APPLICATION OF HIGH RESOLUTION DIGITAL ELEVATION MODELS FOR LANDSLIDE SUSCEPTIBILITY MAPPING IN THE URBAN GROWTH AREA OF URCUQUI, ECUADOR

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.

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

Remote Sensing ◽

10.3390/rs12172718 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2718 ◽

Cited By ~ 3

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.

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

Geosciences ◽

10.3390/geosciences9080360 ◽

2019 ◽

Vol 9 (8) ◽

pp. 360 ◽

Cited By ~ 2

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.

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Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.225.442 ◽

2012 ◽

Vol 225 ◽

pp. 442-447 ◽

Cited By ~ 4

Author(s):

Biswajeet Pradhan ◽

Zulkiflee Abd. Latif ◽

Siti Nur Afiqah Aman

Keyword(s):

High Resolution ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Susceptibility Mapping ◽

Airborne Lidar ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Ratio Model ◽

Landslide Susceptibility Map ◽

Frequency Ratio Model

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

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