W jaki sposób struktura podłoża warunkuje rozwój osuwiska translacyjnego? Przykład osuwiska we Włodowicach w Obniżeniu Noworudzkim (Sudety Środkowe)

Kowalski, Aleksander

doi:10.7306/2022.26

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…About 6% of the country is covered by the Carpathians, where 95% of all the landslides in Poland have been registered. In 2010, 50,000-60,000 landslides were recorded in the Carpathians, an average of one landslide per km 2 [7]. Landslide areas cover about 30-40% of the province areas in this region.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020

View full text Add to dashboard Cite

To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In agricultural areas, soil mixed with the layer located below presents inferior properties for agricultural production. The restoration of soil fertility in such areas is a long-term process [7]. Therefore, to mitigate the negative effects of landslides, it is necessary to effectively estimate landslide susceptibility and hazards [10,11].…”

Section: Introductionmentioning

confidence: 99%