The Use of Polarimetric SAR Data for Forest Disturbance Monitoring

Tropical forest degradation has been a major area of interest for the remote sensing community. Various sensors have been dedicated to monitor its changes; however, due to widespread cloud cover, limited information could be retrieved through optical datasets. Synthetic Aperture Radar (SAR) sensors provide an alternative for such purpose. This paper discusses an application of SAR polarimetry data coupled with the Cloude-Pottier decomposition theorem as a non-invasive method for the assessment of degraded forests in Indonesia. It was found that Cloude-Pottier feature space provides a convenient way to describe degradation levels, especially using P-band datasets. Both L- and P-band data provided appreciable classification accuracy through Support Vector Machine methods. Results suggest that fully polarimetric SAR data, combined with polarimetric parameters, can be useful for operational monitoring.