Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery
Maximillian Van Wyk de Vries, Katherine Arrell, Gopi K. Basyal, Alexander L. Densmore, Alexandre Dunant, Erin L. Harvey, Ganesh K. Jimee, Mark Kincey, Sihan Li, Dammar Singh Pujara, Ram Shrestha, Nick Rosser, Simon Dadson
Abstract
Abstract Landslides are one of the most damaging natural hazards and have killed tens of thousands of people around the world over the past decade. Slow‐moving landslides, with surface velocities on the order of 10 −2 –10 2 m a −1 , can damage buildings and infrastructure and be precursors to catastrophic collapses. However, due to their slow rates of deformation and at times subtle geomorphic signatures, they are often overlooked in local and large‐scale hazard inventories. Here, we present a remote‐sensing workflow to automatically map slow‐moving landslides using feature tracking of freely and globally available optical satellite imagery. We evaluate this proof‐of‐concept workflow through three case studies from different environments: the extensively instrumented Slumgullion landslide in the United States, an unstable lateral moraine in Chilean Patagonia and a high‐relief landscape in central Nepal. This workflow is able to delineate known landslides and identify previously unknown areas of hillslope deformation, which we consider as candidate slow‐moving landslides. Improved mapping of the spatial distribution, character and surface displacement rates of slow‐moving landslides will improve our understanding of their role in the multi‐hazard chain and their sensitivity to climatic changes and can direct future detailed localised investigations into their dynamics.