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Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP)

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Abstract

In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ‘M’) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919.

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References

  • Anbalagan, R. (1992). Landslide Hazard Evaluation and Zonation mapping in mountainous terrain. Engineering Geology, 32, 269–277.

    Google Scholar 

  • Atkinson, P. M., & Massari, R. (1998). Generalized linear modeling of susceptibility to landsliding in the central Apennines, Italy. Computer & Geosciences, 24, 373–385.

    Article  Google Scholar 

  • Barbieri, G., & Cambuli, P. (2009). The weight of evidence statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy), 18th World IMACS/MODSIM Congress, Cairns, Australia, 13–17.

  • Barredo, J. I., Benavidesz, A., & Van Westen (2000). Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. JAG, 2, 9–23.

  • Bathurst, J. C., Bovolo, I. C., & Cisneros, I. F. (2010). Modelling the effect of forest cover on shallow landslides at the river basin scale. Ecological Engineering, 36, 317–327.

    Article  Google Scholar 

  • Borga, M., Dalla Fontana, G., Da Ros, D., & Marchi, L. (1998). Shallow landslide hazard assessment using a physically based model and digital elevation data. Environmental Geology 35(2–3), 81–88.

    Google Scholar 

  • Caiyan, W. U., & Jianping, Q. (2009). Relationship between landslides and lithology in the Three Gorges Reservoir area based on GIS AND Information Value Model. Higher Education Press and Springer-Verlag, 4, 165–170.

    Google Scholar 

  • Carrara, A., Cardinali, M., Guzzetti, F., & Reichenbach, P. (1995). GIS-Based techniques for mapping landslide hazard in Geographical Information System in Assessing Natural Hazards. Dordrecht: Academic.

    Google Scholar 

  • Congalton, R. (1991). A review of assessing the accuracy of classification of remotelysensed data. Remote Sensing of Environment, 37, 35–46.

    Article  Google Scholar 

  • Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS; Lantau Island, Hong Kong. Geomorphology, 42, 213–228.

    Article  Google Scholar 

  • Dhakal, A. S., Amada, T., & Aniya, M. (2000). Landslide hazard mapping and its evaluation using GIS: An investigation of sampling schemes for a grid-cell based quantitative method. Photogrametric Engineering and Remote Sensing, 66(8), 981–989.

    Google Scholar 

  • Donati, L., & Turrini, M. C. (2002). An objective and method to rank the importance of the factors predisposing to landslides with the GIS methodology, application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, 63, 277–289.

    Article  Google Scholar 

  • Einstein, H. H. (1988). Landslide risk assessment procedure. Proceedings of the Fifth International Symposium on Landslides. pp. 1075–1090.

  • Gokceoglu, C., Sonmez, H., & Ercanoglu, M. (2000). Discontinuity controlled probabilistic slope failure risk map of the Altindag (Settlement) region in Turkey. Engineering Geology, 55, 277–296.

    Article  Google Scholar 

  • Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Journal of Geomorphology, 31, 181–216. Elsevier, London.

    Article  Google Scholar 

  • Jibson, W. R., Edwin, L. H., & John, A. M. (2000). A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology, 58, 271–289.

    Article  Google Scholar 

  • Kouli, M., Loupasakis, C., Soupios, P., & Vallianatos, F. (2010). Landslide Hazard Zonation in High risk area of Rethymno Prefecture, Crete Island, Grece, Nat Hazards 52, 599–621 Landslide Hazard Zonation Atlas of India, Building Materials and Technology Promotion Council & Centre for Disaster mitigation and management, Anna University, Chennai, India (2003).

  • Lee, S., & Choi, U. (2003). Development of GIS Based geological hazard information system and its application for landslide analysis in Korea. Geoscience Journal, 7, 243–252.

    Article  Google Scholar 

  • Lee, S., & Pradhan, B. (2006). Landslide hazard assessment at Cameron Highland Malaysia using frequency ratio and logistic regression models. Geophy Res Abstracts, 8: SRef-ID:1607-7962/gra/EGU06-A-03241.

  • Lee, S., Ryu, J. H., Won, J. S., & Park, H. J. (2004). Determination and Publication of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71, 289–302.

    Article  Google Scholar 

  • Lee, S., Choi, J., & Min, K. (2004). Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing, 25, 2037–2052.

    Article  Google Scholar 

  • Luzi, L., Pergalani, F., & Terlien, M. T. J. (2000). Slope vulnerability to earthquake at sub-regional scale, using probabilistic technique and geographic information systems. Engineering Geology, 58, 13–336.

    Article  Google Scholar 

  • Malczewski, J. (1999). GIS and multi-criteria decision analysis (1st ed.). NY: Wiley. 392p.

    Google Scholar 

  • Mason, P. J., Rosenbaum, M. S., & Moore, J. McM. (1998). Digital image texture analysis for landslide hazard mapping, Geohazards in Engineering Geology, Special Publications, Geological Society, London, 15, 297–305.

  • Muthu, K., & Petrou, M. (2007). Landslide Hazard Mapping Using an Expert System and a GIS. IEEE Transaction on Geoscience and Remote Sensing, 45(2).

  • Mwasi, B. (2001). Land use conflicts resolution in a fragile ecosystem using Multi Criteria Evalution (MCE) and a GIS based Decision Support System (DSS).

  • Nagarajan, R., Roy, A., Vinod Kumar, R., Mukherjee, A., & Khire, M. V. (2000). Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng. Geol. Env. 58.

  • Nie, H.F., Diao, S.J., Liu, J.X., & Huang, H. (2001). The application of Remote Sensing technique and AHP-fuzzy method in comprehensive analysis and assessment for regional stability of Chongqing City, China. Proceedings of the 22nd International Asian Conference on Remoye Sensing; November 5–9, 2001; University of Singapore, Singapore, 1:660–665.

  • Nithya, E. S., & Prasanna, R. P. (2010). An integrated approach with GIS and remote sensing technique for landslide zonation. International Journal of Geomatics and Geosciences, 1(1).

  • Pandey, A., Dabral, P. P., Chowdhary, V. M., & Yadav, N. K. (2008). Landslide hazard zonation using remote sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India. Environmental Geology, 54, 1517–1529.

    Article  Google Scholar 

  • Parise, M., & Jibson, W. R. (2000). A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Engineering Geology, 58, 251–270.

    Article  Google Scholar 

  • Pistocchi, A., Luzi, L., & Napolitano, P. (2002). The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environmental Geology, 41, 765–775.

    Article  Google Scholar 

  • Pradhan, B. (2010). Remote Sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45, 1244–1256.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010a). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Science, 60, 1037–1054.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010b). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental Modelling & Software, 25, 747–759.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010c). Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysis. Landslides, 7, 13–30.

    Article  Google Scholar 

  • Quinn, P., Beven, K., Chevallier, P., & Planchon, O. (1991). The prediction of hillslope flow paths for distributed gydrological modeling using digital terrain models. Hydro Processes 5, 59–79.

    Google Scholar 

  • Rautelal, P., & Lakhera, R. C. (2000). Landslide Risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). International Journal of Applied Earth Observation and Geoinformation, 2, 153–160

    Article  Google Scholar 

  • Rowbotham, D., & Dudycha, D. N. (1998). GIS Modelling of slope stability in Phewa Tal Watershed, Nepal. Geomorphology, 26, 151–170.

    Article  Google Scholar 

  • Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281.

    Google Scholar 

  • Saaty, T. L. (1980). The analytical hierarchy process. NY: McGraw Hill. 350p.

    Google Scholar 

  • Saaty, T. L. (1990). The analytical hierarchy process: Planning, priority setting, resource allocation (1st ed.). Pittsburgh: RWS. 502p.

    Google Scholar 

  • Saaty, T. L. (1994). Fundamentals of decision making and priority theory with analytic hierarchy process (1st ed.). Pittsburgh: RWS. 527p.

    Google Scholar 

  • Saaty, T. L. (2000). Models, Methods, Concepts and Application of the Analytical Hierarchy Process, Boston:Kluwer Academic Publishers.

  • Saaty, T. L., & Vargas, L. G. (2001). Models, methods, concepts and applications of the analytic hierarchy process (1st ed.). Boston: Kluwer. 333p.

    Book  Google Scholar 

  • Saha, A. K., Gupta, R. P., & Arora, M. K. (2002). GIS based landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. International Journal of Remote Sensing, 23, 357–369.

    Article  Google Scholar 

  • Sharifikia, M. (2007). “RS and GIS Application in Geo-hazard-A case study part of central Alborz-Iran”-Ph.D. Thesis submitted in Geology department, University of Delhi, India.

  • Sarkar, S., & Kanungo, D. P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering and Remote Sensing, 70, 617–625.

    Google Scholar 

  • Sharma, V. K. (2006). Landslide hazard zonation: an overview of emerging techniques. Journal of Engineering Geology, XXXIII, 73–80.

    Google Scholar 

  • Soeters, R., & Westen, C. J. (1996). Slope instability recognition, analysis and zonation. In A. K. Turner and R. L. Schuster (Eds.), Landslides: Investigation and Mitigation. Transportation Research Board Special Report 247, 129–177.

  • Van Westen, C. J., Castellanos Abella, E., & Sekhar, L. K. (2008). Spatial data for landslide susceptibility, hazards and vulnerability assessment: an overview. Engineering Geology, 102, 112–131.

    Article  Google Scholar 

  • Varnes, D. J. (1984). Lanslide Hazard Zonation: a review of principles and practice. UNESCO, Natural Hazard, No. 3, pp 61.

  • Vijith, H., & Madhu, G. (2008). Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environmental Geology, 55, 1397–1405.

    Article  Google Scholar 

  • Yagi, H. (2003). Development of assessment method for landslide hazardness by Analytical Hierarchy Process (AHP). Abstract volume of the 42nd Annual Meeting of the Japan Landsllide Society, 209–212p.

  • Zhou, C. H., Lee, C. F., Li, J., & Xu, Z. W. (2002). On the spatial relationship between landslide and causative factors on Lantau Island, Hong Kong. Geomorphology, 43, 197–207.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Geography, Raja N.L.Khan Women’s College, Paschim Medinipur, West Bengal, 721102, India

    Sujit Mondal

  2. Department of Geography and Environment Management, Vidyasagar University, Paschim Medinipur, West Bengal, 721102, India

    Ramkrishna Maiti

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  1. Sujit Mondal
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Correspondence to Sujit Mondal.

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Mondal, S., Maiti, R. Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP). J Indian Soc Remote Sens 40, 483–496 (2012). https://doi.org/10.1007/s12524-011-0160-9

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  • DOI: https://doi.org/10.1007/s12524-011-0160-9

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