Modeling Soil Erosion Using Rusle and Gis Tools

International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com MODELING SOIL EROSION USING RUSLE AND GIS TOOLS George Ashiagbor1, Eric K Forkuo1, Prosper Laari2, Raymond Aabeyir2 1. Department of Geomatic Engineering, Kwame Nkrumah University of Science & Technology, Kumasi, Ghana. 2. Department of Environment and Resource Studies, University for Development Studies, Wa Campus, Wa, Ghana. *Corresponding author email: [email protected] or [email protected] Abstract Soil erosion involves detachment and transport of soil particles from top soil layers, degrading soil quality and reducing the productivity of affected lands. Soil eroded from the upland catchment causes depletion of fertile agricultural land and the resulting sediment deposited at the river networks creates river morphological change and reservoir sedimentation problems. However, land managers and policy makers are more interested in the spatial distribution of soil erosion risk than in absolute values of soil erosion loss. The aims of this paper is to model the spatial distribution of soil erosion in Densu River Basin of Ghana using RUSLE and GIS tools and to use the model to explore the relationship between erosion susceptibility, slope and Land use and Land Cover (LULC) in the Basin. The rainfall map, digital elevation model, soil type map, and land cover map, were input data in the soil erosion model developed. This model was then categorized into four different erosion risk classes. The developed soil erosion map was then overlaid with the slope and LULC maps of the of the study area to explore their effects on erosion susceptibility of the soil in the Densu River Basin. The Model, predicted 88% of the basin as low erosion risk and 6% as moderate erosion risk3% as high erosion risk and 3% as severe risk. The high and severe erosion areas were distributed mainly within the areas of high slope gradient and also sections of the moderate forest LULC class. Also, the areas within the moderate forest LULC class found to have high erosion risk had an intersecting high erodibility soil group. causes depletion of fertile agricultural land and the resulting sediment delivered to the river networks creates river morphological change and reservoir sedimentation problems [4]. In Africa it is estimated that the decrease in productivity due to soil erosion is 2-40% with an average of 8.2% for the whole continent and also, an average of 19% of reservoir storage volumes are silted [5]. In addition, excessive sedimentation clogs stream channels and increases costs for maintaining water conveyances. Soil erosion is one of the major non-point pollution sources in many watersheds [6]. Soil erosion, sedimentation, and the subsequent conveyance of fertilizers, pesticides, and herbicides play a significant role in impairing water resources within sub watersheds and watersheds [7]. The need to quantify the amount of erosion in a spatially distributed form has become essential at the watershed scale and in the implementation of conservation efforts [1]. In many situations, land managers and policy makers are more interested in the spatial distribution of soil erosion risk than in absolute values of soil erosion loss [8]. The aims of this paper is to model the spatial distribution of soil erosion at Densu River Basin of Ghana using RUSLE( Revised Universal Soil Loss Equation) and GIS (Geographic Information Systems) tools and explore the relationship between erosion susceptibility, slope and Land use /land cover (LULC). Introduction The identification of the spatially distributed erosion sources will make possible the implementation of special conservation efforts on these source areas. By effectively predicting soil erosion, it is possible to: develop sound land-use practices as they relate to earth disturbing activities, estimate the efficiency of best management practices required to prevent excess sediment loading, and identify target areas for conservation funds or research [9]. Soil erosion involves detachment and transport of soil particles from top soil layers, degrading soil quality and reducing the productivity of affected lands [1]. Problems associated with land soil erosion, movement and deposition of sediment in rivers, lakes and estuaries persist through the geologic ages around the world [2]. Erosion in basin areas creates superficial crust of soil to be eradicated and the arable lands to be reduced [3]. Soil eroded from the upland catchment The Densu River Basin (DRB) serves as a source of water for the Weija Reservoir .This catchment is influenced by the man’s development activities, the soil erosion and sediment transport to the lake which decrease its capacity and quality. The Weija Water Works draws its water from the Weija Reservoir which was constructed in 1952 by damming the Densu River at Weija. The water is treated and supplied to west Accra and some other areas of the city. The DRB covers an Keywords: Soil Erosion, RUSLE, GIS Modelling, Remote Sensing, Densu River Basin ISSN No: 2319-3484 Volume 2, Issue 4, July 2013 7 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com area of about 2564 km2 and forms part of the coastal river basins of Ghana lying between latitudes 5o 30’ N and 6o 20’ N and longitudes 0o 10’ W and 0o 35’ W (as can be seen Figure 1). In addition, the elevation of DRB ranges between 50 to 2750 feet above mean sea level. Effects of rapid urbanization and increasing agricultural and industrial activities in the DRB and around the reservoir have impacted the quality of water in the river and reservoir [10]. The DRB is of great economic importance to Ghana but not much research works have been undertaken on the basin in relation to soil erosion modeling. Runoff estimates into the Weija reservoir and their implication for water supply was modeled by Kuma and Ashley [10]. In their research they examined the hydrological data available on the Weija Reservoir from 1980 to 2007 in an attempt to estimate runoff into the reservoir with the view of determining whether water is available in the basin to meet the present and future demands of reservoir. Bambury and Elgy [11] developed a conceptual sediment yield model for Ghana using sediment transport data based on the CALSITE model to run in the GRASS GIS package for use in Ghana. The model was tested on a 14 km2 basin southwest of the Volta Lake, where streamflow, rainfall, soil moisture and sediment transport data were. GIS facilitates efficient manipulation and display of a large amount of georeferenced data. More importantly, it allows easy definition of spatial subunits of relatively uniform properties. The use of remote sensing and (GIS) techniques makes soil erosion estimation and its spatial distribution feasible with reasonable costs and better accuracy in larger areas [8]. Mapping soil erosion using GIS can easily identify areas that are at potential risk of extensive soil erosion and provide information on the estimated value of soil loss at various locations [7]. Hence, with the aid of GIS, erosion and sediment yield modeling can be performed on the individual subunits. The combined use of GIS and erosion models has been shown to be an effective approach to estimating the magnitude and spatial distribution of erosion [1, 12, 13, 14, and 15]. RUSLE Parameter Estimation for Soil Erosion Modelling This Section describes the basic concepts of Revised Universal Soil Loss Equation (RUSLE) Model and methodology to estimate five parameters rainfall-runoff erosivity factor, soil erodibility, slope length factor, slope steepness factor, and land cover management factor. In addition, modelling the potential erosion susceptible areas are described. ISSN No: 2319-3484 RUSLE Parameter Estimation Several erosion models are available to predict the soil loss and to assess the soil erosion risk [29]. The Universal Soil Loss Equation (RUSLE), an empirical model is the most widely used soil loss estimation method [16, 17, 29, 30, 31, and 32]. The equation is the update of the original Universal Soil Loss Equation [17]. In RUSLE, the rainfall runoff factor of the original USLE (Universal Soil Loss Equation) was replaced by the rainfall erosivity factor while K (soil erodibility factor), LS (slope length and steepness factor), C (land cover management factor) and P (support practice factor) are the same parameters as in the original USLE factors [4]. The RUSLE computes the average annual erosion expected on field slopes and is shown in equation 1. Where: A is annual soil loss from sheet and rill erosion expressed in tons per hectare per year (t/ha/yr), R is rainfall erosivity factor, K is soil erodibility factor, LS is slope length and steepness factor, C is cover management factor, and P is support practice factor. The Rainfall Erosivity(R) Rainfall and runoff play an important role in the process of soil erosion and are together usually expressed as the R factor. The greater the intensity and duration of the rain storm, the higher the erosion potential [18]. The RUSLE rainfallrunoff erosivity factor (R) for any given period is obtained by summing for each rainstorm the product of total storm energy (E) and the maximum 30-minute intensity (I30). Unfortunately, the values of these factors are rarely available at standard meteorological stations. Fortunately, long-term average R-values are often correlated with more readily available rainfall values like annual rainfall or the modified Fournier’s index [19]. For the computation of R factor, data from seven meteorological stations within the catchment was used and Schreiber’s method [20] (as expressed in Equation 2) was then applied for determining the precipitation change according to the elevation. (2) Where Ph is the average annual precipitation (mm) and Po represents the amount of average annual rainfall (mm) at chosen meteorological station and h (measured in mega Joule per hector per year) is elevation of the place which the precipitation is be calculated. From the results of this calculation (as shown in Figure 2), a rainfall factor layer was then generated with ESRI ArcGIS over the whole study area by using an inverse distance weighting interpolation algorithm Volume 2, Issue 4, July 2013 8 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com on a 30m resolution. The resultant Digital Elevation Model (DEM) is shown in Figure 2a whiles the rainfall (Rfactor).distribution is shown in Figure 2b. (4) The Slope Length and Steepness Factors (LS) The Slope Length and Steepness Factors (LS) factor represents erodibility due to combinations of slope length and steepness relative to a standard unit plot. It expresses the effect of topography, specifically hill slope length and steepness, on soil erosion. An increase in hill slope length and steepness results in an increase in the LS factor [21]. The slope length factor (L) is defined as the distance from the source of runoff to the point where either deposition begins or runoff enters a well-defined channel that may be part of a drainage network. On the other hand, the steepness factor(S) reflects the influence of slope steepness on erosion [17]. As already pointed out, the longer the slope length, the greater the amount of cumulative runoff, and the steeper the slope of the land the higher the velocities of the runoff which contribute to erosion. For estimation of the LS factor, theoretical relationship based on unit stream power theory has been adopted as this relation is best suited for integration with the GIS [15]. The relation is given in equation 3. (3) Where As is the specific area (A/b), defined as the upslope contributing area for overland grid (A) per unit width normal to flow direction (b), β the slope gradient in degrees n= 0.4 and m= 1.3. However, with the incorporation of DEM into GIS, the slope gradient (S) and slope length (L) may be determined accurately and combined to form a single factor known as the topographic factor LS. The precision with which it can be estimated depends on the resolution of the DEM [22]. Using the Spatial Analyst Extension (as implemented in ArcGIS), the slope of the catchment area was derived from DEM. Sinks in the DEM were identified and filled. The filled DEM was used as input to determine the Flow Direction which was used as an input grid to derive the Flow Accumulation. The LS factor was then computed using Raster Calculator in ArcGIS to build an expression for estimating LS, based on flow accumulation and slope steepness [23]. The expression is: where Pow (which then means power) is a function in the ArcGIS spatial Analyst. The Land Cover Management Factor (C) The Land Cover Management Factor (C) is used to express the effect of plants and soil cover [21]. Plants can reduce the runoff velocity and protect surface pores. The C-factor measures the combined effect of all interrelated cover and management variables, and it is the factor that is most readily changed by human activities [21]. It is mainly related to the vegetation’s cover percentage and it is defined as the ratio of soil loss from specific crops to the equivalent loss from tilled, bare test-plots [24]. The value of C depends on vegetation type, stage of growth and cover percentage. The vegetation cover has a big impact in the erosion by intercepting the rainfall thus increase the infiltration and reducing the rainfall energy [2]. C-factor is measured as the ratio of soil loss from land cropped under specific conditions to the corresponding loss from tilled land under continuous fallow conditions [25]. By definition, C equals 1 under standard fallow conditions. As surface cover is added to the soil, the C factor value approaches zero. A C-factor of 0.15 means that 15% of the amount of erosion will occur compared to continuous fallow conditions [26]. Since the satellite image data provide up to date information on land cover, the use of satellite images in the preparation of land cover maps is widely applied in natural resource surveys [21]. Traditionally, C-values are assigned to land cover classes from USLE/RUSLE guide tables or field observation. Since the Normalized Difference Vegetation Index (NDVI) values have correlation with C factor many researchers used regression analysis to estimate C-factor values for land cover classes in erosion assessment [21]. These methods employ regression model to make correlation analysis between C factor values measured in field or obtained from guide tables and NDVI values derived from remotely sensed images. The unknown C- factor values of land cover classes can be estimated using equation obtained from linear regression analyses. In this research, a LandSat ETM, August 2004 with resolution 30 m was used in C-factor calculation. The image was processed with a supervised classification to prepare the ISSN No: 2319-3484 Volume 2, Issue 4, July 2013 9 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com LULC map of the study area. This image of the study area was taken through three stages to generate land cover classes of the study area and these include [28]: (1) feature extraction; (2) selection of training data (signatures); and (3) selection of suitable classification approaches. Detailed information on LULC classification, supervised classification techniques, and classification accuracy assessment using error matrix can be found in [28]. In this study, an overall accuracy result of 82% was obtained from maximum likelihood classifier. Using this classifier, the DRB was classified into five LULC classes namely Dense Forest, Moderate Forest, Agricultural Land, build up and water bodies. Figure 4a shows the classified image map with six classes. To Estimate the C-Values NDVI was calculated from the satellite Image. 50 Forest and 30 bare ground NDVI points were sampled randomly with the assumption that there exists a linear correlation between NDVI and C factor using forest and bare ground as reference points with forest as 1 and bare ground as 0 in the regression analysis. The regression equation was found as; Using the raster calculator in ArcGIS, a C-Factor map (as in Figure 4c) was developed for the catchment using the correlation equation on the NDVI image map (as can be seen in Figure 4b. Soil Erodibility (K-factor) The Soil Erodibility (K) factor represents both susceptibility of soil to erosion and the amount and rate of runoff. Soil texture, organic matter, structure and permeability determine the erodibility of a particular soil [20]. The K factor reflects the ease with which the soil is detached by splash during rainfall and/or by surface flow, and therefore shows the change in the soil per unit of applied external force of energy [25]. It is related to the integrated effects of rainfall, runoff, and infiltration on soil loss, accounting for the influences of soil properties on soil loss during storm events on upland areas [8]. A simpler method to predict K was presented by Wischmeier and Smith17which includes the particle size of the soil, organic matter content, soil structure and profile permeability. The soil erodibility factor K can be approximated from a monograph if this information is known. The USLE monograph estimates erodibility as: ISSN No: 2319-3484 where: M is (% Silt + % Very Fine Sand) (100- %Clay), MO is the percent organic matter content, b is soil structure code, and c is the soil permeability rating. For computing the Kfactor, the available soil map (as can be seen in Figure 5a) for the catchment was used. There are 8 different soils on the area (Table 1.) based on the FAO-UNESCO soil map data classification [27]. Not all soils have information about structure and permeability. The percentages of clay, silt, sand and organic matters were determinate for each major soil type using Harmonized World Soil Database (HWSD). To obtain the K-factor for soil the ERFAC (Proposed Alternative Soil Erodibilty Factor) equation 4 was used. With these results, the spatial distribution of the k-factor was computed under a GIS (ArcGIS) at 30 meters resolution and the results are shown in Figure 5b. Table 1: Accuracy of ASTER DEMs Soil Type Clay Silt Sand ERFAC (%) (%) (%) (k) Acrisols 24 27 49 0.255 Lixisols 24 20 56 0.234 Phlinthosols 22 29 49 0.261 Leptosols 23 34 43 0.275 Fluvisols 20 41 39 0.295 Luvisols 24 20 56 0.234 Arenosols 8 10 82 0.194 Solenetz 38 60 2 0.351 Soil Conservation Practice Factor (P) The soil conservation practice factor describes the supporting effects of practices like contouring, strip cropping, and terraces. Most often this variable is assigned a value 1 indicating that there are no support practices in place within the study area. Since this study focuses on the evaluation of soil erosion risk, instead of estimation of actual soil erosion loss, the P-factor value of 1 was used. So the soil erosion risk was developed based on R, K, LS, and C factors in a simplified equation. Modeling Potential Erosion Susceptible Areas Using GIS Volume 2, Issue 4, July 2013 10 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com Figure 6 summarizes the step by step methodology of the erosion modeling process. Raster maps of the R, K, LS, and C grid layers were integrated within the ArcGIS environment using RUSLE equation 1. To generate composite maps of estimated erosion loss within the basin. The resultant soil loss map is shown in Figure 7 and the results are discussed in the subsequent Section. The potential erosion map produce was overlaid on the slope and LULC map in the GIS environment to examine the relationship between slope and LULC on erosion in the catchment. Through the overlaying, the areas with high susceptibility in relation to slope and LULC have been identified and represented in Figures 8a and 8b respectively. Results and Discusion The combined use of GIS and erosion models has been integrated to estimate the magnitude and spatial distribution of erosion of the study area. Five different erosion risk factors including rainfall erosivity, slope length and steepness, land cover management, soil erodibility, and soil conservation were determined. The results of modelling these factors are shown in Figures 2, 3, 4, and 5. In modelling the rainfall erosivity, it can be seen that (as in Figure 2) the greater the intensity and duration of the rain storm, the higher the erosion potential. Figure 3 presents the results of modelling the slope length and steepness. It is noted that the longer the slope length, the greater the amount of cumulative runoff. The soil erosion susceptibility with slope categories shows that the steeper the slope of the land the higher the velocities of runoff which contribute to erosion (Figure 8) as observed by Wischmeier and Smith [17]. Similarly, the land cover management factor was modelled and the results are shown in Figure 4. The results indicate that the vegetation cover has an impact in the erosion by intercepting the rainfall thus reducing the rainfall energy and increasing the infiltration. In Figure 5, soil erodibility factor which represents both susceptibility of soil to erosion and the amount and rate of runoff is shown. The results reflect the ease with which the soil is detached by splash during rainfall and or by surface flow, and therefore shows the change in the soil per unit of applied external force of energy. The final soil loss model (Figure 7) predicts that approximately in 87% of the basin has low erosion risk (i.e., erosion with very gentle runoff speed.) and 6% moderate (i.e., shallow to deep hills mainly found around agricultural lands and moderate forest class). But the erosion risk is high (i.e., very deep hills and some gullies) on 3% and severe on 3% of the catchment area. ISSN No: 2319-3484 In addition to modelling the five risk factors, erosion susceptibility with respect to LULC and slope categories were modelled and are graphed in Figures 8a and 8b. What is the most evident in the results (as can be seen in Figure 8b) is that about 92% area of the watershed has a gradient less than 7°, while the remaining 8% of the area has a gradient greater than 7°. This accounts to the general low susceptibility of the basin to erosion. It is also noted that the steep slopes have much higher rate of erosion compared with flat areas. Generally, it can be seen that the average rate of soil loss and the contribution to the total soil loss from steeper slope is tremendously higher compared with that of gentle slope. The results indicate that (as in Figure 8a) erosion risk was generally low across all the LULC classes. However, traces of severe erosion are available in the moderate forest class. From visual interpretation of the various factors, this was found to be due to the high erodibility of the soil group (leptosols) that intersects the sections of the moderate forest LULC Class. Also the moderate forest class falls within the high rainfall zones of the study area which may contribute to high erosion within the moderate forest cover class. Conclusions and Recommendations In this paper, a soil erosion model at Densu River Basin with the integration of RUSLE and GIS tools has been developed to estimate the annual soil loss. Different components of RUSLE were modelled using various mathematical formulae to explore the relationship between erosion susceptibility, slope and LULC maps. The erosion map produced was then categorized into five different erosion risk classes. According to this model, approximately in 88% of the basin has low erosion risk and 6% moderate erosion risk. But erosion risk is high on 3% and severe on 3% of the basin. The high and severe erosion were found to be distributed mainly within the areas of high slope gradient and also sections of the moderate forest LULC class. The results indicated that areas within the moderate forest LULC class have a high erosion risk and this was due to the presence of an intersecting high erodibility soil group. However, since the vegetative cover is a major factor of soil erosion, in future research, an NDVI should be derived from up to date and higher resolution satellite imagery. This will improve the accuracy of the LULC maps and DEMs generated for land slopes calculations. Also, an additional study is needed to determine the appropriate P-factor values within the study area to realistically estimate the potential soil erosion. In general, it is clear from the results of this study that the developed model is beneficial for the rapid assessment of soil erosion. Volume 2, Issue 4, July 2013 11 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com //www.siue.edu/geogaphy/online/hickey05.pdf. Date Accessed: July 14, 2011. References [1] Fernandez, C., Wu, J., McCool, D., and Stockle, C., Estimating Water Erosion and Sedi-ment Yield with GIS, RUSLE, and SEDD. Journal of Soil and Water Conservation 58 (3), pp 128-136 (2003). [2] Rojas-González, A. M., Soil Erosion Calculation using Remote Sensing and GIS in Río Grande de Arecibo Watershed, Puerto Rico. Proceedings ASPRS 2008 Annual Conference Bridging the Horizons: New Frontiers in Geospatial Collaboration, Portland, Oregon, April 28th– May 2nd, 2008, 6p (2008). [3] Gandomkar, A.,Shaikhe, N., Ahmadi, S., The Valuation of Erosion at Basin Watershed in Mousabad - Tiran by using SlemsaModle. Journal of Geographical Landscape, 3(6), pp 95-108 (2008). [4] Geleta, H. I., Watershed Sediment Yield Modelling for Data Scarce Areas.PhD Disserta-tion, University of Stuttgart (2011). Available at http://elib.unistuttgart.de/opus/volltexte/2011/6152/. Date Accessed: 10 February, 2011. [5] Andersson, L., Soil Loss Estimation Based on the USLE/GIS Approach through Small Catchments - A Minor Field Study in Tunisia. PhD Dissertation, Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Sweden (2010). [6] Wang, X., and Cui, P., Support Soil Conservation Practices by Identifying Critical Erosion Areas within an American Watershed Using the GIS-AGNPS Model. Journal of Spatial Hydrology, Vol. 5, No. 2, pp 31- 44 (2005). [7] Todd, B., Assessment of Soil Erosion Risk within a Sub watershed using GIS and RUSLE with a Comparative Analysis of the use of STATSGO and SSURGO Soil Databases. Re-source Analysis, Volume 8, pp 1-22 (2006). [8] Lu, D., Li, G., Valladares, G. S., and Batistella, M., Mapping Soil Erosion Risk in Rondo-nia, Brazilian Amazonia: using RUSLE, Remote Sensing and GIS. Land Degradation and Development, pp 499–512 (2004). [9] Hickey, R., Burns, E., Bolte, J., and Walker, D., Development of a Statewide Erosion Vul-nerability Screening Tool for Oregon (2005). Available at http: ISSN No: 2319-3484 [10] Kuma, J. S., and Ashley, D. N., Runoff Estimates into the Weija Reservoir and its Impli-cations for Water Supply to Accra, Ghana. Journal of Urban and Environmental Engineer-ing, Vol.2, No.2, pp 33-40 (2008). [11] Bambury, M., and Elgy, J., Development of a Sediment Yield Model for Ghana using Sediment Transport Data, Proceedings of the Oslo Workshop on Erosion and Sediment Transport Measurement in Rivers:Technological and Methodological Advances, IAHS .pp 96-102 (2002). [12] Kothyari, U. C., and Jain, S. K., Sediment Yield Estimation using GIS. Hydrological Science Journal, Vol. 42, No. 6, pp 833-843 (1997). [13] Nasir, A., Uchida, K., and Ashraf, M., Estimation of Soil Erosion by using RUSLE and GIS for Small Mountainous Watersheds in Pakistan. 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Washington, D.C: US, Department of Agriculture, 58p (1978). [18] Stone, R. P., and Hilborn, D., Universal Soil Loss Equation (USLE), Agricultural and Rural Ministry of Agriculture, Food and Rural Affairs, Ontario, Canada (2000).Available at http://www.gov.on.ca/ OMAFRA /english/engineer/facts/00-001.htm. Date Accessed: 5 February, 2011. Volume 2, Issue 4, July 2013 12 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com Pages.82-92. [19] [20] [21] [22] / [23] Sadeghi S. H. R., Moatamednia, M., and Behzadfar, M., Spatial and Temporal Variations in the Rainfall Erosivity Factor in Iran: Journal of Agricultural Science Technology. Vol. 13, pp 451-464 (2011). Efe, R., Ekinci, D., and Curebal, I., Erosion Analysis of Sahin Creek Watershed (NW of Turkey) using GIS Based on RUSLE (3d) Method. Journal of Applied Science, 8 (1), pp 49-58 (2008). Karaburun, A., Estimation of C Factor for Soil Erosion Modeling Using NDVI in Buyukcekmece Watershed. Ozean Journal of Applied Sciences 3(1) ,pp 7785 (2010). Simms, A. D., Woodroffe, C. D., and Jones, B. G., Application of RUSLE for Erosion Management in a Coastal Catchment, Southern NSW, Proceedings of MODSIM 2003, International Congress on Modelling and Simulation, volume 2, pp 678-683 (2003). Mitasova, H., Hofierka, J., Zlocha, M., and Iverson, R., Modelling Topographic Potential for Erosion and Deposition using GIS. International Journal of Geographical Information Systems, 10(5), pp 629-641 (1996). [24] Gitas, I. Z., Douros, K., Minakou, C., Silleos, G. N., and Karydas, C. G., Multi-Temporal Soil Erosion Risk Assessment. In N. Chalkidiki using a Modified USLE Raster Model. Earsel Eproceedings 8, pp.40-52 (2009). [25] Dumas, P., and Printemps, J., Assessment of Soil Erosion Using USLE Model and GIS for Integrated Watershed and Coastal Zone Management in the South Pacific Islands. Proceedings, International Symposium in Pacific Rim, pp 856-866, (2010). [26] Kelsey K., and Johnson T., Determining Cover Management Values (C factors) for Surface Cover Best Management Practices (BMPs). Proceedings of the International Erosion Control Association’s 34th Conference, pp 319-328, (2003). [27] FAO-UNESCO., FAO-UNESCO (Food and Agriculture Organization and United Nations Educational, Scientific and Cultural Organization) Soil Map of the World, 1:5,000,000, Volume VI, UNESCO, Paris (1987). [28] Forkuo, E. K., and Adubofour. Y., 2012. Analysis of Forest Cover Change Detection. International Journal of Remote Sensing Applications, Volume 2, Issue 4, ISSN No: 2319-3484 [29] Dumas, P., Printemps, J., Mangeas, M., & Luneau, G. Developing Erosion Models for Integrated Coastal Zone Management: A Case Study of The New Caledonia West Coast. Marine Pollution Bulletin, 61(7), 519-529. (2010). [30] Diodato, N., Estimating RUSLE’s Rainfall Factor in the Part of Italy with a Mediterranean Regime. Hydrology and Earth System Sciences, 8(1), pp 103-107 (2004). 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Volume 2, Issue 4, July 2013 13 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com Figure 1: Map of DRB Showing Major Rivers in the Catchment Figure 2: DEM of Study Area (a) and R-factor Distribution Map (b) ISSN No: 2319-3484 Volume 2, Issue 4, July 2013 14 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com Figure 3: Slope map of basin (a) and LS-Factor distribution (b) Figure 4: Classified LULC thematic map of basin (a), NDVI (b), and C-Factor map (c) ISSN No: 2319-3484 Volume 2, Issue 4, July 2013 15 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com Figure 5: Soil Type map (a) and K-Factor map (b) Figure 6: Flow Chart for Potential Soil Erosion Model Design ISSN No: 2319-3484 Volume 2, Issue 4, July 2013 16 International Journal of Remote Sensing & Geoscience (IJRSG) www.ijrsg.com Figure 7: Erosion Risk Map Figure 8: Erosion Susceptibility with Respect to LULC (A) and Slope Categories (B) ISSN No: 2319-3484 Volume 2, Issue 4, July 2013 17