Abstract
This paper expands the previous efforts by other researchers to present a quantitative and deterministic approach for terrain analysis. This study evaluates both spatial and temporal factors contributing landslides utilizing Light Detection and Ranging (LiDAR) point clouds in conjunction with the frequency ratio model (PFR) than has previously been used in the Alborz Mountains. The study area is Marzan Abad of the Alborz Mountain in Iran. The significance of this study is the performance of a high-resolution digital elevation model (DEM) derived from LiDAR point clouds in order to provide detailed information in improving landslide susceptibility evaluation. This study discusses how we improve the quality of landslide susceptibility evaluation. We apply the PFR model to consider the effect of landslide-related factors associated with Google Earth’s high-resolution images and field observations. The LiDAR point cloud data and GIS-based analysis have allowed performing high quality ways of landslide hazard assessments using inventory dataset as compared to previous studies. We contributed an improved landslide inventory map of the Mazandaran Province. We used image elements interpretation from the available ASTER DEM (30 m), LiDAR-DEM (5 m), and the Google Earth high spatial resolution images in conjunction with the field observations. This study evaluates factors such as geology, geomorphology, landuse, soil, slope, and distance from roads and drainage to represent, manipulate, and analyze factors. Also, we evaluated the performance success of the rate curve of landslides susceptibility. The results have indicated an improved landslide susceptibility map from LiDAR-derived DEMs implementing the PFR model with 92.59% of accuracy performance as compared to the existing data and previous studies in the same region. Furthermore, this study reveals that all considering factors have relatively positive effects on the landslides susceptibility mapping in the study, however, the most effective factor on the landslide occurrence is the lithology with 13.7%.
Highlights
Landslides are one of the most common deformation scenarios in the real-world environment
We concluded that how Light Detection and Ranging (LiDAR) digital elevation model (DEM) highresolution impacts the PFR model outcomes and increases the precision and quality of the susceptibility mapping as compared with the ASTER DEM with 15 m in resolution
The PFR model applies on the high-resolution DEM, and its derivative such as slope has provided an improved quality of outcomes of landslide susceptibility mapping in conjunction with the ASTER DEM and Google Earth’s images
Summary
Landslides are one of the most common deformation scenarios in the real-world environment. McKean and Roering (2003) studied the low-density digital elevation model (DEM) to determine the potential to differentiate morphologically components within a landslide (Lee and Dan 2005; Glen et al 2006; Lee and Pradhan 2006; Yilmaz 2010; Niculită 2016). They explored how to provide insight into the material type and activity of the slide. The literature review indicated that these techniques and low pixel resolutions of DEM and satellite imageries could not provide sufficient enough accuracy to visualize the objects extracting an informative description of the landslide locations and to predict the probability of the landslides occurrence
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