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

Abstract

The 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10/spl times/10 independent sampling window. The classification approach included two interdependent stages: 1) a supervised maximum a posteriori Baysian approach to classify the mean backscatter image into 5 general land cover categories of forest, savanna, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonomic information and biomass levels. Fourteen classes were successfully separated at 1 km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.