GIS based spatial data analysis for landslide susceptibility mapping (original) (raw)
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GIS Based Landslide Susceptibility Mapping — A Case Study in Indian Himalaya
The paper presents GIS based spatial data analysis for landslide susceptibility mapping in parts of Sikkim Himalaya. Six important causative factors for landslides were selected and corresponding thematic data layers were prepared in GIS. The input data were collected from the topographic maps, satellite image, field data and published maps. Numerical weights for dierent categories of these factors were determined based on a statistical approach and then integrated in GIS environment to arrive at landslide susceptibility map of the area. The landslide susceptibility map classifies the area into five classes of landslide susceptible zones i.e., very high, high, moderate, low and very low. An attempt was also made to validate the map with the existing landslides of the area.
2020
Occurrence of landslides is very common and frequent phenomenon in hilly terrain of Indian Himalayan region leading to severe environmental and socio-economic issues. The current research used the method of weighted parameter, Remote Sensing (RS) and Geographic Information System (GIS) for landslide susceptibility mapping in the study area, East Sikkim district of Sikkim Himalaya. The different thematic layers were produced from high-resolution terrain corrected ALOS PALSAR DEM of 12.5 meter spatial resolution, Sentinel-2A data of 10 meter spatial resolution multi-spectral satellite information, LANDSAT 8 multi-spectral satellite information and multiple other landslide-related sources such as rainfall distribution, slope and structural/linear features (faults, thrusts, roads). These thematic map layers were integrated in a GIS platform (ArcGIS10.7) to delineate vulnerable landslide prone zones. The weighted assigned values were used for assigning weightage ranging from 0 to 10 for ...
landslide susceptibility mapping of north sikkim using geospatial techniques
Assessment of landslide is an important aspect for evaluation, zoning and risk management .Numerous methods and techniques were discussed by the researchers for landslide hazard zonation mapping of different landslide sensitive zones of the world for decision making. The Present research work is an integrated approach of geospatial technology for landslide susceptibility mapping for part of North Sikkim Himalaya using spatial Analyst tool of ARC GIS software. The important factors which are necessary landslide susceptibility mapping in hard rock terrain particularly for Himalayan region such as geology/structure, drainage density, Land use /land cover and slope were discussed and evaluated by using satellite images , field based data and published maps. All the extracted thematic maps were reclassified on the basis of their importance and characteristics for landslide susceptibility mapping of the area under GIS environment by applying weighted Overlay method in ARC GIS. Finally integrated landslide susceptibility map of the area generated and classified under four major categories from low to very high landslide susceptibility zones. Keywords: Landslide Susceptibility Mapping, Remote Sensing, ARC GIS, North Sikkim Himalaya.
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/mapping-of-landslide-susceptibility-using-geospatial-technique-a-case-study-in-kothagiri-region-western-ghats-tamil-nadu-india https://www.ijert.org/research/mapping-of-landslide-susceptibility-using-geospatial-technique-a-case-study-in-kothagiri-region-western-ghats-tamil-nadu-india-IJERTV2IS121059.pdf Landslides present a significant constraint to development in many parts of the study area which experience frequent landslides. Landslides are among the costliest and more damaging natural hazards in mountainous regions, triggered mainly under the influence of earthquakes and/or rainfall. Landslides cause adverse effects on human lives and economy worldwide. Through scientific analysis of landslides, we can assess and predict landslide-susceptible areas, and thus decrease landslide damage though proper preparation. This paper presents modeling of landslide susceptibility mapping using remote sensing data, GIS tools In this study, the landslide susceptibility maps are prepared based on the causative factors of slope, aspect, geology, geomorphology, soil, drainage density, lineament density and land use and land cover. All these factors are extracted from the spatial database constructed using remotely sensed data and topographic maps. The different classes of thematic layers were assigned the corresponding rating value, as non spatial information in the GIS was used to generate for each data layer. The weighted parametric approach was applied to determine degree of susceptibility to landslides. The landslide susceptibility map was prepared using this technique and is reclassified into four classes showing low to very high susceptibility classes. The analysis of the susceptibility modeling results shows the high significance of slope, drainage density, geological and land cover parameters. The landslide susceptibility map can be used to reduce damage associated with landslides and to land cover planning
2013
Landslides present a significant constraint to development in many parts of the study area which experience frequent landslides. Landslides are among the costliest and more damaging natural hazards in mountainous regions, triggered mainly under the influence of earthquakes and/or rainfall. Landslides cause adverse effects on human lives and economy worldwide. Through scientific analysis of landslides, we can assess and predict landslide-susceptible areas, and thus decrease landslide damage though proper preparation. This paper presents modeling of landslide susceptibility mapping using remote sensing data, GIS tools In this study, the landslide susceptibility maps are prepared based on the causative factors of slope, aspect, geology, geomorphology, soil, drainage density, lineament density and land use and land cover. All these factors are extracted from the spatial database constructed using remotely sensed data and topographic maps. The different classes of thematic layers were ass...