Estimation of soil loss by USLE Model using Remote Sensing and GIS Techniques - A Case study of Coastal Odisha, India

Year 2019, Volume: 8 Issue: 4, 321 - 328, 01.10.2019

Ramasamy Srinivasan Surendra Kumar Singh Dulal Chandra Nayak Rajendra Hegde Muniasami Ramesh

https://doi.org/10.18393/ejss.598120

Cited By: 10

Abstract

Globally,
Soil erosion is the major land degradation problem, which impacts seriously on
economic and environmental status. Geospatial techniques support and provided
quantitative approach to estimate soil erosion in different conditions. In the
present study, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS
has been used to estimate soil loss in the part of coastal Odisha system. The
study area, Ganjam block have undulating topography covering 0-35% slopes. The
quantitative soil loss was estimated and classified into different classes and
soil erosion map was generated. The soil erosion map is classified into seven
classes from very slight (<5 t ha^-1 yr^-1) to extremely
severe (>80 t ha^-1yr^-1). The results indicate that
90.9% (22330 ha) of the study area falls in very low erosion category, which
may be due to level topography and regular vegetation cover. The other erosion classes such as moderate,
high and very high erosion occurred in the range of 2.12%, 2.23% and 1.49 %,
respectively. The high soil erosion risk is spatially situated in the
foothills and upper steep slope of the area. The results can certainly aid in
implementation of soil management and conservation practices to reduce the soil
erosion in the coastal Odisha regions of Eastern India.

Keywords

Soil erosion risk, land use, Remote sensing, GIS, coastal Odisha

References

• Bai, Z.G., Dent, D.L., Olsson, L., Schaepman, M.E., 2008. Proxy global assessment of land degradation. Soil Use and Management 24(3): 223–234.
• Bandyopadhyay, A.K., Bhargava, G.P., Bandyopadhyay, B.K., 1984. Coastal Saline Soils of Orissa, CSSRI, RRS, Canning Town, West Bengal, 56p.
• Bandyopadhyay, B.K., Bandyopadhyay, A.K., 1983, Effect of salinity on mineralization and immobilization of nitrogen in a coastal saline soil of West Bengal. IndianAgriculturist 27: 41-50.
• Behera, S.K., 2015. Estimation of soil erosion and sediment yield on ONG Catchment, Odisha, India. McS Thesis. National Institute of Technology, Rourkela, Department of Civil Engineering, India. Available at [Access date : 18.01.2019]: http://ethesis.nitrkl.ac.in/7561/1/2015_ESTIMATION_OF_SOIL_Behera.pdf
• Bera, A., 2017. Estimation of Soil loss by USLE Model using GIS and Remote Sensing techniques: A case study of Muhuri River Basin, Tripura, India. Eurasian Journal of Soil Science 6(3): 206-215.
• Chatterjee, S., Krishna, A.P., Sharma, A.P., 2014. Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India. Environmental Earth Sciences 71(1): 357-374.
• Chaudhary, D.R., Ghosh, A., Boricha, G.N., 2008. Characterization and classification of coastal saline soils of Paradip, Orissa. Agropedology 18(2): 129-133.
• Chen, S., Chen, L., Liu, Q., Li, X., Tan, Q., 2005. Remote sensing and GIS-based integrated analysis of coastal changes and their environmental impacts in Lingding Bay, Pearl River Estuary, South China. Ocean & Coastal Management 48(1): 65-83.
• Dabral, P.P., Baithuri, N., Pandey, A., 2008. Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resources Management 22(12): 1783-1798.
• Devatha, C.P., Deshpande, V., Renukaprasad, M.S., 2015. Estimation of Soil loss Using USLE Model for Kulhan Watershed, Chattisgarh- A Case Study. Aquatic Procedia 4: 1429-1436.
• Fernandez, C., Wu, J.Q., McCool, D.K., Stockle, C.O., 2003. Estimating water erosion and sediment yield with GIS, RUSLE, and SEDD. Journal of Soil and Water Conservation 58 (3): 128-136.
• 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.
• Griffin, M.L., Beasley, D.B., Fletcher, J.J., Foster, G.R., 1988. Estimating soil loss on topographically non uniform field and farm units. Journal of Soil and Water Conservation 43(4): 326-331.
• Hurni, H., 1985. Erosion-productivity conservation systems in Ethiopia. roceedings of 4th International Conference on Soil Conservation, Maracay, Venezuela, 3-9 November 1985, pp. 654-674.
• Jain, S.K., Kumar, S., Varghese, J., 2001. Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resources Management 15(1): 41-54.
• Karthick, P., Lakshumanan, C., Ramki, P., 2017. Estimation of soil erosion vulnerability in Perambalur Taluk, Tamilnadu using revised universal soil loss equation model (RUSLE) and geo information technology. International Research Journal of Earth Sciences 5(8): 8-14.
• Kim, J.B., Saunders, P.F., Finn, J.T., 2005. Rapid assessment of soil erosion in the Rio Lempa Basin, Central America, using the universal soil loss equation and geographic information systems. Environmental Management 36(6): 872-885.
• Lee, G.S., Lee, K.H., 2006. Scaling effect for estimating soil loss in the RUSLE model using remotely sensed geospatial data in Korea. Hydrology and Earth System Sciences 3: 135-157.
• Lu, H., Prosser, I.P., Moran, C.J., Gallant, J.C., Priestley, G., Stevenson, J.G., 2003. Predicting sheetwash and rill erosion over the Australian continent. Australian Journal of Soil Research 41(6): 1037-1062.
• Mishra, S.P., Das, K., 2017. Management of soil losses in South Mahanadi Delta, India. International Journal of Earth Sciences and Engineering 10(2): 222-232.
• Monalisha, M., Panda, G.K., 2018. Coastal erosion and shoreline change in Ganjam Coast along East Coast of India. Journal of Earth Science and Climatic Change 9 (4): 1-6.
• Narayana, D.V.V., Babu, R., 1983. Closure to estimation of soil erosion in India. Journal of Irrigation and Drainage Engineering 11(4): 408-410.
• Ozsoy, G., Aksoy, E., Dirim, M.S., Tumsavas, Z., 2012. Determination of soil erosion risk in the Mustafakemalpasa river Basin, Turkey, using the revised universal soil loss equation, Geographic Information System, and Remote Sensing. Environmental Management 50(4): 679-694.
• Pandey, A., Chowdary, V.M., Mal, B.C., 2007. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resources Management 21(4): 729-746.
• Parveen, R., Kumar, U., 2012. Integrated approach of universal soil loss equation (USLE) and Geographical Information System (GIS) for soil loss risk assessment in upper South Koel Basin, Jharkhand. Journal of Geographic Information System 4(6): 588-596.
• Parveen, R., Kumar, U., 2012. Integrated approach of universal soil loss equation (USLE) and Geographical Information System (GIS) for soil loss risk assessment in upper South Koel Basin, Jharkhand. Journal of Geographic Information System 4(6): 588-596.
• Poyya Moli, G., Balachandran, N., 2008. Strategies for conserving ecosystem services to restore coastal habitats. UNDP-PTEI Conference on “Restoration of Coastal Habitats”, 20-21 August 2008, Mahabalipuram, Tamil Nadu, India.
• Renard, K.G., Yoder, D.C., Lightle, D.T., Dabney, S.M., 2011. Universal soil loss equation and revised universal soil loss equation. In: Handbook of Erosion Modeling. Morgan, R.P.C., Nearing, M.A. (Eds.). Blackwell Publishing Ltd. Oxford, England. pp. 137–167.
• Renschler, C.S., Mannaerts, C., Diekkrüger, B., 1999. Evaluating spatial and temporal variability in soil erosion risk—rainfall erosivity and soil loss ratios in Andalusia, Spain. Catena 34(3-4): 209-225.
• Schwab, G.O., Frevert, R.K., Edminster. T.W., Barnes, K.K., 1981. Soil Water Conservation Engineering, 3rd Ed, Wiley, New York, USA.
• Sehgal, J., Mandal, D.K., Mandal, C., Vadivelu, S., 1992. Agro-ecological zones of India. Second Edition. Nagpur, India. Technical Bulletin, No. 24. NBSS&LUP (ICAR). 130p.
• Sharda, V.N., Mandal, D., Ojasvi, P.R., 2013. Identification of soil erosion risk areas for conservation planning in different states of India. Journal of Environmental Biology 34(2): 219-226.
• Shi, Z.H., Cai, S.F., Ding, S.W., Wang, T.W., Chow, T.L., 2004. Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the Three Gorge Area of China. Catena 55(1): 33-48.
• Shinde, V., Tiwari, K.N., Singh, M., 2010. Prioritization of micro watersheds on the basis of soil erosion hazard using remote sensing and geographic information system. International Journal of Water Resources and Environmental Engineering 2(3): 130-136.
• Singh, G., Babu, R., Narain, P., Bhushan L.S., Abrol, I.P., 1992. Soil erosion rates in India. Journal of Soil and Water Conservation 47(1): 97-99.
• Singh, R., Kundu, D.K., Verma, H.N., 2002. Hydro physical characteristics of odisha soil and their water management implications, Publication 12, Water Technology center for eastern region (Indian Council of Agricultural research, Chandrasekharpur, Bhubaneswar, India. Available at [Access date: 18.01.2019]: http://www.iiwm.res.in/pdf/Bulletin_12.pdf
• Srinivasan, R., Reza, S.K., Nayak, D.C., Singh, S.K., Sarkar, G.C., 2015. Characterization and classification of major vegetables growing soils of odisha coastal system- A case study. Agropedology 25 (2): 232-239.
• Stone, R.P., Hilborn, D., 2000. Universal Soil Loss Equation (USLE), Ontario Ministry of Agriculture, Food and Rural Affairs Factsheet. Available at [Access date: 18.01.2019]: http://www.omafra.gov.on.ca/english/engineer/facts/12-051.htm#1
• Tideman, E.M., 1996. Watershed management: guidelines for Indian conditions. Omega Scientific Publisher, New Delhi, India. 101p.
• Tirkey, A.S., Pandey, A.C., Nathawat, M.S., 2013. Use of Satellite Data, GIS and RUSLE for estimation of average annual soil loss in Daltonganj Watershed of Jharkhand (India). Journal of Remote Sensing Technology 1(1): 20-30.
• Toy, T.J., Foster, G.R., Renard, K.G., 2002. Soil erosion: processes, prediction, measurement, and control. John Wiley & Sons. New York, USA. p 338.
• van Remortel R.D., Hamilton, M.E., Hickey, R.J., 2001. Estimating the LS Factor for RUSLE through ıterative slope length processing of digital elevation data within arclnfo grid. Cartography 30(1): 27-35.
• Vinayaraj, P., Johnson, G., Dora, G.U., Philip, C.S., Kumar, V.S., Gowthaman, R., 2011. Quantitative estimation of coastal changes along selected locations of Karnataka, India: A GIS and remote sensing approach. International Journal of Geosciences 2(4): 385-393.
• Wischmeier, W.H., Johnson, C.B., Cross, B.V., 1971. A soil erodibility nomograph for farmland and construction sites. Journal of Soil Water Conservation 26: 189–193.
• Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses - A Guide to Conservation Planning. Agriculture Handbook No. 537. US Department of Agriculture Science and Education Administration, Washington, DC, USA, 163 p.
• Yildirim, U., 2012. Assessment of soil erosion at the Degirmen Creek watershed area, Afyonkarahisar, Turkey. Proceedings of ISEPP (International Symposium on Environmental Protection and Planning: Geographic Information Systems (GIS) and Remote Sensing (RS) Applications). 28-29 June 2011, İzmir, Turkey. pp. 73-80.