Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India

Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.

Landslide Susceptibility Mapping along Manipur-Assam NH-37

Landslides are a natural hazard in steep places that occur regularly and cause significant damage. To avoid and minimise hazards, comprehensive landslide remediation and control, landslide assessment, and hazard zonation are required. Various methods are established based on different assessment methodologies, which are essentially split into qualitative and quantitative approaches. GIS-based landslide susceptibility mapping was carried out along the National Highway 37, which connects Assam and Manipur and is a vital lifeline for the state, to identify and demarcate possible failure zones. A field visit was used to create a landslide inventory map along the road network. Google Earth and LANDSAT satellite imagery To perform landslide susceptibility zonation, thematic layers of several landslide causative elements were constructed in the study region. The study region has been divided into five endangered zones i.e. (“very low, low, moderate, high, and extremely high”). The landslide susceptibility zonation map was validated using the AUC and landslide density methods. The final map will be helpful to a variety of stakeholders, including town planners, engineers, geotechnical engineers, and geologists, for development and construction in the study region.

Landslide Susceptibility Zonation of Rongga District and Surrounding Areas Using Weight of Evidence (WoE) Method

Rongga District is located on West Bandung Regency, West Java, which is prone to landslide disaster. Morphological conditions in the form of steep hills become the one of landslide controlling factors. There are many landslide occurrences happen in this area, such as Nyomplong, Cibitung Village on March 23, 2020. The incident was triggered by heavy rain and strong winds. This area was chosen to assess the landslide susceptibility using the Weight of Evidence (WoE) Method. WoE is probabilistic bivariate method which connecting parameters causes landslide against distribution of landslide in research area. Landslide data which generated from direct observation in the field and satellite imagery morphology are 572 landslide events. The data is divided into two groups, the analysis data set (70%) and the validation data set (30%). The parameters used in the analysis are land use, slope, slope direction, curvature, elevation, rainfall, lithology, NDWI, NDVI, distance from road, distance from the river, distance to lineament, flow direction, lineament density, stream density and river density. The parameters validated by determining the value of the area under curve (AUC). AUC value> 0.6 will be used in the landslide susceptibility zonation next analysis. Validation of landslide susceptibility zonation was carried out using 172 landslide events. The result of the WoE validation shows the AUC success rate of 0.70 and AUC prediction rate of 0.76. The value of AUC shows that the modelling is good and acceptable.

GIS-Based Study To Develop Landslide Susceptibility Zonation Map Of District Mandi, Himachal Pradesh

Rising Incidents of landslide at district Mandi is issue of concern in Himachal Pradesh. Every year many people losses their life and property in these landslide event. This study is conducted with aim to preparation of landslide susceptibility zonation map of district Mandi using method of frequency ratio. Causative factor of landslide involved in preparation of Landslide susceptibility zonation map is Lithology, Slope, Drainage density, Aspect and Land use land cover. Slope, Drainage density, Aspect map are extracted through digital elevation model. Source of Digital elevation model used here is based on SRTM data whereas lithology map is based on data of geological survey of India. Land use land cover map is extracted by images of Landsat 8 satellite. Total of 52 existing landslides are used to model final map. LSZ map show 40.42% area is falling under medium susceptibility class, 34.5 % under low and 25.07% is under high susceptibility class which cover tehsils Mandi, Chachyot, Thunag and some part of Padhar, Aut and Bali Chowki. Further to validate these result areas under curve (AUC) method is use which give prediction rate of 76.06%.