Dynamic Landslide Susceptibility Modeling and Risk Forecasting in the Nilgiris Using Geospatial Approaches

Authors

  • Yuvaraj R M

    Department of Geography, University of Madras, Chennai, Tamil Nadu 600005, India
  • S. Sanjeevi Prasad

    Department of Geography, University of Madras, Chennai, Tamil Nadu 600005, India
  • Bhagyasree Dolui

    Department of Geography, Queen Mary’s College, Chennai, Tamil Nadu 600004, India
  • Mijing Gwra Basumatary

    Department of Geography, B. Borooah College (Autonomous), Assam 781007, India
  • Usha Rani

    Department of Geography, Shivaji College, University of Delhi, New Delhi 110027, India
  • Rody Vanrohnem Faihriem

    Department of History, Goalpara College, Goalpara, Assam 783101, India
  • Harish Kumar

    Department of Geography, Delhi School of Economics, University of Delhi, Delhi 110007, India
  • Deshraj Meena

    Department of Geography, IP College for Women, University of Delhi, Delhi 110054, India
  • Arun Pratap Mishra

    Office of the Registrar General of India, Ministry of Home Affairs, Government of India, New Delhi 110011, India
  • Alka Gagan

    Department of Geography, Delhi School of Economics, University of Delhi, Delhi 110007, India
  • Arvind Kumar Sahani

    Department of African Studies, University of Delhi, Delhi 110007, India
  • Anjali Bhati

    Department of Geography, Delhi School of Economics, University of Delhi, Delhi 110007, India

DOI:

https://doi.org/10.30564/re.v7i3.10004
Received: 13 May 2025 | Revised: 23 May 2025 | Accepted: 9 June 2025 | Published Online: 25 July 2025

Abstract

Landslides remain a significant environmental hazard in India's hill regions, particularly in the Nilgiris district of Tamil Nadu, due to its steep terrain, fractured geology, and heavy seasonal rainfall. This study applies the Frequency Ratio (FR) model within a GIS and remote sensing framework to map landslide susceptibility and identify key contributing factors to slope instability. Ten thematic layers were used, including land use/land cover (LULC), NDVI, slope gradient, soil type and depth, geomorphology, aspect, rainfall, lineament density, and lineament proximity—derived from geological databases, DEMs, and satellite imagery. A landslide inventory was analyzed statistically to evaluate each factor’s role in landslide occurrence. Results indicate that slope gradient (9.15%) and LULC (8.37%) are the most influential factors, followed by geomorphology (7.78%), soil type (7.48%), and lineament density (4.50%). A key innovation of this study is the integration of lineament buffer zones to assess the influence of structural discontinuities, often overlooked in regional models. The model's predictive performance was validated using the Area Under the Curve (AUC) method, yielding a value of 0.879, indicating high accuracy. The resulting susceptibility map categorizes the landscape into low, moderate, and high-risk zones, providing a critical tool for regional planning, infrastructure development, and disaster management. This research supports climate-resilient development and sustainable land-use planning in vulnerable hill regions, emphasizing that both natural terrain characteristics and human-induced land alterations significantly contribute to landslide risk.

Keywords:

Slope Failure Risk; Frequency Ratio Analysis; GIS-based Mapping; Nilgiris Terrain; Landslide-prone Zones

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How to Cite

Yuvaraj R M, S. Sanjeevi Prasad, Bhagyasree Dolui, Mijing Gwra Basumatary, Usha Rani, Rody Vanrohnem Faihriem, Harish Kumar, Deshraj Meena, Arun Pratap Mishra, Alka Gagan, Arvind Kumar Sahani, & Anjali Bhati. (2025). Dynamic Landslide Susceptibility Modeling and Risk Forecasting in the Nilgiris Using Geospatial Approaches. Research in Ecology, 7(3), 234–253. https://doi.org/10.30564/re.v7i3.10004

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Vol. 7, Iss. 3 (August 2025)(in progress)

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Article

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Copyright © 2025 Yuvaraj R M, S. Sanjeevi Prasad, Bhagyasree Dolui, Mijing Gwra Basumatary, Usha Rani, Rody Vanrohnem Faihriem, Harish Kumar, Deshraj Meena, Arun Pratap Mishra, Alka Gagan, Arvind Kumar Sahani, Anjali Bhati

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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