| 黄河流域生态系统服务价值时空演化及影响因素 |
| 摘要点击 3777 全文点击 632 投稿时间：2023-06-20 修订日期：2023-08-03 |
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| 中文关键词 生态系统服务价值（ESV） 时空演化 GWR模型 影响因素 黄河流域 |
| 英文关键词 ecosystem service value（ESV） spatial-temporal evolution GWR model influencing factor Yellow River Basin |
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| 中文摘要 |
| 探析生态系统服务价值的外部时空演化及内在影响机制,对理解区域生态系统问题和提升人类生态福祉具有重要意义.基于格网数据,利用当量因子法与归一化植被指数（NDVI）对黄河流域的生态系统服务价值进行测度,分析流域沿线城市生态系统服务价值的时空格局演变,并在利用地理探测器确定主要影响因素的基础上,构建地理加权回归（GWR）模型,探讨各影响因素的空间异质性.结果表明：①2000~2020年黄河流域生态系统服务价值先上升再下降最后上升,呈现“南部高于北部”和“下游低、上中游高”的空间分布格局,且调节服务对流域生态系统服务价值的贡献最大.②地理探测结果表明,各因素的影响程度存在差异,社会因素对黄河流域生态系统服务价值的解释作用最强,经济因素次之,自然因素的解释作用最弱,且上游的高值区主要与河流湖泊有关,中游的高值区主要与山地有关.③GWR模型结果表明,人口密度、土地垦殖率与生态系统服务价值呈负相关,年均降水量呈正相关,作用强度均由东向西递增；单位面积GDP与整体生态系统服务价值呈负相关,但在上游区域呈正相关. |
| 英文摘要 |
| The external spatiotemporal evolution and intrinsic impact mechanisms of ecosystem service value are of great significance for understanding regional ecosystem issues and enhancing human ecological well-being. Based on grid data, this study used the equivalent factor method and NDVI to measure the ecosystem service value of the Yellow River Basin, analyzed the spatial-temporal evolution of urban ecosystem service value along the basin, and established a GWR model to explore the spatial heterogeneity of each influencing factor on the basis of determining the main influencing factors via geographic detector. The results showed that:① The ecosystem service value of the Yellow River Basin increased first, then decreased, and finally increased from 2000 to 2020, showing a spatial distribution pattern of "the south was higher than the north;" "the lower reaches were lower, and the upper and middle reaches were higher;" and the regulation service contributed the most to the ecosystem service value of the basin. ② The results of geographical exploration showed that the degree of influence of various factors was different. Social factors played the strongest role in explaining the ecosystem service value of the Yellow River Basin, followed by economic factors, and natural factors played the weakest role. The high value areas in the upper reaches were mainly related to rivers and lakes, and the high value areas in the middle reaches were mainly related to mountains. ③ The results of the GWR model showed that population density and land reclamation rate were negatively correlated with ecosystem service value, whereas average annual precipitation was positively correlated, and the effects increased from east to west. The GDP per unit area was negatively correlated with the overall ecosystem service value but positively correlated in the upstream region. |
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