Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based landslides that have already occurred; however, investigation potential higher value. In this study, progressive landslide identification method proposed with application SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) subsequently combined Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) UAV (Unmanned Aerial Vehicle) imagery, DEM (Digital Elevation Model) for further validation. employed determine Tuanjiecun by integrating multi-period data simultaneously analyze mechanism landslide. long short-term memory then adopted predict trend accumulative deformation from SBAS-InSAR. results suggest will increase −238.57 mm. This article provides new approach disaster prevention mitigation determining using composite remote sensing predicting development indirect contact monitoring.