Mapping landslides from space: a review

Novellino, Alessandro; Pennington, Catherine; Leeming, Kathryn; Taylor, Sophie; Gonzalez Alvarez, Itahisa; McAllister, Emma; Arnhardt, Christian; Winson, Annie. 2024 Mapping landslides from space: a review. Landslides, 21. 1041-1052. https://doi.org/10.1007/s10346-024-02215-x

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Official URL: http://dx.doi.org/10.1007/s10346-024-02215-x

Abstract/Summary

Landslide hazards have significant social, economic, and environmental impact. This work provides a critical review of the main existing literature using satellite data for mapping landslides. We created and examined an extensive bibliographic database from Web of Science (WoS) consisting in 291 outputs from > 1,000 authors who studied almost 700,000 landslides across all continents, for a total of 52 countries represented with China and Italy on top of the list with more authors. The outputs are equivalent to ~ 5% of the whole landslide-related production for the period 1996–2022, with a 600% increase in the number of papers after 2014 driven by the availability of Sentinel-1 and Sentinel-2 data. Analysis of the geographical location across the 66 different countries analysed shows that, within the total number of contributions, the satellite imagery was used to detect and map two main types of landslides: flows and slides. When specified in the manuscripts, the events have been triggered by rainfall (104 cases), earthquakes (32 cases), or both (17 cases). Slope instabilities in these areas were predominantly identified through manual detection (40%); but since 2020, the advent of artificial intelligence is suppressing all other techniques. Despite the undisputed progress of EO-based landslide mapping over the last 26 years, which makes it a consolidated tool for many landslide-related applications, challenges still remain for an effective and operational use of EO images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s10346-024-02215-x
ISSN:	1612-510X
Date made live:	12 Feb 2024 14:59 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536888

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1007/s10346-024-02215-x

ISSN:

1612-510X

Date made live:

12 Feb 2024 14:59 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/536888