Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

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Loche M., Lombardo L., Görüm T., Tanyas H., Scaringi G.

REMOTE SENSING, vol.14, no.6, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.3390/rs14061321
  • Journal Name: REMOTE SENSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: landslide susceptibility, landslide inventory, controlling factor, slope unit, generalised additive model, LOGISTIC-REGRESSION, INVENTORY, DELINEATION, PREDICTION, RESOLUTION, QUALITY, EVENTS, MODELS, UNITS
  • Istanbul Technical University Affiliated: Yes


To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate.