Investigating deformation patterns of a mining-induced landslide using multisource remote sensing: The songmugou landslide in Shanxi Province, China

Landslides induced by mining always cause serious economic losses and human deaths in mining areas. The nature of these disasters argues for a reverse analysis of slope monitoring data to assess the cause, size, and temporal evolution and to further predict the potential risks of future disasters. In this paper, we investigated the Songmugou Landslide based on multisource remote sensing techniques. More specifically, we first investigated the geomorphological features and the time series subsidence rates across this slope from December 7, 2015 to April 26, 2019, by exploiting unmanned aerial vehicle (UAV) and small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) monitoring, we then analyzed the deformation characteristics, active zonation, and secondary landslide risk of the study area by integrating geological, engineering, and rainfall data. Finally, we discussed reasonable evolution sequence and potential risk of the landslide and provided several suggestions for subsequent geological hazard prevention. The results indicate that this comprehensive approach can be used to back-analyze and reasonably predict the evolutionary mechanism of landslides.