期刊
REMOTE SENSING
卷 14, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/rs14061321
关键词
landslide susceptibility; landslide inventory; controlling factor; slope unit; generalised additive model
类别
资金
- Charles University Grant Agency (GAUK) [337121]
- Grant Agency of the Czech Republic (GAR) [20-28853Y]
- Fund for international mobility of researchers at Charles University (MSCA-IF IV) [CZ.02.2.69/0.0/0.0/20_079/0017987]
- King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia [URF/1/4338-01-01]
Statistical approaches are typically used to understand the factors that make certain areas prone to landslides, but interpretation can be challenging in areas with complex geological and geomorphological patterns. A study in Northwestern Turkey showed that active and relict landslides could be related to different triggers, with the former being regulated by current rainfalls and the latter potentially representing past earthquake activity on the landscape.
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.
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