Van Wyk de Vries, M. orcid.org/0000-0001-7752-8813, Arrell, K., Basyal, G.K. et al. (10 more authors) (2024) Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery. Earth Surface Processes and Landforms, 49 (4). pp. 1397-1410. ISSN 0197-9337
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 10−2–102 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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2024 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
Keywords: | hazard inventory; hillslope monitoring; optical feature tracking; remote sensing; slow-moving landslides |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Geography (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Feb 2024 09:10 |
Last Modified: | 08 Nov 2024 12:52 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/esp.5775 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209622 |