Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 25-29, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/25/2016/
doi:10.5194/isprs-archives-XLI-B1-25-2016
 
02 Jun 2016
APPLICATION OF LiDAR DATE TO ASSESS THE LANDSLIDE SUSCEPTIBILITY MAP USING WEIGHTS OF EVIDENCE METHOD – AN EXAMPLE FROM PODHALE REGION (SOUTHERN POLAND)
Mirosław Kamiński Polish Geological Institut-National Research Institut, 4, Rakowiecka street, 00-975 Warsaw, Poland
Keywords: Susceptibility map, landslide, Weights of Evidence, LiDAR, Podhale region Abstract. Podhale is a region in southern Poland, which is the northernmost part of the Central Carpathian Mountains. It is characterized by the presence of a large number of landslides that threaten the local infrastructure. In an article presents application of LiDAR data and geostatistical methods to assess landslides susceptibility map. Landslide inventory map were performed using LiDAR data and field work. The Weights of Evidence method was applied to assess landslides susceptibility map. Used factors for modeling: slope gradient, slope aspect, elevation, drainage density, faults density, lithology and curvature. All maps were subdivided into different classes. Then were converted to grid format in the ArcGIS 10.0. The conditional independence test was carried out to determine factors that are conditionally independent of each other with landslides. As a result, chi-square test for further GIS analysis used only five factors: slope gradient, slope aspect, elevation, drainage density and lithology. The final prediction results, it is concluded that the susceptibility map gives useful information both on present instability of the area and its possible future evolution in agreement with the morphological evolution of the area.
Conference paper (PDF, 1175 KB)


Citation: Kamiński, M.: APPLICATION OF LiDAR DATE TO ASSESS THE LANDSLIDE SUSCEPTIBILITY MAP USING WEIGHTS OF EVIDENCE METHOD – AN EXAMPLE FROM PODHALE REGION (SOUTHERN POLAND), Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 25-29, doi:10.5194/isprs-archives-XLI-B1-25-2016, 2016.

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