The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3/W4
06 Mar 2018
 | 06 Mar 2018


N. A. Correa-Muñoz and C. A. Murillo-Feo

Keywords: Backscattering Coefficient, Dispersion Mechanism, Landslides, Polarimetry SAR (PolSAR), Entropy – Alpha Parameter Classification, Principal Component Analysis

Abstract. SAR polarimetry (PolSAR) is a method that can be used to investigate landslides. Polarimetric scattering power decomposition allows to separate the total power received by the SAR antenna, which is divided in surface scattering power, double bounce scattering and volume scattering power. Polarimetric indices are expected to serve for landslide recognition, because landslides’ scattering properties are different from those of the surrounding forested areas. The surface scattering mechanism is mainly caused by rough surfaces like bare soil and agricultural fields, so we hope that this will be the predominant dispersion mechanism in landslides. In a study area located in south-western Colombia, we used dual-Pol provided by ESA’s Sentinel-1 satellites and quad-pol from NASA’s UAVSAR aerial platform. Using C-band and L-band radar images, we analysed the interaction between radar signals and landslides. First, with dual-pol we found backscatter calibrate coefficients over four GRD radar images acquired between 2015 and 2017. The analysis gave an average backscatter value of −14.47 dB for VH polarisation and −8.40 dB for VV polarisation. Then, using H-a decomposition for quad-pol data, we validated the high relationship between entropy and alpha parameter, which has the highest contribution to the first axis in a principal component analysis. These results were used to obtain an unsupervised classification of landslides, that separated the Colombian Geological Service landslide inventory in three classes characterized by the mechanism of dispersion. These results will be combined with InSAR parameters, morphometric parameters and optical spectral indexes to obtain a local detection model of landslides.