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Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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ISPRS Int. J. Geo-Inf. 2019, 8(12), 545; https://doi.org/10.3390/ijgi8120545
Received: 15 September 2019 / Revised: 6 November 2019 / Accepted: 27 November 2019 / Published: 29 November 2019
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.
Keywords: digital elevation models (DEMs); landslide hazards; landslide susceptibility mapping; landslide conditioning factors; landslide risk assessment digital elevation models (DEMs); landslide hazards; landslide susceptibility mapping; landslide conditioning factors; landslide risk assessment
MDPI and ACS Style

Saleem, N.; Huq, M.E.; Twumasi, N.Y.D.; Javed, A.; Sajjad, A. Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review. ISPRS Int. J. Geo-Inf. 2019, 8, 545.

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