Title: Wetland Mapping in the Congo Basin Using Optical and Radar Remotely Sensed Data and Derived Topographical Indices
Authors: BWANGOY Jean-Robert B.HANSEN Matthew C.ROY DavidDE GRANDI GianfrancoJUSTICE Chris
Citation: REMOTE SENSING OF ENVIRONMENT vol. 114 no. 1 p. 73-86
Publication Year: 2010
JRC N°: JRC55150
ISSN: 0034-4257
URI: http://www.elsevier.com/locate/rse
DOI: 10.1016/j.rse.2009.08.004
Type: Articles in periodicals and books
Abstract: This study reports results of a classification tree approach to mapping the wetlands of the Congo Basin, focusing on the Cuvette Centrale of the Congo River watershed, an area of 1,176,000 km2. Regional expert knowledge was used to train passive optical remotely sensed imagery of the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, JERS-1 active radar L-band imagery, and topographical indices derived from 3 arc sec elevation data of the Shuttle Radar Topography Mission (SRTM). All data inputs were resampled to a common 57 m resolution grid. A classification tree bagging procedure was employed to produce a final map of per-grid cell wetland probability. Thirty bagged trees were ranked and the median result was selected to produce the final wetland probability map. Thresholding the probability map at <0.5 yielded a proportion of wetland cover for the study area of 32%, equivalent to 360,000 km2. Wetlands predominate in the CARPE Lake Tele¿Lake Tumba landscape located in the western part of the Democratic Republic of the Congo and the south-eastern Republic of Congo, where they constitute 56% of the landscape. Local topography depicting relative elevation for sub-catchments proved to be the most valuable discriminator of wetland cover. However, all sources of information (i.e. optical, radar and topography) featured prominently in contributing to the classification tree procedure, reinforcing the idea that multi-source data are useful in the characterization of wetland land cover. The method employed freely available data and a fully automated process, except for training data collection. Comparisons to existing maps and in situ field observations indicate improvements compared to previous efforts
JRC Institute:Institute for Environment and Sustainability

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