Characterization of the vertical distribution of soil organic carbon(C), nitrogen(N), and phosphorus(P) may improve our ability to accurately estimate soil C, N, and P storage. Based on a database of 21 354 records in...Characterization of the vertical distribution of soil organic carbon(C), nitrogen(N), and phosphorus(P) may improve our ability to accurately estimate soil C, N, and P storage. Based on a database of 21 354 records in 74 long-term monitoring plots from 2004 to 2013 in the Chinese Ecosystem Research Network(CERN), we built fitting functions to quantify the vertical distribution of soil C, N, and P(up to 100 cm depth) in the typical Chinese terrestrial ecosystems. The decrease of soil C, N, and P content with depth can be well fitted with various mathematical functions. The fitting functions differed greatly between artificial(agriculture) and natural(desert, forest, and grassland) ecosystems, and also differed with respect to soil C, N, and P content. In both the artificial and natural ecosystems, the best fitting functions were exponential functions for C, quadratic functions for N, and quadratic functions for P. Furthermore, the stoichiometric ratios of soil C, N, and P were ranked in descending order: grassland > forest > agriculture > desert, and were also associated with climate. This study is the first to build the fitting functions for the profile distribution of soil C, N, and P in China at a national scale. Our findings provide a scientific basis to accurately assess the storage of C, N, and P in soils at a large scale, especially for the integrative analysis of historical data.展开更多
以雅鲁藏布江流域林芝段为研究区域,运用GIS空间分析方法和ENVI遥感影像处理技术,获取2017年研究区域景观类型分布,利用Fragstats v 4.2.1计算景观格局指数,并将21个景观格局指数通过相关性分析法、主成分和聚类分析法,剔除冗余指数,最...以雅鲁藏布江流域林芝段为研究区域,运用GIS空间分析方法和ENVI遥感影像处理技术,获取2017年研究区域景观类型分布,利用Fragstats v 4.2.1计算景观格局指数,并将21个景观格局指数通过相关性分析法、主成分和聚类分析法,剔除冗余指数,最终确定8个代表性景观指数对对景观格局空间水平差异性进行分析。结果显示:(1)PAFRAC-MN:耕地>建设用地>水域>草地>林地>未利用地;PD:水域>草地>未利用地>林地>耕地>建设用地;ED边缘密度:草地>水域>林地>未利用地>耕地>建设用地;PLAND:林地>草地>未利用地>水域>耕地>建设用地;AREA_MN:林地>未利用地>草地>水域>建设用地>耕地;AI:林地>未利用地>草地>水域>耕地>建设用地;IJI:建设用地>草地>水域>耕地>林地>未利用地;LSI:水域>草地>耕地>未利用地>林地>建设用地;(2)林地类型斑块结构稳定,连通性好;水域景观类型景观异质性指数高,水资源空间形态复杂;耕地类型板块分维数值高,呈现不规则性,说明受人为干扰度强;草地景观类型的单位面积周长长度大,破碎化程度高,对整个景观的影响大。本研究可对高原生态景观规划、生态安全评价提供一定参考。展开更多
基金under the auspices of Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA05050702)National Natural Science Foundation of China(No.31270519,31470506)Kezhen Distinguished Talents in Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences(No.2013RC102)
文摘Characterization of the vertical distribution of soil organic carbon(C), nitrogen(N), and phosphorus(P) may improve our ability to accurately estimate soil C, N, and P storage. Based on a database of 21 354 records in 74 long-term monitoring plots from 2004 to 2013 in the Chinese Ecosystem Research Network(CERN), we built fitting functions to quantify the vertical distribution of soil C, N, and P(up to 100 cm depth) in the typical Chinese terrestrial ecosystems. The decrease of soil C, N, and P content with depth can be well fitted with various mathematical functions. The fitting functions differed greatly between artificial(agriculture) and natural(desert, forest, and grassland) ecosystems, and also differed with respect to soil C, N, and P content. In both the artificial and natural ecosystems, the best fitting functions were exponential functions for C, quadratic functions for N, and quadratic functions for P. Furthermore, the stoichiometric ratios of soil C, N, and P were ranked in descending order: grassland > forest > agriculture > desert, and were also associated with climate. This study is the first to build the fitting functions for the profile distribution of soil C, N, and P in China at a national scale. Our findings provide a scientific basis to accurately assess the storage of C, N, and P in soils at a large scale, especially for the integrative analysis of historical data.
文摘以雅鲁藏布江流域林芝段为研究区域,运用GIS空间分析方法和ENVI遥感影像处理技术,获取2017年研究区域景观类型分布,利用Fragstats v 4.2.1计算景观格局指数,并将21个景观格局指数通过相关性分析法、主成分和聚类分析法,剔除冗余指数,最终确定8个代表性景观指数对对景观格局空间水平差异性进行分析。结果显示:(1)PAFRAC-MN:耕地>建设用地>水域>草地>林地>未利用地;PD:水域>草地>未利用地>林地>耕地>建设用地;ED边缘密度:草地>水域>林地>未利用地>耕地>建设用地;PLAND:林地>草地>未利用地>水域>耕地>建设用地;AREA_MN:林地>未利用地>草地>水域>建设用地>耕地;AI:林地>未利用地>草地>水域>耕地>建设用地;IJI:建设用地>草地>水域>耕地>林地>未利用地;LSI:水域>草地>耕地>未利用地>林地>建设用地;(2)林地类型斑块结构稳定,连通性好;水域景观类型景观异质性指数高,水资源空间形态复杂;耕地类型板块分维数值高,呈现不规则性,说明受人为干扰度强;草地景观类型的单位面积周长长度大,破碎化程度高,对整个景观的影响大。本研究可对高原生态景观规划、生态安全评价提供一定参考。
