Multitemporal characterization of landslide displacement patterns in the Andean region using InSAR

Abstract

Landslides present significant challenges in spatiotemporal prediction and can lead to devastating consequences. Understanding slope failure mechanisms is complicated by the difficulty in directly measuring physical parameters during these events. However, active remote sensing, particularly interferometric synthetic aperture radar (InSAR), has become a crucial tool for detecting and characterizing slow-moving landslides over large areas. This research focuses on measuring the deformation associated with the catastrophic landslide that occurred in Alausí on March 26, 2023. Persistent scatterer interferometry (PSI), utilizing the StaMPS algorithm, was applied to analyze deformation using ascending and descending Sentinel-1 time series data from 2021 to 2023. The study identifies two critical areas: Area A, corresponding to the leading scarp of the fatal landslide, and Area B, located approximately 1.3 km to the left of Area A, both of which belong to the same ancient scarp. The observed displacements in these areas exhibited average rates in the line-of-sight (LOS) direction for both ascending and descending orbits, as well as in the vertical and horizontal directions, ranging from one to two dozen millimeters per year. This data is invaluable for enhancing early warning systems and supporting post-disaster recovery efforts in the Andean region. Furthermore, the mapping of vertical and horizontal displacements allows for more accurate delineation of landslide boundaries and improved volume calculations, contributing to a clearer understanding of landslide dynamics. These insights are essential for developing more resilient communities and mitigating the risks posed by natural hazards.