Popis: |
Through recent technological developments of radar and optical remote sensing in the areas of temporal, spectral, spatial, and global coverage, the availability of such images either at a low cost or free of charge, and the advancement of tools developed in image analysis techniques and GIS for spatial data analysis, a large variety of applications using remote sensing and GIS as tools are possible. Hence, this study aims to assess the efficacy of using Radar Induced Factors (RIF) in identifying landslide susceptibility using bivariate Information Value method (InfoVal method) and multivariate Multi Criteria Decision Analysis based on the Analytic Hierarchy Process statistical analysis. Using identified landslide causative factors, four landslide prediction models as bivariate without and with RIF, multivariate without and with RIF are generated. Twelve factors topographical, hydrological, geological, land cover and soil plus three RIF are considered. The prediction levels of susceptibility regions are distinguished and categorized into four classes as very low, low, moderate, and high susceptibility to landslides. With integration of RIF, boundary detection between high and very low areas increased by 7 %, and 4 % respectively, and there is an improvement of 2.45 % prediction and 1.12 % validation performances of bivariate analysis than multivariate. |