EOMF-07. Using SAR, EVI, and texture in GEE to identify mass movements: a case study in Southern Chile

Abstract
Identifying landslides and other types of mass movements is critical for disaster response and expanding our understanding of their processes and mechanisms (Handwerger et al., 2021). We develop an open-source, user-friendly tool on Google Earth Engine (GEE) that detects landslides using Synthetic Aperture Radar (SAR), Enhanced Vegetation Index (EVI), and spatial texture. Although there are many landslide detection methods, GEE facilitates cloud-based computing that allows for rapid processing without storage complications. Sentinel-1 A/B SAR and Sentinel-2 optical data are used for the analysis, with an amplitude change approach for the SAR dataset. We additionally use Digital Elevation Models from the Shuttle Radar Topography Mission for calculating slope and accounting for topographic errors. Furthermore, we use the 5th generation European Center for Medium-Range Weather Forecast atmospheric reanalysis (ERA5) to provide hourly estimates of precipitation. The GEE tool is tested on the 2017 Santa Lucia rockslide in Southern Chile, which moved 7.2 x 10^6 m^3 of sediment, water, ice, and vegetation waste (Duhart et al., 2019). We develop a coefficient of mobility that utilizes topography, vegetation, texture, and wetness to evaluate the volume and run-out distance of the landslide and understand how these parameters affected the distribution of material (Jacquemart et al., 2020). Our approach can be applied to other areas similar to Chile, where the high relief energy in combination with glacier dynamics, vegetation patterns, and regional climatic conditions contribute to landslide initiation. References: Duhart, Paul & Sepúlveda, Violchen & Garrido, Natalia & Mella, Mauricio & Quiroz, David & Fernandez, Javier & Roa, Hugo & Hermosilla, Gonzalo. (2019). The Santa Lucía landslide disaster, Chaitén-Chile: origin and effects. 7th International Conference on Debris-Flow Hazards Mitigation. Handwerger, A. L., Jones, S. Y., Huang, M.-H., Amatya, P., Kerner, H. R., and Kirschbaum, D. B.: Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2020-315, 2020.. Jacquemart, M. and Tiampo, K.: Leveraging time series analysis of radar coherence and normalized difference vegetation index ratios to characterize pre-failure activity of the Mud Creek landslide, California, Nat. Hazards Earth Syst. Sci., 21, 629–642, https://doi.org/10.5194/nhess-21-629-2021, 2021.