Available online at Received 15 November 2006; received in revised form 18 July 2007; accepted 21 July 2007

Available online at www.sciencedirect.com Remote Sensing of Environment 112 (2008) 1969 1981 www.elsevier.com/locate/rse Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts G. Duveiller a,, P. Defourny a, B. Desclée a, P. Mayaux b a Department of Environmental Sciences and Land Use Planning, Université Catholique de Louvain, 2/16 Croix du Sud, B-1348 Louvain-la-Neuve, Belgium b Institute for Environment and Sustainability, Joint Research Centre of the European Commission, Ispra, Italy Received 15 November 2006; received in revised form 18 July 2007; accepted 21 July 2007 Abstract Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 10 km sampling sites systematically-distributed every 0.5 over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest. 2008 Elsevier Inc. All rights reserved. Keywords: Tropical forest; Land cover change; Habitat monitoring; Systematic sampling; Object-based image processing 1. Introduction 1.1. Rationale for monitoring tropical forests Tropical forests, although covering less than 10% of the total land surface of the Earth, represent the largest terrestrial reservoir of biological diversity, from the gene to the habitat Corresponding author. Fax: +32 10478898. E-mail address: gregory.duveiller@uclouvain.be (G. Duveiller). level. The value of forests is becoming increasingly evident to the world's population. The importance of their role in our planet's functioning is clearly reflected in Multilateral Environmental Agreements (MEA) such as the Convention on Biological Diversity (CBD) and the United Nations Framework Convention on Climate Change (UNFCCC). However, tropical forests suffer from rapid land use changes (Mayaux et al., 2005). Agricultural expansion, commercial logging, plantation development, mining, industrialization, urbanization and road building are all causing deforestation in tropical regions. These 0034-4257/$ - see front matter 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2007.07.026

1970 G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 land use changes, in respect of other factors like climate, nitrogen deposition, biotic exchange or carbon concentration, are the only really significant processes that have an impact on biodiversity reduction (Sala et al., 2000). The Conference of the Parties of the CBD has adopted in 2002 the 2010 Target, which is to achieve by 2010 a significant reduction of the current rate of biodiversity loss at the global, regional and national levels. Accurate land cover change estimates are among the headline indicators set to evaluate the habitat conservation progress towards its 2010 target (Balmford et al., 2005). While many industrialised countries have set up detailed national forest inventories, assessments of tropical forest distribution and evolution at a global scale have only received the attention of the scientific community since the early 1990s. Current operational programs are still diverging in terms of methods and results (Achard et al., 2002). More specifically, the magnitude and the location of deforested area estimates are influenced by the type of approach employed to monitor geographicallyspecific patterns of the forest change. 1.2. The tropical forest in Central Africa Central Africa contains the second largest area of contiguous moist tropical forest of the world, covering about 2 million km 2 (Mayaux et al., 2006). The Congo Basin in particular includes vast and still uninterrupted tracts of rainforests from the Gulf of Guinea to the Albertine Rift. Salient features are the presence of the world's largest area of tropical swamp forests in the central part of the Congo Basin, and of two mountainous regions in Cameroon and in Eastern Democratic Republic of Congo. Central African humid forests are inhabited by a rural population of approximately 12 million people sparsely distributed in low density communities (6.5 inhabitants per km 2 ) and in some urban settlements. Around 80% of the rural population are slash-and-burn cultivators, while hunter-gatherers and fishermen represent about 20% of the population (Joiris, 1997). For the Central African countries, timber production represents a significant part of export incomes. The main ecological gradients that determine the patterns of biological diversity are oriented according to four main drivers (Vande weghe, 2004): latitude (from the evergreen forests at the equator to semideciduous at the fringes of the Basin), proximity of the ocean (with cloudy and per humid coastal areas to the more insolated and drier eastern part), elevation (with mountains in Cameroon and Eastern Democratic Republic of Congo surrounding large depressions) and soil (from low drainage hydromorphic soils in the Cuvette to Kalahari sands). For the Guineo-Congolian region, which includes the two main rainforest blocks (Central Africa and West Africa), the richness in flowering plants is estimated at 8000 species (White, 1983; Plana, 2004; Vande weghe, 2004) out of which 80% are endemic. The main centers of species richness and endemism are in western Cameroon and in central Kivu. Concerning animal biodiversity, Central Africa is first known by its emblematic species, such as gorillas, chimpanzees, bonobos, elephants and buffalos. The Congo Basin forests host about 400 species of mammals, 1000 species of birds, 200 species of amphibians, 300 species of reptiles and more than 900 species of butterflies. The main centers of animal endemism are the coastal part (South Cameroon, Equatorial Guinea and Gabon) and the Albertine Rift (Boitani, 1999; Frost, 2006). It is currently believed that, with the exception of localized clearings, this central region has low deforestation rates (Mayaux et al., 2003). This situation can be explained by the absence of a significant local market for wood products and a poor transportation infrastructure. However, coastal Central Africa has experienced more intensive forest exploitation. Here, population growth, agricultural expansion, as well as emerging marketing opportunities, have exerted a strong pressure on forest resources. In such a regionally variable context, forest management and conservation issues in Central Africa are being debated anew since the mid 1990s. The Congo Basin Forest Partnership (CBFP) was established in September 2002, at the World Summit on Sustainable Development (Johannesburg, South Africa) and brings together some thirty governmental and non-governmental organizations. The goal of the CBFP is to improve coordination between projects and policies in order to enhance the sustainable management of the Congo Basin forests and improve the standard of living of the region's inhabitants. The CBFP has endorsed the concept of ecological landscapes, defined by conservation non-governmental organizations under the initiative of World Wide Fund for Nature (WWF). These priority areas, covering about 700,000 km 2 (36% of the Congo Basin Forest) capture the majority of essential terrestrial and aquatic biodiversity of the Congo Basin Forest and also provide a framework for management planning and implementation. The CBFP Landscape definition was based on goals of representativeness, population viability of main species, sustainability of ecological processes, and ecosystem integrity and resilience. The lack of up-to-date and accurate information on the current state of forested areas in Central Africa has often been cited as a limitation in the design of efficient forest management policies. The last national forest inventories were conducted in the early 1970s for most of the countries of the region. Efforts to improve regional and national capabilities to address the problem of forest and land use monitoring have thus received particular attention in recent years (Laporte et al., 1995; Mayaux et al., 1998). Although tropical forest monitoring has already benefited from remote sensing at global scales, the major challenge is yet to accurately capture local forest change dynamics at sub-continental scale. Such detailed land cover change analyses, conducted over very large extents, are necessary to derive regional and national figures for sustainable forest management and multilateral environmental agreements. 1.3. Estimating deforestation Currently, deforestation estimates are derived either from coarse to medium resolution imagery (Hansen & De Fries, 2004; Carreiras et al., 2006) or from wall-to-wall coverage of limited area (Sanchez-Azofeifa et al., 2001; Guild et al., 2004). Whereas the first approach cannot detect small forest changes

G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 1971 widely spread across a landscape, operational costs limit the mapping extent in the second approach. Sampling high resolution imagery could be a reasonable compromise. The Forest Resources Assessment (FRA) realized a pan-tropical survey of forest cover changes based on a stratified random sampling scheme using 117 sampling units whose size corresponds to a complete Landsat scene, i.e. 185 185 km (FAO, 2001). The sampling efficiency can be increased significantly by using small image extracts as sampling units and having them systematically (rather than randomly) distributed over the forest domain. Furthermore, the FAO photo-interpretation can be much improved in terms of efficiency and robustness thanks to advanced image processing techniques. Among all change detection techniques reviewed by Lu et al. (2004) and Coppin et al. (2004), methods measuring deforestation using fine resolution satellite images at regional scale are mainly based on either post-classification comparison or visual analysis. For wall-to-wall coverage, complete image scenes of different dates have been classified independently and subsequently compared to produce change maps (Steininger et al., 2001; Zhang et al., 2005). The accuracy of these change maps is totally dependent on the accuracy of the initial classifications. Visual analysis of multi-date images has the capacity to rapidly overcome the complexity of land cover change by incorporating key elements such as texture, shape, size and patterns through the skilled analyst's image interpretation. Such an approach has been employed using interdependent visual interpretation of image prints (FAO, 2001) and digital colour composites (Achard et al., 2002). However, the on-screen change delineation is still time-consuming and the approach is subject to interpreter bias. Thanks to the recent improvements in image segmentation, object-based approaches can be used to delineate and classify land cover efficiently. Based on multi-date segmentation, the change detection method developed by Desclée et al. (2006a) has proved its capability to detect forest changes in temperate regions. Object-based techniques such as image segmentation and object-based unsupervised classification have the potential to facilitate visual interpretation. 1.4. Research objectives The present paper aims to demonstrate the effectiveness of advanced image processing techniques for monitoring deforestation in the humid tropics using high resolution imagery at sub-continental scale. First, it proposes new avenues for accurate and cost-effective deforestation estimates at local level by using object-based multi-date unsupervised classifications. Second, these new tools are applied on a very large sample of satellite images over the Central Africa forests to derive national and regional statistics that can be directly used in the reports for the MEA. Third, a spatial analysis was conducted to verify the level of protection in landscapes of high biodiversity identified by conservation organizations. These methods provide information on two main indicators asked for the 2010 target, i.e. the habitat changes in tropical forests and the effectiveness of protected area management. 2. Methodology Forest cover change measurements need to take into account the spatial characteristics of change processes in Central Africa, specifically: Zones of deforestation are relatively small, and their measurement requires data at an appropriately fine spatial scale. Forests are extensive but deforestation is not uniformly distributed in time and space. Change is a distinctly nonrandom process. Large regions, mainly in coastal zones, are under quasipermanent cloud cover. The measurement time scale needs to be adapted to the forest cover change processes and to their spatial distribution. To respond to these specificities, the approach adopted in this study is based on a dense systematic survey of small sampling units where the forest cover changes are analysed at two different dates (ten year interval) at high spatial resolution. The idea is to measure, on every sampling site of the survey, the forest cover using the most recent image extract. For the sites where forest cover change can be observed between both dates, the change trajectory has to be identified and the area measured. Such an approach based on small systematically-distributed sampling units has already been proposed by the FAO for its future Forest Resources Assessment in 2010 (FRA-2010) which will be applied at global scale and include both boreal and temperate forests. Inter-sample and intra-sample heterogeneity is expected due to diversity in land cover types, and variable atmospheric and radiometric image quality. Since no automated change detection method was found to be as efficient as visual interpretation, it was integrated in the image processing protocol. The challenge is to fully exploit the singular property of image interpretation to rapidly overcome the complexity of land cover change and data heterogeneity while greatly reducing its disadvantages, namely the lack of objectivity and important time requisites. In order to do so, a dedicated processing chain based on multi-date image segmentation and object-based unsupervised classifications was designed to optimize the image visual interpretation to the most critical area: land cover labelling. 2.1. Data and sampling strategy The survey consists of 571 sample sites of 10 10 km separated by 0.5 intervals and regularly distributed over the forest domain in the Congo River basin. This represents a sampling rate of 3.3%. For every sample site, two co-registered high spatial resolution imagery extracts are available. Each pair is composed of a Landsat TM image extract acquired in 1990 (± 2 years) and a Landsat ETM+ image extract acquired in 2000 (± 2 years). The spatial resolution is generally 28.5 m and occasionally 30 m. The extracts are colour composites of the TM bands 3, 4 and 5 which correspond respectively to red, near infrared (NIR) and short wave infrared (SWIR). These bands

1972 G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 were considered to have the most relevant and contrasting information for land cover discrimination. 2.2. Land cover classes and change processes The land cover of the study zone, and particularly the areas where change processes occur, can be characterized with 10 classes: dense forest, degraded forest, long fallow and secondary forest, forest-agriculture mosaic, agriculture and short fallow, baresoil and urban area, non-forest vegetation, forestsavannah mosaic, water bodies and no data. These classes depend on the scale at which they are considered. Indeed, some of these classes are composed of variable proportions of different more elementary land cover classes. For example, the class degraded forest is composed of dense forest perforated by small logging clearings or crop fields. The class forestagriculture mosaic is composed of areas where forest patches and galleries are interwoven with agricultural land use. It is important to note that the forest patch in the forest-agriculture mosaic can have an identical spectral signature to a same-sized zone in the dense forest. However, these two identical elementary land covers are in an entirely different land cover context when observed with a smaller scale. An isolated block of forest that is just a few pixels in size and which is surrounded by agriculture cannot be considered as an equivalent land cover type as the vast extents of continuous dense forest. The land cover classification of the image extracts where no change is observed will be resumed to a general forest map. The forest of the binary map of the extracts where no change is observed corresponds to the dense forest and degraded forest classes aggregated together. The change trajectories that the 10 classes can undertake may be regrouped into four forest cover change processes. The main change process of interest for this study is deforestation. Deforestation is here defined as the conversion from dense forest and degraded forest to any other class. Reforestation is the inverse process, i.e. the conversion from any class to either dense forest or degraded forest. Other change processes that are considered in this study are forest degradation, i.e. the conversion of dense forest to degraded forest, and its counterpart, which will be designated as forest recovery. All forest cover change processes are characterized by an annual change rate. These rates are calculated by dividing the total changed area by the time period, typically 10 years, and by the total forest cover area averaged between the two dates. 2.3. Image processing The image processing chain relies on multi-date image segmentation and object-based unsupervised classification techniques to enhance the delineation objectivity, the interpretation repeatability and the processing efficiency of the Landsat extract pairs. A preliminary visual screening of the image pairs serves to identify the sample sites where change has occurred between the two dates. This data stratification removes change detection from the processing chain in order to focus the latter on forest cover change measurement. Afterwards, the image processing can be decomposed into three steps. First, a multidate image segmentation is applied on every pair of extracts. In this step, groups of adjacent pixels that show similar land cover change trajectories between the two dates are delineated into objects. In the second step, objects from every extract are classified by unsupervised clustering procedures. Finally, visual interpretation is conducted to label the classes and edit possible classification errors. Change over one sampling unit can then be measured by comparing the two multi-date classifications at that site. The flowchart on Fig. 1 summarizes the key steps in the image processing chain. 2.3.1. Visual screening The 571 image pairs were subjected to a first visual inspection whose main objective was to identify the sample sites where change has occurred between the two dates. The analyst decides, by comparing both images side by side, whether forest change has occurred or not. This visual screening sets a detection threshold taking into consideration image differences that can arise from other effects than land cover change, i.e. sensors differences, atmospheric conditions, vegetation seasonality, etc. This preliminary visual inspection not only serves to identify the images where change in the forest cover is observed between the two dates, but also to filter out those where the image quality is insufficient for proper digital processing and interpretation. When the 10 10 km samples crossed the Landsat scene boundaries, they were only analysed if the sample contained a substantial part of a single scene (i.e. samples never contained a mosaic of two Landsat scenes). For this study, all operations of visual interpretation were realized by the same analyst. The visual screening categorizes image extract pairs into four sets. The first group contains all images with poor quality which are not usable to estimate forest cover. These samples are discarded. The second group is composed of image pairs where a continuous dense forest cover is present at both dates. These sample sites do not require any measurement since a forest extent covering the entire sample site has remained undisturbed between the two dates. The third group is constituted by images of a mixed land cover landscape that have not undergone any forest cover change between the two dates. For these extracts, the objective is to measure the extent of the forest thanks to an automated procedure. The last group includes all pairs where forest cover change has taken place. For the extract in this last group, the processing chain results in two land cover classifications from which change will then be accurately measured. 2.3.2. Multi-date object delineation The delineation of land cover entities is realised by an automatic segmentation algorithm. Image segmentation is the process of partitioning an image into groups of pixels that are spectrally similar and spatially adjacent. Boundaries of pixel groups delineate ground objects in much the same way a human analyst would do based on its shape, tone and texture. However, delineation is more accurate and objective since it is carried out at the pixel level based on quantitative values. The segmentation

G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 1973 Fig. 1. Flowchart of the main steps in the image processing chain. algorithm is based on homogeneity definitions combined with local and global optimization techniques. It is implemented using the ecognition commercial software (Baatz & Schäpe, 2000). In the present case, delineation is extended to the temporal dimension by using multi-date segmentation (Desclée et al., 2006a). The segmentation algorithm is applied to both image extracts simultaneously, causing the grouping of contiguous pixels which have undertaken similar land cover change trajectories during the time interval, e.g. between 1990 and 2000. The multi-date segmentation partitions satellite imagery into homogeneous regions which are continuous in both space and time. The main objective of the multi-date image segmentation is to define small objects with robust spectrotemporal signatures that can undergo automated discriminant analysis. In the processing chain, multi-date image segmentation is applied on every pair of images at two different spatial levels. These two segmentation levels are hierarchically structured: the objects of the first segmentation are smaller and are nested into the objects of the second segmentation while every object of the second is composed of a set of objects of the first. Hereafter, the smaller objects of the first segmentation will be referred to as micro-objects while the larger ones of the second segmentation will be called macro-objects. The sizes of the micro-objects and macro-objects are respectively determined by statistical and mapping constraints. The micro-objects will be used in unsupervised classification procedures which require an important number of elements well characterised by robust statistics derived from the spectral values of the micro-object pixels. The first segmentation scale is therefore a compromise between

1974 G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 large number of objects and the minimum size of the elementary land cover units that can be characterised from the image. However, these elementary land cover units are too small to correspond to the classes of the proposed legend. The upscaling of the information to a layer of macro-objects is therefore necessary. The size of the macro-objects is constrained on the lower bound by a Minimum Mapping Unit (MMU) of approximately 10 ha. This figure was obtained empirically by testing, on a subset of typical image samples, several segmentations with different scale parameters and classifying the objects with the aforementioned legend in order obtain the most adequate land cover classification. The maximal size of the macro-objects will depend on the spatial heterogeneity of the landscape and on the type of land cover entities that has to be delineated on the specific sample site. 2.3.3. Object clustering Unsupervised classifications were performed in order to cluster the micro-objects delineated by segmentation into land cover classes. The strong inter-sample heterogeneity (due to sensor radiometric differences and atmospheric perturbations) renders supervised classification too time-consuming since training zones would have to be collected for every individual image. Two different unsupervised classification algorithms were used to cluster micro-objects depending on whether the image pair covers a changed region or not. For image extracts with no forest change, an automated forest/non-forest classification was performed to produce a binary mask. For the image pairs where change is observed, the ISODATA algorithm is used to classify micro-objects into clusters which will be subsequently labelled to produce a full thematic land cover classification for both extract dates. The automated forest/non-forest classification, developed by Desclée et al. (2006b), is applied to the image extract pairs when no forest cover change was observed. This classification procedure is based on two steps: (i) the automated identification of forest training sets and (ii) the forest/non-forest classification. A representative sample of forest micro-objects is defined automatically based on a no change detection procedure based on multivariate iterative trimming. This algorithm is applied on the whole set of micro-objects containing information from both image pairs and results in the identification of a selection of unchanged micro-objects. Under the assumption that the sample sites are dominated by forest which is stable over the time window (assumption that can be considered valid after the visual screening operation) these unchanged micro-objects correspond to a forest micro-object training set. Afterwards, the forest/non-forest classification discriminates forest from nonforest micro-objects based on their similarity to the spectral signature of the forest training set. For the image extracts with forest change, an ISODATA algorithm is used to classify micro-objects into clusters which will subsequently be labelled. The object clustering is based on the Red, NIR and SWIR reflectance values averaged at the micro-object level and on the standard deviation of NIR reflectance. These statistics were chosen because they represent, in simple and robust terms, the essential information for each object. The set of mean reflectances of every available band informs on the spectral signature of the corresponding land cover while the standard deviation provides information on the surface texture within the object. In order not to assign the same weight to textural and spectral information, standard deviation was only extracted for the most informative band: NIR. This operation is applied separately to each extract to result in two sets of 20 object clusters (i.e. the same object delineations are used to compute the respective statistics from the 2 extracts and produce 20 unlabelled classes for each of them). The two classifications will be quality-controlled and labelled through a visual examination. 2.3.4. Visual interpretation, upscaling and change measurement The examination of the false colour composite displayed at 1:75,000 scale allows rapid labelling of the micro-object clusters. Furthermore, by simultaneously checking both colour composites for a sample site, this on-screen examination serves also to verify the clustering output and correct obvious classification errors. Since the labelling is done on the micro-objects obtained by the finer segmentation, elementary land cover labels are used. Afterwards, it is necessary to class each macro-object based upon its composition of labelled micro-objects. This upscaling procedure is inspired from the use of macropatterns used as classifiers in the Land Cover Classification System (LCCS) developed by the FAO (di Gregorio, 2005). They define a macropattern as the horizontal spatial distribution of vegetation in a certain area. For the LCCS, a certain structural vegetation type has a continuous macropattern if, inside the mapping unit, it covers more than 80% of the area. A structural vegetation type can be considered to have a fragmented macropattern if, inside the mapping unit, it covers more than 20% but less than 80%. In this study, the macro-objects are the mapping units, and the macropattern is determined by the proportion of elementary land cover classes. The macropatterns are not limited to continuous and fragmented versions of the structural land cover class, but result in new mosaic classes. While the labelling of clusters is a straightforward procedure and the result easily quality-checked, manual editing is sometimes required to correct misclassifications. Finally, adjacent macro-objects showing the same attributes will then be dissolved to finalize the two land cover classifications (1990 and 2000). For the sample sites where no forest cover change occurred, the micro-objects were labelled by the forest/non-forest mask algorithm. In order to have a product with a similar scale as the classifications, the information from the micro-objects had to be upscaled to the macro-objects. A macro-object is labelled as forest if more than half of its surface in the micro-object layer is composed of forest cover. 2.3.5. Quality control of image processing A random set of 25 sample sites was selected to realize a quality control of the interpretation process. This set is composed of 10 sites where no change was observed and 15 sites where change occurred. For each sample site, four randomly distributed points were used to identify four macro-objects in

G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 each extract which will be labelled by an independent interpreter. A total of 100 macro-objects are available to evaluate the quality of the forest/non-forest mask: 40 macro-objects come from the unchanged extracts and 60 macro-objects from the recent extract of the changed pairs. For the changed samples, the four random points identify 60 macro-objects from the historic extract and 60 macro-objects from the recent extract. Although the macro-objects of the historic imagery are not entirely independent from the macro-objects of the recent extracts, a single confusion matrix was created for the combined set of 120 macro-objects. 2.4. Statistics For every sample site, both land cover statistics and the change transition matrix are derived to compute the respective extension of the different land cover classes and the area affected 1975 by the different change processes. However, each individual extract pair was acquired at various different dates oscillating around the pivot dates of January 1990 and January 2000. In order to have sound change estimates for this period and for the entire region, the forest areas and transition matrices were linearly extrapolated to these pivot dates. As mentioned before, the average annual rates of land cover change are calculated by dividing the total changed area by the time period, 10 years in this case, and by the total forest cover area averaged between the two dates. Using as reference the estimated forest area of January 1995 minimizes the linear approximations on the forest surface estimations. The national level estimates are simply derived from an arithmetic average of the non-cloudy samples. The variance is estimated by the classical formula used for random sampling, even though the design of the current study is systematic. This calculation can be considered as conservative since it provides higher figures for variance than the real one. Fig. 2. An example of the detected deforested area over a sample site located at 22 E and 5 S. The Landsat TM extract of the 1990 dataset and the Landsat ETM+ extract of the 2000 dataset are respectively displayed in (a) and (b). In (c) and (d), the detected deforested area between these two dates is delineated and overlaid over the images while the non-deforested areas are veiled in green.

1976 G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 To test the impact of the sampling rate on the stability of the results, a second set of estimates was produced based on a lower sampling rate by dividing the entire population into four subsets, each with one sample site every square degree. The objective is mostly to test whether it is worthwhile to sample at every 0.5. The results were compiled for the entire Congo Basin and for Congo-Brazzaville and Democratic Republic of Congo. 3. Results 3.1. Deforestation estimates Out of the initial 571 sample sites of the survey, 390 image pairs were processed. The remaining images were discarded from the processing chain due to poor radiometric quality or large cloud coverage. The visual screening identified 165 sites where forest cover changes occur between 1990 and 2000. An example of deforestation measurement at local scale is shown on Fig. 2 for a given sample site. Along with the transition matrices computed for each sample site, similar spatial results are also obtained for the other change processes, i.e. forest degradation, reforestation, and forest recovery. The statistics obtained for each sample site of the survey were summarized at national and regional scale. The repartition of the initial, processed and changed samples among the different countries is indicated in Table 1. Table 2 presents the annual rates of gross deforestation, gross reforestation and the subsequent net deforestation for the different countries and for the whole Congo Basin. Similar results concerning the forest degradation and forest recovery processes are reported in Table 3. The annual gross deforestation rate for Central Africa's tropical forest is estimated at 0.21% per year (±0.05%), with an additional gross degradation of 0.15% per year (± 0.03%). On the positive side, 0.05% (±0.01%) of the study area goes annually from non-forest to forest, and 0.06% (± 0.02%) of the area are regenerated from degraded to dense forests. The Democratic Republic of Congo shows the most active deforestation process in the region, followed by Cameroon and Equatorial Guinea. However, the estimates for these two latter countries have a low accuracy, due to the high number of missing data. The forest dynamics is much lower in Congo- Brazzaville, Central African Republic and Gabon but the same Table 1 Number of initial, processed and changed samples for each country and for the entire surface covered by the CBFP Landscapes Country Initial Processed Changed D. R. Congo 338 267 129 Congo-Brazzaville 80 54 15 C. A. Republic 16 14 6 Cameroon 65 32 9 Gabon 63 21 5 Eq. Guinea 9 2 1 Inside CBFP landscapes 239 158 51 Outside CBFP landscapes 332 232 114 Total 571 390 165 Table 2 Basin wide and national figures for annual gross deforestation, gross reforestation and net deforestation rates between 1990 and 2000 Country n Gross deforestation Gross reforestation Net deforestation D. R. Congo 267 0.25% ±0.06% 0.05% ±0.01% 0.20% Congo-Brazzaville 54 0.07% ±0.04% 0.05% ±0.06% 0.02% C. A. Republic 14 0.12% ±0.10% 0.06% ±0.08% 0.06% Cameroon 32 0.20% ±0.26% 0.06% ±0.06% 0.14% Gabon 21 0.12% ±0.11% 0.03% ±0.03% 0.09% Eq. Guinea 2 0.31% ±0.41% 0.69% ±0.91% Central Africa 390 0.21% ±0.05% 0.05% ±0.01% 0.16% The number of processed samples (n), the change rates and their confidence interval are mentioned for every country. The national figures which are less reliable due to missing data (country name in italics) are mentioned for information only. reservation about missing data is still applied (especially for Gabon). The spatial distribution of the deforestation process over the Congo River basin (Fig. 3) highlighted with more detail the deforestation hot spots identified in 1998 by the TREES project (Achard et al., 2001). Deforestation is clearly accentuated along the Congo River, especially in the region between the river and the northern frontier of the forest. Deforestation generally appears more active on the periphery of forest. The approach adopted in this study is capable of showing a spatial link between the different forest cover change processes. Indeed, 56% of the samples sites where more than 0.5 km 2 was deforested also exhibit a significant reforestation of more than 0.5 km 2. Looking at this the other way around is even more significant; out of the 92 sample sites exhibiting significant reforestation, 88% were also affected by the deforestation process. The coupling of deforestation and forest degradation is even more obvious as 94% of the samples showing forest degradation are also affected by the deforestation process. When comparing the forest change observed in the CBFP Landscapes (Table 4), the net deforestation is two times lower in these ecologically important areas (0.09%) than outside of them (0.20%) while the forest cover is rather similar. The net degradation outside the Landscapes (0.11%) is almost double than the same process within the Landscapes. Table 3 Basin wide and national figures for annual gross deforestation, gross reforestation and net forest degradation rates between 1990 and 2000 Country n Gross forest degradation Gross forest recovery Net forest degradation D. R. Congo 267 0.19% ±0.04% 0.07% ±0.03% 0.12% Congo-Brazzaville 54 0.04% ±0.03% 0.04% ±0.03% 0.00% C. A. Republic 14 0.06% ±0.05% 0.04% ±0.04% 0.02% Cameroon 32 0.07% ±0.06% 0.06% ±0.05% 0.01% Gabon 12 0.09% ±0.10% 0.01% ±0.02% 0.08% Eq. Guinea 2 0.00% ±000% 0.32% ±0.62% Central Africa 390 0.15% ±0.03% 0.06% ±0.02% 0.09% The number of processed samples (n), the change rates and their confidence interval are mentioned for every country. The national figures which are less reliable due to missing data (country name in italics) are mentioned for information only.

G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 Fig. 3. Spatial distribution of forest cover change processes that occurred between 1990 and 2000 over the Central African forest: (a) gross deforestation; (b) gross reforestation; (c) gross forest degradation; (d) gross forest recovery. Each circle corresponds to a 10 10 km sample site. The circle size is proportional to the surface affected by the corresponding change process. 1977

1978 G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 3.2. Representativeness and accuracy assessment Table 4 Annual forest change rates derived from samples in and out of Landscapes for forest cover changes between 1990 and 2000 All CBFP landscapes Outside CBFP landscapes Number of samples 158 232 Forest cover 85.7% 83.0% Deforestation 0.15% 0.24% Reforestation 0.06% 0.04% Net deforestation 0.09% 0.20% Forest degradation 0.09% 0.19% Forest recovery 0.03% 0.08% Net degradation 0.06% 0.11% Table 5 Average deforested area (in percentage of forest area) over 10 years as measured using different sampling rates (α) for Congo-Brazzaville, D. R. Congo and for the entire region of Central Africa α Congo-Brazzaville D. R. Congo Central Africa (%) n D.R.E. ±C.I. n D.R.E. ±C.I. n D.R.E. ±C.I. 0.8 14 0.86% ±0.79% 68 2.54% ±1.10% 98 2.20% ±0.88% 0.8 15 0.67% ±0.76% 67 1.95% ±1.03% 101 1.48% ±0.74% 0.8 11 0.03% ±0.06% 65 2.72% ±1.46% 96 1.94% ±0.74% 0.8 14 1.02% ±1.10% 67 2.72% ±1.24% 95 2.73% ±1.22% 3.3 54 0.69% ±0.43% 267 2.45% ±0.55% 390 2.08% ±0.48% From the complete dataset (which has a sampling rate of 3.3%, i.e. 1 sample site every 0.5 ), 4 different subsets of 0.8% sampling rate were created leaving 1 sample site every degree. For every deforestation rate estimate (D.R.E.) the number (n) of sample used and the corresponding confidence interval (C.I.) are given. Over 30% of the initial dataset (181 sample sites) had to be discarded because they were not processable (Table 1). This is the case even though the images were selected as the best available from all the available Landsat catalogs. Large cloud coverage, either on a single extract or on both, is the main cause for this problem. A high proportion of unusable sample sites are found on coastal countries (Gabon, Equatorial Guinea and Cameroon) which renders their nation-wise estimates less reliable. Equatorial Guinea is a drastic example where the national figure would be based on a single changed site which happened to undergo more reforestation than deforestation. With regard to spatial representation, the national figures can only be provided for Central African Republic, Congo- Brazzaville and for the Democratic Republic of Congo where the processed samples are well distributed over the forest domain. For Congo-Brazaville, this is only true for its northern forest and not for the south-western part that is subject to the same important coastal cloud coverage effect. The confidence intervals associated with the estimates confort the robustness for the national estimates for the Democratic Republic of Congo and Congo-Brazzaville. For Central African Republic, even though 14 out of 16 samples were processed, the confidence interval is quite high due to the small size of the sample population. The production of deforestation estimates based on systematic sampling schemes with a lower sampling rate can indicate if the selected sampling rate was adequate. Table 5 compares the deforestation estimates obtained for the entire population with 4 independent populations of lower sampling rate (10 10 km sample sites on a grid of 1 square degree). The deforestation estimates over 10 years show significantly smaller confidence intervals when increasing the sampling rate from 0.8% (one sample site every degree) to 3.3% (one sample site every 0.5 ). The accuracy of the image interpretation provided by the production chain was evaluated from the 25 quality control sample sites. For the forest/non-forest discrimination the accuracy is estimated at 93% (n=100) and at 72% for the 10 land cover classes mapping (n = 120). The observed discrepancies were analysed to better understand the main error sources. For the forest/non-forest map, all discrepancies are located in changed samples where forest mask was derived by merging the dense and degraded forest classes. For five out of seven macroobjects, the labelling difference is due to disagreement between the interpreter and the controller. Therefore, it is not related to the production chain. For the detailed land cover classification, 15 out of 34 errors can be attributed to the production chain while the rest is due to a certain subjectivity of the visual interpretation of 10 land cover classes. However, most of these classification errors do not affect the deforestation estimates. Since the deforestation reforestation dynamic is based on the change trajectories between two groups of classes, dense forest and degraded forest on one side and all the other classes on the other, the confusion matrix can be simplified by reducing its dimension from 10 10 to 2 2. With this simplified matrix, the overall accuracy for the discrimination between the two groups of classes concerned by the deforestation and reforestation processes is 91%. This figure includes the interpreter subjectivity errors. 4. Discussion 4.1. Analysis of the deforestation estimates The regional deforestation figure for Central Africa obtained in the current study (0.16% net per year) is significantly lower than previous estimates for Africa (0.34% for FAO FRA 2000 and 0.36% for TREES). A first reason for this difference is that the estimates for Africa in previous studies also included West Africa and Madagascar, two regions well-known for high deforestation. Another reason is that most scenes used in previous studies were located at the fringes of the Congo Basin forests, where the deforestation is the highest. However, this difference can certainly be attributed to a better spatial resolution for pattern change detection and a more detailed land cover typology in the current study. Indeed, the net deforestation was separated from the net forest degradation which accounts for 0.09% in this study. The forest cover changes in Central Africa are by far the lowest observed in the pan-tropical belt, with a net deforestation rate two times higher in South America and four times higher in South-east Asia. Nonetheless, some areas show a dramatic increase of deforestation due to agricultural encroachments, particularly in regions affected by human conflicts (i.e. in the Kivu region).

G. Duveiller et al. / Remote Sensing of Environment 112 (2008) 1969 1981 1979 The estimates on the CBFP Landscapes show that the deforestation and forest degradation processes are half as important inside the Landscapes as outside of them. Even though the sampling strategy does not allow reliable estimates for individual Landscapes the overall result is significant. Biodiversity richness and endemism are the criteria on which the delimitation of the Landscapes was primarily based upon. However, the results of this study suggest that the Landscapes are also appropriate for an efficient management aiming at preserving the habitats since the intensity of change processes for the period preceding their delimitation was already lower within the Landscapes than outside of them. The uniqueness of this study is the monitoring of different forest cover changes including deforestation, reforestation, forest degradation and forest recovery. Whereas other studies have been only focused on deforestation, the other change processes are also crucial for forest monitoring as depicted by the close spatial association of opposite processes. Reforestation is required for deriving net forest cover loss. Forest degradation provides an index of forest vulnerability status and forest recovery show the status of on-going recovering of the forest. 4.2. Comments on the methodology Using multi-date segmentation for delineating image objects is probably the step that most reduces the processing time of image analysis and thus limiting the cost of the whole methodology. The delineation provided by this approach is not only rapid and automatic but also finer than what could be achieved using a manual approach. It is repeatable and therefore more objective than a visual delineation by an analyst. However, given the heterogeneity in the forest spectral signature and the occasionally poor radiometric conditions, the image analysis by a skilled interpreter is indispensable to map land cover and land cover change with high overall accuracy. In the present methodology, visual analyses were restricted to two crucial steps: sorting image extracts by visual screening; and labelling the multi-date object clusters. The forest/non-forest automated classification algorithm applied on the non-changed extracts has the advantage of avoiding the labelling operation. However, the trimming procedure relies on the hypothesis that the majority of the land cover under the extract is constituted of forest. Hence, this automated binary approach might not be applicable directly for landscapes not dominated by a relatively homogeneous land cover type. The quality of the classification can deteriorate if there is too much of a difference between extracts due to sensor properties, vegetation phenology or forest types. More advanced classification methods can be used but this often demands more interactive operations increasing time requirements. 4.3. Sampling design A controversial debate has recently posed two options for accurately estimating tropical deforestation: the wall-to-wall mapping (Tucker and Townshend, 2000) and sampling of Landsat scenes (Czaplewski, 2003). The current study, largely inspired from the FAO proposal for FRA-2010, aims at combining the advantages of both sampling and mapping. It was made possible by processing improvements and better access to remote sensing data. On one hand, the processing limited to small areas (10 10 km) with rather uniform atmospheric effects is rather straightforward and allows spending more time on the thematic interpretation of the data. On the other hand, the sampling scheme, with a large number of samples, is designed for capturing the spatial heterogeneity of the forest change processes. The sampling rate plays a critical role for getting representative and robust estimates. Our results (Table 5) show that even in the case of large forest blocks, such as the entire Congo Basin, the annual deforestation estimates can considerably vary (0.15% to 0.27% per year) and the confidence intervals are very broad when the sampling rate is low. For national estimates, the situation is even worse, especially in the case of countries like Congo-Brazzaville (about 20 million hectares of forests) with a very low deforestation. Although a sampling rate of 1 is clearly too coarse, it remains unclear whether a sampling rate of 0.5 is sufficient. This has yet to be verified using a more intensive sampling strategy. Furthermore, to fully take advantage of the robustness of systematic sampling it is necessary to reduce the proportion of missing data. Although the sampling is systematic, its real density is inferior in the regions obscured by clouds. Having a stratified sampling scheme with different sampling densities to compensate for the lack of optical data would both increase the sampling complexity and the number of samples to process (and potentially discard). In the current context of observation systems, Synthetic Aperture Radar (SAR) technology appears to be the best candidate to deal with the cloudy coastal regions. 4.4. Legend Unlike most large-scale monitoring exercises, this study considered different land cover classes in order to depict various forest change processes, including reforestation, forest degradation and forest recovery which are often neglected. However, the radiometric and atmospheric quality of the Landsat images available over such cloudy regions can affect the classification accuracy between classes such as degraded forest and long fallow and secondary forest or forest-agriculture mosaic and agriculture and short fallow. These confusions concern limited area and do not affect significantly the forest change figures. In this study, the 10 classes were aggregated into 2 groups to characterize deforestation: forest (composed of dense forest and degraded forest ) and non-forest. The individual classes of the group forest were retained to characterize the internal process of forest degradation. This first experience shows that a 5-class typology would provide better classification accuracy by reducing the impact of the interpreter's subjectivity. The hierarchical approach based on macropatterns is an efficient and original way to take advantage of high resolution imagery for depicting small deforestation patches, logging and