Study of Land-Use and Deforestation In Central African Tropical Forest Using High Resolution SAR Satellite Imagery

Abstract

In this study, we propose to use high resolution satellite imagery to map areas of forest clearing and general land cover types for the entire Central African tropical region. Images acquired by JERS-1 SAR (Synthetic Aperture Radar) instrument during its global rain forest mapping (GRFM) phase will be the main source of data for this study. By using a combination of radar backscatter and texture analysis, and a SAR-specific classifier, we propose to classify the images into five categories of forest, nonforest, savanna, flooded forest, and open water. Approximately 4000 high resolution (12.5 m) images for two periods of dry and wet seasons will be processed and delivered to the NASA/Jet Propulsion Laboratory and the European Commission Joint Research Center in Italy for generating a geocoded 100 m resolution mosaic of image data and a land cover map. The thematic interpretation of JERS-1 data and the validation of the land cover map will be supported by available Landsat TM images acquired by NASA pathfinder project, ERS-1 mosaic of Africa generated by ESA TREES (Tropical Ecosystem Environment Monitoring by Satellites) project, and vegetation maps and field data provided by collaborators from national institutions in the region. The resulting land cover map will be integrated in a socio-economic study to assess the trends and patterns of deforestation by including demographic and social and environmental policies.

Contents

• Rationale

• Objectives

• Background
  1. Rain forest of Central Africa
  2. Deforestation and Land-Use

• Approach
  1. Data Processing and Storage
  2. Land Cover Classification
  3. Socio-economic Analysis of Deforestation

• Expected Results

• Management and Cost Plan
  1. Management Plan
  2. Cost Plan Summary

• References

• Appendix A

• Appendix B

• Curiculum Vitae

As the destruction of tropical rain forests increased during the past 25 years, attempts in understanding its causes have multiplied. In general, the agents of deforestation can be found among the commercial loggers, cattle ranchers, and slash-and-burn cultivators. Since mid-1960s, the shifting cultivators of traditional type that practiced the small scale migratory agriculture has been gradually replaced by what is called the 'shifted' cultivators according to Myers (1984). These are farmers who moved from long-established farmlands of territories far distant from the forests into unoccupied lands available to them in tropical forests. The cause of this move resides in population growth, misdistribution of established farmlands, lack of agrotechnologies for intensive cultivation, and inadequate rural developments (Burgess, 1983). These shifted cultivators have grown exceptionally numerous in recent decades (World Resources Institute and World bank, 1985). The mass immigration of about 1.5 million small-scale farmers in Rondonia, Brazil between 1975 and 1988 is a notable example (Myers, 1991).

There is a general agreement among environmental experts and social science scholars that social configurations influence land use (Hecht, 1985; Hecht and Cockburn, 1989). The relation between social structure and ecological processes is key to understanding the degradation or enhancement of natural resources. The clearance of African rainforest is strongly tied to the socio-economic forces within the developing countries of the region, and is influenced by current and future environmental, political, and demographic conditions. The population of Africa is doubled in the past 25 years, it will double in 24 years and it will not stabilize until the year 2050 (World Bank, 1992). The greatest loss of forest in recent years has been in countries with high population growth. World Bank research suggests that shifting cultivation and conversion of the natural forest cover into perennial cash crops are the direct causes of forest degradation in the region. These causes have their origin in the rapid population growth, depending on the magnitude of the internal and external migration and the agricultural stagnation (Cleaver et al., 1992; Cleaver and Schreiber, 1994).

The costs of the deforestation in the region is only beginning to be understood. The most obvious costs are the loss of future wood for forest industry, destruction of biological diversity, changes in biogeochemical cycles and climate by altering local rainfall and hydrological processes, and desertification. The degradation of vegetation cover has caused northern areas of some West and Central African countries that were previously under forest to alter to savanna grasslands and degraded savanna. Investigations show that Africa contributes to gas emissions from biomass burning more than other tropical region of the world (Andreae, 1990). Although most of the biomass burning occurs in a small number of countries and in dry regions, there is an uncertainty in the geographical locations and the type of the forest (closed or open forest) being burned. Given this uncertainty, the estimate of forest biomass burned in tropical Africa because of deforestation is about 130 MT dm (megatons dry matter) per year (Delmas, et al., 1991). This estimate depends on three parameters: the area annually cleared, the average phytomass, and the burning efficiency. There is an increasing demand to reduce the uncertainty in all three parameters, and monitoring land use changes and deforestation is a key element of this process. By improving this estimate, our understanding of the rate of trace gas release to the atmosphere, and the current status of global climate models, can also improve.

In general, it is difficult to establish how big the problem of deforestation is neither it is easy to say categorically why the forest is being cleared because the reasons may vary from one area to another and may change through time. A coordinated program has been designed to conduct rigorous comparative studies at the regional and local scales to understand the wide diversity of causes for tropical deforestation and to identify cross-national and regionally specific types of deforestation by quantitative measures (Turner et al., 1994). These studies depend heavily upon remote sensing data from high resolution satellite imagery and aerial photography backed up by field data (Myers, 1988; Skole and Tucker, 1993; Moran, 1994; Skole et al., 1994). Satellite data provides large scale quantitative data for estimating the extent of deforestation and other forms of land cover changes. Landsat Thematic Mapper (TM) has been the most effective instrument for deforestation studies and mapping land cover types (Malingreau and Tucker, 1985; Skole and Tucker, 1993). Currently, Landsat TM data are used for routinely estimating the rate of deforestation in the Amazon basin (INPE, 1992). Similar efforts are underway to study the deforestation in African tropical region. The NASA Landsat Pathfinder program has been collecting Landsat images for 1970s, 1980s, and 1990s period over Central Africa. However, due to continuous cloud cover the frequent acquisition of data has been impeded for some areas, making the estimation of the annual rate of deforestation in Africa is a difficult if not an impossible task (see Figure 1).

Synthetic aperture radar (SAR) data can be used to circumvent this problem. Moreover, it has been shown that SAR images are useful for land cover classification because of their sensitivity to vegetation structure and biomass (Saatchi et al., 1996a; Rignot et al., 1996). In late 1995, JERS-1 (Japanese Earth Observation Satellite) entered into its tropical rain forest mapping (GRFM) phase and has been collecting high resolution data (12.5 m resolution, L-band, HH polarization) over the entire tropical rainforest including West and Central Africa. The JERS-1 coverage of African rain forests consists of about 4000 frame images (75 km x 75 km) for two periods of high and low flood seasons that can be used to map general vegetation classes and areas of deforested in the region.

1. To improve the current estimates of deforestation in the Central African tropical region by producing a mosaic land cover map (100 m resolution) of each country in the region using the JERS-1 high resolution (12.5 m) image data.

2. To integrate the resulting land cover map in a socio-economic comparative study in order to draw conclusions on the general patterns and causes of deforestation in countries of the region.

3. To integrate the JERS-1 images with the Landsat Pathfinder data in order to fill the gaps in Landsat TM coverage of 1990s and improve the 1990s regional forest cover map.

Tropical forest of Africa is 18 per cent of the world total and covers over 3.6 million square kilometers of land in West, East and Central Africa. This total area can be subdivided to 2.69 million square kilometers (74%) in Central Africa, 680,000 square kilometers (19%) in West Africa, and 250,000 square kilometers (7%) in East Africa (Sommer, 1976). In West Africa, a belt of rain forests up to 350 km long stretches from the eastern border of Sierra Leone all the way to Ghana. In Ghana the forest zone gradually dissipates near the Volta river, following a 300 km stretch of Dahomey savanna gap. The rain forest of West Africa continues from east of Benin through southern Nigeria and officially ends at the border of Cameroon along the Sanaga river. FAO study divides the two regions along the national border of Cameroon for practical purposes, however, the distribution of many West African plant and animal species ends at the Sanaga river (Richards, 1979; White, 1983; Hamilton, 1984).

Schematically the rain forest vegetation of the Guinea-Congolian transition region, extending from Senegal to western Uganda are constituted of two main types:

1. The semi-deciduous rain forest which is characterized by a large number of trees who lose their leaves during dry season. This type of forest appears in areas where the dry period (rainfall below about 100 mm) reach three months (White, 1983).

2. The evergreen or the semi-evergreen rain forest is climatically adapted to slightly more humid conditions than the semi-deciduous type and is usually present in areas where the dry period is shorter than two months. This forest is usually richer in legumes and variety of species and its maximum development is around the Baie of Biafra, from east Nigeria to Gabon (Figure 2), and with some large patches more to the west from Ghana to Liberia and to the east of Zaïre-Congo basin (White, 1983).

The most recent vegetation map of Africa was published by UNESCO (White, 1983), and the main vegetation features of Central African rain forest can be divided to the following categories:

1. Dense Forests: This category comprises of dense humid evergreen forests, dense humid deciduous forests and dense dry semi-deciduous forests. According to Lebran and Gilbert (1959), the lowland and central part of the Congo basin is covered by ombrophilous equatorial forest with deciduous upper-strata trees in well-drained sites and evergreen lower-strata trees at wetter valley bottoms. This type of forest does not show any noticeable seasonal behavior. At the border of the central basin is the mesophilous semi-deciduous forest with mixed deciduous and evergreen trees in the upper-stratum, irregular age distribution, continuous shrub stratum at the lower canopy, and a more marked seasonality (Mayaux and Malingreau, 1996).

2. Secondary Forests: Outside the forest reserves, much of the remaining part of Central African rain forest on well drained soils are old secondary forests. There also exit younger secondary forests dominated by parasol tree, Musanga ceropiodes . This is the most abundant and characteristic secondary forest in Africa. These trees can be found in upper layers of secondary regrowth along the old road networks in Zaïre . This pioneer species have a rapid height growth in the clearings (up to 15 m in 3 years) and their water content and photosynthetic activities are continuously high. The distribution of secondary forests are important in regional analysis because they show different floristic and faunistic characteristics than primary forests, and represent centers of human activity and history of land-use changes (Mayaux and Malingreau, 1996).

3. Savanna: The forest at the northern and southern limits of the rain forest domain has been destroyed by cultivation and fire and replaced by grassland which often occurs in mosaic with patches of original or secondary forests. The boundary of forest and savanna may change depending on the fire intensity (invasion of forest during reduced fire intensity and retreat of forest during increased fire intensity). This mosaic can be further subdivided into savanna with sparse forest blocks, savanna with gallery forest and forest with enclosed savanna. Gallery forests are tree formations developed along the river banks. In the north of the Congo basin this transition is very sharp and gallery forests cover a large portion of the transition zone. In the south, however, this transition zone is more diffuse due to the presence of woodland "Miombo" which is an intermediate type (Hamilton, 1984; Mayaux and Malingreau, 1996). Further away from the transitional zones to the north or south, the forest/savanna mosaic is replaced by savanna.

4. Non-forest: The non-forest category includes degraded lands, shifting agriculture, and plantations. This class represents both the deforested lands and fragmented forests. Plantations have a geometrical lay-out with uniform canopy cover and follow a different vegetation cycle as their surrounding features. These areas are located near the transportation networks and are dominated by economic tree crops such as cacao, rubber, oil palms and Kola.

5. Swamp and Flooded Forests: Swamp forests , inundated forests in floodplains, and riparian forests are treated collectively here. Swamp forests are found extensively in the Zaïre basin and throughout the Congo basin where conditions are suitable. In most areas, swamp forests is similar in appearance to rain forest and the tallest tress attain a height of 45 m. The main canopy is often irregular and open and sometimes resembles the secondary forests caused by disturbance (White, 1983). The forest has a diversity in endemic flora but it is poor in species. Recently, large areas of swamp forests have been cleared for rice farming. Swamp forests in Zaïre, Congo and other lowland forests have seasonal variations that correspond to the level of forest inundation.

The term deforestation refers to complete destruction of forest canopy cover through clearing for agriculture, cattle ranching, plantations, or other non-forest purposes. Other forms of land-use changes such as forest fragmentation (altering the spatial continuity and creating a mosaic of forest blocks and other land cover types), and degradation (selective logging of woody species for economic purposes that affects the forest canopy and the biodiversity) are often included in estimating deforestation. The characterization of forest into one of these categories depends on the temporal and spatial scale of observation. A small farmer's plot may be seen as deforestation in local scale but a forest fragmentation in regional scale. At the same time, if the density of crops is low enough to keep the forest ecological function intact and the return period is long enough to allow regeneration, then shifting cultivation may not imply deforestation. The subjective meaning of the term deforestation is thus not only linked to a value system but to the nature of the measurement designed to assess it (Poor, 1976; Mayaux and Malingreau, 1996). Adopting different perspectives of deforestation in data analysis have caused considerable variations in estimation of the area of forest cleared.

The tropical forest of Africa is 18 per cent of the world total and covers over 20 million km2 of land in West and Central Africa. This region has been facing deforestation with various degrees of intensity throughout this century. The actual pace of deforestation varies from one country to another and accurate data do not yet exist. Recent estimates indicate that the annual rate of deforestation in the region can vary from 15,000 hectares in Gabon to 290,000 ha in Cote d'Ivoire (FAO, as of 1980). Remaining tropical forest still cover significant areas in Central Africa but are reduced to patches in West Africa. Table 1 and 2 show the latest estimates of forest area and forest loss rates for Central African countries for which data are available (FAO/UNEP, 1988).

Table 1. Distribution of closed broad-leaved forests and fallow (deforested) in Central Africa at the end of 1980 (FAO/UNEP, 1988)

Central Africa	Forest (km2)	Fallow (km2)
Cameroon	179200	49000
CAR	35900	3000
Congo	213400	11000
Equatorial Guinea	12950	11650
Gabon	205000	15000
Zaïre	1056500	78000
Angola	29000	48500

Table 2. Distribution of closed broad-leaved forests and fallow (deforested) in Central Africa at the end of 1985 (FAO/UNEP, 1988)

Central Africa	Forest (km2)	Fallow (km2)
Cameroon	175200	52800
CAR	35600	3300
Congo	212300	12000
Equatorial Guinea	12800	11800
Gabon	204250	15750
Zaïre	1047500	85500
Angola	26800	50000

In general, estimates of deforestation in African rain forests suffer from lack of accurate maps and up-to-date survey data. In many of the countries in the region, the rate and patterns of clearance vary rapidly depending on several socio-economic factors. The time scales used in measurements and the availability of accurate baseline surveys in some countries against which to compare recent changes are important for assessing the deforestation on a regional scale. It is possible to obtain deforestation estimates using tabular summaries from standard government census sources. However, these data do not exist in all countries in the region and if available, they do not directly report the area deforested, but instead they provide estimates of land in various forms of permanent and temporary agriculture and pasture. These data can be used as a proxy for deforestation but cannot provide a direct measure.

Remote sensing holds much promise for revealing exactly where and when deforestation is taking place and for providing baseline information for assessing land-use changes. In recent years, the use of satellite imagery has shown that earlier statistics are not reliable and are open to many errors and distortions. Analysis of remote sensing data suggest that the overall rate of loss of rain forests (204,000 km2) is over 80 percent higher than the estimates by FAO in 1980 (114,000 km2) (United Nations Environment Program, 1987). There have been a number of applications of remote sensing to estimate deforestation rates and total area cleared over large regions (Tucker, 1984; Nelson and Holben, 1986; Malingreau and Tucker, 1988; Skole and Tucker, 1993). Although initial utilization of regional estimates diverged widely, they converged in recent years as a result of a wider utilization of high-resolution (< 100 m) imagery (Townshend and Justice, 1988). Deforestation has been quantified using Landsat TM imagery over the entire legal Amazon (Sokle and Tucker, 1993) and in southeast Asia through NASA's Landsat Pathfinder project (Chomentowski et al., 1994). Over Africa, JRC TREES (Tropical Ecosystem Environmental Observation by Satellites) project has been using a multi-satellite observation to map land-cover and deforestation in Central Africa (Mayaux and Malingreau, 1996; TREES, 1995). Radar remote sensing technology has also been used in tropical forest studies primarily for their mapping purposes (TREES, 1995). Imaging radar instruments can acquire continental-scale data sets without being constrained by cloud cover. In addition, they can provide information about the density, structure, biomass, and understory vegetation in forests and the type of clearing practices because radar signal probe the larger size constituents of the forest and penetrate deep into the forest canopy. Results from the analysis of Shuttle Imaging Radar SIR-C/X-SAR, JERS-1 and ERS-1 data indicate that the imaging radars can map deforestation in the tropical rain forest environment and when combined with optical data can improve the detection of secondary growth and plantations(Saatchi et al., 1996; Rignot et al., 1996; TREES, 1995; Sader, 1987).

Approach

JERS-1 images acquired during the GRFM phase of the satellite are being processed and will be delivered to JPL and JRC as the PIs of the NASDA project over African rain forest. The entire African rain forest was mapped by about 2000 scenes between January and March of 1996. Another data acquisition is scheduled for October-November 1996 to map the high flood area of the Congo basin in order to provide a seasonal variability of flooded forests. Landsat images are available for 1970s, 1980s, and 1990s years. In areas that the 1990 Landsat images are not available, JERS-1 will help to fill the gaps, and in other areas it can improve the classification of Landsat data for generating regional forest map. In addition, the Africa mosaic of ERS-1 data will be available through institutional agreements. We will investigate the use of this data set in interpreting the JERS-1 data or improving the classification accuracy. A brief outline of our approach follows.

Data Processing and Management

The analysis of the JERS-1 SAR data requires a number of interrelated processing tools that can be applied to a large volume of data in order to make feasible the extraction of thematic information from the signal. These tools function at various levels. The first layer processes data to correct the geometry and radiometry, reduce spatial sampling and increase the signal to noise ratio. The image files received from NASDA are processed to 12.5 m pixel spacing and will go through the following chain of processing tools:

1. Speckle Filtering: An adaptive speckle filter based on statistic signal processing that will generate an estimated radar backscatter image with mean radiometry, and strong scatterers and fine edge preservation. This process will improve the signal to noise ratio.

2. Calibration: The image data will be absolutely calibrated using external targets. The image products will then be corrected for the variable ground range pixel size with respect to incidence angle.

3. Spatial Filtering: To reduce the data volume for the image mosaic and enhance the signal to noise ratio, the spatial resolution will be compromised. This filter will cut higher frequencies in space to abate the speckle. The resampling will performed in an adaptive way using wavelet theory in order not to destroy the natural features of interest. The product derived from this process is an amplitude image with 100 m pixel spacing.

4. Texture Extraction: Spatial filtering of the SAR data will cause the loss of texture or local statistics. Since tonal averages are not adequate to separate land-cover types of interests, texture information can be used as part of the feature space in the classifier. We propose to use several first and second order texture features to test the contribution of each texture feature in the classification mechanism over some test sites. A suitable distant measure will be employed to choose features to maximize the discrimination of land-cover classes (Soares et al., 1996; Baraldi and Parmiggiani, 1995). We intend to select two most important texture features from the original high resolution data (12.5 m) in the reduced 100 m resolution to be stored along with the mean backscatter data for further data analysis.

Land-Cover Classification

The choice of land-cover classes depends on two factors: (1) the importance of the classes in the global change and land-use change studies, 2) the characteristics and the dynamic range of the JERS-1 SAR backscatter data. The general categories of the land-cover types for global change ecosystem process studies have been suggested by the core project of the International Geosphere Biosphere Program (IGBP, 1990). We propose to use these categories as our guideline to study class reparability in JERS-1 images. The use
of texture and backscatter data suggests that six general classes can be separated in JERS-1 data: forest, secondary forest, nonforest, savanna, flooded forest and swamps, and open water. Other classes such as pasture and crop lands and some stages of secondary growth within the rain forest region are important in land use studies but may not be reliably distinguished in the one band JERS-1 data.

We propose to implement a supervised MAP (Maximum a posteriori Baysian) classifier based on the a priori knowledge of tonal and texture features of the radar backscatter. The training areas will be selected among various types of land cover visually separated in JERS-1 backscatter images over test sites. Labeling of training sites into land cover types will be based on the classes identified in Landsat TM images, existing maps and field data over test sites, and the knowledge of the radar scattering from vegetated terrain. For example, the areas of lowest radar cross section are related to either the deforested areas or grassland savanna, or high radar cross section may correspond to inundated/swamp forests and urban areas. Texture information extracted over these areas can help to identify them more accurately. The classifier has been tested on JERS-1 mosaic backscatter image acquired over the Amazon rain forest (Figure 4). In this study, we propose to apply the classifier on individual images and generate 100 m resolution land cover maps. These maps will be georeferenced, projected and used to form a land cover mosaic map over the entire Central African rain forest.

We have chosen test sites to verify the accuracy of land cover maps. These sites cover a wide range of land cover types: areas representing a cross-section of geographic locations, deforestation and land use practices, undisturbed regions of ecological importance, forest fragments under biodiversity and conservation studies (protected areas and wildlife reserves), areas within the flooded forest of the Congo basin, and areas overlapping the IGBP transects and NASA Landsat pathfinder test sites. We have established the initial contacts and intend to work closely with the IGBP Core projects, NASA Landsat pathfinder, TREES projects, The World Bank environmental and policy departments, and several conservation societies in order to valid the local and regional vegetation maps. The test sites will be chosen from areas where Landsat TM date are available. Depending on the time of data acquisition and intensity of land-use changes, TM images can be used either in the classifier or as for validation purpose. The list of potential sites for validation of the regional land cover map and the correspoding collaborating individuals and agencies are given in Appendix A.

On the regional scale, we will compare and verify the land cover map with the AVHRR derived vegetation map of Central Africa produced by TREES project (Laporte, 1995; Mayaux and Malingreau, 1996). The comparison will allow us to produce a more accurate map of the region where ambiguities in forest-savanna and secondary growth cover types in AVHRR vegetation map are potentially removed.

Socio-economic Analysis of Deforestation in Africa

A. Analytic Context and Approach

Deforestation is driven by social and economic dynamics. The prevailing models rely largely on demographic and market factors to explain clearing, in part because these data are collected by governments, and seem to correlate with land use change. These models tend to view the evolution of clearing patterns as straightforward linear processes that diffuse through the landscape. Recent scholarship on tropical deforestation has, however, brought more complex, socially nuanced analyses to bear. This research has focuses on how social factors organize land use through mediation of markets, institutions, communities and the political economy of local, national and international actors. These approaches, which now fall under the rubric of the political economy of natural resources, or political ecology, have strong explanatory powers and thus are far more useful for understanding the outcome and options of policy and the processes of tropical development. Correlative demographic models while powerful statistically, reveal little about dynamics, equity issues or potential sustainability of land uses. In the main such models generate unsophisticated policy tools, since in many cases the drivers underlying deforestation may have little to do with population (Hecht, l989; Angelson, l995; Repetto l985; Berry, l993; Bassett and Crummey, l993)

Recent studies examine the logic and dynamics of deforestation at several different levels and introduce many innovations. At the international level, recent scholarship has emphasized the role of structural adjustment and the need for international currencies, debt dynamics, and the increasing internationalization of markets to understand how these potentially effect patterns of deforestation. (Lean et al l990). The surge of deforestation in Cameroon largely reflects the need for debt repayment, while the demand for rubber has actually intensified the maintenance of forest cover in Indonesian small holder settlements (Angelsen l995).

At the national level, research emphasizes the role of macroeconomics policy ( and its inherent political interests) in stimulating deforestation by its ability to distort market functions. Logging in Indonesia, livestock in Amazonia, Cacao in the Ivory Coast are classic examples of policy distortions driving clearing (with very inequitable social as well as environmental consequences) (See Repetto and Gillis l988, Hecht and Cockburn l989, Peluso l993) Another dimension is the state's role in guiding the colonization into humid tropical zones for various reasons, including revenues, geopolitical concerns, as a means of defusing political conflict, and as a counter insurgency tactic (Chazen l988, Bates l981, Hart l982). National kleptocracies may find it very useful to permit predatory exploitation of tropical resources by political cronies, a problem often observed in undemocratic regimes such as that of Nigeria and Zaire (MacGaffey, l987, l991).Environmental policy is also a factor, but an equivocal one since the way it is implemented can trigger resistance which can result in sharp clearing and resource poaching (Western et al l995).

At the local level several dynamics affect deforestation, including those linked to institutions, resource control and acquisition, as well as to subsistence. In cases of unclear property rights in contexts of limited local sanctions and powers, deforestation to claim land and its ancillaries (timber, minerals etc.) often occurs. (Berry l988). But, it is important to also assert that local populations may inhibit such incursions by invoking the claims of traditional rights. The arena of property rights is thought to be significant in deforestation processes, although how property regimes unfold in clearing remains contradictory (Berry, 1988).

Widely cited as the dominant driver of tropical deforestation, (Bandy et al, l994) in many contexts this system of successional management may actually be among the most important means of intensifying tropical agriculture in humid tropical settings. A great deal of data from Asia, Africa and many Latin American countries points to spontaneous intensification of land uses by shifting cultivators responding to market opportunities for forest products (Hecht et al l996, Angelsen l995, Guyer, and Lambin l993) Dvorak l991.

The complexity of deforestation processes is further compounded by the fact that several mechanisms may be operating at the same time. Given the relative paucity of detailed studies of the dynamics of deforestation in Africa, (especially when compared with Latin America and Asia), the research we outline has the potential :

1. to expand the regional knowledge about deforestation;

2. to generate an abundance of comparative data;

3. to inform public policy.

Step 1: Historical Patterns of Deforestation

The countries of central and West Africa that are the subject of this research are extremely heterogeneous in their ecology, ethnic diversity, colonial history, political structures, economic configurations, institutional arrangements and development approaches to mention only a few of the variables that play out in the region. Any meaningful analysis of such diversity requires both inductive and deductive techniques. This involves explaining deforestation patterns as they have developed using the international, state and local approaches outlined above to generate a recent historical, policy, institutional, and economic analysis for each relevant area to construct a general environmental history of regional forests. Archives and current remote sensing data can provide more precise data on land use and land cover change. This exercise provides the background for more detailed hypothesis development, which will be established in consultation with the entire research team in such a way that social analysis, the remote sensing archives and current images are used to their maximum advantage. Finally policy analysis and suggestions can be from these efforts.

Step 2. Hypothesis development and evaluation.

This section will be developed with a concern for understanding economic, social and environmental policy effects on deforestation patterns in the region. While the exact questions cannot be finalized at this time, certain kinds of questions already suggest themselves because they reflect policy changes that should be detectable in changes in land use.

1. With structural adjustment programs and neoliberal reforms of national markets, many countries with marketing boards for tropical tree crops such as Cacao and coffee have eliminated them. Has this affected patterns of deforestation?

2. Do countries with higher debt burdens have higher rates of deforestation whether for export, annual crops or timber.

3. Are there differences in deforestation rates among countries with differing political formations (i.e. authoritarian, democratic, or socialist).

4. Many countries of West Africa have embarked on policies of control of shifting agriculture, and indeed these are part of a major GEF (Global Environmental Facility) initiative. In those countries of special priority in Central Africa (Cameroon, Gabon, Zaire) what is the magnitude of shifting agriculture's contribution to deforestation as compared with logging and commercial agriculture, and are policies to control SC having an effect on deforestation patterns?

5. Do national environmental programs affect clearing patterns in general, and specifically near parks.

The combination of analytic models with the current and archival empirical data generated from remote sensing should provide some exciting insights into policy performance. Since this work will not be uniquely "at a distance", and will incorporate a complex analysis of policy and its context, we are confident that this study can make major contributions to the study of the technical dimensions of deforestation monitoring, the understanding of the social dynamics of clearing, and the performance of policy in mediating the patterns of cutting we observe

Expected Results

1. Central African Regional mosaic of JERS-1 image data with 100 m spatial resolution. The mosaic image is the calibrated and georeferenced radar backscatter data. The mosaic image will be provided in the CDROM format and includes the individual post-processed 100 m resolution image data.

2. For each backscatter image file two texture features will be provided in order to preserve the information lost as a result of spatial filtering of original data. The texture feature can be used for classification and further analysis of JERS-1 SAR data.

3. Central African regional vegetation map based on JERS-1 data at 100 m spatial resolution. Along with the map, a set of documents about the validation and accuracy of the classification procedure will be provided.

4. We will also provide georeferenced JERS-1 image data and vegetation maps for each country in the region to be used for national land-use and deforestation assessment. In addition, the total area of forest cover and deforested will be computed for each country.

5. A final regional scale vegetation map of Central Africa with 1 km spatial resolution derived from JERS-1 and AVHRR based vegetation maps will be produced.

6. A final report summarizing the methodology, data processing steps, map validation, and field data for each test site and the general data for socio-economic analysis will be published jointly by the investigators.

Management and Cost Plan

The proposed work will have three phases to complete: 1) post-processing the data and generating a geocoded mosaic of 100 m resolution image data, development of classification algorithms, and accumulation of ancillary data for land use and socio-economic studies, 2) implementing the classifier over the images and validation of maps by incorporating field data maps acquired by our collaborators in Central African countries, 3) geocoding the land cover map and determining the general patterns and trends of deforestation and land use over the entire region. The project will require three years, starting January 1997, to complete. The socio-economic research will be performed on local and regional scales as maps and information over test sites and region become available. A portion of the data required for socio-economic research will be provided by World Bank, international environmental and Central African national institutions (see Appendix A).

Travel costs are for trips to Washington for science team meeting and the use of libraries and research facilities at the World Bank. If necessary, we will have site visits to coordinate the proposed study with our collaborators, acquire field data, maps and sociological and land-use information from national institutions, and provide educational material.

S. Saatchi-(PI) Task management, development of classification algorithm, validation, geocoding and producing regional and national land cover maps.

E. Njoku-(Co-I) Data analysis and validation.

S. Hecht-(CO-I) Socio-economic analysis.

F. De Grandi-(Co-I) Post-processing and generating geocoded JERS-1 mosaic image data, classification algorithm.

J-P Malingreau-(Co-I) Validation, and coordination with IGBP and TRESS projects, comparison with regional AVHRR-based vegetation map.

M. Massart-(Co-I) Validation of results using Landsat TM and field data, coordination with NASA Landsat Pathfinder/Central Africa program.

This work has not been funded by NASA or any other institutions. The NASDA GRFM project will provide JERS-1 data without any costs to investigators (JPL and JRC). The level of effort will be divided among JPL, UCLA, and UMD (partially funded by NASA Landsat Pathfinder Program). The effort of JRC will be funded by TREES project. Here, a summary of the required budget is outlined for each year. The fringe benefits and burdens are included in the costs. The detailed cost plan for each institution is provided in Appendix B.

Cost Plan Summary

Jet Propulsion Laboratory	1997	1998	1999
S. Saatchi (P-I), 0.35 AY	36.0	37.6	39.6
Contractor 0.6 AY	35.6	36.8	38.8
3 science team meetings and three site visits, once/year	13.0	13.0	13.0
Computer hardware and software	20.2	3.0	13.0
Publications	2.0	2.0	4.0
Indirect costs	50.9	50.9	54.2
Total	159.0	143.0	162.5
University of California
S. Hecht (Co-I) , summer	22.3	23.5	24.6
Graduate Student Research Assistant	9.0	9.5	9.9
3 trips to Washington, 1 Site visit	3.0	14.1	3.3
Indirect costs	16.8	23.1	18.6
Total	51.3	70.2	56.5
University of Maryland
M. Massart (Co-I), 0.1 AY	6.6	6.8	14.3
1 Site Visit		14.9
Research Material	4.5	4.5	5.9
Total	11.1	26.1	20.2
Annual Total	221.4	239.3	239.2

References

Appendix A

Appendix B