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The Significance of Land Cover Delineation on Soil Erosion Assessment

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Abstract

The study aims to evaluate the significance of land cover delineation on soil erosion assessment. To that end, RUSLE (Revised Universal Soil Loss Equation) was implemented at the Upper Acheloos River catchment, Western Central Greece, annually and multi-annually for the period 1965–92. The model estimates soil erosion as the linear product of six factors (R, K, LS, C, and P) considering the catchment’s climatic, pedological, topographic, land cover, and anthropogenic characteristics, respectively. The C factor was estimated using six alternative land use delineations of different resolution, namely the CORINE Land Cover (CLC) project (2000, 2012 versions) (1:100,000), a land use map conducted by the Greek National Agricultural Research Foundation (NAGREF) (1:20,000), a land use map conducted by the Greek Payment and Control Agency for Guidance and Guarantee Community Aid (PCAGGCA) (1:5,000), and the Landsat 8 16-day Normalized Difference Vegetation Index (NDVI) dataset (30 m/pixel) (two approximations) based on remote sensing data (satellite image acquired on 07/09/2016) (1:40,000). Since all other factors remain unchanged per each RUSLE application, the differences among the yielded results are attributed to the C factor (thus the land cover pattern) variations. Validation was made considering the convergence between simulated (modeled) and observed sediment yield. The latter was estimated based on field measurements conducted by the Greek PPC (Public Power Corporation). The model performed best at both time scales using the Landsat 8 (Eq. 13) dataset, characterized by a detailed resolution and a satisfactory categorization, allowing the identification of the most susceptible to erosion areas.

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References

  • Alexandridis TK, Sotiropoulou AM, Bilas G, Karapetsas N, Silleos NG (2015) The effects of seasonality in estimating the C-factor of soil erosion studies. Land Degrad Dev 26(6):596–603

    Article  Google Scholar 

  • Angulo-Martinez M, Lopez-Vicente M, Vicente-Serrano SM, Beguerıa S (2009) Mapping rainfall erosivity at a regional scale: a comparison of interpolation methods in the Ebro Basin (NE Spain). Hydrol Earth Syst Sci 13:1907–1920

    Article  Google Scholar 

  • Auerswald K, Fiener P, Martin W, Elhaus D (2014) Use and misuse of the K factor equation in soil erosion modeling: an alternative equation for determining USLE nomograph soil erodibility values. Catena 118:220–225

    Article  Google Scholar 

  • Ballhorn U (2007) Pre-processing of remote sensing data. Lecture, 19/08-31/08/2017. Bogor Agricultural University (IPB), Indonesia

    Google Scholar 

  • Bathrellos G, Skilodimou H, Chousianitis K (2010) Soil erosion assessment using GIS in Zakynthos Island. In: Proceedings of the 9th National conference of the Geographical Society of Greece, Greece, 4–6 November 2010 (in Greek)

  • Beasley DB, Huggins LF, Monke EJ (1980) ANSWERS: a model for watershed planning. T ASAE 23(4):938–944

    Article  Google Scholar 

  • Brakensiek DL, Rawls WJ, Stephenson GR (1986) Determining the saturated hydraulic conductivity of a soil containing rock fragments. Soil Sci Soc Am J 50(3):834–835

    Article  Google Scholar 

  • Cihlar J (1987) A methodology for mapping and monitoring cropland soil erosion. Can J Soil Sci 67(3):433–444

    Article  Google Scholar 

  • Clapp RB, Hornberger GM (1978) Empirical equations for some soil hydraulic properties. Water Resour Res 14(4):601–604

    Article  Google Scholar 

  • CLC (2017a) CORINE Land Cover 1990 seamless vector data. http://www.eea.europa.eu/data-and-maps/data/clc-1990-vector. Accessed 01 Feb 2017

  • CLC (2017b) CORINE Land Cover 2000 seamless vector data. http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-database-4. Accessed 01 Feb 2017

  • CLC (2017c) CORINE Land Cover 2006 seamless vector data. http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-4. Accessed 01 Feb 2017

  • CLC (2017d) CORINE Land Cover 2012 seamless vector data. http://www.eea.europa.eu/data-and-maps/data/clc-2012-vector. Accessed 01 Feb 2017

  • Correa EA, Pinto SAF (2011) Estimating the C factor of the Universal Soil Loss Equation (USLE) using the Normalized Difference Vegetation Index (NDVI) in the hydrographic basin of the Monjolo Grande River, Ipeuna, SP. In: Proceedings of the XV Brazilian symposium on remote sensing, INPE, Brazil, 30 Apr–5 May 2011 (In Portuguese)

  • De Jong SM (1994) Application of reflective remote sensing for land degradation studies in a Mediterranean environment. Dissertation, University of Utrecht

  • De Jong SM, Brouwer LC, Riezebos HT (1998) Erosion hazard assessment in the Peyne catchment, France. Working paper DeMon-2 Project, University of Utrecht

  • De Vente J, Poesen J, Govers G, Boix-Fayos C (2009) The implications of data selection for regional erosion and sediment yield modelling. Earth Surf Proc Land 34(15):1994–2007

    Article  Google Scholar 

  • Desmet P, Govers G (1996) A GIS procedure for automatically calculating the ULSE LS factor on topographically complex landscape units. J Soil Water Conserv 51:427–433

    Google Scholar 

  • Dimopoulou E (2012) Towards an integrated cadastral system fulfilling LPIS requirements. In: Proceedings of the FIG Working Week, Knowing to manage the territory, protect the environment, evaluate the cultural heritage session, TS04C-Cadastre and Spatial Information. Italy. pp 11, 6–10 May 2012

  • Efthimiou N, Lykoudi E, Karavitis C (2014) Soil erosion assessment using the RUSLE model and GIS. Eur Water 47:15–30

    Google Scholar 

  • Efthimiou N (2016a) Developing strategies for estimating sediment yield by using Decision Support Systems at mountainous hydrological catchments. Dissertation, Agricultural University of Athens

  • Efthimiou N (2016b) Performance of the RUSLE in Mediterranean mountainous catchments. Environ Process 3(4):1001–1019. https://doi.org/10.1007/s40710-016-0174-y

  • Efthimiou N, Lykoudi E, Karavitis C (2017) Comparative analysis of sediment yield estimations using different empirical soil erosion models Hydrol Sci J 62(16):2674–2694. https://doi.org/10.1080/02626667.2017.1404068

    Article  Google Scholar 

  • Evans R (1980) Mechanics of water erosion and their spatial and temporal controls: an empirical viewpoint. In: Kirkby MJ, Morgan RPC (eds) Soil erosion. Wiley, Chichester, p 109–128

    Google Scholar 

  • Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The shuttle radar topography mission. Rev Geophys 45:RG2004. https://doi.org/10.1029/2005RG000183

    Article  Google Scholar 

  • Ferreira V, Panagopoulos T (2014) Seasonality of soil erosion under Mediterranean conditions at the Alqueva Dam watershed. Environ Manag 54(1):67–83. https://doi.org/10.1007/s00267-014-0281-3

    Article  Google Scholar 

  • Food and Agriculture Organization of the United Nations (1990) Guidelines for Soil Description, 3rd edn. FAO Publications Division, Rome

    Google Scholar 

  • Gyssels G, Poesen J, Bochet E, Li Y (2005) Impact of plant roots on the resistance of soils to erosion by water: a review. Prog Phys Geogr 29(2):189–217

    Article  Google Scholar 

  • Hrissanthou B, Pyliotis A (1995) Estimation of sediment inflow into a reservoir under construction. In: Proceedings of the 6th National Conference NHC, Greece (in Greek)

  • Jensen JR (2000) Remote sensing of the environment: an earth resource perspective. Prentice Hall, New Jersey

    Google Scholar 

  • Hazarika MK, Honda K (2001) Estimation of soil erosion using remote sensing and GIS: its valuation and economic implications on agricultural production. In: Stott DE, Mohtar RH, Steinhardt GC (eds) Sustaining the Global Farm. Purdue University, Indiana, p 1090–1093

    Google Scholar 

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

    Google Scholar 

  • Karamesouti M, Petropoulos PP, Papanikolaou DI, Kairis O, Kosmas K (2016) Erosion rate predictions from PESERA and RUSLE at a Mediterranean site before and after a wildfire: comparison & implications. Geoderma 261:44–58

    Article  Google Scholar 

  • Karydas CG, Sekuloska T, Silleos GN (2008) Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete. Environ Monit Assess 149(1–4):19–28

    Google Scholar 

  • Kinnell PIA (2010) Event soil loss, runoff and the universal soil loss equation family of models: a review. J Hydrol 385:384–397

    Article  Google Scholar 

  • Koutsoyiannis D (2000) Broken line smoothing: a simple method for interpolating and smoothing data series. Environ Modell Softw 15:139–149

    Article  Google Scholar 

  • Lillesand TM, Kiefer RW, Chipman JW (2004) Remote Sensing and Image Interpretation, 5th edn. John Wiley & Sons, New York

    Google Scholar 

  • Lin CY, Lin WT, Chou WC (2002) Soil erosion prediction and sediment yield estimation: the Taiwan experience. Soil Res 68:143–152

    Google Scholar 

  • Lykoudi Ε (2000) Geomorphological evolution of the Upper Acheloos River catchment. Dissertation, National Technical University of Athens, In Greek

    Google Scholar 

  • Lykoudi E, Zarris D (2002) Identification of regions with high risk of soil erosion in the island of Cephalonia using the Universal Soil Loss Equation. In: Proceedings of the 6th National Conference of the Geographical Society of Greece, Greece. Volume II, pp 412–419, 3–6 October 2002 (In Greek)

  • Maggina K (2003) Effects of intense rainfall events on a catchment’s sediment yield. Thesis, National Technical University of Athens (In Greek)

  • Mather MP (1999) Computer Processing of Remotely Sensed Images, 2nd edn. John Wiley & Sons, UK

    Google Scholar 

  • Mitchell JK, Bubenzer GD (1980) Soil Loss Estimation. In: Kirkby MJ, Morgan RPC (eds) Soil Erosion.. Wiley, Chichester, p 17–62

    Google Scholar 

  • Morgan RPC, Morgan DDV, Finney HJ (1984) A predictive model for the assessment of soil erosion risk. J Agr Eng Res 30(1):245–253

    Article  Google Scholar 

  • Morgan RPC (2005) Soil Erosion and Conservation. 3rd edn. Blackwell, New Jersey

  • Nearing MA, Foster GR, Lane LJ, Finkner SC (1989) A process-based soil erosion model for USDA: water erosion prediction project technology. T ASAE 32:1587–1593

    Article  Google Scholar 

  • Ostovari Y, Ghorbani-Dashtaki S, Bahrami HA, Naderi M, Dematte JAM (2017) Soil loss prediction by an integrated system using RUSLE, GIS and remote sensing in semi-arid region. Geoderma Reg 11:28–36

    Article  Google Scholar 

  • Pagonas M, Kontopoulos N (2007) Soil erosion estimation with the Universal Soil Loss Equation: a case study of three Hellenic basins, NW Peloponnese, Greece. In: Proceedings of the 8th National Conference of the Geographical Society of Greece, Greece. Volume I, pp 149–156, 4–7 October 2007 (In Greek)

  • Panagos P, Karydas CG, Gitas IZ, Montanarella L (2012a) Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service Int J Digit Earth 5(6):461–487

    Article  Google Scholar 

  • Panagos P, Meusburger K, Alewell C, Montanarella L (2012b) Soil erodibility estimation using LUCAS point survey data of Europe Environ Model Softw 30:143–145

    Article  Google Scholar 

  • Panagos P, Ballabio C, Yigini Y, Dunbar M (2013) Estimating the soil organic carbon content for European NUTS2 regions based on LUCAS data collection. Sci Total Environ 442:235–246

    Article  CAS  Google Scholar 

  • Panagos P, Meusburger K, Ballabio C, Borrelli P, Alewell C (2014) Soil erodibility in Europe: A high-resolution dataset based on LUCAS. Sci Total Environ 479-480:189–200

    Article  CAS  Google Scholar 

  • Panagos P, Borrelli P, Meusburger C, Alewell C, Lugato E, Montanarella L (2015a) Estimating the soil erosion cover-management factor at European scale Land Use Policy 48(C):38–50. https://doi.org/10.1016/j.landusepol.2015.05.021

    Article  Google Scholar 

  • Panagos P, Ballabio C, Borrelli P, Meusburger K, Klik A, Rousseva S, Tadic MP, Michaelides S, Hrabalikova M, Olsen P, Aalto J, Lakatos M, Rymszewicz A, Dumitrescu A, Begueria S, Alewell C (2015b) Rainfall erosivity in Europe Sci Total Environ 511:801–814. https://doi.org/10.1016/j.scitotenv.2015.01.008

    Article  CAS  Google Scholar 

  • Panagos P, Borrelli P, Meusburger K (2015c) A new European slope length and steepness factor (LS-factor) for modeling soil erosion by water Geosciences 5:117–126

    Article  Google Scholar 

  • Panagos P, Borrelli P, Meusburger K, van der Zanden EH, Poesen J, Alewell C (2015d) Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European scale Environ Sci Pol 51:23–24

    Article  Google Scholar 

  • Panagos P, Ballabio C, Borrelli P, Meusberger K (2016) Spatio-temporal analysis of rainfall erosivity and erosivity density in Greece. Catena 137:161–172

    Article  Google Scholar 

  • Patriche CV, Capatana V, Stoica DL (2006) Aspects regarding soil erosion spatial modeling using the USLE/RUSLE within GIS. Geographia. Technica 2:87–97

    Google Scholar 

  • Poesen JW, Torri D, Bunte K (1994) Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23(1–2):141–166

    Article  Google Scholar 

  • Psomiadis E, Dercas N, Dalezios N, Spyropoulos N (2016) The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices. In: Proceedings SPIE 9998, Remote Sensing for Agriculture Ecosystems, and Hydrology XVIII, 99981L, https://doi.org/10.1117/12.2241316

  • Rawls WJ, Brakensiek CL, Saxton KE (1982) Estimation of soil water properties. T ASAE 25(5):1316–1320

    Article  Google Scholar 

  • Renard KG, Foster GR, Weesies GA, Porter JP (1991) RUSLE: Revised Universal Soil Loss Equation. J Soil Water Conserv 46(1):30–33

    Google Scholar 

  • Renard KG, Foster GR, Yoder DC, McCool DK (1994) RUSLE revisited: Status, questions, answers, and the future. J Soil Water Conserv 49(3):213–220

    Google Scholar 

  • Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1996) Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation. USDA (US), Washington (DC), Agricultural Handbook No 703

    Google Scholar 

  • Renfro GW (1972) Use of erosion equations and sediment delivery ratios for predicting sediment yield. In: Proceedings of Sediment yield workshop: Present and prospective technology for predicting sediment yield and sources, USDA Sedim Labor, Oxford

  • Richter G, Negendank JFW (1977) Soil erosion processes and their measurement in the German area of the Moselle river. Earth Surf Process 2:261–278

    Article  Google Scholar 

  • Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: 3rd ERTS symposium, Washington, DC. NASA SP-351.

  • Sader SA, Winne JC (1992) RGB-NDVI color composites for visualizing forest change dynamics. Int J Remote Sens 13:3055–3067

    Article  Google Scholar 

  • Schwertmann U, Vogl W, Kainz M (1990) Soil erosion by water. Eugen Ulmer, Stuttgart, In German

    Google Scholar 

  • Seutloali KE, Dube T, Mutanga O (2017) Assessing and mapping the severity of soil erosion using the 30-m Landsat multispectral satellite data in the former South African homelands of Transkei. Phys Chem Earth Parts A/B/C 100:296–304. https://doi.org/10.1016/j.pce.2016.10.001

    Article  Google Scholar 

  • Sigalos G, Loukaidi V, Dasaklis S, Alexouli-Livaditi A (2010) Assessment of the quantity of the material transported downstream of Sperchios river, Central Greece. In: Proceedings of the 12th international congress of the Geological Society of Greece, Greece, 19–22 May 2010

  • Smith B, Sandwell D (2003) Accuracy and resolution of shuttle radar topography mission data. Geophys Res Lett 30(9):20.1–20.4. https://doi.org/10.1029/2002GL016643

    Article  Google Scholar 

  • Soil Science Division Staff (2017) Soil survey manual. In: Ditzler C, Scheffe K, Monger HC (eds) USDA Handbook 18. US Government Printing Office, Washington DC

    Google Scholar 

  • Symeonakis E, Drake N (2004) Monitoring desertification and land degradation over sub-Saharan Africa. Int J Remote Sens 25(3):573–592

    Article  Google Scholar 

  • Tadesse L, Suryabhagavan KV, Sridhar G, Legesse G (2017) Land use and land cover changes and soil erosion in Yezat Watershed, North Western Ethiopia. Int Soil Water Conserv Res 5(2):85–94

    Article  Google Scholar 

  • Toth G, Jones A, Montanarella L (2013) The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union. Environ Monit Assess 185(9):7409–7425

    Article  CAS  Google Scholar 

  • Van der Knijff JM, Jones RJA, Montanarella L (2000) Soil erosion risk assessment in Italy. Luxembourg: Office for Official Publications of the European Communities. Report No.: EUR 19044 EN. pp 44

  • Vrieling A (2006) Satellite remote sensing for water erosion assessment: a review. Catena 65(1):2–18

    Article  Google Scholar 

  • 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, Johnson CB, Cross BW (1971) A soil erodibility nomograph for farmland and construction sites. J Soil Water Conserv 26(5):189–193

    Google Scholar 

  • Wischmeier WH (1975) Estimating the Soil Loss Equation's Cover and Management Factor for Undisturbed Areas. p. 118–124. In: Present and Prospective Technology for Predicting Sediment Yields and Sources. USDA-ARS-40.

  • Wischmeier WH, Smith DD (1978) Predicting Rainfall Erosion Losses, A guide to conservation planning. USDA (US), Washington (DC), Agricultural Handbook No 537

    Google Scholar 

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Acknowledgements

The authors wish to thank the EU and Greek NAGREF for the provision of the soil samples data, the Greek NAGREF and Greek PCAGGCA for the provision of the land cover datasets, the Greek PPC and Greek MEECC for the provision of the precipitation data, the Greek PPC for the provision of the discharge, sediment discharge, and discharge–sediment discharge pairs measurements. The LUCAS topsoil, Erosivity Density, LS-factor, and P-factor datasets used in this work were made available by the European Commission through the European Soil Data Centre managed by the Joint Research Centre (JRC), http://esdac.jrc.ec.europa.eu/.

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Efthimiou, N., Psomiadis, E. The Significance of Land Cover Delineation on Soil Erosion Assessment. Environmental Management 62, 383–402 (2018). https://doi.org/10.1007/s00267-018-1044-3

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