Study on estimation method for landslide areas using elevation data and post-disaster SAR image

Abstract

Recently, SAR (Synthetic Aperture Radar) has been increasingly adopted to observe landslide areas. In previous studies, various types of image processing anddeep learning havebeenapplied to SAR images to detect andpredict landslide areas with high accuracy. However, SAR observations are conducted under various conditions, such as the direction and position of the satellite, and the conditions of the targets to be observed. Inthis study, we applied Random Forest to SARimages and geographic condition data such as elevation, slope angle, and slope direction to construct amodel to predict landslide areas and to evaluate the influence of these geographic condition data. It is shown quantitatively that the accuracy of landslide prediction changeswith changes in the values of these geographic information data.