Skip to main content

Advertisement

SpringerLink
Log in

Land use and Land Cover change and its resultant erosion susceptible level: an appraisal using RUSLE and Logistic Regression in a tropical plateau basin of West Bengal, India

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

Abstract

Soil erosion trend depends on effective land use and land cover dynamics since overwhelming population growth in tropical region. The objective of this paper is to assess potential mean annual soil erosion rate, and conversion of erosion class incorporate with land use and land cover change in plateau fringe, undulating and low land of Kangsabati basin using Revised Universal Soil Loss Equation (RUSLE) and multiple logistic regression (MLR). Both models denote potential mean soil erosion zone as 55% corresponds at low level in low land than medium level as 30% in undulating topography and high level as 15% in plateau fringe site. RUSLE indicates erosion rate increases with expanding area in degraded forest (169 ton ha−1 year−1, 137 km2), dense forest (134 ton ha−1 year−1, 55 km2) and settlement area (30 ton ha−1 year−1, 105 km2), whereas erosion rate decreases with reducing the area in barren land with laterite outcrop (− 154 ton ha−1 year−1, − 93 km2), double crop (− 40 ton ha−1 year−1, − 201 km2) and single crop yield (− 1 ton ha−1 year−1, − 62 km2) from 2002 to 2016. MLR predicts barren land with laterite outcrop and dense forest play a crucial role to determine the erosion susceptibility in plateau fringe, while degraded forest and single crop signify erosion susceptibility in undulating topography. Settlement and double crop are more significant in low land with proper validation. Model comparison depicts same class conversion finds out as 63.73% (low-to-low class, 41%) in low land, whereas high-to-low class finds in undulating topography (23%) and low-to-high class in plateau fringe (13%).

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

Access this article

Log in via an institution

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Akgün, A., & Türk, N. (2011). Mapping erosion susceptibility by a multivariate statistical method: A case study from the Ayvalık region, NW Turkey. Computers & Geosciences, 37(9), 1515–1524.

    Google Scholar 

  • Angima, S. D., Stott, D. E., O’neill, M. K., Ong, C. K., & Weesies, G. A. (2003). Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agriculture, Ecosystems & Environment, 97(1–3), 295–308.

    Google Scholar 

  • Bakker, M. M., Govers, G., van Doorn, A., Quetier, F., Chouvardas, D., & Rounsevell, M. (2008). The response of soil erosion and sediment export to land-use change in four areas of Europe: The importance of landscape pattern. Geomorphology, 98(3–4), 213–226.

    Google Scholar 

  • Beguería, S. (2006). Identifying erosion areas at basin scale using remote sensing data and GIS: A case study in a geologically complex mountain basin in the Spanish Pyrenees. International Journal of Remote Sensing, 27(20), 4585–4598.

    Google Scholar 

  • Bhattacharya, R. K., Das Chatterjee, N., & Das, K. (2020). Estimation of erosion susceptibility and sediment yield in ephemeral channel using RUSLE and SDR model: Tropical plateau fringe region, India. In P. Shit, H. Pourghasemi, G. Bhunia (Eds.), Gully erosion studies from India and surrounding regions. Advances in Science, Technology & Innovation (pp. 163–185). Cham: Springer. https://doi.org/10.1007/978-3-030-23243-6_10.

    Chapter  Google Scholar 

  • Biswas, S. (2012). Estimation of soil erosion using remote sensing and GIS and prioritization of catchments. International Journal of Emerging Technology and Advanced Engineering, 2(7), 124–128.

    Google Scholar 

  • Bready, N. C., & Well, R. R. (2005). The nature and properties of soil. Singapore: Pearson Prentice Hall.

    Google Scholar 

  • Cox, C., & Madramootoo, C. (1998). Application of geographic information systems in watershed management planning in St. Lucia. Computers and Electronics in Agriculture, 20(3), 229–250.

    Google Scholar 

  • Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3–4), 213–228.

    Google Scholar 

  • Desmet, P. J. J., & Govers, G. (1996). A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation, 51(5), 427–433.

    Google Scholar 

  • Dissanayake, D., Morimoto, T., & Ranagalage, M. (2018). Accessing the soil erosion rate based on RUSLE model for sustainable land use management: A case study of the Kotmale watershed, Sri Lanka. Modeling Earth Systems and Environment, 5(1), 291–306.

    Google Scholar 

  • Djoukbala, O., Hasbaia, M., Benselama, O., & Mazour, M. (2019). Comparison of the erosion prediction models from USLE, MUSLE and RUSLE in a Mediterranean watershed, case of Wadi Gazouana (NW of Algeria). Modeling Earth Systems and Environment, 5(2), 725–743.

    Google Scholar 

  • Fernandez, C., Wu, J. Q., McCool, D. K., & Stöckle, C. O. (2003). Estimating water erosion and sediment yield with GIS, RUSLE, and SEDD. Journal of Soil and Water Conservation, 58(3), 128–136.

    Google Scholar 

  • Ganasri, B. P., & Ramesh, H. (2016). Assessment of soil erosion by RUSLE model using remote sensing and GIS—A case study of Nethravathi Basin. Geoscience Frontiers, 7(6), 953–961.

    Google Scholar 

  • Hembram, T., Paul, G. C., & Saha, S. (2019). Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: A comparison of information value and logistic regression models. Modeling Earth Systems and Environment, 5(2), 689–708.

    Google Scholar 

  • Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2), 142–152.

    Google Scholar 

  • Hoyos, N., Waylen, P. R., & Jaramillo, Á. (2005). Seasonal and spatial patterns of erosivity in a tropical watershed of the Colombian Andes. Journal of Hydrology, 314(1–4), 177–191.

    Google Scholar 

  • Imam, E., & Kushwaha, S. P. S. (2013). Habitat suitability modelling for Gaur (Bos gaurus) using multiple logistic regression, remote sensing and GIS. Journal of Applied Animal Research, 41(2), 189–199.

    Google Scholar 

  • Indian Council of Agricultural Research & National Academy of Agricultural Science (ICAR & NAAS). (2010). Report on degraded and wastelands of India status and spatial distribution.

  • Jiang, Z., Lian, Y., & Qin, X. (2014). Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Science Reviews, 132, 1–12.

    Google Scholar 

  • Jordan, G., Van Rompaey, A., Szilassi, P., Csillag, G., Mannaerts, C., & Woldai, T. (2005). Historical land use changes and their impact on sediment fluxes in the Balaton basin (Hungary). Agriculture, Ecosystems & Environment, 108(2), 119–133.

    Google Scholar 

  • Kachouri, S., Achour, H., Abida, H., & Bouaziz, S. (2015). Soil erosion hazard mapping using Analytic Hierarchy Process and logistic regression: A case study of Haffouz watershed, central Tunisia. Arabian Journal of Geosciences, 8(6), 4257–4268.

    Google Scholar 

  • Kalambukattu, J. G., & Kumar, S. (2017). Modelling soil erosion risk in a mountainous watershed of Mid-Himalaya by integrating RUSLE model with GIS. Eurasian Journal of Soil Science, 6(2), 92.

    Google Scholar 

  • Kavian, A., Hoseinpoor Sabet, S., Solaimani, K., & Jafari, B. (2017). Simulating the effects of land use changes on soil erosion using RUSLE model. Geocarto International, 32(1), 97–111.

    Google Scholar 

  • Kijowska-Strugała, M., Bucała-Hrabia, A., & Demczuk, P. (2018). Long-term impact of land use changes on soil erosion in an agricultural catchment (in the Western Polish Carpathians). Land Degradation and Development, 29(6), 1871–1884.

    Google Scholar 

  • Lai, C., Wang, Z., Chen, X., Xu, C. Y., Yang, B., Meng, Q., et al. (2016). A procedure for assessing the impacts of land-cover change on soil erosion at basin scale. Hydrology Research, 47(5), 903–918.

    Google Scholar 

  • Li, R., Bennett, J., & Wang, X. (2013). Predicting environmental impacts for assessing land use change options in Sichuan Province, China. Land Use Policy, 30(1), 784–790.

    CAS  Google Scholar 

  • López-Vicente, M., Lana-Renault, N., García-Ruiz, J. M., & Navas, A. (2011). Assessing the potential effect of different land cover management practices on sediment yield from an abandoned farmland catchment in the Spanish Pyrenees. Journal of Soils and Sediments, 11(8), 1440–1455.

    Google Scholar 

  • Mahala, A. (2018). Soil erosion estimation using RUSLE and GIS techniques—A study of a plateau fringe region of tropical environment. Arabian Journal of Geosciences, 11(13), 335.

    Google Scholar 

  • McCool, D. K., Brown, L. C., Foster, G. R., Mutchler, C. K., & Meyer, L. D. (1987). Revised slope steepness factor for the Universal Soil Loss Equation. Transactions of the ASAE, 30(5), 1387–1396.

    Google Scholar 

  • Millward, A. A., & Mersey, J. E. (1999). Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena, 38(2), 109–129.

    Google Scholar 

  • Mukhopadhyay, S. (1992). Terrain analysis of river basin. Delhi: Vora publication.

    Google Scholar 

  • Nasir, N., & Selvakumar, R. (2018). Influence of land use changes on spatial erosion pattern, a time series analysis using RUSLE and GIS: the cases of Ambuliyar sub-basin, India. Acta Geophysica, 66(5), 1121–1130.

    Google Scholar 

  • National forest report, India http://fsi.nic.in/foresreport(2005) (2017).

  • Pal, S., & Talukdar, S. (2018). Application of frequency ratio and logistic regression models for assessing physical wetland vulnerability in Punarbhaba river basin of Indo-Bangladesh. Human and Ecological Risk Assessment: An International Journal, 24(5), 1291–1311.

    CAS  Google Scholar 

  • Perović, V., Jakšić, D., Jaramaz, D., Koković, N., Čakmak, D., Mitrović, M., et al. (2018). Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia). Environmental Monitoring and Assessment, 190(11), 675.

    Google Scholar 

  • Poesen, J., Nachtergaele, J., Verstraeten, G., & Valentin, C. (2003). Gully erosion and environmental change: Importance and research needs. CATENA, 50(2–4), 91–133.

    Google Scholar 

  • Pradhan, A. M. S., & Kim, Y. T. (2018). GIS-based landslide susceptibility model considering effective contributing area for drainage time. Geocarto International, 33(8), 810–829.

    Google Scholar 

  • Prasannakumar, V., Shiny, R., Geetha, N., & Vijith, H. (2011). Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India. Environmental Earth Sciences, 64(4), 965–972.

    Google Scholar 

  • Renard, K. G., & Freimund, J. R. (1994). Using monthly precipitation data to estimate the R factor in the revised USLE. Journal of Hydrology, 157(1), 287–306.

    Google Scholar 

  • Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) (Vol. 703). Washington, DC: United States Department of Agriculture.

    Google Scholar 

  • Sahaar, A. S. (2013). Erosion mapping and sediment yield of the Kabul River Basin. Fort Collins, CO: Afghanistan.

    Google Scholar 

  • Sarkar, T., & Mishra, M. (2018). Soil erosion susceptibility mapping with the application of logistic regression and artificial neural network. Journal of Geovisualization and Spatial Analysis, 2(1), 8.

    Google Scholar 

  • Santos, J. C. N. D., Andrade, E. M. D., Medeiros, P. H. A., Guerreiro, M. J. S., & Palácio, H. A. D. Q. (2017). Land use impact on soil erosion at different scales in the Brazilian semi-arid. Revista Ciência Agronômica, 48(2), 251–260.

    Google Scholar 

  • Shit, P. K., Nandi, A. S., & Bhunia, G. S. (2015). Soil erosion risk mapping using RUSLE model on Jhargram sub-division at West Bengal in India. Modeling Earth Systems and Environment, 1(3), 28.

    Google Scholar 

  • Syvitski, J. P., Vörösmarty, C. J., Kettner, A. J., & Green, P. (2005). Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science, 308(5720), 376–380.

    CAS  Google Scholar 

  • Toubal, A. K., Achite, M., Ouillon, S., & Dehni, A. (2018). Soil erodibility mapping using the RUSLE model to prioritize erosion control in the Wadi Sahouat basin, North-West of Algeria. Environmental Monitoring and Assessment, 190(4), 210.

    Google Scholar 

  • UN-FAO. (2001). Strategic environmental assessment: An assessment of the impact of cassava production and processing on the environment and biodiversity (Vol. 5). Rome.

  • Vijith, H., Hurmain, A., & Dodge-Wan, D. (2018). Impacts of land use changes and land cover alteration on soil erosion rates and vulnerability of tropical mountain ranges in Borneo. Remote Sensing Applications: Society and Environment, 12, 57–69.

    Google Scholar 

  • Wang, J., Rich, P. M., Price, K. P., & Kettle, W. D. (2004). Relations between NDVI and tree productivity in the central Great Plains. International Journal of Remote Sensing, 25(16), 3127–3138.

    Google Scholar 

  • Williams, J., Nearing, M., Nicks, A., Skidmore, E., Valentin, C., King, K., et al. (1996). Using soil erosion models for global change studies. Journal of Soil and Water Conservation, 51(5), 381–385.

    Google Scholar 

  • Wischmeier, W. H. (1960). Cropping-management factor evaluations for a universal soil-loss equation 1. Soil Science Society of America Journal, 24(4), 322–326.

    Google Scholar 

  • Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses—A guide to conservation planning. Washington, DC: U.S. Department of Agriculture-Agriculture Handbook 537. U.S. Government Printing Office.

    Google Scholar 

  • Xiao, L., Yang, X., Chen, S., & Cai, H. (2015). An assessment of erosivity distribution and its influence on the effectiveness of land use conversion for reducing soil erosion in Jiangxi, China. CATENA, 125, 50–60.

    Google Scholar 

Download references

Acknowledgements

Authors are thankful to Survey of India (SOI), Irrigation Office of Paschim Medinipur and Bankura, District Land & Land Reforms officer of the Paschim Midnapore and Bankura districts, WB in India. Authors are also grateful to the anonymous reviewers for their valuable comments and suggestions to improve the quality of this article.

Author information

Authors and Affiliations

  1. Department of Geography, Vidyasagar University, Midnapore, West Bengal, 721101, India

    Raj Kumar Bhattacharya, Nilanjana Das Chatterjee & Kousik Das

Authors
  1. Raj Kumar Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nilanjana Das Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kousik Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raj Kumar Bhattacharya.

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

Bhattacharya, R.K., Das Chatterjee, N. & Das, K. Land use and Land Cover change and its resultant erosion susceptible level: an appraisal using RUSLE and Logistic Regression in a tropical plateau basin of West Bengal, India. Environ Dev Sustain 23, 1411–1446 (2021). https://doi.org/10.1007/s10668-020-00628-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-020-00628-x

Keywords

Search

Navigation