Keywords
susceptibility
AHP model
inventory
regional planning
How to Cite
Abstract
The findings of the statistical models based on geographic information systems (GIS) for creating landslip susceptibility maps utilising remote sensing data and geographic information systems for the Rishikesh to Yamunotri corridor of Uttarakhand are presented in this study. Cartosat, Landsat, IMD, and India water resources data were used to extract ten factors: slope, aspect, soil, lithology, NDVI, LULC, distance to stream, precipitation, distance to road, and elevation. Using GIS-based statistical models, such as the analytical hierarchy process (AHP), which assigns ranks and weights to various factors to determine which factors are more responsible for landslides, the relationships between the detected landslide locations and these ten related factors were identified. The three landslide zone categories, high, medium, and low, on the landslip inventory map were developed using various things like field surveys and digital aerial photos. Regional planning and hazard mitigation would benefit from these landslip susceptibility maps.
References
1. Barnard, P. L., Owen, L.A., Sharma, M.C., and Finkela, R.C. (2001). Natural and human induced landsliding in the Garhwal Himalayas of Northern India. Geomorphology, 40(1-2), 21-35. https://doi.og/10.1016/S0169-555X(01)00035-6
2. Guzzetti et al., (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy, Geomorpholo-gy, 31, pp. 181-216
3. Myronidis, D., Papageorgiou, C. & Theophanous, S. (2016). Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81, 245–263. https://doi.org/10.1007/s11069-015-2075-1
4. Negi, I.S., Kumar, K., Kathait, A. et al. (2013). Cost assessment of losses due to recent reactivation of Kaliasaur landslide on National Highway 58 in Garhwal Himalaya. Nat Hazards 68, 901–914. https://doi.org/10.1007/s11069-013-0663-5
5. Sarkar et al., (2023). An event-duration based rain-fall threshold model for landslide prediction in Ut-tarkashi region, North-West Himalayas, India, Int. J. Earth Sci., 112, pp. 1923-1939
6. Sharda, Y.P., (2009). Landslide studies in India, glimpse of geoscience research in India, the Indian report to IUGS,2004-2008, Indian science academy, Silver Jubilee, (2009), pp. 98-101
7. Sujatha et al., (2012). Landslide susceptibility anal-ysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed, India, J. Earth Syst. Sci., 121 (5), pp. 1337-1350
8. Taloor et al., (2021b). Tectonic imprints of land-scape evolution in the Bhilangana and Mandakini basin, Garhwal Himalaya, India: a geospatial ap-proach, Quat. Int., 575–576, pp. 21-36, 10.1016/j.quaint.2020.07.021
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