Preprints
https://doi.org/10.5194/nhess-2021-283
https://doi.org/10.5194/nhess-2021-283

  19 Oct 2021

19 Oct 2021

Review status: this preprint is currently under review for the journal NHESS.

Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine

Alexander L. Handwerger1,2, Shannan Y. Jones3, Pukar Amatya4,5,6, Hannah R. Kerner7, Dalia B. Kirschbaum6, and Mong-Han Huang3 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. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change detection approach that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to detect areas with high landslide density using the cloud-based Google Earth Engine (GEE). Importantly, our approach does not require downloading a large volume of data to a local system or specialized processing software. We provide strategies, including a landslide density heatmap approach, that can aid in rapid response and landslide detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries, and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

Alexander L. Handwerger 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-2021-283', Anonymous Referee #1, 02 Nov 2021
  • RC2: 'Comment on nhess-2021-283', Anonymous Referee #2, 03 Nov 2021

Alexander L. Handwerger et al.

Model code and software

Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine Handwerger, A. L., Jones, S. Y., Amatya, P., Kerner, H. R., Kirschbaum, D. B., and Huang, M.-H. https://github.com/MongHanHuang/Codes-for-Handwerger-et-al-2021-preprint

Alexander L. Handwerger et al.

Viewed

Total article views: 618 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
435 174 9 618 2 4
  • HTML: 435
  • PDF: 174
  • XML: 9
  • Total: 618
  • BibTeX: 2
  • EndNote: 4
Views and downloads (calculated since 19 Oct 2021)
Cumulative views and downloads (calculated since 19 Oct 2021)

Viewed (geographical distribution)

Total article views: 571 (including HTML, PDF, and XML) Thereof 571 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Dec 2021
Download
Short summary
Rapid detection of landslides is critical for emergency response and disaster mitigation. Here we develop a global landslide detection tool in Google Earth Engine that uses satellite radar data to measure changes in the ground surface properties. We find that we can detect areas with high landslide density within days after a triggering event. Our approach allows the broader hazards community to utilize these state-of-the-art data for improved situational awareness of landslide hazards.
Altmetrics