A simple GIS-based tool for the detection of landslide-prone zones on a coastal slope in Scotland

Alejandro Gonzalez Ollauri*, Slobodan B. Mickovski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
154 Downloads (Pure)


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.
Original languageEnglish
Article number685
Number of pages15
Issue number7
Early online date28 Jun 2021
Publication statusPublished - Jul 2021


  • GIS
  • climate action
  • climate change
  • sustainability
  • resilience
  • erosion
  • landslides
  • machine learning
  • adaptive management
  • eco-engineering
  • geographical information system (GIS)
  • natural hazards
  • conservation
  • restoration
  • Scotland

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General Engineering
  • General Environmental Science
  • General Materials Science
  • General Earth and Planetary Sciences
  • General Computer Science
  • General Decision Sciences
  • Nature and Landscape Conservation
  • Global and Planetary Change
  • Ecology


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