[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding authors, upon reasonable request.

References

  • Abbas, S., Kousar, S., Yaseen, M., Ali Mayo, Z., Zainab, M., Mahmood, M. J., & Raza, H. (2019). Impact assessment of socioeconomic factors on dimensions of environmental degradation in Pakistan. SN Applied Sciences. https://doi.org/10.1007/s42452-020-2231-4.

    Article  Google Scholar 

  • Abbas, S., Hussain, M. S., Shiraji, S. A., & Khurshid, M. (2020). Assessment of physiographic features and changing climate of Kabul River Catchment area in Northwestern Pakistan. Pakistan Journal of Science, 72(2), 112.

    Google Scholar 

  • Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey. Landslides, 9, 93–106. https://doi.org/10.1007/s10346-011-0283-7.

    Article  Google Scholar 

  • Allen, S. K., Rastner, P., Arora, M., Huggel, C., & Stoffel, M. (2015). Lake outburst and debris flow disaster at Kedarnath, June 2013: Hydrometeorological triggering and topographic predisposition. Landslides. https://doi.org/10.1007/s10346-015-0584-3.

    Article  Google Scholar 

  • Ambrosi, C., Strozzi, T., Scapozza, C., & Wegmuller, U. (2018). Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on Earth-Observation data. Engineering Geology, 237(2018), 217–228. https://doi.org/10.1016/j.enggeo.2018.02.020.

    Article  Google Scholar 

  • Bhukosh (2020). Geoscientific data of Geological Survey of India. http://bhukosh.gsi.gov.in/Bhukosh/Public.

  • Bruschi, V. M., Bonachea, J., Remondo, J., Gomez-Arozamena, J., Rivas, V., Barbieri, M., et al. (2013). Land management versus natural factors in land instability: Some examples in northern Spain. Environmental Management, 52(2), 398–416.

    Article  Google Scholar 

  • Catani, F., Lagomarsino, D., Segoni, S., & Tofani, V. (2013). Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Naturnal Hazards and Earth System Science, 13, 2815–2831. https://doi.org/10.5194/nhess-13-2815-2013.

    Article  Google Scholar 

  • Chen, W., Chai, H., Sun, X., Wang, Q., Ding, X., & Hong, H. (2016). A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab Journal of Geoscience, 9, 1–16.

    Article  CAS  Google Scholar 

  • Chen, L., Guo, Z., Yin, K., Shrestha, D. P., & Jin, S. (2019). The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuanen County (Hubei, China). Natural Hazards and Earth System Sciences, 19(203), 2207–2228. https://doi.org/10.5194/nhess-19-2207-2019.

    Article  Google Scholar 

  • De Sy, V., Schoorl, J. M., Keesstra, S. D., Jones, K. E., & Classens, L. (2013). Landslide model performance in a high resolution small-scale landscape. Geomorphology, 190, 73–81. https://doi.org/10.1016/j.geomorph.2013.02.012.

    Article  Google Scholar 

  • Deng, X., Li, L., & Tan, Y. (2017). Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity. ISPRS International J Geo-Information, 6, 103. https://doi.org/10.3390/ijgi6040103.

    Article  Google Scholar 

  • Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., & Alamri, A. M. (2020). Rainfall induced landslide studies in Indian Himalayan Region: A critical review. Applied Science, 10, 2466. https://doi.org/10.3390/app10072466.

    Article  CAS  Google Scholar 

  • Fu, S., Chen, L., Woldai, T., Yin, K., Gui, L., Li, D., et al. (2020). Landslide hazard probability and risk assessment at the community level: A case of western Hubei, China. Natural Hazards and Earth System Sciences, 20(2), 581–601. https://doi.org/10.5194/nhess-20-581-2020.

    Article  Google Scholar 

  • Gabet, E. J., Burbank, D. W., Putkonen, J., Pratt-Sitaula, B., & Ojha, T. (2004). Rainfall thresholds for landsliding in the Himalaya of Nepal. Geomorphology, 63(3), 131–143. https://doi.org/10.1016/j.geomorph.2004.03.011.

    Article  Google Scholar 

  • Galve, J. P., Cevasco, A., Brandolini, P., & Soldati, M. (2015). Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modeling. Landslides, 12, 101–114. https://doi.org/10.1007/s10346-014-0478-9.

    Article  Google Scholar 

  • Gao, H., Fam, P. S., Low, H. C., Tay, L. T., & Lateh, H. (2019). An overview and comparison on recent landslide susceptibility mapping methods. Disaster Advances, 12, 46–64.

    Google Scholar 

  • Geertsema, M., Highland, L., & Vaugeouis, L. (2009). Environmental Impact of Landslides. In K. Sassa & P. Canuti (Eds.), Landslides - Disaster risk reduction. NewYork: Springer.

    Google Scholar 

  • Glade, T. (2003). Landslide occurrence as a response to land use change: A review of evidence from New Zealand. Catena, 51, 297–314. https://doi.org/10.1016/s0341-8162(02)00170-4.

    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-study, Central Italy. Geophys J R Astr Soc, 31, 181–216.

    Google Scholar 

  • Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth Science Reviews, 112, 42–66.

    Article  Google Scholar 

  • Haoyuan, H., Pourghasemi, H. R., & Pourtaghi, J. S. (2016). Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259, 105–118. https://doi.org/10.1016/j.geomorph.2016.02.012.

    Article  Google Scholar 

  • Heping, S., Hürlimann, M., Molowny-Horas, R., González, M., Pinyol, J., Abancó, C., et al. (2019). Relation between land cover and landslide susceptibility in Val d'Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction. Science of The Total Environment, 693, 133557. https://doi.org/10.1016/j.scitotenv.2019.07.363.

    Article  CAS  Google Scholar 

  • Khanduri, S. (2017). Landslide hazard around mussoorie: The lesser Himalayan tourist destination of Uttarakhand, India. Journal of Geograph Natural Disaster. https://doi.org/10.4172/2067-0871000200.

    Article  Google Scholar 

  • Kumar, A., & Gorai, A. K. (2018). Geo-spatial estimation and forecasting of LULC vulnerability assessmentof mining activity: A case study of Jharia coal field, India. Journal of Remote Sensing & GIS, 2018(7), 4. https://doi.org/10.4172/2469-4134.1000253.

    Article  Google Scholar 

  • Kwan, J. S. H., Chan, S. L., Cheuk, J. C. Y., & Koo, R. C. H. (2014). A case study on an open hillside landslide impacting on a flexible rock fall barrier at Jordan Valley, Hong Kong. Landslides, 11, 1037–1050. https://doi.org/10.1007/s10346-013-0461-x.

    Article  Google Scholar 

  • Li, Y., Zhou, R., Zhao, G., Li, H., Su, D., Ding, H., et al. (2014). Tectonic uplift and landslides triggered by the Wenchuan earthquake and constraints on orogenic growth: A case study from Hongchun Gully, Longmen Mountains, Sichuan, China. Quaternary International, 349, 142–152. https://doi.org/10.1016/j.quaint.2014.05.005.

    Article  Google Scholar 

  • Marinos, V., Stoumpos, G., & Papazachos, C. (2019). Landslide hazard and risk assessment for a natural gas pipeline project: The case of the trans adriatic pipeline, Albania Section. Geosciences, 2019(9), 61. https://doi.org/10.3390/geosciences9020061.

    Article  Google Scholar 

  • Meena, S. R., Ghorbanzadeh, O., & Blaschke, T. (2019). A Comparative study of statistics-based landslide susceptibility models: A Case study of the region affected by the gorkha earthquake in Nepal. ISPRS International Journal of Geo-Information, 8, 94. https://doi.org/10.3390/ijgi8020094.

    Article  Google Scholar 

  • Meneses, B. M., Pereira, S., & Reis, E. (2019). Effects of different land use and land cover data on the landslide susceptibility zonation of road network. Natural Hazards Earth System Science, 19, 471–487. https://doi.org/10.5194/nhess-19-471-2019.

    Article  Google Scholar 

  • NASA/Goddard Space Flight Center (2020). Climate change could trigger more landslides in High Mountain Asia. Science News. https://www.sciencedaily.com/releases/2020/02/200211121512.htm

  • Nseka, D., Mugagga, F., Bamutaze, Y., & Bob, N. (2019). The fragility of agricultural landscapes and resilience of communities to landslide occurrence in the tropical humid environments of Kigezi Highlands in South Western Uganda. In Y. Bamutaze, S. Kyamanywa, B. R. Singh, G. Nabanoga, & R. Lal (Eds.), Agriculture and Ecosystem Resilience in Sub Saharan Africa. Cham: Springer.

    Google Scholar 

  • Pachauri, A.K. (2010). Landslide hazard mapping and assessment in Himalayas. In: Fifth International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics 22, May 24-29 2010 San Diego, California USA, http:scholarsmine.mst.edu/icrageesd/05Sicrageesd/session04b/22.

  • Pandey, V. K., & Sharma, M. C. (2017). Probabilistic landslide susceptibility mapping along Tipri to Ghuttu highway corridor, Garhwal Himalaya (India). Remote Sensing Applications, Society Environmental, 8(2017), 1–11.

    Article  Google Scholar 

  • Pham, B. T., Bui, D. T., Prakash, I., & Dholakia, M. B. (2017). Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena, 149, 52–63.

    Article  Google Scholar 

  • Pisano, L., Zumpano, V., Malek, Z., Rosskopf, C. M., & Parise, M. (2017). Variations in the susceptibility to landslides, as a consequence of landcover changes: A look to the past, and another towards the future. Science of the Total Environmental, 601–602, 1147–1159. https://doi.org/10.1016/j.scitotenv.2017.05.231.

    Article  CAS  Google Scholar 

  • Pourghasemi, H. R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? Catena, 162, 177–192. https://doi.org/10.1016/j.catena.2017.11.022.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental Model Software, 25, 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016.

    Article  Google Scholar 

  • Rai, P. K., Mohan, K., & Kumra, V. K. (2014). Landslide hazard and its mapping using remote sensing and GIS techniques. Journal of Scientific Research, 58, 1–13.

    Google Scholar 

  • Randall, W. M., Thomlinson, J. R., & Larsen, M. C. (1997). Predicting landslide vegetation in patches on landscape gradients in Puerto Rico. Landscape Ecology, 12(299–307), 1997. https://doi.org/10.1023/A:1007942804047.

    Article  Google Scholar 

  • Reichenbach, P., Busca, C., Mondini, A. C., & Rossi, M. (2014). The influence of land use change on landslide susceptibility zonation: the Briga Catchment test site (Messina, Italy). Environmental Management, 54, 1372–1384. https://doi.org/10.1007/s00267-014-0357-0.

    Article  CAS  Google Scholar 

  • Roy, J., & Saha, S. (2019). Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenvironmental Disasters. https://doi.org/10.1186/s40677-019-0126-8.

    Article  Google Scholar 

  • Sartohadi, J., Pulungan, N. A. H. J., Nurudin, M., & Wahyudi, W. (2018). The ecological perspective of landslides at soils with high clay content in the middle bogowonto watershed, central java, Indonesia. Applied and Environmental Soil Science. https://doi.org/10.1155/2018/2648185.

    Article  Google Scholar 

  • Schmaltz, E. M., Steger, S., & Glade, T. (2017). The influence of forest cover on landslide occurrence explored with spatio-temporal information. Geomorphology, 290, 250–264. https://doi.org/10.1016/j.geomorph.2017.04.024.

    Article  Google Scholar 

  • Schuster, R. L., & Highland, L. (2007). Overview of the effects of mass wasting on the natural environment. Environmental and Engineering Geoscience, 13(1), 25–44. https://doi.org/10.2113/gseegeosci.13.1.25.

    Article  Google Scholar 

  • Sharma, A., Sur, U., Singh, P., Rai, P. K., & Srivastava, P. K. (2020). Probabilistic landslide hazard assessment using statistical information value (SIV) and GIS techniques: A case study of Himachal Pradesh, India. In Technique for disaster risk management and mitigation (pp. 197–208). https://doi.org/10.1002/9781119359203.ch15.

  • Shastri, S., Singh, P., Verma, P., Rai, P. K., & Singh, A. P. (2020). Land cover dynamics and their impacts on thermal environment of dardi block, Gautam Budh Nagar, India. Journal of Landscape and Ecology. https://doi.org/10.2478/jlecol-2020-0007.

    Article  Google Scholar 

  • Singh, P., and Sharma, A. (2015). Probabilistic Landslide susceptibility mapping using binary logistic regression model and Geospatial Techniques: A case study of Uttarakhand. In: 16th ESRI User Conference, New Delhi, India, December 2015. https://doi.org/https://doi.org/10.1007/s10668-020-00811-0

  • Singh, P., Sharma, A., Sur, U., & Rai, P. K. (2020). Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India. Environment Development and Sustainability. https://doi.org/10.1007/s10668-020-00811-0.

    Article  Google Scholar 

  • Stokes, A., Norris, J. E., van Beek, L. P. H., Bogaard, T., Cammeraat, E., Mickovski, S. B., et al. (2008). How vegetation reinforces soil on slopes. Slope Stability and Erosion Control: Ecotechnological Solutions. https://doi.org/10.1007/978-1-4020-6676-4_4.

    Article  Google Scholar 

  • Sur, U., & Singh, P. (2019). Landslide Susceptibility Indexing using geospatial and geostatistical techniques along Chakrata-Kalsi road corridor, India. Journal of the Indian National Cartographic Association (INCA), 38, 487–495.

    Google Scholar 

  • Sur, U., Singh, P., & Meena, S. (2020). Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomatics, Natural Hazards and Risk, 11(1), 2176–2209. https://doi.org/10.1080/19475705.2020.1836038.

    Article  Google Scholar 

  • Tien, Bui D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J. F., Melesse, A. M., et al. (2019). Flood spatial modeling in Northern Iran using remote sensing and GIS: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sensing, 11, 1589. https://doi.org/10.3390/rs11131589.

    Article  Google Scholar 

  • Tien Bui, D., Pradhan, B., Revhaug, I., Nguyen, D. B., Pham, H. V., & Bui, Q. N. (2015). A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics, Natural Hazards and Risk, 6, 243–271. https://doi.org/10.1080/19475705.2013.843206.

    Article  Google Scholar 

  • Torkashvand, A. M., Irani, A., & Sorur, J. (2014). The preparation of landslide map by landslide numerical risk factor (LNRF) model and geographic information system (GIS). Egyptian Journal of Remote Sensing and Space Science, 17(2), 159–170. https://doi.org/10.1016/j.ejrs.2014.08.001.

    Article  Google Scholar 

  • Tsangaratos, P., & Llia, I. (2016). Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena, 145, 164–179. https://doi.org/10.1016/j.catena.2016.06.004.

    Article  Google Scholar 

  • Vanacker, V., Vanderschaeghe, M., Govers, G., Willems, E., Poesen, J., Deckers, J., & De Bievre, B. (2003). Linking hydrological, infinite slope stability and land-use change models through GIS for assessing the impact of deforestation on slope stability in high Andean watersheds. Geomorphology, 52, 299–315. https://doi.org/10.1016/S0169-555X(02)00263-5.

    Article  Google Scholar 

  • Wang, Q., Guo, Y., Li, W., He, J., & Wu, Z. (2019). Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor. Geomatics, Natural Hazards and Risk, 10(1), 820–835. https://doi.org/10.1080/194757.2018.1549111.

    Article  Google Scholar 

  • World Bank. (2013). Turn down the heat: Climate extremes, regional impacts, and the case for resilience. A Report for the World Bank by the Potsdam Institute for Climate Impact Research and Climate Analytics. https://www.pik-potsdam.de/members/olivias/tdth2-exec-summ.pdf.

  • Xu, C., Xu, X., Shen, L., Yao, Q., Tan, X., Kang, W., et al. (2016). Optimized volume models of earthquake-triggered landslides. Scientific Reports. https://doi.org/10.1038/srep29797.

    Article  Google Scholar 

  • Zhang, J., He, P., Xiao, J., & Xu, F. (2018). Risk assessment model of expansive soil slope based on Fuzzy-AHP method and its engineering application. Geomatics, Natural Hazards Risks, 9(1), 389–402. https://doi.org/10.1080/19475705.2018.1445664.

    Article  Google Scholar 

Download references

Acknowledgements

Authors like to thank the anonymous reviewers for their constructive inputs on the manuscript.

Author information

Authors and Affiliations

  1. Amity Institute of Geo-Informatics and Remote Sensing, Amity University, Sector 125, Noida, UP, India

    Ujjwal Sur & Prafull Singh

  2. Department of Geology, Central University of South Bihar, Gaya, 824236, India

    Prafull Singh

  3. Department of Geography K.M.C, Language University (U.P. State Govt. University), Lucknow, UP, India

    Praveen Kumar Rai

  4. Umwelt und Information stechnologgie, UIZ, Berlin, Germany

    Jay Krishna Thakur

Authors
  1. Ujjwal Sur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prafull Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Praveen Kumar Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jay Krishna Thakur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prafull Singh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sur, U., Singh, P., Rai, P.K. et al. Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India. Environ Dev Sustain 23, 13526–13554 (2021). https://doi.org/10.1007/s10668-021-01226-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-021-01226-1

Keywords