Skip to main content

Advertisement

SpringerLink
Log in

Modeling cover management factor of RUSLE using very high-resolution satellite imagery in a semiarid watershed

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Vegetation cover is regarded as one of the most important protection measures for controlling soil erosion caused by water. Numerous articles have been published about the fact that more delicate, practical, and reliable estimations can be made through normalized difference vegetation index (NDVI) for calculating “cover management (C) factor” in the Revised Universal Soil Loss Equation (RUSLE), the most commonly recognized erosion prediction model worldwide. In this study, the C-factor map of the Tortum-North sub-watershed in the mountainous northeastern part of Turkey was estimated using NDVI values derived from the 50-cm resolution WorldView-2 satellite imagery. The C-factor values, collected from 55 sampling plots by measuring crown closure, canopy height, litter layer depth, and surface cover of the study area, were plotted against the NDVI values and then curved using the simple linear regression method. The resulting regression models (linear, cubic, exponential, growth) and five other best-known NDVI-related models from the literature (Knijff, Smith, Karaburun, De Jong, and Durigon) were compared using model diagnostic statistics (\(R_{{\text{adj}}}^{2}\), RMSE, MAE, Mallows’ Cp) and information criterion statistics (Akaike’s information criterion, the Sawa’s Bayesian information criterion, Schwarz’s Bayesian criterion). The curve estimation results showed that the cubic model (R 2 = 0.83, RMSE = 0.063), the Knijff et al. (1999)’s model (R 2 = 0.85, RMSE = 0.059), and the linear model (R 2 = 0.81, RMSE = 0.067) were the top three estimators of the C-factor. The least estimator of the C-factor was the growth model (R 2 = 0.46, RMSE = 0.113). The residual analysis results showed that the cubic model performed well (total score of 57) by the best fitting of the overall regression model selection process. It was concluded that the C-factor estimation can be improved by the NDVI-based per-pixel approach using very high-resolution satellite imagery in the semiarid mountainous areas.

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

Similar content being viewed by others

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F (eds) Proceeding of the second international symposium on information theory. Academiai Kiado, Budapest, pp 267–281

    Google Scholar 

  • Akpınar A, Kömürcü Mİ, Kankal M (2011) Development of hydropower energy in Turkey: the case of Çoruh river basin. Renew Sust Energy Rev 15:1201–1209

    Article  Google Scholar 

  • Anache JAA, Bacchi CG, Alves-Sobrinho T (2014) Modeling of (R) USLE C-factor for pasture as a function of normalized difference vegetation index. Eur Int J Sci Technol 3(9):214–221

    Google Scholar 

  • Atalay İ (1988) The geography of lake Tortum district (North-Eastern Anatolia). Aegean Geogr J 4(1):19–40

    Google Scholar 

  • Baskan O, Cebel H, Akgul S, Erpul G (2010) Conditional simulation of USLE/RUSLE soil erodibility factor by geostatistics in a Mediterranean catchment, Turkey. Environ Earth Sci 60:1179–1187

    Article  Google Scholar 

  • Bettinger P, Boston K, Siry JP, Grebner DL (2009) Forest Manag Plan. Academic Press, USA

    Google Scholar 

  • Bozdogan H (2000) Akaike’s information criterion and recent developments in informational complexity. J Math Psychol 44:62–91

    Article  Google Scholar 

  • Brooks KN, Ffolliott PF, Gregersen HM, Thames J (1997) Hydrology and the management of watersheds. Iowa State University Press, Iowa

    Google Scholar 

  • Buttafuoco G, Conforti M, Aucelli PPC, Robustelli G, Scarciglia F (2012) Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Environ Earth Sci 66:1111–1125

    Article  Google Scholar 

  • Çam A, Fırat O, Yılmaz A (2013) The orthophoto and digital surface model production activities in the general command of mapping. In: TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara, p 6 (in Turkish)

  • Castrignano A, Buttafuoco G, Comolli R, Castrignano A (2011) Using digital elevation model to improve soil pH prediction in an alpine doline. Pedosphere 21(2):259–270

    Article  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250

    Article  Google Scholar 

  • Chen P, Lian Y (2016) Modeling of soil loss and its impact factors in the Guijang Karst River Basin in Southern China. Environ Earth Sci 75:352

    Article  Google Scholar 

  • Chi Z, Yao Z, Shen S, Hiroyuki N, Haruyoshi I, Peng C, Jun F (2008) Development of GIS-based FUSLE model in a Chinese fir forest sub-catchment with a focus on the litter in the Dabie Mountains, China. For Ecol Manag 255:2782–2789

    Article  Google Scholar 

  • Conforti M, Buttafuoco G, Leone AP, Aucelli PPC, Robustelli G, Scarciglia F (2013) Studying the relationship between water-induced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis: a case study in southern Italy. Catena 110:44–58

    Article  Google Scholar 

  • Conforti M, Buttafuoco G, Rago V, Aucelli PPC, Robustelli G, Scarciglia F (2016) Soil loss assessment in the Turbolo catchment (Calabria, Italy). J Maps 12(5):815–825

    Google Scholar 

  • Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Boca Raton

    Google Scholar 

  • De Jong SM (1994) Derivation of vegetative variable from a Landsat TM image for modeling soil-erosion. Earth Surf Process Landf 19(2):165–178

    Article  Google Scholar 

  • De Jong SM, Riezebos HT (1997) SEMMED: a distributed approach to soil erosion modelling. In: Spiteri A (ed) Remote sensing’96: integrated applications for risk assessment and disaster prevention for the Mediterranean. Balkema, Rotterdam, pp 199–204

    Google Scholar 

  • Demirci A, Karaburun A (2012) Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed, northwest Turkey. Environ Earth Sci 66(3):903–913

    Article  Google Scholar 

  • Durigon VL, Carvalho DF, Antunes MAH, Oliveira PTS, Fernandes MM (2014) NDVI time series for monitoring RUSLE cover management factor in a tropical watershed. Int J Remote Sens 35(2):441–453

    Article  Google Scholar 

  • ERDAS (2009) ERDAS imagine 2010. ERDAS Inc, Norcross

    Google Scholar 

  • Erencin Z (2000) C-factor mapping using remote sensing and GIS: a case study of Lom Sak/Lom Kao, Thailand. M.Sc thesis, International Institute for Aerospace Survey and Earth Sciences (ITC)

  • ESRI (2010) ArcGIS for Desktop. Environmental Systems Research Institute Inc, California

    Google Scholar 

  • Farhan Y, Nawaiseh S (2015) Spatial assessment of soil erosion risk using RUSLE and GIS techniques. Environ Earth Sci 74:4649–4669

    Article  Google Scholar 

  • Folly A, Bronsveld MC, Clavaux M (1996) A knowledge-based approach for C-factor mapping in Spain using Landsat TM and GIS. Int J Remote Sens 12:2401–2415

    Article  Google Scholar 

  • Fournier A (2011) Soil erosion: causes, processes and effects. Nova Science Publishers, New York

    Google Scholar 

  • Ge Y, Thomasson JA, Morgan CL, Searcy SW (2007) VNIR diffuse reflectance spectroscopy for agricultural soil property determination based on regression-kriging. T ASABE 50(3):1081–1092

    Article  Google Scholar 

  • General Directorate of Forestry (2013) Ecosystem-based functional forest management plan of Tortum Forest Sub-district Directorate. Forest Management and Planning Department, Ankara

    Google Scholar 

  • General Directorate of Forestry (2015) Act No 299. Forest Management and Planning Department, Ankara

    Google Scholar 

  • Goldman SJ, Jackson K, Bursztynsky TA (1986) Erosion and sediment control handbook. McGraw-Hill Inc, New York

    Google Scholar 

  • Hocking RR (1976) The analysis and selection of variables in linear regression. Biometrics 32:1–49

    Article  Google Scholar 

  • Irmak MA, Yılmaz H (2008) Determination of the usability of woody plant species in Tortum—creek watershed for functional and aesthetical uses in the respect of landscape architecture. Biol Divers Conserv 1(1):1–12

    Article  Google Scholar 

  • Jamshidi R, Dragovich D, Webb AA (2012) Native forest C factor determination using satellite imagery in four sub-catchments. Revisiting experimental catchment studies in forest hydrology. In: Proceedings of a workshop held during the XXV IUGG General Assembly in Melbourne, June–July 2011, IAHS Publ 353, 2012

  • Jamshidi R, Dragovich D, Webb AA (2013) Estimating catchment-scale annual soil loss in managed native eucalypt forests, Australia. For Ecol Manag 304:20–32

    Article  Google Scholar 

  • Ju CY, Cai TJ, Yang XH (2008) Topography-based modeling to estimate percent vegetation cover in semi-arid Mu Us sandy land, China. Comput Electron Agric 64(2):133–139

    Article  Google Scholar 

  • Judge GG, Griffiths WE, Carter Hill R, Lütkepohl H, Lee TC (1985) The theory and practice of econometrics. Wiley, New York

    Google Scholar 

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

    Google Scholar 

  • Karpilo RD, Toy TJ (2004) RUSLE C-factors for slope protection applications. In: National Meeting of American Society of Mining and Reclamation and 25th West Virginia Surface Mine Drainage Task Force, 18–24 April 2014, Published by ASMR, Lexington

  • Knijff JM, Jones RJA, Montanarella L (1999) Soil erosion risk assessment in Italy. European Soil Bureau, Joint Research Centre EUR 19022 EN

  • Knijff JM, Jones RJA, Montanarella L (2000) Soil erosion risk assessment in Europe. European Soil Bureau, Joint Research Centre EUR 19044 EN

  • Kobya Y, Taşkın H, Yeşilkanat CM, Varinlioğlu A, Korcak S (2015) Natural and artificial radioactivity assessment of dam lakes sediments in Çoruh river, Turkey. J Radioanal Nucl Chem 303(1):287–295

    Article  Google Scholar 

  • Kouli M, Soupios P, Vallianatos F (2009) Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environ Geol 57:483–497

    Article  Google Scholar 

  • Laar AV, Akça A (2007) Forest mensuration. Springer, The Netherlands

    Book  Google Scholar 

  • Laflen JM, Foster GR, Onstad CA (1985) Simulation of individual-storm soil loss for modeling the impact of soil erosion on crop productivity. In: El-Swaify SA, Moldenhauer WC, Lo A (eds) Soil erosion and conservation. Soil Cons Soc Am, Ankeny, pp 285–295

    Google Scholar 

  • Li X, Wu B, Zhang L (2013) Dynamic monitoring of soil erosion for upper stream of Miyun reservoir in the last 30 years. J Mt Sci 10(5):801–811

    Article  Google Scholar 

  • Loague K, Green RE (1991) Statistical and graphical methods for evaluating solute transport models: overview and application. J Contam Hydrol 7:51–73

    Article  Google Scholar 

  • LTI (2016) Laser Technology Inc. TruPulse360 Laser range finder. http://www.lasertech.com/TruPulse-360-Rangefinder.aspx. Accessed 22 March 2016

  • Mallows CL (1973) Some comments on Cp. Technometrics 15(4):661–675

    Google Scholar 

  • McCoy RM (2005) Field methods in remote sensing. The Guilford Press, New York

    Google Scholar 

  • Morgan RPC (2005) Soil erosion & conservation. Blackwell, Massachusetts

    Google Scholar 

  • Özevren E, Tekin SN (2014) The integrated participatory watershed rehabilitation approach-Best practices from Turkey. Planet@Risk 2(4):217–223

    Google Scholar 

  • Özhan S, Balcı AN, Özyuvaci N, Hızal A, Gökbulak F, Serengil Y (2005) Cover and management factors for the Universal Soil-Loss Equation for forest ecosystems in the Marmara region, Turkey. For Ecol Manag 214:118–123

    Article  Google Scholar 

  • Özşahin E, Uygur V (2014) The effects of land use and land cover changes (LULCC) in Kuseyr plateau of Turkey on erosion. Turk J Agric For 38:478–487

    Article  Google Scholar 

  • Pan JH, Wen Y (2014) Estimation of soil erosion using RUSLE in Caijiamiao watershed China. Nat Hazards 71(3):2187–2205

    Article  Google Scholar 

  • Panagos P, Borrelli P, Meusburger K, Christine A, Lugato E, Montanarella L (2015) Estimating the soil erosion cover-management factor at the European scale. Land Use Policy 48:38–50

    Article  Google Scholar 

  • Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou HD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Eng Geol 158:77–88

    Article  Google Scholar 

  • Perovic V, Jaramaz D, Zivotic L, Cakmak D, Mrvic V, Milanovic M, Saljnikov E (2016) Design and implementation of WebGIS technologies in evaluation of erosion intensity in the municipality of NIS (Serbia). Environ Earth Sci 75:211

    Article  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. Environ Earth Sci 64:965–972

    Article  Google Scholar 

  • Ranzi R, Le TH, Rulli MC (2012) A RUSLE approach to model suspended sediment load in the Lo river (Vietnam): effects of reservoirs and land use changes. J Hydrol 422–423:17–29

    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). Agricultural Handbook, vol 703. US Department of Agriculture, Washington, DC

    Google Scholar 

  • Renard KG, Yoder DC, Lightle DT, Dabney SM (2011) Universal Soil Loss Equation and Revised Universal Soil Loss Equation. In: Morgan RPC, Nearing MA (eds) Handbook of erosion modelling, 1st edn. Blackwell, Massachusetts, pp 137–167

    Google Scholar 

  • Richards JA (2013) Remote sensing digital image analysis: An introduction. Springer, Berlin

    Book  Google Scholar 

  • Rosewell CJ (1993) SOILOSS—a program to assist in the selection of management practices to reduce erosion. Technical handbook no 11, 2nd edn. Soil Conservation Service, Sydney, p 86

  • SAS (2011) The SAS system for Windows Release 9.2. SAS Institute, Cary

    Google Scholar 

  • Sawa T (1978) Information criteria for discriminating among alternative regression models. Econometrica 46:1273–1282

    Article  Google Scholar 

  • Saygın SD, Özcan AU, Başaran M, Timur OB, Dolarslan M, Yılman FE, Erpul G (2014) The combined RUSLE/SDR approach integrated with GIS and geostatistics to estimate annual sediment flux rates in the semi-arid catchment, Turkey. Environ Earth Sci 71:1605–1618

    Article  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Article  Google Scholar 

  • Smith SV, Bullock SH, Hinojosa-Corona A, Franco-Vizcaino E, Escoto-Rodriguez M, Kretzschmar TG, Farfan LM, Salazar-Cesena JM (2007) Soil erosion and significance for carbon fluxes in a mountainous Mediterranean-climate watershed. Ecol Appl 17(5):1379–1387

    Article  Google Scholar 

  • SOILPRO (2008) Explaination of the use of SMS models. European Commission Life + Programme LIFE08ENV/IT/000428, Belgium. http://www.soilpro.eu/en/documents/public-documents/manuals. Accessed 26 April 2015

  • SPSS (2006) SPSS 15.0 for Windows. Statistical Package for the Social Sciences Inc, New York

    Google Scholar 

  • State Meteorological Service (2016) Temperature and precipitation records of the Tortum Weather Station. Ministry of Forestry and Water Affairs Turkish State Meteorological Service, Ankara

    Google Scholar 

  • Sumner ME, Stewart BA (1992) Soil crusting: chemical and physical processes. Lewis Publishers, Boca Raton

    Google Scholar 

  • Suriyaprasit M, Shrestha DP (2008) Deriving land use and canopy cover factor from remote sensing and field data in inaccessible mountainous terrain for use in soil erosion modelling. In: Technical Session TS-34:SS-7 Global Monitoring for Environment and Security (GMES), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol XXXVII Part B7, Beijing

  • Tang Q, Xu Y, Bennett SJ, Li Y (2015) Assessment of soil erosion using RUSLE and GIS: a case study of the Yangou watershed in the Loess Plateau, China. Environ Earth Sci 73:1715–1724

    Article  Google Scholar 

  • Toy TJ, Foster GR, Renard KG (1999) RUSLE for mining, construction and reclamation lands. J Soil Water Conserv 54(2):462–467

    Google Scholar 

  • Trimble SW, Mendel AC (1995) The cow as a geomorphic agent: a critical review. Geomorphology 13:233–253

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  • URL1 (2012) Coruh River Watershed Rehabilitation Project 2012–2019. http://www.coruhhavzasi.com/en/. Accessed 27 April 2015

  • URL2 (2012) Project outcomes of Coruh River Watershed Rehabilitation Project. http://www.coruhhavzasi.com/en/64-project-outcomes.html. Accessed 18 July 2016

  • Vatandaşlar C (2015) Estimation of cover and crop management factor in soil erosion risk analysis by remote sensing methods. M.Sc. thesis, Artvin Coruh University Graduate School of Natural and Applied Sciences (in Turkish)

  • Wang G, Wente S, Gertner GZ, Anderson A (2002) 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(18):3649–3667

    Article  Google Scholar 

  • Wischmeier WH (1975) Estimating the soil loss equation’s cover and management factor for undisturbed area. In: Present and prospective technology for predicting sediment yields and sources. USDA ARS Publication, ARS-S-40, pp 118–124

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—a guide for conservation planning. Agriculture Handbook 537, US Department of Agriculture, Washington, DC

  • Woo M, Luk S (1990) Vegetation effects on soil and water losses on weathered granitic hillslopes, South China. Phys Geogr 11:1–16

    Google Scholar 

  • Wu B, Zhou Y, Huang J, Tian Y, Huang W (2004) Spatial pattern of soil and water loss and its affecting factors analysis in the Upper Basin of Miyun reservoir. IEEE, pp 4700-4702

  • Yılmaz H, Karahan F, Bulut Z, Demircan N, Alper H (2002) Evaluation at the view of biological restoration of some secondary plants in arid region watershed planning (Kurak bölgelerde havza planlamasında bazı sekonder bitkilerin biyolojik onarım yönünden değerlendirilmesi). In: Su Havzalarında Toprak ve Su Kaynaklarının Korunması Geliştirilmesi ve Yönetimi Sempozyumu, Hatay, pp 77–84

  • Yüksek T (2012) The restoration effects of black locust (Robinia pseudoacacia L.) plantation on surface soil properties and carbon sequestration on lower hillslopes in the semi-humid region of Coruh Drainage Basin in Turkey. Catena 90:18–25

    Article  Google Scholar 

  • Zengin M, Özer S, Özgül M (2009) Determining of erosion situation of the Coruh watershed by GIS and solution suggestions. Ataturk Universitesi Ziraat Fakultesi Dergisi 40(1):9–19 (in Turkish)

    Google Scholar 

  • Zhang H, Sun Y, Cheng Y, Cheng J (2006) Effect on surface runoff coefficient of different vegetation types in Jinyun mountain of Chongqing. J Soil Water Conserv 20(6):11–14

    Google Scholar 

  • Zhang Y, Degroote J, Wolter C, Sugumaran R (2009) Integration of modified universal soil loss equation (MUSLE) into a GIS framework to assess soil erosion risk. Land Degrad Dev 20:84–91

    Article  Google Scholar 

  • Zheng FL (2006) Effect of vegetation changes on soil erosion on the Loess Plateau. Pedosphere 16(4):420–427

    Article  Google Scholar 

  • Zhou P, Luukkanen O, Tokola T, Nieminen J (2008) Effect of vegetation cover on soil erosion in a mountainous watershed. Catena 75:319–325

    Article  Google Scholar 

  • Zobel JM, Ek AR, Burk TE (2011) Comparison of forest inventory and analysis surveys, basal area models, and fitting methods for the aspen forest type in Minnesota. For Ecol Manag 262(2):188–194

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially supported by the Coruh River Watershed Rehabilitation Project (2012–2019). The authors would like to thank the project funding agencies General Directorate of Forestry’s and the Japanese International Cooperation Agency’s (JICA) managers and staff, Prof. Dr. Aydin Tufekcioglu, Asst. Prof. Dr. Mustafa Tufekcioglu, Deniz Akdeniz, Res. Asst. Ahmet Duman, and Res. Asst. Musa Dinc for their contributions to the work. The authors would also like to thank to the reviewers for their constructive comments.

Author information

Authors and Affiliations

  1. Department of Forest Engineering, Artvin Coruh University, 08100, Artvin, Turkey

    Can Vatandaşlar & Mehmet Yavuz

Authors
  1. Can Vatandaşlar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehmet Yavuz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Can Vatandaşlar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vatandaşlar, C., Yavuz, M. Modeling cover management factor of RUSLE using very high-resolution satellite imagery in a semiarid watershed. Environ Earth Sci 76, 65 (2017). https://doi.org/10.1007/s12665-017-6388-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-017-6388-0

Keywords

Search

Navigation