The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China

2020 ◽  
Vol 101 (3) ◽  
pp. 853-877 ◽  
Author(s):  
Zhuo Chen ◽  
Fei Ye ◽  
Wenxi Fu ◽  
Yutian Ke ◽  
Haoyuan Hong
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Nw China

scholarly journals Landslide susceptibility mapping of the Sera River Basin using logistic regression model

2016 ◽  
Vol 85 (3) ◽  
pp. 1323-1346 ◽  
Author(s):  
Nussaïbah B. Raja ◽  
Ihsan Çiçek ◽  
Necla Türkoğlu ◽  
Olgu Aydin ◽  
Akiyuki Kawasaki
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
River Basin ◽  
Landslide Susceptibility ◽  
Logistic Regression Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping

scholarly journals Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas

Geosciences ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 360 ◽  
Author(s):  
Sansar Raj ◽  
Thimmaiah
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Mountainous Regions ◽  
Digital Elevation ◽  
Kullu Valley ◽  
Elevation Model ◽  
The Impact

Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.

scholarly journals DEM Resolutions for Landslide Susceptibility Modeling

2021 ◽  
Author(s):  
Azemeraw Wubalem
Keyword(s):  
Statistical Methods ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Good Accuracy ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Dem Resolution ◽  
Predictive Rate

Abstract In landslide susceptibility mapping, the digital elevation model (DEM) is one of the most essential data sets, which is frequently used. Therefore, evaluate the effects of the spatial resolution of DEM on the landslide susceptibility model is very important. Hence, this paper is analyzed only the effects of the spatial resolution of DEM, Advanced Spaceborne Thermal Emission, and Reflection (ASTER) was used for DEM data source. The ASTER DEM was resampled to 45, 60, 75, and 90 m spatial resolutions. A set of geodatabases were built using Geographic Information System (GIS), which contains landslide governing factors and landslide inventory. Frequency ratio (FR) and certainty factor (CF) statistical methods were employed to generate a landslide susceptibility map. Landslide density and area under the curve (AUC) were applied to evaluate the model's performance for each DEM resolution. The results of the predictive rate curve value of AUC showed a coarser DEM resolution (90 m) produced the best performance and prediction accuracy. This indicated that a coarser DEM resolution produced higher predictive accuracy than fine resolution. Concerning the statistical models, the frequency ratio model produced very good accuracy at the coarser DEM resolutions (75 and 90 m). The predictive rate curve value of AUC ranges from 86–92% for the FR model and 81–89% for the CF model which indicating very good accuracy of the models to predict future landslide incidence in the study area. Therefore, it is possible to endorse statistical methods (frequency ratio, and certainty factor) respect with to DEM resolution, is satisfactory to landslide susceptibility mapping.

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