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Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery

Van Wyk De Vries, Maximillian; Arrell, Katherine; Basyal, Gopi; Densmore, Alexander; Dunant, Alexandre; Harvey, Erin L; Ganesh, Jimee; Kincey, Mark; Li, Sihan; Pujara, Dammar Singh; Pujara, Singh; Shrestha, Ram; Rosser, Nick; Dadson, Simon

Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery Thumbnail


Authors

Maximillian Van Wyk De Vries

Katherine Arrell

Gopi Basyal

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Dr Erin Harvey erin.l.harvey@durham.ac.uk
Post Doctoral Research Associate

Jimee Ganesh

Mark Kincey

Sihan Li

Dammar Singh Pujara

Singh Pujara

Ram Shrestha

Simon Dadson



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.

Citation

Van Wyk De Vries, M., Arrell, K., Basyal, G., Densmore, A., Dunant, A., Harvey, E. L., …Dadson, S. (2024). Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery. Earth Surface Processes and Landforms, https://doi.org/10.1002/esp.5775

Journal Article Type Article
Acceptance Date Jan 8, 2024
Online Publication Date Feb 21, 2024
Publication Date Feb 21, 2024
Deposit Date Feb 22, 2024
Publicly Available Date Feb 23, 2024
Journal Earth Surface Processes and Landforms
Print ISSN 0197-9337
Publisher British Society for Geomorphology
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1002/esp.5775
Keywords optical feature tracking, slow‐moving landslides, hillslope monitoring, hazard inventory, remote sensing
Public URL https://durham-repository.worktribe.com/output/2272093

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