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The Impact of Quality of Digital Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence

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Mirosław Kamiński
Mirosław Kamiński
Mirosław Kamiński
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Earth and its Atmosphere: 2nd Edition
1
Book Chapter
e Impact of Quality of Digital
Elevation Models on the Result of
Landslide Susceptibility Modeling
Using the Method of Weights of
Evidence
Mirosław Kamiński
Polish Geological Institute–National Research Institute, Poland
*Corresponding Author: Mirosław Kamiński, Polish Geological Institute–National Research
Institute, Rakowiecka 4, 02-519 Warszawa, Poland
is Book Chapter is a republication of an article published by Mirosław Kamiński at Geosci-
ences in December 2020. (Kamiński, M. e Impact of Quality of Digital Elevation Models
on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence.
Geosciences 2020, 10, 488. https://doi.org/10.3390/geosciences10120488)
How to cite this book chapter: Mirosław Kamiński. e Impact of Quality of Digital
Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of
Weights of Evidence. In: Earth and its Atmosphere: 2nd Edition. Hyderabad, India: Vide Leaf.
2021.
© e Author(s) 2021. is article is distributed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Published November 18, 2021
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Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction
Preprint
Full-text available
  • Nov 2024
Digital Elevation Models (DEMs) play a key role in slope instability studies, ranging from landslide detection and recognition to landslide prediction. DEMs assist these investigations by reproducing landscape morphological features and deriving relevant predisposing factors, such as slope gradient, roughness, aspect, and curvature. Additionally, DEMs are useful for delineating map units with homogeneous morphological characteristics, such as Slope Units (SUs). In many cases, the selection of a DEM depends on factors like accessibility and resolution, without considering its actual accuracy. In this study, we compared freely available global DEMs (ALOS, COP, FABDEM) and a national DEM (TINITALY) with a reference DEM (local airborne LiDAR) to identify the most suitable DEM for representing fine-scale morphology and delineating SUs in the Marche Region, Italy, for landslide susceptibility mapping. Furthermore, we proposed a novel approach for selecting the optimal SUs partition. The DEM comparison was based on several criteria, including elevation, residual DEMs, roughness indices, slope variations, and the ability to delineate SUs. TINITALY, resampled at a 30x30 m pixel size, was found to be the most suitable DEM for representing fine-scale terrain morphology. It was then used to generate the optimal SUs partition among 18 combinations. These combinations were evaluated using both existing and newly integrated metrics alongside mapped landslide inventories to optimize terrain delineation and produce landslide susceptibility maps.
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