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Rapid Landslide Detection Following an Extreme Rainfall Event Using Remote Sensing Indices, Synthetic Aperture Radar Imagery, and Probabilistic Methods

Author

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  • Aikaterini-Alexandra Chrysafi

    (Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, Greece)

  • Paraskevas Tsangaratos

    (Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, Greece)

  • Ioanna Ilia

    (Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, 15780 Zografou, Greece)

  • Wei Chen

    (College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

The rapid detection of landslide phenomena that may be triggered by extreme rainfall events is a critical point concerning timely response and the implementation of mitigation measures. The main goal of the present study is to identify susceptible areas by estimating changes in the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), and Synthetic Aperture Radar (SAR) amplitude ratio before and after extreme rainfall events. The developed methodology was utilized in a case study of Storm Daniel, which struck central Greece in September 2023, with a focus on the Mount Pelion region on the Pelion Peninsula. Using Google Earth Engine, we processed satellite imagery to calculate these indices, enabling the assessment of vegetation health, soil moisture, and exposed soil areas, which are key indicators of landslide activity. The methodology integrates these indices with a Weight of Evidence (WofE) model, previously developed to identify regions of high and very high landslide susceptibility based on morphological parameters like slope, aspect, plan and profile curvature, and stream power index. Pre- and post-event imagery was analyzed to detect changes in the indices, and the results were then masked to focus only on high and very high susceptibility areas characterized by the WofE model. The outcomes of the study indicate significant changes in NDVI, NDMI, BSI values, and SAR amplitude ratio within the masked areas, suggesting locations where landslides were likely to have occurred due to the extreme rainfall event. This rapid detection technique provides essential data for emergency services and disaster management teams, enabling them to prioritize areas for immediate response and recovery efforts.

Suggested Citation

  • Aikaterini-Alexandra Chrysafi & Paraskevas Tsangaratos & Ioanna Ilia & Wei Chen, 2024. "Rapid Landslide Detection Following an Extreme Rainfall Event Using Remote Sensing Indices, Synthetic Aperture Radar Imagery, and Probabilistic Methods," Land, MDPI, vol. 14(1), pages 1-30, December.
    • Handle: RePEc:gam:jlands:v:14:y:2024:i:1:p:21-:d:1553629
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    References listed on IDEAS

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    1. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 451-472, November.
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