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Geospatial assessment of urban sprawl and landslide susceptibility around the Nainital lake, Uttarakhand, India

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Abstract

Landslide is one of the challenges faced by mountainous regions due to natural phenomena and human activity. Nainital district in the state of Uttarakhand is one of the popular tourist spots in India. It is situated in a lesser Himalayan belt facing experiences number of landslides every year. This region comes under the Main Boundary Thrust and Main Central Thrust which are considered to be very sensitive for landslides. Landslide susceptibility mapping is a proficient tool to identify vulnerable zones for landslides. Remote sensing and geographic information system are very effective tools for collecting, analysing and interpreting land use data, and on the other hand, multi-criteria valuation (MCE) allows users for decision-making by considering various factors affecting the process of the landslide. The MCE technique was applied considering present land use/land cover, slope, drainage, lithology, geomorphology, and type of soil. Overlay analysis and land susceptibility mapping was carried out for the area around the Nainital lake. The study concludes with hot spot analysis and recommends mitigation measures like geotextiles, retaining walls and strict building by-laws for preventing landslides.

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Data availability

Satellite Data models or code used during the study were downloaded from USGS Earth Explorer and procured from NRSC, Hyderabad, India. Direct requests for these materials may be made to the provider as indicated in the Acknowledgements. All data, models, and code generated or used during the study appear in the submitted article.

References

  • Althuwaynee, O. F., Pradhan, B., Park, H., & Hyun, J. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA, 114, 21–36.

    Article  Google Scholar 

  • Anbalagan, R., & Singh, B. (1996). Landslide hazard and risk assessment mapping of mountainous terrains: A case study from Kumaun Himalaya, India Engineering Geology, pp. 237–246

  • Anderson, J.R. (1976) A land use and land cover classification system for use with remote sensor data (vol. 964). US Government Printing Office.

  • Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area. Bangladesh. Landslides, 12(6), 1077–1095.

    Article  Google Scholar 

  • Ayalew, L., & Yamagishi, H. (2005). The application of GISbased logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65, 15–31.

    Article  Google Scholar 

  • Baatz, M., & Schape, A., (2000). Multiresolution segmentation: An optimization approach for high-quality multi-scale image. In Angewandte Geographische information.

  • Bathrellos, G. D., Kalivas, D. P., & Skilodimou, H. D. (2009). GIS-based landslide susceptibility mapping models applied to natural and urban planning in Trikala, Central Greece. Estud Geol, 65(1), 49–65.

    Article  Google Scholar 

  • Bell, J., & Morrison, T. (2015). Land use planning for flood risk: A comparative case of adaptive and precautionary governance systems. Journal of Environmental Policy and Planning.

  • Ben-Joseph, E., Ishii, H., & Ratti, C. (2002). 11.304 J Site and Urban Systems Planning, Department of Urban Studies and Planning Department of Architecture, Massachusetts Institute of Technology, Spring 2002.

  • Besson, L., Durville, J. L., Garry, G., Graszk, E, Hubert, T. & Toulemont, M., (1999). Plans de prévention des risques naturels (PPR): Risques de mouvements de terrain. Guide mé- thologique. La Documentation Française, Paris.

  • Borgatti L., &  Soldati M. (2005). Geomorphological hazard and human impact in mountain environments: an introduction. Geomorphology, pp. 1–390

  • Brabb, E. E. (1985). Innovative approaches to landslide hazard and risk mapping. In International landslide symposium proceedings, Toronto, Canada (vol. 1, pp. 17–22).

  • Carrara, A., Guzzetti, F., Cardinali, M., & Reichenbach, P. (1999). Use of GIS technology in the prediction and monitoring of landslide hazard. Natural Hazards, 20(2–3), 117–135.

    Article  Google Scholar 

  • Cascini, L., Bonnard, C., Corominas, J., Jibson, R., & Montero-Olarte, J. (2005). Landslide hazard and risk zoning for urban planning and development. In Landslide risk management (pp. 209–246). CRC Press.

  • Casey, K. A., Kääb, A., & Benn, D. I. (2012). Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal. The Cryosphere, 6(1), 85–100.

    Article  Google Scholar 

  • Chung, C. J. F., Fabbri, A. G., & Van Westen, C. J. (1995). Multivariate regression analysis for landslide hazard zonation. In Geographical information systems in assessing natural hazards (pp. 107–133). Springer, Dordrecht.

  • 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. Photogrammetric Engineering and Remote Sensing, 66(8), 981–989.

    Google Scholar 

  • Dey, J., Sakhre, S., Gupta, V., Vijay, R., Pathak, S., Biniwale, R., et al. (2018). Geospatial assessment of tourism impact on land environment of Dehradun, Uttarakhand, India. Environmental Monitoring and Assessment, 190(4), 181.

    Article  Google Scholar 

  • Erginal, A. E., Türkeş, M., Ertek, T. A., Baba, A., & Bayrakdar, C. (2008). Geomorphological investigation of the excavation-induced dündar landslide, bursa—turkey. Geografiska Annaler: Series A, Physical Geography, 90(2), 109–123.

    Article  Google Scholar 

  • Fanyu, Z., Gao, L., Wenwu, C., Wenfeng, H., & Shouyun, L. (2009). The evolution mechanism of the Yuanjiawan landslide in the process of cutting slope and excavation. Chinese Journal of Geotechnical Engineering, 31(8), 1.

    Google Scholar 

  • Fell, R., Ho, K. K., Lacasse, S., & Leroi, E. (2005). A framework for landslide risk assessment and management. In Landslide risk management (pp. 13–36). CRC Press.

  • Garry, G. & Graszk, E. (Ed.) (1997). Plans de prévention des risques naturels (PPR) – Guide général. La Documentation Fran- çaise, Paris.

  • Gautam, V. K., Gaurav, P. K., Murugan, P., & Annadurai, M. (2015). Assessment of surface water Dynamicsin Bangalore using WRI, NDWI, MNDWI, supervised classification and KT transformation. Aquatic Procedia, 4, 739–746.

    Article  Google Scholar 

  • Gorsevski, P. V., Gessler, P. E., Foltz, R. B., & Elliot, W. J. (2006). Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS, 10(3), 395–415.

    Article  Google Scholar 

  • Guedjeo, C. S., Kagou, Dongmo A., Wotchoko, P., Nkouathio, D. G., Chenyi, M. L., Wilson Buma, G., et al. (2017). Landslide susceptibility mapping and risk assessment on the bamenda mountain (cameroon volcanic line). Journal of Geosciences and Geomatics, 5(4), 173–185.

    Article  Google Scholar 

  • Gupta, R. P., & Joshi, B. C. (1990). Landslide hazard zoning using the GIS approach: A case study from the Ramganga catchment. Himalayas. Engineering Geology, 28(1–2), 119–131.

    Article  Google Scholar 

  • Gupta, V., Bhasin, R. K., Kaynia, A. M., Tandon, R. S., & Venkateshwarlu, B. (2016). Landslide hazard in the Nainital township, Kumaun Himalaya, India: the case of September 2014 Balia Nala landslide. Natural Hazards, 80(2), 863–877.

    Article  Google Scholar 

  • Guzzetti, F., Cardinali, M., Reichenbach, P., & Carrara, A. (2000). Comparing landslide maps: A case study in the Upper Tiber River Basin, Central Italy. Environmental Management, 25(3).

  • Hartlen, J., & Viberg, L. (1988). General report: Evaluation of landslide hazard. In International symposium on landslides. 5 (pp. 1037–1057).

  • Hennig, H. (2014) Multi-temporal and multi-sensor thermal analyses for submarine groundwater discharge detection over large spatial scales. Available on https://www.hydrology.uni-freiburg.de/abschluss/Hennig_H_2014_MA.pdf

  • Hukku, B. M. , Srivastava, A. K. , Jaitly, G. N. (1997). Measurement of slope movements in Nainital area Engineering Geology.

  • Hungr, O., Leroueil, S., & Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides., 11(2), 167–194.

    Article  Google Scholar 

  • Jade, S., & Sarkar, S. (1993). Statistical models for slope instability classification. Engineering Geology, 36(1–2), 91–98.

    Article  Google Scholar 

  • Jamloki D., (2010). District groundwater brochure of Nainital district, Uttarakhand. Available on https://cgwb.gov.in/District_Profile/Uttarakhand/Nainital.pdf

  • Jawak, S. D., Wankhede, S. F., & Luis, A. J. (2019). Explorative study on mapping surface facies of selected glaciers from Chandra Basin, Himalaya using WorldView-2 data. Remote Sensing, 11(10), 1207.

    Article  Google Scholar 

  • Jayaweera, S., (2007). Importance of planning guidelines in landslide disaster risk reduction. Available on https://www.nbro.gov.lk/images/content_image/publications/symposia/importance_of_planning_guidelines.pdf

  • Jensen, J. R. (2007). Remote sensing of the environment: an earth resource perspective: Pearson Prentice Hall. NJ: Upper Saddle River.

    Google Scholar 

  • Jones, H. G., & Vaughan, R. A. (2010). Remote sensing of vegetation: Principles, techniques, and applications. Oxford: Oxford University Press.

    Google Scholar 

  • Kavzoglu, T., Yildiz, M., & Tonbul, H. (2016). Evaluating performances of spectral indices for burned area mapping using object-based image analysis. In 12th International symposium on spatial accuracy assessment in natural resources and environmental sciences (pp. 5–8).

  • Kindu, M., Schneider, T., Teketay, D., & Knoke, T. (2013). Land use/land cover change analysis using object-based classification approach in Munessa-Shashemene landscape of the Ethiopian highlands. Remote Sensing, 5(5), 2411–2435.

    Article  Google Scholar 

  • Kuniyal, J. C., Vishvakarma, S. C. R., Badola, H. K., & Jain, A. P. (2004). Tourism in Kullu valley: An environmental assessment. Dehradun, India: Bishen Singh & Mohindra Pal Singh.

    Google Scholar 

  • Laliberte, A. S., & Rango, A. (2009). Texture and scale in object-based analysis of sub decimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 761–770.

    Article  Google Scholar 

  • Lee, S. G., & Hencher, S. R. (2009). The repeated failure of a cut-slope despite continuous reassessment and remedial works. Engineering Geology, 107(1–2), 16–41.

    Article  Google Scholar 

  • Lemenkova, P. (2015). Topology, homogeneity and scale factors for object detection: application of eCognition software for urban mapping using multispectral satellite image. In Proceedings of 7th international scientific and practical conference. INSO2015 (Akaki Tsereteli State University ATSU (pp. 80–85).

  • Lucas, R., Mitchell, A., & Bunting, P. (2008). Hyperspectral data for assessing carbon dynamics and biodiversity of forests (pp. 47–86). New York, NY: Taylor & Francis Group.

    Google Scholar 

  • Magliulo, P., Lisio, A. D., Russo, F., & Zelano, A. (2008). Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Natural Hazards, 47, 411–435.

    Article  Google Scholar 

  • Martire, D., De Rosa, M., Pesce, V., Santangelo, M. A., & Calcaterra, D. (2012). Landslide hazard and land management in high-density urban areas of Campania region, Italy. Natural Hazards & Earth System Sciences, 12(4).

  • McFeeters, S. K. (2013). Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561.

    Article  Google Scholar 

  • Middlemiss, C. S. (1890). Geological sketch of Nainital with some remarks on the natural conditions governing mountain slopes. India, 23(4), 213–234.Survey

    Google Scholar 

  • Mukherji, A., Sinisalo, A., Nüsser, M., Garrard, R., & Eriksson, M. (2019). Contributions of the cryosphere to mountain communities in the Hindu Kush Himalaya: A review. Regional Environmental Change, 19(5), 1311–1326.

    Article  Google Scholar 

  • Nagarajan, R., Mukherjee, A., Roy, A., & Khire, M. V. (1998). Technical note temporal remote sensing data and GIS application in landslide hazard zonation of part of Western Ghat, India.

  • National Disaster Management Guidelines, Govt. of India (2009) available on https://nidm.gov.in/pdf/guidelines/new/landslidessnowavalanches.pdf

  • Ozyavuz, M., Bilgili, B. C., & Salici, A. (2015). Determination of vegetation changes with NDVI method. Journal of Environmental Protection and Ecology, 16(1), 264–273.

    Google Scholar 

  • Pal, B., & Samanta, S., (2012). Topography and land temperature investigation

  • Pande, I. C., (1974). Tectonic interpretation of the geology of the Nainital area Himalayan Geology, pp. 532–546

  • Pande, R. K., & Pande, N. (2008). Nainital: A landslide town of Uttarakhand (India). Disaster Prevention and Management: An International Journal.

  • Puniya M. K., Joshi P., Pant P. D. (2013). Geological investigation of Nainital Bypass: a special emphasis on slope stability analysis, Kumaun Lesser Himalaya, Himalayan vulnerability, Uttarakhand, 2013: learning for planning and action, Xpressions Print & Graphics Pvt. Ltd, pp. 65–72

  • Pasang, S., & Kubíček, P. (2018). Information value model based landslide susceptibility mapping at Phuentsholing, Bhutan. AGILE 2018–Lund, June 12–15.

  • Rasul, A., Balzter, H., Ibrahim, G. R. F., Hameed, H. M., Wheeler, J., Adamu, B., et al. (2018). Applying built-up and bare-soil indices from landsat 8 to cities in dry climates. Land, 7(3), 81.

    Article  Google Scholar 

  • Rawat A. S. (2016). Geology of Nainital and History of Nainital.

  • Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 80, 60–91.

    Article  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(2), 357–369.

    Article  Google Scholar 

  • Sah, N., Kumar, M., Upadhyay, R., & Dutt, S. (2018). Hill slope instability of Nainital City, Kumaun Lesser Himalaya, Uttarakhand, India. Journal of Rock Mechanics and Geotechnical Engineering, 10(2), 280–289.

    Article  Google Scholar 

  • Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific reports, 5(1), 1–15.

    Article  CAS  Google Scholar 

  • Sarkar, S., Kanungo, D. P., Patra, A. K., & Kumar, P. (2012). GIS based landslide susceptibility mapping: A case study in Indian Himalaya.

  • Sarkar, S., & Kanungo, D. P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70(5), 617–625.

    Article  Google Scholar 

  • Sarkar, S., Kanungo, D. P., & Mehrotra, G. S. (1995). Landslide hazard zonation: A case study in Garhwal Himalaya, India. Mountain Research and Development, pp. 301–309.

  • Salcedo, D., Almeida, O. P., & Toulkeridis, B. M. T. (2018) Landslide susceptibility mapping using fuzzy logic and multi-criteria evaluation 1 techniques in the city of Quito, Ecuador 2.

  • Serrano, J., Shahidian, S., & Marques da Silva, J. (2019). Evaluation of normalized difference water index as a tool for monitoring pasture seasonal and inter-annual variability in a Mediterranean agro-silvo-pastoral system. Water, 11(1), 62.

    Article  Google Scholar 

  • Sikdar, P. K., Chakraborty, S., Adhya, E., & Paul, P. K. (2004). Land use/land cover changes and groundwater potential zoning in and around Raniganj coal mining area, Bardhaman District, West Bengal - a GIS and remote sensing approach. Journal of Spatial Hydrology, 4(2), 1–24.

    Google Scholar 

  • Sri Lanka Urban Multi-hazard Disaster Mitigation Project, Guidelines for Construction in Landslide Prone Areas (2003).

  • Sterlacchini, S., Frigerio, S., Giacomelli, P., Brambilla M., (2007). Landslide risk analysis: A multi-disciplinary methodological approach.

  • Turner, A. K., & Schuster, R. L. (1996). Landslides-Investigation and mitigation: Transportation Research Board. National Research Council, Special Report, 247, 672.

    Google Scholar 

  • United Nations Environment Programme (UNEP). Year Book. (2011). Emerging issues in our global environment; UNEP/Earthprint: Nairobi, Kenya.

  • Uniyal, R., (2018). Nainital at risk of major landslides Study. Available on https://timesofindia.indiatimes.com/city/dehradun/nainital-at-risk-of-major-landslidesstudy/articleshow/63124942.cms

  • Urban Development in the Lake Region: A historical perspective. (2017). Available on https://shodhganga.inflibnet.ac.in/bitstream/10603/44102/2/chapter%202.pdf

  • Van Westen, C. J. (1994). GIS in landslide hazard zonation: A review, with examples from the Andes of Colombia. In Mountain environments & geographic information systems (pp. 135–166). Taylor & Francis.

  • Valdiya, K. S. (1988). Geology and natural environment of Nainital Hills, Kumaun Himalaya. Nainital: Gyanodaya Prakashan.

    Google Scholar 

  • Valdiya, K. S. (1975). Lithology and age of the Tal Formation in Garhwal, and implication on stratigraphic scheme of Krol Belt in Kumaun Himalaya

  • Vijay, R., Kushwaha, V. K., Chaudhury, A. S., Naik, K., Gupta, I., Kumar, R., et al. (2016). Assessment of tourism impact on land use/land cover and natural slope in Manali, India: A geospatial analysis. Environmental Earth Sciences, 75(1), 20.

    Article  Google Scholar 

  • Vijay, R., Dey, J., Sakhre, S., & Kumar, R. (2020). Impact of urbanization on creeks of Mumbai, India: A geospatial assessment approach. Journal of Coastal Conservation, 24(1), 1–16.

    Article  Google Scholar 

  • Web-1: Nainital mall road collapsing, falling into Naini lake, daily hunt news. https://m.dailyhunt.in/news/india/english/newsroom+post-epaper-newspost/watch+here+nainital+mall+road+collapsing+falling+into+naini+lake-newsid-94976936; https://www.amarujala.com/photo-gallery/dehradun/nainital-mall-road-collapse-in-naini-lake.

  • Web-2: Indian Geo-platform of ISRO, Thematic Services, Geomorphology. https://bhuvan.nrsc.gov.in.

  • Web-3: Landslide Potential Index, Information Note 8/2014, June 2014, Hong Kong Slope Safety, Geotechnical Engineering Office, Civil Engineering and Development Department. https://hkss.cedd.gov.hk/hkss/eng/landslipwarn/IN_2014_08E.pdf.

  • Web-4: Disaster Risk Reduction and Resilience in The 2030 Agenda for Sustainable Development, United Nations Office for Disaster Risk Reduction. https://www.unisdr.org/iles/46052_disasterriskreductioninthe2030agend.pdf.

  • Weih, R. C., & Riggan, N. D. (2010). Object-based classification vs. pixel-based classification: Comparative importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4), 7.

    Google Scholar 

  • Wold, R. L., & Jochim, C. L. (1989). Landslide loss reduction: A guide for state and local government planning. In Earthquake Hazards reduction series (vol. 52). US Federal Emergency Management Agency (FEMA).

  • Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033.

    Article  Google Scholar 

  • Xu, H. (2008). A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276.

    Article  Google Scholar 

  • Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparison of results and confirmations. CATENA, 72, 1–12.

    Article  Google Scholar 

  • Yin, K. L. & Yan, T. Z. (1988). Statistical prediction models for slope instability of metamorphosed rocks. In Bonnard, C. (Ed.), Landslides, proceedings of the fifth international symposium on landslides, (vol. 2, pp. 1269–1272). Balkema, Rotterdam.

  • Yin, H., Udelhoven, T., Fensholt, R., Pflugmacher, D., & Hostert, P. (2012). How normalized difference vegetation index (ndvi) trendsfrom advanced very high resolution radiometer (AVHRR) and système probatoire d’observation de la terre vegetation (spot vgt) time series differ in agricultural areas: An inner mongolian case study. Remote Sensing, 4(11), 3364–3389.

    Article  Google Scholar 

  • Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.

    Article  Google Scholar 

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Acknowledgements

Authors are thankful to the Director CSIR-NEERI, Nagpur for providing necessary infrastructure and support to carry out this research study. Authors are also thankful to USGS earth explorer for downloading satellite data for this research study.

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  1. Centre for Strategic Urban Management, CSIR-NEERI, Nagpur, Maharashtra, 440020, India

    Jaydip Dey, Saurabh Sakhre, Ritesh Vijay, Hemant Bherwani & Rakesh Kumar

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Dey, J., Sakhre, S., Vijay, R. et al. Geospatial assessment of urban sprawl and landslide susceptibility around the Nainital lake, Uttarakhand, India. Environ Dev Sustain 23, 3543–3561 (2021). https://doi.org/10.1007/s10668-020-00731-z

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