Modelado de deslizamiento utilizando técnicas de sensores remotos: Caso de estudio vía a la Costa-Guayas.

Abstract

Landslides represent a significant risk in Ecuador due to geological and climatic conditions, exacerbated by human activities such as open-pit mining. This project focused on modeling the landslide that occurred in 2023 in the vía a la Costa sector using remote sensing techniques, particularly the Differential Interferometric Synthetic Aperture Radar (DInSAR) methodology. The main objective of this study was to model the 2023 landslide in Vía a la Costa using remote sensors to obtain surface deformation data, including the generation of deformation maps, validation of results with LiDAR data, and analysis of vulnerability indices for at-risk areas. The methodology involved acquiring and processing radar images using SNAP software. Key steps included orbital corrections, interferogram generation, phase unwrapping, and conversion to deformation maps. The results revealed significant surface displacements in two critical areas, referred to as zones of interest, allowing the identification of a lineament from the DInSAR results and a possible fault line detected using LiDAR in the study area. In the post-event period, deformations continued, particularly around the landslide’s crown and flanks, following low-slope directions. Deformations were also observed in areas where rock extraction occurs, indicating that mining activities continue to affect the stability of the terrain. Additionally, vulnerability maps were created to highlight critical areas, contributing to a better understanding of the dynamics of the zones of interest and providing key information for decision-making in quarry planning and risk management. Keywords: Landslide modeling, DInSAR, remote sensing, Sentinel-1, vía a la Costa.