Version 1
: Received: 22 February 2023 / Approved: 22 February 2023 / Online: 22 February 2023 (15:33:14 CET)
How to cite:
Lindsay, E.; Jarna Ganerød, A.; Devoli, G.; Reiche, J.; Nordal, S.; Frauenfelder, R.; Tokle, L. Understanding Landslide Expression in SAR Backscatter Data: A Global Study. Preprints2023, 2023020390. https://doi.org/10.20944/preprints202302.0390.v1
Lindsay, E.; Jarna Ganerød, A.; Devoli, G.; Reiche, J.; Nordal, S.; Frauenfelder, R.; Tokle, L. Understanding Landslide Expression in SAR Backscatter Data: A Global Study. Preprints 2023, 2023020390. https://doi.org/10.20944/preprints202302.0390.v1
Lindsay, E.; Jarna Ganerød, A.; Devoli, G.; Reiche, J.; Nordal, S.; Frauenfelder, R.; Tokle, L. Understanding Landslide Expression in SAR Backscatter Data: A Global Study. Preprints2023, 2023020390. https://doi.org/10.20944/preprints202302.0390.v1
APA Style
Lindsay, E., Jarna Ganerød, A., Devoli, G., Reiche, J., Nordal, S., Frauenfelder, R., & Tokle, L. (2023). Understanding Landslide Expression in SAR Backscatter Data: A Global Study. Preprints. https://doi.org/10.20944/preprints202302.0390.v1
Chicago/Turabian Style
Lindsay, E., Regula Frauenfelder and Lars-Christian Tokle. 2023 "Understanding Landslide Expression in SAR Backscatter Data: A Global Study" Preprints. https://doi.org/10.20944/preprints202302.0390.v1
Abstract
During disaster response, clouds or darkness can prevent the use of optical images for detecting consequences of natural disasters, including landslides. In these situations, radar images can be used to detect changes more rapidly. However, Synthetic Aperture Radar (SAR) backscatter intensity images are underutilized for landslide detection. Unfortunately, there remains a lack of understanding about how to interpret landslide signatures in SAR imagery. In this study, we investigate how the morphometric features and material properties of landslides, and preexisting land cover, control their expression in SAR backscatter intensity change images. Trends in the spatial and temporal signatures of over 1000 landslides in 30 diverse case studies are investigated, using multi-temporal composites and dense time-series of Sentinel-1 C-band SAR backscatter intensity data. The results show that the orientation of landslide surfaces relative to the sensor, pre-existing land cover, and the roughness of the landslide surface, determine whether landslides will produce an increase or decrease in backscatter intensity values. In certain cases, we can identify morphometric features of landslides (e.g. scarps, transit zone, deposits, ponding) and material properties. Generally, we see that landslides appear most clearly with a strong increase in intensity when they occur in herbaceous vegetation or non-vegetated ground surfaces, due to an increase in surface roughness. While in forested or densely vegetated areas, landslides produce a more complex signature with both decreases due to radar shadow and vegetation removal, and an adjacent edge of increased intensity due to double bounce and direct return from vertical tree trunks and convex edges. In most cases, rough deposits produce an increase in intensity, while smooth deposits (e.g. from mudslides) exhibit specular reflection, and thus show decreased values. Landslides are less visible in cases with pre-event very rough ground, or mixed vegetation conditions. The conceptual model developed can aid interpretation of landslides in SAR imagery, and provide domain knowledge needed to train models for automatic landslide detection.
Keywords
change detection; time-series; landslide detection; land cover; Sentinel-1; backscatter; Google Earth Engine;
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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