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

Marco Loche, Luigi Lombardo, Tolga Gorum, Hakan Tanyas, Gianvito Scaringi*

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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.

Orijinal dilİngilizce
Makale numarası1321
DergiRemote Sensing
Basın numarası6
Yayın durumuYayınlandı - 1 Mar 2022

Bibliyografik not

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


M.L. appreciates the financial support given by the Charles University Grant Agency (GAUK) with project number 337121. Data analysis, manuscript preparation and publication were funded by the Grant Agency of the Czech Republic (GAČR Grant No. 20-28853Y) and the Fund for international mobility of researchers at Charles University (MSCA-IF IV; Project No. CZ.02.2.69/0.0/0.0/20_079/0017987). The research was also partially supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia (Grant URF/1/4338-01-01).

FinansörlerFinansör numarası
Grantová Agentura, Univerzita Karlova
Grantová Agentura České RepublikyCZ.02.2.69/0.0/0.0/20_079/0017987, 20-28853Y
King Abdullah University of Science and TechnologyURF/1/4338-01-01

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