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中国八大经济区GPP变化及影响因子协同机制
引用本文:徐勇,赵纯,郭振东,戴强玉,盘钰春,郑志威.中国八大经济区GPP变化及影响因子协同机制[J].中国环境科学,2023,43(1):477-487.
作者姓名:徐勇  赵纯  郭振东  戴强玉  盘钰春  郑志威
作者单位:桂林理工大学测绘地理信息学院, 广西 桂林 541006
基金项目:广西自然科学基金资助项目(2020GXNSFBA297160);广西科技基地和人才专项(桂科AD21220133);国家自然科学基金资助项目(42061059,42161028)
摘    要:以中国和八大综合经济区为研究区,全面分析人文因子、土地利用类型、气候因子和地形因子对植被总初级生产力(GPP)空间分异的影响差异.利用MODIS GPP数据、气象数据、土地利用类型、DEM数据、夜间灯光和人口密度数据等,基于Theil-Sen Median趋势分析、Mann-Kendall显著性检验和地理探测器模型,在全国和经济区尺度上分析2000~2020年植被GPP时空变化特征,探测植被空间分异的影响因子及影响因子间的协同机制.结果表明,2000~2020年中国及八大经济区植被GPP整体呈波动上升趋势,呈上升趋势的区域占总面积的84.46%,其中,呈极显著上升区域占19.86%,主要分布在黄河中游综合经济区中部和大西北综合经济区东部.影响因子探测结果表明,湿度、日照时数、降水和土地利用类型是中国植被GPP空间分异的主要影响因子,其中,湿度的影响力最高,q值为0.64.经济区尺度上,湿度、日照时数、降水是影响东北、黄河中游、大西南和大西北综合经济区植被GPP空间分异的主导因子,而人文因子对东部和南部沿海综合经济区植被空间分异的影响较大.交互作用探测结果表明,中国植被GPP空间分异主要...

关 键 词:八大经济区  植被总初级生产力  地理探测器  人文因子  土地利用类型
收稿时间:2022-06-17

Spatio-temporal variation of gross primary productivity and synergistic mechanism of influencing factors in the eight economic zones,China
XU Yong,ZHAO Chun,GUO Zhen-dong,DAI Qiang-yu,PAN Yu-chun,ZHENG Zhi-wei.Spatio-temporal variation of gross primary productivity and synergistic mechanism of influencing factors in the eight economic zones,China[J].China Environmental Science,2023,43(1):477-487.
Authors:XU Yong  ZHAO Chun  GUO Zhen-dong  DAI Qiang-yu  PAN Yu-chun  ZHENG Zhi-wei
Institution:College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
Abstract:China and the eight economic zones were considered as the study area. This study aimed to comprehensively analyze the impact of anthropogenic factors, land use types, climatic factors, and topographic factors on the spatial differentiation of vegetation GPP. Using MODIS GPP time series, in situ meteorological data, land use type, DEM, nighttime light, and population density data based on Theil-Sen Median trend analysis, Mann-Kendall significance test, and geo-detector model, the spatio-temporal variation of vegetation GPP from 2000 to 2020 were analyzed, and the influencing factors affecting the spatial differentiation of vegetation GPP were detected both on country and regional scales. The results showed that the vegetation GPP showed a fluctuating upward trend both in China and the eight economic zones from 2000 to 2020. The areas with an upward trend accounted for 84.46% of the total area, of which the areas with extremely significant increases accounted for 19.86%, mainly distributed in the middle of the Yellow River economic zone and east of the Northwest economic zone. The factor detection results showed that relative humidity, sunshine duration, precipitation, and land use types were the dominant factors affecting the spatial differentiation of vegetation GPP in China. On regional scale, relative humidity, sunshine duration, and precipitation were the dominant factors affecting the spatial differentiation of vegetation GPP in the Northeast, middle reaches of the Yellow River, Southwest, and Northwest economic zones, while anthropogenic factors exerted the spatial differentiation of vegetation GPP in the Eastern and Southern coastal economic zones. Interaction detection results showed that the interaction between land use type and relative humidity exhibited the greatest influence on the spatial differentiation of vegetation GPP in China with a q value of 0.75. On regional scale, the spatial differentiation of vegetation GPP in the middle reaches of the Yellow River and Southwest economic zones was mostly affected by the interaction between precipitation and other influencing factors, while the spatial differentiation of vegetation GPP in other economic zones was mainly affected by the interaction between land use type and other influencing factors or relative humidity and other influencing factors.
Keywords:eight economic zones  vegetation gross primary productivity  geo-detector model  anthropogenic factor  land use types  
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