DC pole | Wartość | Język |
dc.contributor.author | Gaidzik, Krzysztof | - |
dc.contributor.author | Ramírez-Herrera, María Teresa | - |
dc.date.accessioned | 2021-11-19T14:33:36Z | - |
dc.date.available | 2021-11-19T14:33:36Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | "Scientific Reports", Vol. 11, 2021, art. no. 19334, s. 1-14 | pl_PL |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/21926 | - |
dc.description.abstract | Landslide detection and susceptibility mapping are crucial in risk management and urban planning.
Constant advance in digital elevation models accuracy and availability, the prospect of automatic
landslide detection, together with variable processing techniques, stress the need to assess the
effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this
study is to evaluate the influence of variations in input data on landslide susceptibility mapping using
a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory
(manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslidecausing
factors, and (4) sampling technique. We showed that models based on automatic landslide
inventory present comparable overall prediction accuracy as those produced using manually detected
features. We also demonstrated that finer resolution of topographic data leads to more accurate
and precise susceptibility models. The impact of the number of landslide-causing factors used for
calculations appears to be important for lower resolution data. On the other hand, even the lower
number of causative agents results in highly accurate susceptibility maps for the high-resolution
topographic data. Our results also suggest that sampling from landslide masses is generally more
befitting than sampling from the landslide mass center. We conclude that most of the produced
landslide susceptibility models, even though variable, present reasonable overall prediction accuracy,
suggesting that the most congruous input data and techniques need to be chosen depending on the
data quality and purpose of the study. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | landslide detection | pl_PL |
dc.subject | type of landslide inventory | pl_PL |
dc.title | The importance of input data on landslide susceptibility mapping | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.identifier.doi | 10.1038/s41598-021-98830-y | - |
Pojawia się w kolekcji: | Artykuły (WNP)
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