Effects of the Spatial Resolution of Digital Elevation Models and Their Products on Landslide Susceptibility Mapping

2017 ◽  
pp. 133-150 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Maher Ibrahim Sameen
Keyword(s):  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Digital Elevation Models ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Digital Elevation

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 Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

2019 ◽  
Vol 8 (12) ◽  
pp. 545 ◽  
Author(s):  
Nayyer Saleem ◽  
Md. Enamul Huq ◽  
Nana Yaw Danquah Twumasi ◽  
Akib Javed ◽  
Asif Sajjad
Keyword(s):  
Risk Assessment ◽  
Landslide Susceptibility ◽  
3D Visualization ◽  
Digital Elevation Models ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Conditioning Factors ◽  
Digital Elevation ◽  
The Impact

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.

scholarly journals Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh

2020 ◽  
Vol 12 (17) ◽  
pp. 2718 ◽  
Author(s):  
Yasin Wahid Rabby ◽  
Asif Ishtiaque ◽  
Md. Shahinoor Rahman
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Digital Elevation Models ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Assessment ◽  
Alos Palsar ◽  
Bivariate Model ◽  
Landslide Susceptibility Assessment ◽  
Digital Elevation

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.

Influence of DEM spatial resolution on the susceptibility mapping with SHALSTAB in the Rio Garcia hydrographic basin, municipality of Blumenau/SC / Influência de la resolución espacial del MDE en la cartografía de susceptibilidad con SHALSTAB en la cuenca hidrográfica del Río García, municipio de Blumenau/SC

2021 ◽  
Vol 5 (3) ◽  
pp. 1475-1491
Author(s):  
Gisele Marilha Pereira Reginatto ◽  
Regiane Mara Sbroglia ◽  
Camilo Andrade Carreño ◽  
Bianca Rodrigues Schvartz ◽  
Pâmela Betiatto ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Hydrographic Basin ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
Stability Model ◽  
Elevation Model ◽  
Del Rio

In translational landslide susceptibility analysis with SHALSTAB (Shallow Landsliding Stability Model), the resolution of the digital elevation model (DSM) is determinant for defining the type of mapping generated (preliminary or not). In this study, in order to verify the influence of the SDM scale on the SHALSTAB stability classes, susceptibility maps were prepared at two scales: 1:50,000 and 1:10,000. The study area was the Garcia River watershed, belonging to the municipality of Blumenau, Santa Catarina, affected by landslides in the 2008 catastrophe, which enabled the validation of the simulations with the scars mapped in the field. Thus, the influence of scale on the distribution of the model's stability classes and on its performance was verified. SHALSTAB performed better at the 1:10,000 scale, predicting 70% of the instabilities in a percentage of unstable area approximately three times smaller than at the 1,50,000 scale.

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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