Slope length and slope steepness are critical topographic factors(L and S)in the Universal Soil Loss Equation(USLE)and Chinese Soil Loss Equation(CSLE)for soil erosion modelling.Both slope length and slope gradient ar...Slope length and slope steepness are critical topographic factors(L and S)in the Universal Soil Loss Equation(USLE)and Chinese Soil Loss Equation(CSLE)for soil erosion modelling.Both slope length and slope gradient are potentially sensitive to spatial resolution when calculated in a GIS framework.The resolution effect on the LS factor and approaches suitable for improving the LS factor at a coarse resolution have not been well identified.To address this problem,the LS factor at 5-m and 30-m resolution in twenty-four watersheds with various terrains was estimated.And a downscale model based on matching of the lower resolution LS cumulative frequency curves to a higher resolution("Histogram Matching"method)was tested for its potential to improve LS factor estimation accuracy.In the larger relief mountainous area,compared to 5-m resolution,the 30-m resolution generated LS was generally overestimated by more than 20%and in lower relief areas underestimated by more than 15%.This bias is less than 10%in medium relief areas.The downscale model improved LS factor estimates compared to the 30-m resolution estimate by more than 10%when comparing frequency distribution curves and more than 20%in mean values in larger relief areas.The downscale model worked well in all regions except for the low relief areas,which intuitively are the low soil erosion potential areas.The results of this research help quantify the uncertainty in soil erosion estimates and may ultimately help to improve the assessment of soil erosion through its impact on LS factor estimates,especially at regional and global scales.展开更多
土壤侵蚀是影响全球土地退化和可持续发展的重大环境问题。土壤侵蚀模型可估算土壤流失的空间和时间分布,为制定流域水土资源保护政策与实施有效战略提供支持。修订版通用土壤流失方程(RUSLE,Revised Universal Soil Loss Equation)模...土壤侵蚀是影响全球土地退化和可持续发展的重大环境问题。土壤侵蚀模型可估算土壤流失的空间和时间分布,为制定流域水土资源保护政策与实施有效战略提供支持。修订版通用土壤流失方程(RUSLE,Revised Universal Soil Loss Equation)模型以其兼具简单性与准确性,成为全球应用最广泛的土壤侵蚀模型之一。在RUSLE模型的输入参数中,地形因子(LS因子)对土壤流失潜力的影响最为显著,而LS因子的输入数据和计算方法对最终RUSLE模型计算的质量会有直接影响。为此构建了中国区域30米分辨率LS因子数据集(LS_China)。本数据集采用开源工具SAGA(自动化地球科学分析系统)和GDAL(地理空间数据抽象库),基于公开的SRTM30米高程数据集(SRTMGL1)计算获得,覆盖整个中国区域。数据被组织成单独的栅格化图块,每个栅格化图块覆盖1°×1°的范围,以Geotiff格式存储。在大尺度范围的数据计算过程中,基于对数据的空间分解,采用邻域依赖的计算方法,同时采用多流算法,保证LS因子计算的准确性。在数据质量控制方面,通过变异系数(CV)验证并与同一区域的其他数据集进行比较。结果表明,LS_China数据集的变异系数为1.24,相较于其他数据集,其内部异质性较小,具备高质量和高可靠性。本数据集可用作各种尺度(地方、区域、国家)的任何土壤侵蚀评估的输入数据。展开更多
目的观察人重组骨桥蛋白(recombinated human osteopontin,rhOPN)对大肠癌LS-174T细胞增殖、侵袭及其相关基因表达的影响,探讨骨桥蛋白在大肠癌演进中的作用。方法添加不同浓度rhOPN于人大肠癌LS-174T细胞培养液中,采用四甲基偶氮唑蓝(M...目的观察人重组骨桥蛋白(recombinated human osteopontin,rhOPN)对大肠癌LS-174T细胞增殖、侵袭及其相关基因表达的影响,探讨骨桥蛋白在大肠癌演进中的作用。方法添加不同浓度rhOPN于人大肠癌LS-174T细胞培养液中,采用四甲基偶氮唑蓝(MTT)试验检测细胞的增殖能力,重组人工基底膜实验评价细胞侵袭能力,并运用实时荧光定量RT-PCR检测大肠癌细胞中尿激酶型纤溶酶原激活物(uPA)、基质金属蛋白酶-2(MMP-2)和血管内皮生长因子(VEGF)等mRNA表达水平。结果LS-174T细胞经不同浓度rhOPN作用24h后,实验组的MTT值显著高于对照组(P<0.01),并呈剂量依赖关系。Transwell上室加入5nmol/L、30nmol/L的rhOPN后,实验组LS-174T细胞穿膜数量明显多于对照组(P<0.05)。经5nmol/L、30nmol/L的rhOPN处理24h后,实验组LS-174T细胞的uPA mRNA及VEGF mRNA的表达水平明显高于对照组(P<0.05),而MMP-2 mRNA表达水平与对照组无显著性差别(P>0.05)。结论rhOPN促进人大肠癌LS-174T细胞的增殖,并提高其侵袭能力,可能与uPA和VEGF基因表达上调有关。展开更多
The prevalence of unwholesome land use practices and population pressure exacerbates soil loss which is worsening the problem of sedimentation of the Kubanni dam. This study was conducted at the Kubanni drainage basin...The prevalence of unwholesome land use practices and population pressure exacerbates soil loss which is worsening the problem of sedimentation of the Kubanni dam. This study was conducted at the Kubanni drainage basin covering a spatial area of 56.7 Km2 in Samaru, Zaria, Nigeria to estimate annual soil loss using the RUSLE model. Satellite images of Landsat OLI for December 2014, 2016, 2018, February, July and November 2022;soil data, rainfall data from 2010 to 2022, and DEM of 30-meter resolution were utilized for the study. All factors of the RUSLE model were calculated for the basin using assembled data. The erosivity (R-factor) was discovered to be 553.437 MJ∙mm∙ha−1∙h−1∙yr−1. The average erodibility (K-factor) value was 0.1 Mg∙h∙h∙ha−1∙MJ−1∙mm−1∙yr−1. The Slope Length and Steepness factor (LS-factor) in the basin ranged between 0% and 13.47%. The Crop Management Factor (C-factor) values were obtained from a rescaling of the NDVI values derived for the study area and ranged from 0.26 to 0.55. Support practice (P-factors) were computed from the prevalent tillage practice in the basin and ranged from 0.27 to 0.40. The soil loss amount for the Kubanni basin was found to be 28441.482 tons∙ha−1∙yr−1, while the annual soil loss for the entire Kubanni drainage basin was found to be 49780.257 tons∙yr−1. The study has demonstrated the viability of coupling RUSLE model and Remote Sensing and Geographic Information System (GIS) techniques for the estimation of soil loss in the Kubanni drainage basin.展开更多
地形是影响地表水文和土壤侵蚀的主要环境因素,坡度、坡长和LS因子是土壤侵蚀模型的重要参数。该文以第四次全国土壤侵蚀普查项目为依托,在ANUDEM软件环境中建立25m分辨率文地貌关系正确的DEM(Hydrologically Correct Digital Elevation...地形是影响地表水文和土壤侵蚀的主要环境因素,坡度、坡长和LS因子是土壤侵蚀模型的重要参数。该文以第四次全国土壤侵蚀普查项目为依托,在ANUDEM软件环境中建立25m分辨率文地貌关系正确的DEM(Hydrologically Correct Digital Elevation Model,Hc-DEM),提取了坡度、坡长并计算了LS因子,对中国主要水蚀地区的土壤侵蚀地形因子的空间及统计特征进行了分析,并将该数据与目前应用较为广泛的2种遥感高程数据进行了对比。结果表明,25m分辨率Hc-DEM可用以表达各典型样区地形特征,其上提取的坡度和坡长,符合一般地貌学原理和常规认识;坡度在东北地区最为平缓(0.8°),而在黄土丘陵区最陡(22.3°);坡长则在东北地区最长而黄土丘陵区最短(479m和69m);在地势比较低的河谷和地势较高的分水地带坡度比较平缓,而在分水岭到河谷冲积平原之间坡度较陡;在地形起伏较大的陡坡丘陵或坡度平缓的丘陵,坡长均比较大;LS因子的空间分布格局与坡度基本一致;该文得到的数据与ASTER和SRTM遥感高程数据对比具有明显优势,全国土壤侵蚀普查项目建立的DEM,在全国、省区和大流域尺度上的土壤侵蚀评价制图中具有不可替代性。该文阐明了中国主要水蚀区的侵蚀地形特征,为土壤侵蚀学、水文学中地形因子的提取提供了参考。展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDA20040202SKL Foundation Grant No.A314021402-1718+1 种基金Na tional Natural Science Foundation of China,Grant No.41977062,41601290,41771315,41930102Program for Key Science and Technology Innovation Team in Shaanxi Province,Grant No.2014KCT-27
文摘Slope length and slope steepness are critical topographic factors(L and S)in the Universal Soil Loss Equation(USLE)and Chinese Soil Loss Equation(CSLE)for soil erosion modelling.Both slope length and slope gradient are potentially sensitive to spatial resolution when calculated in a GIS framework.The resolution effect on the LS factor and approaches suitable for improving the LS factor at a coarse resolution have not been well identified.To address this problem,the LS factor at 5-m and 30-m resolution in twenty-four watersheds with various terrains was estimated.And a downscale model based on matching of the lower resolution LS cumulative frequency curves to a higher resolution("Histogram Matching"method)was tested for its potential to improve LS factor estimation accuracy.In the larger relief mountainous area,compared to 5-m resolution,the 30-m resolution generated LS was generally overestimated by more than 20%and in lower relief areas underestimated by more than 15%.This bias is less than 10%in medium relief areas.The downscale model improved LS factor estimates compared to the 30-m resolution estimate by more than 10%when comparing frequency distribution curves and more than 20%in mean values in larger relief areas.The downscale model worked well in all regions except for the low relief areas,which intuitively are the low soil erosion potential areas.The results of this research help quantify the uncertainty in soil erosion estimates and may ultimately help to improve the assessment of soil erosion through its impact on LS factor estimates,especially at regional and global scales.
文摘土壤侵蚀是影响全球土地退化和可持续发展的重大环境问题。土壤侵蚀模型可估算土壤流失的空间和时间分布,为制定流域水土资源保护政策与实施有效战略提供支持。修订版通用土壤流失方程(RUSLE,Revised Universal Soil Loss Equation)模型以其兼具简单性与准确性,成为全球应用最广泛的土壤侵蚀模型之一。在RUSLE模型的输入参数中,地形因子(LS因子)对土壤流失潜力的影响最为显著,而LS因子的输入数据和计算方法对最终RUSLE模型计算的质量会有直接影响。为此构建了中国区域30米分辨率LS因子数据集(LS_China)。本数据集采用开源工具SAGA(自动化地球科学分析系统)和GDAL(地理空间数据抽象库),基于公开的SRTM30米高程数据集(SRTMGL1)计算获得,覆盖整个中国区域。数据被组织成单独的栅格化图块,每个栅格化图块覆盖1°×1°的范围,以Geotiff格式存储。在大尺度范围的数据计算过程中,基于对数据的空间分解,采用邻域依赖的计算方法,同时采用多流算法,保证LS因子计算的准确性。在数据质量控制方面,通过变异系数(CV)验证并与同一区域的其他数据集进行比较。结果表明,LS_China数据集的变异系数为1.24,相较于其他数据集,其内部异质性较小,具备高质量和高可靠性。本数据集可用作各种尺度(地方、区域、国家)的任何土壤侵蚀评估的输入数据。
文摘The prevalence of unwholesome land use practices and population pressure exacerbates soil loss which is worsening the problem of sedimentation of the Kubanni dam. This study was conducted at the Kubanni drainage basin covering a spatial area of 56.7 Km2 in Samaru, Zaria, Nigeria to estimate annual soil loss using the RUSLE model. Satellite images of Landsat OLI for December 2014, 2016, 2018, February, July and November 2022;soil data, rainfall data from 2010 to 2022, and DEM of 30-meter resolution were utilized for the study. All factors of the RUSLE model were calculated for the basin using assembled data. The erosivity (R-factor) was discovered to be 553.437 MJ∙mm∙ha−1∙h−1∙yr−1. The average erodibility (K-factor) value was 0.1 Mg∙h∙h∙ha−1∙MJ−1∙mm−1∙yr−1. The Slope Length and Steepness factor (LS-factor) in the basin ranged between 0% and 13.47%. The Crop Management Factor (C-factor) values were obtained from a rescaling of the NDVI values derived for the study area and ranged from 0.26 to 0.55. Support practice (P-factors) were computed from the prevalent tillage practice in the basin and ranged from 0.27 to 0.40. The soil loss amount for the Kubanni basin was found to be 28441.482 tons∙ha−1∙yr−1, while the annual soil loss for the entire Kubanni drainage basin was found to be 49780.257 tons∙yr−1. The study has demonstrated the viability of coupling RUSLE model and Remote Sensing and Geographic Information System (GIS) techniques for the estimation of soil loss in the Kubanni drainage basin.
文摘地形是影响地表水文和土壤侵蚀的主要环境因素,坡度、坡长和LS因子是土壤侵蚀模型的重要参数。该文以第四次全国土壤侵蚀普查项目为依托,在ANUDEM软件环境中建立25m分辨率文地貌关系正确的DEM(Hydrologically Correct Digital Elevation Model,Hc-DEM),提取了坡度、坡长并计算了LS因子,对中国主要水蚀地区的土壤侵蚀地形因子的空间及统计特征进行了分析,并将该数据与目前应用较为广泛的2种遥感高程数据进行了对比。结果表明,25m分辨率Hc-DEM可用以表达各典型样区地形特征,其上提取的坡度和坡长,符合一般地貌学原理和常规认识;坡度在东北地区最为平缓(0.8°),而在黄土丘陵区最陡(22.3°);坡长则在东北地区最长而黄土丘陵区最短(479m和69m);在地势比较低的河谷和地势较高的分水地带坡度比较平缓,而在分水岭到河谷冲积平原之间坡度较陡;在地形起伏较大的陡坡丘陵或坡度平缓的丘陵,坡长均比较大;LS因子的空间分布格局与坡度基本一致;该文得到的数据与ASTER和SRTM遥感高程数据对比具有明显优势,全国土壤侵蚀普查项目建立的DEM,在全国、省区和大流域尺度上的土壤侵蚀评价制图中具有不可替代性。该文阐明了中国主要水蚀区的侵蚀地形特征,为土壤侵蚀学、水文学中地形因子的提取提供了参考。
