Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synth etic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to d emonstrate the application of spaceborne L-band radar in tropical forest st udies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine t he information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resamp led to 100 m resolution and mosaicked into a seamless image of about 8 mill ion km(2), including the entire Amazon basin. This image was used in a clas sifier to generate a 1 km resolution land cover map. The inputs to the clas sifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent samplin g window. The classification approach included two interdependent stages. F irst, a supervised maximum a posteriori Baysian approach classified the mea n backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforeste d areas and open water. A hierarchical decision rule based on texture measu res was then applied to attempt further discrimination of known subcategori es of vegetation types based on taxonomic information and woody biomass lev els. True distributions of the general categories were identified from the RADAM BRASIL project vegetation maps and several field studies. Training and vali dation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The ac curacy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with th e RADAM- and AVHRR-based 1 km resolution land cover maps.