Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considere...Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and展开更多
Dynamic monitoring of plant cover and soil erosion often uses remote sensing data, especially for estimating the plant cover rate (vegetation coverage) by vegetation index. However, the latter is influenced by atmosph...Dynamic monitoring of plant cover and soil erosion often uses remote sensing data, especially for estimating the plant cover rate (vegetation coverage) by vegetation index. However, the latter is influenced by atmospheric effects and methods for correcting them are still imperfect and disputed. This research supposed and practiced an indirect, fast, and operational method to conduct atmospheric correction of images for getting comparable vegetation index values in different times. It tries to find a variable free from atmospheric effects, e.g., the mean vegetation coverage value of the whole study area, as a basis to reduce atmospheric correction parameters by establishing mathematical models and conducting simulation calculations. Using these parameters, the images can be atmospherically corrected. And then, the vegetation index and corresponding vegetation coverage values for all pixels, the vegetation coverage maps and coverage grade maps for different years were calculated, i.e., the plant cover monitoring was realized. Using the vegetation coverage grade maps and the ground slope grade map from a DEM to generate soil erosion grade maps for different years, the soil erosion monitoring was also realized. The results show that in the study area the vegetation coverage was the lowest in 1976, much better in 1989, but a bit worse again in 2001. Towards the soil erosion, it had been mitigated continuously from 1976 to 1989 and then to 2001. It is interesting that a little decrease of vegetation coverage from 1989 to 2001 did not lead to increase of soil erosion. The reason is that the decrease of vegetation coverage was chiefly caused by urbanization and thus mainly occurred in very gentle terrains, where soil erosion was naturally slight. The results clearly indicate the details of plant cover and soil erosion change in 25 years and also offer a scientific foundation for plant and soil conservation.展开更多
泥沙连通性可以反映泥沙源汇的潜在联系,识别流域水土流失热点区域及泥沙迁移路径。研究泥沙连通性的影响因素有助于更好地理解泥沙连通性的时空变化特征。该研究在已有泥沙连通性指数(Index of Connectivity,IC)模型基础上,考虑影响连...泥沙连通性可以反映泥沙源汇的潜在联系,识别流域水土流失热点区域及泥沙迁移路径。研究泥沙连通性的影响因素有助于更好地理解泥沙连通性的时空变化特征。该研究在已有泥沙连通性指数(Index of Connectivity,IC)模型基础上,考虑影响连通性的功能性因素,并采用修正泥沙连通性指数(Revised Index of Sediment Connectivity,ICr)探讨了植被覆盖度和降雨侵蚀力耦合作用下的季节与年际变化对天目湖中田舍河流域泥沙连通性特征的影响。结果表明:2019年夏冬季植被覆盖度分别为85%、57%,对应的泥沙连通性指数均值是-9.39、-6.85,植被覆盖度变化对泥沙连通性具有重要影响,利用NDVI值获取模型中的地表综合系数,可以动态反映地表植被和土地利用的区域及季节性变化;降雨影响泥沙的功能连通性,年尺度上的连通性指数均值同流域泥沙量的相关系数达0.91。说明在流域植被覆盖变化不明显时或者在林地为主的流域中,降雨因子具有主导作用;植被覆盖度升高28%,IC均值降低37%,而单独考虑降雨因子的IC-R均值则反映出雨量升高,指数值随之升高,修正连通性指数ICr综合考虑了植被与降雨因子,但在应用中要依据流域实际情况适当调整两者的权重。研究结果指出泥沙连通性指数在中国东南部区域运用中存在的问题,将对气候变化背景下中国湿润区湖泊小流域水土保持与水环境治理等提供科学参考。展开更多
文摘Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and
文摘Dynamic monitoring of plant cover and soil erosion often uses remote sensing data, especially for estimating the plant cover rate (vegetation coverage) by vegetation index. However, the latter is influenced by atmospheric effects and methods for correcting them are still imperfect and disputed. This research supposed and practiced an indirect, fast, and operational method to conduct atmospheric correction of images for getting comparable vegetation index values in different times. It tries to find a variable free from atmospheric effects, e.g., the mean vegetation coverage value of the whole study area, as a basis to reduce atmospheric correction parameters by establishing mathematical models and conducting simulation calculations. Using these parameters, the images can be atmospherically corrected. And then, the vegetation index and corresponding vegetation coverage values for all pixels, the vegetation coverage maps and coverage grade maps for different years were calculated, i.e., the plant cover monitoring was realized. Using the vegetation coverage grade maps and the ground slope grade map from a DEM to generate soil erosion grade maps for different years, the soil erosion monitoring was also realized. The results show that in the study area the vegetation coverage was the lowest in 1976, much better in 1989, but a bit worse again in 2001. Towards the soil erosion, it had been mitigated continuously from 1976 to 1989 and then to 2001. It is interesting that a little decrease of vegetation coverage from 1989 to 2001 did not lead to increase of soil erosion. The reason is that the decrease of vegetation coverage was chiefly caused by urbanization and thus mainly occurred in very gentle terrains, where soil erosion was naturally slight. The results clearly indicate the details of plant cover and soil erosion change in 25 years and also offer a scientific foundation for plant and soil conservation.
文摘泥沙连通性可以反映泥沙源汇的潜在联系,识别流域水土流失热点区域及泥沙迁移路径。研究泥沙连通性的影响因素有助于更好地理解泥沙连通性的时空变化特征。该研究在已有泥沙连通性指数(Index of Connectivity,IC)模型基础上,考虑影响连通性的功能性因素,并采用修正泥沙连通性指数(Revised Index of Sediment Connectivity,ICr)探讨了植被覆盖度和降雨侵蚀力耦合作用下的季节与年际变化对天目湖中田舍河流域泥沙连通性特征的影响。结果表明:2019年夏冬季植被覆盖度分别为85%、57%,对应的泥沙连通性指数均值是-9.39、-6.85,植被覆盖度变化对泥沙连通性具有重要影响,利用NDVI值获取模型中的地表综合系数,可以动态反映地表植被和土地利用的区域及季节性变化;降雨影响泥沙的功能连通性,年尺度上的连通性指数均值同流域泥沙量的相关系数达0.91。说明在流域植被覆盖变化不明显时或者在林地为主的流域中,降雨因子具有主导作用;植被覆盖度升高28%,IC均值降低37%,而单独考虑降雨因子的IC-R均值则反映出雨量升高,指数值随之升高,修正连通性指数ICr综合考虑了植被与降雨因子,但在应用中要依据流域实际情况适当调整两者的权重。研究结果指出泥沙连通性指数在中国东南部区域运用中存在的问题,将对气候变化背景下中国湿润区湖泊小流域水土保持与水环境治理等提供科学参考。
