Remote Sensing Landslide Monitoring Based on Machine Learning Method

Abstract

The susceptibility evaluation of landslides has become one of the key environmental issues that people are concerned about. This study took the landslides in Xishuangbanna, Yunnan Province as the study object, and selected 10 evaluation factors such as digital elevation model (DEM), slope aspect, precipitation, land use, water system, roads, population density, lithology, faults, and NDVI. Different machine learning methods were compared and studied, and the ROC (receiver operating characteristics) curve verification revealed that the accuracy of the random forest evaluation model was high. In the prediction and evaluation of the susceptibility of landslides, five risk levels were divided. After the superimposed analysis, 87.26% of the disaster points fell in the first and second susceptibility areas. The spot analysis found that the distribution of hot spots is consistent with the distribution of disaster spots. In a word, the results of this study can provide better technical support for the evaluation and early warning of landslides in Southwest China.