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 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.

Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS

2007 ◽  
Vol 35 (1) ◽  
pp. 31-42 ◽  
Author(s):  
P. Rajakumar ◽  
S. Sanjeevi ◽  
S. Jayaseelan ◽  
G. Isakkipandian ◽  
M. Edwin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landslide Susceptibility ◽  
Remote Sensing And Gis ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hilly Terrain

Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

2019 ◽  
pp. 384-398 ◽  
Author(s):  
Arzu Erener ◽  
Gulcan Sarp ◽  
Sebnem H. Duzgun
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Gis And Remote Sensing ◽  
Data Layers

In recent years, geographical information systems (GISs) and remote sensing (RS) have proven to be common tools adopted for different studies in different scientific disciplines. GIS is defined as a set of tools for the input, storage, retrieval, manipulation, management, modeling, analysis, and output of spatial data. RS, on the other hand, can play a role in the production of a data and in the generation of thematic maps related to spatial studies. This study focuses on use of GIS and RS data for landslide susceptibility mapping. Five factors including normalized difference vegetation index (NDVI) and topographic wetness index (TWI), slope, lineament density, and distance to roads were used for the grid-based approach for landslide susceptibility mappings. Results of this study suggest that geographic information systems can effectively be used to obtain susceptibility maps by compiling and overlaying several data layers relevant to landslide hazards.

scholarly journals Landslide Susceptibility Assessment: Identification and Hazard Mapping of Gandaki Province, Nepal

2020 ◽  
Vol 3 ◽  
pp. 11-21
Author(s):  
Khagendra Raj Poudel ◽  
Ramesh Hamal ◽  
Naresh Paudel
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Entropy Model ◽  
Risk Zone ◽  
Property Loss ◽  
And Coping ◽  
Steep Slopes

 Landslides considered as a common hazard, affecting constantly the administrative territory of Gandaki province, located in the central part of Nepal. Impact of landslides is significant due to its specific geological, anthropic, vegetation and other circumstances. The main aim of this study was to identify the factors determining landslides and forming a landslide susceptibility mapping of study area. The fieldwork was conducted, where 128 GPS locations was recorded throughout the study area. This study also used the maximum entropy model using MaxEnt software, taking into account of various landslide-causing factors, resulting major variables of landslides risk and formed susceptibility mapping of landslide. It is identified that slope and land use land cover are most important variables to increase the landslide risk. Findings highlight that lands around the riversides and steep slopes are more risky area in terms of landslides. Moreover, it is found that the area of 3371.32 km2 measured as landslide risk zone in this province, where Gorkha district categorized as most vulnerable place for landslide, comprising of largest area of landslide risk zone while Parbat district has low amount of risk land. Since the human casualties and property loss are the major consequences of the disaster, it is essential to identify and analyse the factors determining for landslide and developing the landslide susceptibility mapping of Gandaki province, which could be taken into account while developing mitigation and coping strategies.

scholarly journals Landslide susceptibility mapping by remote sensing and geomorphological data: case studies on the Sorrentina Peninsula (Southern Italy)

2019 ◽  
Vol 56 (6) ◽  
pp. 940-965 ◽  
Author(s):  
Claudia Spinetti ◽  
Marina Bisson ◽  
Cristiano Tolomei ◽  
Laura Colini ◽  
Alessandro Galvani ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Case Studies ◽  
Landslide Susceptibility ◽  
Southern Italy ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping
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