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.

Use of GIS and Remote Sensing for Landslide Susceptibility Mapping

2018 ◽  
pp. 3503-3514 ◽  
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 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 Mapping and Assessment of Ketti Micro Watershed in Nilgiris District using Remote Sensing and Gis Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 142-148
Author(s):  
P. Kodanda Rama Rao ◽  
C. Rajakumar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Field Study ◽  
Landslide Susceptibility ◽  
Remote Sensing And Gis ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Thematic Maps ◽  
Soil Thickness ◽  
Gis And Remote Sensing

The GIS and Remote sensing technologies have been useful in the field of mapping in recent days. It is possible to integrate spatial data’s of different layers to determine the influence of various factors on landslide incidences. Based on the parameters such as slope, geomorphology, lineament, aspect, and present land use and soil thickness various thematic maps were prepared. By assessing proper ranks and weights the final landslide susceptible map was prepared. These maps were validated during field study

scholarly journals GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

2019 ◽  
Vol 11 (1) ◽  
pp. 708-726
Author(s):  
Zorgati Anis ◽  
Gallala Wissem ◽  
Vakhshoori Vali ◽  
Habib Smida ◽  
Gaied Mohamed Essghaier
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Satellite Image ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
North Western ◽  
Landslide Susceptibility Maps

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

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

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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