Using Satellite Data to Develop Rainfall-Induced Landslide Susceptibility and Forecasting Models

The systematic use of Earth observation (EO) data and technologies (i.e. satellites) can contribute effectively to detect, map, and monitor landslides and landslide-prone hillsides, in different physiographic and climatic regions. Information on the meteorological conditions contributing to slope failures can be extracted from satellite]based precipitation data such as the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission using the Integrated MultisatellitE Retrievals (IMERG) for GPM. In addition, satellite-borne Multi-temporal synthetic aperture radar interferometry (MT-InSAR) time-series analyses provide valuable insight into the spatio-temporal evolution of geohazards like landslides. It is hypothesized that combined, these two workflows can be exploited for the potential of predicting landslides. The aim of this study is to test the feasibility of combining ground surface displacement data derived from MT-InSAR techniques with precipitation data obtained from IMERG to produce a model to assess the conditions at the onset of a landslide. In particular, it is the intent of this research project to test the hypothesis that satellite]based data can be used effectively to assess conditions leading to landslide initiation at the local site level.