Abstract
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.
Highlights
Over the past three decades, developments in the field of computer vision, remote sensing and algorithms, have made it possible to conduct digital terrain analysis accurately in several types of analyses, assessments, and applications
Validation Results: 1class-Support Vector Machine (SVM) ~70.50%, Kernel Density Estimation (KDE) ~ 72.00%, artificial neural networks (ANN) ~92.90% 2class-SVM ~95.10% Methods were tested for rainfall-induced landslide susceptibility and overall efficiency of ARF is found better the Decision Tree (DT) results
digital elevation models (DEMs) have facilitated the researchers for risk assessment of landslide hazards by estimating the probability of landslide event using derived topographic attributes
Summary
Over the past three decades, developments in the field of computer vision, remote sensing and algorithms, have made it possible to conduct digital terrain analysis accurately in several types of analyses, assessments, and applications. The United States Geological Survey (USGS) [7] has defined these models as a digital cartographic representation method for the elevation of the terrain at regularly spaced intervals x and y directions using z (elevation) values referenced to a common vertical datum. The accuracy of a DEM is always dependent on the quality of the field survey data collection methods [22], and these include contour insertion/plotting, scanning quality, digitization accuracy, map scale, and interpolation techniques. To achieve sub-meter accuracy of elevation models, LiDAR, aerial photography and ground survey techniques are most suitable methods but at high unit cost [24].
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