Landslide Susceptibility Mapping for the Thao River Catchment with High Spatial Resolution Rainfall Data

2021 ◽  
pp. 97-103
Author(s):  
The Viet Tran ◽  
Manh Cuong Nguyen ◽  
Quang Toan Trinh ◽  
Hoai Nam Do ◽  
Trung Kien Nguyen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
High Spatial Resolution ◽  
Susceptibility Mapping ◽  
Rainfall Data ◽  
Landslide Susceptibility Mapping ◽  
River Catchment

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.

scholarly journals Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

2014 ◽  
Vol XL-1 ◽  
pp. 293-300 ◽  
Author(s):  
O. E. Mora ◽  
M. G. Lenzano ◽  
C. K. Toth ◽  
D. A. Grejner-Brzezinska
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Surface Features ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Landslide Morphology

Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.

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, which is satisfactory to landslide susceptibility mapping.

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