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Lookup NU author(s): Dr David MilledgeORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Information on the spatial distribution of triggered landslides following an earthquake is invaluable to emergency responders. Manual mapping using optical satellite imagery, which is currently the most common method of generating this landslide information, is extremely time consuming and can be disrupted by cloud cover. Empirical models of landslide probability and landslide detection with satellite radar data are two alternative methods of generating information on triggered landslides that overcome these limitations. Here we assess the potential of a combined approach, in which we generate an empirical model of the landslides using data available immediately following the earthquake using the random forest technique and then progressively add landslide indicators derived from Sentinel-1 and ALOS-2 satellite radar data to this model in the order they were acquired following the earthquake. We use three large case study earthquakes and test two model types: first, a model that is trained on a small part of the study area and used to predict the remainder of the landslides and, second, a preliminary global model that is trained on the landslide data from two earthquakes and used to predict the third. We assess model performance using receiver operating characteristic analysis and r2, and we find that the addition of the radar data can considerably improve model performance and robustness within 2 weeks of the earthquake. In particular, we observed a large improvement in model performance when the first ALOS-2 image was added and recommend that these data or similar data from other L-band radar satellites be routinely incorporated in future empirical models.
Author(s): Burrows K, Milledge D, Walters RJ, Bellugi D
Publication type: Article
Publication status: Published
Journal: Natural Hazards and Earth System Sciences
Year: 2021
Volume: 21
Issue: 10
Pages: 2993-3014
Online publication date: 07/10/2021
Acceptance date: 22/09/2021
Date deposited: 19/10/2021
ISSN (print): 1561-8633
ISSN (electronic): 1684-9981
Publisher: Copernicus Publications
URL: https://doi.org/10.5194/nhess-21-2993-2021
DOI: 10.5194/nhess-21-2993-2021
Data Access Statement: https://doi.org/10.5281/zenodo.2577300 https://doi.org/10.5066/F7DZ06F9
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