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Abstract
Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.
Highlights
Local terrain conditions, including terrain relief, hydrology, geology, and land use, are crucial for the assessment of landslide susceptibility [1]
We found that the prediction rates (PRs) of the slope, aspect, and elevation derived from three Digital elevation models (DEMs) (ASTER, Shuttle Radar Topographic Mission (SRTM), and Advanced Land Observation Satellite (ALOS) PALSAR) are similar
For ASTER, SRTM, and ALOS PALSAR DEMs, we found that the slope class 14–23o had the highest probability of landslides
Summary
Local terrain conditions, including terrain relief, hydrology, geology, and land use, are crucial for the assessment of landslide susceptibility [1]. These local features are often termed as “causal factors” [2]. Identifying these causal factors is considered as the steppingstone of landslide susceptibility assessment. Landslide susceptibility represents the likelihood of landslide occurrence in an area It assumes that future landslides may occur in previous landslide locations where the causal factors already created conducive environments for triggering landslides [2,3,4]. The success of landslide susceptibility assessment largely depends on the selection of causal factors and the quality of landslide inventories
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