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Evaluation of remotely sensed soil moisture for landslide hazard assessment

Zhuo, Lu, Dai, Qiang, Han, Dawei, Chen, Ningsheng, Zhao, Binru and Berti, Matteo 2019. Evaluation of remotely sensed soil moisture for landslide hazard assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (1) , pp. 162-173. 10.1109/JSTARS.2018.2883361

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Soil moisture is important in the triggering of many types of landslides. However, in situ soil moisture data are rarely available in hazardous zones. The advanced remote sensing technology could provide useful soil moisture information. In this study, an assessment has been carried out between the latest version of the European Space Agency Climate Change Initiative soil moisture product and the landslide events in a northern Italian region in the 14-year period 2002–2015. A clear correlation has been found between the satellite soil moisture and the landslide events, as over four-fifths of events had soil wetness conditions above the 50% regional soil moisture line. Attempts have also been made to explore the soil moisture thresholds for landslide occurrences under different environmental conditions (land cover, soil type, and slope). The results showed slope distribution could provide a rather distinct separation of the soil moisture thresholds, with thresholds becoming smaller for steeper areas, indicating dryer soil condition could trigger landslides at hilly areas than in plain areas. The thresholds validation procedure is then carried out. Forty five rainfall events between 2014 and 2015 are used as test cases. Contingency tables, statistical indicators, and receiver operating characteristic analysis for thresholds under different exceedance probabilities (1%–50%) are explored. The results have shown that the thresholds using 30% exceedance probability provide the best performance with the hitting rate at 0.92 and the false alarm at 0.50. We expect this study can provide useful information for adopting the remotely sensed soil moisture in the landslide early warnings.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Environmental Sciences
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1939-1404
Last Modified: 26 Oct 2022 13:15

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