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 m²/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.