Tropical land cover change detection with polarimetric SAR data

There is an increasing need for fast and accurate data on tropical land cover status, and a baseline for land cover monitoring. Remotely sensed SAR data are not sensitive to cloud cover and can be useful for such purpose. Polarimetric SAR data are available in orbital systems, such as RADARSAT-2, and still have to be tested for the classification of tropical land cover and the detection of land cover change, particularly forest conversion. This work presents a study of RADARSAT-2 polarimetric images, acquired in two different dates (September 2008 and October 2009), to assess their potential in classifying forest and non-forest classes in Brazilian Amazonia. SAR images were acquired following different orbit and incidence angles, which anticipated varied conditions for images interpretation and classes discrimination. The complex SAR data were classified based on the distance of Wishart, and information from field campaigns was used for the training and test samples. Classification results were compared to evaluate possibilities for change detection in the forest cover. Classification accuracy figures were around 80%. The use of RADARSAT-2 images allowed the mapping of land cover and land cover change, considering forest and non-forest classes.