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.
Highlights
Landslides, in particular, have been recognised as one of the main drivers of soil loss globally [1] with a severity and recurrence which is most likely to increase under the predicted scenarios of climate change due to the potential intensification of the hydrological cycle [2], which is linked to the occurrence of rainfall-induced landslides [3]
We considered that false positive cases of landslide detection were those landslide-prone zones detected by the tool in which an active or past landslide is not visible on the ground
84.7% of the polygons generated by the tool fell on zones presenting slope failure events (i.e., 89 out of 105 polygons), or they appeared to be adjacent to zones with clear landslide signs
Summary
Landslides, in particular, have been recognised as one of the main drivers of soil loss globally [1] with a severity and recurrence which is most likely to increase under the predicted scenarios of climate change due to the potential intensification of the hydrological cycle [2], which is linked to the occurrence of rainfall-induced landslides [3]. Landslides can be classified in accordance with scale, location of occurrence, cause, material (e.g., mud, soil, rock) and predominant type of movement (e.g., falls, topples, flows etc.; [4]). An effective and timely detection of landslides is needed in order to substantially reduce the risks derived from such a natural hazard. The physical factors such as past history, slope steepness, and bedrock are the minimum parameters needed for assessing the likelihood of landslide occurrence
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