Remote Sensing Data Derived Parameters and its Use in Landslide Susceptibility Assessment Using Shannon’s Entropy and GIS

Abstract

In recent years, the growth of urban populations in hazardous areas has increased the impact of natural disasters in both developed and developing countries. The purpose of the current study is to assess the landslide susceptibility in Kalaleh township of Golestan province, Iran. In this study the Shannon’s entropy approach was applied. A total of 82 landslide locations were identified primarily from aerial photographs and field surveys. Then eighteen landslides conditioning factors were prepared in GIS. These landslide conditioning factors are: slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, soil texture, distance from faults, distance from rivers, distance from roads, fault density, road density, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). Using these conditioning factors, landslide susceptibility index was calculated using Shannon’s entropy. For model validation, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curves for landslide susceptibility maps were drawn and the area under curve values was calculated. Verification results showed 82.15% accuracy. According to the results of the AUC (area under curve) evaluation, the map produced exhibits satisfactory properties.