Visual interpretation of synthetic aperture radar data for assessing land cover in tropical savannas

Satellite SAR data offers land managers an affordable, all-weather capability for detailed land cover mapping. Visual classification of these data may be more appropriate to the resource base in many developing countries and human interpreters Cian often overcome problems of speckle more effectively than automated classification procedures. We report work in progress on the visual interpretation of SAR data to classify land cover types within tropical savannas. Airborne L-band SAR data for a region in Belize, Central America is degraded to approximate the single polarisation hh and dual polarization hh/hv data that is expected from the ALOS PALSAR satellite sensor. Interpretations of these two types of data by multiple interpreters were compared to explore how the number of polarizations, the effective spatial resolution and the visual presentation of the SAR data affected the ability of interpreters to classify land cover. An average classification accuracy of 78% for hh and 85% for hh/hv data were achieved for all classes and interpreters. Denser high forest areas were accurately interpreted using both data sets, whilst a red-green colour composite of the hh/hv data allowed grass dominated areas to be separated from areas of savanna woodland. Conclusions are drawn about the benefits of certain presentations of backscatter data to assist visual interpretation.