Integrating multisensor data and RADAR texture measures for land cover mapping

This study evaluated multisensor spaceborne data from Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) for East African landscapes including settlements, natural vegetation, and agriculture. An extensive landscape that has been difficult to accurately map with spaceborne remote sensing has been subtropical areas of savanna or woodland, especially when mixed with scattered agricultural practices. These varied vegetation communities are often very difficult to spectrally differentiate with optical data. This study examines the utility of radar to accurately locate areas of natural vegetation, scattered agricultural, and settlements. Radar data were able to accurately map these features with approximately the same accuracy as TM. In addition to comparing these two sensor types, four different geospatial manipulations of the radar data were examined. Those manipulations included measures of texture, the size of the texture window, data filtering prior to extraction of texture values, and post-classification smoothing. The variance (2nd order) measure of texture provided the best classification accuracies. The optimum window size for texture was a 13 x 13 pixel array and both pre- and post-classification filtering of the radar data significantly improved results. (C) 2000 Elsevier Science Ltd. All rights reserved.