Remote sensing–based analysis of accelerated surface movements in Joshimath Town, Chamoli, India: first insights into deep-seated gravitational slope processes

Abstract

This study presents a process-based integrated remote sensing framework to explain ground deformation crisis in Joshimath, Garhwal Himalayas (India), from deep-seated gravitational slope deformation (DSGSD) perspective. Integrated multisensor remote sensing, including high-resolution unmanned aerial vehicle (UAV) mapping and field validation, produced a new comprehensive landslide inventory, revealing Joshimath’s location on a large rock avalanche (RA) deposit hosting three active deep-seated landslides (DSLs) with head, core, and frontal domains. Sloping local base level (SLBL) modeling constrained failure depths (~ 350 m for RA deposit; ~ 100–250 m for DSLs), delineating first landslide geometries. Three-fold Sentinel-1 interferometric synthetic aperture radar (InSAR) framework (2020–2023) enabled new insights on inter–intra-landslide kinematic analysis, revealing spatial correlation with mapped DSLs, rainfall-driven seasonal displacement with minor snowmelt influence, first identification of 7 February 2021 debris flow–induced toe cutting on displacement acceleration and rainfall-driven localized pore pressure rise. SLBL failure-surface modeling reproduced observed InSAR displacement patterns, while stability modeling using new DSL geometries confirmed erosion and pore pressure as key instability drivers and lowest stability at landslide fronts consistent with higher InSAR displacement. Modeled strain–displacement patterns matched SLBL-derived failure surfaces. Together these results link landslide mapping, geometry, kinematics, and stability into a unified explanation of Joshimath deformation, providing a transferable framework for high-risk, data-limited Himalayan settings.