Author:
Mani Ashish,Kumari Maya,Badola Ruchi
Abstract
AbstractThe occurrence of landslides is a costly and cataclysmic natural hazard that mainly occurs in hilly areas due to factors like earthquakes, cloud burst, extreme rainfall, human pressure, etc. leading to loss of biodiversity, property, and life. Effective and comprehensive landslide risk management is crucial to address landslide susceptibility. Utilizing remote sensing and Geographic Information System (GIS) techniques, this work focuses on the landslide hazard zonation (LHZ) mapping. The study is specifically conducted in the Doon Valley. These advanced technologies help in identifying and categorizing the areas prone to landslides. The Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data at 30 m resolution and Sentinel-2B data at 10 m resolution were used to perform the remote sensing and GIS operations in ArcGIS Software. Thematic layers such as Land Use/Land Cover (LULC), Slope, Geology, Soil Type, Elevation, Drainage Density, Vegetation, and Aspect were produced utilizing remote sensing and GIS data. The weighted overlay, a multi-criteria analysis method, was applied to assign attribute values to each thematic layer based on their importance, which was then combined to calculate the landslide hazard zone. The findings of this study demonstrate that landslides are more likely to occur near and above the Main Boundary Thrust/Fault (MBT). The high to very high hazard zone covers 16.64% of the total area, making landslides occurring more frequently. Additionally, the study found that the Doon Valley rivers’ upper segments are more susceptible to landslides than their lower segments. By integrating Remote Sensing and GIS techniques, it is possible to obtain extensive knowledge of regions prone to landslides. This information will be helpful for decision-makers and planners to reduce the impact of landslides in the near future.
Publisher
Springer Science and Business Media LLC
Reference49 articles.
1. Aristizábal E, Sánchez O. Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous Colombian Andes. Disasters. 2020;44(3):596–618.
2. Tiwari PC, Tiwari A, Joshi B. Urban growth in Himalaya: understanding the process and options for sustainable development. J Urban Reg Stud Contemp India. 2018;4(2):15–27.
3. Sujatha ER, Rajamanickam GV. Landslide hazard and risk mapping using the weighted linear combination model applied to the Tevankarai Stream Watershed, Kodaikkanal, India. Human Ecol Risk Assess. 2015;21(6):1445–61.
4. Geological Survey of India. Landslide hazard; 2023. https://www.gsi.gov.in/webcenter/portal/OCBIS/pages_pageGeoInfo/pageLANDSLIDEHAZRD
5. Allen SK, Rastner P, Arora M, Huggel C, Stoffel M. Lake outburst and debris flow disaster at Kedarnath, June 2013: hydrometeorological triggering and topographic predisposition. Landslides. 2016;13:1479–91. https://doi.org/10.1007/s10346-015-0584-3.