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Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

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Abstract

A comprehensive Landslide Susceptibility Zonation (LSZ) map is sought for adopting any landslide preventive and mitigation measures. In the present study, LSZ map of landslide prone Ganeshganga watershed (known for Patalganga Landslide) has been generated using a binary logistic regression (BLR) model. Relevant thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, land use and land cover, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. The coefficients of the causative factors retained by the BLR model along with the constant have been used to construct the landslide susceptibility map of the study area, which has further been categorized into four landslide susceptibility zones from high to very low. The resultant landslide susceptibility map was validated using receiver operator characteristic (ROC) curve analysis showing an accuracy of 95.2 % for an independent set of test samples. The result also showed a strong agreement between distribution of existing landslides and predicted landslide susceptibility zones.

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Acknowledgements

Sanjit Kundu is grateful to all the personnel of DIGIT, for rendering unstinted support and encouragement. Dr K P Sharma, General Manager, RRSC-North, Dehradun, ISRO, Department of Space, for providing part of the relevant data. The authors sincerely acknowledge Director, Map and Cartography Division, Geological Survey of India, Lucknow for permission to refer Geological map. This manuscript has been greatly benefited from the suggestions of both the anonymous reviewers.

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Authors and Affiliations

  1. Department of Geology, Kumaun University, Nainital, 263001, India

    S. Kundu & C. C. Pant

  2. Defence Institute of Geospatial Information and Training, Delhi, 110010, India

    S. Kundu & D. C. Sharma

  3. Department of Geography, University of Delhi, Delhi, 110007, India

    A. K. Saha

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  1. S. Kundu
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  2. A. K. Saha
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Correspondence to A. K. Saha.

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Kundu, S., Saha, A.K., Sharma, D.C. et al. Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas. J Indian Soc Remote Sens 41, 697–709 (2013). https://doi.org/10.1007/s12524-012-0255-y

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