Monitoring Landslides along the Jinsha River Basin Based on Dempster-Shafer Evidence Theory with Multi-Source INSAR Time Series

Synthetic aperture radar Interferometry (InSAR) has proven to be an effective landslide monitoring technique, especially for very slow landslides without observable morphological features. Integration of multi-source InSAR observations from different satellites/tracks could help reduce omissions and misjudgements of potential landslides, showing a promising trend toward automatic landslide detection and monitoring at regional or national scale. However, existing methods present poor error suppression performance, and are not capable of describing and solving potential information conflicts, due to the neglect of observation uncertainties during the integration. Here we propose a new integrated method based on Dempster-Shafer evidence theory to address these deficiencies. The two key steps are multi-source InSAR integration based on DST and a two-step decision rule. We apply the proposed method to the entire Jinsha River Basin using both ascending and descending Sentinel-1 SAR data from 2014 to 2023. The preliminary result on the Luoshui-Baini section based on two sources (ascending and descending orbits) identified 68 landslides, which is nearly the same between our method and the existing mosaic method.