Effect of cover management factor in quantification of soil loss: case study of Sungai Akah subwatershed, Baram River basin Sarawak, Malaysia

Abstract The present study evaluates the effectiveness and suitability of cover management factors (C factor) generated through different techniques like land use/land cover-based arbitrary value (CLULC), Normalised Different Vegetation Index-based methods CNDVI1 and CNDVI2 and Modified Soil Adjusted Vegetation Index 2-based method (CMSAVI2). The C factors generated using these four methods were tested in the calculation and assessment of annual average soil loss from an upland forested subwatershed in the Baram river basin using the Revised Universal Soil Loss Equation (RUSLE). The four cover management factor maps generated by this analysis show some variation among the results. The LULC method uses a single arbitrary value for each LULC type mapped in the subwatershed. The other three methods show a range of C values within each mapped LULC type. The effects of these variations were tested in the RUSLE by keeping the factors such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS) constant. The maximum annual average soil loss is 1191 t. ha−1. y−1 based on the CLULC. Soil losses estimated with other three methods are very different compared to those estimated with the CLULC method. The highest calculated soil loss values were 1832, 1674 and 1608 t. ha−1. y−1 in the study area based, respectively, on CNDVI1, CNDVI2 and CMSAVI2 C factors. These maximum values represent the worst pixel scenario values of soil loss in the region. The statistical analysis performed indicates different relationship between the parameters and suggests the acceptance of the methodology based on CNDVI2 for the study area, instead of a single value method such as CLULC. Among the other two methods, the CMSAVI2 was found to be more consistent than the CNDVI1 method, but both methods lead to over-prediction of annual soil loss rate and therefore need to be reconsidered before applied in the RUSLE.

[1]  Xu Yue-qing,et al.  Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China , 2008, Environmental monitoring and assessment.

[2]  T. Lihan,et al.  Integration of remote sensing, RUSLE and GIS to model potential soil loss and sediment yield (SY) , 2013 .

[3]  A. Ziegler,et al.  Erosion processes in steep terrain—Truths, myths, and uncertainties related to forest management in Southeast Asia , 2006 .

[4]  Lei Wang,et al.  Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE , 2015, Natural Hazards.

[5]  Luca Montanarella,et al.  Soil erosion risk assessment in Italy , 1999 .

[6]  Ahmet Karaburun,et al.  Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed , 2010 .

[7]  Georgios N. Silleos,et al.  Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete , 2009, Environmental monitoring and assessment.

[8]  Biswajeet Pradhan,et al.  Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models , 2016 .

[9]  Dominik Bänninger,et al.  stimating vegetation parameter for soil erosion assessment in an alpine atchment by means of QuickBird imagery , 2010 .

[10]  H. Fathizad,et al.  The estimation of erosion and sediment by using the RUSLE model and RS and GIS techniques (Case study: Arid and semi-arid regions of Doviraj, Ilam province, Iran) , 2014 .

[11]  S. M. Jong.  Applications of reflective remote sensing for land degradation studies in a Mediterranean environment , 1994 .

[12]  Yasser Alashker,et al.  Risk assessment of soil erosion in semi-arid mountainous watershed in Saudi Arabia by RUSLE model coupled with remote sensing and GIS , 2014 .

[13]  Luca Montanarella,et al.  Soil erosion risk assessment in Europe , 2000 .

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

[15]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[16]  Wen-Chieh Chou,et al.  Soil erosion prediction and sediment yield estimation: the Taiwan experience , 2002 .

[17]  W. Zhou,et al.  Risk assessment of water soil erosion in upper basin of Miyun Reservoir, Beijing, China , 2009 .

[18]  J. Adinarayana,et al.  Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia , 2011, Environmental Monitoring and Assessment.

[19]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[20]  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 .

[21]  V. Prasannakumar,et al.  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 , 2012 .

[22]  Panos Panagos,et al.  Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service , 2012, Int. J. Digit. Earth.

[23]  Liang Zhang,et al.  Research trends and hotspots in soil erosion from 1932 to 2013: a literature review , 2015, Scientometrics.

[24]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[25]  Panos Panagos,et al.  Estimating the soil erosion cover-management factor at the European scale , 2015 .

[26]  R. Crippen.  Calculating the vegetation index faster , 1990 .

[27]  S. M. Jong,et al.  Erosion hazard assessment in the La Peyne catchment, France. , 1998 .

[28]  I. D. Moore,et al.  Modelling Erosion and Deposition: Topographic Effects , 1986 .

[29]  I. Moore,et al.  Physical basis of the length-slope factor in the universal soil loss equation , 1986 .

[30]  F. Baret,et al.  TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[31]  G. Asner,et al.  Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations , 2002 .

[32]  Soil loss assessment in the Tasik Chini catchment, Pahang, Malaysia , 2010 .

[33]  Jiao Feng,et al.  Stratified vegetation cover index: A new way to assess vegetation impact on soil erosion , 2010 .

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

[35]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[36]  M. B. Gasim,et al.  Soil loss assessment in the Tasik Chini catchment, Pahang, Malaysia , 2010 .

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

[38]  Panos Panagos,et al.  Modelling monthly soil losses and sediment yields in Cyprus , 2016, Int. J. Digit. Earth.

[39]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[40]  Filippos Vallianatos,et al.  Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece , 2009 .

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

[42]  Fernando Bação,et al.  Self-organizing Maps as Substitutes for K-Means Clustering , 2005, International Conference on Computational Science.

[43]  Loredana Antronico,et al.  Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: An application model for Calabria (southern Italy) , 2009 .

[44]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[45]  G. R. Foster,et al.  RUSLE: Revised universal soil loss equation , 1991 .