The Revised Universal Soil Loss Equation (RUSLE)'s cover and management factor (C-factor) is one of the most difficult factors to obtain,mainly because long-term monitoring soil erosion plots under natural rainfal...The Revised Universal Soil Loss Equation (RUSLE)'s cover and management factor (C-factor) is one of the most difficult factors to obtain,mainly because long-term monitoring soil erosion plots under natural rainfall are needed.Therefore,remote sensing approaches have been used as an alternative for obtaining this factor.However,there is a lack of studies comparing values of this factor computed from remote sensing approaches with measured data.In this study,we compare two widely used remote sensing approaches (CrA and CVK) to estimate the C-factor based on the Normalized Difference Vegetation Index (NDVI) with the literature (CLIT) and field experimental data..We also investigated the influence of C-factor methods on the prediction of soil loss and sediment yield (SY) using measured data in the Guariroba basin,Central-West Brazil.We obtained mean C-factor values of 0.032,0.023 and 0.137 for CLIT,CrA and CVK,respectively.We found an average annual soil loss of 2.20 tha-1 yr-1,2.02 t ha-1 yr-1 and 10.07 tha-1 yr-1 and SY values of 6875 tyr-1,6468 tyr-1 and 33,435 t yr1,for CLIT,CrA and CVK respectively.Our results indicated a significant improvement in soil loss and SY estimations by using the CrA approach developed for tropical regions,with a bias of 13% to the measured SY (5709tyr-1).We conclude that the CrA method present the most suitable alternative to compute soil loss and SY in tropical regions.Furthermore,this approach allows large-scale evaluation and temporal monitoring,therefore enhancing multi spatial and temporal assessment of soil erosion processes.展开更多
We present a new approach for calculating the C-factor of RUSLE considering the effect of low-reflectance vegetation cover areas on the reduction of the effects on erosion caused by rainfall seasonality.For this,we pr...We present a new approach for calculating the C-factor of RUSLE considering the effect of low-reflectance vegetation cover areas on the reduction of the effects on erosion caused by rainfall seasonality.For this,we propose the coefficients Cr2(rescaled 2)and C-PC(Precipitation Correction),which represent the Cfactor,and an adaptation in NDVI calculation,according to the seasonality of precipitation(NDVI-PC).The Cr2 factor is used when there is no seasonal effect of rainfall on vegetation,while the C-PC factor is calculated for localities under the influence of seasonality,from NDVI-PC.The proposed approaches were tested using different satellites images in the Palmares-Ribeir~ao do Saco watershed,Rio de Janeiro,Brazil.The values of Cr2 and C-PC factors were compared to the Cr factor(rescaled)and to mean values from the literature for different land covers.Our results indicated that the Cr2 factor represents an improvement in accuracy in relation to Cr by considering specific values of the studied area to normalize the data without generalizations.Furthermore,the C-PC factor is able to simulate the effect of seasonality,providing more realistics values of soil loss by the RUSLE as a function of the proportion of area affected by the rainfall seasonality obtained from NDVI-PC.We conclude that both Cr2 and C-PC factors generate values similar of the C-factor observed in the literature,and therefore are able to provide better soil loss estimation than that using the Cr factor.展开更多
基金supported by the Ministry of Science,Technology,Innovation and Communication-MCTIC and National Council for Scientific and Technological Development-CNPq [grant numbers 441289/2017-7 and 306830/2017-5].
文摘The Revised Universal Soil Loss Equation (RUSLE)'s cover and management factor (C-factor) is one of the most difficult factors to obtain,mainly because long-term monitoring soil erosion plots under natural rainfall are needed.Therefore,remote sensing approaches have been used as an alternative for obtaining this factor.However,there is a lack of studies comparing values of this factor computed from remote sensing approaches with measured data.In this study,we compare two widely used remote sensing approaches (CrA and CVK) to estimate the C-factor based on the Normalized Difference Vegetation Index (NDVI) with the literature (CLIT) and field experimental data..We also investigated the influence of C-factor methods on the prediction of soil loss and sediment yield (SY) using measured data in the Guariroba basin,Central-West Brazil.We obtained mean C-factor values of 0.032,0.023 and 0.137 for CLIT,CrA and CVK,respectively.We found an average annual soil loss of 2.20 tha-1 yr-1,2.02 t ha-1 yr-1 and 10.07 tha-1 yr-1 and SY values of 6875 tyr-1,6468 tyr-1 and 33,435 t yr1,for CLIT,CrA and CVK respectively.Our results indicated a significant improvement in soil loss and SY estimations by using the CrA approach developed for tropical regions,with a bias of 13% to the measured SY (5709tyr-1).We conclude that the CrA method present the most suitable alternative to compute soil loss and SY in tropical regions.Furthermore,this approach allows large-scale evaluation and temporal monitoring,therefore enhancing multi spatial and temporal assessment of soil erosion processes.
基金Paulo Tarso S.Oliveira was supported by the Brazilian National Council for Scientific and Technological Development(CNPq)(grants 441289/2017e7 and 306830/2017e5)the Coordination of Superior Level Staff Improvement-Brazil(CAPES)(Finance Code 001).
文摘We present a new approach for calculating the C-factor of RUSLE considering the effect of low-reflectance vegetation cover areas on the reduction of the effects on erosion caused by rainfall seasonality.For this,we propose the coefficients Cr2(rescaled 2)and C-PC(Precipitation Correction),which represent the Cfactor,and an adaptation in NDVI calculation,according to the seasonality of precipitation(NDVI-PC).The Cr2 factor is used when there is no seasonal effect of rainfall on vegetation,while the C-PC factor is calculated for localities under the influence of seasonality,from NDVI-PC.The proposed approaches were tested using different satellites images in the Palmares-Ribeir~ao do Saco watershed,Rio de Janeiro,Brazil.The values of Cr2 and C-PC factors were compared to the Cr factor(rescaled)and to mean values from the literature for different land covers.Our results indicated that the Cr2 factor represents an improvement in accuracy in relation to Cr by considering specific values of the studied area to normalize the data without generalizations.Furthermore,the C-PC factor is able to simulate the effect of seasonality,providing more realistics values of soil loss by the RUSLE as a function of the proportion of area affected by the rainfall seasonality obtained from NDVI-PC.We conclude that both Cr2 and C-PC factors generate values similar of the C-factor observed in the literature,and therefore are able to provide better soil loss estimation than that using the Cr factor.
