Title: Deforestation in Central Africa: Estimates at Regional, National and Landscape Levels by Advanced Processing of Systematically-distributed Landsat Extracts
Authors: DUVEILLER GrégoryDEFOURNY PierreDESCLÉE BaudouinMAYAUX PHILIPPE
Citation: REMOTE SENSING OF ENVIRONMENT vol. 112 p. 1969-1981
Publisher: ELSEVIER SCIENCE INC
Publication Year: 2008
JRC Publication N°: JRC40002
ISSN: 0034-4257
URI: http://dx.doi.org/10.1016/j.rse.2007.07.026
http://publications.jrc.ec.europa.eu/repository/handle/JRC40002
DOI: 10.1016/j.rse.2007.07.026
Type: Articles in Journals
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 with highest biodiversity rates. 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 were 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. 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 adequate combination of automated image processing and interactive labelling renders this method cost-efficent. The proposed 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. Around 60% of the 390 cloud-free images do not show any forest cover change. For the other 165 sites, the results are resumed by a change matrix for every sample site describing 4 land cover change processes, e.g. 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. The results also show that the Landscapes designed 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.
JRC Institute:Institute for Environment and Sustainability

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