Skip to main content

Advertisement

SpringerLink
Log in

Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS—a case study

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

This paper discusses the application of the Revised Universal Soil Loss Equation (RUSLE) in conjunction with LANDSAT-7 ETM+ remote sensing data, and geographical information system (GIS) to the spatial mapping of soil erosion risk in Jhagrabaria Watershed Allahabad, U.P., India. Soil map and topographical data were used to develop the soil erodibility factor (K), and a digital elevation model (DEM) image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a LANDSAT Enhanced Thematic Mapper Plus (LANDSAT-7 ETM+) image, and support practice factor (P) was developed by crossing operation between land use/land cover classification map and slope map. Assuming the same climatic conditions in the study area, the rainfall-runoff erosivity (R) factor was not used. The value of K for the study area lies between 0.25 and 0.485, LS values were less than 1.4, and C and P values were less than 1. A soil erosion risk map with five classes (very low, low, medium, medium high, and high) was produced based on the simplified RUSLE within the GIS environment and was linked to land use/land cover (LULC) image to explore the relationships between soil erosion risk (SER) and LULC distribution. The results indicated that the land use/land cover (LULC) having the most succession and mature vegetation are in low-erosion-risk areas, while the barren and fallow lands are usually associated with high- to medium-erosion-risk areas. The spatial maps of the SER has been generated that can be utilized in the policy matter and planning for the watershed studied. This research also implied that the remote sensing and GIS tools and techniques provide the highly promising and important tools for evaluating and mapping soil erosion risk in the Jhagrabaria Watershed.

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

Similar content being viewed by others

References

  • Adediji A, Tukur AM, Adepoju KA (2010) Assessment of Revised Universal Soil Loss Equation (RUSLE) in Katsina area, Katsina state of Nigeria using remote sensing (RS) and geographic information system (GIS). Iran J Energy Environ 1(3):255–264

    Google Scholar 

  • Adinarayana J, Rao KG, Krishna NR, Venkatachalam P, Suri JK (1999) A rule-based soil erosion model for a hilly catchment. Catena 37:309–318

    Article  Google Scholar 

  • Ahmet K (2010) Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean J Appl Sci 3(1):77–85

    Google Scholar 

  • Amore E, Modica C, Nearing MA, Santoro VC (2004) Scale effect in USLE and WEPP application for soil erosion computation from three Sicilian basins. J Hydrol 293:100–114

    Article  Google Scholar 

  • Ananda J, Herath G (2003) Soil erosion in developing countries: a socio-economic appraisal. J Environ Manag 68:343–353

    Article  Google Scholar 

  • Angima SD, Stott DE, O’Neill MK, Ong CK, Weesies GA (2003) Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agric Ecosyst Environ 97:295–308

    Article  Google Scholar 

  • Aniello C, Morgan K, Busbey A, Newland L (1995) Mapping micro-urban heat islands using LANDSAT TM and a GIS. Environ Geol 21:965–967

    Google Scholar 

  • Ashwini (2007) Analysis of land use classification using remote sensing and GIS. M.E. Thesis in Civil Engineering, Bangalore University, India

  • Bacchi OOS, Reichard K, Sparovek G, Ranieri SBL (2000) Soil erosion evaluation in a small watershed in Brazil through 137Cs fallout redistribution analysis and conventional models. Acta Geol Hisp 35:251–259

    Google Scholar 

  • Bartsch KP, Miegroet H, Boettinger J, Dobrwolski JP (2002) Using empirical erosion models and GIS to determine erosion risk at Camp Williams. J Soil Water Conserv 57:29–37

    Google Scholar 

  • Batistella M, Robeson S, Moran EF (2003) Settlement design, forest fragmentation, and landscape change in Rondonia, Amazonia. Photogramm Eng Remote Sens 69:805–812

    Article  Google Scholar 

  • Bhattacharyya T, Ram B, Sarkar D, Mandal C, Dayani BL, Nagar AP (2007) Soil loss and crop productivity model in humid subtropical India. Curr Sci 93:1397–1403

    Google Scholar 

  • Boggs G, Devonport C, Evans K, Puig P (2001) GIS-based rapid assessment of erosion risk in a small catchment in the wet/dry tropics of Australia. Land Degrad Dev 12:417–434

    Article  Google Scholar 

  • Bonilla CA, Reyes JL, Magri A (2010) Water erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, central Chile. Chile J Agric Res 70(1):159–169

    Google Scholar 

  • Cerri CEP, Dematte JAM, Ballester MVR, Martinelli LA, Victoria RL, Roose E (2001) GIS erosion risk assessment of the Piracicaba River Basin, southeastern Brazil. Mapp Sci Remote Sens 38:157–171

    Google Scholar 

  • Chen T, Niu R, Li P, Zhang L, Du B (2010) Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed. North China Environ Earth Sci. doi:10.1007/s12665-010-0715-z

    Google Scholar 

  • D’Ambrosio D, di Gregorio S, Gabriele S, Gaudio R (2001) A cellular automata model for soil erosion by water. Phys Chem Earth Part B Hydrol Oceans Atmos 26:33–39

    Article  Google Scholar 

  • Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of north eastern India using USLE, GIS and remote sensing. Water Resour Manag 22:1783–1798

    Article  Google Scholar 

  • Dan J, Yaloon DH, Koyundjinsky H, Raz Z (1976) The soils and association map of Israel. ISA Division of scientific Publication. Volcanic Center, Beit Dagan

    Google Scholar 

  • De Jong SM, Paracchini ML, Bertolo F, Folving S, Megier J, De Roo APJ (1999) Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. Catena 37:291–308

    Article  Google Scholar 

  • Dengiz O, Yakupoglu T, Baskan O (2009) Soil erosion assessment using geographical information system (GIS) and remote sensing (RS) study from Ankara-Guvenc Basin, Turkey. ISSN 2224–3208 (Paper) ISSN 2225-093X (www.iiste.org Online)

  • Devi GMS, Kumar KSA (2008) Remote sensing and GIS application for land quality assessment for coffee growing area of Karnataka. J Indian SocRemote Sens 36:89–97

    Article  Google Scholar 

  • Dhruvanarayana VV (1993) Soil and water conservation research in India. ICAR, Krishi Anusandhan Bhavan, Pusa, New Delhi

    Google Scholar 

  • Diana A, Ramón Bienes, Marta Ruiz-Colmenero, María José Marqués (2011) Comparison of soil erosion models (USLE, RUSLE) and a new adapted model in a basin of central Spain. Geophys Res Abstracts 13:EGU2011-10332-2

  • Favis Mortlock DT (1998) Evaluation of field-scale erosion models on the UK South Downs. In: Boardman J, Favis–Mortlock DT (eds) Modelling soil erosion by water, vol Series I-55, NATO-ASI. Springer Verlag, Berlin, pp 41–157

    Google Scholar 

  • Freddy H (2011) Soil erosion prediction using RUSLE for rain fed crops under Conservation Agriculture practices in the Lake Alaotra region in Madagascar. Wageningen University, Netherlands, Master thesis in Agriculture

    Google Scholar 

  • Fullen MA (2003) Soil erosion and conservation in northern Europe. Prog Phys Geogr 27:331–358

    Article  Google Scholar 

  • Gay M, Cheret V, Denux JP (2002) Apport de la télédétectiondansl’identification du risqued’érosion. La Houille Blanche 2002:81–86. doi:10.1051/lhb/2002013

    Article  Google Scholar 

  • Gitas IZ, Douros K, Minakou C, Silleos GN, Karydas CG (2009) Multi-temporal soil erosion risk assessment. In N. Chalkidiki, Using a modified USLE raster model. EARSeL eProceedings 8:40–52

    Google Scholar 

  • Hickey R (2000) Slope angle and slope length solutions for GIS. Cartography 29:1–8

    Article  Google Scholar 

  • Hoyos N (2005) Spatial modeling of soil erosion potential in a tropical watershed of the Colombian Andes. Catena 63:85–108

    Article  Google Scholar 

  • ILWIS user guide (2007) http://www.itc.nl/ilwis/downloads/ilwis33.asp

  • Ioannis Z G, Kostas D, Chara M, George NS, Christos G K (2009) Multi-temporal soil erosion risk assessment In: N. Chalkidiki, Using a modified USLE raster model, EARSeL eProceedings 8(1):40–52

  • Jahun BG, Ibrahim R, Dlamini NS, Musa SM (2015) Review of soil erosion assessment using RUSLE model and GIS. J Biol Agric Healthcare 5(9):36–47

    Google Scholar 

  • Jain SK, Goel MK (2002) Assessing the vulnerability to soil erosion of the Ukai Dam catchments using remote sensing and GIS. Hydrol Sci J 47:31–40

    Article  Google Scholar 

  • Kim H (2006) Soil erosion modeling using RUSLE and GIS on the IMHA Watershed, South Korea. Master thesis in Civil Engineering, Colorado State University, Fort Collins, Colorado

  • King C, Baghdadi N, Lecomte V, Cerdan O (2005) The application of remote-sensing data to monitoring and modelling of soil erosion. Catena 62:79–93

    Article  Google Scholar 

  • Kumar S, Kushwaha SPS (2013) Modelling soil erosion risk based on RUSLE-3D using GIS in a Shivalik sub-watershed. J Earth Syst Sci 122(2):389–398

    Article  Google Scholar 

  • Lal R (1998) Soil erosion impact on agronomic productivity and environment quality: critical reviews. Plant Sci 17:319–464

    Article  Google Scholar 

  • Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12:519–539

    Article  Google Scholar 

  • Lee GS, Lee KH (2006) Scaling effect for estimating soil loss in the RUSLE model using remotely sensed geospatial data in Korea. Hydrol Earth Syst Sci Discuss 3:135–157

    Article  Google Scholar 

  • Lim KJ, Sagong M, Engel BA, Tang Z, Choi J, Kim KS (2005) GIS based sediment assessment tool. Catena 64:61–80

    Article  Google Scholar 

  • Liu Y, Luo Z (2003) A spatial information technology approach for the mapping and quantification of gully erosion. Catena 50:293–308

    Article  Google Scholar 

  • Liu JG, Hilton F, Mason P, Lee H (2000) A RS/GIS study of rapid erosion in SE Spain using ERS SAR multi-temporal interferometric coherence imagery. In: Owe M, Zilioli E, D’Urso G (eds) Remote sensing for agriculture, ecosystems, and hydrology II. SPIE International, Barcelona, pp 367–375, Proceedings of SPIE Vol. 4171

    Google Scholar 

  • Lu D, Mausel P, Batistella M, Moran E (2004) Comparison of land-cover classification methods in the Brazilian Amazonia basin. Photogramm Eng Remote Sens 70:723–731

    Article  Google Scholar 

  • Ma JW, Xue Y, Ma CF, Wang ZG (2003) A data fusion approach for soil erosion monitoring in the Upper Yangtze River Basin of China based on Universal Soil Loss Equation (USLE) model. Int J Remote Sens 24:4777–4789

    Article  Google Scholar 

  • Markham BL, Barker JL (1987) Radiometric properties of U.S. processed Landsat MSS Data. Remote Sens Environ 22:39–71

    Article  Google Scholar 

  • Mati BM, Morgan RPC, Gichuk FN, Quintor JN, Brewer TR, Liniger HP (2000) Assessment of erosion hazard with the USLE and GIS: a case study of the upper EwasoNg’iro North basin of Kenya. Int J Appl Earth Obs Geoinf 2:78–86

    Article  Google Scholar 

  • Merritt WS, Letcher RA, Jakeman AJ (2003) A review of erosion and sediment transport models. Environ Model Softw 18(8):761–799

    Article  Google Scholar 

  • Millward AA, Mersey JE (1999a) Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38:109–129

    Article  Google Scholar 

  • Millward AA, Mersey JE (1999b) Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38(2):109–129

    Article  Google Scholar 

  • Mikhailova EA, Bryant RB, Schwager SJ, Smith SD (1997) Predicting rainfall erosivity in Honduras. Soil Science Society of America Journal 61:273–279

  • Morgan RPC (1995) Soil erosion and conservation, 2nd edn. Longman Group Ltd., Harlow, UK

  • Nearing MA, Lane LJ (1989) USDA-water erosion prediction project: hillslope profile model documentation. NSERL repot 2 (USDA-ARS Nation soil erosion laboratory), West Lafayette

    Google Scholar 

  • Odongo VO (2006) Evaluating the topographic factor in the universal soil loss equation using GIS techniques for Malewa Catchment, Nawasha, Kenya.Ph.D Thesis, ARCSSTEE, Ile-Ife, Nigeri 35–37

  • Onori F, De Bonis P, Grauso S (2006) Soil erosion prediction at the basin scale using the revised universal soil loss equation (RUSLE) in a catchment of Sicily (southern Italy). Environ Geol 50(8):1129–1140

    Article  Google Scholar 

  • Pandey A, Chowdary VM, Mal BC (2007) Identification of critical erosion prone areas in the small agricultural watershed using USLE GIS and remote sensing. Wat Resour Manag 21:729–746

    Article  Google Scholar 

  • Parysow P, Wang G, Gertner GZ, Anderson AB (2003) Spatial uncertainty analysis for mapping soil erodibility based on joint sequential simulation. Catena 53: 65–78

  • Prasannakumar V, Vijith H, Abinod S, Geetha N (2012) Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geosci Front 3(2):209–215

    Article  Google Scholar 

  • Rawat KS, Mishra AK, Sahgel VK, Ahmed N, Tripathi VK (2012) Comparative evaluation of horizontal accuracy of elevations of selected ground control points from ASTER and SRTM DEM with respect to CARTOSAT–1 DEM: a case study of district Shahjahanpur (Uttar Pradesh), India. Geocarto Int 28(5):439–452. doi:10.1080/10106049.2012.724453

    Article  Google Scholar 

  • Rawat KS, Mishra AK, Tripathi VK (2013) Hydro-morphometrical analyses of sub-Himalayan region in relation to small hydro-electric power. Arab J Geosci 6:2889–2899. doi:10.1007/s12517-012-0586-6

    Article  Google Scholar 

  • Rawat KS, Mathur GK, Mishra A, Singh J (2014a) Effect of DEM data resolution on low relief region watershed boundaries delineating using of SWAT model and DEM derived from CARTOSAT-1 (IRS-P5), SRTM and ASTER. J Appl Nat Sci 6(1):144–151

    Google Scholar 

  • Rawat KS, Tripathi VK, Mishra AK (2014b) Sediment yield index mapping and prioritization of Madia sub-watershed, Sagar District of M.P. (India). Arab J Geosci 7:3131–3145. doi:10.1007/s12517-013-1007-1

    Article  Google Scholar 

  • Renard KG, Ferreira VA (1993) RUSLE model description and database sensitivity. J Environ Qual 22:458–466

    Article  Google Scholar 

  • Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Handbook #703.US Department of Agriculture. Washington, DC 404

  • Reusing M, Schneider T, Ammer U (2000) Modeling soil erosion rates in the Ethiopian Highlands by integration of high resolution MOMS-02/ D2-stereo-data in a GIS. Int J Remote Sens 21:1885–1896

    Article  Google Scholar 

  • Richard C (2003) Modeling soil erosion risk in Los Maribios volcanic chain, Nicaragua. Trop Res Bull 25:50–56

    Google Scholar 

  • Rinos Mohamed MH, Aggarwal SP, Silva Ranjith Premalal De (2005) The application of remote sensing and GIS on soil erosion assessment at Bata River Basin. India. Research report

  • Romken MJM (1983) The soil erodibility factor: a perspective. Soil Erosion and Conservation 10, 445–461

  • Rose CW (1999) Soil erosion models and implications for conservation of sloping tropical lands. 10th International soil conservation organization meeting, May 24–29, At Purdue University and the USDA-ARS National Soil Erosion Research Laboratory

  • Saavedra C (2005) Estimated spatial patterns of soil erosion and deposition in the Andean region using geo-information techniques: a case study in Cochabamba, Bolivia, Ph.D thesis in Geo-information. Wageningen University, Netherlands

  • Sadeghi SHR, Mizuyama T (2007) Applicability of the modified universal soil loss equation for prediction of sediment yield in Khanmirza watershed, Iran. Hydrol Sci 52:1068–1075

    Article  Google Scholar 

  • Sang DP, Kyu SL, Seung SS (2011) A statistical soil erosion model for burnt mountain areas in Korea-RUSLE approach. J Hydrol Eng 17(2):292–304

    Google Scholar 

  • Shakesby RA, Coelho COA, Schnabel S, Keizer JJ, Clarke MA, Contador JFL, Walsh RPD, Fereira AJD, Doerr SH (2002) A ranking methodology for assessing relative erosion risk and its application to Dehesas and Montados in Spain and Portugal. Land Degrad Dev 13:129–140

    Article  Google Scholar 

  • Sharma A (2010) Integrating terrain and vegetation indices for identifying potential soil erosion risk area. Geo-Spatial Inf Sci 13(3):201–209

    Article  Google Scholar 

  • Shi ZH, Caia CF, Dinga SW, Wanga TW, Chowb TL (2004) Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the Three Gorge area of China. Catena 55:33–48

    Article  Google Scholar 

  • Shiono T, Kamimura K, Okushima S, Fukumoto M (2002) Soil loss estimation on a local scale for soil conservation planning. Jpn Agric Res Q 36(3):157–161

    Article  Google Scholar 

  • Tamene L, Le QB (2015) Estimating soil erosion in sub-Saharan Africa based on landscape similarity mapping and using the revised universal soil loss equation (RUSLE). Nutr Cycl Agroecosyst 102:17–31. doi:10.1007/s10705-015-9674-9

    Article  Google Scholar 

  • Thiam AK (2003) The causes and spatial patterns of land degradation risk in southern Mauritania using multitemporal AHVRR-NDVI imagery and field data. Land Degrad Dev 14:133–142

    Article  Google Scholar 

  • User’s Reference Guide (2008) Revised Universal Soil Loss Equation Version 2, Prepared for USDAAgricultural Research Service Washington, D.C. May 15, 2008

  • Veihe A, Mati BM (2001) Application of the USLE in a Savannah environment: comparative experiences from east and West Africa. Singap J Trop Geogr 22:138–142

    Article  Google Scholar 

  • Vrieling A (2002) Satellite remote sensing for water erosion assessment. Catena 65:2–18

    Article  Google Scholar 

  • Wang G, Gertner GZ, Liu X, Anderson A (2001) Uncertainty assessment of soil erodibility factor for revised universal soil loss equation. Catena 46:1–14

    Article  Google Scholar 

  • Wang G, Gertner G, Singh V, Shinkareva S, Parysow P, Anderson A (2002a) Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall–runoff erosivity R factor. Ecol Model 153:143–155

    Article  Google Scholar 

  • Wang G, Wente S, Gertner GZ, Anderson A (2002b) Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with landsat thematic mapper images. Int J Remote Sens 23:3649–3667

    Article  Google Scholar 

  • Wang ZG, Ma JW, Xue Y, Ma CF (2003) A data fusion approach for soil erosion monitoring in the upper Yangtze river basin of China based on universal soil loss equation (USLE) model. Int J Remote Sens 24:4777–4789

    Article  Google Scholar 

  • Warrena SD, Hohmannb MG, Auerswald K, Mitasovad H (2004) An evaluation of methods to determine slope using digital elevation data. Catena 58:215–233

    Article  Google Scholar 

  • Wayne D, Mahmoudzadeh A, Myers C (2002) Land use effects on sediment yields and soil loss rates in small basins of Triassic sandstone near Sydney, New Australia. Catena 49:271–287

    Article  Google Scholar 

  • Wilson JP, Lorang MS (2000) Spatial models of soil erosion and GIS. In: Wegener M, Fotheringham AS (eds) Spatial models and GIS: new potential and new models. Taylor and Francis, London, pp 83–108

  • Wischmeier WH Smith DD (1965) Predicting rainfall-erosion losses from cropland east of the rocky mountains. Agriculture Handbook No. 282.U.S. Department of Agriculture, Washington DC

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. Agric. Handbook 537. USDA, SEA. Agric Res 58

  • Xiea K, Wub Y, Maa X, Liuc Y, Liub B, Hesseld R (2002) Using contour lines to generate digital elevation models for steep slope areas: a case study of the Loess Plateau in North China. Catena 54:161–171

    Article  Google Scholar 

  • Young RA, Onstad CA, Bosch DD, Anderson WP (1987) An agricultural non point source pollution model (AGNPS). Conservation Research Report 35, US Department of agricultural Research Services, WA

  • Yuksel A, Gundogan R, Akay AE (2008) Using the remote sensing and GIS technology for erosion risk mapping of Kartalkaya Dam watershed in Kahramanmaras, Turkey. Sensors 8(8):4851–4865

    Article  Google Scholar 

  • Zhang Shuhua, Zhou Lijun (2011) Assessment of soil erosion in Suihua City based on RUSLE and GIS. Computer Distributed Control and Intelligent Environmental Monitoring 1979–1982 DOI:10.1109/CDCIEM.2011.366

Download references

Author information

Authors and Affiliations

  1. Centre for Remote Sensing and Geoinformatics, Sathyabama University, Chennai, 600-119, Tamil Nadu, India

    Kishan Singh Rawat, Ainl Kumar Mishra & Ranjan Bhattacharyya

  2. Water Technology Centre, Indian Agricultural Research Institute, New Delhi, 110012, India

    Ainl Kumar Mishra

  3. CESCRA, Indian Agricultural Research Institute, Nuclear Research Laboratory Building, New Delhi, 110012, India

    Ranjan Bhattacharyya

Authors
  1. Kishan Singh Rawat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ainl Kumar Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ranjan Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kishan Singh Rawat.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rawat, K.S., Mishra, A.K. & Bhattacharyya, R. Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS—a case study. Arab J Geosci 9, 288 (2016). https://doi.org/10.1007/s12517-015-2157-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-015-2157-0

Keywords

Search

Navigation