Landslide Susceptibility Zonation Mapping Using Bivariate Statistical Frequency Ratio method and GIS: A Case Study in Pa
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RESEARCH ARTICLE
Landslide Susceptibility Zonation Mapping Using Bivariate Statistical Frequency Ratio method and GIS: A Case Study in Part of SH 37 Ghat Road, Nadugani, Panthalur Taluk, The Nilgiris S. E. Saranaathan1
•
S. Mani1 • V. Ramesh2 • S. Prasanna Venkatesh3
Received: 14 August 2019 / Accepted: 7 October 2020 Ó Indian Society of Remote Sensing 2020
Abstract Landslide is more universal calamity in mountain areas. It is a threat to life and socio-economy. The ghat section Calicut– Nilampur–Gudalur State Highway 37 (SH-37) comes under the Survey of India toposheet 58A/7. It is important road network to connect Calicut. The study area is covered in 6.5 km ghat section of 103/6 to 109/8 km stone on SH 37. This study was carried out to prepare landslide susceptibility zonation (LSZ) mapping on 1:50,000 scale using frequency ratio model. Seventeen parameters such as elevation, slope, slope aspect, curvature, road buffer, drainage buffer, lineament buffer, land use, geomorphology, run-off, drainage density, drainage frequency, lineament density, lineament frequency, weathering condition, soil thickness and geology were considered as landslide-inducing factors. LSZ map was organized by manipulating association between the landslide persuade factors and old landslide using this model. Study area has grouped into five levels of susceptibility groups such as very low, low, moderate, high and very high. The LSZ map was validated by the old landslide record data collected from field. The landslide inventory percentage fall in very low hazard is 1.02%, 1.03% under low-susceptibility, 7.22% in moderate-susceptibility, 39.18% present in high-susceptibility and 51.55% noticed in very high-susceptibility zone, and using these data, the 18 rock vulnerable cut slopes were identified. Keywords Landslide susceptibility Remote sensing and GIS Frequency ratio model
Introduction Landslides are important natural hazards that can be devastating to life and property. Landslides in mountainous regions are triggered by natural factors like seismicity, precipitation, groundwater fluctuation, rainstorm and fast river erosion (Naranjo et al. 1997; Dai et al. 2002) and anthropogenic activities such as creation of cut slopes to build road network and unplanned development of & S. E. Saranaathan [email protected] 1
School of Civil Engineering, SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India
2
Centre for Geoinformatics, Jamsetji Tata School of Disaster Studies, Tata Institute of Social Sciences, Mumbai, Maharashtra, India
3
School of Earth & Atmospheric Science, Department of Applied Geology, University of Madras, Chennai, Tamil Nadu, India
township along hill slopes (Kannan et al. 2011a; Mani and Saranaathan 2017). Therefore, the need for prediction of landslide occurrence and its mitigation is inevitable. In order to ease the losses caused by landslides, many landslide susceptibility zonation (LSZ) mapping approaches have been identified and tested (Carrara 1988). The LSZ mapping involves separation of th
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