Preprints
https://doi.org/10.5194/nhess-2021-148
https://doi.org/10.5194/nhess-2021-148

  19 May 2021

19 May 2021

Review status: a revised version of this preprint is currently under review for the journal NHESS.

Improved rapid landslide detection from integration of empirical models and satellite radar

Katy Burrows1,a, David Milledge2, Richard J. Walters1, and Dino Bellugi3 Katy Burrows et al.
  • 1COMET, Department of Earth Sciences, Durham University, Durham, U.K.
  • 2School of Engineering, Newcastle University, Newcastle, U.K.
  • 3Department of Geography, University of California, Berkeley, U.S.A.
  • anow at: Géoscience Environnement Toulouse, CNES, Toulouse, France

Abstract. 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 Forests 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 find that the addition of the radar data can considerably improve model performance and robustness within two 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.

Katy Burrows et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-148', Anonymous Referee #1, 09 Jun 2021
    • AC1: 'Reply on RC1', Katy Burrows, 07 Aug 2021
  • RC2: 'Comment on nhess-2021-148', Anonymous Referee #2, 10 Jun 2021
    • AC2: 'Reply on RC2', Katy Burrows, 07 Aug 2021

Katy Burrows et al.

Katy Burrows et al.

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Short summary
When cloud cover obscures optical satellite imagery, there are two options remaining for generating information on earthquake-triggered landslide locations: 1) models which predict landslide locations based on, e.g. slope and ground shaking data and 2) satellite radar data, which penetrates cloud cover and is sensitive to landslides. Here we show that the two approaches can be combined to give a more consistent and more accurate model of landslide locations after an earthquake.
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