Integration of remote sensing, RUSLE and GIS to model potential soil loss and sediment yield (SY)

Abstract. Land use activities within a basin serve as one of the contributing factors which cause deterioration of river water quality through its potential effect on erosion. Sediment yield in the form of suspended solid in the river water body which is transported to the coastal area occurs as a sign of lowering of the water quality. Hence, the aim of this study was to determine potential soil loss using the Revised Universal Soil Loss Equation (RUSLE) model and the sediment yield, in the Geographical Information Systems (GIS) environment within selected sub-catchments of Pahang River Basin. RUSLE was used to estimate potential soil losses and sediment yield by utilizing information on rainfall erosivity ( R ) using interpolation of rainfall data, soil erodibility ( K ) using field measurement and soil map, vegetation cover ( C ) using satellite images, topography (LS) using DEM and conservation practices ( P ) using satellite images. The results indicated that the rate of potential soil loss in these sub-catchments ranged from very low to extremely high. The area covered by very low to low potential soil loss was about 99%, whereas moderate to extremely high soil loss potential covered only about 1% of the study area. Sediment yield represented only 1% of the potential soil loss. The sediment yield (SY) value in Pahang River turned out to be higher closer to the river mouth because of the topographic character, climate, vegetation type and density, and land use within the drainage basin.

[1]  Chongfa Cai,et al.  Soil conservation planning at the small watershed level using RUSLE with GIS: a case study in the Three Gorge Area of China , 2004 .

[2]  Wen-Chieh Chou,et al.  Assessment of vegetation recovery and soil erosion at landslides caused by a catastrophic earthquake: A case study in Central Taiwan , 2006 .

[3]  R. Efe,et al.  Erosion Analysis of Sahin Creek Watershed (NW of Turkey)Using GIS Based on Rusle (3d) Method , 2008 .

[4]  Jasmin Ismail,et al.  RUSLE2 Model Application for Soil Erosion Assessment Using Remote Sensing and GIS , 2008 .

[5]  W. Cornelis,et al.  Influence of landuse on soil erosion risk in the Cuyaguateje watershed (Cuba) , 2008 .

[6]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[7]  R. Mathieu,et al.  Contribution of multi-temporal SPOT data to the mapping of a soil erosion index. The case of the loamy plateaux of northern France , 1997 .

[8]  Andrew A. Millward,et al.  Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed , 1999 .

[9]  Soo Huey Teh Soil erosion modeling using RUSLE and GIS on Cameron highlands, Malaysia for hydropower development , 2011 .

[10]  Carlos Rogério de Mello,et al.  Soil erosion prediction in the Grande River Basin, Brazil using distributed modeling , 2009 .

[11]  G. R. Foster,et al.  Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) , 1997 .

[12]  R. Hickey,et al.  Estimating the LS Factor for RUSLE through Iterative Slope Length Processing of Digital Elevation Data within Arclnfo Grid , 2001 .

[13]  Donald K. McCool,et al.  Modeling the impacts of no-till practice on soil erosion and sediment yield with RUSLE, SEDD, and ArcView GIS , 2006 .

[14]  Dengsheng Lu,et al.  Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS , 2004 .

[15]  Pierre Goovaerts,et al.  Using elevation to aid the geostatistical mapping of rainfall erosivity , 1999 .

[16]  J. Peters,et al.  Application of geographic information systems and remote sensing for quantifying patterns of erosion and water quality , 2003 .

[17]  Claudio O. Stöckle,et al.  Estimating water erosion and sediment yield with GIS, RUSLE, and SEDD , 2003 .

[18]  Rattan Lal,et al.  Soil Erosion Impact on Agronomic Productivity and Environment Quality , 1998 .

[19]  R. Hickey,et al.  Slope Angle and Slope Length Solutions for GIS , 2000 .

[20]  Guangxing Wang,et al.  Mapping Multiple Variables for Predicting Soil Loss by Geostatistical Methods with TM Images and a Slope Map , 2003 .

[21]  S. D. Angima,et al.  Soil erosion prediction using RUSLE for central Kenyan highland conditions , 2003 .

[22]  T. Tokola,et al.  Effect of vegetation cover on soil erosion in a mountainous watershed , 2008 .