DC pole | Wartość | Język |
dc.contributor.author | Gaidzik, Krzysztof | - |
dc.contributor.author | Ramírez-Herrera, María Teresa | - |
dc.contributor.author | Bunn, Michael | - |
dc.contributor.author | Leshchinsky, Ben A. | - |
dc.contributor.author | Olsen, Michael | - |
dc.contributor.author | Regmi, Netra R. | - |
dc.date.accessioned | 2020-02-24T15:06:45Z | - |
dc.date.available | 2020-02-24T15:06:45Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Geomatics, Natural Hazards and Risk, Vol. 8, no. 2 (2017), s. 1054-1079 | pl_PL |
dc.identifier.issn | 1947-5705 | - |
dc.identifier.issn | 1947-5713 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/12765 | - |
dc.description.abstract | Landslides are a pervasive natural disaster, resulting in severe social,
environmental and economic impacts worldwide. The tropical,
mountainous landscape in South-West Mexico is predisposed to landslides
because of frequent hurricanes and earthquakes. The main goal of this
study is to compare landslide susceptibility maps in Guerrero derived
using high-resolution LIDAR (light detection and ranging) data from both
a manual landslide event inventory and an automated landslide
inventorying algorithm. The paper also highlights the importance of
applying LIDAR data in landslide inventorying and susceptibility mapping.
We mapped landslides based on two approaches: (1) manual mapping
using satellite images and (2) automatic identification of landslide
morphology employing the Contour Connection Method (CCM). We
produced a landslide susceptibility map by computing the probability of
landslide occurrence from statistical relationships of inventoried landslides
detected with LIDAR digital terrain models (DTMs) and derived landslidecausing
factors using the logistic regression method.
Our results suggest that the automated inventory derived through the
CCM algorithm with LIDAR DTMs effectively minimizes the timeconsuming
and subjective manual inventorying process. The high overall
prediction accuracy (up to 0.83) from logistic regression demonstrates the
validity and applicability deriving reliable landslide susceptibility maps
from an automated inventory; however, LIDAR data are required. | 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 | pl_PL |
dc.subject | landslide susceptibility map | pl_PL |
dc.subject | Contour Connection Method | pl_PL |
dc.subject | logistic regression | pl_PL |
dc.subject | landslide inventory | pl_PL |
dc.title | Landslide manual and automated inventories, and susceptibility mapping using LIDAR in the forested mountains of Guerrero, Mexico | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.relation.journal | Geomatics, Natural Hazards and Risk | pl_PL |
dc.identifier.doi | 10.1080/19475705.2017.1292560 | - |
Pojawia się w kolekcji: | Artykuły (WNP)
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