Burnt area mapping from ERS-SAR time series using the principal components transformation

Each year thousands of hectares of forest burn across Southern Europe. To date, remote sensing of this phenomenon has focused on the use of optical satellite imagery. However, the presence of clouds and smoke prevents the acquisition of this type of data. It is possible to overcome this problem by using synthetic aperture radar (SAR) data. Principal component analysis (PCA) was performed to quantify differences between pre- and post- fire images and to investigate the separability over a European Remote Sensing (ERS) SAR time series. Moreover, the transformation was carried out to determine the best conditions to acquire optimal SAR imagery according to meteorological parameters and the procedures to enhance burnt area discrimination for the identification of fire damage assessment. A comparative neural network classification was performed in order to map and to assess the burn scars using a complete ERS time series or just an image before and an image after the fire according to the PCA. The results suggest that ERS-SAR is suitable to highlight areas of localized changes associated with forest fire damage-in Mediterranean landcover.