In this paper, a multiscale texture-based classifier for mapping tropical forest land cover types is discussed. The classifier was implemented using the Japanese Earth Remote Sensing Satellite (JERS-1) 100 m resolution radar data acquired over the Amazon Rainforest as part of the Global Rainforest Mapping (GRFM) Project. Demonstrated here is the use of the information content present in different texture measurements at different scales to separate three categories of land cover types: forest from nonforest, terre firme from floodplain vegetation, and grassland from woodland savanna. Various combinations of first-order image statistics known as texture measures were used at different scales as feature dimensions to aid the class discrimination. Eight of the most common first-order texture measures found in the literature were used. The best combination of texture measures at each scale were determined by employing a class separability test using the Bhattachuryya distance. The results were then used as input images into a supervised multiscale maximum likelihood estimation classifier. The classified maps were validated against independent test sites, and by comparison with a Landsat Thematic Mapper (TM) classification. It was found that JERS-1 backscatter and texture measures can discriminate forest from nonforest types with very high accuracy (above 90%). Old secondary forest or regrowth areas were often mixed with forest. Radar backscatter alone was able to separate terre firme and floodplain vegetation. However, texture measures were important in separating open from dense floodplain vegetation. Similarly, the backscatter sensitivity to low biomass values was instrumental in separating woodland from grassland savanna. Texture had a lesser role in separating these two vegetation types but was important to separate the woodland savanna from dense evergreen forest and secondary forests.