Characterization of post‐event kinematics of Baige landslide using multi‐source remotely‐sensed imagery

Abstract The Baige landslide, which experienced two major collapses on October 10 and November 3, 2018, resulted in the formation of a landslide dam on the Jinsha River, causing significant socio‐economic damage. Despite these catastrophic events, ongoing deformation has been observed, indicating persistent landslide activity and a continued risk of future failures. In this study, we integrated multi‐source remote sensing imagery to investigate the post‐failure kinematics of the Baige landslide from 2019 to 2023. Small baseline subset interferometric synthetic aperture radar (SBAS‐InSAR) was employed to derive slow moving displacement rates of Baige landslide from the Sentinel‐1 and ALOS‐2 PALSAR‐2 datasets. Two‐dimensional (2D) displacement by integration of InSAR measurements revealed maximum vertical and eastward displacement rates of −357.1 mm/yr and 382.1 mm/yr, respectively. Pixel offset tracking (POT) analysis of Sentinel‐2 and ALOS‐2 PALSAR‐2 datasets further facilitated the derivation of three‐dimensional (3D) displacement rates, with maximum vertical and horizontal displacements of −7.2 m/yr and 5.4 m/yr in the upper sections, respectively. The significant variations in displacement rates are related to the fractured surfaces within the landslide. A one‐dimensional pore pressure diffusion model estimated the hydraulic diffusivity of the landslide as approximately 4.95 × 10 −5 m 2 /s, with an unstable mass thickness of ~ 65 m near the head scarp. Seasonal accelerations correlated with rainfall highlight the role of hydrological factors in landslide dynamics. This study demonstrates the value of integrating multi‐source remote sensing data to monitor landslides, providing critical insights for hazard assessment and mitigation in the Jinsha River Basin and similar high‐risk regions.