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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2020-315
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-315
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal NHESS.

Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine

Alexander L. Handwerger1,2, Shannan Y. Jones3, Mong-Han Huang3, Pukar Amatya4,5,6, Hannah R. Kerner7, and Dalia B. Kirschbaum6 Alexander L. Handwerger et al.
  • 1Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 3Department of Geology, University of Maryland, College Park, MD, USA
  • 4Universities Space Research Association, Columbia, MD, USA
  • 5Goddard Earth Sciences Technology and Research, Columbia, MD, USA
  • 6Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 7Department of Geography, University of Maryland, College Park, MD, USA

Abstract. The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and improving our understanding of where landslides occur. Satellite-based synthetic aperture radar (SAR) can be used to identify landslides, often within days after triggering events, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Although there are many landslide detection methods using SAR, most require downloading a large volume of data to a local system and specialized processing software and training. Here we present a SAR-based amplitude change detection approach designed for those without SAR expertise that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to identify landslides on Google Earth Engine (GEE). We provide strategies that can aid in rapid response and event inventory mapping. We test our GEE-based approach in a ~ 277 km2 area in Hiroshima Prefecture, Japan where ~ 3,800 landslides were triggered by rainfall in July 2018. Our ability to detect landslides improves with the total number of SAR images acquired before and after the landslide event, by combining both ascending and descending acquisition geometry data, and by using topographic data to mask out flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize these state-of-the-art remote sensing data.

Alexander L. Handwerger et al.

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Alexander L. Handwerger et al.

Model code and software

Google Earth Engine SAR landslide detection Mong-Han Huang https://doi.org/10.5281/zenodo.4060268

Alexander L. Handwerger et al.

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Short summary
The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and understanding landslide processes. Here we present a new approach to detect landslides anywhere in the world using freely available synthetic aperture radar data and open source tools in Google Earth Engine. Importantly, our methods do not require specialized processing software or training, which allows the broader hazards community to utilize these state-of-the-art remote sensing tools.
The rapid and accurate mapping of landslides is critical for emergency response, disaster...
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