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A Simple GIS-Based Tool for the Detection of Landslide-Prone Zones on a Coastal Slope in Scotland

Author

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  • Alejandro Gonzalez-Ollauri

    (The BEAM Research Centre, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Slobodan B. Mickovski

    (The BEAM Research Centre, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

Abstract

Effective landslide detection is crucial to mitigate the negative impacts derived from the occurrence of these natural hazards. Research on landslide detection methods has been extensively undertaken. However, simplified methods for landslide detection requiring a minimum amount of data inputs are still lacking. Simple approaches for landslide detection should be particularly interesting for geographical areas with limited information or resources availability. The aim of this paper is to present a refined, simple, GIS-based tool for the detection of landslide-prone and slope restoration zones. The tool only requires a digital elevation model (DEM) dataset as input, it is interoperable at multiple spatial scales, and it can be implemented on any GIS platform. The tool was applied on a coastal slope prone to instability, located in Scotland, in order to verify the functionality of the tool. The results indicated that the proposed tool is able to detect both shallow and deeper landslides satisfactorily, suggesting that the spatial combination of steep and potentially wet soil zones is effective for detecting areas prone to slope failure.

Suggested Citation

  • Alejandro Gonzalez-Ollauri & Slobodan B. Mickovski, 2021. "A Simple GIS-Based Tool for the Detection of Landslide-Prone Zones on a Coastal Slope in Scotland," Land, MDPI, vol. 10(7), pages 1-15, June.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:7:p:685-:d:583913
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    References listed on IDEAS

    as
    1. Gonzalez-Ollauri, Alejandro & Thomson, Craig S. & Mickovski, Slobodan B., 2020. "Waste to Land (W2L): A novel tool to show and predict the spatial effect of applying biosolids on the environment," Agricultural Systems, Elsevier, vol. 185(C).
    2. Vorpahl, Peter & Elsenbeer, Helmut & Märker, Michael & Schröder, Boris, 2012. "How can statistical models help to determine driving factors of landslides?," Ecological Modelling, Elsevier, vol. 239(C), pages 27-39.
    3. 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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