ESTIMATING TROPICAL DEFORESTATION IN THE CONGO BASIN BY SYSTEMATIC SAMPLING OF HIGH RESOLUTION IMAGERY

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1 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover ESTIMATING TROPICAL DEFORESTATION IN THE CONGO BASIN BY SYSTEMATIC SAMPLING OF HIGH RESOLUTION IMAGERY Gregory Duveiller 1, Pierre Defourny 1 and Philippe Mayaux 2 1. Université Catholique de Louvain, Department of Environmental Sciences and Land Use Planning, Louvain-la-Neuve, Belgium; duveiller@enge.ucl.ac.be 2. Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy; philippe.mayaux@jrc.it ABSTRACT The use of high resolution remotely-sensed imagery to detect fine change in land cover within large forested environments is limited by the amount of data that has to be processed and interpreted. Deforestation being spatially concentrated where forest accessibility and population density are high, random sampling is likely to commit gross estimation errors at a local scale. However, systematic sampling based on a large number of small units may grasp local spatial variability without requiring a complete wall-to-wall coverage of entire scenes. By combining a systematic sampling scheme with object-based unsupervised classification techniques, this study designed and implemented a new cost-effective survey approach to estimate deforestation accurately at a regional scale. Furthermore, national and ecosystemspecific deforestation figures (such as gross and net deforestation rates) can be derived from the results. The survey was composed of km sampling sites systematically distributed every 0.5, corresponding to an approximate sampling density of 3.3%. For each site, data was extracted from both Landsat TM and ETM+ imagery acquired in 1990 (±2 years) and 2000 (±2 years) respectively. The gross deforestation rate for the entire basin is estimated at 0.21% per year. INTRODUCTION Tropical forests, although covering less than 10% of the land area, represent the largest terrestrial reservoir of biological diversity, from the gene to the habitat level. They suffer from rapid land use changes (i). Agricultural expansion, commercial logging, plantation development, mining, industry, urbanization and road building are all causing deforestation in tropical regions (ii). These land-use changes, in respect of other factors like climate, nitrogen deposition, biotic exchange or carbon concentration, are the most significant processes that impact in biodiversity reduction (iii). Although tropical forest monitoring has already greatly benefited from remote sensing at global scale, the major challenge is yet to accurately capture local forest change dynamics at sub-continental scale. Such detailed land cover change, detected over very large extents, are necessary to derive regional, national and sub-national figures for multilateral environmental agreements and sustainable forest management. Currently, deforestation estimates are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited area. Whereas the first approach cannot grasp small forest changes widely spread across a landscape, the operational costs limit the mapping extent in the second approach. Sampling high-resolution imagery could be a reasonable compromise. The Forest Resources Assessment (iv) realized a pan-tropical survey of forest cover changes using a stratified random sampling over the world s tropical forests using 117 sampling units each corresponding in size to a Landsat scene. Deforestation being spatially concentrated where forest accessibility and population density are high, random sampling is likely to commit gross estimation errors at a local scale. However, systematic sampling based on a large number of small units may grasp local spatial variability by significantly increasing the sampling efficiency. Furthermore, 364

2 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 this approach is considered by the FAO for the next global assessment of the FRA programme. Surveying a vast region through sampling high spatial resolution imagery requires intensive processing of the image extracts in order to detect change at every sample site. For this type of processing visual analysis remains a preferred approach due to the complexity of land cover change, combined with the potential effects of sensor differences, atmospheric conditions, vegetation seasonality, etc. This technique is very time-consuming and subject to a potential lack of objectivity due to interpreter's bias. However, these disadvantages can be amended by integrating object-based techniques such as image segmentation and objectbased unsupervised classification to assist visual interpretation. This research presents accurate estimates for deforestation and other forest cover changes over the entire Congo Basin countries. They are obtained through a survey using a systematic sampling scheme to extract Landsat data at two different dates. The processing of these samples is realized through a visual analysis assisted by object-based unsupervised classification techniques. METHODS The survey of the Central African tropical forest consists of 571 sample sites of 10 x 10 km separated by 0.5 intervals and regularly distributed over the forest domain in the Congo River basin. This represents a sampling density of 3.3%. A Landsat TM image extract and a Landsat ETM+ image extract, acquired respectively in the years 1990 (±2 years) and 2000 (±2 years), are available at every sample site. The objective of the survey is to measure, on every site, 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 concerned surface must be measured. In order to do so, the sample extract pairs are first screened visually in order to separate the changed samples form the rest. The extracts are processed in order to obtain two classifications (1990 & 2000) for the changed set of sample sites and a single binary forest mask for the unchanged samples. Change is then quantified for the changed set of sample site by comparing the two classifications (figure 1). Figure 1: Brief illustration of the methodology applied on the 571 image extract pairs of the Congo Basin survey. The 571 image pairs were screened by a first visual inspection whose main objective is to identify the sample sites where change has occurred between the two dates. The analyst decides, by looking at both images side by side, whether forest change has occurred or not. This visual screening sets a land cover change detection threshold taking into consideration image differences that can arise from other effects than land cover change (i.e. differences of instruments (TM vs. ETM+), atmospheric conditions, vegetation seasonality, etc.). This preliminary visual inspection not only serves to identify the images where change in the forestcover is observed between the two dates, but also to filter out those where the image quality is insufficient for a proper processing and interpretation. 365

3 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover Forest cover change is estimated by applying a dedicated processing chain to a population of image extracts that constitute the sampling units of the survey. The processing chain can be decomposed into three steps. First, a multi-date objects segmentation is obtained for every pair of extracts. In this step, groups of adjacent pixels that show a similar land cover change trajectories between the 2 dates are delineated into objects. In a second step, the objects of every extract are classified by unsupervised procedures. Finally, visual interpretation is conducted to label the classes and edit possible classification errors. The time required to interpret each extract is significantly reduced since a whole group of objects, considered as similar by the automatic classification, is treated simultaneously. Ten land cover classes are used: "dense forest", "degraded forest", "long fallow & secondary forest", "forest-agriculture mosaic", "agriculture & short fallow", "bare soil & urban area", "non forest vegetation", "forestsavannah mosaic", "water bodies" and "no data". The land cover classifications of 1990 and 2000 can be confronted to identify and quantify the land cover change and to compile transition matrices for each image extract pair. The main change process of interest for this study is deforestation. Deforestation is here defined as the conversion from the classes "dense forest" and "degraded forests" to any other class. However, since 10 thematic land cover classes are considered, several other change processes can be analysed to identify into what the forest is converted and understand the dynamics of tropical forest change. For example, an idea of the intensity of deforestation can be obtained by dividing the resulting classes into two groups. In this way, deforestation is considered low if the resulting class is either long fallow & secondary forest, "forest-agriculture mosaic" or forest-savannah mosaic and high if the forest is converted into agriculture/short fallow, "bare soil & urban area", "non forest vegetation" or "water bodies". Some processes represent a unique trajectory such the passage from a dense forest to degraded forest which is described as forest degradation. The reverse process of forest degradation was called forest regeneration, and reforestation opposes deforestation. RESULTS & DISCUSSION Important cloud coverage limited the processing to 390 image extract pairs out of the initial 571 sample sites. Forest cover change was observed and measured on 165 sites while the remaining 225 samples did not show any forest change. The general transition matrix of the changed sites (table 1) shows that dense forest composed 70.2% of the total area of changed samples in 1990 and only 65.5% in For the other 225 sites, only the recent forest cover extent was measured. These measures are integrated with those of the changed sites to have a total forest cover for all the sampling area. In this case the classes "dense forest" and "degraded forest" are considered equivalent to the forest mask for the non-changed samples. Annual rates of change for the different processes are calculated by dividing the concerned changed area by the total forest area. Gross deforestation between 1990 and 2000 for the study zone is estimated at 0.21% (table 2). Subtracting the estimated annual reforestation rate leaves a 0.16% of net deforestation. A high proportion of unusable sample sites are found on the coastal countries (Gabon, Equatorial Guinea and Cameroon) which render the national estimates hardly reliable. Equatorial Guinea is a drastic example where the national figures are based on a single site which happened to undergo more reforestation than deforestation. The national figures can only be considered as robust for Congo and for the Democratic Republic of Congo. The latter has undergone a forest cover change which is significantly more important than the first. The annual net deforestation rate in D. R. Congo amounted to 0.20% for 0.02% in Congo. The systematic survey approach enables to display the spatial repartition of deforestation and reforestation over the Congo River basin (figure 2). Not surprisingly, deforestation is accentuated along the Congo River and on the eastern border of the Central African forest. 366

4 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 Table 1: Transition matrix for the 165 changed samples. The values are in squared kilometres. Dense Forest Degraded Forest Forest-sav. Mosaic L Fallow/ Sec. Forest Forest-agri. Mosaic Agri. / S. Fallow Bare soil / Urban Non Forest Veg. Water Bodies Dense Forest Degraded Forest Forest-sav. mosaic Long Fallow / Sec. Forest Forest-agri. Mosaic Agri. / Short Fallow Bare soil / Urban Non Forest Veg. Water Bodies No Data Total 1990 % of total land area No Data Total % of total land area Table 2: National figures for annual gross and net deforestation rates between 1990 and 2000 as obtained by this survey. The number (n) of used samples where change was observed is mentioned next to each country. The figures are in italic when the number n is considered too low for the estimates to be reliable on their own. Country Gross Deforestation Reforestation Net Deforestation Cameroon (n = 8) 0.21% 0.06% 0.15% Congo (n = 15) 0.07% 0.05% 0.02% Gabon (n = 5) 0.12% 0.03% 0.10% Equatiorial Guinea (n = 1) 0.31% 0.69% -0.37% Central African Republic (n = 6) 0.13% 0.06% 0.07% D. R. Congo (n = 129) 0.25% 0.05% 0.20% Central Africa (n =164)* 0.21% 0.05% 0.16% * The total number of changed sample is 165 but one of them is actually in Nigeria which is not considered as a Congo Basin country 367

5 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover (a) (b) Figure 2: Spatial distribution of deforestation (a) and reforestation (b) in the Congo basin. The circle size is proportional to the concerned changed surface for each corresponding process. CONCLUSIONS The results of this study demonstrate that the proposed operational methodology is applicable to estimate forest cover, deforestation and other forest cover change processes at a local scale over a vast region. In this case, which concerns tropical forest monitoring, an important amount of samples had to be discarded due to intense cloud covering. Perhaps the incorporation of SAR imagery could help achieve more satisfying national estimates for the coastal countries. Thematic discrimination might be improved with a clear-cut definition of the land cover typology. Furthermore, a reduced number of land cover classes should render the estimates more robust. In any case, the lessons learned thanks to this large scale forest change 368

6 Center for Remote Sensing of Land Surfaces, Bonn, September 2006 estimation are of great interest for any global initiative related to forest monitoring and land cover change. REFERENCES i ii iii iv Mayaux P, P Holmgren, F Achard, H Eva, H-J Stibig & A Branthomme, Tropical forest cover change in the 1990s and options for future monitoring. Philosphical Transactions of the Royal Society B., 360, Geist H J & E F Lambin, Proximate causes and underlying driving forces of tropical deforestation. BioScience, 52, Sala O E, F S Chapin, J J Armesto, R Berlow, J Bloomfield, R Dirzo, E Huber-Sanwald, L F Huenneke, R B Jackson, A Kinzig, R Leemans, D Lodge, H A Mooney, M Oesterheld, N L Poff, M T Sykes, B H Walker, M Walker & D H Wall, Global biodiversity scenarios for the year Science 287: FAO, Global forest resources assessment Main Report. FAO Forestry Paper 140 (Food and Agriculture Organization of the United Nations, Rome). 369