 
registration, inadequate atmospheric correction and comparison of classifications 
at different scales). The validation for operational monitoring systems is 
undeveloped. 



26 EOS-93/090-TN-002 



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5. 
Summary and Conclusions 



5.1 Summary 

There is a considerable body of literature relating to the use of remotely sensed data for 
vegetation mapping and monitoring. There is relatively much less concerrung tropical 
forestry in particular. 

There have been several major initiatives for acquiring and managing data either indirectly 
(IGBP) or directly (FAO) for tropical forests. The use of AVHRR LAC/HRPT data is 
demanding in terms of management and the problem is far from solved, although recent 
work on catalogue interoperability and the establishment and consolidation of HRPT 
groimd stations in tropical areas gives cause for hope. Due to the spatial and temporal 
resolution of LAC, AVHRR is seen by many v^^orkers as a viable mapping and morutoring 
source. However, other groups espouse the use of Landsat images for mapping (GIMMS 
group, FAO) and possibly for monitoring (FAO sample strategy). There is therefore no 
consensus on the optimum data source. 

Radar work has been ongoing and apparently somewhat separate from work with other 
sensors. This is partly because of the local scale of results and partly as a result of the 
different techniques required for iriformation extraction (e.g. segmentation). Given the 
potential of radar, and the need for merging of data sources, this is unfortunate. 

Discriminants of parameters and classification of AVHRR and Landsat data is well 
established for vegetation in general in the form of the NDVI (GVI) data product. For 
tropical forests, the identification of discriminants is less well developed and the subsequent 
classification is almost totally vmdeveloped with only a handful of papers in the literature. 

5.2 Current Initiatives in Relation to TREES 

The stated objectives of TREES I are to produce a wall-to-wall map, the detection and 
monitoring of active deforestation areas and thirdly modelling of forest dynamics. The 
review in this document shows that these are ambitious objectives. In particular, the wall- 
to-wall map cannot easily be created from techniques and technology existing in early 1993. 
In particular AVHRR data in a consistent processed state are not readily available for all 
tropical areas; and discriminants of tropical forest parameters some of the many data 
sources which have been discussed are known and documented. However, classification on 
an operational basis (which implies the ability systematically to extract the same classes 
across time and space) is in its infancy. This is a major technical problem. 

The TREES I project has therefore had to break a considerable amount of new grovmd and 
can be seen to be addressing a significant gap in capabilities. 



EOS-93/090-TN-002 27 



Re\'iew of the CapabiliHes of Remote Sensing TREES II Pre-feasibility Study 



6. 
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Kummer, D. M., 1992. Remote Sensing and Tropical Deforestation: A Cautionary Note from 
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Technical Note No. 3 



Analysis of Users and Requirements 



WCMC-93/TN-003 



Analysis of Users and Requirements TREES II Pre-feasibility Study 



Contents 



1 . Introduction 1 

2. The User Community 3 

2. 1 Intergovernmental Organisations 3 

2.1.1 Commission of the European Communities 3 

2.1.2 The World Bank 5 

2.1.3 Global Environment Facility 5 

2.1.4 Regional Development Banks 6 

2.1.5 International Tropical Timber Organisation 

(TITO) 7 

2.2 United Nations Agencies 8 

2.2.1 UN Food and Agriculture Organisation 

(FAO) 8 

2.2.2 United Nations Environment Programme 

(UNEP) 9 

2.2.3 United Nations Development Programme 

(UNDP) 10 

2.2.4 Unesco 10 

2.2.5 EEC member state governments 12 

2.3 Governments of Non-tropical Countries 12 

2.4 Governmental Agencies in Tropical Forest Countries 14 

2.5 International Non-governmental Organisations 16 

2.5.1 International Union for Conservation of Nature and Natural 
Resources (lUCN) 16 

2.5.2 Birdlife International 17 

2.5.3 World Wide Fund for Nature (WWF) 

International 17 

2.5.4 Worid Resources Institute (WRI) 18 

2.5.5 The Nature Conservancy 18 

2.5.6 Friends of the Earth International (FoE) 18 

2.5.7 Forest Stewardship Council 19 

2.6 National Non-governmental Agencies 19 

2.7 The Forest Research Community 19 

2.7. 1 International Union of Forestry Research 

Organisations (lUFRO) 20 

2.7.2 Centre for International Forestry Research 

(CIFOR) 20 

2.7.3 European Tropical Forest Research Network 

(ETFRN) 20 

2.7.4 CIRAD-Foret 20 



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Analysis of Users and Requirements 



4. 



2.7.5 The International Geosphere-Biosphere 
Programme (IGBP) 

2.7.6 The World Climate Research Programme 
(WCRP) 

2.8 Global Change Community 

2.8.1 The Intergovernmental Panel on Climate 
Change (IPCC) 

2.8.2 Global Change and Terrestrial Ecosystems 
(GCTE) 

Analysis of User Requirements 

3.1 Resolution of Information 

3.2 Coverage of Information 

3.3 Information Frequency 

3.4 Information Categories 

3.5 Requirement Priorities 

3.6 User Group Requirements 

3.7 Conclusions 

Translating Information into Action Programmes 
4.1 Key Considerations in the Development of TFIS 



21 

21 
22 

22 

22 

23 
26 
29 
32 
32 
37 
37 
41 

44 
46 



References 



48 



Appendix A Letter Introducing TREES 

Appendix B TREES - Provision of Tropical Forest Information 

Appendix C Questionnaire - User Profile and User Needs 

Appendix D List of Respondents and Summary Comments 

Appendix E List of Current Sources of Supply 

Appendix F Requests for Information Summarised by Information Type 



A-1 
B-1 
C-1 
D-1 
E-1 
F-1 



WCMC-93/TN-(X)3 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



1. 

Introduction 

Deforestation is a major global issue of concern, recognised by the general public, business 
community, scientists, policy makers and politicians throughout the world. Tropical 
deforestation has received the most attention over the past two decades with concern spreading 
more recently to problems of deforestation in temperate and boreal regions. The degradation and 
loss of tropical forests remains a particularly critical issue in biodiversity conservation and of 
foremost importance in the global change debate. 

Despite the attention received by tropical forests, understanding of the effects of tropical 
deforestation have often been superficial. The research and information base on tropical forests 
remains generally weak, particularly at an international level. The extent and rate of loss of the 
tropical forest resource have not yet been effectively measured at a global coverage, with a 
particular weakness being the lack of spatial registration of statistical data. There are as many 
estimates as there are reports about the subject. The most comprehensive study so far is the 
FAO Tropical Forest Resources Assessment Project (1980 and 1990 baselines), compiled at the 
national level. 

Despite the fundamental information gaps, a wide range of organisations are mandated to 
reverse the trend of tropical deforestation, using currently available information to guide their 
policies. A review of the objectives and policies of organisations using tropical forest 
information is essential to guide future information collection, retrieval priorities and decision- 
making. 

The analysis of users and their requirements carried out for the TREES II Pre-feasibility Study 
has drawn on a knowledge of organisations involved in tropical forest issues and their data needs 
as understood by WCMC and the results of a questionnaire, which was specifically designed for 
the study. The questionnaire focused on the use of satellite-derived data and its integration in 
geographic information systems (GIS), with various forms of ancillary data. Computerised 
mapping and monitoring systems that once were expensive and controlled mainly by government 
agencies are now being more widely used by the private sector, non-governmental organisations 
(NGOs) and individuals. The primary objectives of the questionnaire were to gauge the level 
of interest in TREES, assess potential users' requirements and applications of such systems in 
monitoring tropical deforestation, and to enable potential users to input their suggestions into 
further development of TREES. 

Names and addresses of users of forest information are stored in a contacts database maintained 
by WCMC. For the purposes of the questionnaire survey users have been divided into nine 
broad categories as follows: 

• global change community (30) 

• government agencies of tropical forest countries (229) 

(forest departments, protected area agencies and some research organisations) 



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Analysis of Users and Requirements TREES 11 Pre-feasibility Study 



• United Nations agencies (18) 

• intergovernmental agencies and programmes (59) 

• international non-governmental organisations (22) 

• governments of non-tropical forest countries (13) 

• national non-governmental agencies (180) 

• forest research organisations (92) 

• timber traders (12) 

Total (655) 

The figures in brackets show the number of users who received questionnaires in each category. 
As can be seen the number of questionnaires sent to different user groups varies considerably, 
and for example, relatively few commercial organisations (such as timber traders) were 
contacted. In some cases more than one individual was contacted in a particular organisation, 
or a range of regional offices of an organisation may have been contacted. In total 655 potential 
users have been identified and contacted for the current survey. The responses to the 
questionnaire are stored in a FoxPro database and an analysis of the results is presented in 
Section 3. 

The objectives and activities of some of the major forest information users, all of which were 
contacted in the questionnaire survey, are described in Section 2. Because of the European basis 
to the TREES Project, this brief review of the main users concentrates to a certain extent on the 
European perspective on tropical deforestation by looking, for example, at initiatives of the 
Commission of the European Communities (CEC). The inter-linkages between European 
governments and other governments in tropical forest initiatives are considered through the work 
of UN agencies and membership of international conventions and agreements. The policies and 
bilateral aid programmes of selected donor countries are briefly summarised and the information 
needs of tropical forest countries are considered in relation to the enhancement of their data 
sources by international means. The policies and information requirements of the broad 
spectrum of NGOs involved in tropical forest issues are also analysed together with the needs 
of the forest research and global change communities. 



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2. 
The User Community 



The user community of tropical forest information is large and diverse and it would be 
extremely difficult to cover all users in this report. However, the following section outlines the 
forest activities and profiles of selected users and presents a useful summary of some of the 
organisations and programmes concerned with the conservation of tropical forests. 



2.1 Intergovemmental Organisations 

The intergovemmental organisations and programmes involved in tropical forest conservation 
and land use issues have in general developed their own information systems or have contractual 
arrangements with other environmental agencies such as lUCN, WCMC and national NGOs or 
environmental consultancies. In addition certain international organisations and programmes 
have arrangements to receive forest information from national members on a regular basis, e.g. 
ITTO. 



2.1.1 Commission of the European Communities 

The role of the European Economic Community in tropical forest conservation is set out in the 
communication *The Conservation of Tropical Forests: the role of the Community' (Anon, 
1989). Five main elements are specified for a community tropical forest conservation strategy: 

• development aid and cooperation 

• actions relating to the trade in timber 

• development of additional resources 

• 'debt for conservation ' strategies 

• research and development. 

Information needs in relation to developing and implementing the strategy are not specified in 
the original document. The importance of satellite monitoring is however stressed as a means 
of assessing deforestation. The remote sensing techniques utilised by JRC are stated to be: 'a 
useful tool for research into deforestation and, therefore, they can be utilised for the control of 
the tropical forestry situation'. 

Coordination with other international initiatives was envisaged as central to the strategy, 
particularly in the implementation of elements 1 and 2 above through the Tropical Forestry 
Action Programme (TFAP) and International Tropical Timber Organisation (ITTO). The 
strategy set out in the communication was only adopted in part by the Council, who developed 
their response largely in the context of TFAP and ITTO. The Council Resolution, 'Tropical 
forests: Development aspects' was adopted by the Council of Ministers for Development 



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Cooperation in May 1990. The information requirements necessary for implementation of TFAP 
and ITTO are discussed below. 

The current Lom6 Convention gives considerable emphasis to the importance of tropical forests, 
both in the general provisions, and in articles on environment, agricul^J^al cooperation and food 
security, drought and desertification control, energy development, and regional cooperation. 

The Council resolution, together with the Lom6 Convention and other cooperation agreements 
provide a broad outline of CEC policy relating to tropical forest conservation. The Directorate- 
Generals (DGs) of the Commission which have remits covering tropical forest activities are 
shown in Table 1. 

The provision of financial assistance is one of the main ways by which the Commission assists 
forest conservation and development in tropical countries. Over the past ten years more than 250 
projects have been funded, in 61 countries, principally in Africa. A major project underway at 
present with support from the Commission is the, 'Pilot Programme to conserve the Brazilian 
rain forest', co-funded by the World Baidc. 

The main sources of CEC funds for forestry conservation and development were, until 1992, 
those in the context of the Lom6 Convention and financial and technical assistance to Latin 
America and Asia. A supplementary budget line was opened in 1992: 'Actions in favour of 
tropical forests ' resulting from an initiative of the European Parliament. The overall strategy 
for funding, under this budget line, based on the 1989 Communication, is to promote tropical 
forest conservation by '/j implementing - and verifying -protection and sustainable management 
measures for significant areas of forest; and ii) developing, diffusing and monitoring the impact 
of technologies and systems for increasing living standards of the populations of forest regions 
through sustainable resource use'. 

Applied research and monitoring are also considered important aimed both at increasing 
knowledge of the overall trend of deforestation and how it relates to policy factors and actions 
in the field, and at identifying at a micro level the impacts of EC cooperation in the fields of 
sustainable development and protected areas. Remote sensing and geogrz^hic information 
systems are two areas of interest. 

In March 1993, the Commission published a, 'Proposal for a Council Regulation (EEC) on 
operations to promote tropical forests ' . This sets out the criteria and procedures for Community 
support to conservation and sustainable management of tropical forests. Under the Regulation 
the Commission is required to submit an annual rq)ort to the Council and to the European 
Parliament, specifying the actions that have been financed and summarising their progress and 
conclusions so that monitoring of activities can take place. 

The Commission is integrating environmental considerations into the appraisal process for all 
aid programmes and projects. Evaluations based on monetary estimation of environmental 
impacts are planned. 



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Table 1: Responsibilities for Tropical Forest Activities within the CEC 



- 

DG 






I 


External 
Relations 


Development and aid in Asian and Latin American countries 

Amazonian rainforest protection programme 

UNCED 


VI 


Agriculture 


Relationship with FAO 


vm 


Overseas 
Development 


Aid to ACP countries under the Lom^ Convention 

nro 


XI 


Environment 


Global change 
CITES 


xn 


Research and 
Development 


Consultative Group on International Agricultural Research (CGIAR) 
European Tropical Forest Research Network 


XVI 


Regional Policy 


EC's Regional Fund (ERDF) - supporting work in Guyane Franjais 



2.1.2 The World Bank 

The World Bank's environmental activities involve policy dialogue, lending, technical 
assistance, and research and aid coordination. They are designed to: 



• assist member countries in setting priorities, building institutions, and 
implementing programmes for sound environmental stewardship 

• ensure that potential adverse environmental impacts from Bank-financed 
projects are addressed 

• assist member countries in developing linkages between poverty reduction 
and environmental protection 

• address global environmental challenges through participation in the Global 
Environment Facility (GEF). 

In its Forest Policy Paper adopted on 18 July 1991, the World Bank stated that it will not fund 
commercial logging in tropical moist forests. Full Environmental Impact Assessments (EIAs) 
are required for all infrastructure projects, for example roads, dams and mines, that may affect 
tropical moist forests or other primary forest. The Bank will continue to emphasise programmes 
that involve institutional development, forest protection measures and non forest income- 
generating projects, the primary objective of which are the preservation of tropical moist forest. 



2.1.3 Global EnTironment Facility 

The Global Bivironment Facility (GEF) is a three year pilot facility created to address four 
global environmental issues: conservation of biodiversity; limitation of greenhouse gas 
emissions; reduction of pollution in international waterways; and protection of the ozone layer. 
The GEF is jointly managed by the World Bank, UNEP and UNDP and is housed in the World 
Bank. Overall funding for the GEF is estimated to be about $1.3 billion over three years. 
Funding has been pledged by 24 countries, including nine from the developing world. Most of 
the biodiversity projects under the GEF are concerned with creating protected areas or building 



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park management capacity. 

2.1.4 Regional Development Banks 

The African Development Bank (AfDB) produced an Environment Policy Paper in June 1990. 
This provides guidelines for environmental impact assessment of both project and non-project 
loans and includes a brief on biodiversity. More recently AfDB has developed a draft forestry 
policy paper. The Bank proposes not to fund logging in primary forests; not to engage in 
developments affecting forest dwellers without their consent; and to routinely carry out 
environmental impact assessments for projects in forested areas. 

The Asian Development Bank (AsDB) has developed guidelines for incorporating EIAs into the 
Bank's project cycle and has incorporated biodiversity conservation into its Forest Sector Policy 
Paper. AsDB is very interested in remote sensing and related capabilities and has compiled 
several papers on the subject. The Bank has an Office of the Environment. 

The Inter-American Development Bank (IDB) has an Environmental Protection Division created 
in 1990 to ensure that IDB operations comply with member country legislation and the 
guidelines on EIA developed by the Bank. EIAs are required for IDB projects that have minor 
or major environmental impacts. 



Table 2: Global Environment Facility (GEF) biodiversity projects 



GEF inTestment projects 
COUNTRY PROJECT 



TARGET 



Congo Congo Tropical Forest Lowland rain forest 

Preservation 
Kenya Lower Tana River Primates 

Uganda Gorilla Reserve Bwindi Forest 

Bhutan Trust Fund for Environment 

Conservation 
Laos Wildlife and Protected Areas 

Management 
Philippines Conservation of Priority 

Protected Areas 
Brazil National Conservation Units 

Mexico Biodiversity Conservation 



Riverine forest 

Lowland and montane forest 

Lowland, temperate and alpine 

forests 

Lowland and montane forest 



10 high priority protected 

areas 

25 conservation units 

20 protected areas 



GEF technical assistance projects 



COUNTRY 

East Afiica 

West/Central 

Viet Nam 
South Pacific 
Colombia 

Guyana 
Amazon 



PROJECT DESCRIPTION 



ASSOCIATED 
PROJECT 

Free-standing 

EBRD US$30m 

Free-standing 

Free-standing 

IBRD USSlOm 

IBRD US$lS8m 

IBRD US$1 17m 
IBRD US$30m 



FUNDING 
US$iniIIions 

10.00 

6.20 

4.00 

10.00 

5.50 

20.00 

30.00 
30.00 



Subtotal 140.70 



Support for traming, research, equipment and institutional development of 
government, umversity, and NGOs worfang in protected area management 
Alhca Establish a regional TRAFHC office in Zaire and develop capacity to monitor both 
legal and illegal trade in wildlife 

Training/institutional development to prq>are a plan for protected areas 
Establish and manage 20 conservation areas with threatened biodiversity 
Assess diversity of the Choco Region through capacity-building research with a view 
to developmg plans for protection and sustainable use 
Protect a large tract of rain forest, study the impact of local management 
Institutional strengthening within the eight members of the Treaty for Amazonian 
Cooperation 



FUNDING 

Smillions 

10.00 

1.00 

3.00 
8.20 
9.00 

3.00 
4.50 



Total GEF biodiversity funding 

Source: World Conservation Monitoring Centre, 1992 



Subtotal 38.70 
179.40 



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2.1.5 International Tropical Timber Organisation (TTTO) 

ITTO was created in 1985 under the auspices of UNCTAD. Since its establishment, riTO has 
increasingly been called upon to promote and instigate tropical forest conservation, although this 
is only one of the eight original objectives of the International Tropical Timber Agreement 
(HI A). The CEC, in its tropical forest conservation strategy, and European governments, such 
as Denmark, the Netherlands and UK cite membership of ITTO and support for its conservation 
initiatives, as a major element in their contribution to tropical forest conservation. 

illO's stated objective in relation to conservation is: 

'to encourage the development of national policies aimed at the sustainable utilisation 
and conservation of tropical forests and their genetic resources and at maintaining 
the ecological balance in the regions concerned '. 

The means by which ITTO is implementing this objective are through: 

• projects relating, for example, to sustainable timber production and the 
development of extractive reserves in Brazil and Bolivia 

• the production of guidelines for sustainable management and guidelines for 
conserving biological diversity in production forests 

• Target 2000. 

In 1990 the 46 member governments of ITTO agreed to ensure that all tropical timber marketed 
internationally should, by the year 2000, come from forests that are managed sustainably. 
Implementation of sustainable management for timber production requires information on 
resource extent, size and condition, management intention and quality of execution. Mechanisms 
for reaching ITTO's ambitious target of transforming forest management have not been fully 
formulated. One procedure that has been introduced, however, is regular reporting by members 
to the International Tropical Timber Council (TTTC) on progress towards reaching Target 
lOOOwhich will. 

The ITTO Forest Resource Accounting Activity is designed to supply a framework, first for 
accurate and comprehensive international reporting and secondly for regular national assessment 
and quality control of forest management. The Forest Resource Accounting system is being 
designed for ITTO by TTFD and WCMC to provide a system whereby countries make progress 
towards reaching Target 2000. This will be achieved by assessing and monitoring the location, 
legal status, area, vegetation type, condition and management status of all forest lands in a 
country and by specifying targets for each of these measures. The system can be used by each 
country as a tool for monitoring the progress of its policies, designed in such a way that more 
information that is relevant to management can be added where desired. There are three levels 
of information to be recorded: 

• site details (management area/concession area level) 

• country statistics compiled by the national Forest Authority from all information 
about sites within the country 

• global statistics compiled by ITTO from country statistics from all ITTO member 
countries. 



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The ITTA is now being renegotiated and its future direction is unclear. Many producer 
organisations feel that the agreement should cover all timbers, both temperate and tropical, and 
that the mandate of ITTO should be more focused on trade-related policies that have an impact 
on limiting deforestation. 



2.2 United Nations Agencies 

The General Assembly of the United Nations and its component bodies provide the main focus 

for intergovernmental discussion and action on global change issues. 

These are amongst the major users and distributors of global forest information. 



2.2.1 UN Food and Agriculture Organisation (FAO) 

The mandate of FAO is to support development efforts in fisheries, forests and agriculture. The 
Forestry Department of FAO has responsibility for the promotion of national and international 
action for the effective management and use of forest and related resources as an integral part 
of sustainable land use. There are three main areas of activity: 

• the collection, analysis and dissemination of data on all aspects of forestry to 
assist member countries in the formulation of policies and the planning of 
forestry investment 

• assistance with the development and adaptation of appropriate systems for 
integrating resource management with industrial processing to ensure socio- 
economic development needs are met equitably 

• promotion of the systematic development of the forestry profession so as to 
maximise the contribution of the forestry sector to sustainable development. 

FAO through its professional staff at the headquarters in Rome, regional offices, and field 
projects, is involved in a wide range of forest conservation initiatives. The conservation-related 
initiative which has provided a focus for political support and which has received most public 
attention is the international Tropical Forestry Action Programme (TFAP). 

FAO has been the primary agency involved in the collection of global information on rates of 
tropical deforestation. Forest Resources Assessments have been carried out to 1980 (FAO, 1981) 
and 1990 (not yet published) baselines. These global figures have been generated from the 
national level and are based on national assessments of the extent of forest cover. 

FAO together with the Netherlands National Aerospace Laboratory (NLR), Wageningen 
Agricultural University (WAU) and the International Institute for Aerospace Surveys and Earth 
Sciences (TTC) have also initiated a feasibility study on a global operational forest cover 
monitoring network using satellite remote sensing data within the framework of TFAP (Looyen 
et al. , 1993). The development of a Forest Assessment and Monitoring Environment (FAME) 
is proposed. Firstly, the establishment of a pilot project (PFAME) is suggested which will 
include: 1) selection of pilot countries; 2) making demonstration products available for the pilot 
countries for local to national use; 3) evaluating the products with the local users; 4) refining 



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products, to cover the requirements of the end user, and 5) assessing the remote sensing products 
and developing a prototype Remote Sensing Preprocessing and Archiving System (RESPAS). 
Execution of this pilot project will lead to the development of a full scale FAME. 



2.2.2 United Nations Environment Programme (UNEP) 

UNEP was established in 1973 and charged with working with governments, other UN 
organisations and NGOs around the world to coordinate and catalyse action on the global 
environment. 

UNEP coordinates the Global Environment Monitoring System (GEMS) and the Global 
Resource Information Database (GRID), elements of the United Nations Earth watch programme. 
GEMS is a collective international programme to acquire, through global monitoring and 
assessment, the data that are needed for the rational management of the environment. GRID 
provides an environmental data management service throughout the United Nations. Data from 
satellites, aircraft and ground survey are incorporated into the system. WCMC is currently 
negotiating formal status as the biodiversity node of GEMS. 

UNEP Harmonization of Environmental Measurement office (UNEP-HEM) as part of GEMS 
is involved in a number of catalytic and coordinating activities aimed at improving the 
compatibility of environmental data on a global coverage. UNEP-HEM has recognised the 
importance of developing an improved, practical and widely-acceptable global classification 
scheme for vegetation classification. It has been considering various approaches to this problem 
in close cooperation with WCMC and also the International Geosphere Biosphere Programme 
core project Global Change and Terrestrial Ecosystems (IGBP-GCTE). The role of IGBP-GCTE 
is considered below. 

UNEP was the coordinating body for the United Nations Conference on Environment and 
Development (UNCED) held in Rio de Janeiro, Brazil, June 1992. The principal outputs of 
UNCED, in relation to forest conservation are: 

• Convention on Climate Change. This was negotiated prior to Rio and signed at the 
Rio meeting by 153 States. 

• Convention on Biological Diversity. This was also negotiated prior to Rio and 
signed by 153 States, together with the European Community, during the meeting. 

• Forest Principles. (Non-legally Binding Authoritative Statement of Principles for a 
Global Consensus on the Management, Conservation and Sustainable Development 
of aU Types of Forests.) Element 1(c) of the Forest Principles states that, 'the 
provision of timely, reliable and accurate information on forests and forest 
ecosystems is essential for public understanding and informed decision-making and 
should be ensured '. 

• The Rio Declaration on Environment and Development. This is a set of 27 
Principles intended to encapsulate the results of discussions at the Rio Conference. 



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Agenda 21. This is a comprehensive action plan aimed at reversing the effects of 
environmental degradation and promoting environmentally sound and sustainable 
development in all countries. Chapter 11 deals with combatting deforestation and 
identifies the actions needed to achieve this. The stated objectives are to sustain the 
multiple roles and functions of all types of forests, to enhance their protection, to 
sustainably manage and conserve forests through rehabilitative means, to promote 
efficient utilisation of forest goods and services through full valuation of these 
resources, and to strengthen capacities for the planning, assessment and systematic 
observation of forests. 



2.2.3 United Nations Development Programme (UNDP) 

UNDP has developed Environmental Management Guidelines, as a means of incorporating 
principles of environmental management into its work. UNDP funds a wide range of 
environmental projects including those which encourage sustainable development and the 
improvement of the 'quality of human life' . 



2.2.4 Unesco 

Unesco has responsibility for the World Heritage Convention (The Convention concerning the 
protection of the World Cultural and Natural Heritage). This aims to protect natural and cultural 
areas of 'outstanding universal value' as World Heritage Sites. There are currently thirteen 
World Heritage tropical rain forest sites, as listed in Table 3 below. 

The principal aim of listing sites of universal value, under the World Heritage Convention, is 
to foster international cooperation in safeguarding these important areas. Monitoring the 
integrity of sites is of major importance but, at present, there is no standard methodology in 
place to do this. 

The conservation of tropical forests is an integral part of the Unesco Man and the Biosphere 
(MAB) Programme. A set of five interlinked types of tropical research activity is undertaken 
within the framework of the MAB Programme and related Unesco activities: 

• biological diversity, traditional ecological knowledge, and integrated 
conservation in the humid tropics 

• ecological and economic sustainability of tropical rain forest management 

• forest regeneration and ecosystem rehabilitation in the humid tropics 

• tropical soil fertility and its biological management 

• savaima ecology and management; responding to stress and disturbance. 

The overall objective of this work is to contribute to the development of sustainable land- 
use systems appropriate for the social, cultural and biological characteristics of the 
peoples and ecological systems of the humid and sub-humid tropics. Under the MAB 
Programme, internationally important areas are protected as Biosphere Reserves. These 
are selected and managed as natural or minimally-disturbed representative examples of 
the world's ecosystem types. They are also selected to demonstrate the relationship 
between conservation and development and may include buffer zones with varying levels 



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of human use and exploitation. Tropical moist forest Biosphere Reserves are listed in 
Table 4. 



Table 3: World Heritage Tropical Rain Forest Sites 



PROTECTED AREA 


COUIMIKY 


SIZE (ha) 


Dja Fauna] Reserve 


Cameroon 


526,000 


Mount Nimba Reserve 


Guinea 
Cote d'lvoire 


5,000 


Tai National Park 


Cote d'lvoire 


350,000 


Kahuzi-Bi6ga National Park 


Zaire 


600,000 


Salonga National Park 


Zaire 


3,656,000 


V^runga National Park 


Zaire 


780,000 


Talamanca Range and La Amistad Reserves 


Costa Rica 


193,929 


Rio Platano Biosphere Reserves 


Honduras 


350,000 


Darien National Park 


Panama 


597,000 


Manu National Park 


Peru 


1,532,806 


Northeast wet tropics 


Australia 


■> 


Sundarbans National Park 


India 


1 


Sinharaja Forest Reserve 


Sri Lanka 


7,648 



In cooperation with various UN and other agencies, including lUCN and the U.S. Smithsonian 
Institution, MAB is engaged in a programme of inventorying and monitoring the vegetation in 
the biosphere reserves. A system of inventory is being created to make a global estimate of the 
extent to which biosphere reserves assist in the conservation of biodiversity. WCMC maintains 
for Unesco a structured database for both World Heritage sites and Biosphere reserves. 

Table 4: Biosphere Reserves Containing Tropical Moist Forest 



PROTECTED AREA 


COUNTRY 


SIZE (ha) 


Pilon-Lajas 


Bolivia 


100,000 


Reserve Forestiere et de Faune du Dja 


Cameroon 


500,000 


Basse-Lobaye Forest 


CAR 


18,200 


Dinghu Nature Reserve 


China 


1,200 


Odzala National Park 


Congo 


111,000 


Reserve Naturelle integralle d'Ipassa-Makokou 


Gabon 


15,000 


Bia National Park 


Ghana 


7,700 


Monts Nimba 


Guinea 


17,130 


Ziama Massif 


Guinea 


116,170 


Tal National Park 


Cote d'lvoire 


330,000 



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Gunung Gede-Pangrango 


Indonesia 


14,000 


Lore Lindu 


Indonesia 


231,000 


Tanjung Puting 


Indonesia 


205,000 


Gunung Leuser 


Indonesia 


946,000 


Sibenit 


Indonesia 


56,000 


MoDtes Azules 


Mexico 


331,200 


Omo Reserve 


Nigeria 


460 


Parque Nacional Fronterizo Darien 


Panama 


597,000 


Manu Reserve 


Peru 


1,881,200 


Puerto Galera 


Philippines 


23,545 


Sinharaja Forest Reserve 


Sri TanVa 


8,850 


La Luki Forest Reserve 


Zaire 


33,000 


Reserve Floristique de Yangambi 


Zaire 


250,000 



2.2.5 EEC member state govermnents 

Within the EEC, the Dutch Government is the only Government to have published a 
comprehensive policy document on tropical rain forests. Implementation of the policy is by 
means of nine basic strategies: 

• active protection of surviving virgin rain forest 

• in principle, no collaboration with projects and developments that are harmful or 
potentially harmfiil to the rainforest 

• encouraging planned land use and land management along with sustainable 
agriculture and forestry 

• the tropical timber trade: controlled harvesting; encouraging the formulation and 
implementation of long-term planned timber production 

• national and international encouragement for afforestation and re-afforestation 
projects 

• strengthening institutions and legislation; empowering local populations 

• strengthening the political and social base in tropical nations 

• improving economic relations and relieving the debt burden 

• increasing scope for national and international tropical rain forest policy by 
strengthening research and institutions. 



2.3 GoTemments of Non-tropical Countries 

Governments of non-tropical countries have become increasingly involved in tropical forest 
conservation over the past fifteen years, in response to scientific and public concern. In some 
cases this has resulted in the formulation of national tropical forest policies. Governmental 
agencies within European and other bilateral aid donor countries require information to support 
tropical forest policies and funding mechanisms. These are usually contracted out to independent 



12 



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research organisations. 

In 1987 the German Government appointed the Enquete Kommission to carry out an extensive 
study on the protection of tropical forests (Deutscher Bundestag, Drucksache 1 1/7220, 24.5.90). 
Policy recommendations of the Enquete' s report focus primarily on national initiatives with a 
programme of initiatives at EC level also suggested. These include the reform of agricultural 
policies, a ban on tropical timber imports from primary forests, reforms in aid policies and 
procedures and a substantial increase in community level funding for tropical forest protection. 

The UK Government does not have a comprehensive stated policy on tropical forest 
conservation. In 1990, a House of Lords Select Committee reviewed the EC Communication 
'The Conservation of Tropical Forests: the role of the Community' and its conclusions include 
the following: 

• The primary aim of the Community's strategy should be to support 
improved management of tropical forests and the areas which surround 
them. It is essential to develop locally appropriate forms of natural forest 
management to ensure that the forests have a long term value as a source of 
income for local people. 

• It is important to establish an adequate network of protected areas where 
biological conservation is the main priority. More intensive economic activities, 
such as agriculture, plantation crops, and processing facilities should be 
encouraged in land beyond the natural forest boundaries. 

• Aid from the Community should be directed towards those activities which 
contribute to sustainable forest management. These are likely to include the 
rehabilitation of disused and degraded land, support for agricultural 
improvements on land adjoining forests and the selective establishment of 
plantations, as well as improved natural forest management and the creation of 
reserves. 

The UK Government's Overseas Development Administration (ODA) takes a lead on 
international forest conservation matters, including ITTO and TFAP, and is responsible for 
bilateral and multilateral forestry aid provision. The main objectives of the ODA Forestry 
Initiative are to help: 

• enable developing countries to maximise the economic and social benefits they 
enjoy from their forests in a sustainable way 

• limit deforestation by tackling its causes and supporting forest departments and 
other institutions in developing countries charged with conservation and 
management 

• promote reforestation of degraded land and agroforestry 

• increase the productivity of forests through research 

• conserve the planet's bank of plant and animal species, most of which are imique 
to tropical forests 

• promote the more productive use of existing agricultural land and the 
identification of alternatives for people on the margins of forests to unsustainable 
exploitation of forest resources. 



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The UK supports around 200 forestry projects in developing countries. Over 180 are funded 
through ODA, some as part of TFAP, and the remainder are managed by the Commonwealth 
Development Corporation (CDC). A current project relating to the conservation and sustainable 
development of the forests of the Western Ghats in India will be the largest forestry project ever 
funded by the UK. Management will be based on five zones with the innermost zone designated 
for biodiversity conservation. 

The USDA Forest Survey conducts forest research in tropical and subtropical forests of the US, 
including tropical areas ie Puerto Rico, Hawaii, Florida, and provides assistance overseas. 
USAJD currently provides more than $60 million annually for biological diversity conservation. 
USAID programs in natural forest management, buffer zone management and forestry policy 
reform are aimed at stopping deforestation and protecting biodiversity while developing the 
economic potential of natural areas and their buffer zones for both timber and non-timber forest 
products. Other Agency activities focus on environmental education; efforts to strengthen 
legislation, policies and institutions relevant to conservation of biodiversity and tropical forests. 
US-MAB also supports work in tropical forests. 



2.4 Governmental Agencies in Tropical Forest Countries 

The governments of tropical forest countries have the primary responsibility for tropical forest 
utilisation and conservation through the formulation of national policies. In reality these are 
unlikely to be successful unless the needs of the fuU range of forest users within the countries 
are satisfied. Mechanisms to control unplanned deforestation at a national level are generally 
weak at present because of 1) limited financial and technical resources, 2) strong external and 
internal pressures which promote over-exploitation of forest resources and forest conversion to 
other land uses. 

Some countries continue to promote deforestation through legislation which requires forest 
clearance as a means of establishing legal claim to imoccupied lands. In other cases deforestation 
is indirectly encouraged through fiscal incentives and subsidies for cattle ranching (Blockhus et 
al., 1992). 

Initiatives to control deforestation at a national level include the formulation of forest 
conservation policy, improved land use planning, the imposition of legal and fiscal controls on 
logging, the reduction of timber felling and other steps towards meeting the ITTO Target 2000 
and the extension of protected area systems. A few examples of national measures are: 

• India introduced a new National Forestry Policy in 1988 with the achievement 
of ecological stability stated to be the primary objective of all forest 
management. 

• The Philippines and Thailand have introduced logging bans in recent years in 
response to problems caused by deforestation. 

• Indonesia has 18.7 million ha of totally protected areas and 30.3 million ha of 
protection forests, the primary function of which is to protect watersheds. 



14 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



International factors clearly have a major influence on tropical deforestation but the extent to 
which international solutions can be found depends on the response of national governments. 
Obligations to the international community arise primarily from membership of international 
legal agreements. 

In order to formulate forest policies and prevent unplanned deforestation access to all available 
data should be guaranteed. There has, in the past, been a tendency to accumulate information 
on forest resources and biodiversity in non-tropical countries and little of the information has 
been repatriated. Space-derived data on tropical forests has also largely been processed in a 
limited number of non-tropical centres of expertise and are not easily accessible. 

The majority of forestry agencies in tropical forest countries will have access to detailed national 
forest information carried out by them or on Lheir behalf. National departments very often have 
their own up-to-date, large scale (down to compartment level) paper maps and satellite imagery 
which they use for management and planning. Some national departments already provide their 
own statistics from satellite-derived imagery; e.g. INPE (National Institute for Space Research) 
in Brazil, or work in collaboration with FAO, which uses high resolution imagery to estimate 
forest extent from national level sampling. However, national users may wish to compare their 
own data with neighbouring countries and problem areas could be identified on the less detailed 
imagery which may be missed by less-frequently gathered Landsat or SPOT imagery. Certainly, 
AVHRR imagery would be extremely useful for tracking threats to the forest such as fires; often 
countries are not aware of where forest fires are burning because of inaccessibility. It may be 
useful to receive information on incursions into legally gazetted forest areas. National 
departments may also be pleased to acquire repatriated information, in digital or map format and 
may welcome prototype information to initiate or enhance existing in-country monitoring 
systems. It is often evident that important data exist outside a country but which are not 
available in-country. 

The extent to which government agencies in tropical countries are already using data derived 
from remote-sensing for forest assessment varies considerably. The Royal Forest Department 
in Thailand, for example, has a highly developed information management system including 
remote sensing and a cartography department, whereas the Forestry Department of Cambodia 
is lacking in resources. The Directorate General of Forest Inventory and Land Use Planning, 
Indonesia has a National Forest Inventory Project which will be completed by 1996. The 
nationwide mapping using satellite data, mainly Landsat MSS (1986-91) has been completed in 
phase-I of the Forest Resource Monitoring activities, and, in phase-H, starting 1992, a Digital 
Image Analysis System has been implemented using Landsat TM. The Forestry Department of 
Sri Lanka prepared a national forest map at nominal 1:250,000 scale in late 1992 by 
interpretation of 1992 TM imagery. This preliminary map is available in digital (ARCINFO) 
form. A more detailed and field-checked map at nominal 1:50,0(X) scale is currently being 
prepared fi-om the same imagery and will be complete by mid 1994. This will form a major 
input to a national forest GIS and will serve as a baseline against which to measure future 
change in forest area. The new map will also be available in ARCINFO format. The national 
forest map divides closed canopy forest into seven categories of natural forest, based on 
elevation, rainfall, and other characteristics (eg mangroves) and four species categories of forest 
plantations (W.R.M.S. Wickramasinghe, in litt. 1993) 



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Analysis of Users and Requirements TREES n Pre-feasibility Study 



2.5 International Non-governmental Organisations 

There is a very wide range of NGOs involved in the conservation of tropical forests around the 
world. The policies, activities and related data needs of a selection of these are given below. 

International environmental NGOs collect information on forest ecosystems at the project level 
as well as utilising global assessments from a variety of sources. Few currently have the 
capacity to effectively monitor forest extent nationally or internationally although they require 
these data for use in formulating and assessing proposals and strategies. Others, such as WCMC 
and lUCN, are already trying to address these deforestation issues and have established 
sophisticated geographic information systems. 



2.5.1 International Union for Conservation of Nature and Natural Resources (lUCN) 
lUCN - The World Conservation Union is unique amongst NGOs in that it brings together 
States, government agencies and NGOs in a world partnership. The work of lUCN is 
coordinated with the environmental responsibilities of FAO, Unesco and UNEP through the 
Ecosystem Conservation Group. 

The forest conservation activities of lUCN are coordinated by the Forest Conservation 
Programme based at lUCN's headquarters in Gland, Switzerland. The Programme focuses on 
the conservation of species and ecological processes, and promotes forest uses which are 
compatible with the conservation of biological diversity. Special emphasis is given to the 
development of compatible uses for buffer zones around national parks and reserves. 

The Forest Conservation Programme deals with global and local policy issues and undertakes 
field projects. The Central African Project for the conservation and sustained use of forest 
ecosystems, funded by the EC (DGVIU), is an example of an approach developed by lUCN to 
regional forest conservation. Over one hundred sites of critical importance for biodiversity 
conservation have been identified and measures proposed to ensure their conservation. A 
network of pUot projects has been designed to demonstrate different aspects of a community- 
based approach to forest resource management. 

The Forest Conservation Programme works closely with WCMC, and utilises, for example, the 
Centre's spatial data on tropical forests maintained on a GIS, data on plant and animal species, 
and information on forest sites important for conservation. The Centre also acts as a rgwsitory 
for data gathered under lUCN auspices. 

The WCMC database of tropical forest maps held on an ARC/INFO GIS called the Biodiversity 
Map Library (BML), based on the most recently available sources, provides the only currently 
available systematic global forest cover database in digital form. The data are available for 
dissemination to the user community in digital or paper format and form the basis for a three 
volume joint lUCNAVCMC publication The Conservation Atlas of Tropical Forests (Collins, 
N.M., Sayer, J.A. and Whitmore, T.C., 1991; Sayer, J.A., Harcourt, C.S. and Collins, N.M, 
1992). 



16 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



The Biodiversity Programme of lUCN promotes the concept of biodiversity among lUCN 
members and partners. It provides a comprehensive overview for the wide range of lUCN 
activities which contribute to conserving biodiversity. The Programme provides a focus for 
requests for policy advice from governments, lUCN members and the lUCN Regional Offices. 
The Biodiversity Programme also advises GEF, UNEP and UNDP on biodiversity issues. 

The lUCN Plant Conservation Programme has undertaken a project with collaboration from 
botanists around the world to identify the several hundred major Centres of Plant Diversity 
(CPD). These are defined as places particularly rich in plant life which would, if protected, 
safeguard the majority of wild plants in the world. A table showing the tropical forest sites for 
which data sheets have been prepared is given in Appendix D. 



2.5.2 Birdlife International 

Birdlife International is the new name for the International Council for Bird Preservation 
(ICBP). Birdlife International has identified priority areas for global conservation by analysis 
of information on bird distribution (Bibby et al. , 1992). Birds are good indicators because they 
occur in most land habitats throughout die world, and are sensitive to environmental change. 
Bird taxonomy and distribution are better known than for any other reasonably large group of 
animal or plant taxa. 

In order to analyse patterns of bird distribution locality records were gathered for all bird 
species with breeding ranges below 50,000 sq km. In total, about 51,000 separate locality 
records of these birds have been computerised and mapped with the aid of GIS. Analysis shows 
that species of restricted range tend to occur together in places which are often islands or 
isolated patches of a particular habitat, especially montane and other tropical forests. Boundaries 
of these species groupings have been identified and defined as Endemic Bird Areas (EBAs). 221 
EBAs have been identified, 76% of which are located in the tropics. Birdlife International works 
closely with WCMC and are currently adding mapped EBAs to the WCMC Biodiversity Map 
Library. By overlaying EBAs onto forest cover and protected areas information, the 
conservation status of these areas can be evaluated. 



2.5.3 World Wide Fund for Nature (WWF) Intemational 

WWF is the world's largest private intemational conservation organisation with 28 Affiliate and 
Associate National Organisations around the world and over 4.7 million regular supporters. The 
intemational headquarters of WWF is in Switzerland. 

The conservation and rehabilitation of forests and woodlands has been one of WWF's three 
primary goals for the five years 1987-1992, the other two major goals being preservation of 
global biodiversity and the conservation of wetlands and coasts. The WWF Forest Programme 
covers the conservation of both tropical and temperate forests. A WWF Intemational Position 
Paper published in 1989 sets out the main areas of interest of WWF Intemational in tropical 
forest conservation (WWF Intemational, 1989). These are: 

• establishment and management of protected areas 

• sustainable forest management 



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• rights of indigenous forest-dwellers 

• TFAP 

• ITTO 

• regulation of trade in tropical timbers 

• macro-economic issues: debt, agricultural subsidies and policies, terms of trade, 
development aid agencies. 

At present WWF does not have an integrated strategy for forest conservation work. The 
organisation has a series of policy activities, relating to the above issues, conducted mostly at 
international level and, over one hundred field projects, mostly concentrating on protected area 
management in the tropics. 

The Forest Working Group of WWF has proposed that links between international policy 
objectives and the field programme should be improved by the preparation of country plans 
(Forest Working Group, 1992). These would be prepared nationally with targets for forest 
conservation listed and a strategy for how to achieve them. To measure progress towards the 
targets Country Data Sheets, containing such data as protected area coverage and deforestation 
rates would be prepared and updated annually as part of a monitoring and evaluation process. 
At an international level, the country plans would be taken together to assess progress towards 
achievement of global targets and to identify priority areas for further action. 



2.5.4 World Resources Institute (WRI) 

WRI, sited in Washington DC, is an independent research and policy institute founded in 1982 
to help governments, environmental and development organisation and the private sector, 
address sustainable resource use and development issues. The work of WRI focuses on six broad 
areas: climate, energy and pollution; forests and biodiversity; economics; technology; resource 
and environmental information; and institutions. 



2.5.5 The Nature Conservancy 

Established in 1951, The Nature Conservancy is an international, non-profit environmental 
organisation, sited in the USA, which is committed to the protection of natural diversity. The 
Nature Conservancy works with local conservation organisations throughout Latin America, 
helping to build their conservation capacity. A current cooperative campaign involving more 
than 30 conservation organisations, is 'Parks in Peril'. This aims to improve management of 
200 key protected sites in Latin America and the Caribbean by the end of the century. 

The Nature Conservancy is involved in the establishment and operation of a network of 
Conservation Data Centres (CDCs), through the provision of technical, scientific and 
administrative support and training. The Conservancy also makes available the computer 
technology, data inventory and management methodology and procedure manuals on which the 
CDC network is based. 



2.5.6 Friends of the Earth International (FoE) 

FoE International is a network of member groups in over 50 countries, with an international 



18 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



secretariat based in Amsterdam. Various national groups campaign on tropical forest issues with 
policy and research priorities decided at a national level. 

In 1989, FoE commissioned a comprehensive survey carried out, firstly, to determine the 
current rates of tropical deforestation and, secondly, to appraise the consequences thereof for 
emissions of greenhouse gases, notably carbon dioxide (Myers, 1989). 



2.5.7 Forest Stewardship Council 

The goal of the Forest Stewardship Council (FSC) is to set a worldwide standard for good forest 
management by promoting widely recognised and respected Principles of Good Forest 
Management. It will act as a forum to provide consistency in forest certification schemes. The 
draft FSC Principles and Criteria for certification will complement existing national and 
international laws and forestry initiatives of ITTO, lUCN, TFAP, UNCED and the Forest 
People's Charter. 

FSC is an initiative involving British NGOs and industry and acting in consultation with 
governments. An increasing number of influential NGOs and major companies are becoming 
involved. USAID, the EEC, GTZ and private corporations have expressed interest in funding 
the work of FSC. 



2.6 National Non-goyemmental Agencies 

There is a very wide range of environmental NGOs concerned with tropical forest conservation, 
with groups based in both tropical and non-tropical countries. These range from radical single- 
issue campaigning organisations through to long-established learned societies. The resources and 
expertise of national NGOs varies equally widely. Their role in tropical forest conservation is 
increasingly being recognised by governments and inter-governmental organisations. The 
International Tree Project Clearinghouse (TTPC), a programme of the UN Non-Govemmaital 
Liaison Service, exists to support the NGO and grass roots communities involved in forestry 
related activities. This support takes the form of information collection and dissemination. 

As an example, the Malayan Nature Society, founded in 1940, is a non-government organisation 
dedicated to the study, conservation and enjoyment of the Malaysian natural heritage and the 
surrounding region. The Society has about 2800 members in 34 countries and is the oldest as 
well as biggest scientific organisation in Malaysia. From a relatively passive conservation body, 
the Society has become a more active campaigning organisation. It has, for example, lobbied 
against the logging of forests of Endau-Rompin and the buUding of a dam in Taman Negara. 



2.7 The Forest Research Community 

A large number of individuals and organisations are working in forestry research. This research 
may cover a wide range of topics and it is more difficult to pin-point specific user needs. These 



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Analysis of Users and Requirements TREES II Pre-feasibility Study 



may comprise international programmes and research organisations such as CIFOR (the new 
Centre for International Forestry Research), or universities, such as Wageningen in the 
Netherlands, and Reading in the UK, who are actively involved in remote sensing work. Other 
research organisations may be involved in plantation forestry or working at the forest species 
level to identify tree species for reforestation for instance, such as the Oxford Forestry Institute 
(OFI), Edinburgh, Bangor and Aberdeen universities in Britain. 



2.7.1 International Union of Forestry Research Organisations (lUFRO) 

lUFRO is currently preparing guidelines for international forest monitoring. The intention of 
the guidelines is to promote international uniformity between forest data collectors. Institutions 
and researchers conducting remote sensing studies or setting up permanent plots on forested 
lands, will be encouraged to follow the standards and definitions provided in the guidelines. 



2.7.2 Centre for International Forestry Research (CIFOR) 

Established in early 1993, CIFOR is one of 18 international agricultural research centres 
supported by the Consultative Group on International Agricultural Research (CGIAR), and is 
the focal point within CGIAR for strategic forestry research. CIFOR is a non-profit, 
autonomous, scientific research and training institute with headquarters at Bogor in Indonesia. 

CIFOR has a global mandate, but wiU concentrate in the tropics during the next five years. Four 
research programmes are planned: 

• social sciences, economics, policy analysis and development 

• ecology, conservation, management of natural forests 

• rehabilitation of degraded and depleted forest lands 

• utilisation and marketing of forest goods and services. 

In addition to these four research programmes, CDFOR will have one programme to provide 
generic support through database and library systems, together with dissemination of research 
results, education and training. CIFOR will be concentrating on tropical forests for the next five 
years and will require regular, accurate data on forest extent, condition, type, protection and 
deforestation. 



2.7.3 European Tropical Forest Research Network (ETFRN) 

ETFRN was established in 1991 in order to provide information and services to support research 
on tropical humid and dry forests. This includes all research areas related to the tropical forest 
environment. The aim of ETFRN is to increase the cooperation and consultation of research 
institutions, governments and industry of European and tropical countries through weU targeted 
information management. 



2.7.4 CIRAD-Foret 

CIRAD-Foret (formeriy known as CTFT) is one of the seven departments of CIRAD (Centre 
de cooperation intemationale en recherche agronomique pour le diveloppement) a French state- 



20 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



owned organisation. It is the agency responsible for tropical forestry and wood science. CIRAD- 
Foret's main partners are FAO Forestry Department, Man and Biosphere Programme of 
Unesco, lUCN, ITTO and the International Council for Research in Agroforestry (ICRAF). One 
of the organisation's main areas of research is natural forest management in the humid tropics. 
Forest survey and mapping is an important aspect of this work, using remote sensing combined 
with ground checking. 



2.7.5 The International Geosphere-Biosphere Programme (IGBP) 
IGBP was established in 1986 by the International Council of Scientific Unions (ICSU). Over 
50 countries have national IGBP Committees, including most EC member states with the 
exception of Luxembourg, Portugal and Spain. The aim of IGBP is: 

To describe and understand the interactive physical, chemical and biological 
processes that regulate the total earth system, the unique environment that it provides 
for life, the changes that are occurring in this system, and the manner in which they 
are influenced by human activities. 

Six core projects are currently being undertaken within the IGBP framework. The one most 
relevant to tropical forest conservation issues is Global Change and Terrestrial Ecosystems 
(GCTE), which is described in Section 2.8.2 below. 

Each core project of IGBP is involved in data analysis and model buUding. Synthesis of these 
activities is guided by the IGBP Task Force on Global Analysis, Interpretation and Modelling 
(GAIM). The aim of GAIM is 'to advance the development of comprehensive prognostic models 
of the global biogeochemical system, and to couple such models with those of the physical 
climate system ' . 

The IGBP Data and Information System (IGBP-DIS) addresses the generic issues of data 
processing and management for land, ocean and atmosphere systems, in close liaison with 
GAIM and IGBP core projects. IGBP-DIS has paid particular attention to the use of AVHRR 
data in the provision of information relevant to global change issues. IGBP-DIS provides the 
link between IGBP and the Committee on Earth Observation Satellites (CEOS), an international 
association of agencies with interests in the collection, validation and dissemination of space data 
relevant to global change. It is expected that IGBP will be involved with the five working 
groups set up by the Space Agency Forum on the International Space Year (SASIFY) including 
those on: global land cover change; the World Forest Watch; and the global satellite image 
mapping project. 



2.7.6 The World Clunate Research Programme (WCRP) 

The WCRP is the research component of the World Climate Programme, jointly sponsored by 
ICSU; the World Meteorological Organisation (WMO) and the Intergovernmental 
Oceanographic Commission. 



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Analysis of Users and Requirements TREES n Pre-feasibility Study 



2.8 Global Change Community 

One of many research activities within the global change community is to understand the effects 
of changes in climate, atmospheric composition and land use on terrestrial ecosystems and to 
determine feedback effects to the physical climate system. Forest conversion will have a local 
affect and may have a global affect on numerous physical processes. To predict affects on the 
natural ecosystem, using modelling techniques, this user group requires forest information at 
periodic intervals to evaluate changes in forest cover and the consequences of this loss at a 
global level. 



2.8.1 The Intergovernmental Panel on Climate Change (IPCQ 

IPCC was jointly established in 1988 by the World Meteorological Organisation (WMO) and 
UNEP. The Panel's charge was to: 

• assess the scientific information that is related to the various components of the 
climate change issue, such as emissions of major greenhouse gases and 
modification of the Earth's radiation balance resulting tiiere from, and that is 
needed to enable the environmental and socio-economic consequences of climate 
change to be evaluated; and 

• formulate realistic response strategies for the management of the climate change 
issue. 

The Panel has established three working groups to assess available scientific information on 
climate change; assess the environment^ and socio-economic impacts of climate change; and 
formulate response strategies. The Panel has prepared its First Assessment Report which 
summarises the findings of the three working groups. 

EPCC collects data from a wide variety of sources to support the work of its Working Groups. 



2.8.2 Global Change and Terrestrial Ecosystems (GCTE) 

The overall objective of GCTE is 'to develop a predictive understanding of the effects of 
changes in climate, atmospheric composition and land use on terrestrial ecosystems, and to 
determine feedback effects to the physical climate system ' . A global vegetation map is urgently 
needed by IGBP-GCTE for model verification and development in predicting global change 
effects. IGBP-GCTE has therefore been working closely with UNEP-HEM and WCMC on the 
development of a global vegetation classification scheme (see above). 

GCTE is currently developing the LEMA (Long-term Ecological Modelling Activity) network 
which should be operational by the end of 1993. LEMA will link together ecological modelling 
groups around the world to facilitate model development through exchange of data, models and 
model components (Steffen, in lift. 1993). 



22 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 

3. 
Analysis of User Requirements 



I 



The results of the questionnaire survey conducted for this report indicate the high level of 
interest amongst a wide range of forest information users in the provision of information 
resulting from TREES. The response rate itself is indicative of the strong demand for an 
information system based on satellite-derived imagery. Despite the tight deadlines of this 
evaluation of user needs, by the end of May the overall response rate was 21 % from a total of 
over 655 questionnaires sent out. In many of the organisations contacted the staff resources 
available to deal with forest conservation issues in general are very limited, particularly in the 
NGO sector. Furthermore travel to international meetings and involvement in field-based 
activities restricts the office time available to respond to enquiries. This became particularly 
apparent in following up questionnaire responses by telephone. 

Indicative responses showing enthusiasm for the development of the TREES project are: 

• 'It certainty appears that TREES will produce a most valuable set of data on tropical 
forests, one that will be very usefUlfor GCTE modellers. ' - GCTE project of IGBP. 

• 'UNEP-HEM is cooperating with WCMC and IGBP in a project to look at ways of 
developing a new system for vegetation classification, in particular for global 
mapping and monitoring. In this context we are very interested in the type of 
information available from the TREES project and ways of using it in the 
classification. ' - UNEP-HEM 

• 'The Commission has recently started a special programme on tropical forests that 
will greatly benefit from the data collected and processed under the TFIS. ' - CEC- 
DGI/J, Directorate for Asia 

• 'We realised that the TREES project is very usefid and would like to congratulate the 
Commission of the European Communities and the European Space Agency (ESA)for 
their joint effort. FRIM will be looking forward to actively participating and 
contributing to the project. ' - FRIM (Forest Research Institute Malaysia) 

• 'TREES is a very interesting and valuable project. WWF HK has been using CIS to 
establish an ecological database of Hong Kong for more than two years. We welcome 
any opportunity to participate in the TREES project and to integrate our database for 
the integrated forest information system which you have mentioned. ' - WWF-HK 



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Analysis of Users and Requirements 



TREES n Pre-feasibility Study 



• 7 look forward to working closely with WCMC and the TREES Project and agree 
that rapid access to an integrated forest information system will contribute immensely 
to conservation efforts in the future/ - Olson, WWF-USA 

• "The information you can send to us will be of great use to the work that Fundacion 
Natura carries out. This information will be shared out through the Latin American 
forest network which our institute coordinates. ' - Fundacion Natura 

The response rate to the questionnaire by different user groups is shown in Table 5 below. It 
is important to take care when comparing percentage responses from different user groups. 
Information given about user group G (national NGOs) represents 40 respondents (22% of the 
total sent to that group) while information about group I (timber traders) represents only one 
respondent (8% of the total sent to that group) and therefore cannot be taken as representative. 
It is also important to note that the overall size and resources of different user groups varies 
considerably, and the numbers of questionnaires sent to these user groups varies accordingly. 
However, no attempt has been made in this evaluation to compile a definitive list of potential 
users. 



Table 5: User response to questionnaire survey 




II USERS 


Response - end of May | 


Sent 


Received 


% 


A Global change community 


30 


11 


37 


B National forestry departments etc 


229 


31 


14 


C UN agencies 


18 


5 


28 


D Intergovernmental agencies 


59 


10 


17 


E International NGOs 


22 


8 


36 


F National governmental agencies 


13 


3 


23 


G National NGOs 


180 


40 


22 


H Forest research community 


92 


31 


34 


I Timber traders 


12 


1 


8 


Total 


655 


140 


21 



The response rate varies between the different user groups. Group B (national agencies and 
research organisations in tropical forested countries) have responded (14%) below the mean % 
response (24%), whereas group A (global change community), group E (international non- 
governmental organisations) and group H (forest research) have aU responded with enthusiasm. 
However, out of those in group B who have responded, the need for TFIS is very apparent and 
individuals concerned are keen to be kept up to date with TREES progress. Indeed, a few 
organisations have stressed their interest in becoming involved with TREES; e.g. FRIM will be 
looking forward to actively participating and contributing to the project (Hamzah, in litt, 1993). 



24 



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This difference in response rate may also be attributable to slower and less reliable 
communication links in Southern countries. 

Only one respondent from the timber traders has answered and therefore no deductions can be 
drawn for this user groups. This may be because the timber traders are not interested in the 
service being offered by TREES or because only a small number (10) were sent the 
questionnaire. 

Appendix D lists all those who have responded within the targeted user groups and their 
comments on the usefulness of TREES to them and some of their own activities. These 
comments are an extremely valuable addition to the yes/no type answers which the questionnaire 
mostly comprises and should be taken fully into account. Specific comments are incorporated 
into the discussion below. Appendix E contains a list of potential users' current sources of forest 
information. The specific sources of forest survey data most frequently cited by respondents 
of the survey are FAO followed by WCMC/IUCN, but this varies from user group to user 
group. Government agencies in tropical forest countries cited data from a variety of national 
sources and in very few instances mentioned international organisations as a source of 
supplementary data. High resolution satellite data, aerial photographs and ground surveys are 
currently of great importance to the national organisations. Of the organisations currently using 
satellite data, Landsat TM is the most frequently cited source, followed by SPOT XS and 
Landsat MSS. 

The format in which respondents required data is shown in Figure 1. 

Only 4% requested raw satellite data, the preferred options being paper maps 19%, reports 
20%, digital format 17%. There is clearly a need for centralised processing of satellite-derived 
forest data and its provision in appropriate formats. There appears to be a higher incidence in 
use of GIS in the global change (73%) and forest research communities (77%) compared with 
other user groups (51% total). It is interesting to note that compared to the national 
organisations in tropical forested countries with an average occurrence of GIS, are not 
particularly interested in an on-line service (group B, Figure 2), whereas the global change 
community preferred this as a method of data delivery (group A, Figure 2). However, 
interestingly, for GIS occurrence, group B (52%) fair better than intergovernmental agencies 
(30%) and international non-governmental agencies (13%). The detection of vegetation type and 
vegetation condition from satellite data are cited most frequently as applications of processed 
data being considered. 



WCMC-93/TN-003 25 



Analysis of Users and Requirements 



TREES II Pre-feasibility Study 



Figure 1 Information format requirements 



N»vs I etrer * 



Paper mpfl 19X 




nvorta ^4* 



On-I fne servto^T* 



ClasEffled sat. datMX 



Nwre letter ITK 
Classified sat. datlOX 

^^^^^^^ satelllts dstsIX 

Dlflltal rormat 1''«^^™'^^hoto products 1CH Paper mapa » 

Overa I I response __™._.^ 

Olfllt.l fcr™t 2« """' "'^ products » 

User group A 

Navstetter UK 




~r>-llr» eervlceZSSE 



Paper impa ZQK 



Digital rormt 15X 




Reports 22SE 



Oft- II no eervlceix 



ClBseiriad aat. datlQX 



%v sets I I Its dstaax 
Phcrto products ISX 



User group B 



The questionnaire survey has highlighted major requirements of the satellite-derived data and 
these are indicated below. Each database record represents one targeted potential user who has 
responded. (The list of contacts for forest information are maintained in a contacts database, 
ordered by user group.) Simple statistics and counts have been derived from the database and 
are examined below. Unfortunately, potential users were given no more than 6 weeks to 
respond, but an overall response rate of 21% is sufficient to draw conclusions. Users replying 
after the end of May have not been included in this evaluation. 



3.1 Resolution of Information 

Demand for information at high and medium resolutions is much higher than demand for low 
resolution information. The questionnaire results are summarised in Table 6 and Figures 2 and 
3, while more detailed results are given in Table 7 and Figure 4. 

Figure 2 shows the number of respondents requesting each level of resolution while Figure 3 
shows the mean percentage response. To derive this latter figure, the percentage response of 
each user group was calculated and an average of all nine figures was calculated. This figure 
adjusts for distortion in the results caused by the varying number of responses from each user 
group. For example. Figure 2 shows that a larger number of respondents requested high 
resolution information compared to medium resolution. Table 7 shows that the greatest demand 



26 



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Analysis of Users and Requirements 



for high resolution data comes from the national forestry departments, of whom 74% requested 
this information. However, there were 31 respondents in this group (22% of all respondents) 
compared with, for example, only three in group F (national NGOs). Their requirements, 
therefore, make a much greater contribution to the overall results than those of lower-responding 
groups. However, until further responses are received even the mean percentage response should 
be treated with caution since, by giving equal weight to user groups rather than to respondents, 
it assumes that user group results are always representative. This certainly cannot be assumed 
for groups C, F and I where only five, three and one responses respectively have been received 
to date. 

Table 6: Resolution of data requested by respondents 



% respondents 
requesting data 



% information 
requests 



high < 1:100,000 

medium 

low > 1:1,500,000 



62 
55 
19 



47 
41 
12 



All user groups require some information at all resolutions, with the exception of the timber 
traders where only one response has been received to date. 



Figure 2 Resolution - number of responses 



£ 




<i:iDD.ODO ymafim irvsintiiron 

Level of roeolution requirement 



WCMC-93/TN-003 



27 



Analysis of Users and Requirements 



TREES II Pre-feasibility Study 



Figure 3 Resolution - mean % response 




<1:10a,0Da IMIUB 1:1-3 nlllron 

Level of r&solutlon requirement 



Table 7: Information requirements - resolution of data 



Users 


Level of resolution requirement | 


<1:1{)0,000 


Medium 


1:1-5 million | 


n= 


% 


n= 


% 


n= 


% 


A 


5 


45 


6 


55 


1 


9 


B 


23 


74 


17 


55 


2 


6 


C 


2 


40 


2 


40 


3 


60 


D 


5 


50 


6 


60 


2 


20 


E 


5 


63 


4 


50 


3 


38 


F 


1 


33 


2 


67 


1 


33 


G 


25 


63 


22 


55 


6 


15 


H 


21 


68 


17 


55 


8 


26 


I 








1 


100 








Total 


87 


62 


77 


55 


26 


19 


Mean % response 




48 




60 




23 



28 



WCMC-93/TN-003 



TREES II Pre-feasibility Study 



Analysis of Users and Requirements 



Figure 4 Resolution requirement per user group 




IcItlQQ.OOQ 



User groLps 
iuedtum I 



1 1:1-5 ml I I Ion 



Key: 

A = 
B = 



C 
D 

E 
F 
G 
H 
I 



Global change community 

National forestry departments, protected areas agencies 

and research organisations in tropical forested countries 

UN agencies 

Intergovernmental agencies and programmes 

International non-governmental agencies 

National governmental agencies 

National non-governmental agencies 

Forest research community 

Timber traders 



3.2 Coverage of Information 

Respondents' requests for infonnation at coverages from local to global are given in Table 8 
and Figures 5, 6 and 7. Most respondents requested national infonnation, but more respondents 
required local information than global information. The order of requests was as follows: 



national 


104 


respondents 


local 


77 




regional 


73 




subnational 


67 




global 


59 





WCMC-93/TN-003 



29 



Analysis of Users and Requirements 



TREES n Pre-feasibility Study 





rable 8: Information requirements 


- coverage 








Users 


Coverage of information requirement as recorded 


Local 


Subnational 


National 


Regional 


Global 


n= 


% 


n= 


% 


n= 


% 


n = 


% 


n= 


% 


A 


4 


36 


5 


45 


6 


55 


7 


64 


8 


73 


B 


16 


52 


12 


39 


22 


71 


12 


39 


9 


29 


C 


2 


40 


3 


60 


2 


40 


3 


60 


4 


80 


D 


6 


60 


5 


50 


8 


80 


5 


50 


6 


60 


E 


5 


63 


3 


38 


8 


100 


7 


88 


6 


75 


F 














2 


67 


1 


33 


1 


33 


G 


21 


53 


20 


50 


33 


83 


20 


50 


12 


30 


H 


23 


74 


19 


61 


22 


71 


17 


55 


13 


42 


I 














1 


100 


1 


100 








Total 


77 


55 


67 


48 


104 


74 


73 


52 


59 


42 


Mean % response 




42 




38 




74 




60 




47 



Figure 5 Scale of information - number of respondents 




Scale of Information requirement 



30 



WCMC-93/TN-003 



TREES n Pre-feasibility Study 



Analysis of Users and Requirements 



Figure 6 Scale of information - mean % response 



c 
o 
a 
u 
o 




acaie of imormatlon requirement 



F^ure 7 Scale of information by user group 



S « - 



« 




■Local 
i Regional 



User 9-OLFis 
Isubnatlonat ^National 
I Global 



Seven of the nine user groups, representing 97% of respondents, requested information at all 
five levels. The scope of the user groups, not surprisingly, reflected the scope of their 
requirements. So, for example, the global change community and UN agencies requested 
information with more or less increasing frequency as the coverage moved from local to global. 
International NGOs, national forestry departments etc., intergovernmental agencies and national 
NGOs showed greatest need for national information with demand decreasing at both higher and 



I WCMC-93/TN-003 



31 



Analysis of Users and Requirements 



TREES II Pre-feasibility Study 



lower resolutions. The forest research community showed a preference for information at more 
detailed (local, sub-national and national) levels. 

3.3 Information Frequency 

All information types were requested yearly more frequently than at any other interval. There 
was little preference shown between 3- and 5-year intervals. There were few requests for 
information at 10-year intervals, but slightly more requests for information to be received once 
only. Overall responses were as follows: 



yearly 
3-yearly 
5-yearly 
10-yearly 
once only 



% of 

respondents 

56 

40 

36 

12 

17 



% of total 

requests 
40.0 
23.5 
22.5 

4.5 

7.5 



The full results are presented in Table 9. 



Table 9: Information requirements - frequency 





Frequency requirement 


Users 


Yearly 


3-yearly 


5-yearly 


lO-yearly 


Once only || 




n= 


% 


n= 


% 


n= 


% 


n= 


% 


n= 


% 


A 


3 


27 


1 


9 


7 


64 


2 


18 


3 


27 


B 


17 


55 


15 


48 


11 


35 


4 


13 


8 


26 


C 


1 


20 


2 


40 


4 


80 


1 


20 








D 


5 


50 


4 


40 


4 


40 


2 


20 


1 


10 


E 


7 


88 


4 


50 


1 


13 














F 


1 


33 


1 


33 


3 


100 


1 


33 








G 


29 


73 


16 


40 


8 


20 


4 


10 


6 


15 


H 


15 


48 


13 


42 


12 


39 


3 


10 


6 


19 


I 


1 


100 


























Total 


79 


56 


56 


40 


50 


36 


17 


12 


24 


17 


Mean % 




55 




34 




43 




14 




11 



3.4 Information Categories 

Number of requests for information in each of the eight categories (forest boundaries, fires, 
roads etc) are shown in Table 10. 



32 



WCMC-93/TN-003 



I 



TREES n Pre-feasibDity Study 



Analysis of Users and Requirements 









Table 10: Information requirements 


- categories 








Users 


Type of information required | 


CFB 


FF 


R 


PA/FR 


B 


FT 


TV 


BIO 


n = 


% 


n = 


% 


n = 


% 


n = 


% 


n = 


% 


n = 


% 


n = 


% 


n = 


% 


A 


8 


73 


5 


45 


5 


45 


6 


55 


8 


73 


8 


73 


4 


36 


8 


73 


B 


26 


84 


14 


45 


14 


45 


23 


74 


14 


45 


24 


77 


16 


52 


24 


77 


C 


5 


100 


2 


40 


4 


80 


5 


100 


3 


60 


5 


100 


3 


60 


5 


100 


D 


8 


80 


6 


60 


6 


60 


8 


80 


5 


50 


8 


80 


6 


60 


9 


90 


E 


6 


75 


4 


50 


6 


75 


8 


100 


2 


25 


6 


75 


4 


50 


5 


63 


F 


2 


67 




















2 


67 


2 


67 








2 


67 


G 


32 


80 


21 


53 


24 


60 


34 


85 


16 


40 


36 


90 


17 


43 


29 


73 


H 


25 


81 


13 


42 


15 


48 


27 


87 


15 


48 


26 


84 


15 


48 


27 


87 


1 


1 


100 


























1 


100 


1 


100 








Total 


113 


81 


65 


46 


74 


53 


111 


79 


65 


46 


116 


83 


66 


47 


109 


78 


Mean% 




82 




37 




52 




65 




45 




83 




50 




70 



More respondents requested information on changes in forest boundaries, protected areas/forest 
reserves, forest types and areas of high biodiversity (varying from 78% to 83% of respondents - 
see below). Requests for information on forest fires, roads, biomass and timber volume were 
much lower, at between 46 and 53% of respondents. 



forest types (FT) 

changes in forest boundaries (CFB) 

protected areas/forest reserves (PA/FR) 

areas of high biodiversity (BIO) 

roads (R) 

timber volume (TV) 

forest fires (FF) 

biomass (B) 



83% 
81% 
79% 
78% 
53% 
47% 
46% 
46% 



In general, responses for each group followed the patterns presented by total responses. There 
were a few exceptions: 

• 73% of respondents from the global change community requested information 
on biomass, compared with 46% of all respondwits; 

• only 55% of respondents from the global change community requested 
information on protected areas/forest reserves, compared with 79% of all 
respondents; 

• 75 % of international NGOs requested information on roads, compared with 53 % 

of all respondents. 

Requests for information categories in relation to resolution, coverage and frequency are shown 
in Figures 8, 9 and 10. For most categories of information, more respondents requested 



WCMC-93/TN-003 



33 



Analysis of Users and Requirements 



TREES n Pre-feasibility Study 



information at national coverage than any other coverage. Exceptions were timber volume and 
roads where local information was more important. A similar pattern of frequency requirement 
was shown for all information types, except biomass and areas of high biodiversity where the 
preference for annual information was less marked. 

Figure 8 Resolution required by information type 




UMIW laMI 

Level of rasolutlon 



. frras 
i fr-«l«ctad arm 



O 
V 



s 




Leve I or reeo I ut I on 



1:V5 mlllcn 



lero 
I Tin 



I PoTAst lyp«A 

itrmma of high blodlvaralty 



34 



WCMC-93/TN-003 






TREES n Pre-feasibility Study 



Analysis of Users and Requirements 



Figure 9 Scale required by information type 



o « - 



i 



in 

« 

i. 
s 

1. 
o 




tlsrml 

Scale of mrormatron 



3 mxKJtcxma •rBBB/rorMt raearvso 






o 



z 




tiermi iMtieml P^ 

Scale of Informerion 



i fortex types 

iArmmm of high biodivwBity 



I 



L 



WCMC-93/TN-003 



35 



Analysis of Users and Requirements 



TREES n Pre-feasibiiity Study 



Figure 10 Frequency required by information type 



S 



c 

& 

u 
o 

I. 



- 




{ ! 




, 


i 




1 







tftry a ymr9 Ev«ry 5 y«ars Cwy 10 ymr» One* only 

Froqudncy 



I Omvgtm rn ftrtmt I 



lF«r«*t f iriB 




I Bio 

I Tin 



B^m" 3 yvvi 



Frequency 

^M Forast typM 

^aArcftB of high blodlvoTBlty 



36 



WCMC-93/TN-003 



TREES II Pre-feasibility Study Analysis of Users and Requirements 



3,5 Requirement Priorities 

Most of the information relating to user requirements was collected in the large matrix on page 
two of the questionnaire. The numbers of requests for each category of information in this 
matrix are given in the tables in Appendix F. The first table gives the number of requests while 
in the second table these numbers are expressed as a percentage of the total number of 
respondents. 

To assess the importance to the user of the various information requirements, the average 
number of cell entries per requirement type (coverage, frequency, response speed and 
resolution) was calculated (as total number of cell entries divided by the number of cells). The 
results were as follows: 



coverage 


41 


resolution 


35 


response speed 


27 


frequency 


15 



From Appendix F it can be seen that the most frequently requested information types were: 

• national level information on changes in forest boundaries, protected areas/forest 
reserves, forest types and areas of high biodiversity 

• information on changes in forest boundaries, roads, protected areas/forest 
reserves and forest types within one month of request 

• annual changes in forest boundaries at national and regional level within one 
month of request and at resolutions less than 1:1(X),(XX). 

Other firequently requested information included: 

• forest types at all levels from local to regional, and at high and medium 
resolution 

• areas of high biodiversity at local and regional levels and at high and medium 
resolutions 

• roads, protected areas and forest reserves at high and medium resolution. 



3.6 User Group Requirements 

There were some variations in the requirements of individual user groups compared with 
requirements of respondents as a whole. The global change community expressed a 
higher demand for information on biomass and a lower demand for information on 
protected areas and forest reserves. In general, they required information less fi^uently 
than other users: 

frequency global change total 

community (%) (%) 

yearly 27 56 

3-yearly 9 40 



WCMC-93/TN-(X)3 37 



Analysis of Users and Requirements 



TREES II Pre-feasibility Study 



5-yearly 
lO-yearly 
once only 



64 
18 

27 



They also required more global information than other users: 
coverage 



local 

sub-national 

national 

regional 

global 



global change 
community (%) 

36 
45 
55 
64 
73 



36 
12 
17 



total 
(%) 

55 
48 
74 
52 
42 



National forestry departments, protected areas agencies and research organisations in 
tropical countries had a lower requirement for regional and global information and 
requested more accurate information than other users: 



resolution 


national forestry 
departments etc (%) 


total 
(%) 


local 

sub-national 

national 

regional 

global 


52 
39 
71 
39 
29 


55 
48 
74 
52 
42 


resolution 


national forestry 
departments etc (%) 


total 
(%) 


high 

medium 

low 


74 
55 
6 


62 
55 
19 



United Nations agencies expressed a higher demand for information at longer intervals: 



frequency 


UN agencies 


total 




(%) 


(%) 


yearly 


20 


56 


3-yearly 


40 


40 


5-yearly 


80 


36 


10-yearly 


20 


12 


once only 





17 



38 



WCMC-93/TN-003 






TREES n Pre-feasibility Study 



Analysis of Users and Requirements 



They also expressed a higher demand for global data and for low resolution data: 



coverage 


UN 


agencies 


total 






(%) 


(%) 


local 




40 


55 


sub-national 




60 


48 


national 




40 


74 


regional 




60 


52 


global 




80 


42 


coverage 


UN 


agencies 


total 






(%) 


(%) 


high 




40 


62 


medium 




40 


55 


low 




60 


19 



Intematioiial NGOs have a higher requirement for regional, global and low resolution 
data: 



coverage 


international 


total 




NGOs (%) 


(%) 


local 


63 


55 


sub-national 


38 


48 


national 


100 


74 


regional 


88 


52 


global 


75 


42 


resolution 


international 


total 




NGOs (%) 


(%) 


high 


63 


62 


medium 


50 


55 


low 


38 


19 



I WCMC-93/TN-003 



39 



Analysis of Users and Requirements TREES n Pre-feasibility Study 



Their need for information on roads is higher than average (75% compared to 53% of 
all respondents), as is their need for frequent information: 



frequency 


international 


total 




NGOs (%) 


(%) 


yearly 


88 


56 


3-yearly 


50 


40 


5-yearly 


13 


36 


10-yearly 





12 


once only 





17 



The responses of intergovemmental agencies, national NGOs and of the forest 
research community tended to reflect average patterns of all respondents, although the 
forest research community requires slightly more local and subnational data: 



coverage 


forest research 


total 




community (%) 


(%) 


local 


74 


55 


sub-national 


61 


48 


national 


71 


74 


regional 


55 


52 


global 


42 


42 



The results of the questionnaire survey will clearly be usefial in planning the future 
development of the TREES project and an integrated forest information system. It is also 
important to note certain reservations expressed by the respondents. These mainly relate 
to: actual and potential overlap with otiier programmes; limitations imposed by cost; 
availability of technical resources and expertise to access and utilise data. 

In relation to overlap of satellite monitoring activities, Rodenburg (WRI) points out that 
TREES is duplicative with at least two other programmes, the work of EROS and at 
Woods Hole. Lund (USDA Forest Service) stresses the need for coordination between 
TREES, RESPAS and NASA Pathfinder activities, pointing out that whilst there is 
unnecessary duplication of work, not much progress is being made in making satellite 
data available to the countries which need it. Furthermore he suggests that the key groups 
should be urged to define their areas of responsibility and work together to provide 
information for resource inventory management and monitoring. Eden (CEDAR) points 
out that collaboration will allow effective comparison of the divergent results, and thus 
real appraisal of the various methodologies. 

Others recognised the complementary objectives of TREES and their own organisation's 
activities and requested collaboration; e.g. K.D. Singh (in litt, 1993) of the FAO Forest 
Resources Assessment project, suggests that his work and TREES 'seem to serve a 
complementary purpose and it would be most desirable if the two activities could be 



40 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



integrated in the framework of a combined or complementary FAO/EC Programme. ' 

Cost was cited most frequently as a problem by current users of satellite-derived data, 
being indicated in 40 responses. Various NGOs commented that they would not utilise 
a forest information system based on TREES unless it was inexpensive. 

A considerable number of respondents commented that they would like to be updated on 
progress in the work, and receive, when available, more specific information on the 
proposed forest information service. In the absence of detailed specifications, several 
respondents indicated a general interest in any available information from TREES. 
Halpin, for example, pointed out that the Global Systems Analysis Program at the 
University of Virginia is potentially interested in the development/acquisition of most 
types of forest inventory data suggested in the questionnaire. 'It is difficult for me to 
objectively prioritize our potential data requirements and our possible contributions to the 
TREES system when our focus is so variable between individual research projects. So the 
bottom line is that at some point in time we may be sincerely interested in most anything 
you may produce under the program. This is obviously not very helpful in establishing 
your priorities, but I am certain that this would not be an uncommon observation from 
many academic institutions. ' 



3.7 Conclusions 

The following list is a summary of preferred user requirements: 

A: Global change community 

A.l The global change community has requirements for information on: 

changes in forest boundaries, areas of high biodiversity, forest types and 

biomass. 
A. 2 Requirements for this information are satisfied by a frequency of years. 
A. 3 Coverage required is regional and global. 
A.4 Medium resolution satisfies the majority of the community requirement. 

B: National forestry departments and protected areas agencies 

B.l National forestry departments require information on: changes in forest 

boundaries, protected areas/forest reserves, forest types and areas of high 

biodiversity. 
B.2 Required frequencies for information delivery are yearly, and every three 

years. 
B.3 Information is required at local and national coverage. 
B.4 Information is required at high and medium resolutions. 

C: United Nations agencies 

C.l UN agencies require information on: changes in forest boundaries, 
protected areas/forest reserves, forest types and areas of high 
biodiversity. 



WCMC-93/TN-003 41 



Analysis of Users and Requirements TREES II Pre-feasibility Study 



C.2 Information is required every five years. 
C.3 Information is required with global coverage. 
C.4 Information is required at low resolution. 

D: Intergovernmental agencies and programmes 

D.l Intergovernmental agencies and programmes require information on: 

changes in forest boundaries, protected areas/forest reserves, forest types, 

forest fires and areas of high biodiversity. 
D.2 Information is required annually, and at three year intervals. 
D.3 National coverage is required. 
D.4 Information is required at medium resolution. 

E: International non-governmental agencies 

E. 1 International non-governmental agencies require information on: changes 

in forest boundaries, roads, protected areas/forest reserves and forest 

types. 
E.2 Information is required annually. 
E.3 National and regional coverage is required. 
E.4 High and medium resolution is required. 

F: National government agencies 

F. 1 National government agencies require information on: changes in forest 

boundaries, forest types and areas of high biodiversity. 
F.2 Information is required every five years. 
F.3 National coverage is required. 
F.4 Information is required at medium resolution. 

G: National non-government agencies 

G.l National non-government agencies require information on: changes in 

forest boundaries, protected areas/forest reserves and forest types. 
G.2 National coverage is required. 
G.3 Information is required at high and medium resolutions. 

H: Forest research community 

H. 1 The forest research community requires information on: changes in forest 

boundaries, protected areas/forest reserves, forest type and areas of high 

biodiversity. 
H.2 Information is required annually. 
H.3 Local and national coverage is required. 
H.4 High resolution information is required. 



Other requirements: 

1.1 The majority of potential users require high level information products, 
not semi-processed data. 

1.2 Most users prefer deliver of information in paper and digital form, with 
the exception of the global change community who prefer an on-line 



42 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



delivery. 

1.3 Several users stressed the desire to see cooperation between TREES and 
other similar activities worldwide. 

1.4 Uncertainty regarding potential costs of information were cited as an area 
for concern by many respondents. 

1.5 Many users requested that they be kept updated on the progress of the 
TREES programme. 



WCMC-93/TN-003 43 



Analysis of Users and Requirements TREES 11 Pre-feasibility Study 



4. 

Translating Information into Action Programmes 



It is generally acknowledged that there is an urgent need for regular and reliable 
information concerning tropical forest cover and deforestation. 

• Effective management of forest resources requires suitable, up-to-date 
information on the state, extent and distribution of forest. 

• Remotely sensed data, supported by other important ancillary data can assist in 
providing this information. 

• These data should be in a format that is easUy accessible and simply assimilated 
into decision making programmes and environmental modelling. 

• These data are not currentiy freely available. Spatial and statistical data that are 
not 'restricted' must be made freely available and must be open to scrutiny by 
all users. 

• Collaboration between TREES and the user is vital if the data are to be accepted 
and used wisely to truly assess the status of the world's tropical forests. 

• Several potential users have expressed their interest in collaborating with TREES 
and this should be considered carefully. 

At present there appears to be a fundamental lack of mechanisms for translating space data 
derived from earth observation systems into practical forest management and conservation 
programmes. It has been pointed out that the international forest research community needs to 
address the requirements of a global satellite monitoring programme from the forest 
management perspective. Methods and procedures need to be developed to link satellite-based 
forest monitoring with forest management and regional planning, with lUFRO a suggested 
forum for discussion (Justice, 1992). 

Certain general priority uses for satellite-derived forest data can already be suggested. These 
include: 

• modelling biomass levels 

• monitoring areas protected for forest conservation under international agreements 
or internationally agreed programmes: 

World Heritage Sites 
Biosphere Reserves 

• monitoring priority areas potentially in need of international conservation 
assistance: 

Centres of biodiversity 
Deforestation zones 
Centres of Plant Diversity 
Endemic Bird Areas 



44 WCMC-93/TN-003 



TREES n Pre-feasibility Study 



Analysis of Users and Requirements 



• inclusion in Forest Resource Accounting - monitoring progress towards ITTO's 
Target 2000 

• prioritising activities under regional and country programmes which requires a 
knowledge of extent of tropical forest cover and relative and absolute 
deforestation rates. 

Specific uses mentioned in the questionnaire survey include the placing of forest management 
areas in a regional context (Programme for Belize); use of data in university teaching and 
research (University of Brunei Darussalam); use of data to monitor logging and encroachment 
into sensitive areas (Responsible Forestry Programme [FSC]); use of data to suggest changes 
in forest policy (Asociacion Ecologista Panamena); use of independent satellite derived data to 
verify official forest survey information (WWF India). 

The use of satellite derived data in forest conservation, management and research programmes 
is greatly enhanced when it is used in conjunction with other forms of data. Integration in GIS 
offers a mechanism to overlay the specific datasets required by the wide range of organisations 
and individuals involved in forest conservation. Additional datasets which could usefuUy be 
integrated into the TFIS system relate to: 



Environmental 
factors: 



forest extent 
forest types 
forest condition 
forest boundaries 
forest biomass 
seasonality 
hydrology 

topography (e.g. slope) 
soil erodibility/erosion 



Forest management: 



protected areas 
forest reserves 
forest concessions 
local rights 



Forest policy: 



land use planning 

forest legislation and policy 

colonisation policies 



Threats: 



fires 

logging 

roads 

railways 

population movements/growth 

industry (mining etc.) 

major projects (dams etc.) 

other natural phenomena 



WCMC-93/TN-003 



45 



Analysis of Users and Requirements TREES n Pre-feasibility Study 



It is clear that the development of an integrated forest information system based on TREES 
should build on existing programmes and networks. This will facilitate the collection of datasets 
itemised above and will increase the accessibility of the system to the user community. As has 
already been pointed out the development of TREES and the FAO Forest Resources Assessment 
1990 Follow-up Project using multi-date high resolution data on a sampling basis would seem 
to be complementary. A considerable number of respondents indicated that their organisation 
is in a position to be involved in verification and data exchange. 

Most users are interested in a value-added service and are less interested in obtaining raw 
(unprocessed) satellite data. This is probably an indication that most users do not have the 
money or technical capability of dealing with these unprocessed data. Therefore, maintenance 
of a system, acquisition of data and distribution of data are of paramount importance to TREES 
and the potential user and also supports the notion of an integrated forest information system. 
Contacts, networking and collaboration are important aspects for efficient dissemination of data. 
Precise knowledge of user requirements, at a prototype level, may be necessary to ensure that 
these methodologies are put in place. It is also important to note that until a potential user is 
exposed to the TREES data, precise needs cannot be fully assessed, or stated requirements may 
change. WCMC, an organisation which already has a well established network of contacts in 
forest conservation and management, may well have a key role to play in the provision and 
dissemination of data. 



4.1 Key Considerations in the Development of TFIS 

The following list summarises the salient points which have been deduced from this users 
survey. These key considerations should be taken into account in the future development of an 
integrated tropical forest information system. 

• There is a clear demand for an integrated forest information system based on satellite- 
derived forest cover data and potential applications which suggest that this will 
contribute to forest conservation. 

• The demand is wide-ranging with national government agencies, intergovernmental 
agencies, and NGOs all expressing strong interest. 

• It is important to consider the requirements of different user groups in identifying the 
potential users of TFIS, and to design the system accordingly. High priority should be 
given to user groups who have shown a particular interest in the service. 

• Requirements may change once the user starts to employ data provided by TREES and 
therefore products should be sufficiently flexible to account for these possible 
refinements in requirements. 

• AVHRR satellite data appear to be relatively infrequently used by tropical forest 
conservation and management organisations and is considered to have limited 
applications in forest monitoring. 

• Data with a sufficiently high resolution to be used for national forest monitoring is the 
primary requirement. 

• The most frequently requested information is national level information on changes in 
forest boundaries, protected areas and forest reserves, to be received within one month 



46 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



of request. 

' Global level information on forest extent and deforestation rates is of more interest to 
the Global Change Community for modelling purposes. 

' Close liaison should be maintained with similar global forest monitoring programmes - 
EROS, RESPAS, NASA Pathfinder. 

TFIS should be developed in collaboration with the organisations which are currently 
the main sources of international data on forest cover and deforestation notably FAO 
and WCMC. 

Regular updates on the development of TFIS should be provided to the potential user 
community. 

Forest research organisations such as lUFRO and CIFOR should be involved in 
coordinating TFIS and the forest research community. CIFOR can for example help 
to extend the forest map library (GIS) of WCMC for integration into TFIS. 
Financial support from CEC, UNDP or other appropriate bodies should be considered 
for the provision of GIS systems to national government agencies and national NGOs 
to encourage collaboration and guarantee the effectiveness of TFIS. 
National agencies have expressed their interest in ground-truthing satellite derived data 
and this should be taken into account in planning TFIS, with a view to developing 
collaborative ventures. 

The use of GIS is becoming much more common and TFIS should be designed to cope 
with conversion between the wide variety of software packages used. 
A regular standard reporting system should be designed for aU users. This could for 
example be in the form of a published twice yearly report available at low cost or free 
of charge. Most users prefer paper maps, reports and newsletters for data acquisition 
although on-line service should not be ignored. 



WCMC-93/TN-003 47 



Analysis of Users and Requirements TREES II Pre-feasibility Study 



References 



Anon. (1989). The conservation of tropical forests: the role of the Community. OfficialJoumal 
of the European Communities, Notice No. 89/C 264/01. 

Anon. (1992). ITTO and thefiuure in relation to sustainable development. AID Environment 
and the International Institute for Environment and Development. 

Baldock, D. and Hewett, J. (1991). European Community policy and tropical forests. World 
Wide Fund for Nature, Gland. 

Bibby, C. et al. (1992). Putting Biodiversity on the map: priority areas for global conservation, i 
ICBP, Cambridge. ' 

Blockhus, J.M. et al. (eds.) 1992 Conserving biological diversity in managed tropical forests. 
The lUCN Forest Conservation Programme lUCN/ITTO. 

Collins, N.M., Sayer, J. A. and Whitmore, T.C. (eds) (1991). The Conservation Mas of 
Tropical Forests - Asia and the Pacific. Compiled by lUCN and WCMC, MacMillan Press Ltd, 
London. 

FAO (1981) Tropical Forest Resources Assessment Project. 3 vols. FAO, Rome. 

Forest Working Group (1992) Minutes of meeting, Kindrogan, Scotland. 21-23 September, 

1992. WWF International, Gland. 

House of Commons (1991) Climatological and environmental effects ofrairforest destruction. 
Select Committee on the Environment, Third Report, House of Commons, 11 March 1991. 

House of Lords (1990) Select Committee on the European Communities: Tropical Forests, with 
Evidence. Session 1989-90, 11th Report, House of Lords, March 1990, HMSO, London. 

Johnson, B. (1991) Responding to tropical deforestation: an eruption of crises - an array of 
solutions. World Wildlife Fund and The Conservation Foundation. 

Justice, C. (1992) Satellite monitoring of tropical forests: a commentary on current status and 
institutional roles. In: Malingreau, J.P., da Cunha, R. and Justice, C. (Eds.) Proceedings World 
Forest Watch Conference, May 27-29, 1992, Sao Jose dos Campos. Brazil. Joint Research 
Centre, Commission of the European Communities. 

Looyen, W.J. et al (1993) FAME: A forest assessment and monitoring environment scenario. 
Paper presented at the International Symposium "Operalisation of remote sensing", 19-23 April 

1993, rrc Enschede, the Netherlands. 



48 WCMC-93/TN-003 



I 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Matthews, J.T. and Tunstall. D.B. (1991) Moving towards Ecodevelopment: generating 
environmental information for decision makers. WRI Issues and Ideas. World Resources 
Institute, Washington. 

Myers, N. (1989) Deforestation rates in tropical forests and their climatic implications. Friends 
of the Earth Ltd, London. 

Sayer, J.A., Harcourt, C.S. and Collins, N.M. (eds) (1992). The Conservation Atlas of Tropical 
Forests - Africa. Compiled by lUCN and WCMC, MacMillan Press Ltd, London. 

World Conservation Monitoring Centre (1992). Global Biodiversity: Status of the Earth 's living 
resources. Chapman & Hall, London, xx + 594pp. 

Witty, R.W. (1993) European initiatives in global environmental change data management: the 
Centre for Earth Observation Project of the CEC. Paper presented at the International Meeting 
on Global Environmental Change Data Management, February 16-17, 1993, London, UK. 

WWF International (1989) Tropical Forest Conservation. A WWF International Position Paper. 
WWF International, Gland. 



WCMC-93/TN-003 49 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Appendix A 
Letter Introducing TREES 

Dear Colleague 

THE FUTURE DEVELOPMENT OF THE TREES PROJECT 

Objective data on the extent and condition of tropical forests are urgently needed for planning forest 
conservation and management into the next century. There is a global demand for such data as expressed 
at the Rio Earth Summit and recognised in the Statement of Forest Principles. 

The TREES (Tropical Ecosystem Environment Observations by Satellites) project, a joint initiative of 
the Commission of the European Communities and the European Space Agency (ESA), sets out to meet 
some of these data needs, using the latest technology to provide pan-tropical forest information. 

WCMC and EOS (Earth Observation Sciences Ltd) are currentiy advising on the future of the TREES 
project. We will be looking at die objectives and targets, the extent to which these meet the needs of 
the user conmiunity and the ways forward for the development of the project. This will include an 
integrated forest information system. 

WCMC is seeking the views of a wide range of users of forest information, and we would greatly value 
your involvement. Please spare the time to read the brief account of the TREES project which is 
enclosed and to fill in the questionnaire. We need to know if an integrated tropical forest information 
system based on the work of TREES could meet your information requirements, and if so how the 
development of the project could best meet your specific needs. We will take into account the broad 
range of technological capabilities. 

We will ensure that all organisations that respond to the survey are informed of the developments relating 
to the proposed tropical forest information system. It would be greatly appreciated if you could return 
the questionnaire to WCMC by 20 May 1993. Thank you for your help. 

Yours sincerely. 



N M Collins, Ph.D. 
Director of Programme 



WCMC-93/TN-(X)3 A-1 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Appendix B 



TREES - Provision of Tropical Forest Information 



"The provision of timely, reliable and accurate information on forests and forest 
ecosystems is essential for public understanding and informed decision-making 
and should be ensured.' {Non-legaUy Binding Authoritative Statement of Principles 
for a Global Consensus on the Management, Conservation and Sustainable 
Development of all Types of Forests, UNCED, 1992) 



The Tropical Ecosystem Environment Observations by Satellite (TREES) project is a joint 
initiative of the Commission of the European Communities (CEC) and the European Space 
Agency (ESA). Over the past two years a research team has been designing and implementing 
a system to assess the world's tropical forest cover using satellite-derived data and ancillary data 
from numerous other sources. Implementation of the TREES project involves close 
collaboration with those countries in which tropical forests are located and cooperation with 
other relevant initiatives around the world. The TREES project, which is being undertaken by 
the Institute for Remote Sensing Applications of the CEC Joint Research Centre at Ispra in 
northern Italy, is currently acquiring spatial information derived from satellite receiving stations, 
to compile a pan-tropical baseline map showing forest extent and forest type - the first such map 
based on satellite-derived data. A method is being set in place to classify the satellite data 
automatically and make regular updates, concentrating on those areas of rapid deforestation. 
These data are being augmented with important ancillary information, such as vegetation maps 
from national and regional sources, land-use information, biodiversity attributes, management 
data and other ecological information. Data will be maintained in a geographic information 
system (GIS) to facilitate data manageability, analysis, access and distribution. When the 
system has been designed and tested, a procedure will be implemented and data will be made 
available to a wide range of users through an integrated Tropical Forest Information System 
(TFIS). 

What are the data sources? 

The main type of satellite imagery used by the TREES project is from the AVHRR (Advanced 
Very High Resolution Radiometer) on-board the NOAA satellites. AVHRR images have 1km 
resolution and are being coUectad for the project from receiving stations in Africa, Latin 
America and Southeast Asia. As procedures improve, there may also be the possibility of 
using ERS-1 satellite radar coverage. The forest categories currently being investigated for 
identification are: closed canopy tropical rain forest; degraded tropical rain forest, where 
regional forest cover is <70%; secondary forest; seasonal moist forests, and areas of active 
deforestation, identified using indicators of change (roads, fires, boundary changes, appearance 
of forest canopy). AVHRR data are supplemented by higher resolution SPOT and Landsat TM 
images. Analysis of the seasonality of the vegetation is being undertaken using GAC (Global 



WCMC-93/TN-003 B-1 



Analysis of Users and Requirements TREES n Pre-feasibility Study 

Area Coverage) data. Fieldwork and other methods of verification also form an important part 
of the validation and information gathering process. As well as the satellite-derived data, other 
relevant information from many sources is being incorporated into the system and managed 
within the TFIS. These ancillary data will help provide a more holistic picture of the world's 
tropical forests. 

How will TREES develop? 

The TREES project was established in 1990, and the first phase of the project (TREES I), 
which concentrated on methodological aspects, data gathering and mapping, is nearing 
completion. Phase two of TREES (TREES II) is now being planned to build on this work, to 
develop TFIS and provide a continual forest monitoring service. EOS (Earth Observation 
Sciences Ltd.) and the WCMC (World Conservation Monitoring Centre) are helping in this 
process by objectively assessing TREES goals and achievements over the past two years, by 
looking at modifications which may be necessary as a result of changing views on tropical 
deforestation, by assessing the actual and potential needs of the user community and, as a result, 
by proposing ways forward. EOS and WCMC will be analysing the requirements for 
operational forest monitoring and making recommendations for the orientation of further 
research under the umbrella of TREES H. From the outset of the TREES project it was agreed 
that, "unless the data are put to use in an effort to control deforestation then the TREES work 
win have been wasted". It is essential that TREES is designed and maintained to the required 
standards and needs of the user community - the organisations and individuals who manage, 
study and conserve the world's tropical forests. 



B-2 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Appendix C 

Questionnaire 
USER PROFILE AND USER NEEDS 



WCMC-93/TNf-003 C-1 



Questionnaire 
USER PROFILE AND USER NEEDS 



In order to plan the next phase of the TREES project the Commission of the European 
Communities has asked WCMC to coraaa users of tropical forest information to find out their 
data needs. Your views will help to determine the development of an integrated tropical forest 
information system incorporating satellite-derived data. Please fill in text or tick box(es) 
where appropriate, make any additional comments, and return your views to Qare Billington, 
WCMC, 219 Huntingdon Road, Cambridge, CBS ODL, UK. Tel: 44.223 277314 and Fax: 
44.223 277136. THANK YOU 



1. Major Activities 



A. Your organisation 's full name: 

Contact person (capitals please): 

Address: 



Tel: Fax: Telex: email: 

B. Please indicate which of the following activities your organisation undertakes: 

Bl. Forest resource surveys D 

B2. Forest management for production D 

B3. Forest management for nature conservation D 

B4. Forest management for recreation D 

B5. Forest policy making D 

B6. Forest research D 

B7. Land use evaluation and planning D 

B8. Global change research n 

B9. Vegetation classification/mapping D 

BIO. Protected area systems planning D 

Bll. Provision of information service D 

B12. Raising of public awareness D 

B13. Provision of forest goods D 

B14. Provision of consultancy services D 

B15. Provision of development aid D 

B16. Other, please state what 

C. Does your organisation use forest survey information? If yes, what activity are these data used 
for: 

CI. Resource management D 
C2. Global change research D 
C3. Biodiversity conservation D 
C4. Other research, please state what 
C5. Other, please state what 

If yes, what are your current sources of simply? 
Please elaborate: 



2. Information Requirements 



D. 



The TREES project is currently developing a prototype forest information system incorporating 
satellite-derived data and important ancillary information. Would you be interested in receiving 
forest information from such a system? 
Dl. Yes D 

D2. No D 




J. How would you wish to receive this information/data? 

Jl. On-line service D 

J2. Reports D 

J3. Newsletter D 

J4. Paper maps D 

J5. Digital format files (e.g. ARC/INFO, ERDAS, IDRISI) D 

J6. Photographic products D 

J7. Raw satellite data D 

J8. Computer classified satellite imagery D 

J9. Other, please state how 



3. Information Analysis and Supply 

K, Does your organisation maintain a geographic information system? 
Kl. Yes D 
K2. No D 



If yes what type of software do you employ? 



Kl.l. 


ARC/INFO D 


K1.2. 


ERDAS D 


K1.3. 


IDRISI D 


K1.4. 


SPANS D 


K1.5. 


ILWIS D 


K1.6. 


Other, please state what 



The verification of satellite data and supply of other data using local knowledge is essential. 
Is your organisation in a position to be involved in verification and data exchange? 
LI. Yes D 
L2. No D 



M. Do you distribute forest survey information? 

Ml. Yes D 
M2. No D 

If yes, in what form are these data distributed: 

Ml.l. P^er reports D 

Ml. 2. Computer database tables and text D 

M1.3. Paper maps D 

Ml. 4. Digital maps D 

Ml. 5. Others, please state how 



4. Forest Monitoring 

N. Does your organisation monitor forests? 

Nl. Yes D 

N2. No D 

If you answered 'no* then there is no need to carry on with the questionnaire. Please note, 
however, that there is a section at the end of the questionnaire for you to make further comment 



if you wish. Many thanks 

If you answered yes please tick a box(es) to indicate at which level the forest surveying is being 
undertaken and by which method: 





Global 


Regional 


National 


Sub-national 


Local 


Ground survey 












Aerial survey 












Satellite imageiy 













O. If your organisation uses satellite imagery which satellite data do you use? 

01. LandsatTM D 

02. Landsat MSS D 

03. SPOT XS D 

04. SPOT P D 

05. AVHRR (local area coverage) D 

06. AVHRR (global area coverage) D 

07. Other, please state what 

P. If you have used satellite-derived data to assess forest cover, what, if any, are the problems 
you have experienced? 
PI. Data availability D 
P2. Data access D 
P3. Cost D 
P4. Distribution D 
P5. Interpretation D 
P6. Data processing D 
P7. Hardware/software D 
P8. Available data content not suitable D 
P9. Other, please state what 

Q. If your organisation is considering the use of satellite data for forest monitoring, what classes 
would you need to detect? 

Ql. Vegetation type D 
Q2. Vegetation condition D 
Q3. Timber volume D 
Q4. Biomass D 
Q5. Other, please state what 



R. For your purposes do you require: 

Rl. Low resolution satellite derived data (1km resolution) at regular intervals □ 

R2. Higher resolution satellite derived data (30m resolution) at less frequent intervals D 



Additional comments: 



TREES II Pre-feasibility Study Analysis of Users and Requirements 



Appendix D 

List of Respondents 
and Summary of Comments 

This appendix represents the list of users who responded to the questionnaire by the end of 
May 1993. 



Global change community 
Agren 

Department Ecology and Environmental Research, Swedish Univ Agric. Sci 

Steffen 

Global Change and Terrestrial Ecosystems Core Project of the IGBP 
Thank you for your questionnaire on the TREES project. I have completed it on behalf of 
GCTE, as the ongoing data that will arise from TREES will be very useful to our modelling 
groups. You will note that I have listed the GCTE Core Project Office as the contact. TTiis 
is an interim arrangement, as we hope to have our LEMA (Long-term Ecological Modelling 
Activity) network established and operational by the end of the year. LEMA will link together 
ecological modelling groups around the world to facilitate model development through 
exchange of data, models and model components. LEMA will be coordinated by a small 
secretariat, to be established at the University of Virginia with Prof Hank Shugart's group. 
When the LEMA Secretariat is established, it will be the point of contact for data exchange 
between GCTE and other groups, such as TREES. It certainly appears that TREES will 
produce a most valuable set of data on tropical forests, one that will be very usefidfor GCTE 
modellers. Best of luck in the next phase of the project! 

Jorgensen 

DFH Institut A, Environment. Chemistry, Int. Society of Eco. Modelling 

Congalton 

Department of Natural Resources, University of New Hampshire 

Brown 

Department of Forestry, University of Illinois 

Glantz 

National Center for Atmospheric Research 

Windhorst 

Ecosystem Research Project Centre 

Though we are not a forest research institution jrom our origin, our interest in the TREES 



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Analysis of Users and Requirements TREES II Pre-feasibility Study 



project results out of the following: 1) as an institution devoted to ecosystem research we are 
also involved in forest research. This includes also activities to develop an integrated 
ecological information system, which will allow a question oriented access to the elaborated 
knowledge stored in models, in a relational data-base (ORACLE) and in the GIS 
(ARC/INFO). Just now we are discussing the structural scheme for the system and the layout 
for the documentation schemes in order to meet our needs for integrated ecological 
interpretations. The creation of transparent interfaces to other information-systems is 
therefore still possible. A more detailed knowledge about the TREES approach would possibly 
enhance the necessary harmonization process in this field; 2) together with our Danish 
partner Prof. S.E. Jorgensen we started an activity to install an International Centre of 
Ecological Modelling (ICEM). The success of this activity and the benefit for the participants 
of the courses will increase with the availability of information elaborated and documented 
in the TREES project. Thus we would be glad to receive more information about your work 
and the conclusions you draw out of the returned questionnaires. 

Spence 

Global Climate Observing System 

Halpin 

Global Systems Analysis Program, Department of Environmental Sciences, 
The Global Systems Analysis Program at the University of Virginia conducts forest ecosystem 
studies which range in spatial scale from fidl global coverage to regional and site specific 
case studies of ecosystem dynamics depending on the current mix of research projects 
undertaken at a given time. For example at this time we have projects ranging from the 
analysis of global climate change on world forest systems conducted at extremely coarse 
spatial resolutions, while at the same time have projects dealing with individual nature 
reserves using data at 30m spatial scales. Because of the broad range of projects we conduct 
under this program, we could be potentially interested in the development/acquisition of most 
types of forest inventory data suggested in the questionnaire depending on the type of 
research activity currently luiderway. This necessarily generalist position is reflected in the 
many "all of the above ' responses to most of the questions posed in the survey. It is difficult 
forme to objectively prioritize our potential data requirements and our possible contributions 
to the TREES system when our focus is so variable between individual research projects. So 
the bottom line is that at some point in time we may be sincerely interested in most anything 
you may produce under the program. This is obviously not very helpfidfor establishing your 
priorities, but I am certain that this would not be an uncommon observation from many 
academic institutions. With that said, I have a personal iraerest in seeing the development 
of global coverage (1km) vegetation index and monthly NDVI phenology mapping (similar to 
the work Loveland (PE&RS 1992) use for the US) to replace the coarse GVI data sets now 
in use. I am extremely interested in the development and dissemination of data bases for this 
type of research and appreciate being informed about future developments. I also want to 
state that I am very willing to offer assistance where possible and have always valued my 
contacts with WCMC. Thanks for keeping me in mind and please stay in touch. 

Dale 

Environmental Sciences Division, Oak Ridge National Laboratory 



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Leemans 

National Inst, for Public Health & Env Protection, Global Change Dept 
/ believe this could be a very imponant initiative, but from the documentation it is not clear 
to me how this initiative fits in with some other research activities in research programmes 
like IGBP-DIS. I hope that all these activities will be well directed, so that a minimum of 
overlap occurs and some products of good quality are developed. Currently my research has 
changed from climate impact research to land cover modelling. We have assembled a group 
ofc.15 researchers to further develop the IMAGE model. This model included sub-models 
for energy-use, ocean-atmosphere interactions, the terrestrial biosphere and all their 
linkages. It is now one of the first global change models which is aimed at developing and 
evaluating policy scenarios. I am responsible for the terrestrial biosphere models and an 
explicit simulation of land cover change, such as deforestation and agricultural 
intensification. The preliminary results are very promising. I envision that the approach taken 
can also be useful for evaluating sustainable development, conservation and biodiversity 
issues. 

National forestry departments, protected areas agencies and national 

research organisations (mainly in tropical forested countries) 

Hamzah 

Forest Research Institute Malaysia 

We realised that the TREES project is very useful and would like to congratulate the 
Commission of the European Communities and the European Space Agency (EDA) for their 
joint effort. FRIM will be looking forward to actively participating and contribute to the 
project. 

Jonkers 

Tropenbos-Cameroon Programme 

Our research project in Cameroon is just starting. Inventories etc. will commence in the 

second half of 1993. We do not maintain a CIS at present but will use ILWIS or ARC/INFO 

soon. 

Johns 

Makerere University Biological Field Station, Uganda 

Abdel Nour 

Forests National Corporation, Sudan 
Trying to obtain a CIS 

Marguba 

Nigeria National Parks 

Senekane 

Lesotho Ministry of Agriculture, Co-operatives and Marketing 

Please note: Lesotho is not in the tropics but sub/tropics. It has 8% of its land area 
dominated by tree and predominantly shrub species, generally knee high to head high, which 
are an importaru source of local household fuel and help protect steep slopes. The country 
is classed as virtually treeless. The main forestry activity by Government and local people 



WCMC-93/TN-003 D-3 



Analysis of Users and Requirements TREES II Pre-feasibility Study 



is firewood plantation development in areas fi-om 0.1 ha to 400 ha. The average size of 
Government plantations is c. 15 ha and of private plantations c. 0.15 ha. Government 
plantations total 450 and are all actual or proposed forest reserves. Private plantations have 
not yet been surveyed, assessed or even counted but will exceed 2,000. 

Pomeroy 

Makerere University, Institute of Environment and Natural Resources, Uganda 
The National Biodiversity Data Bank (NBDB) is a unit within MUIENR and is involved in all 
aspects of Uganda's biodiversity. The use of remote sensing and GIS are likely within the 
next 2 years or so. 

Hoeft 

Wau Ecology Institute, Papua New Guinea 

Wau Ecology Institute is presently starting to set up GIS for land use and conservation 
planning on a regional scale. We are working on soil and vegetation classification and 
attempt to come up with thematic maps including land-use and conservation suggestion. For 
this goal we have used aerial photographs up to now. However, prints were of rather poor 
quality and only part of our area was covered. Delineation of different forest types from these 
photographs was impossible at our scale of interest. I have recently seen satellite images 
composed from a mixture of infrared and far red which impressed me by showing clear 
differences between "primary" and "secondary' forest. For our Institute being a non-profit 
oriented place of research and education relying almost entirely on outside funds price is a 
major impediment to purchasing and using those data. Up to now, we have therefore worked 
primarily on the ground truth survey. We are looking forward to getting further information 
from you. 

Wickramasinghe 

Forest Inventory and Management, Forest Department, Sri Lanka 

A national forest map at nominal 1:250,000 scale was prepared in late 1992 by interpretation 
of 1992 TM imagery. This prelindnary map is available in digital (ARCINFO) form. A more 
detailed and field-checked map at nominal 1:50,000 scale is currently being prepared from 
the same imagery and will be complete by mid 1994. This will form a major input to a 
national forest GIS and will serve as a baseline against which to measure fiaure change in 
forest area. The new map will also be available in ARCINFO format. Updating is planned 
every five years. The national forest map divides closed canopy forest into seven categories 
of natural forest, based on elevation, rainfall and other characteristics (e.g. mangroves) and 
four species categories of forest plantations. 

James 

Forestry and Wildlife Division, Dominica 

Deravariere 

British Virgin Islands National Parks Trust 

My organisation is responsible for the management of terrestrial parks in the British Virgin 
Islands. In this regard some of the forested areas, which are national parks, come under its 
jurisdiction. Information that is available in these parks may need to be updated. 



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Preston 

Department of Forestry, Queensland, Australia 

Leduc 

Direction de I'Agriculture et de la Foret, French Guiana 

Teo 

National Parks Board, Singapore Botanical Gardens 

Kumar 

Department of Agriculture, British Virgin Islands 

Eaton 

University of Brunei Darussalam 

Certain aspects of the TREES project are obvious value to our university carrying out 
teaching and research in a tropical rain forest area. My own particular interests are in 
protected areas and resource management. We are not involved in forest monitoring although 
have used LANDSAT imagery identifying forest boundary changes, especially in relation to 
protected areas. We do not have a GIS but one is being developed in association with the 
Kuala Belalong Rainforest Research Project. 

Director General 

Tanzania National Parks 

Tanzania National Parks is maifify concerned with managemera and conservation of the 
protected areas or national parks. The use of wildlife or the biotic natural resources is 
strictly on non consumptive use, i.e. tourism and scientific research which is mainly in the 
form of ecological monitoring to provide informationfor management decision making. Other 
research projects are normally specific solution seeking or knowledge. They are long term 
and short term. The long term projects normally even if they were not meant to answer 
specific management problems become very usefid. The major managemera objective is to 
answer biodiversity in our protected areas by preventing human activities such as poaching 
and their related fires during illegal hunting, honey gathering and cattle raiding across 
parks. The other thing we do is to develop roads and hotels in a well planned way so as not 
to jeopardise the conservation goals. 

Sonuna 

Adiministracion de Parques Nacionales, Argentina 

Lopez 

CroDEBENI, Trinidad 

Our institution is beginning to work with GIS. We believe that within a shortish time, 3-6 

months, we will be able to exchange information on digital maps. 

Liang for Sastrosemito 

Directorate General of Forest Inventory & Land Use Planning, Ministry of Forestry, 

Indonesia 

The points you raised in your letter related to global demand for the objective data on the 

extent and condition of tropical forests has also become the Government of Indonesia 



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concern. The first National Forest Inventory Project is undergoing and will be completed by 
1996. The wall to wall mapping using satellite data mainly Landsat MSS (1986-91) has been 
completed inphase-1 of the Forest Resource Monitoring activities, hence in phase-II starting 
1992 a Digital Image Analysis System has been implemented using Landsat TM. 

Siddiqui for Nazir 

Ministry of Food and Agriculture, Government of Pakistan 

The Pakistan Forest Institute, Peshawar is responsible fi)r conducting research and education 
in forestry and allied disciplines. Apart from research the Economics Branch of the Institute 
compiles forestry statistics of the county and publishes it anmially. This information is 
collected from the provincial/regional forest departments as well as field surveys. In near 
future the Institute would develop GIS facilities and would conduct training and survey for 
forest inventory in the country. 

Dias for Barbosa 

National Institute for Space Research - INPE, Brazil 

Pinso 

Rakyat Berjaya Sdn Bhd, Sabah, Malaysia 

Playfau* 

Suriname Forest Service, Planning Division 

We are applying for an ILWIS GIS. We need to detect vegetation type and condition for 

mixed tropical high forest. 

Incer 

IRENA - Institute Nicaraguense de Recursos Naturales y del Ambiente, Nicaragua 

Compadre 

Ministere de I'Environnement et du Tourisme, Burkina Faso 

Gratia 

Conselho Estadual do Meio Ambiente (CONSEMA), Brazil 

We are seruling a folder that tells about our Monitoring Sistem by Satellites, named 'Olho 

Verde " (Green Eye). 

Gwyan 

Forestry Development Authority, Liberia 

Soundele 

SODEFOR (Societe de Developpement des Plantations Forestieres) 

Appiah 

Forestry Service, Government of Mauritius 



Timber traders 
Franco 

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TREES n Pre-feasibility Study Analysis of Users and Requirements 



Asociacion Ecujtoriana de Industriales de Madera, Franco 

UN agencies 
Olesen 

UNEP-GRID 

GRID cooperates with TREES. FAO. GEMS and WCMC already. 

Srestha 

UNEP/GRID Bangkok 

GRID-Bangkok mandate is to supply geo-referenced environmental information for the user 
community. A digital spatial database will be developed very soon through collaborations in 
Asia. The database combines tables and geographic information systems data related to the 
main physical features and some socio-economic variables. The scoop is the sub-national 
level or the 1:1,000,000 scale. TREES II could be one of the contributors to this Asia 
database for the land cover/vegetation information. GRID-Bangkok plans also to use NOAA 
data to update the existing information as we do now for a few countries (Cambodia, 
Thailand) already. 

Klune 

UNEP-HEM (Harmonisation of Environmental Measurement) 

UNEP-HEM is cooperating with WCMC and IGBP in a project to look at ways of developing 
a new system for vegetation classification, in particular for global mapping and monitoring. 
In this context, we are very interested in the type of information available from the TREES 
project, and ways of using it in the classification. A major part ofHEM's task is concerned 
with collection and dissemination of information about who is doing what, where, how and 
why in the field of environmental measurement. This is our general interest in the TREES 
programme. 

Bangoura 

UNEP-GEMS National, Guinea 

Our organisation was created under UNEP-GEMS/PAC as an Environmental Monitoring and 
Assessment Centre; however, lack of funds means that the Centre is not yet operational, but 
we do what we can. The Cerure's work programme was to comprise: the use of aerial point 
sampling (APS): GISfor data collection and integration; and finally data distribution and 
training. Since the Centre is not functional, our Service faces serious problems as Guinea is 
covered in wet tropical and shade forest. The long-term objectives of the Centre have become 
a focal point for networks such as UNEP-GRID, AFRINEM, PAEMN, FAO 
ARTEMIS/DIANA, CUSS andAGRIMET. The Centre should continue to assess, develop and 
apply methods of monitoring resources all over Africa, and assist in the adoption of such 
methods in similar situations. We request all institutions, organisation etc. to assist us to 
bring our efforts to fruition and to see the Centre in operation. 

Singh 

FAO, Forest Resources Assessment 

EEC TREES-2 (Follow-up Project) using NOAA and FAO Forest Resources Assessment 1990 
Follow-up Project using multi-date high resolution data on a sampling basis seem to serve 
a complementary purpose and it would be most desirable if the two activities could be 
integrated in the framework of a combined or complementary FAO/EEC Programme. 



I 



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Analysis of Users and Requirements TREES II Pre-feasibility Study 

Intergovernmental agencies and programmes 
Kerr 

Commonwealth Secretariat, UK 

JTiis orgamsation is interested in national level statistics for forest cover in all fifty 
Commonwealth countries. A baseline data set with an update every three years and some 
early warning system for major changes by use of a Newsletter would be very welcome. We 
are not in a position to feed data into the system. 

Fottland 

NORAD - The Catchment Forestry Project, Norway /Tanzania 

The Catchment Forestry Project is working with management of forest resources, mostly 
protected in parks of Ta/izania. We have thus interest in and will assemble any knowledge, 
relevant to the management of forest. [It is usually so in Western countries that forest 
knowledge and management starts on the ground and information have successively been 
collected from fitrther away i.e space at present. The problem about satellites is that you do 
not avoid the necessity of ground truth, being the most important part, and which Western 
countries have had for a long time. I do unfortunately see limited use of satellites as long as 
the ground truth is lacking. 

Kotari 

JICA, Japan 

We, as the bilateral technical cooperation implementation Agency of Japan, are very much 

interested in receiving an integrated tropical forest information by our project. 

Wagner 

World Bank 

It is difficult to fill this in accurately for the whole World Bank! Obviously, we have projects 
in many countries, which often use data such as you supply in design, monitoring etc. Any 
improvement on the paucity of reliable, high quality data now available would be good. 
Projects are designed in conjunction with client countries, consultants, FAO etc; they all use 
different sources of data. The Bank would probably only use TREES type data 'second hand' 
as it were, through these initial sources. 

Beese 

Commission of the European Communities, DGXn B-4 

Anz (for Unto) 

CEC - DG for Agriculture - UNIT VI FII 2 

As you probably know, DG VI and in particular my Directorate, is not directly concerned 
by tropical forestry matters, as these are among the competencies ofDG Vin (cooperation 
and development) andDG I (external affairs). In the forestry sector, the competencies ofDG 
VI are limited to matters concerning the Community 's forests and, in particular, to the 
implementation of EC legislation relating to forestry. Particular aspects ofDG VI activities 
which may interest you are those relating to the Community's action for the protection of 
forests against atmospheric pollution andftres, where important monitoring and irtformation 
collecting activities have been developed. For your information, I join in the annex the last 
Commission report on the condition afforests in Europe. The answers given to your 
questionnaire by my department are relating only to the Community's forests (European). As 



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/ already said above, we have no direct information needs concerning tropical forests. 

Busca and Corda (for Fossati) 

CEC - DGI/J, Directorate for Asia 

As you probably know, the Commission has recently started a special programme on Tropical 

Forests that will greatly benefit from the data collected and processed under the TFIS. Please 

do not hesitate to get in touch with my collaborators for further information and exchange 

of views. 

Fraser 

Overseas Development Administration (ODA), UK 

Persson 

SIDA 

FAO is after all trying to develop a system for continuously following changes in forest 
resources. What can you add to this? Global information is of rather limited importance. 
What's the difference if we know that deforestation is 12.16 or 20 million ha per year. No 
meaningful global action will be taken I fear (whatever the figures say). 1 hardly know which 
actions can be taken at the national level. TREES data will be of limited value to control 
deforestation! Data is needed at the national level! It is at the national level that something 
can be done about deforestation. I doubt ifNOAA satellites can give information of value at 
the national level. In summary I don 't thirik this irrformation in practice will be of great value 
to SIDA. In the countries we work, more detailed information ought to be available. For me 
as a professor in iraemational forestry the information would of course be of value. Every 
year I try to tell my students about the global forestry situation. But that knowledge or those 
lectures doesn't change the world. In summary once more. Information is needed at the 
national level. 

Lutz for Tuyll 

Deutsche Gesellschaft fur Technische Zusammenarbeit (GTZ) 



International non-goyernmental agencies 
Smeraldi 

Amazonia Program-Friends of the Earth 

We would like to stay updated about the developmera of your work and receive, upon 

availability, a more specific information on the service (terms, conditions, etc.). 

•> 

Association Technique Internationale des Bois Tropicaux, 

Spire 

ATIBT - Association Technique Internationale des Bois Tropicaux, France 

Renard 

Caribbean Natural Resources Institute 



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Analysis of Users and Requirements TREES 11 Pre-feasibility Study 



Stattersfield 

Birdlife International (ICBP) 

We met with Jane D 'Souza a few months ago and so heard about the project firsthand. We 
are keen to collaborate by providing our bird data (= 'biodiversity attribute data ") and in 
return would like to receive up-to-date forest boundaries/types for areas which we consider 
important. However, we were concerned with the accuracy of the maps which she showed us 
e.g. large areas of lowland forest shown in southern Thcdland. To be of practical value the 
data must be more accurate! In addition, high resolution data is best, because we are often 
concerned about the conservation of relatively small areas of forest which can be vital for 
biodiversity conservation. 

Herrera 

Red Nacional de Informacion Forestal (REDINFOR), Peru 

Anderson 

Greenpeace International 

We wouldn't use your material much, unless it was inexpensive. We have forest campaign 
staff working on the ground in Siberia, Vancouver, the Amazon, PNG, Finland, Karelia, Nth 
west USA, Guatemala for whom your maps could be very useful. Please let me know about 
rough costings for your materials. TTianks 

Gilmour 

lUCN Forest Conservation Programme 



National goyeriunental agencies 
Hunter 

Natural Resoiu-ces Institute, UK 

Lund 

USDA Forest Service (for Puerto Rico, Hawaii, Florida) 

TJie USDA Forest Service conducts forest inventories of the forested lands of the US and also 
provides assistance abroad. Hence, I completed two questionnaires; one reflecting our 
domestic work (we have tropical lands in Puerto Rico, Hawaii, Florida, and the territories 
in the S. Pacific) and one reflecting our international work. A general comment - there are 
many groups starting to monitor the world forests using satellite imagery. These include 
TREES, FAO, UNEP, NASA, Woodshole etc. The World Conservation Monitoring Centre can 
do us alia great service by urging that these groups define their areas of responsibility and 
work together toward providing the information we need to inventory, manage and monitor 
our resources. 

Lund (2) 

USDA Forest Service (International work) 

TTtere has to be coordination between TREES, RESPAS, NASA Pathfinder activities. We are 

unnecessarily duplicating each other work and not making much progress in making satellite 

data available to countries in need. 



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TREES n Pre-feasibility Study Analysis of Users and Requirements 



National non-governmental agencies 
Burton 

World Wide Land Conservation Trust, UK 

/ have completed this in order to ensure some input. However, I hope that copies can also 
be sent to Roger Wilson c/o Programme for Belize [done], Belize City and also to Dr Peter 
Furley, Department of Geography, Edinburgh [done]. Their comments, taken in conjunction 
with those ofODA/NRI should give an accurate assessment of the WWLCT/PFB activity in 
Belize. The WWLCT is also currently investigating Costa Rica and the Philippines and may 
become involved in other parts of the world. 

Bomer 

WWF-Switzerland 

Member of WWF International's Forest Advisory Group. Can get additional material from 

WWF International. 

Williams 

Sierra Club, USA 

Wiile the Sierra Club does not currently use satellites, we might in thefiuure. 

Muchoney 

Remote Sensing, The Nature Conservancy 

TNC require high resolution data at frequent imervals; frequency in receiving data depends 
on specific areas; for level of accuracy generally a scale of 1:100,000 is desirable, 
classification accuracy standards should be 80-85%. 

Juniper/Rice 

Friends of the Earth 

Some of the answers given (such as distribution of forest survey information) are based on 
FoE's future work on Geographical Information Systems (which as far as the forest campaign 
is concerned, is currently being developed). Presently, we have no facility to 'directly ' 
monitor forests (aside from on-the-gromd inspection). This is something we may want to 
develop in thefiuure fi.e. use of satellite imagery). All monitoring is 'indirectly ' taken from 
other credible sources reporting on the issue. 

Johnson 

Tropical Agricultural Development, USA 

Currently using WCMC to provide maps for Global Action Plan for Palm Conservation. 

Verhagen 

Friends of the Earth International 

Kuhlmann 

ARA - Arbeitgemeinschaft Regenwald und Artenschutz, Germany 

Bogaert 

WWF-Switzerland 



WCMC-93/TN-003 D-H 



Analysis of Users and Requirements TREES 11 Pre-feasibility Study 



Rodenburg 

World Resources Institute 

As you know, WRJ is interested in both forests and the institutionalization of monitoring. 
Global monitoring of earth resources is what I want to see implemented. TREES is 
duplicative with at least two other programs I know ofl) Tom Loveland (?) at EROS, Sioux 
Falls, South Dakota is extending his 1 km land cover database of the USA to North America 
and then the entire world over the next five years. 2) George Woodwell of Woods Hole is 
going to map all tropical forests. Call a meeting as all of you involved in these global efforts 
can work together. 

Wilson 

Programme for Belize The principal use to which TREES could be put would be to place 
PFD management areas in a regional context. At national and local level there is already 
adequate provision for monitoring and landuse purposes. 

Colchester 

World Rainforest Movement 

The World Rainforest Movement monitor policy and project impacts on forests. Also want 
mapped information on concession areas (mining, logging, oil prospecting, etc.) and tenure 
classification (PAs, PFE, Statelands, ejido, private property etc.). 



Chaudhry 

WWF-Pakistan 

Jackson 

Responsible Forestry Programme (FSC) 

Our timber certification and labelling scheme involves inspections of concessions to determine 
that good management practices are being upheld. We are therefore interested in satellite 
information which can tell us things such as whether logging has encroached on demarcated 
areas (e.g. set-aside zones of biological importance, or indigenous lands), whether more 
timber is being extracted than desirable, whether logging is taking place too near 
watercourses, erosion along roads - and so on. Normally, this information will be obtained 
by inspectors visiting sites and making assessments using structured inspection forms. There 
is thus scope for ground-truthing some of your satellite data. Our interest is the satellite 
scheme depends in on how accurate the information is, and to what level of resolution, the 
level of expertise needed to interpret the data supplied, and of course, the cost. 

de Freitas 

Carmabi Foundation, Netherlands Antilles 

Country representative 

lUCN-Laos 

Fox? 

East- West Center, Hawaii 



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TREES n Pre-feasibility Study Analysis of Users and Requirements 



Godoy . 

lUCN-Guatemala 

lUCN has a ARCINFO CIS in San Jose Costa Rica, which can be used to give a regional 
service. However, the lUCN representation in Guatemala has only restricted access to is 
because of costs. The Unit for Planning in Peten (UNEPETJ has another ARCINFO CIS 
which Junctions at relatively low cost and which could cooperate with ourselves and WCMC. 

Rios 

CDC-Peru 

Tang 

WWF-Hong Kong 

TREES is a very interesting and valuable project. WWF HK has been using GIS to establish 
an ecological database of Hong Kong for more than two years. We welcome any opportunity 
to participate in the TREES project and to integrate our database for the integrated forest 
information system which you have mentioned in the near future. 

Pereira 

Fundacao de Tecnologia do Estado do Acre, Brazil 

Thank you for sending us information about the TREES Project. We believe it will be helpful 
and usefidfor ail who work on tropical forests. About the question K, nowadays we are using 
a Brazilian software made by INPE (National Institute of Spatial Research) called SGI. but 
we have a project to work with ARC/INFO and PCI (Canadian software) probably at the end 
of tins year (93). 

Nairn 

lUCN-Pakistan 

Gomez 

Asociacion Ecologista Panamena, Panama 

The information on tropical forests in Panama, which you can give us, will be useful to 
suggest changes in national forest policy. Furthermore, the data can be used for monitoring 
deforestation and how it affects global climate change. 

Oliver 

Forests Forever 

Jean and Rodriguez 

Instituto Nacional de Recursos Naturales Renovables, Panama 

CasteUo 

lUCN-Ecuador 

Bass 

International Institute for Environment and Development, UK 

We would not want information annually, but whenever we receive information we would 

want it to be this years! 



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Campredon 

lUCN Guinea-Bissau 

Of course we are interested by information you can provide but note that we have very few 
people able to devote time to new activity, that the average technical level of people in 
Guinea-Bissau is very low, and also that we are dealing with a very patchy environment in 
a very small country. Anyway I'm pleased to look forward.. 

Galvarro 

CEDOIN (Centre de Documentacion e Informacion, Bolivia 

Acevedo 

Centre de Datos para la Conservacion, Paraguay 

Broekhoyen 

lUCN East AMca, Forest Conservation Programme 

Would be able to verify satellite data in specific localities, where we have field projects. 

Michel 

lUCN-Burkina Faso 

Leblanc 

Environmental Defense Fund 

/ recently mailed to Dr Collins a copy of a paper entitled 'Preserving Tropical Forests and 
Climate: The Role of Trees in Greenhouse Gas Emissions Trading, ' written by myself and 
Dan Dudek. This paper advocates the establishment of a forest monitoring system to 
implement the climate convention in a way that includes forests as sinks and that allows joint 
implementation forestry projects. Such a monitoring system would need to be implemented 
under the auspices of a scientific body set up by the Conference of the Parties. See pages 
33-40 of this paper for a proposal including specification of data requirements for the 
monitoring system. 

Olson 

WWF-USA, Conservation Science Program 

We are in the initial stages of developing a GIS program here at WWF-US and anticipate a 
wide variety of information needs in the future. We would like to have access to databases 
of regional and local forest cover, forest condition, and surrounding landuse. We have been 
currently working with forest coverages for some regions in Southeast Asia in order to 
develop effective methods for prioritizing habitat fragments for conservation action. The forest 
coverages have been most useful for testing a variety of spatial models and for integrating 
biotic inventories with landscape-scale analysis. If possible, we would like to be able to 
obtain similar coverages for other regions and receive updates on a regular basis. Temporal 
sequences in forest cover data would allow us to conduct a number of analyses that examine 
temporal patterns of isolation and to assess rates of deforestation for threat evaluation. The 
Conservation Science Program will also support WWF regional programs and field projects 
with GIS. Information on forest cover and condition for local regions or specific conservation 
areas around the World will likely be necessary in the future. Ideally, if a field project asks 
for current and past information on forest coverage and landuse for a particular area we 
would like to be able to obtain it for them in a timely fashion, both raw images and 



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processed or digitized data. We hope to develop GIS capabilities for all of the regional 
programs, but we will likely be responsible for obtaining the majority of databases. I can 
envision a wide variety of needs based on the diversity of activities conducted here. I look 
forward to working closely with WCMC and the TREES Project and agree that rapid access 
to an integrated forest information system will contribute immensely to conservation efforts 
in the future. 

Kumar 

WWF India 

TTie unfortunate situation is that in India, capability does not exist in any NGO for an 
independent verification of the Forest Survey of India data. WWF-India too does not have a 
specialised forestry policy evaluation desk or person. Currently this task is assigned to Mr 
Ashok Kumar, Director, TRAFFIC-India. Visual observations (which are not comprehensive) 
and anecdotal accounts contirme to point to degradation of forests, unauthorised removal and 
theft of timber, pressure of head loaders and graziers. In some states still having rich forest 
cover such as Arunachal and Andamans virtual plunder by corrupt officials is reported. In 
this situation, access to independent remote sensing data will enable us to verify the FSI data 
- to begin with as it relates to the Protected Area network or other sensitive forest rich states. 
We have reasons to believe that FSI may be under pressure to conceal reduction of forest 
cover. Ideally, we would like a full project for the evaluation of the FSI reports of 1989 and 
1991 with independent ground truth verification in critical forest areas. There is a need to 
develop greater capability to monitor forest cover and forest policy in India but for the 
present, we are not able to create an independent desk owing to lack of funds. 

Benitez 

Fundacion Natura, Latino Americana de Bosques Tropicales, El Salvador 
The information you can send to us will be of great use to our work in that Fundacion Natura 
carries out. TTiis information will be shared out through the Latin American forest network 
which our institute coordinates. 

JunkoT 

ORCA - lUCN Central America Office, Costa Rica 

The frequency with which the information is required varies depending on the projects being 
carried out. In some cases information is needed annually, especially when you are dealing 
with projects on a local level. To determine changes on a regional scale the information may 
be gathered every three years. The precision level of the information also depends, in our 
case, on the scale of the project. It could be less than 1:100,000 for local projects or 
1:100,000 for the whole of the Central American region. Both the studies with satellite 
imagery and those on a regional level will be initiated in the next few months in the GIS unit 
oflUCN. Generally the projects in which we participate have an initial planning period for 
the work to be carried out, for which, in the majority of cases, we require quite quickly lists 
of the information already available for the area of the project. The information itself would 
be required on a slightly longer time-scale, which could be about one moruh. 

Skyum 

The National Forest and Nature Agency, Denmark 

The use of satellite data has for the present a low priority, but the Agency would be pleased 

to receive current information about the TREES-project. 



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Hulse 

WWF-Vietnam 

In our organisation we have done the following activities related to your subject: undertaking 
the national forest inventory and monitoring (five-year rotation). The forest map of this 
project is made from TM satellite images and stored in GIS system; establishing a map 
database at national and provincial level. Digitised map as follows: elevation map 
(1:100,000); administrative map by communes (1:50,000); forest map (1:250,000); road 
network map (1:100,000); soil map (1:100,000); land use map (1:100,000). For these 
activities we have developed one GIS system named FEWGIS (writing in C language) and one 
digitising section with about 40 staff. 

Donovan 

Rainforest Alliance, USA 



Forest research community 
Barnes 

Department of Biology, University of California, USA 

Jacobsen 

Tropical Forest Foundation, USA 

DeWulf 

Laboratory of Remote Sensing and Forest Management, University of Gent, Belgium 

Burley 

Oxford Forestry Institute, UK 

Eden 

Centre for Developing Areas Research (CEDAR), Royal HoUoway-Univ 
1. Since other organisations than TREES (CEC/ESA) are monitoring tropical forests (eg. 
FAO) it is important that sufficient collaboration occurs between organisations to allow 
effective comparison of the no doubt divergent results, and thus real appraisal of the various 
methodologies. 2. Emphasis on monitoring deforestation is obviously important, but precise 
evaluation of replacement land covers also needs to be undertaken, i.e. agricultural land, 
pasture (+-weeds), woody regeneration etc. 'Degraded tropical rainforests' is insufficiertt 
for land management or carbon budgeting purposes. 3. If the Brazilians can monitor 
deforestation annually on Landsat TMfor Amazonia, would not an extrapolation of their 
methods to the tropics as a whole provide a sounder medium-term basis for TREES than 
automated AVHRR analysis? At this stage, I believe a low-tech approach is probably more 
reliable and useful. 

Evers 

Institute for Forestry and Nature Research in coop with TROPENBOS, The Netherlands 
Timber volume is needed at individual tree level or multiple sites per ha. 

Freiberg 

European Tropical Forest Research Network (ETFRN) 



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ETFRN can possibly ground truth satellite derived data via the ETFRN network. From 
TREES I would expect that: a) derived from global satellite data (pre-stratified) it should be 
necessary to request these data on-line or CD-ROM/Floppy disk, to manage these data on 
the users desk (resolution of 1 x 1 km); b) further it should be possible, indicating e.g. the 
region or country, to obtain a more detailed data output (eg. 80 x 80 m, 30 x 30 m) from 
TREES (or another listed data supplier) to process/manage these data on their own PC(e.g./ 
local/regional management plans, landscape planning to make printouts on printer); c) for 
all R+D projects and commercial projects/activities this could be an interesting tool, if the 
user costs are reasonable I 

Longh 

Centre for Environmental Science, Leiden University, The Netherlands 

Carver 

Map and Imagery Laboratory Library, University of California, USA 
As you can probably tell, I had a tough time filling this out. since its the users of the Map 
and Imagery Lab who build GIS and work with mapping of vegetation - and 25 years of 
experience as a university spatial-data librarian have taught me that there is no predicting 
exactly what users will need, beyond the obvious - eg. spatial data covering the state in 
which the library is located. UCSB's role is that of a research institution, with emphasis on 
graduate and faculty research, so at the same time, someone will probably work on just about 
any one of the research categories you have listed. 

Poorter 

Tropenbos Foundation, The Netherlands 

Leakey 

Institute of Terrestrial Ecology, Scotland, UK. 

It would be interesting to have information on changes in cloud cover with time. 

Wyngaarden 

International Institute for Aerospace Surveys and Earth Sciences, The Netherlands 

ter WeUe 

Utrecht University, The Netherlands 

Sayer 

CIFOR - Center for International Forestry Research 

CIFOR is still some way from defining precisely what its role, if any, might be in global 
monitoring efforts. We do, however, have a programme area dealing with 'location and types 
of global forest resources to satisfy future demands for goods and services". At the meeting 
last week in Delhi of the incipient World Commission on Forestry and Sustainable 
Development, there was a good deal of enthusiasm amongst one or two participants for 
CIFOR to collaborate with the Commission on certain global monitoring issues. The 
Commission sees itself as having a role in helping to define methodologies and the respective 
roles of the differeru agencies involved in this sort of work. George Woodwell, in particular, 
was very keen on convening a meeting of all the major players during the coming months (at 
Woods Hole) to clarify responsibilities and seek enhanced collaboration. Underlying these 



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discussions was the idea that the Commission, possibly in collaboration with CIFOR, would 
promote the production of a bi-annual "state of the World's forests ". This would be somewhat 
similar to the sorts of ideas that we discussed with Robin Pellew when we last met in 
Cambridge. The Commission was very well aware that a great deal is already being done to 
harmonize approaches and to iraegrate data derived from different sources. A question is 
whether arty of these efforts are sufficiently resourced to really achieve an adequate global 
product. The other question is the respective roles of the principal UN agencies which have 
a mandate to do this sort of work. I suggest that there might be some collaborative work 
between WCMC and CIFOR in this area. CIFOR, subject to the Board's approval, may be 
able to help WCMC expand its forest map library work and to make it inore readily available 
to CIFOR' s partners. This might constitute a contribution toward the product that the 
Commission would be seeking. 

Ricker 

Yale University, School of Forestry and Environmental Studies, USA 
Not being an expert on Geographic Information System's analysis, it would be useful to get 
information about what data and in which form can be provided by WCMC (and costs). 
Furthermore, it would be important to know the accuracy of data (in particular for 
deforestation data in a region it would be necessary to know the possible error range). 

Evans 

Forest Inventory and Analysis, Southern Forest Experiment Station 
Please see enclosed reprints on examples of research interests. 

W illiams 

School of Agricultural and Forest Sciences, Bangor University, UK 

Tomppo 

Finnish Forest Research Institute 

Possibly, our institute produces similar information as TREES. Need satellite data to detect 
timber volume and growth by tree species, site fertility, mean age of trees, damages etc. i.e. 
all variables of National Forest Inventory - about 100 variables. 

Doolittle 

International Society of Tropical Foresters (ISTF), USA 

Henry 

World Forest Institute, World Forestry Center, USA 

Marshall 

Royal Society South-east Asian Rain Forest and Aberdeen University 

Thompson 

Division of Forest Science and Technology, CSIR, South Africa 

Heden 

Swedforest International, Sweden 



D-18 WCMC-93/TN-003 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Barborak 

Wildlife Conservation Society 

I highly recommend you contact CCAD - Central American Commission on Conservation and 
Development, based in Guatemala, via Juan Carlos Godoy oflUCN's office there. With help 
from NASA. CI and University of Florida, they are involved in regional data-base 
development on these themes in Central America. WCS is collaborating with the U. of 
Florida in developing an up to date map of the region's protected area network in relation 
to remaining forests, lands needing restoration, and potential forest corridors linking 
protected areas. 

Condit 

Center for Tropial Forest Science, Panama/USA 

Smith 

Seed Conservation Section, Jodrell Laboratory, Kew Botanical Gardens, UK 
From my own standpoint, I would hope for a greater emphasis on dryland forests. 

Gillman 

Open University, UK 

Our work focuses on forests in Central Guyana and Trinidad (northern range and southeast). 
Local biodiversity assessments in those areas are put into a regional context (Guianas, 
Venezuela. Caribbean). Any support for that work would be gratefully received. 

Jack 

Environmental Management Unit, Monash University, Australia 

An email network newsgroup (e.g. "forestNet") might be useful - depends on attitudes to 

'teleresearch ' and perhaps this is worth encouraging. 

Rudel 

Rutgers University, USA 

Adam 

CSIR, South Africa 

Winser 

Royal Geographical Society, UK 

In consideration there are three things you should perhaps be aware of: l)Geographical 
Observatories - the RGS has embarked on a programme to create a network of environmental 
field centres for long-term research. It is hoped to provide a ten-year commitment to such 
programmes. The Society is about to embark on a two-year period of consultation and a 
number of forest sites are being considered; 2) GTOS (Global Terrestrial Observing System) 
-following discussions with both UNEP and IGBP the RGS is looking at the possibility of 
including monitoring in its fieldwork programme, either at its permanent research centres at 
its sites that it has worked in the past; 3) Links with EOS - we have responded to Chris 
Justice's request for centres that can validate EOS data and this we have begun in Jordan 
and Tanzania. I hope they will be developed for any future forests programmes. 



WCMC-QSA-N-OOS D-19 



TREES n Pre-feasibility Study Analysis of Users and Requirements 

Appendix E 
List of Current Sources of Supply 



Global change community 

Swedish Forest Survey 

Various, as obtained by individual research groups contributing GCTE 
- FAO, WCMC mainly 

UNFAO reports, surveys other NGO surveys 

Own measurements, cooperation with other scientists 

GCOS is currently in the planning stage 

Existing digital data bases; manually digitised vegetation maps; 

AVHRR-GVI,GAC,LAC; TM; SPOT; Aerial photo 

USDA Forest Service, FAO; published papers; in-house research and data storage 

(CDIAC) 

Data from national surveys Worid Bank, UNEP, WRI and FAO 

Forest departments, protected area agencies and research organisations in tropical forested 
countries 

Information from National Forest Inventory and also from field survey 

Own surveys, reports on previous surveys 

Contracted aerial surveys, satellite info (GIS etc.) 

National 1:20,000 & 1:50,000 maps, ground survey, 13yr old aerial photo (b+w) lOyr 

col aerial photos 

There are good data on Uganda's forests from sources: Hamilton and Howard and 

various on-going projects 

Vegetation and soil analysis, interpretation of aerial photographs 

Mapping by R/S and ground surveys 

a) own research, b) short term consultancies, c) ordinance surveys: 1:26,000 b/w aerial 

photography 

Site data (plots), aerial photos, satellite imagery 

Saw mills, loggers and research organisations 

Aerial photographs (10,000 ft), field recoiuiaissance survey, topographic map (1 :25,000) 

Own studies; publications from local institutions; FAO; CDC; Governmental 

organisations; lUCN 

Terrestrial surveys, remote sensing data interpretation (visual and digital) 

LANDSAT, SPOT and Brazilian environment agencies; eventually international 

environmental agencies like EOSAT, NASA, SPOTIMAGE etc. 

Aerial photos, transet inventories 
- FAO, UICN, WWF 

Field work; satellite imagery (Landsat TM); bibliography 

Soil inventories; aerial photos (1:20,000); SPOT or TM images; aerial survey 



WCMC-93/TN-003 E-1 



Analysis of Users and Requirements TREES II Pre-feasibility Study 



UN agencies 

All currently available datasets in the public domain, or for which distribution 

agreements can be obtained 

Existing maps, National Survey Department, UN Agencies (FAO), national 

environmental statistics dept 

National organisations and institutions; members of Infoterra 

FAO is mandated by its member governments to collect and report periodically on 

global forest resource data 

Intergovernmental agencies and programmes 

FAO estimates and material based on FAO figures 

From client countries' organisations FAO, or any other source, SPOT satellite 

information etc. 

EC Forest Health Inventory, national reports 

Relevant information is provided by consultants mainly during project preparation 

Own surveys, forest dept. surveys, forest concession holder's surveys; NOAA data for 

forest fires, forest cover change and land-use planning 

National sources 

Aerial photos; existing topographical maps; satellite images (Landsat and SPOT) 

International non-governmental agencies 
INPE, Brazil 

- FAO 

Donated and purchased materials; CD-Rom 

Field knowledge, literature, organisations like WCMC/FAO etc. 

National non-govemmeiudl agencies 

Various university data houses, including Edinburgh. Manomet Bird Observatory, JPL 

etc. 

Biological and Conservation Database (BCD), TNC 

- WCMC 

FAO national statistics 

Studies, books, scientific and fieldwork WWF/IUCN 

Self generated 

- WRI, FAO, WCMC aflases 
Local fieldwork 

Forest inventory data from national department of forestry 
National and international reports Landsat and SPOT images 

- PAFG-GUA, WCMC 

Secondary information produced by research centres, private researchers, NGOs etc; 

primary information produced by CDC (Conservation Data Centre) 

Local ecologists & scientists. Biodiversity conservation of local fauna, flora & habitats 

by assessing potential environmental impacts of developments & identifying conservation 

areas 

Our current sources of supply are ground survey and ancilliary data (research, books, 

papers, newsletters and odiers) 

Smithsonian Tropical Research Institute 



E-2 WCMC-93/TN-(X)3 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Survey data supplied by forestry depts. of timber producing countries, by FAO and 

ITTO. Timber trade data supplied by FAO, ITTO 

Members of lUCN, FAO, lUCN Forest Programme, various country data 

National and local surveys, international literature 

Ministry of the Environment, Direction Generale de la Recherche Scientifique et 

Technique; various scientific organisations 

Scientific and technical papers 

Publications, WCMC 

Reports of Forest Survey of India and observations of ground truth 

International and national libraries; meetings and workshops; 

Paper maps; photographic material; satellite images; GPS locations 

Usually country specific; The Nature Conservancy US 

Forest research community 

Various sources e.g. FAO, WRI 
UNEP/GRID, information from bilateral partners 
National forest departments, lUCN 
National survey data e.g. INEP, Brazil 

- SAREX-gz 

- WCMC 

Resource monitoring/assessments, dissemination of data, remote sensing/GIS 

Federal and state inventories 

A variety of published information, both original research papers and books/atlases 

Ground surveys by scientists and collaborating organisations; air photo coverage; 

Landsat and SPOT data from Satellite Application Centre, South Africa 

Remote sensing; field inventories; reports; public databases 

Bibliographies, forest departments 

Individual researchers' arrangements 

Ground survey. Literature/personal communications 

Surveys, mapping, aerial photos; aerial/sateUite data from Eros Data Center; sister 

organisations (Natural History Museum, Kew etc.) 

Timber traders 

- FAP; NNUU; UNCTAD/GATT 



WCMC-93/TN-003 E-3 



TREES n Pre-feasibility Study Analysis of Users and Requirements 



Appendix F 

Requests for Information Summarised by Information 

Type 



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List of Acronyms 



ACP African, Caribbean and Pacific 

AVHRR Advanced Very High Resolution Radiometer 

CDC Conservation Data Centre 

CEC Commission of the European Communities 

CEO Centre of Earth Observation 

CEOS Committee on Earth Observation Satellites 

CGIAR Consultative Group on International Agricultural Research 

CIFOR Centre for International Forestry Research 

CIRAD Centre de cooperation intemationale en recherche agronomique pour le 

developpdment (formerly known as CTFT) 

CPD Centres of Plant Diversity 

DIS Data and Information System 

EBA Endemic Bird Area 

EC European Community 

EOS Earth Observation Sciences 

EROT European Communities' Regional Fund 

ESA European Space Agency 

ETFRN European Tropical Forest Research Network 

FAO United Nations Food and Agriculture Organisation 

FoE Friends of the Earth 

FRA Forest Resource Assessment 

GAIM Global Analysis, Interpretation and Modelling 

GATT General Agreement on Trade and Tariffs 

GCTE Global Change and Terrestrial Ecosystems 

GEMS Global Environment Monitoring System 

GIS Geographic Information System 

GRID Global Resources Information Database 

HTFI Humid Tropics Forest Inventory 

ICBP Birdlife International (formerly International CouncU for Bird Preservation) 

ICSU International Council of Scientific Unions 

IGBP International Geosphere - Biosphere Programme 

EED International Institute for Environmental Development 

IPCC International Panel on Climate Change 

ITTA International Tropical Timber Agreement 

mO International Tropical Timber Organisation 

ITTO International Tropical Timber Organisation 

lUCN International Union for Conservation of Nature and Natural Resources 

lUFRO International Union of Forestry Research Organisations 

JRC Joint Research Centre 

MAB Unesco Man and the Biosphere Programme 

NASA National Aeronautic and Space Administration 

NGO Non-governmental Organisation 

SASIFY Space Agency Forum on the International Space Year 

TFAP Tropical Forestry Action Programme 

TFIS Tropical Forest Information System 

UN United Nations 

UNCED United Nations Conference on Environment and Development 

UNCTAD United Nations Conference on Trade and Development 

UNDP United Nations Development Programme 

UNEP United Nations Environment Programme 



UNEP/HEM UNEP/Harmonisation of Environmental Measurement 

Unesco United Nations Educational Scientific and Cultural Organisation 

WCMC World Conservation Monitoring Centre 

WCRP World Climate Research Programme 

WMO World Meteorological Organisation 

WRI World Resources Institute 

WWF World Wide Fund for Nature 



TREES II Pre-feasibility Study Problems and Recommendations for Solutions 



Technical Note No. 4 



Problems and Recommendations for Solutions 



EOS-93/090-TN-004 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



Contents 



1. Introduction 1 

2. Technical Requirements 2 

2.1 Introduction 2 

2.2 User Requirements 2 

2.3 Problems in Meeting Information Requirements 5 

2.4 Problems in Meeting Scale Requirements 7 

2.5 Problems in Meeting Frequency Requirements 8 

2.6 Recommendations 9 

3. Non-technical Requirements 14 

3.1 Extension of TREES to Non-Tropical Areas 14 

3.2 Technology Transfer and Data Dissemination 14 

3.3 Collaborative Research Effort 15 

4. Products Specifications 16 

4.1 Introduction 16 

4.2 Tropical Monitoring Product 17 

4.3 Boreal Monitoring Product 22 

4.4 Tropical Inventory Product 22 

4.5 Tropical Local Product 25 

4.6 Report Generation 27 

5. Conclusions 32 

6. References 33 



EOS-93/090-TN-004 



TREES II Pre-feasibility Study Problems and Recommendations for Solutions 

1. 

Introduction 



The analysis of user requirements is reported in Technical Note No. 3. Specific user needs 
related to the monitoring of tropical forests were determined. The purpose of this technical 
note is to examine these user needs in relation to the TREES II programme and determine 
how they may best be met by TREES II. Problems in meeting the user requirements are 
identified and discussed, and potential solutions are suggested. 

Global monitoring requirements can only realistically be met by using sateUite-based data. 
It is therefore assiimed throughout this Technical Note that satellite based data is to be used 
by TREES II. 

In Section 2 the user requirements identified in Technical Note No. 3 are examined in 
relation to the possibility of their provision via sateUite data with respect to the questions of 
whether the required information can be extracted from the data, whether it can be extracted 
at the required scale, and whether the data volumes are such that it can be extracted at the 
required frequency. 

In Section 3 some non-technical issues are examined, in particular the political sensitivity of 
the TREES programme. 

In Section 4 a number of 'products' and 'reports' are proposed which are designed to meet 
as many of the user requirements as possible within the constraints of the TREES 
programme. Each product and report is described in detail, and outstanding problems in 
their production are listed. 

Section 5 summarises the findings of this technical note and leads in to the specific issues to 
be covered in Technical Note No. 5, the Work Plan for TREES II. 



EOS-93/090-TN-004 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 

2. 
Technical Requirements 



2.1 Introduction 

The user requirements analysed in Technical Note No. 3 are discussed below in relation to 
their potential provision via satellite-based data. In Section 2.2 the requirements specified in 
Technical Note No. 3 are considered. Each of the main areas on which users were 
questioned is treated separately and the most frequently requested data are considered to 
form a user requirement (cf Technical Note No. 3, Section 3). Each requirement so identified 
is then discussed in the following sections in relation to its potential provision via remote 
sensing and in particular within the constraints of the TREES project. 

For each user requirement the following questions must be addressed: 

• Is the required information potentially extractable from remotely sensed data? 

• Can the information be presented at the required spatial scale? 

• Can the information be extracted with the required frequency? 

The first issue depends upon the spatial and radiometric properties of the data; the second 
depends on the spatial resolution of the data; the third depends upon the data volume in 
relation to the available manpower and hardware resources. The first two issues are 
therefore primarily technical in nature, whereas the third has both technical and managerial 
implications. 

In Section 2.3 the information requirements are discussed in relation to their potential 
provision via satellite data. The issues involved in spatial scale are discussed in Section 2.4 
and in frequency of provision in Section 2.5. In Section 2.6 the identified user requirements 
which could feasibly be met by a TREES II programme are identified, and the reasons for 
failing to meet user requirements are discussed. The requirements which can be met are 
then synthesised into a set of three related requirement groups. 



2.2 User Requirements 

Technical Note No. 3, Section 3.7 lists a number of requirements which are categorised by 
user group. In order to extract specific information requirements from this list the common 
information requirements from each group have been extracted. For example, both groups 
B (national forestry departments) and D (intergovernmental agencies) require forest 
boundary data annually at medium resolution (see Requirement 2 below). The following 
list of requirements is therefore categorised by information type. Numerical data 
supporting the requirements are derived from Technical Note No. 3, Appendix F. 



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Boundaries 

81% of users requested boundary information, making it the most frequently requested 
information category after forest type. Fine scaled >1:100,000, data is most frequently 
requested (68)2, j^^t medium scale, 1:100,000 to 1:1,000,000, (45) and coarse scale, 1:1-1.5 
million, (16) are also required. Most users require data annuaOy (57); however, data every 
3/5 years would be almost as popular as the annual data (54). Boundary data is requested 
by all the surveyed user groups, as shown below: 

Requirement 1 Forest boundaries (113), annually (57) at fine scale (68) 

Requested by groups B, E, G and H^. 

Requirement 2 Forest boundaries (113), annually (57) at medium scale (45) 

Requested by groups B, D, E and G. 

Requirement 3 Forest boundaries (113), every 3 years or more (54) at coarse 

scale (16) 
Requested by groups A and C. 

Requirement 4 Forest boundaries (113) every 3 years or more (54) at medium 

scale (45) 
Requested by groups B, D and F. 

Requirement 5 Forest boimdaries (113), every 3 years or more (54) at fine scale 

(68) 
Requested by group B. 



Forest Fires 

Forest fixe data are requested by 46% of users, with the most frequent requests coming from 
groups D, G and E. Data are requested annually at medium to fine scales. 

Requirement 6 Forest fire data (65), annually (43), at medium scale (29) 



I 



The terms 'fine' and 'cocirse' are used throughout this report instead of the more usual large' and 
'small' which can be confusing for the non-specialist 
'Fine' scale 1:100,000 or greater 

'Medium' scale 1:100,000 to 1:1,000,000 
'Coarse' scale 1:1,000,000 or less 

2 Numbers in brackets refer to the number of users requesting data. 
The codes for user groups are as follows: 

A global change corrununity 

B national forestry departments 

C UN agencies 

D intergovemmentcd agencies 

E international NGOs 

F national governmental agencies 

G national NGOs 

H forest research community 

I timber traders 



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Roads 



Road data are requested by 53% of users, with the most frequent requests coming from 
groups C and E. Data are mostly requested at annual frequency, but there are also users 
requiring less frequent data. 



Requirement 7 



Road data (74), annually (33) at medium scale (54) 



Protected Areas 

Protected area information is requested by 79% of users, with the most frequent requests 
coming from groups B, C, D, E, G and H. Data are required annually (41), or every 3 years 
or more (68). Users are almost evenly divided between fine (52) and medium (54) scale 
requirements. 



Requirement 8 
Requirement 9 
Requirement 10 
Reqiiirement 11 



Protected area data (111), 3 yearly (68), fine scale (52) 
Protected area data (111), 3 yearly (68), medium scale (54) 
Protected area data (111), annually(41), fine scale (52) 
Protected area data (111), annually (41), medium scale (54) 



Biomass 

Biomass data are requested by 46% of users, with the most frequent requests coming from 
groups A and F. Data are required every 3 years or more (40) at both fine (35) scale and 
medium scale (27). 



Requirement 12 
Requirement 13 
Requirement 14 



Biomass (65), every 3 years or more (40), fine scale (35) 
Biomass (65), every 3 years or more (40), medium scale (27) 
Biomass (65), annually (22), fine scale (35) 



Forest Types 

Forest type information is the most frequently requested data type, required by 83% of 
users, and required by users from all groups. Data are required annually (36) and at 3 
yearly or greater frequency (74), with users evenly divided between fine (58) and medium 
(55) scale products. 



Requirement 15 
Requirement 16 
Reqioirement 17 
Requirement 18 



Forest types (116), annually(36), fine scale (58) 

Forest types (116), annually (36), medium scale (55) 

Forest types (116), every 3 years or more (74), fine scale (58) 

Forest types (116), every 3 years or more (74), medium scale 
(55) 



( 



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Timber Volume 

Timber volume information is requested by 47% of users, mainly from groups C and D. 
Data are requested annually (29) or at intervals of 3 years or more (34) and at fine scale (37) 
and medium scale (25). 

Requirement 19 Timber volume (66), every 3 years or more (34), fine scale (37) 

Requirement 20 Timber volume (66), every 3 years or more (34), medium scale 

(25) 

Requirement 21 Timber volume (66), annually (29), fine scale (37) 

Requirement 22 Timber volume (66), annually(29), medium scale (25) 



Biodiversity 

Biodiversity data are requested by 78% of users, v^ith the most frequent requests coming 
from groups B, C, D and H. Data are requested annually (35) and at 3 yearly or greater 
intervals (61). Scale requirements are evenly divided between fine (57) and medium (50) 
scales. 

Requirement 23 Biodiversity (109), every 3 years or more (61), fine scale (57) 

Requirement 24 Biodiversity (109), every 3 years or more (61), medium scale 

(50) 



2.3 Problems in Meeting Information Requirements 

In this section each topic on which users were questioned is discussed with respect to the 
ability of satellite data to provide the required information. Consideration is also given to 
the scale and frequency of provision as discussed later in Sections 2.4 and 2.5. 



Boundaries 

The boundary between forest and non-forest classes can be extracted from remotely sensed 
data by classifying each pixel as either 'forest' or 'non-forest'. This implies that there must 
be a radiometric separation between the two classes. Research described in Technical Note 
No. 2 has shown that this is the case for the major data sources i.e. TM, SPOT and AVHRR. 
However, discrimination is a combination of both the radiometric and the spatial properties 
of the data. Less success is to be expected at coarser resolutions largely because each pixel 
may contain a mixture of cover types, thus making the decision as to its class more complex. 

The spatial resolution of the data also affects the ability to map the bovmdary at different 
spatial scales. Accurate recording of boundaries is most important for the detection of 
boundary changes. Using TM data with a resolution of 30 m registered to ±0.5 pixels would 
imply that a boundary change of 60 m could be represented accurately. At a scale of 
1:1,000,000 this would be equivalent to a 0.06 mm line width. For 1 km AVHRR data a 2 km 
boundary change could be represented. At a scale of 1:1,000,000 this would be a line 2 mm 
wide. Boundary changes could only be represented using AVHRR GAC data, with 4km 
resolution, correct to 8 km. 



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The above discussion assumes that the user wishes to know the precise location of the 
boundary itself. For users requiring mapping at fine scales (e.g. 1:100,000) this is almost 
certainly the case. However for users requiring coarser scale data it can be assumed that 
their primary interest is in the actual amount of forest, and in its variation over time, as 
opposed to the precise location of the boundary. For these users provision of statistics 
indicating the percentage forest:non-forest cover at regional or national level may be 
adequate. This point is returned to later. 



Fires 

Information concerning biomass burning can be both an indication of active deforestation 
and supply important information for climate modelling. It is not clear in what form the 
users might require this information. However, the FIRE project at JRC is dealing explicitly 
with the provision of fire data. It would be unnecessary for TREES to duplicate this effort, 
but there should be some coordination in the dissemination of the data. If TREES were to 
focus on the provision of annual data in some form, then the FIRE data could be included in 
this. 



Roads 

Roads which are open to the sky and which have sufficient contrast with their background 
can certainly be detected with TM data using automated techniques even when they are 
relatively narrow (Gumey, 1983). In areas such as Rondorua where deforestation follows 
the road network closely roads can be clearly seen on AVHRR data (Malingreau et al, 1989). 
However, many logging roads in S.E. Asia are narrow and not open to the sky, so that 
detection with satellite data is problematic (T. Whitmore, pers. comm.). Therefore, it might 
not be possible to provide road data for all the tropical forest regions on a corisistent and 
routine basis. This could become a research area for TREES II if there was sufficient user 
interest in monitoring roads using satellite data. 



Protected Areas 

Aspects relating to protected areas are covered by discussion under boundaries above and 
forest types below. 



Biomass 

This requirement is achievable with satellite-based data in that biomass can be taken as 
correlated with NDVI. The NOAA GVI product already provides NDVI at 4 km globally, 
and the IGBF-DIS provides NDVI at 1 km. There therefore seems no need for TREES II to 
duplicate this provision. However, there is currently no routine provision for NDVI at finer 
scales. 



Forest Types 

Radiometrically, TM data have narrower bandwidths than AVHRR and are hence more 
likely to provide forest type information. However, research using 1 km AVHRR data (see 
Technical Notes Nos. 1 and 2) has shown that these data can be used to derive a limited 
range of forest types such as evergreen, seasonal and degraded. Many users, especially 
those interested in detailed mapping, may require a more extensive classification. For the 



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TREES II Pre-feasibility Study Problems and Recommendations for Solutions 



information provided by TREES II to be of use there must be agreement among users as to 
an acceptable set of forest types. The questionnaire sent to users (see Technical Note No. 3) 
did not elicit any information concerning required classes. Also, as discussed in Technical 
Note No. 2, there is no clear agreement among researchers as to the optimum classification 
method. 



Timber volume 

This would be possible only if percentage forest coverage and forest type relates to timber 
volume, in that timber volume cannot be directly measured using satellite data. Some 
research is needed into the relatiomhips between biomass, percent forest coverage, forest 
type and timber volume. 



Biodiversity 

It is not clear how biodiversity is actually measured using remotely sensed data. If it is 
correlated with biomass, forest type, forest age and fragmentation it can be derived from a 
combination of NDVl, forest type classification and analysis of the shape and size of the 
forest areas. To be able to analyse the shapes and sizes of areas requires data at high 
resolution. Only in this sense would it be feasible to provide these data. This is a research 
area which needs to be addressed before an operational TREES programme can be in a 
position to generate products for biodiversity. 



2.4 Problems in Meeting Scale Requirements 

In Technical Note No. 3 analysis of user requirements indicated that the majority of users 
were interested in data at scales finer than 1:100,000. Data were overwhelmingly requested 
at the national level, but with interest in global/regional level data from the global change 
commimity and UN agencies. 

TM data with a spatial resolution of 30 m can be used for mapping at scales of the order of 
1:100,000. However, it is generally accepted that 1 km AVHRR data caxmot be used reliably 
for mapping at scales finer than 1:1,000,000 (Hausler et al, 1993). The inability to map at 
fine scales is primarily a function of the variability of land cover types with respect to the 
spatial resolution of the sensor. Large pixels represent ground areas in which there may be 
a high variability in the cover type and the classification accuracy is therefore reduced. The 
groimd area which can be represented is in any event no smaller than the pixel size itself. 
Mis-registration acciiracy introduces a further source of imcertainty. 

The user therefore has to make a choice between the use of TM data which provide the 
required spatial detail, but have the accompanying cost and data volvime disadvantages 
discussed in Section 2.5, and the use of 1 km AVHRR data which are cheaper and have a 
much lower data volimie, but cannot produce maps at fine spatial scales. The use of data at 
coarser resolutioris than 1 km AVHRR data would not be able to meet user scale 
requirements. 

Users were primarily interested in provision of information at national level (see Technical 
Note No. 3, Section 3), with less of an interest in regional and global provision. The 
provision of information at any level has implications for data dissemination. If information 



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Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



is supplied for the entire tropical region at 1:100,000 this implies a large number of maps, 
e.g. the tropical rainforest regions of Africa would require 1900 Al sheets. If users are to 
receive only data for selected areas then consideration must be given to how such data 
would be made avaOable e.g. can a user request data for any specified nation or combination 
of nations? The provision of data at coarser scales such as 1:1,000,000 has similar 
implications, but the number of map products required is more manageable, e.g. the tropical 
rainforest regions of Africa would require 19 Al sheets. 



2.5 Problems in Meeting Frequency Requirements 

The majority of users are interested in an annual provision of information, although there is 
also considerable interest in less frequent provision i.e. on a 3-yearly or 5-yearly basis 
(Section 2.2). If information is to be provided annually consideration must be given to the 
data volumes and processing cost and load that this would imply. 

The basic data characteristics of the potential satellite data sources currently available are 
summarised below with respect to data volumes and costs: 

• Landsat TM 

TM data are available at 30 m resolution with six bands containing 8 bits/pixel 
and one containing 6 bits/pixel. Thus 54 bits are required to represent a single 
pixel. One scene covers 34225 km2 and costs $4,400 at present. There is a 16 day 
repeat cycle. 

• AVHRR LAC 

AVHRR LAC data are available at 1 km resolution with five bands each 
containing 10 bits/pixel. Thus 50 bits are required to represent a single pixel. 
The usable portion of a scene covers approximately 3,000,000 km2 and the 
nominal cost per scene is $100. There is daily coverage. 

• AVHRR GAC 

AVHRR GAC data are sampled from LAC at 4 km resolution. This seems 
initially attractive in terms of data volumes. However, considering that user 
requirements discussed in Technical Note No. 3 were strongly in favour of fine 
mapping scales it must be concluded that sufficient spatial accuracy to meet user 
needs could not be achieved using these data (see Section 2.4). 

• SPOT 

SPOT data are available at 10 m resolution in 1 band and 20m resolution in 3 
bands. One scene covers 3600 km2 with repeat coverage every 26 days. Each 
pixel is represented by 8 bits. 

• ERS-1 SAR 

ERS-1 SAR data are not available with sufficiently frequent and extensive 
coverage to be of use in provision of complete monitoring of tropical forests on a 
routine basis. However, the data could be used where less frequent monitoring 
of particular areas is required. Each image covers a ground area of 
approximately 10000 km2 with a spatial resolution of 30 m and costs about $1000; 
each pixel is represented by 16 bits. 



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The reader is referred to Technical Note No. 1, Section 2 for more detail concerning 
radiometric properties. 

For coverage of the tropical forest regions 1 km AVHRR data and TM data are the only 
realistic possibilities in the near to mid-term. GAC data are at too coarse a resolution to 
provide sufficient accuracy and the nature of the resampling is such that entire features can 
be omitted (see also Technical Note No. 2). SPOT data rates are higher by a ratio of 
approximately 1:6 than the already very high TM rates discussed below and are 
radiometrically less suitable than TM or AVHRR for tropical forest monitoring. 

The equivalent ground area of a 1 km AVHRR pixel requires 60,000 bits to be represented 
using TM data and 50 bits to be represented using 1 km AVHRR data; a ratio of 
approximately 1:1,200. The spatial resolution difference means that for every AVHRR pixel 
classified 1,111 TM pixels would have to be classified with a corresponding increase in 
processing time and load. The increased processing required to use TM data in preference 
to 1 km AVHRR data is therefore considerable. Data costs must also be considered in that a 
single TM scene can be purchased for $4,400, and an AVHRR LAC scene for $100. With the 
spatial extent of a TM scene being 34225 km^ and the usable portion of an AVHRR scene 
being on the order of 3,000,000 km2, the cost differential is therefore $(100/3,000,000): 
$(4,400/34225), i.e. approximately 1:3,857 . Should current proposals make Landsat 6 data 
available at cost come to fruition this differential would be substantially reduced. However 
the data processing load would remain unchanged. 

It can be concluded from this that the use of TM data to cover the tropical regions on a 3 
yearly or more frequent basis is prohibitive in terms of data volume and cost. Although 
frequent coverage is a major user concern (see Section 2.2 and Technical Note No. 3) 
requirements which would need TM data across the entire tropics must be considered to be 
infeasible imless CEC is prepared to invest considerably more manpower and money than 
have been the case for TREES I. 



2.6 Recommendations 

Each requirement identified in Section 2.2 is now considered with respect to the constraints 
and problems discussed in Sections 2.3 to 2.5. For each requirement the optimum data 
source is identified and potential problems summarised. 

Requirement 1 fim scale, annual boundaiy data 

This requirement would need TM data to achieve sufficient 
accuracy. TM has a 16-day repeat cycle, so that obtaining 
sufficient cloud free data could be a problem. Areas would 
have to be labelled as 'unclassified' if cloud free data were 
unavailable. 1 km AVHRR data could be used if areal 
statistics were satisfactory as opposed to mapping the exact 
boundary. Cloud cover problems would still exist, but would 
be less severe with the daUy repeat coverage. The major 
problem, in meeting this requirement using TM would be the 
volume and cost of data (Section 2.5). 



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Requirement 2 



Requirement 3 



Requirement 4 



Requirement 5 



Requirement 6 
Requirement 7 

Requirement 8 



Requirement 9 
Requirement 10 

Requirement 11 
Requirement 12 



medium scale, annual boundary data 

It would be possible to achieve this with 1 km AVHRR data at 
a mapping scale of about 1:1,000,000, provided that it is 
possible to mark areas as 'unclassified' if cloud free data are 
not obtained during the year. Provision of areal statistics is 
feasible. Satellite data at spatial resolutions more than 1 km 
would not provide sufficient accuracy. 

coarse scale, 3 yearly boundary data 

This is certainly possible with 1 km AVHRR data, and cloud 

free data should be available during this time frame. 

medium scale, 3 yearly boundary data 

The 3-year time period would mean that greater attention 
could be paid to the accuracy of the classification than for 
Requirement 1 so that the results woiild be more meaningful. 
While use of TM data would be desirable, 1 km AVHRR data 
would be adequate. 

fine scale, 3 yearly boundary data 

As with requirement 1, this would require TM data with 
correspondingly high data volumes and costs. The 3-year 
time-frame would however reduce these considerably from 
those of requirement 1. 

medium scale, annual fire data 

This is provided by the FIRE programme. 

medium scale, annually, roads 

This would require TM data except in areas with clearly 

delineated roads where 1 km AVHRR data might be adequate. 

fine scale, every 3 years, protected area monitoring 
To derive these data would reqiiire the use of TM, possibly 
supplemented by airborne data (both optical and SAR). This 
would be possible, although a five-year repeat period might be 
more realistic. 

medium scale, every 3 years, protected area monitoring 
This would require 1 km AVHRR data at a minimum. 

fine scale, annually, protected area monitoring 

This would require TM data on an armual basis with 

correspondingly high data volumes and costs. 

medium scale, annually, protected area monitoring 

This would require 1 km AVHRR data at a minimum. 

fine scale, every 3 years or more, biomass 

At the required scale of less than 1:100,000 it would be 

necessary to use TM data, or even airborne data. It would be 



10 



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Problems and Recommendations for Solutions 



Requirement 13 
Requirement 14 

Requirement 15 

Requirement 16 
Requirement 17 

Requirement 18 

Requirement 19 



Requirement 20 
Requirement 21 

Requirement 22 

Requirement 23 

Requirement 24 



difficult to cover the whole tropical area at this scale, but it 
should be feasible to provide data for selected areas. 

medium scale, every 3 years, biomass 

This is provided by GVI data and IGBP-DIS data.. 

fine scale, annually, biomass 

Similar comments apply as for Requirement 12. To achieve 

this annually would be an extremely heavy processing load. 

fine scale, annually, forest types 

TM data would be required, probably supplemented by 
airborne SAR and airborne optical data. The data volume and 
costs would be very high. 

medium scale, annually, forest types 
See discussion for requirement 15. 

fine scale every 3 years, forest types 

This would be possible using TM data, but possibly only for 

selected areas (see discussion on protected areas). 

medium scale every 3 years, forest types 

Provision of these data would be achievable with 1 km 

AVHRR. 

fine scale, e^jery 3 years or more, timber volume 
Provided the relationships exist as discussed above in Section 
2.3 this would be achievable using TM data. It would be 
difficult to cover the whole tropical area at this scale, but data 
could be provided for selected areas. 

medium scale, every 3 years or more, timber volume 

1 km AVHRR data would be a minimum reqviirement. 

fine scale, annually, timber volume 

This would require TM data with corresponding high data 

volumes and costs, see also comments for requirement 19. 

medium scale, annually, timber volume 

This would be possible using 1 km AVHRR data, but see 

comments under requirement 19. 

fine scale, every 3 years, biodiversity 

TM or SPOT data supplemented by airborne optical and SAR 
data would be required, providing the restrictions outlined in 
Section 2.3 are overcome. 

medium, every 3 years, biodiversity 

TM data should provide adequate spatial resolution providing 

that the restrictions outlined in Section 2.3 are overcome. 



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11 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



Of the total of 24 identified requirements outlined above 13 could be met by the products 
and systems outlined in the rest of this report. However, note must be taken of the 
constraints discussed in the description of each requirement. 

• Requirements 2, 11 and 22 could be met by a product derived annually from 
1 km AVHRR data - this product w^ould give percent forest coverage and map 
the forest boundary. 

• Requirements 3, 4, 9 ,18 and 20, would be met by a product derived every 3 years 
using 1 km AVHRR data - this product would provide percent forest coverage of 
selected forest types and map their locations. 

• Requirements 5, 8, 12, 17 and 19 would be met by a fine scale product derived 
every three years using TM and supplementary data, possibly only for selected 
areas - this would provide detailed mapping of these areas. It is not considered 
to be feasible to meet these requirements for the whole tropical area. 

Requirements which are not met fall into three groups: 

• Data handling constraints 

Requirements 1, 10, 14, 15, 16, 21. 

These requirements all would involve annual classification of TM data over the 
entire tropical area. This can be achieved in the technical sense, but in 
considering the data volumes involved Section 2.5) and the required manpower 
it is considered extremely imlLkely that this would be feasible in practice. 

• Technical difficulties 

Requirements 7, 23 and 24. 

These requirements could not be met because of technical difficulty in that the 

information is not directly derivable from remotely sensed data on a consistent 

basis. 

• Requirements covered by other programmes 

Requirements 6 and 13. 

Considering the numbers of user requests for a requirement (see Techrucal Note No. 3, 
Appendix F), the major areas which would not be met would be: 

• fine scale annual boundary data and protected area monitoring 

• annual forest type data at any scale 

• biodiversity. 

The major information reqiiirement areas met in terms of nxmibers of requests (see Technical 
Note No. 3, Appendix F) would be: 

• bovmdary data at other than fine scale annually 

• protected area monitoring at other than fine scale annually 

• forest types at 3 yearly or greater intervals. 



12 EOS-93/090-TN-004 



i 



TREES II Pre-feasibility Study Problems and Recommendations for Solutions 



As can be seen from the above discussion, the primary impediment to supplying the users 
with data at their required scales is data volume and cost. The many users who require 
pan-tropical data annually at scales of 1:100,000 or less will be unable to look to TREES for 
their information. To some extent these users' requirements could be partially met by 
providing fine scale data via TM, but this would probably have to be for selected areas only. 
This implies that the user groups best served by TREES will be those reqmring coarser scale 
data at lower temporal frequencies namely the Global Change Community, UN Agencies, 
National Governmental Agencies and Intergovernmental Agencies. 



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Problems and Recommendations for Solutions TREES II Pre-feasibility Study 

3. 
Non-technical Requirements 



In this section some non-technical issues connected with the future development of TREES 
are examined. 



3.1 Extension of TREES to Non-tropical Areas 

Requirement 25: Extend TREES to Non-tropical Areas 

Deforestation is not confined to tropical areas, and indeed recent deforestation rates in 
Siberia since the dissolution of the former Soviet Union are considered to be as great as 
those in the tropics. While the user survey reported in Technical Note No. 3 did not cover 
non-tropical areas, there is known to be considerable interest in such areas; in particular, 
global change studies must of course take account of forest cover change wherever it occurs. 

Further, the possible perception of TREES as being an incursion into the sovereignty of 
tropical nations by the developed world should be avoided. This is necessary to minimise 
political problems and to increase the potential for collaboration with TREES work and more 
generally in the implementation of international treaties such as the Convention on 
Biological Diversity and the statement on Forest Principles (see Technical Note No. 3, 
SecHon 2.2). 

To extend TREES to global provision in the short term would have a severe impact on its 
potential for success. Data collection problems would multiply. Classification and 
geometric registration procedures would be different. Expertise in the specific geographical 
areas developed during TREES I would be lacking. Therefore, it is suggested that TREES 
implement a 'pathfinder' study to a single non-tropical area in an attempt to assess the 
difficulties involved. It is suggested that Siberia, as being both an area of concern and a 
major area where no other monitoring is known to be taking place (other areas such as 
Canada, W coast of USA and Scandinavia all have their own monitoring services), should be 
focused on as an example for boreal forest monitoring. If this exterision is successful then in 
future years TREES could gradually be extended to cover additional areas. 



3.2 Technology Transfer and Data Dissemination 

Requirement 26 : Provide outputs from TREES in low cost, low technology 
forms 

It is essential that the results of TREES work be disseminated effectively to interested users. J 
Many users have little or no access to sophisticated computing facilities such as CIS and will " 

14 EOS-93/090-TN-004 fl 



TREES II Pre-feasibility Study Problems and Recommendations for SoluHons 



require paper products at low cost. Users with GIS or other computing facilities may also 
require paper data, but are likely also to be interested in electronic data, again at as low a 
cost as possible. TREES must be able to meet these requirements. 

Many users are also in a position to provide ground survey data for verification (see 
Technical Note No. 3). In particular national agencies have expressed an interest in 
providing data. Perhaps there could be an exchange system whereby users would receive 
'free' information in exchange for verification activity or supplying other data. 

During the period of TREES II while TFIS is being enhanced it could be made available to 
users in a demonstration form so that they could input comments and suggestions. There 
could also be provision of free 'sample' data to users during TREES II for comment. 



3.3 Collaborative Research Effort 

Requirement 27: Collaborate with other projects using remotely sensed data for 

forestry morutoring 

Many of the respondents to the user survey drew attention to the existence of similar 
research efforts in tropical forest monitoring (see Technical Note No. 3, Appendix D). 
Particular mention was made of: 

IGBP-DIS 

FAO 

UNEP 

Woods Hole 

RESPAS 

NASA Pathfinder 

EROS, 1 km land cover database 

CIFOR (connected to Woods Hole) 

TREES can choose to collaborate in providing information which is different from the other 
groups in terms of its information content, scale or frequency. Alternatively collaboration 
can be on a geographic basis in that TREES would focus on particular areas, and each group 
would combine its results to provide global data. 

Despite the perception of other groups, TREES is unique in attempting to provide 1 km data 
at annually updated frequencies. The provision of data at finer scales could be left to others. 

There is also a role for either shared raw data provision, or for a centralised provision to 
which TREES and others could look for their data. Data supply has been one of the major 
stumbling blocks of TREES I (see Technical Note No. 1) and a streamlined data supply 
would vastly increase the chances of success. 



EOS-93/090-TN-004 15 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 

4. 
Product Specifications 



4,1 Introduction 

The reqioirements analysis presented above attempts to encapsulate the requirements into a 
set of realisable outputs. This was done by considering the following factors: 

• the data supply rate and quality 

• the likely classes of data which could be extracted from different EO datasets 

• efficient means of grouping requirements into single outputs by considering 
which parameters could be produced from a particular process and grouping 
those together. 

The resvilt is a set of four products and two reports which it is hoped will meet the largest 
number of requirements in the smallest number of realisable, reasonable products. For each 
product, the following sections have been specified: 

1. Principal Applications 

2. Sensors/Data Inputs Used 

3. Classes 

4. Coverage 

5. Frequency of Product Production 

6. Precision, Acciu^acy and Resolution 

7. Auxiliary Data Inputs . 

8. Investigations required. 

Each specification statement is followed by discussion points. 
The products are: 

Product Acronym 

Tropical Monitoring Product TMP 

Boreal (Siberian) Monitoring Product BMP 
Tropical Inventory Product TIP 

Tropical Local Product TLP 

Active Areas Report AAR 

Expert Analysis Report EAR 



16 EOS-93/090-TN-004 



TREES II Pre-feasibility Study Problems and Recommendations for Solutions 



4.2 Tropical Monitoring Product (TMP) 

This is a pan-tropical statistical product with graphical output as a backup designed to 
provide simple but robust and accurate inputs for policy and management makers. It will 
satisfy requirements 2, 11 and 22. Figure 1 shows an example of the classes and global 
extent of the TMP. Table 1 shows an example statistical table while Figure 2 shows an 
example chloropleth representation of the national area statistics from the table. 

1. Principal Applications 

Mmitoring of changes in the ureal coverage of forest within tropical nations. Assessment of gross 
boundary changes from map output. Production oflmrdcopy maps at 1:5,000,000. 
The emphasis is on area rather than boundary because the ability to monitor accurate 
boundary changes at this scale is unresolved. It is believed that area within nations should 
prove more robust given the average size of a nation compared to registration accuracy. 
Hence this is primarily a numerical rather than a graphical product. While boundary 
changes may be visible on the hardcopy output, this product does not claim to indicate the 
size or direction of boundary changes with any degree of confidence. This is left to the AAR 
which synthesises this product with other data soxrrces at a much higher scale. 

2. Sensors/Data Inputs Used 

1 km AVHRR. 

This product uses the major advantage of AVHRR; i.e. its frequent coverage. There must be 
a clear upgrade path to other sources of similar data such as 'Vegetation' and MERIS. 
Future satellite programmes are discussed in Technical Note No. 2. 

3. Classes 

Forest:non-forest:other 

There has been much debate about which classes can be extracted from the data. This 
product takes a pragmatic view and uses the minimum possible ntmiber of classes in order 
to provide a simple, robust and repeatable product. The classification would be carried out 
separately for each tropical region with results reported for each coimtry (see Figure 1) 
Slightly different classifiers may be required between continents but in general it is 
suggested that simplicity should predominate. From year to year, the same techniques and 
metiiodology must be used in order to maintain a consistent base for the statistics. There 
will inevitably be discussion over the best technique to use but some classification accuracy 
may need to be sacrificed for consistency and repeatability to monitor changes. The 'other' 
class includes cloud and haze where clear images have not been collected during the period. 

4. Coverage 

Pan tropically, divided by region and nation with very small tuitions not covered. 
National level breakdown is the key level required by the user to give statistics at a level 
comparable to those collected on odier subjects. In addition, any smaller unit would force 
the error term associated with mis-registration unacceptably high. Research is required into 
the error term for the area of each nation. 

5. Frequency of Product Production 

Yearly. 

The minimimi time required to collect a cloud free image is about three months and it is 
better to limit collection to the end of the dry season (where there is a dry season). 
Degrading temporal resolution to one year means that there should be sufficient cloud free 
images for most regions although a few areas will not be covered. There is no user 
requirement for monitoring at less than one year. 



EOS-93/090-TN-004 17 



Problems and Recommendations for Solutions 



TREES II Pre-feasibility Study 




+- 




c 



lUffl 


LU 


rest 
rest 
her 


CO 


O OK 


0) 

o 

II. 


c 
o 

z 



i 



Figure 1 : Example for Africa of the Classes of the Tropical Monitoring Product 



18 



EOS-93/090-TN-004 



TREES II Pre-feasibility Study 



Problems and Recommendations for Solutions 























Tropical Monitoring Inventory Product (TMP) 
TREES II Product 1998 




Country 


Forest 


Non- Forest 


Other 


Country 
size 


7o forest 


7o Change 
1996-98 


Treeland 


532 


24 


2 


558 


87 


4 


West Trees 


624 


214 


31 


849 


76 


-2 


Middle Trees 


247 


384 


5 


633 


36 


-0.4 


Nortti Trees 


523 


17 


56 


596 


93 


12 


Leafy Island 


3416 


274 


27 


3423 


96 


1 


Northern Stem 


5847 


34 


17 


5898 


95 


-0.75 


Western Stem 


327 


257 


17 


601 


52 


-34 



Table 1: An Example Statistical Table (TMP) 

6. Precision, Acctiracy and Resolution 

Precision isX% or +/-Y kml; Accuracy should he a minimum of 80%; Resolution is 1 km. 
The precision of the national statistics will vary with the size of nation and the spatial 
distribution and 'shape' of forest formations. The relationship between area and boimdary 
is complex. A pixel resolution of 1km implies that boimdaries can be located to within 1 km. 
and changes in botmdaries of less than 2km are not discemable. The measurement of forest 
area, while subject to the same 1km resolution implication, wiU usually be more accurate, 
provided that the area of forest is large relative to the number of boundary pixels. Where 
the forest is very fragmented, so that the number of boundary pixels is very large relative to 
the number of non-botmdary pixels, areal measurement will decrease in accviracy. A similar 
argtmnent applies for changes through time i.e. that the measurement of forest area wUl 
suffer from less uncertainty through time than the mapping of the boundary location. The 
work of Kuntz and Kleinn (1993) needs to be examined by the TREES 11 team in order to 
make firmer statements about relationship between pixel size, forest distribution and 
accuracy of results. 

Overall classification accuracies of 80% have been achieved elsewhere and using a simple 
classifier should enable a consistent high level to be achieved. 

7. Auxiliary Data Inputs 

Coastlines and national boundaries. 

The best soiorce is almost certainly DON. 



EOS-93/090-TN-004 



19 



Problems and Recommendations for Solutions 



TREES II Pre-feasibility Study 



8. Investigations and trade-offs to be made 

The discussions above can be summarised in terms of research required: 

• precision and accuracy definitions 

• minimum size of nation for which statistics can meaningfully be produced 

• the most appropriate and simplest classifier to use to achieve high accuracy 

• the scale at which hardcopy output can meaningfully be produced in relation to 
significant boundary changes and registration errors. 



20 



EOS-93/090-TN-004 



TREES II Pre-feasibility Study 



Problems and Recommendations for Solutions 




Figure 2 : An Example Chloropleth Representation of the National Area Statistics (TMP) 



EOS-93/090-TN-004 



21 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



4.3 Boreal Monitoring Product (BMP) 

This is equivalent to the TMP in function but is specific to boreal environments, e.g. Siberia. 
It is will satisfy the requirements 2, 1 1, 22 and 25. 

1. Principal Applications 

Monitoring ofcJmnges in the ureal coverage of forest within Boreal (Siberian) regions. Production of 

Iwrdcopy maps at 1:5,000,000. 

Much as for the TMP; the major difference being that a different classifier wiU be required. 

2. Sensors/Data Inputs Used 

As for the TMP 

3. Classes 

As for the TMP. 

4. Coverage 

Siberia divided by federal regions. 

As for the TMP. However the projection may need to change. 

5. Frequency of Product Production 

As for the TMP, although cloud free coverage might be easier to obtain in this period. 

6. Precision, Accuracy and Resolution 

As for the TMP 

7. Auxiliary Data Inputs 

As for the TMP, although regions of Siberia are not obviously available. Division into 
squares of 10° in latitude and longitude might be necessary. 

8. Areas of Investigation 

As for the TMP. 



4.4 Tropical Inventory Product (TIP) 

This is a pan-tropical graphical product with statistical output designed to show the extent 
of and relationships between forest types and boundaries of other political and physical 
units. It wUl satisfy requirements 3, 4, 9, 18 and 20. Figure 3 illustrates a specimen TIP over 
S.E. Asia while Table 2 shows a section of the statistical table which might accompany it. 

1. Principal Applications 

Area, boundaries and types of forest per nation and sub-national region. Principally a graphical 
product to show relationships between and areas of many classes of forest sub-classified by other 
spatial auxiliary data such as protected areas, national parks, vegetation zones etc.. 
This is a more rigorously defined version of TMP which is made possible by the more 
extensive use of groimd survey for more detailed classification. The TMP is not necessarily 
directly derivable from the TIP since data from different times may have been used in its 
production (see Technical Note No. 5, section 5). The output is numerical for area plus 
graphical output for distribution of forest tjrpes and possibly boundary changes at scale to 
be decided (perhaps 1:1,000,000). 



22 EOS-93/090-TN-004 



TREES II Pre-feasibility Study 



Probleffvs-and Reeoffunendations for Solutions 



E 

8 




1 


■ 


■ 


"w 


■ 

U3 




k- 


O 


O 


© 




rV 


< 


o 


o 






T3 
t> 


u. 


u. 


(1) 




c 
© 

CD 


O 

c 

2 
o 
© 


0) 

a 

F 




O 


© 




Q. 


> 


CO 


o 






LU 




x 










Ul 











Figure 3 : A Set of Possible Classes for S.E. Asian Tropical Inventory Product 



I 



EOS-93/090-TN-004 



23 



Problems and Recommendations for Solutions 



TREES II Pre-feasibility Study 



Tropical Nations Inventory Product (TIP) 
TREES II Product 1996 



Countiy 

Treeland 55 

West Treesland 04 

Middle Treesland 57 

North Treesland 49 

Leafy Island 64 

Northern Stemland 5g 

Western Stemland 37 



Fvenn-een DegradedSecondary Forest , ^ Seasonal Forest protected Evergreen Seasonal Forest 
fcvergreen ^^^^ Non-Forest ^_.^ ^^^^^ in protected 

in protected 



Forest 



Forest 



I protected 
area 



23 

24 
34 

17 

24 

34 
24 



13 

31 

5 

27 
18 

17 
17 



4 
18 



28 
7 
9 



5 

7 
24 

18 
2 

21 
5 



2 

3 
5 

3 
5 
2 
7 



2 

1 
4 
1 
1 

2 
3 



5 
1 

4 

6 

4 

2 



Table 2 : A Section of a Statistical Table for TIP 

While boimdary changes may be visible on the hardcopy output, this product does not 
claim to indicate the size or direction of boundary changes with any degree of confidence. 

2. Sensors/Data Inputs Used 

I km AVHRRfor classification, TMfor survey and validation. 

This is still basically a 1km based product but with much increased use of TM and groimd 
survey for training classifiers. The use of separate TM and/or ground sxirvey for each region 
(sub-continental scale related to AVHRR image/strip extent) is anticipated. 

3. Classes 

evergreen forest : degraded forest : secondary forest : non-forest : seasonal forest (all classes tbd 
following appropriate research). 

The definition of classes is critical and must be done carefully in light of research within and 
outside the TREES project. The classifier may be modified to produce a set of classes 
appropriate for each continent, region or even sub-region; i.e. the aim is to optimise the 
classification accuracy and obtain maximum information content in terms of forest type. 
This implies the use of separate TM and/or ground survey for each region and also that 
changes in the classifier will occur at each inventory. 

4. Coverage 

Pan-tropical, by region, nation and sub-national area (with a minimum size cutoff- tbd). 
For large coimtries, e.g. Brazil, the states (e.g. Rondonia) must be apparent. Such sub- 
division is made to maximise the information extractable and to take account of the forest 
type changes as one moves from tropical through sub-tropical zones. 



24 



EOS-93/090-TN-004 



J 



TREES II Pre-feasibility Study Problems and Recommendations for Solutions 



5. Frequency of Product Production 

3-5 years. 

This length of time is required to collect TM and ground survey data and make the 

appropriate number of decisions about classifiers in each area. 

6. Precision, Acciu-acy and Resolution 

The specification and discussion is as for the TMP. However the division of nations into 
smaller units using other auxiliary data (e.g. protected areas) will tend to decrease precision 
and accuracy, although the Tjetter' classifier will counter this. 

7. Auxiliary Data Inputs 

As for the TMP with the addition of sub-national area definitions and a range of other 
auxiliary data including protected area, national parks, vegetation zones. 

8. Areas of Investigation 

As for the TMP with the addition of the need to research multi-class classifiers for a nimiber 
of different regions. 



4.5 Tropical Local Product (TLP) 

This is designed to provide highly detailed information over a small area; in particular to 
satisfy requirements 5, 8, 12, 17 and 19. Figvire 4 illustrates how a TLP might appear when 
using airborne SAR strips and ground survey as input. 

1. Principal Applications 

It is envisaged that ad-hoc requests for detailed investigation of a small area (10,000 knfi or less) will 
drive the production and definition of this product. For example the mapping of protected areas for 
species diversity, hydrology, deforestation etc.. 

This product is characterised by being highly detailed at a local scale only. It is the 
antithesis of the TMP and TIP being geared towards one-off analysis for special tasks. It 
encapsulates all of the techniques whidi are not viable at a global or even regional scale, but 
uses TM as a base and the specific strengths of SAR and ground survey for the uses to which 
they are best suited at the current level of knowledge. It is unlikely that it can be specified in 
any generic detail since local data availability will determine the form of the product. 

2. Sensors/Data Inputs Used 

Landsat TM, SAR (space and air borne), ground survey. 

The base data for the product might be a single TM to which is added SAR and any other 

detail that can be foimd. 

3. Classes 

The minimum which should be extracted is forest :non-f or est at 100 m (minimum) accuracy/ 
precision. However, a much larger number of classes should be possible; for example a number of 
tree species and land use categories. 

This product could contain a large quantity of very specific information about the surveyed 
area including tree species type, tree height, etc., all at 100 m or less spatial resolutiori. 



EOS-93/090-TN-004 25 



Problems and Recommendations for Solutions 



TREES II Pre-feasibility Study 



Tropical Local Product (TLP) 

TREES II Product '°^''°"^ 

July 1996 



Lat/Long 



eSAR 




TM image 



Scale: I 20 km , 
Region: Africa 
Nation: Testland 
Investigator: J. Smith 



nt/Long 



TM Analysis with SAR and Ground Survey interpretation 



Figvire 4 : Example Form of Tropical Local Product 



26 



EOS-93/090-TN-004 



TREES II Pre-feasibility Study 



Problems and Recommendations for Solutions 



4. Coverage 

As requested by users; e.g. treaty verification group for specific protected areas, biodiversity areas 
etc.. 

5. Frequency of Product Production 

On request only. 

If the project is requested to monitor a protected area, where TIP provides inadequate 

resolution then regular production of TLP may be required. 

6. Precision, Accuracy and Resolution 

Dependent on data availability but at least 100 x 100 m resolution and possibly much more. 
Precision and accuracy will vary but should be very 'high' given the narrow spatial focus 
and the combination of data types, especially if groimd survey is involved. 

7. Auxiliary Data Inputs 

National boundaries, boundaries of areas of special interest, high biodiversity and protected 
areas. In addition, DEMs for SAR correction and local weather conditions will provide 
highly defined classifications. 

8. Investigation required 

SAR image segmentation procedures for forest type classification. 



4.6 Report Generation 

In addition to the four products proposed above, it is considered useful to specify two 
reports. These are derived from the other products but concentrate on small areas of 
particular interest where additional expertise or auxiliary data are available. The 
relationship between the products and reports and input data is shov^m below: 



Tropical 

Monitoring 

Product 



Boreal 

Monitoring 

Product 



Tropical 

Inventory 

Product 



Tropical 

Local 

Product 




Other 
Inputs 



EOS-93/090-TN-004 



27 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



4.6.1 Active Areas Report (AAR) 

This is a derivative of the other products designed to provide detaOed information on areas 
where deforestation is rapid. It meets the same requirements as TIP and TMP but 
concentrates on boundary changes over small areas and could be seen as meeting 
requirement 1 for fine scale boimdary changes. Figtire 5 illustrates the type of information 
which might be extracted from this report. 

1. Principal Applications 

Areas of active deforestation may be identified in the TMP, BMP, TIP or TIP products if they are 
overlaid with previous products of the same type. Large scale graphical output showing changed 
boundaries or areas of degradation could be produced. The area of change, direction and 'shape' of 
the change might be outlined. 

Whether change can be detected in the products continues to be a matter of debate and 
research. While the products will show changes which may be genuine (rather than as a 
result, for example of mis-registration), it is difficult to determine the reasons for such 
change without more detailed investigation. The AAR provides for such an investigation, in 
particular by concentrating on further algorithms for change detection and by prescribing a 
'report' format which entails the involvement of human intervention, the use of additional 
auxiliary data and the inclusion of text results from the investigation. 

2. Sensors/Data Inputs Used 

Based on any of TMP, BMP, TIP or TLP. 

3. Classes 

Areas of change between each combination of classes. 

Particuilarly when using TIP there wiU be a potentially large nvtmber of polygons created by 
an overlay, e.g. forest becomes non-forest, secondary forest becomes forest, etc.. It will be 
necessary to remove obviously spurious change polygons and those which are too small to 
be of interest. The remaining polygons wUl form the valid change classes which should then 
be assessed by the operator. The form of assessment and the rules by which polygons are 
judged valid or invalid must be the subject of research. 

4. Coverage 

Any area within that of TMP, BMP, TIP or TLP. 

Large scale output will be appropriate of area over which change is detected. Natior^ or 

sub-national scale might be most appropriate. 

5. Frequency of Product Production 

When the other products are generated and a change is obvious or even suspected from examination 
of the graphical or statistical output then AAEs could be produced. 

Discussion. A judgement wiU be required each time one of the products is generated as to 
whether detailed change detection should be undertaken. This will depend on which 
products are used as basis for detection, how tightly defined the thresholds are and what is 
actually knov^Ti to be happerung on the groimd. One could imagine the TMP/BMP being 
routinely checked against the previous version in a TFIS function and where a significant 
change in statistics is seen within a nation, then an AAR would be produced for that nation. 
A change detection function designed to register consecutive products to a high degree of 
accuracy and then match borders using particular algorithms might be applied. 



28 EOS-93/090-TN-004 



TREES II Pre-feasibility Study 



Problems and Recommendations for Solutions 



Lat/long 



Active Areas Report 

June 1995 

TREES II Analysis 



Lat/long 



Seasonal Forest 




Evergreen Forest 



Lat/long 



Lat/long 



Scale: i 1 

Product: TIP 1993, TIP 1995 

Region: SE Asia 

Nation: Nowhereland 

Report Analyst: J Smith (Asia Forestry Expert) 

Change Detection Algorithm: Type A 

Discussion: 



Figiure 5: Illustration of Information Content of the Active Area Report 



EOS-93/090-TN-004 



29 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



6. Precision, Accuracy and Resolution 

Depends on the parameters of the product in use and the results of change detection 
research. 

7. Auxiliary Data Inputs 

As for TMP, BMP, TIP or TLP but using large scale features from the DCW, such as roads, 
plus auxiliary HD data (population) and any local knowledge. 

8. Investigations Required 

Change detection algorithms. 

4.6.2 Expert Analysis Report (EAR) 

This report provides a specific point for the products, human expertise and all manner of 
other related data to be combined. Other requirements not met by the products or the AAR 
might be met in the EAR, by viewing TREES output as just one of a nimiber of inputs. 
Hence biodiversity mapping, while not possible from the products, could be reported by 
another means and supported by TREES products. This would be encapsulated in the EAR 
thus indirectly meeting requirements 23 and 24. Figvire 6 illustrates what sections from an 
EAR might contain. 

1. Principal Applications 

To combine many inputs into a text report on a specific area of the globe; for example a combination 
of a specific TIP area, treaty data, an AAR and forestry expert combine to produce a report on 
conformance of nation to a treaty covering particular protected areas. 

2. Sensors/Data Inputs Used 

As TMP, BMP, TIP, TLP, AAR , auxiliary data from all sources. 

The major input is not a sensor but a hiunan expert who makes use of products and 

auxiliary data on an interactive basis. 

3. Classes 

n/a 

4. Coverage 

Any area within that of TMP, BMP, TIP or TLP. 

5. Frequency of Product Production 

Produced on an interactive and ad-hoc basis. 

6. Precision, Accuracy and Resolution 

n/a 

7. Auxiliary Data Inputs 

As for TMP, BMP, TIP, AAR or TLP plus a wide range of other data sources which may become 
primary while the TREES products become auxiliary to it. 

8. Investigations Required 

n/a 



30 EOS-93/090-TN-004 



TREES II Pre-feasibilitv Study 



Problems and Recommendations for Solutions 



Expert Analysis Report 

1993 

TREES II Product 




Forest Agreement 



%^ 



Q>r4i 



^ 



^ 



^ 



_2l 



=G 



5 



^ 



2S^ 









1(X) Km section 
corresponding to AAR 



3: 



^ 



^-% 



^g 



3:^^ 






^ 



% 



§ 



et 



^ 



% 



CL 



Figure 6 : Illustration of Expert Analysis Report Content 



EOS-93/090-TN-004 



31 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 



5. 
Conclusions 



The products described in Section 4 are designed to meet the user requirements as fully as 
possible within the technical and institutional constraints of the TREES project. Table 3 
svimmarises each requirement with respect to the suggested products. Three reasons were 
given for not meeting eleven of the identified requirements (labelled 1 to 3 in Table 3): 

• the data volume required is prohibitively high. 

• the required information cannot be directly extracted from satellite data given the 
current technology 

• the requirement is met elsewhere 

From the description of the suggested products given in Section 4 it can be seen that there 
are several areas for which research issues need to be resolved before operational 
production can proceed: 

• classes to be included in the TIP 

• classification procedures to be used for all products 

• procedures for determining accuracy of areal statistics, boundary location and 
change statistics for all products 

• methods for improving registration accuracy. 

These research areas are further expanded and discussed in Technical Note No. 5, Section 3. 
In meeting operational production there are also problems relating to: 

• data supply 

• data management 

• project management. 

These are further discussed and expanded upon in Technical Note No. 5 in the context of a 
framework for the operational TREES system. 



32 EOS-93/090-TN-004 



TREES II Pre-feasibility Study 



Problems and Recommendations for Solutions 



Requirem 


t Requirunent 


Rcauiremcnti Met 


Umt 


iRequirtan'ts Not Me^ 


Number 


Descriplion 


Comments TMP 


TIP ;rLP 


GroupJ l| 2| 3] 




1 fine scale, annua] 
boundary data 


This r«juiremefil would need TM data lo achieve Kjffiiaent accuracy. TM has a 16-daj 
repeat cyde, so thai obtaining suffiocnl cloud free data could be a problem. Arcaj 
would have to be labelled as 'unclassified' if cloud free data were unavailable. 








B,E.G, 
H 


>?■- 








I medium scale, annual 
boundary data 


It would be possDale lo achieve this with 1 km AVHRK data at a mapping scale of about V 
1:1,OX,000, provided that h is possible to mark areas ai 'unclassified' if cloud free data 
are not obtained during the year. Provision of arcal statistics is feasible 






BXJ.E, 
G 










cx»rse scale, 3 yearly 
boundary data 


This is certainly possible with 1 km AVHRR data, and cloud free data should be 
available during this time frame. 




V 




A,C 








< 


medium scale, 3 yearly 
boundary data 


The 3-year time period would mean that greater attention could be paid to the acniracj 
of the classification than for Requirement 1 so that the results would be more 
meaningful. VNTJle use of TM data would be desirable, 1 km AVHRR data would be 
adequate. 




V 




B,D,5 










fine scale, 3 yearly 
boundary' data 


As with requirement 1, this would require TM data with correspondingly high data 
volumes and costs. The 3-year time-frame would however reduce these considerably 
from those of requirement 1. 






V 


B 








6 


medium scale, ai\nual fine 
dau 


This is provided by another programme 








D,G,E 






V 


7 


medium scale, annually, 
roads 


This wculd require TM data except in areas wilh dearly delineated roads where 1 km 
AVHRR data might be adequate. 








CE 




V 




6 


fine scale, every 3 years, 

protected area monitoring 


To derive these data would require the use of TM, possibly supplemented by airborne 
data (both optica] and SAR). This would be possible, although a five-year repeat pericx 
might be more realistic 






V 


B,CJ>, 
E,G,H 








9 


medium scale, every 3 
years, protected area 
monitoring 


This would require 1 km AVHRR data at a miniirtim. 




>/ 




B.C^, 
E,G,H 








10 


fine scale, annually, 
protected area monitoring 


This would require TM data on an annual basis with correspondingly high data 
volurries and costs. 








B,C,D, 
E.G,H 


J 






n 


medium scale, annually, 
protected area monitoring 


This wcTuld require 1 kmAVHRR data at a minimum. 


V 






E.G.H 








12 


fine scale, cver^- 3 years or 
more, bJomass 


At the required scaleof less than 1 J 00,000 it woiild be necessary to use TM data, or 
even airborne data. It would be difficult to cover the whole tropical area at this scale, 
but it should be feasible to provide data for selected areas. 






V 


A,F 








13 


medium scale, every 3 
years, biomass 


This is provided by another prograrrune. 








AJ 






r-- 


14 


fine scale, annually, 
siomass 


Similar comments apply as for Requirement 12. To achieve this annually would be an 
extremely heavy processing load. 








AJ 


V 






15 


fine scale, annually, forest 
types 


TM dau would be required, probably supplennented by airborne SAR and airborne 
optica] daU. The data volume and costs would be very high. 








All 


V 






16 


medium scale, annually, 
'orest tj-pes 


See discussion for icqulrcment 15. 








AU 


V 






17 


fine scale eveiy 3 years, 

forest types 


This would be possible using TM data, but possibl}' only for selected areas (see 
discussion on protected areas). 






V 


All 








18 


medium scale every 3 

yea re, forest type* 


Provision of these data would be achievable with 1 km AVHRR. 




; 




AU 








19 


ine scale, every 3 years or 
more, timber vohime 


Vovided the relationships exist as discussed above in Section 23 this would be 
achievable using TM data. It would be difficult to cover the whole tropica] area at this 
Kal^ but data could be provided for selected areas. 






V 


CD 








20 


tned ium scale, every 3 
'ears or more, timber 
k>olume 


km AVHRR data would be a minimum requirement 




s/ 




ZD 








21 


Ine scale, annually, timber 
«]ume 


rhis would require TM data with corresponding high data vohimes and costs, see also 
■ommcnts for requirement 19, 








Z^D 


J 






22 


Twdium scale, annually, 
imber volume 


rhis would be possible using 1 km AVHRR dat*, but see corruiientE under requirerrwnl 
9. 


J 






:.D 








23 


ine scale, ever^' 3 year^ 
iodiversity 


rM or SPOT data supplenwnted by- airborne optical and SAR data would be required, 
jroviding the restrictions outlined in Section 23 (TK-04) are overtome. 






I 


3,QD, 


1 


1 




24 I 


nedium, every 3 years, 
riodiverstty c 


"M data should provide adequate spatial resohjtion providing that the restnctloni 
njtllned in Section 23 C7N-04) are overcome. 






I 

I 


,QD, 


> 







I 



Table 3 : Siunmary of Requirements and Recommendations 



EC)S-93/090-TN-004 



33 



Problems and Recommendations for Solutions TREES II Pre-feasibility Study 

6. 
References 



Gumey, C. M., 1983, The use of linear feature detection to investigate thematic mapper 
performance and processing, NASA Conf. Pub. 2355, vol 111, part 2 

Hausler, T., Saradeth, S. and Amitai, Y., 1993, NOAA_AVHRR forest map of Europe, Proc 
Int Symp on Operationalisation of Remote Sensing, Van Genderen, J.L. et al (eds), Enschede, vol 
8, pp 37-4 

Kimtz, S. and Kleinn, C., 1993, Some aspects of the role of definitions of land use classes 
based on satellite remote sensing - an example from forestry, Proc Int Symp on 
Operationalisation of Remote Sensing, Van Genderen, J.L. et al (eds), Enschede, vol 8, pp 83-90 

Malingreau, J. P., Tucker, C.J. and Laporte, N., 1989, AVHRR for monitoring tropical 
deforestation. Int. ]. Remote Sensing, vol 10, nos 4 & 5, pp 855-867 



4 



34 EOS-93/090-TN-004 



TREES II Pre-feasibility Study Work Plan 



Technical Note No. 5 



Work Plan 



EOS-93/090-TN-005 



Work Plan TREES II Pre-feasibility Study 



Contents 

1. Introduction 1 

2. System Components 2 

2.1 Introduction and Methodology 2 

2.2 Data Suppliers 4 

2.2.1 Routine Data Supply 4 

2.2.2 One-off Data Supply 10 

2.3 Data Management 14 

2.4 Images 15 

2.5 Products 18 

2.5.1 Area Analysis Function 18 

2.5.2 Change Analysis 18 

2.5.3 Interactive Consultation 19 

2.6 Validation 21 

2.7 Users 23 

2.8 System Development 30 

3. Research Requirements and Recommendations 

3.1 Introduction 32 

3.2 Research Topics 32 

3.2.1 Decision on TIP Qasses 32 

3.2.2 TMP Classification Technique and Rules 32 

3.2.3 TIP Classification Technique and Rules 35 

3.2.4 BMP Classification Technique and Rules 35 

3.2.5 Selection and Use of Training and Test Sites 35 

3.2.6 Methods for Improving Registration Accuracy 37 

3.2.7 Projections for Production of Area! Statistics 37 

3.2.8 Accuracy and Precision of Area] Statistics 38 

3.2.9 Accuracy of Boundary Locations 38 

3.2.10 Change Detection 39 

3.2.11 Methods for Segmenting SAR Data 39 

3.3 Recommendations 40 

4 Management and Team Development 41 

4.1 Introduction 41 

4.2 Plarming and Control 41 

4.3 Schedule and Deliverables 42 

4.4 Human Resources 42 

45 
45 
45 
50 
51 
53 
53 
54 
54 
54 

63 

65 

Appendix : Key for Object Oriented Figures 



EOS-93/090-TN-005 



5. 


Deliverables and Schedule 




5.1 


Introduction 




5.2 


Data 




5.3 


Research Report Deliverables 




5.4 


Product Deliverables 




5.5 


Work Schedule 

5.5.1 Full Functionality Scenario 

5.5.2 Reduced Functionality Sceiiario 

5.5.3 Basic Functionality Scenario 




5.6 


System Development Schedule 


6 


Recommendations 


7. 


References 



TREES II Pre-feasibility Study Work Plan 

1. 

Introduction 



The requirements gathered earlier in this study and reported in Technical Note No. 3 form 
the basis for the Work Plan in this technical note. In Technical Note No. 4, the requirements 
were analysed and reconciled with the potential of current and possible future remote 
sensing technologies. This analysis led to the conclusion that the clearest means of meeting 
requirements was to propose a set of products towards which TREES should work. The 
products outhned in that analysis are built on below as the basis for the Work Plan. 

Section 2 is concerned with the systems elements required to service the formation of the 
products. This is not an attempt to design a system for TREES II in either software or 
hardware terms. Iristead, the system elements have been developed and described in order 
to provide a framework for analysing options and to provide a means by which a clear path 
for the development of a fully operational system can be planned. 

Section 3 details the various 'questions to be answered' which have arisen both during the 
TREES I project and during the analysis of requirements, product proposals and system 
elements. Research topics are those areas where data must be further analysed in order to 
provide a firm foundation for the product specification. 

Section 4 addresses the critical issue of management and manning while in section 5, the 
system and research elements are tied down to a schedule. Cost implications of various 
outputs are considered in accordance with CEC wishes to imderstand what might be 
delivered by TREES II with various levels of funding. Recommendations are summarised in 
section 6. 



EOS-93/090-TN-005 



Work Plan TREES U Pre-feasibility Study 

2. 
System Description 



2.1 Introduction and Methodology 

The product specifications outlined in Technical Note No. 4 imply a number of supporting 
system elements. Some of these elements are found in the TREES I project in more or less 
well developed forms. A systems view is necessary so that a managed move towards an 
operational system can begin. The purpose of this section is to provide a description of a 
system outlining how these elements might be knitted together to establish a semi- 
operational product system. Where studies are required to determine the best set of 
elements to use, then these are identified and discussed. 

The approach taken to describe the proposed system is 'object oriented'. This means that 
elements of the system are grouped according to natural boimdaries defined by physical or 
logical 'things' into objects or components. This is in contrast to the more traditional 
function-oriented design method where functional elements are the main building blocks. 
The elements in TREES I and those proposed for TREES II lend themselves to such an 
approach for the following reasons: 

• the elements are varied, some being physical (hardware, data), some conceptual, 
some managerial and some institutional 

• the proposed products are complex outputs consisting of data from a range of 
sources in addition to human expertise and cannot therefore be seen from a 
simple 'data flow-function' viewpoint. 

The advantages of this viewpoint are that complexity (in data and function terms) can be 
hidden inside the components and interfaces can be fixed. This means that changes within 
components (such as changes in data supply or classification or product specification) do 
not disrupt the rest of the system. In addition, components can be distributed to different 
physical locations, different institutioris or projects, again with the minimum of disruption. 

If readers are unfamiliar with object orientation, then the figures should be viewed as 
r\atural, intuitive descriptions and there need be no concern with the background theory. 
The main point is that each component has a boimdary (see following figures). Procedures 
and data items within the boundary are liidden' from other components while if they 
traverse boundaries then they are visible to other components and interact with them. 

An analysis of the system requirements for TREES II leads to the development of six 
components: 

• Data Suppliers 

• Data Management 



EOS-93/090-TN-005 



TREES II Pre-feasibility Study 



Work Plan 



• Images 

• Products 

• Validation 

• Users. 

These components are shown in Figure 1 (for object oriented symbol key, see Appendix). 




r 


^ 




Data 
suppliers 








V 


_J 




^' 


"^ 






^ Data 
"^ Management 


V 


\ 


V 


) 




\ 


i 


1 












s 


Product 
validation 


\ 1 


' 


J 


) 




> 


X 






Products 








V 


_J 






\ 









J 



Users 



Figure 1 : High Level System Components 



EOS-93/090-TN-005 



Work Plan TREES 11 Pre-feasibility Study 



The following sections describe each component in terms of the internal elements: 
procedures, data, inputs and outputs. Each is described in terms of TREES I heritage (where 
applicable) and proposed TREES II development. The problems and possibilities for the 
physical and logical location of components is also discussed. The research topics in each 
section are further elaborated in section 3. 



2.2 Data Suppliers 

This is divided into two further components: 

• routine data supply 

• one-off data supply 

2.2.1 Routine Data Supply 

The basic input for most of the proposed products is a routine supply of data having the 
following characteristics: 

• regular coverage of all areas of interest realising a reasonable supply of cloud 
free images in appropriate wavelengths 

• resolution sufficiently high to provide useful results while not resulting in an 
unmanageable data flow. 

The data were supplied in TREES I by AVHRR 1 km coverage. However, there are four 
other sources of comparable data which might be used in the future: 

• The French 'Vegetation' instrument proposed for flight in 1996 and which would 
be purpose built for vegetation monitoring. The major advantage over AVHRR 
is an on-board recorder capable of global storage which would very considerably 
simplify the data collection process. 

• MERIS; scheduled for flight on board ESA's EnviSat in 1998. MERIS shoiild 
provide high resolution coverage including a vegetation product. 

• MODIS; similar to MERIS also scheduled for 1998 on-board one of the US EOS 
satellites. 

• ATSR-2; available from 1994, but suffering a swath width disadvantage 
compared to AVHRR which limits coverage. 

In the future, one or more of these sources, notably Vegetation and MERIS, might weU be 
capable of furnishing the data requirements of TREES more efficiently than AVHRR. It is 
therefore critically important to TREES II and beyond that the system for forest analysis and 
products output are as far as possible independent of data source. This has important 
implications for TREES II and beyond: 

• the routine data collection component (including all internal procedures) should 
be separated logically and possibly even physically from the rest of the system to 



EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



encourage independence and non-reliance on one data source and one set of 
preparation procedures 

• subsequent analysis and processing algorithms should be generic and 
configurable for different source data characteristics 

• a long term view should be taken in terms of influencing known future 
programmes to provide data suitable for the TREES project. 

The use in TREES 1 of AVHRR 1 km data was implemented through contact with various 
bodies including HRPT stations in the tropics. Thus the procedures of collection, selection 
of cloud free images and subsequent geometric and atmospheric correction were provided 
at JRC. This proved a heavy load; something recognised internationally by the fact that 
major efforts are being made vmder the IGBP umbrella to collate a global AVHRR data set. 
This fact combined with the increasingly routine natvire of such processing and the level of 
effort required indicates that the use of an external data collection agency is the way 
forward for TREES 11. 

Each procedure shown in Figure 2 is now expanded to analyse the options further. 

Procedtires - Routine Collection and Selection of Images 

TREES 1 obtained low level products from individual HRPT stations (Australia, Bangkok) 
and higher level products from NASA (S. America, Africa). These were then processed to a 
standard form at JRC or using external contractors. The options for the future are: 

• Continue and expand the direct contact with HRPT stations. The Bangkok 
station has proved reliable in providing data for S.E. Asia (personnel are 
contracted by JRC). However, the other two continents have not yielded data 
from direct contact and intermediate sources have been used. The source of 
these data in turn has been EDC where LAC collection is being combined with 
their own direct contact with HRPT stations. TREES II could continue this direct 
contact, with ESA taking an indirect role through their support to ground station 
development notably in Africa. The deployment of mobile stations is another 
possibility which could be pursued where data gaps occur (for example in 
Central America). The selection of cloud free images shovdd be performed 
locally if possible through contracting with suitably qualified agents in the field. 

• Indirect collection and selection. The use of an external supplier to provide the 
required output. There are several possibilities here. Firstly, a commercial 
company might be contracted to perform the collection and selection of cloud 
free images. Secondly, as indicated above, EDC is currently developing a global 
1 km AVHRR product imder the auspices of the IGBP. The data are collected 
from HRPT stations and LAC recorded downlink. The data are being collated 
from 1 April 1992 for 30 months under current funding and a composited, 
mosaicked product is being produced for the globe. The first is already available 
for a 10-day period in June 1992. If this product is suitable for use in TREES then 
is would appear to be an obvious candidate for supply. The advantages of using 
an international body are that the costs are spread, expertise in processing is 
centralised (at EDC) and LAC coverage of areas without HRPT (or as backup for 
failed HRPTs) is more likely. Discussion of the pre-processing and suitability of 
this product for TREES use is found below. 



EOS-93/090-TN-005 



Work Plan 



TREES II Pre-feasibility Study 



routinely collect 
images 




geometrically & 

atmospherically 

correct 




cloud free 1km 
composited mosiacs 
OR corrected images 



( MODIS j 



( ATSR-2 j 



L 



Figure 2 : Routine Data Supply 

The institutional and political situation must also be considered under this heading. ESA 
and CEC are aiming to work together in a data supplier - user relationship respectively. 
With its interests in HRPT station and ongoing support of the IGBP project, ESA has taken a 
dear stake in the supply of suitable data. It would appear sensible to recommend that ESA 
perform the data supply role for future TREES work. There is also clear advantage in such a 
link when future missions are considered since ESA is obviously the agency with a mandate 
in data for MERIS, ATSR-2 and possibly Vegetation. 



EOS-93/090-TN-005 



TREES 11 Pre-feasibility Study Work Plan 



However, two outstanding questions remain: 

• During TREES I, ESA was not able to supply suitable AVHRR data to the project 
team at JRC. Clearly, if ESA is the data supplier for TREES 11 then a very clear 
agreement backed by plans and assured finance must be in place to ensure that 
the correct data is supplied in the appropriate time frame. 

• The superficially more attractive route of the centralised IGBP data collection and 
product (covering all relevant HRPTs and supported by ESA) relies on continued 
funding at EDC by IGBP. The future production of the IGBP product is currently 
uncertain. A major meeting of the Land Working Group on 5 July 1993 should 
clarify some of these issues. 

In conclusion, it is dear that ESA as supporter of HRPT stations and of the IGBP project has 
a major role to play in supplying data. It would appear unwise for the TREES project to 
continue with the collection of data by direct means due to the cost and duplication of effort 
involved (it should be noted that TREES 1 data collection began before IGBP). Therefore the 
main conclusion must be that ESA should be seen as the major route for future routine data 
supply. The second conclusion is that considerable effort must be expended to clarify the 
current and future ESA policy, funding and capability in relation to the supply of AVHRR 
data to the TREES II project. This effort should be made quickly so that TREES II can 
proceed on a firm footing. This much is dear. However, the exact form of ESA involvement 
is the subject of several technical trade-offs. These are discussed below. 

Procedures - Composite and Mosaic, Geometric and Atmospheric Correction 

The use of the IGBP product noted above appears to be the most attractive option since the 
data are collected, selected and pre-processed centrally. Some pre-processing steps meet 
TREES technical pre-requisites: 

• Atmospheric Correction 

The IGBP product accounts for Rayleigh scattering and ozone and indudes NMC 
water vapour fields for optional correction. Aerosol correction is not applied. 
This is more than TREES I prescribed, and probably acceptable. 

• Geometric Correction 

The extent of correction is not entirely clear but is aimed towards sub-pixel (1 
km) accuracy with nearest neighbour resampling. 

• Compositing 

The IGBP product is composited using the standard technique whereby 
maximum NDVl is used to identify suitable pixels over a 10-day period. This is 
probably unsuited to TREES because of the use made in TREES I of channels 3 
and 4, not just NDVI for classification. This could prove to be a terminal problem 
for the use of the product in TREES II. 

• Mosaicking 

Consideration must be given to the requirements of TREES whereby training 
sites must be related to images from similar dates in order that classification is 
not impaired. This is discussed further in section 3. It is not known how the 
IGBP product is handled in this respect. It may also be necessary to move this 
function to the products component, thus preventing the use of the product. 



EOS-93/090-TN-005 



Work Plan TREES II Pre-feasibility Study 



• Projection 

The IGBP product uses a Goode interrupted equal area projection which has not 
been used by TREES. However, reprojection is not corisidered to be a major 
problem. 

Given the potential unsuitability of the final IGBP output product, the following trade-off 
study needs to be made: 

• Is the IGBP product unsuitable due to the compositing technique and due to the 
fact that mosaicking is done before classification? What reduction in 
classification quality might result from its use? Is the possible reduction in 
quality offset by the savings made by using an external fully pre-processed 
product? Is re-projection feasible (given the large volume of the product) and 
what are the software and hardware implications for handling the product 

If the outcome of the trade-off study is that the product is imsuitable, then a second option is 
to obtain a lower level or intermediate product from the same source, which has not been 
composited or mosaicked. It is known that ESA receive the Level and the Level lb 
product from EDC. Consequently, it can be tentatively concluded that an agreement with 
ESA to supply the Level or lb IGBP product to the TREES project should be made. It is 
also known that there are plans to reduce the data handling load by joining together 
contiguous images into orbit stiips. This will be done by both the EDC and ESA supplied 
Level data (with both being coUated first at EDC and supplied to ESA). Thus the strip 
Level 0/lb could be supplied to the TREES project. ESA might also be tasked with 
supplying the most cloud-free strip during a three month period for appropriate locations. 

Unforttmately, the Level lb product content is much as the Level and EDC have no plai\s 
to archive the more useful intermediate data set produced after corrections, but before 
compositing due to its very large volvime. Consequently, even though the atmospheric and 
geometric correction may be appropriate to TREES requirements, they will not be available. 
Therefore, once the low level data are received, calibration, geometric and atmospheric 
corrections should be done using appropriate software. The operation of this software 
could again be done by ESA, as it is primarily an operational task. This assimies that the 
software has been developed to the state where an operator in (say) Frascati could run it 
routinely. A particular concern here is that control point selection is a procedure involving 
manual intervention. 

Assuming that IGBP pre-processing does not meet TREES requirements, TREES II must plan 
to develop a software suite for the task. In order to permit standardisation and 
maintainability, it is strongly recommended that the software be fuUy engineered. The 
options here are in-house development or contracted development. In-house development 
could only be recommended if one of the TREES II team was experienced in software 
development to industrial standards and dedicated to the task. If the development is 
confracted out, then the JRC TREES team should specify the pre-processing. The latter 
option is recommended. 



EOS-93/090-TN-005 



TREES II Pre-feasihilih' Study Work Plan 



Since many of the pre-processing steps are widely used, the softAvare itself is best deri\'ed by 
tailoring similar systems. This means that either the TREES I softvk'are could be upgraded or 
that systems such as the MARS SPACE or perhaps the EDC pre-processing software be 
used. In both of these latter cases, some customisation will be required. For SPACE, this 
means: 

• Atmospheric correction 

During TREES I, contractors performing pre-processing were asked to apply 
Rayleigh scattering corrections only. SPACE also applies ozone correction and 
water vapour correction (both using a climatic model). SPACE could be adjusted 
to meet TREES I standards. However, there is some doubt as to the wisdom of 
the Hmited number of corrections applied in TREES I. This is taken up imder 
research topics in chapter 3 and Section 6.2. Neither model applies corrections 
for aerosol. 

• Geometric correction 

The SPACE coastline matching may need to be extended to cover non-European 
conditions vk^ith perhaps image chip type techniques used for areas far from the 
coast. 

• Compositing 

The compositing module in SPACE would be removed. 

• Mosaicking 

Mosaicking is also performed by SPACE. The training site consideration is the 
same as discussed for the IGBP mosaicking. It is possible that mosaicking will 
be moved to the products component such that classification is performed on 
individual images and the classified images are mosaicked. 

The development of software to a standard which can be used for pre-processing on a 
routine basis either at JRC or by ESA, is not a trivial task. The necessary robustness, ease of 
use, maintainability, etc., requires an engineering approach. This must be carefully 
considered early in TREES 11. A trade-off study needs to be imdertaken to determine: 

• the current state of SPACE software (note that re-engineering of SPACE software 
is currently being considered; additional requirements could be introduced now) 

• the cost of changing SPACE software to meet TREES requirements by the TREES 
team or an external contractor 

• the possible use of other systems such as the EDC software ^ 

A further option might be to contract a commercial company to receive the low level data 
from ESA/EDC, then to select, atmospherically and geometrically correct and possibly 
mosaic the data for delivery to JRC. Various scenarios involving delivery of some 
intermediate form might also be considered where JRC completes the pre-processing. The 
major advantage is that JRC retain a very large elament of control on the whole data 
preparation process, which might not be the case were ESA performing pre-processing. 



^ Other systems such as GEOCOMP might also be considered. Customisation would again be 
required. 



EOS-93/090-TN-005 



Work Plan TREES 11 Pre-feasibility Study 



Output Data - Cloud Free 1 km Composited Mosaics 

The output from this component may be one of several data sets depending on the result of 

research (section 3) and the trade-off studies discussed above. 

In the scenario where compositing and mosaicking is acceptable before classification, the 
output is a cloud-free 1 km resolution product containing appropriate visible NIR, IR and 
thermal IR band data. The area coverage should be of the tropics and selected boreal areas 
in mosaicked form (perhaps four files). The time period covered should be no greater than 
three months (TBC) from the first to the last image used in the mosaic probably in the 
second half of the year to catch dry season effects. It is expected that compositing of images 
will occur in order to locate cloud free pixels in a manner suitable to facilitate classification. 
Thus, the number of entries into the data manager (See section 2.3) will be of the order of ten 
per year. 

In the scenario where the raw images are classified, then the output from this component is 
of selected images (or image strips) with minimal cloud contamination. The channels, 
geographic and time coverage are similar to the first scenario but the number of entries 
passed to the data manager will be sigruficantly larger. This scenario significantly alters the 
activity required in different components with more functionality and effort required in 
images and products and less in data suppliers. 

It should be re-emphasised that these outputs could be provided from one of a number of 
instrument data sources and that the discussion over AVHRR data pre-processing wUl also 
apply in some degree to these others. 

In summary, the recommendations made are: 

1 ESA should be the data supplier 

2 An intermediate form from the IGBP processing at EDC should be the basic input 

3 The pre-processing should be specified by the TREES II team 

4 Pre-processing software based on SPACE should be developed by a softv^'are contractor 

5 The pre-processing shoiild be performed separately from the TREES 11 project 

6 Further study of the options is vital 



2.2.2 One-off Data Supply 

The supply of various other data (see figure 3) has been separated from routine data for 
several reasons: 

• the project must initiate requests for these data implying a two-way interaction 
with the suppliers 

• the requirements for data vdll vary through time and some of the acquisition 
may be performed by the TREES project team itself. 

Due to the diversity of requirements, it would be advisable to appoint a single person to act 
as point of contact to be resporisible for the acquisition of these types of data into the TREES 
II project; i.e. a 'Data Gathering Leader'. This would ensvu-e that the costs in terms of 
purchasing data (where applicable) and the liaison with outside bodies would be efficiently 
executed. In particular, where research activities require different data types from specific 



10 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



TMP development would normally be the same, but could also be encouraged to coincide 
with areas of investigation for the TLP). In addition, consultation with other groups should 
be pursued in order that cost of data purchase or field work is minimised by pooling 
resources and/or sharing results. For example: 

• ground survey undertaken by FAO as part of their national based efforts in 
forestry management 

• field work undertaken by the Finnish forestry department for their own 
monitoring of national forests might provide important insight and background 
data for the BMP 

• the many possibilities for cooperation in the formation of TLPs; e.g. INPE 
airborne projects, use of EARSEC project results. This point meets Technical 
Note No. 4 requirement 27. 

Each of the data types which might be acquired by the Data Gathering Leader (DGL) are 
outlined below: 

• The supply of TM images is well established and semi-commerciaJised. The 
DGL's role is therefore only to identify within the project which images are 
required (taking possible synergy into accoimt) and then contact the European 
supply point - EURIMAGE. At the ciurent cost of $4400 per image, the total cost 

for TM images is Ukely in the order of $44000 ■^ (this assume approximately 10 
images, a figure which is variable with the cost option scenarios discussed later). 

• Ground svirveys must be undertaken for all four main products. The costs are 
likely to be high and therefore the DGL must vmdertake a careful analysis of 
requirements including synergy between requirements and cooperation with 
other groups. The use of TREES II project staff in some field survey work is 
advisable in order that the JRC team builds up a reaKstic picture of real field 
conditions. Several potential users contacted by the survey expressed an interest 
in cooperating in this area. 

• The acquisition and processing of spacebome SAR images wiU be made by ESA 
from one of the ERS platforms or the Canadian Radarsat. The DGL's task is to 
coordinate requirements and liaise with the appropriate agency. 

• Due to the nature of the TLP product and the cost of airborne survey, the TREES 
DGL will usually obtain airborne SAR data in cooperation with other 
programmes such as those run by INPE, SAREX and the EARSEC project In this 
sense, acquisition is opportunistic and very highly dependent on prevailing 
programmatic, institutior\al and political situations. This points to the need for 
the appointed DGL to be aware of and competent in negotiation with a wide 
range of programmes. 

• The data type marked in Figure 3 as 'Treaties' covers any and aJl political 
agreements made by relevant countries or organisations concerning forestry or 
related areas. Where such agreements are accessible in document form, then 



^ For LandSat 6, a significant cost reduction for TM images is expected 



EOS-93/090-TN-005 11 



Work Plan TREES 11 Pre-feasibility Study 



these should be obtained for use in the extended TFIS (product) element of the 
system. CIESIN is named as a source of some documents and a knowledge base 
for the existence and location of others. Other institutions such as the World 
Bank, and the Inter-American, Asian and African Development Banks might be 
contacted. A yearly search of such databases might be suggested. Once 
obtained, paper versions couJd be scanned into electronic format for use as 
auxiliary data with aO products. 

• Other reports are ad-hoc in supply and may be obtained from any number of 
sources; for example TREES consultants such as Myers and Lucas. 

• Protected areas, tropical forest extent from other sources and population are all 
examples of spatial information which would form a valuable input to the 
proposed products. In particular, data on forest extent and type from other 
sources might be used in the development of classifiers. However, the major use 
of these data is to provide a backdrop to eriable the interpretation of products. 
For example, TIP classes might be compared with protected areas boundaries. 
This is discussed further in Section 2.4.3. Some of these data have already been 
collected by the TREES I team. An on-going programme by the DGL to liaise 
with groups such as WCMC and CIESIN should be imdertaken. 

The role of the proposed CEO development must also be considered in relation to these 
auxiliary data; i.e. the development of non sateUite data sets for user services element. 

The DGL role is critical to the development of the system processes and to the enhancement 
of product value. Leadership for the routine data supply might also be handled in this role. 



12 EOS-93/090-TN-005 



TRIES II Pre-feasibilit>' Study 



Work Plan 



TM image 
selected area 



of L, 



EOSAT 



select image 



-c 



TM images 



fonnnlate request 



f ground survey V^ — — 



perfonn survey 



identify source & ^ 
consult other groups 



f spacebome SARV^- — — 



( 



acquire & 

process scene 

(ESA) 



identify source & 

consult other 
groups 



airborne SAR 



>--- 



acquire & 
process scene 



identify source & 

consult other 
groups 



( treaties j^ 



consult with 
CIESIN 



{ reports T^ 



consult with 
experts 



(protected areas,\^ 
parks etc ^ z 



i; 



consult with 
WCMC 



Figure 3 : One-off data supply 



i 



EOS-93/090-TN-005 



13 



Work Plan TREES 11 Pre-feasibility Study 



2.3 Data Management 

Figure 4 indicates the various interfaces between elements of the proposed s)'stem. It can be 
seen that the pivotal element is 'data catalogue and archive'. The importance of this element 
arises because of the move towards a semi-operational system in TREES II especially: 

• The requirement to maintain the integrity and value of data and products. This 
means that all data should be catalogued and archived with an appropriate level 
of metadata. 

• The number of products, auxiliary and intermediate data sets will increase as the 
operationalisation of TREES progresses, necessitating central control of data 
resources. 

This trend has been identified in the JRC MTV Unit and has led to the development of a 
prototype data management and browse system known as the 'Image Browse Information 
System' (IBIS). This system is being developed in part to service the TREES project. The 
proposed functionality of IBIS is adequate to service much of TREES II although if the 
system proposed here is implemented, then the following additional functions will be 
required: 

• cataloguing and archiving of vector data sets 

• cataloguing and archiving of electronic text based data sets 

• cataloguing and archiving of hard copy text and graphical hardcopy 

• association of hardcopy text, electronic text, graphical (h/c and electronic) in the 
database with a particular report title. 

These functions are required to handle auxiliary data of all types, output products 
(especially statistical output) and output reports (which consists of several media types). 
Since IBIS is principally an image management system, some or all of these requirements 
might be better handled by extending the data management functionality of TFIS (see 
section 2.5) or by manual document cor\figviration maintenance. 

If it is decided to extend IBIS (perhaps for vector datasets), there are two implications. 
Firstly, consideration should be given to early discussion with the sponsors of IBIS such that 
functionality can be added as required and secondly, the role of a 'Data Manager' should be 
considered. This role might be MTV unit wide or specific to TREES. In either scenario, the 
importance of a Data Manager cannot be over emphasised in maintaining the value of 
project work. 



14 EOS-93/090-TN-005 



TREES II Pre-feasibility Study 



Work Plan 



Suppliers 
of Ikm Data 



Data Gathering 
Leader "^ 



( Ikm output \ 



Suppliers of 
other data 



formulate request 



/ ( SAR ) 
\ ^ \ auxiliary J 
\ \^^ 



Images 



1 



, ^ ^^ 

( TM, Ikm image y ^ — — — 
^ suryey, coastline "y^ — — 



c 



SAR 



:i: 



>•-- 



DaU 

Catalogue 

and archive 



Product 



r classified images } — — — 1 

^ .^ 

/ / 



^ 



(auxilary data \^ j^ I 

& classified images y ^ ' I — 



C products/reports J 



classified image & 

independent auxiliaiy 

data 



Validation ^ 

( report ^ 



t: 





^ Users ^ 






disseminate 












collate report rqs 




















collate products rqs 



/ 



/ 



/ 



y 



2.4 Images 



Figure 4 : System Interfaces 



One of the main areas of research during TREES I has been in the classification of images. 
This will continue to be a major component of TREES II both in a research and system sense. 

The image component (Figure 5) comprises the assemblage of various images (AVHRR, TM 
and SAR) with ground sur\'ey in the classification procedure to produce a classified image 
(the coastline data set aids the classification process). In the case of the monitoring and 
inventory products (TMP, BMP, TIP, Figure 5) the major input is the 1 km product. For the 



EOS-93/090-TN-005 



15 



Work Plan TREES II Pre-feasibility Study 



training of the classifier, the other inputs are auxiliary but once trained on a certain area (at 
least for the IMP), these are dropped and the classification process becomes more routine. 
There are a number of research subjects here: 

• Should the classifier operate on a mosaicked product or individual corrected 
images (this is the driving study required to determine how the data suppliers 
component operates)? 

• What form is the classifier; supervised, unsuper\'ised etc? 

• WTiat part can be played by expert system and neural network technology? 

• How often are test sites revisited for each product? 

The internal structvire of this component cannot be further developed until these questions 
are answered (see section 3, requirements R1,R2, R3, R4, R5). However, there are several 
important issues which must be considered in moving towards an operational system, 
whatever the precise form of the classifier. These issues are: 

• The classification procedure is likely to be a mixture of manual and automated 
processes. In order that consistent products are created, the classification procedure 
must be dearly and extensively documented. The documentation must be oriented 
towards enabling non-experts to operate the procedures. The level of skill, 
experience and supervision necessary to operate the procedure must be stated since 
it is anticipated that as operationalisation proceeds then the system wiU be gradually 
handed over to non-specialist operatioris staff. In addition, the time required to 
classify each image or mosaic needs to be estimated so that overall time to produce 
classified images to meet image requirements can be calculated. This is critical to the 
management of the whole system and its ability to output products to the prescribed 
time-table. 

• The software in the 'images' component interfaces to data management (Figure 4). 
The interface of the IBIS system must be available to that system on which the 
classification procedure is operated such that the image or mosaicked products can 
be extracted and classified image returned to the archive. The inventory must also 
be updated. The classification procedure itself may be executed using a standard 
package configured to the detemiined procedure. It is important for procedures 
operated frequently (e.g. for TMP classification) that the software is optimised to 
minirruse load on the available hardware platform. 

For TMP, BMP and TIP classifications, the 1 km product forms the basis of the output 
image. Depending on the mosaicking scenario, the output will be a classified image or a 
classification of a continental or regional scale mosaic. For TMP and BMP this will be: 

• AVHRR S.E.Asia forest:non-forest: other 

• AVHRR Africa forest:non-forest:other 

• AVHRR S. America forest:non:forest:other 

• AVHRR (eg) Siberia forest:non-forest: other 

For TIP: 

• pan-tropical regions /nations multi-class 



16 EOS-93/090-TN-005 



TREES II Pre-feasibilin' Study 



Work Plan 



Figure 5 shows the structure of the TLP component. The major cJifference is that the base 
data are a TM scene, which is supplemented by SAR and/or ground survey. In addition, 
since the TLP is less routine in production, the classification procedure is likely to be more 
ad-hoc and involve an expert on an ongoing basis. Therefore the importance of specified 
procedures is less. The content of the product is highly variable and will depend on the 
inputs available. 



I class! 



71 ^ 

( ground survey j 



/ 



/ 



/ 



/ 



classified image 



> 



classify 



( selected TM image j 



> 



. ^(^ 



S ( spacebome SAR ) 



\ 



"V^ airborne SAR J 



classified 
image/mosaic 



I^ 



( ground survey 1 



/ 



/ 



/ 



/ 



\^ — classify 






/' 



cloud free corrected 

composited mosaics or 

corrected images 



\ 



-G 



selected TM image 



\ 



S 






D 



coastline 



J 



3 



Figure 5 : Images Component 
(a) top: TLP (b) bottom: TMP, BMP and TIP 



EOS-93/090-TN-005 



17 



Work Plan TREES II Pre-feasibility Study 



2.5 Products 

The 'products' component is similar to the system developed in TREES I known as TFIS. 
However, in moving towards an operational system, the functionality of TFIS will need to 
be significantly expanded. 

This component has two roles. In the short term, it will assist in research into classification 
criterion for all products by providing a platform for comparing experimental classifications 
with auxiliary data (for example WCMC tropical forest atlas data). Its more general and 
longer term function is to synthesise the various input sources (including non-satellite data) 
into forms suitable for output as the products and to provide an information system 
supporting expert analysis. The current TFIS has some functionality, but it will need to be 
significantly enhanced in order to be able to output products and support interactive 
consultation- Specific functions are outlined below. 

2.5.1 Area Analysis Function 

The functionality required is to rehim area extent per nation for each class for the classified 
TMP, BMP and TIP products. This function will use the coastline, national boundary and 
other auxiliary maps to overlay with the classified TMP, BMP and TIP images. The output 
wiU be the number of km^ of each class by area. Sub-national areas may also be analysed in 
this way if large enough. Three preparatory stages are implied: 

• all image and auxiliary data will have to be matched to a standard coastline - 
probably DCW; this is a major task 

• aU images will have to be transformed to an equal area projection before area 
coverage is output 

• the minimum size of nation/area which can meaningfully be analysed in this 
way must be determined (see section 3, requirement R8). 

The normal GIS function of Arclnfo available through AML are sufficient to enable the 
auxiliary and image data to be combined to produce the appropriate statistics. This is done 
by rasterising the vector national boundaries and producing classes from the resulting raster 
overlay. It would therefore be useful to prepare such a raster version of appropriate tropical 
and boreal areas to enable the analysis to be performed quickly. This analysis is required 
for TMP, BMP, TLP and TIP. 

2.5.2 Oiange Analysis 

Change in this context means identifying specific areas where changes in boundaries or the 
patchy degradation of forest is particularly marked. Whether such changes can be 
monitored in either TMP, BMP or TIP products is unknown (see section 3, research topic 
R.9). The basic process is that of matching the current with the previous classified image. 
Output should be in the form of 'active area' analysis; i.e. identification of the area where 
change exceeds critical thresholds. Specific investigations are covered in section 3. 

Once these questions are answered, the overlay of current with previous classified images 
using Arclnfo functions will enable changes to be highlighted. The overlay of two raster 
images (e.g. TMP from consecutive years) produces an intersected raster image. The vector 
version of this image contains 'change polygons' which can be analysed automatically or 
interactively to reject polygons which are imlikely, spurious or too small to be significant 
(eg. changes at the scale of the registration accuracy). Where meaningful polygons remain 
then these can be output in the form of large scale hardcopy maps of the areas showing 



18 EOS-93/090-TN-005 



TREES II Pre-feasibiliU' Study Work Plan 



changes with boundary changes or patchy degradation indicated (i.e. the AAR). The 
functionality to perform this type of analysis can be developed in Arclnfo. 

2.5.3 Interactive Consultation 

The TMP, BMP, TIP and TLP products can be produced in a relatively mechanical v^'ay. The 
AAR and EAR however are the output from an interactive session with TFIS. The EAR in 
particular results from an examination of all other products and combining these with other 
non-spatial information. It should be possible to pan in and out to TMP, BMP, TIP, TLP and 
AAR in order to examine particular featuures. V^Tiere non-spatial data such as treaties or 
reports are in electronic form, then presentation on the screen with the spatial data shoiild 
be possible by associating text pages with polygons (see CIESIN GREENpages for example 
where pages of treaty information are linked to nation polygons so that spatial and textural 
information can be presented and viewed together). The resulting hard copy output should 
form a central part of the expert's report the rest of which is prepared uising non-electronic 
external sources such as reports, newspapers etc. (see EAR iUustration in Technical Note No. 
4). Association of text with, for example, national polygons is possible in Arcinfo although 
additional AML programming is required. 

A number of auxiliary data sets could be gathered for use in producing EARs. For example, 
CIESIN allows access to the following: 

FAO comprehensive demographic estimates and projections 

Global change encyclopaedia (GEOSCOPE) 

Global ecosystems data 

UN demographic yearbook 

CIA world factbook 

GEMS/Food 

Global environmental policy database 

UNCED collection 

World resources database (WRD) 

World demographic estimates and projections (1950-2025) 

Demographic indicators (1990) 

Sex and age (1990) 

Interpolated national populations (1950-2000) 

Interpolated populations by sex and age (1950-2000). 

The EPA (NGDC) has recently released two CD-ROMs containing a wide range of spatial 
data including: 

• FNOC elevation, terrain and surface characteristics 

• Terrainbase (an updated ETOP05). 



The overlay and mosaic functions require some development although both are 
straightforward. A further function not found on the figure is product management. 
Section 2.3 indicates that additional functionality in terms of cataloguing and archiving of 
multi-media reports is required. This may best be met by development of the INFO 
capabilities of AJclnfo. 



EOS-93/090-TN-005 19 



Work Plan 



TREES II Pre-feasibility Study 



Non-functional Development 

As TREES II progresses, the products component will change its emphasis from being a 
support tool for classification to being a product producer (and possibly a data manager) 
and then an interactive tool for experts to use. As this occurs and users become more 
involved, there will be opportunity physically to disseminate the component to other sites. 
In this scenario, one might see TMP, BMP and TIP products being routinely produced by the 
TREES II team at JRC but then disseminated to two or three sites where the extended TFIS 
will be used by others further to analyse the products and produce AARs, EARs and other 
outputs. These other sites could be Universities, the CEC, WCMC or more far flung users. 
Obviously these users would require Arclnfo and associated software and hardware 
facilities. The fully operational TREES III could be seen as an even more distributed version 
of this scenario. 



na ti onal,Tegi onal, 

area statistics 

TMP, BMP,TIP, 

TLP 



area analysis 



J 



C coastline & 
national 
boundaries 



/ 



/ 



/ 



/ 



hardcopy 

output 

TMP, BMP,TIP, 

TLP 



— overlay 



/ I \ 



I — — — — f treaties. 



J 



reports 



mosaic (trade) 



; 



change analysis 
7 



\ 



/ 



AAR 



J 



K \ 

I \ \ 



J 



i 



\ 



classified 
image/mosaic 



\ 



interactive analysis 
7" 



\ 



\ 



\ 



\ 



1 



\ 



EAR 



r 



\ 



protected areas, 
parks, populat'n 
etc 



\ 



t 



previous images 



7 



) 



Figure 6 : Products Component 



20 



EOS-93/090-TN-005 



TREES II Pre-feasibiliU' Study Work Plan 



2.6 Validation 

A validation exercise (Figure 7) was carried out in TREES I involving TM and ground 
sur\'ey in S.E. Asia. It is necessary to identify such a component for TREES II for several 
reasons: 

• one of the major distinctions between a research programme and an operatior\al 
system is the need to provide independent and on-going validation of product 
quality 

• to separate and hence avoid confusion behveen ground survey and TM data 
collected to train the classifier and that collected to validate the classification. 

• dearly to identify 'validation' as logically separate from other parts of the system 
and which may therefore be performed by a separate group and/or perhaps on a 
different physical site. 

Validation applies primarily to the IMP, BMP and TIP products. The input is each product 
in turn. The comparison function uses the independent data from one or more sources: 

• Ground survey data which may have been collected at the same time and from 
similar sites as the training data but was not used to develop the classifier. 

• TM images; either those used in training (using different sub-images) or different 
TM images altogether. It is assumed that visual inteipretation of the TM images 
woiild provide classes appropriate to the product being validated. 

• Data from external sources such as the WCMC tropical forest data or the FAO 
statistics (compared for example to TMP statistical tables). 

The comparison function itself should comprise two types of procedure: 

• Visual comparison between hardcopy map output and point values from grovmd 
survey, TM interpretation or maps from external sources (which would probably 
not be global in extent). 

• Use of TFIS functions to overlay products with map data from external sources 
appropriate to the time period of the product. Since TREES provides the only 
global product, there are no external global sources regularly updated. 
However, suitable maps produced through the 1990s should be collected if 
possible into elecfronic form. 

Careful consideration must be given to the significance of such comparisons in relation to 
statistical sampling theory and die quality of the independent data. 

The report generated should detail the comparison made and discuss implications for the 
classifiers used in producing the product. Where the validation report shows mismatches 
between independent data and products, then a decision is required as to whether the 
product classifier should be re-assessed. Factors influencing this are: 

• the size of the mismatch - is it inside or outside the tolerance of the product 



I 



EOS-93/090-TN-005 21 



Work Plan 



TREES II Pre-feasibility Study 



results 

• the significance of mismatches varies between the IMP, BMP and TIP; in 
particular it may be considered more important to maintain consistency in the 
monitoring product over time than make fine adjustment. 

There is a great deal of overlap between the functions of this component and those in 
products and images. Once sampling and statistical questions are solved, the development 
of this component should therefore be straightforward. Despite this, it must be considered 
vitally important. In addition, it may be possible to involve users in validation work; i.e. 
supplying products in exchange for validation data. Technical Note No. 3 contains 
comments from users to this effect. 

This component will always require expert input in the interpretation of the comparison 
report. This, and the fact that independence of operation is required, points to the eventual 
separation of validation from the rest of the system. For example, validation covild stay at 
JRC when TREES III goes to an operational site. 



TMP, BMP, TIP 



a 




Independent 
1 ground survey 



Independent 
TM 



i^ 



/ 
/ / 



/ 



/ 



companson 



external sources 



7 



Figure 7 : Validation Component 



22 



EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



2.7 Users 

One of the most important considerations in moving from TREES I research into TREES II 
semi-operation is to establish a clear link with the users of the products. A component has 
therefore been identified which encapsulates users (see Figure 8) and the functions 
necessary to relate the TREES II system to them. 

The collation functions are split into those for products and those for reports. The first 
might consist of responses to publicity. A yearly flyer indicating the availability of TMP, 
BMP, TIP and TLP during the year ahead could be produced and posted to all respondents 
from the user sur\'ey (Technical Note No. 3). The flyer would indicate product availability 
and form. Responses to this could be serviced quite simply (also see discussion on 
dissemination below). TLPs may be produced on a more ad-hoc basis, although this is 
likely to be driven at a programme level by instrument and data availabOity than directly by 
user request. 

The collation of requests for reports is more problematic since some degree of interaction 
and choice of area is required. It is likely that a smaU number of major users would retain 
contact with the project and that discussion of results from the products would generate 
requests for specific reports. For AARs, requests could be serviced by TREES project staff 
while for EARs, an outside expert from one of the user groups (or an independent expert) 
would interact directly with TFIS. 

The monitoring of feedback from users is a critical procedure during TREES II. This will 
enable the team to be kept in touch with their users and bmld up a realistic picture of what 
is required. 

The disseminate function could consist of one or more of the following: 

• distribution by mail of hardcopy maps and statistical tables (TMP, BMP ,TLP and 
TIP) 

• distribution by mail of paper reports (AAR, EAR) 

• distribution on electronic media (e.g. CD-ROM, floppy, network) of Arclnfo (or 
other GIS) format graphics and statistical tables for use in users GIS (TMP, TIP 
and BMP) 

• distribution on electronic media (e.g. CD-ROM, floppy, network) of selected 
statistical and graphical information in hypertext formats for use in non-GIS 
applications. 

The most important of these will probably be the traditional map format output. This is 
particularly to meet Technical Note No. 4, requirement 26. Figure 9 shows how many TIPs 
would be produced in terms of map sheets at 1:1,000,000 for Africa; while Figure 10 shows 
the level of detail which would be shown on the Al map sheet at the same scale. Similarly, 
TMP coverage at 1:5000,000 is shown in Figures 11 and 12. The BMP would be similar. The 
TLP and AAR may be produced as a large format graphic at 1:100,000 as iUustrated in 
Figure 5. in Technical Note No. 4. The EAR may be simple A4 text and graphic documents 
containing pages as illustrated in Figure 6. in Technical Note No. 4. 



EOS-93/090-TN-005 23 



Work Plan TREES II Pre-feasibility Study 



The electronic versions of TMP, BMP, TIP and TLP could be produced in vector format in 
one of several GIS formats; e.g. Arclnfo, SPANS, ILWIS or ASCII. Statistical tables would be 
in ASCII. Non-GIS display systems could be supported although the volume of graphical 
data would probably preclude fuU resolution display. 

User Development 

It is critical to TREES II that products are used. In particular, routes for dissemination must 
be developed. It would be advisable to develop the user component as follows. A number 
of pilot users should be identified. These should include at very least two sophisticated user 
(having their own GIS); two unsophisticated user (requiring paper products) and two from 
an intermediate category (having PC range machines). Interim products output by the 
project should be forwarded to these users in several of the formats suggested above. Close 
contact should be maintained with this user group which would have perhaps 12 members. 
Comments should be sought on products and a sub-set of users could be in\'ited to review 
meetings at periodic intervals. Such meetings might be hosted by JRC or another body such 
as WCMC or the CEC. This feedback from users is a critical element of TREES II. In 
particular, this feedback wiU enable the team to understand which of the requirements met 
by each product is more or less important to users. This will help the team to continue to 
develop and refine the product specifications and to maintain a realistic understanding of 
the requirements. 

It is recommended that this work is at least partially vmdertaken by outside experts with 
user contacts. This would lead naturally to the splitting off of the component as TREES II 
matures and TREES III (fuUy operational) is considered. The collation of orders, 
dissemination of products and monitoring of feedback might then pass to a separate body. 

A second major development aim may be met by the proposed Centre for Earth Observation 
(CEO). Access to the CEO is planned to be via networks and interoperable catalogues. 
Users wiU dial in, see what data are available and possibly retrieve data to their own site 
across international networks. Should TREES II be linked to the CEO network then there is 
a dear opportunity for disseminating products via this route. The link between the TREES 
system and CEO would probably be via a shared data management and browse function 
such as that represented by the component discussed above. Such a Unk would enable the 
TREES II team to assess the usefulness of different electronic formats (e.g. GIS standards). 
The types of users would be those classed above as sophisticated and intermediate. 



24 EOS-93/090-TN-005 



TREES 11 Pre-feasibilih' Studv 



Work Plan 



Disseminate 



Monitor Feedback 



Collate requests 
for reports 



Collate requests 
for products 



Users 



Global change 
community 



National forest 
departments 



UN 
Agencies 



Inter- 
governmental 
Organisations 



Inter- 
national 
NGOs 



Figure 8 : User Component 



EOS-93/090-TN-005 



25 



Work Plan 



TREES II Pre-feasibility Study 




Figure 9 : Al Maps Sheet Coverage of Africa at 1:1000,000 (TIP) 



26 



EOS-93/090-TN-005 



TREES II Pre-feasibilih' Study 



Work Flan 




Figure 10 : Details Level for Al Maps at 1:1000,000 (TIP) 






EOS-93/090-TN-005 



27 



Work Plan 



TREES ]I Pre-feasibility Study 




Figure 11 : Al Maps Sheet Coverage of S America at 1:5000,000 (TMP) 



28 



EOS-93/090-TM-005 



TREES I] Pre-feasibility Study 



.Work Plan 




Figure 12 : Detail Level for Al Maps at 1:5000,000 (TMP) 



EOS-93/090-TN-005 



29 



Work Plan 



TREES II Pre-feasibility Study 



2.8 System Development 

Figure 4 showed the system as a whole with data flow between components. As already 
noted, a number of components (both suppliers, images, products) have been partly 
developed during TREES 1. The data management and users components are proposed to 
complete a system view. The question now arises as to how these components should be 
developed during TREES II. 

Most of the research activity is centred on the image component and as such the resolution 
of research issues must be seen as a main driver on the timetable for development. In 
particular, the classification procedure used will determine the functions required in routine 
suppliers and products in respect of mosaicking and compositing. In turn, the use or not of 
mosaicked products input to images will determine the load on the data management 
function; with load increasing massively if raw images are ingested by the project. 
Similarly, the extent to which images received by the project can be pre-processed (i.e. 
atmospheric and geometric corrections) will determine the extent of the procedures 
required. Source independence (looking to MERIS and 'Vegetation') is an important factor 
to consider in these decisions in order to avoid the danger of building a system too 
dominated by the peculiarities of AVHRR. 

The two components 'routine data supplier' and 'images' should therefore be carefully 
considered. Reasoned decisions need to be made before the development of other 
components goes ahead. The development of the products and data management 
components is the second priority. Tlie existing functionality requires enhancing as 
described in Sections 2.3 and 2.5. However, the extension of TFIS to support classification 
work should be the higher priority of the two. Validation and user components are the 
lowest priority in the short term although it must be stressed that these components are 
critical to the operational TREES system. 

The point made in the introduction to this chapter that components analysed in this way 
could be distributed must also be considered. The removal of routine data supply from the 
TREES II team has been recommended. However, other components, notably validation, 
products and users have also been discussed in this light. The latter two in particular ought 
to receive attention as candidates for separation in the sense discussed in the individual 
sectioris; i.e. the duplication of the enhanced TFIS (products) to other sites to enable higher 
level information products to be derived by users and for the user component, the 
involvement and guidance of user oriented groups. In summary, a scenario for component 
distribution might be: 



Component 


Current site 


TREES II site 


i KEES III site ? 


Routine data supply 


JRC 


ESA? 


ESA 


One-off data supply 


JRC 


JRC 


CEC 


Data Management- 


JRC 


JRC 


CEC 


Images 


JRC 


JRC 


CEC 


Validation 


JRC 


JRC 


JRC 


Products 


JRC 


JRC/multipIe 


CEC/multiple 


Users 


none 


JRC/expert group 


expert group 



30 



EOS-93/090-TN-005 



TREES II Pre-feasihiliU' Study Work Plan 



The final point to be made concerning system development is the need fully to document 
the various components. The component breakdown proposed here couJd easily form the 
basis of a document. Important elements to include are: 

• results of trade-off studies, especially those into the source of AVHRR data, pre- 
processing steps, the load on the data management component etc. 

• a plan for the acquisition of auxiliary data for both the training of classifiers and 
the interpretation of products which must be related to product outputs 

• requirements statements for the software functions required of IBIS and TFIS 
over the TREES II development period 

• classification procedures (as in Section 2.4). 

A high level development plan for components is found in section 5. 



EOS-93/090-TN-005 31 



Work Plan TREES II Pre-feasibility Study 

3. 
Research Requirements and Recommendations 



3.1 Introduction 

Research areas were identified in general terms in Technical Note No. 4 following each 
product description. The following list outlines these areas and assigns a reference number 
to each which is then used in subsequent parts of this report: 

Research topic Ref. number 

Decision on the classes to be contained in TIP R.1 

Derivation of classification techniques and rules for TMP R.2 

Derivation of classification techniques and rules for TIP R.3 

Derivation of classification techniques and rules for BMP R.4 

Selection and use of training and test sites for all products R.5 
Methods for improving registration accuracy between images, and between 

image and coastUne/national boundary data R.6 
Determination of appropriate projections for production of areal statistics R.7 

Methods for determining accuracy and precision of areal statistics R.8 

Methods for determining accuracy of boundary locations R.9 

Methods for determining change detection R.10 

Methods for segmenting SAR data R.11 

In the following sections each research topic is discussed in tiun. 



3.2 Research Topics 

3.2.1 Decision on TIP classes (R.l) 

This is an important component of TIP production. The classes actually required by users 
which are also feasible to extract from the data need to be established before any serious 
classification work can be undertaken as defined in R.3. The product specification given in 
Technical Note No. 4 (Section 4.4) indicates that evergreen, seasonal, and degraded and 
secondary forest are required. This list needs to be refined and its appUcabOity to each of 
the three tropical forest regions needs to be examined. It is suggested that a short 
consxiltancy project is initiated which would report during the first part of Year 1 of TREES 
II. The consultants should be a mix of those with tropical forest experience and those with 
remote sensing experience. The output from this consultancy would be a set of classes for 
each tropical region to be included in the TIP product. 

3.2.2 TMP Classification Technique and Rules (R.2) 

TMP is intended to be a rapidly produced and disseminated product. Using data collected 
over the period of a year a forest:non-forest classification is produced with areas containing 
cloud (or corrupted for some other reason) flagged as unclassified. Areal statistics are then 



32 EOS-93/090-TM-005 



TREES n Pre-feasibility Study Work Plan 



derived on a national and regional basis, with hardcopy maps produced at 1:5,000,000 (see 
Technical Note No. 4, Section 4.2). An accuracy of 80% in the classification is required. 

In order to achieve this goal there must be a trade-off between classification accuracy and 
speed. A classifier must be used which does not require extensive user interaction and 
ground sur\'ey. To this end the classifier must be simple and use repUcable rules which do 
not have to be drastically modified from year to year. 

The goal of this research topic will be to determine the classifier which best meets these 
requirements. It should be noted that the majority of previous research into classification 
techniques has-fociosed on the production of the 'most accurate' map. For the purposes of 
this research the 'most accurate' is not necessarily the best; i.e. the 'most consistently 
repeatable' is of equal importance. 

The research should be carried out on an empirical basis by testing a large variety of 
classifiers on areas for which there is sufficient data for validation and which a forest expert 
could help locate The most reasonable set to use would be the data collected for TREES I 
since these are readily available, familiar to the researchers, and have sufficient supporting 
data. It is envisaged that the optimmn classification procedure might not be the same for 
each of the three continental tropical regions (although it would naturally be desirable) due 
to floristic differences, so that test areas shoiild be selected for each of these regions and 
analysed separately. 

In examining various classifiers the researchers shoiold consider the impact of reducing the 
number of bands and/or the number of scenes required. Processing costs are likely to be 
substantially reduced if fewer scenes are required. Simplicity and speed are improved by 
reducing the number of bands. However, if imagery from a particular date is required the 
research shotdd establish how sensitive the classi^cation would be to the precise date, in the 
event that data would have to be used from an alternative date. 

Kimtz and Kleinn (1993) describe the relationship between the percentage of an area 
identified as forest, the pixel size, and- the percent of a. pixel occupied by forest. An 
appropriate percent pixel occupied by forest, which implies that that pixel is classified as 
forest, needs to be selected to correspond to the imposed pixel size of 1 km. This also relates 
to the work of Cross, et al (1991), into the application of mixtiire modelling techniques to 
forest cover classification vising AVHRR. One of their findings was that AVHRR data 
tended to underestimate forest cover, particularly in areas with large numbers of pixels of 
mixed cover. 

It is suggested that the following classification procedures be examined, although this list is 
not intended to be exhaustive or prescriptive. All of these techniques are known to 
researchers in the field and are represented in the literature. 

1. Simple Threshold of Band 3 (Thermal) 

Band 3 has been shown (see Technical Note No. 2) to provide the best basic forest:non-forest 
discrimination. Would a Band 3 threshold provide sufficient accuracy in all the regions, and 
how dependent would this be on the date of the imagery used? 

2. NDVI 

While NDVI is known not to provide good tropical forest discrimination (see Technical 
Note No. 2) its use in provision of a simple forest:non-forest product should not be 



EOS-93/090-TN-005 33 



Work Plan TREES II Pre-feasibility Study 



neglected. The variation in NDVI over a season is known to provide phenological 
information which could be input into a classifier. Calibration problems are reduced since 
the basic measure is a normalised index. However, to be of use, it is probable that data from 
at least three dates would be required in order to determine the seasonal variations. For the 
TMP which is dependent on data collected over a very short time period, it may not be 
realistic to rely on a classifier which requires several images. Being NDVI based, the IGBP 
product should provide insight here. 

3. Simple Box Classifier using Bands 1-3 

This has been used with some success by INPE and may well be adequate for the accuracy 
levels required for this product. 

4. RaHo/Combination of NDVI and AVHRR Band 3 

Various combinations of NDVI and band 3 data could be examined to see which offers the 
best discrimination. 

5 Supervised Maximum Likelihood 

This should be examined with a variation of bands and number of images. The selection of 
training areas has always been a difficulty with this classifier, since the trairiing area 
statistics have to follow a normal distribution which is not required of the box method. 

6. Unsupervised Clustering Procedures 

TREES I work has already estabUshed the utility of this unsupervised classification (see 
Technical Note No. 1), however no work has been undertaken on attempting to determine 
whether a sub-set of bands/images would provide equally, (or sufficiently satisfactory) 
results. The difficulty with unsupervised methods is that many classes are produced which 
must then be labelled, requiring considerable user-interaction. For a product containing 
only two classes this would be a considerable burden since typically an unsupervised 
method may produce about twenty initial clusters. The supervised maximum-likelihood 
method would probably be less user-intensive for the TMP product. 

7. Mixture Modelling 

This technique has been applied with some success to forest mapping with AVHRR data 
(Cross, et al, 1991). While the technique has been shown to improve classification accuracy 
its application for the TMP classification may be too time<onsuming to be useful Expertise 
in Neural Nets is available within IRSA. 

8. Neural Nets 

For a fast procedure which only requires extensive user intervention in the preliminary 
'training' phase the application of neural nets to the production of the forest:non-forest 
classification should be seriously considered. 

9. Visual Interpretation 

This is an interpretation technique which should not be imderestimated. The problems with 
visual interpretation are replicabUity and consistency. However, it is a rapid and low-tech 
method which in this case might prove to yield results that are sufficiently accurate for the 
requirements. 

The result of the research described above will be a report (Section 5.3) which indicates for 
each classifier the accuracy, man-hours, CPU time and robustness (e.g. sensitivity to precise 
dates of imagery or sensitivity to which bands are used). From these results it should be 



34 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



possible to select a maximum of tvv'o potential classifiers which could then be used for 
preliminary TMP production. Results from assessment of this product could then be used 
to select a single classifier for each region which would be used for operational production. 

3.2.3 TIP Classification Techniques and Rules (R.3) 

The research issues for the TIP classifier have much in common with that for TMP in that a 
simple cost-effective procedure should be found. TIP differs from TMP in that more classes 
are derived (as defined from results of R.1), and the time-scale is three-yearly rather than 
annually. The emphasis in the procedure is therefore less on speed and more on accuracy. 
Further, the procedure is assumed to utUise input from TM data and to some extent from 
ground survey. Nevertheless a classification scheme needs to be selected and then used 
repeatedly, rather than selecting a different procedure each time the product is made. 

The specific classifiers to be examined should be those used for the TMP evaluation (see 
Section 3.2.1), although it seems unlikely given previous research experience that the simple 
threshold methods would prove adequate. More attention should therefore be paid to the 
maximiam likelihood, unsupervised, mixture modelling, neural nets and visual methods, 
and to variations in these. 

The result from the investigation would be a research report along similar lines to that 
produced for the TMP classifiers showing which method was likely to provide the best 
results for each tropical region. 

3.2.4 BMP Classification Techniques and Rules (R.4) 

The BMP product is similar to the TMP, but is produced only for Siberia. The classification 
methods to be used must be investigated in a similar manner to the TMP classifications in 
order to establish the quickest and least user-intensive procedure to derive a forest:non- 
forest classification at 80% accuracy. 

It is suggested that the same classifiers be investigated as for R2 and R3. The use of NDVI is 
expected to provide better results for this area than for the tropics and should receive more 
attention. TTiis may be particularly important since cloud-free data are more likely to be 
available so thiat use of a time-series of data may be more feasible than for the tropics. 
Although it must be remembered that data will only be available in the summer months 
when there is sufficient light. 

For the Siberian region there are no TREES I data available for testing, so data will have to 
be collected during the first year of the project. This may also include some TM data for 
provision of validation. An alternative would be to collaborate with the already extensive 
Scandinavian research, especially that of Tomppo of the Finnish Forest Research Institute, 
and use their data for derivation of a classification procedure which could then be tested on 
Siberia. The Finnish research involves extensive ground validation, and the vise of both 
AVHRR and TM data, so that there should be a wealth of test data. 

The result of this investigation should be a report along similar lines to that produced for 
TMP. 

3.2.5 Selection and Use of Training and Test Sites (R.5) 

An important issue is the use of training sites from year-to-year. All the classification 
methods require that some threshold is chosen, or some labelling is carried out, so that there 
must be areas whose class is 'known'. It is clearly desirable to have a minimum of these 



EOS-93/090-TN-005 35 



Work Plan TREES II Pre-feasibility Study 



sites so that field survey and checking can be minimised. Further, it is desirable to use the 
same test sites for as long a period as possible to minimise the effort involved in their 
selection. 

The TMP/BMP classification is produced annually and requires a minimum of user 
interaction. There is insufficient time to visit test sites and undertake extensive checking, so 
that a set of sites needs to be chosen v^hose classification is unlikely to change each year. 
For the TIP classification there is a three year time-frame so that field survey and /or use of 
TM data for checking is possible. To minimise effort, the TMP sites must be a sub-set of the 
TIP sites which are likely to remain unchanged over a three-year period. The classification 
of the TMP sites would be checked every three years. These sites need to be chosen in the 
expectation that surface cover will not change over three years. The BMP sites should be 
equally stable. 

An additior\aI problem is that sxifficient sites must be available to allow threshold 
selection/derivation of training statistics/class labelling (according to which classification 
method is used) over the whole of a tropical region. In an ideal situation, with infinite time 
and money, there would be a set of sites for each AVHRR image (or strip section). This 
would require a dense site network, since from image-to-image the actual ground area 
covered will obviously vary, or it would require site selection after the imagery was 
obtained (as was the case for the TREES I work). For an operational product it is not 
realistic to keep re-selecting sites, nor is it feasible to select the sites after the data have been 
obtained. It is therefore suggested that some research effort be expended in determining a 
minimum number of suitable sites which are sufficiently dense to allow separate 
classification of each image. For the TMP/BMP data this would be a set of sites which are 
dearly 'forest' and whose classification is unlikely to change in a three-year period. For the 
TIP data a similar set would be required covering every class and well spaced across the 
region. Again the assistance of a knowledgeable botanist would help. 

An additional consideration here concerns the relationship between the number and 
location of ground survey sites and the spatial extent of the images making up the input. 
Mosaicked images reduce the data handling load but create difficulty in associating sites 
with single date sections of the mosaic. This has been noted in Section 2.8 on system 
development as a major study point since it influences the design of several elements. 

In the foreseeable future the major data source for TREES may switch from AVHRR to an 
alternative source (see discussion in Section 2.1.1). The potential impact of such a switch on 
the selection of training sites must be considered. For example, a data source with a smaller 
swath width might require more sites; although the stabOity of its radiometric calibration 
might mean that fewer sites would be necessary. The important point is that TREES must 
not become locked into a situation where the data source and the training site selection are 
too heavily interdependent. 

It is not envisaged that ground survey be carried out at each of these sites. Validation of 
their class can be made using TM data. However at least one site per region should be 
selected for detailed study and ground survey. TTiese sites would then form the basis of the 
TLP products described in Technical Note No. 4, Section 4.5. 

The results of analysis of this problem should be a brief report produced in conjunction with 
the results of the classification tests for the TIP classification (R.3). This report should 
indicate the selected test sites for each region and the sub-set of these which are to be used 



36 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



for IMP. 

3.2.6 Methods for Improving Registration Accuracy (R.6) 

The TREES I work registered the AVHRR data to the WDBII coastline and national 
boundaries. If accurate areal statistics are to be derived on a national as well as regional 
basis then the accuracy both of this registration and of the WDBII data themselves is 
important. Where data are being compared for different times a change from forest to non- 
forest must not be confused with an unintended boundary mis-registration. 

This research area is concerned with establishing methods to improve the registration 
accuracy so that such effects are minimised. For TREES I geometric registration was carried 
out using the AVHRR orbit model then nearest neighbour resampling using ground control 
points derived from the WDBII data. Some effort needs to be invested in assessing 
alternative resampling methods which might give greater accioracy. 

Inaccuracies in WDBII coastline data and their impact both on image registration and on the 
production of national and protected area statistics also needs to be considered. If small 
islands are missing or inaccurately represented what impact does this have on the 
presentation of the corresponding statistical data? If the coastline boimdaries or the national 
boimdaries are inaccurate how does this impact the areal statistics, the botmdary statistics 
and the accuracy of change detection? It is knovvn for example, that in the Amazon basin, 
some rivers are mislocated by up to 15 rrdles (A. MiUington pers. comm.). This has major 
implications for the registration of images and boimdary change analysis. This relates also 
to the research problems outlined under R.8. 

Recently the DCW coastline and national boimdary data have become available and are 
considered to be more accurate than WDBII. The inaccuracies in both data bases need to be 
examined with respect to the type of data derived from TREES in order to determine which 
data base is the most suitable. Some of the auxiliary data already used in TFIS are fitted to 
national boundary data other than WDBII or C>CW. For example, WCMC use Mundocart as 
a base. The TREES research should cor^sider whether a switch to DCW would be beneficial, 
and should also cor^sider the costs and difficiilties'bf so doing. 

Results of this investigation should be presented by the middle of the second year of the 
TREES II project. A recommendation should be made as to the optimum geometric 
registration procedure. The impact of inaccuracies in registration on the interpretation of 
the products needs to be quantified as far as possible. 

3.2.7 Projections for Production of Areal Statistics (R.7) 

This is a component of the TFIS work which needs to be determined. Currently the 
classified AVHRR data are included in TFIS as Lat/Long. To produce statistical data they 
must be resampled to an appropriate projection that maintains accurate area, distance and 
shape. The data from the non-tropical region must be resampled in a manner which is 
compatible with the tropical data. 

The TFIS upgrade required by the end of the project (see Section 5.6) will have to contain a 
suitable automated procedure. However, it is not considered necessary to produce a 
specific research report during the project. 



EOS-93/090-TN-005 37 



Work rian TREES II Pre-feasibility Study 



3.2.8 Accuracy and Precision of Areal Statistics (R.8) 

Each product described in Technical Note No. 4, section 4 has a component stating the 
required accuracy. In order to determine whether the required accuracy has been met the 
results must be validated against known data. These data will be derived from TM data and 
from ground survey. An additional comparison can be made between the TMP and TIP 
classifications. It should be possible to 'degrade' the TIP classification into a TMP 
classification and so compare the two directly. This would be of particular interest where 
both are derived from the same data set using different classification schemes. The work 
plan outlined in Section 5 indicates that the interim products of each type will indeed be 
produced from the same data and so can be compared in this manner. 

Results for each product are to be presented on a national basis showing statistics for the 
forest coverage within each nation. The precision to which these can be represented 
depends upon the size of nation. TTie requirement for 80% overall accuracy implies that 
80% can be achieved for any nation within a region. However, the inaccuracies may not be 
entirely spatially random, and there may be nations whose size, shape or geographic 
location is such that 80% is not achievable. A study needs to be done of the size of the 
nations within the tropical forest regions to determine whether they are aU capable of 
representation with 1 km AVHRR at the required accuracy level. For the TIP product, if 
protected areas are to be identified within nations, then this study should also determine the 
restrictions on representing and classifying these areas. 

Accuracy considerations tie in closely with the choice of classifier described in R.1 - R.3. It 
may be that in order to achieve a 75% accuracy level for aU nations one classifier would be 
suitable, whereas a different and more complex method would give 90% accuracy for aU, 
but a few nations which would only be achievable to 50% accuracy. A trade-off therefore 
exists between the complexity of the classifier and the desirability of classifying aU natior\s 
to a high level of accuracy. 

Results of this investigation should be included in a report to be delivered by the middle of 
Year 2 of TREES II and which will also contain results for research areas R.9 and R.10. The 
report should state the expected accuracy for each nation/protected area to be classified, 
and should state the minimum size of area which can be represented. 

3.2.9 Accuracy of Boimdary Locations (R.9) 

The product data consist both of areal statistics and boimdary maps. Where boundary maps 
are used these must be consistent with the scale of the data. If registration accuracy is 
correct to ± 0.5 pixel a boundary change for a 1km pixel would have to be 2 km in order to 
be properly identified. Such a shift can be represented at a scale of 1:1,000,000 as a line 2 
mm wide, or at 1:5,000,000 as a line 0.4 mm wide. In areas where there is a clearly moving 
forest front this is a realistic representation. However, in areas where the forest is being 
fragmented into small units the boxmdaries may not be sufficiently clearly identifiable. 
Some additional research to that undertaken in TREES I on the size and shape of deforested 
areas (see Technical Note No. 1 Section 2.3) needs to be undertaken with respect to 
determirung the amovmt of shift which can be adequately monitored in different regions. 
Production of boundary maps for the TMP and BMP products needs to be restricted to those 
areas where the boundary change is sufficiently great. Thus, the TMP data may not include 
boundaries at all if the change is not sufficient to warrant this. There needs therefore to be a 
link with the TFIS output described in the next section and the production of boundary 
statistics. ^ 



^^ EOS-93/090-TN-005 



TREES 11 Pre-feasibilit\' Study Work Plan 



Results of this investigation should be included in a report to be delivered by the middle of 
Year 2 of TREES II and which will also contain results for research areas R.8 and R.IO. 

3.2.10 Change Detection (R.IO) 

The ability to detect change depends on a number of factors : 

• registration accuracy of the data sets (R.6) 

• spatial resolution of the data (R.9) 

• classification accuracy(R.2 - R.5), which in turn depends oru 

- sensor radiometry 

- sensor spatial resolution 

- accuracy of atmospheric correction 

- nature of the classifier itself. 

The minimum size of identifiable boundary change that can be represented must be 
established so that only where changes are greater than this minimum would a boimdary 
change map be produced. 

The reports derived from the data products (see Technical Note No. 4, chapter 4) require an 
analysis of the specific changes. This implies that TFIS must include a function whereby 
such changes can be measured. This is a fairly simple procediire using Arc/Info, but its 
analysis and presentation require consideration. For the forest:non-forest data there is only 
a binary decision, but rules must be derived for dealing effectively with 'unclassified' areas 
which will be different between two dates. For the more complex TIP classification there 
would be certain changes which would be unlikely (e.g. evergreen forest to seasonal forest); 
if such changes are foimd they must be flagged and made the subject of further 
investigation to determine whether they occvirred due to inaccurate classification or to 
inaccurate registration. 

Results of this investigation should be included in a report to be delivered by the middle of 
Year 2 of TREES II and which will also contain results for research areas R.8 and R.9. It 
must include a set of rules which identify whether a change in classification is both possible 
and siifficiently great to warrant investigatioiL 

3.2.11 Methods for Segmenting SAR Data (R.ll) 

During TREES I some work was carried out on methods of segmenting the ERS-1 SAR data. 
This research should be continued in order to establish whether tropical forests can be 
distinguished using ERS-1, and whether different types of forest can also be distingviished. 
The TREES I procedure used a contextual classifier applied after segmentation and local 
averaging to isolate areas of rugged terrain and reduce speckle (see Technical Note No. 1, 
Section 5) The classifier was therefore not applied at all in areas of high relief. To be of use 
for tropical forest monitoring it is unrealistic to be so restricted, since many forested areas 
are in mountainous regions. The segmentation niles must therefore be modified to deal 
with such areas. The problem of geocoding after classification must also be considered in 
the absence of accurate DEMs for the majority of the tropical forests. 

Results of this work should be available by the end of Year 2, so that the methods can be 
applied to derivation of the TLP classifications. Results should be presented in a report 
which must identify segmentation procedures which can be used and their limitations. 



EOS-93/090-TN-005 39 



Work Plan TREES II Pre-feasibility Study 



3.3 Recommendations 

Topics R.l to R.8 inclusive are all critical to the development of TMP, BMP and TIP. The 
remaining three topic (R.9 R.IO and R.ll) relate to the AAR and the TLP. Of the topics, R.4, 
R.IO and R.11 can be considered less critical. The schedule in section 5 reflects this in terms 
of output reports required under various cost options. We can therefore summarise the 
research requirements as t\vo: 

• a set of critical research topics must be addressed (R.1, R.2, R.3, R.5, R.6, R.7, R.8, 
R.9) 

• a set of desirable research topics should be addressed (R.4, R.IO and R.ll) 



40 EOS-93/090-TN-005 



TREES II Pre-feasibilih' Study Work Plan 



4. 
Management and Team Development 



4.1 Introduction 

The original TREES project was focused strongly on research; a challenging target had been 
set at the beginning which had not previously been undertaken and consequently the 
techniques and methodologies required were not apparent at the beginning of the project. It 
was therefore necessary to approach the problem by allowing scientists freedom to devise 
creative solutions to the \'arious problems involved. With so many imcertainties in both 
approach and anticipated results a management structure was employed which allowed 
flexibility in the project direction depending upon the outcome of the research. During 
TREES II, the nature of the project will gradually change, moving away from research and 
toward operational product generation, though there will always be strong elements of 
research to support the operations. This change in focus must be accompanied by a change 
in management structures which will continue to encourage innovative research while 
supporting and controlling the operational aspects of the project. The following sections 
identify the key areas for attention in terms of project management and team development, 
addressing aspects of resourcing which caused difficulties during TREES I and 
recommending changes to be adopted for TREES II. 



4.2 Planning and Control 

The purpose of planning and controlling the project is to attain maximum efficiency in 
running the project, ensuring that the proposed schedule is met within the resources 
allowed, whikt coping with risks to the project in terms of changing internal and external 
circumstances. A planning and reporting cycle should be maintained which allows the 
project leader to maintain control of the direction of the project without constraining the 
scientific innovation of the project staff. 

The cycle should begin with the generation of a project plan by members of the project team 
(not just the project leader). The plan should cover the following key topics: 

overall objectives of the project 

description of the tasks to be undertaken 

list of expected outputs from the project 

schedule showing how the outputs are reached 

definition of the project organisation, staffing and responsibilities 

estimates of effort allocated to each task for each person 

definition of project management procedures 

identification of major risks to the successful completion of the project, including 

action to be taken to ameliorate these risks. 



EOS-93/090-TN-005 41 



Work Plan TREES II Pre-feasibilit)' Study 



If sufficient time is given to planning the project, the plan will serve as a valuable resource 
to the team. Its generation could be seen as an unwelcome chore, but if properly- 
constructed it can free the team from inefficiency associated with poor communication of 
objectives, ineffective allocation of resources and the impact of unexpected circumstances. 
This allows effort to be devoted to the central scientific and operational aims of the project. 

Monitoring of progress against the plan should be undertaken, preferably on a monthly 
basis. This monitoring should lead to a status report which can be used to assess whether 
forthcoming targets wOJ be met. This report should be distributed to all team members, to 
ensure communication of the status and direction of the project to all interested parties. If 
circumstances change during the course of the project, the plan should be updated to reflect 
the changes, which may impact any of the areas covered by the plan. 



4.3 Schedule and Deliverables 

Section 5 of this report proposes a schedule for the accomplishment of key tasks in the 
project, with the associated deliverables at each stage. Once the detailed objectives and 
scope of the project have been set, this schedule should be updated to reflect that which can 
reliably be met with the resources available to the project. 

The setting of milestones and deliverables within the project schedule fulfils a number of 
purposes of value both to the external perception of the project, and the internal planning. 
Many of the deliverables are prototype or interim products, which demonstrate the progress 
of the project to the outside world. These products demonstrate that the funding of the 
project is bearing fruit in tangible results. They also allow relationships with potential users 
to be established and developed since the products can be used to demonstrate the utility of 
the information that the TREES project can generate. The remaining deliverables are 
research reports which are either necessary for the successful undertaking of other project 
tasks, or provide for the dissemination of project results or research findings to the wider 
scientific community. 

The value of these self-imposed deadlines for the production of research reports is in 
maximising the personal research productivity of each of the team members. It is proposed 
that each of the research reports could become self-standing papers in appropriate journals. 
This wUl raise the profile of the staff involved, enhancing their research careers, and 
maintaining maximum motivation through the project. 



4.4 Human Resources 

The success of the TREES 1 project was partly inhibited by the difficulty and slowness of 
recruiting permanent scientific staff through the Commission. This made the planning of 
staff resources difficult, and left shortages in manpower which caused bottlenecks in the 
project process leading to unrealistic demands on the available staff. 

The success of the TREES 11 project, like any research-based project, is critically dependant 
upon the timely availability and motivation of staff with the correct experience and 



42 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



expertise. Accepting that delays and difficulties in recruiting high calibre staff are 
ine\-itable, it is necessary to pay close attention to staff planning to ensure that the key posts 
are filled. Staff resources avaUable to the project may be split into the following categories: 

• permanent staff \\-ho are employees of the Commission on a long term basis 

(5 years) 

• temporary staff who are employees of the Commission on a short term basis 
(1 year) 

• contractors who are employees of other organisations and may be assigned to the 
project for a range of time periods. 

While a wide range of permutations and combinations to staff deployment are available, the 
plan suggested here is based on the following premises: 

• key research should be undertaken by permanent employees, to maintain the 
intellectual base of JRC 

• since JRC is a research establishment employing scientific staff who are best 
equipped and motivated to undertake research, operational tasks should be 
undertaken by temporary staff or contractors 

• to reduce the management overhead on contracts, contractors should where 
possible be assigned significant logical units of work, to be undertaken on- or 
off-site. 

It is proposed that a proportion of the posts to be fiUed in order to execute the TREES II 
project be defined as the "core team", and be assigned to permanent members of staff. The 
core team should comprise 30-50% of the total team and be assigned roles of key scientific or 
organisational importance. Many of the key roles require less than a full-time person, and 
so could be combined within one post. The key roles are as follows: 

• Project Leader: responsible for high level leadership of the team in accomplishing 
the objectives set at the start of the project. Responsible for developing the 
intellectual capacity and cohesion of the team, for strategic planning, and for 
upward commimication to the project's institutional and political controllers. 

• Project Administrator: Responsible for assuring the smooth day-to-day running 
of the project by ensuring all the resources required by the various project 
functions are available. Responsible for detailed project planning and collecting- 
information on progress for reporting to the Project Leader. 



Science and Research Leader: Responsible for coordinating the research aspects 
of the project, ensxiring high standards of science are attained and that the 
intellectual benefit of the research remains within JRC. 

Data Gather Leader: Responsible for ensuring that all the input data resources 
needed by the project are available in a timely manner. These resources include 
all types of remotely sensed and auxiliary data needed through the life of the 
project. Responsibility for these data passes to the Data Manager once the data 



EOS-93/090-TN-005 43 



Work Plan TREES II Pre-feasibility Study 



are ingested into the project. 

• Data Manager: Responsible for ensuring all the data used and produced by the 
project is properly catalogued and securely archived. Responsible for defining 
the project's data management policy and ensuring it is adhered to. Responsible 
for liaison with IRSA and CEO. 

• Software and Systems Engineer; Responsible for the commissioning and 
management of all software developments required to undertake the TREES 
project. 

• External Relations Coordinator: Responsible for developing relatior\s with 
potential end users of TREES information, and maintaining a high profile for the 
project within the user and scientific commimity. Responsible for the 
dissemination of TREES products and the collection of user feedback, through 
newsletters, meetings and workshops. 

• Project Librarian; Responsible for ensuring aU the project procedures, results and 
reports are properly documented collated and archived, and made available to 
external users through the External Relations Coordinator. 

These roles wiU fUl more or less time, depending upon the functionality chosen for the 
project. However, each of them is needed, whatever the funding level; lower fimding levels 
wUl simply mean that more roles wiU need to be combined into single posts. Logical groups 
of roles to combined under different scenarios would be: 

• Data Gather Leader and Data Manager (unless Data Manager is an MTV wide 
post) 

• Science and Research Leader and Data Gather Leader 

• Data Manager and Software and Systems Engineer 

• Project Administrator, External Relations Coordinator and Project Librarian 

The choice of combinations wiU depend upon the exact project structure, and the availability 
of suitably qualified staff. 

In summary, it should be vmderstood that good management is critical to TREES II. This is 
because the project staff represent the most valuable resource. The planning of utilisation of 
hviman resources should be imdertaken with particular attention. The preceding sections 
have set out a number of principles for accomplishing successful use of staff resources, 
which can be modified and adapted to the actual, specific project needs. 



44 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



5. 
Deliverables and Schedule 



5.1 Introduction 

The deliverables of the TREES II project are outlined in the following sections and an 
outline schedule provided. The scheduJe focuses on the data collection, research reports 
and product deliverables required. The data collection is described in Section 5.2 and is 
designed so that smtable data are available for use in the research components and in the 
product derivations. The research reports are described in Section 5.3. Each research report 
attempts to aT\sv^er one or more of the research requirements identified in Section 3. The 
reports are scheduled in such a way as to provide timely information for the production of 
each interim and final product. Section 5.4 describes the product deliverables. For each 
product there is a 'final' version and one or more 'interim' versions using either partial data 
or incompletely defined production rules. The production and subsequent analysis of these 
'interim' products is considered to be an essential component of the development of the 
operational production of the 'final' outputs. The work schedule is given in Section 5.5. A 
schedule is given for full restricted and basic functionality cost scenarios and an attempt is 
made to analyse the trade-offs involved in making a selection between the different cost 
scermrios. 



5.2 Data 

TREES II can be considered as driven by the requirement to produce output products as 
described in section 4 of Technical Note No. 4. This section describes the relationships 
between these products and the data required for their production. 

At the most general level level the 'data' are used to make the 'products'. The products 
themselves are defined as four separate classes, using the acronyms previoiJisly defined in 
Technical Note No. 4. The data are divided into two classes: TREES I data which are 
defined as all the data collected during the TREES I project, and data collected during the 
TREES II project. Each of these classes is further subdivided into different components 
consisting of AVHRR data, TM data, SAR data and ground survey data. These relationships 
are illustrated in Figure 13. 



EOS-93/090-TN-005 45 



Work Plan 



TREES II Pre-feasibility Study 






/7\ 


/C\ 


Q£ 


I o 1 


/ C/3 1 


<r 


1 c^ 1 


CO 


\l/ 


V ta y 




3 
a; 


f^\ 


f^^ 


/^ 


> 


< 


< 1 


I "^ ) 


< 


\j / 


V J 7 


\^/ 


\ 


^^ 


^-^ 


\^ 



Figure 13 : TREES II Data Requirements 



46 



EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



The TREES II data as described in Figure 13 is: 

• LACl refers to LAC data collected during Year 1 of TREES II, LAC2 to LAC data 
collected during Year 2 etc.. Since it may take some time to set up the chosen 
data collection scheme (section 2) it is possible that LACl may be incomplete. 
The deliverables are designed in such a way that this data set would not be 
essentia] except that LACl data are required for the interim BMP product unless 
suitable data can be obtained from E. Tomppo of the Finnish forestry ser\'ice. 

• TM 1-3 refers to TM data collected for specific test sites in each of the three 
tropical regions. Each site chosen for a TLP product will require complete 
coverage with TM data over the 3-year project period. Additional TM data will 
be required for validation, with TM data for eacii test site. 

• TM4 refers to TM data collected for the Siberian test area. 

• SAR data are divided into airborne and spaceborne sets. Within each there 
would be four sets, one for each of the four TREES II regions. At least one 
spaceborne set should be available for each of the TLP sites. 

• The ground sun'ey data are sub-divided in the same maimer as the TM data. It 
is envisaged that ground survey will only be tmdertaken at each of the TLP sites, 
and that only one visit per site will be necessary. This is schedvded to take place 
during Year 2. 

The TREES II data will be used for all the products. TREES I data will be used initially 
during the TREES II project. The reason for this is that new data are unlikely to be available 
until at least the start of Year 2, and new ground survey data until later still. This wUl be the 
case whether or not the data collection is imdertaken in-house or contracted out (see Section 
2). Further, the TREES I data set consists of a valuable resource set containing considerable 
amounts of ground sur\'ey and TM data which it would be unfortimate not to exploit. 

However, the current state of the TREES I data is that collection has been completed for S.E. 
Asia and Africa, but not for S. America. There is considerable uncertainty regarding the 
processing state of these data. We understand that atmospheric correction has not been 
routinely performed on the S. American data and the S.E. Asian data. It is not known 
whether this is also the case for the African data. If these data are to be usable for multi- 
temporal study then atmospheric correction is essential. While it is appreciated that there 
is some uncertainty in the correction over tropical areas this should not be taken as sufficient 
justifi3tion to avoid applying whatever correctioris are available. Correction algorithms are 
available at JRC (possibly used on the MARS project). These should be applied to the 
existing TREES I data so that aU the data is pre-processed to a vmiform standard. Only after 
this has been done can these data be used for further study. 

For each of the four basic product classes TREES II should produce several examples. The 
last product in a series is considered to be 'final', the lower numbered products are 'interim' 
(see Section 5.4) These are described in Figure 14 with an indication of which data set will 
be used for which product. Tl, T2, and T3 refer to the three TMP products described below; 
Bl and B2 to the two BMP products and so forth. Figure 15 illustrates use of the TREES II 
data in more detail. 



EOS-93/090-TN-005 47 



Work Plan 



TREES II Pre-feasibility Study 




Figure 14 : Products and Data Relationships 



48 



EOS-93/090-TN-005 



TREES II Pre-feasibility Study 



Work Plan 



TREES II 
TM 



TLP 




Figure 15 : Detailed Data Relationships 



EOS-93/090-TN-005 



49 



Work Plan TREES 11 Pre-feasibility Study 



5.3 Research Report Deliverables 

Each report contains results of research undertaken to fulfil the various research 
requirements described in Section 3.2. and refers to the research requirement identified in 
the list in Section 3.1. It is envisaged that each report could form the basis for a paper in a 
refereed research journal. Such publications would increase the profile of the TREES 
project. 

1. Report on classification schemes Due Ist quarter Year 1 
This report will contain the results of the research and consultancy required for R.1 

2. Report on test classifications for TMP Due mid Year 1 
This report will contain the results of investigations carried out for R.2. It will identify at 
most two classification schemes suitable for production of the interim TMP classification. 

3. Report on test classifications for TIP Due end of Year 1 
This report will contain the results of investigations carried out for R.3 and R.5. It will 
identify one, or at most two, possible candidates for production of the interim TIP product. 
Test sites suitable for use in both TIP and TMP production will also be identified. 

4. Report on TMP#1 Due 1st quarter Year 2 
Following production of TMP#1 there will be a short evaluation exercise using whatever 
ground TM data are available, plus an evaluation of the effort/cost. This wiB lead to a 
report to be delivered early in Year 2 with recommendatioris, i.e. which method is to be used 
in future. This has to be done quickly so that production of TMP#2 can proceed. 

5. Report on test classifications for BMP#1 Due 1st quarter Year 2 
This report will contain the results of research for R.4, and will identify at most two possible 
candidates for use in the interim BMP production. Suitable test sites should also be 
identified to fulfil R.5. 

6. Report on geometric registration Due mid Year 2 
This report wiU focus on the possibilities for improving registration accuracy between 
images and between images and the DCW coastline data, as identified in R.6. The report 
should also contain a statement concerning suitable projectioris for use in providing areal 
statistics (R.7). 

7. Report on statistical accuracy Due mid Year 2 
This report wiU include results of studies for R.8, R.9 and R.10, i.e. all the work concerrung 
statistical accuracy and change detection procedures. 

8. Report on S AR segmentation Due end Year 2 
This report should contain results from research into ERS-1 SAR segmentation as described 
byR.ll. 

9. Report on TMP#2 and BMP#1 Due early Year 3 
Results of product#2 must be evaluated by the start of Year 3. After this the 'final' rules for 
these products wUl be developed and will not change. It is expected that different 
procedures are likely to be suitable between the tropics and Siberian test areas. However, 
the research should also be aiming towards integrating these as far as possible, i.e. if there is 



50 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



a method which is the same for both areas it would be preferable provided that the accuracy 
is not too low. 

10. TIP#1 validation Due mid Year 3 
Similar to evaluation of TMP/BMP above. Recommendations then feed into the production 
ofTIP#2. 

11. TIP/TMP comparison Due mid year 3 
A comparison of the TIP and TMP products should be interesting see R.S described in 
Section 3.2.8. Every three years both products are going to be produced, and if results are to 
be published which disagree there could be a problem, e.g. an area classed as forest by TIP 
and non-forest by TMP, (or areal statistics which disagree). Even though the products do 
not normally have exactly the same data input (TIP uses the TMP data, plus data from the 
preceding and following years) for the TREES II period the interim TIP product and the 
interim TMP products will both be derived from TREES I data and so will be directly 
comparable. The TIP product should be 'degradable' into a TMP product, and one could 
then produce a confusion matrix between the two classifications. Since TIP is supposed to 
be more accurate than TMP this would give an interesting handle on the accuracy of the 
latter. The TIP:TMP comparison should be reported on by the middle of Year 3, so that 
production of the 'final' products is not hindered. 

12. Final report Due end Year 3 
Summary of results and recommendations for proceeding towards operational 
implementation as TREES III. Final rules for aU products should have been developed. 



5.4 Product Deliverables 

TMP #1 Due before end Year 1 

By the end of Year 1 one or more (see research report discussion section 3) "interim" 
products will be generated. These will use the classification rules and procediires derived 
from the research into the TMP classifications (see Section 3.2.2) and the TREES I data set, 
since the data collection for year 1 (LACl) may not be complete, and the TREES 1 data have 
the supporting TM and groimd survey data, which will be necessary for training and 
validation. It is desirable, but not absolutely essential, that the complete tropical region is 
classified. Sufficient should be classified to enable meaningful validation and subsequent 
modification of the classification method. 

TMP#2 Due mid Year 2 

By mid Year 2, a TMP#2 "modified" product will be generated. The TREES I data will be 
used again, in order to enable comparison with the interim products. Results from 
validation and research will be used to modify the methods used for products #1. 

TMP#3 Due early Year 3 

By early Year 3 a "final"TMP#3 product using LAC set#2 will be generated. Results from 
the TMP and TIP comparison will be used, plus validation thus far to produce a set of final 
"cast-in-stone" rules which should not subsequently change, (and hopefully do not differ 
from deliverables #2). This sets up further routine production such that a TMP/BMP 
product is produced by the middle of the year following the collection of the relevant data 
set, i.e. TMP#4 is due eariy Year 4 using LAC set#3. 



EOS-93/090-TN-005 51 



Work Plan TREES II Pre-feasibility Study 



BMP#1 Due mid Year 2 

This product will use data from the first year of LAC collection (LAC#1) since TREES I data 
are not available for the test area. 

BMP#2 Due early Year 3 

Specifications as for TMP#3. 



TIP#1 Due end Year 2 

By end of Year 2, an "interim" product TIP#1 will be generated. This wUl use data from 
TREES I only in order to enable comparison with TMP#1 and #2, and also to ensure that 
suificient cloud-free data are available. In this respect the product is a re-visiting of the 
wall-to-wall coverage of TREES I in the light of experience, and with increased user inputs 
to define the classes more appropriately, and to work towards a more automated procedure. 

TIP#2 Due end Year 3 

By end of Year 3 a "final" TIP product TIP #2 will be generated. This will mostly use LAC 
set#2, with additional data from set#l and set#3 where required to reduce cloud cover 
problems, plus the corresponding TM data and field data. This sets up the operational 
production such that the next TIP product would be three years later using LAC set#5 plus 
additional data from set#4 and set#6 where required. The TIP product is therefore 
produced at the end of the year for which the main data collection was made. The 
production could be staggered by regions if necessary. 

TLP products Due mid Year 3 

It is envisaged that a TLP product wUl be derived for a set of test sites where there is at least 
one test site per region (regions being defined as S. America, Africa, S.E. Asia and Siberia). 
Thus, there will be a minimum of four products. Should time and costing permit there 
could be additional test sites. In order to minimise costs and logistics the test sites used to 
derive and validate the other products shovild be the same sites as used for the TLP 
products. Data will be TM data, SAR data (both airborne and spacebome), and field data. 
The field work will probably take place during Year 2, so that the TLP products will be in 
final form by the middle of Year 3. It is likely that interim products will also be derived 
during the validation of the other products which can then be used to assist in deriving the 
final products. 

THS Upgrade Due end Year 3 

The upgrading of TFIS occurs in parallel to the product scheduling. The necessary 
components of TFIS are described in Section 2.4, and the research required to achieve the 
upgrades in Section 3. It is not envisaged that any specific reports need be produced, but 
the research imdertaken must be outlined in the final report. 



52 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



5.5 Work Schedule 

This section describes the work schedule for TREES II in terms of the data collection, output 
of reports and products and systems developments required and gives 'rough order of 
magnitude' costs. Three functionality scenarios are defined: full, reduced and basic. 

The products can be divided into 'interim' and 'final' deliverables, in that products 
generated during the final year of the project are considered to be final and therefore should 
be complete and produced in as operational a manner as possible. The interim products are 
considered as stepping stones towards this goal, and may therefore not be complete in their 
coverage, not be produced in an operational manner, nor provide the required accuracy. 

The scheduling of the products leads naturally on into an operational production at the end 
of the project. Thus, the so-called LAC#3 data set would be used the next year for a routine 
TMP production, and TIP#3 would follow on naturally three years after the end of TREES II. 
TMP products are finished within three months of the relevant data collection. TIP within a 
year of the relevant data collection. 

Each research report is designed to address specific research questions raised by the product 
specifications. Their scheduling with respect to the products is important, because they 
provide direct input in terms of the required techniques etc.. Each report could make a 
reasormble journal/conference paper and so help with the dissemination of results (in the 
research sense) that is rather lacking in TREES I, and help with the research careers of those 
involved. 

A schedule showing system development is included. All system components are required 
whatever option is taken; and there is relatively little difference envisaged between the 
general level of development in each case; i.e. the number of products and research reports 
does not directly influence the 'size' of the system- 

5.5.1 Full Functionality Scenario 

The fuU functionality scenario provides for all research topics to be investigated and 
reported and several products of each type to be produced. The cost table (Table 1) gives an 
estimate for the size of team and other costs incurred to cover the work and the timeline for 
the various elements is shown in Schedule 1. 

The discussion of management requirements in Section 4 indicates the need for a 
management/administration team of eight staff and a research and operations team of 
seven staff. One of the operators could work at ESA/ESRIN where pre-processing might be 
sited. The second operator will become important later during the project as image 
classification becomes more routine. The cost of airborne campaigns is speculative in the 
sense that irvformation is not easily available and the need for TREES II to fund such 
campaigns is undecided (collaboration with other programmes may provide input). The 
expert reports are related to research topics where the TREES team requires input from 
outside consiiltants. This occurred in TREES I and it is wise to continue to take such advice. 
The conferences and travel expenses are driven by the need to consult widely with other 
projects and to develop user contacts. 

The total cost over three years is 7.5 million ecu. 



EOS-93/090-TN-005 53 



Work Plan TREES II Pre-feasibility Study 



5.5.2 Reduced Functionality Scenario 

In this option the management/administration team has been reduced by combining roles 
and the research and operations team reduced. The BMP product has been removed along 
with the research and data collection required for its production. The number of TMP and 
TIP products is also reduced, along with field and airborne campaigns. Other costs reduce 
to match the team size and needs for software development. Table 2 shows the breakdown 
and the timing of the activities is shown in Schedule 2. There is of course a major political 
disadvantage in this option in that monitoring of a non-tropical area is a stated user 
requirement (see Technical Note No. A, Section 3). The possibility of extending TREES 
beyond the tropical scope is considerably reduced without this preliminary 'pathfinder' 
effort and once TREES III is operational it becomes much more difficult to add functionality. 
The production of the TMP product is also put at risk, since with only one interim product 
there is less scope for recovery from mistakes, and for refining the production rules. 

The total cost over three years is 5.3 million ecu. 

5.5.3 Basic Fxinctionality Scenario 

In the basic functionality scenario, the TLP product is removed altogether reducing data, 
research and several other costs substantially. In particular, airborne SAR campaigns and 
SAR research would not be required. There are further reductions in research effort along 
with commensurate reductions in supporting areas. Table 3 shows the cost estimate, while 
Schedule 3 timelines the tasks. There is a major disadvantage in this option in that several 
important requirements are not met (5, 8, 12 17 and 19). The TLP products are likely to be 
highly valued by users who are substantially in favour of obtaining information at fine 
scales. They also provide irvformation which can be used ia production of the coarser scale 
products, which if lacking could put these products at risk. Li addition, the use of minimal 
'interim' products reduces the ability to correct mistakes and refine the operational 
procedures. 

The total cost over three years is 2.8 million ecu. 



5.6 System Development Schedule 

Section 2.7 discussed the relationship between system components in terms of the order of 
development. Having outlined a schedule for product and research report generation, it is 
now possible to add component development. The schedule is dependent on the results of 
trade-off studies especially those into data supply (see Schedule 4). The schedule shows two 
levels of activity for each component. The thicker lines show periods when actual 
development occurs while the thinner lines indicate ongoing operation 

Year 1 (1994) 

Data suppliers (routine) dominates the first year when development (directly or by contract) 
of software for the pre-processing operations is undertaken While there is a trade-off issue 
here as to how much pre-processing needs to be done, what is clear is that considerable 
effort needs to be invested by the TREES team on this component; in particular, the effort to 
separate as many operations as possible from the rest of the system by using the EDC/ESA 
route discussed in Section 2. Also in Year 1, an effort to incorporate extra functionality into 
TFIS to enable it to support the research report activity (i.e. the area analysis function). 



54 EOS-93/090-TN-005 



TREES n Pre-feasibilit>' Study 



Work Plan 



Item 


Annual 


Total Programme 




Kecu 


Kecu 


Management and Administration 




2400 


Project Leader 






Project Administrator 






Science and Research Leader 






Data Gather Leader 






Data Manager 






Software and Systems Engineer 






External Relations Coordinator 






Project Librarian 






Research and Operations 




1490 


4 Research Scientists 






1 Application Scientists (Year 3) 






2 Ojjerators 






Data Acquisition 






AVHRR Processing 


90 


270 


SAR Data 


40 


120 


TMData 


90 


270 


Miscellaneous Processing 


10 


30 


Hardware and Software 




350 


Field Campaigns 






Field Work (2) 




300 


Airborne Campaigns (2) 




1000 



Expert Studies (2 per year) 



100 



300 



Software Developments 



TFIS development 
Standard pre-processor 
Standard product generator 




100 
200 
200 


Staff Travel to Conferences/Meetings 


40 


120 


User and Expert Meetings 


50 


150 


Promotional Materials 




160 




TOTAL 


7500 



Table 1 : Full Functionality Scenario 



EOS-93/090-TN-005 



55 



Work Plan 



TREES II Pre-feasibilin- Study 



c: 




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Schedule 1 : Full Funcrionality Scenario 



56 



EOS-93/090-TN-005 



TREES 11 Pre-feasibility Study 



Work Plan 



Item 



Annual Total Programme 

Kecu Kecu 



Management and Administration 

Project Leader 

Project Administrator/Librarian 

Science and Research Leader 

Data Gather Leader 

Data Manager/Softv\''are and Systems Engineer 

External Relations Coordinator 



1860 



Research and Operations 

3 Research Scientists 




990 


1 Application Scientists (Year 3) 

2 Operators (Year 3) 






Data Acquisition 

AVHRR Processing 
SARData 


90 
20 


270 
60 


TMData 


40 


120 


MisceUaneous Processing 


10 


30 


Hardware and Software 




200 


Field Campaigns 

Field Work (1) 
Airborne Campaigns (1) 




150 
500 



Expert Studies (2 per year) 
Software Developments 



100 



300 



TFIS development 
Standard pre-processor 
Standard product generator 




100 
150 
150 


Staff Travel to Conferences/Meetings 


40 


120 


User and Expert Meetings 


50 


150 


Promotional Materials 




150 




TOTAL 


5300 



Table 2 : Reduced Functionality Scenario 



EOS-93/090-TN-005 



57 



Work Plan 



TREES I! Pre-feasihility Study 







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Schedule 2 : Reduced Functionality Scenario 



58 



EOS-93/090-TN-005 



TREES II Pre-feasibilit}' Study 



Work Plan 



Item 



Annual Total Programme 

Kecu Kecu 



Core Team 

Project Leader 

Project Administrator/Librarian/Extemal Relations Coordinator 

Science and Research Leader/Data Gather Leader 

Data Manager/Software and Systems Engineer 



960 



Research and Operations Team 

2 Research Scientists 
1 Operators 

Data Acquisition 

AVHRR Processing 

SAR Data 

TMData 

Miscellaneous Processing 

Hardware and Software 

Field Campaigns 

Field Work 
Airborne Campaigns 

Expert Studies (1 per year) 

Software Developments 

" TFIS development 
Standard pre-processor 
Standard product generator 

Staff Travel to Conferences/Meetings 

User and Expert Meetings 

Promotional Materials 



530 



90 



50 



40 



TOTAL 



270 



100 

150 

150 

60 
150 
100 

120 

75 

100 

2800 



Table 3 : Basic Functionality Scenario 



EOS-93/090-TN-005 



59 



Work rian 



TREES I] Pre-feasibilit)' Study 



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Schedule 3 : Basic Functionality Scenario 



60 



EOS-93/090-TN-0C5 



TREES I] rre-feasibiliU' Studv 



Work Plan 





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Schedule 4 : System Development 



EOS-93/090-TN-005 



61 



Work Plan TREES II Pre-feasibility Study 



Year 2 (1995) 

Once research activities start to yield results, the Lmages component can be built. This will 
involve the development of the procedures (part manual, part automated) needed to classify 
data for products. Later in the year, IBIS will have to be enhanced to cope with the 
increasing demand from the now established data suppliers. Procedures for validation of 
products should also be started. 

Year 3 (1996) 

Images, data management and validation should be completed by early in the year. By this 
stage, the format and content of the products should be clear. The final tasks are firstly an 
upgrade to complete the products component by incorporating the change analysis and 
interactive consultation elements and secondly user development. As indicated, the user 
component should have been receiving some attention during Year 2 in order that contacts 
are established. However, in Year 3 a concerted effort is necessary to disseminate products 
to selected users and receive feedback such that a user base for full operational use of TREES 
can be established. 

Thus by the end of year 3, TREES II should have produced a fully functioning but still pre- 
operational system but should be in a position rapidly to operationaUse. 



62 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 



6. 
Recommendations 



The major feahires of this work plan are a number of recommendations for TREES II . These are 
detailed in Sections 2, 3, 4 and 5 of this technical note. The major recommendations are as 
follows. 

In starting TREES II, a major objective is to move from a phase which has been predominantly 
research-oriented to one which is pre- operational. The system level recommendations which 
seek to drive this move are: 

1 Development and specification of a set of outputs or products which meet user 
requirements. 

This task has been started in this report by specifying a set of products. These are not full 
specifications and a number of questions are outstanding which should be answered during 
TREES II. It may become necessary significantly to alter product specifications, delete products 
altogether or add new ones. The important points are that a clear link exists between user 
requirements and the output from TREES, and that the output from TREES II can be 
characterised. 

2 To engineer a set of system components which will enable products to be 
output 

The system outlined in the work plan is derived from elements many of which were developed 
during TREES I. Additional components have been added in order to create a system which 
leads towards operationaHsation. 

3 To study options and determine the best means of providing a routine supply 
of processed 1 km data to the project 

The ingestion of these data during TREES I was a major operation which must be contained and 
if at aU possible removed from the JRC team. This will enable the TREES II team to concentrate 
on the specialist areas of research and product development. 

TREES I demonstrated that valuable information can be derived from the data sources used. 
However, a number of areas remain outstanding where additional research is required: 

4 A set of critical research topics must be addressed 

5 A set of desirable research topics should be addressed 



EOS-93/090-TN-005 63 



Work Plan TREES II Pre-feasihility Study 



The management and team development aspects are critical: 

6 Clearly identify roles and responsibilities for all team members including 
special positions such as data management, data gathering, systems 
development, science leader etc.. 

The necessity to meet schedules and deliver value for money means that responsibilities must 
be clearly understood at technical and non-technical levels. This is not easy in an environment 
of permanent and contracted staff. 

7 To involve a mix of people with skills in research, operation, engineering, 
management, negotiation and liaison. 

The user component of the system discussion emphasised the need to communicate with users. 
The contact with potential users reported in this study must not be seen as a one-off. It is vital 
to develop a clear understanding of the user community and its requirements. 

8 Establish links with users, distribute products and monitor feedback. 



Lastly, it is necessary to estimate the financial cost of the TREES II project. There are 
however a large number of research topics and trade-off studies involved in its 
development. This means that the final form of a system in terms of necessary functionality 
cannot be determined at this stage. In turn this means that the costs of development, 
research, software and data acquisition can be estimated only very generally. 

However, the discussions and recommendations above have been brought together into the 
the three options discussed in the section above. It is not possible here to recommend which 
of these options is taken. It is a matter for CEC to determine the value of meeting or at least 
probably meeting the requirements Ln each option. 

In summary, it can be said that with the full functior\ality scenario, a TREES II project 
following the suggestions and recommendations in the technical note will almost certak\ly 
be able to meet around half of the requirements identified in Technical Note No. 4. Thus 
TREES wUl be able to make a significant contribution to the management of tropical forests 
and associated activities. In the reduced functionality scerurio the boreal monitoring 
requirement is not met and research and products generation elements are much reduced. 
Boreal monitoring is a non-technical requirement whose value must be weighed by the CEC. 
In the basic functionality scenario a significant number of requirements are not met, 
especially those associated with SAR. 

In conclusion it can be said that TREES II presents a valuable opporixmity to utilise remote 
sensing technology in tropical forest monitoring. With dear requirements, the project can 
make a corisiderable contribution to the knowledge and understanding of this vital issue of 
global significance. 



64 EOS-93/090-TN-005 



TREES II Pre-feasibility Study Work Plan 

7. 
References 



Cross, A.M., Settle, J.J. , Drake, N.A. and Paivinen, R.T.M., 1991, Subpixel measiirement of 
tropical forest cover using AVHRR data, IJRS, vol 12, no 5, pplll9-1129 

Kuntz, S. and Kleinn, C, 1993, Some aspects of the role of definitions of land use classes 
based on satellite remote sensing - an example from forestry, Proc Int Symp on 
Operationalisation of Remote Sensing, Van Genderen, J.L. et al (eds), Enschede, vol 8 pp 83 - 90 



f 



EOS-93/090-TN-005 65 



Work Plan 



TREES 11 Pre-feasibility Study 



Appendix 
Key for Object Oriented Figures 

Key to class diagrams 



Symbol 




C3 



Key 



Class 



Object 



Instance 



Object/class is derived 
from object/class at arrow 
head 



Object/class/instance at 
bullet end loses the other 
object/ class /instance 



c 



) 



Data set in object 



Procedure in Object 



Data movement in and 
between Objects 



66 



EOS-93/090-TN-005