长江中游城市群生态系统健康及其关键驱动因素遥感评价[j]。

Q2 Environmental Science
Jin Guo, Xiao-Jian Wei, Fu-Qing Zhang, Jin Cai, Yu-Bo Ding
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引用次数: 0

摘要

长江中游城市群是中国中部地区重要的城市群,其生态系统健康对促进城市生物多样性保护和可持续发展具有重要作用。基于多源遥感数据,采用“活力-组织-弹性”综合模型对城市生态系统健康状况进行了系统评价。利用地理收敛交叉映射(GCCM)模型识别生态系统健康的关键驱动因子,揭示生态系统健康与驱动因子之间的因果关系。研究表明:①20 a来,长江中游城市群生态系统健康水平由2010年的0.784提高到2020年的0.801,总体呈上升趋势;东北和西部整体生态系统健康状况好于中部和南部,且差异显著。②基于GCCM模型,人类活动与生态系统健康的相互影响相对稳定,而自然环境与生态系统健康的相互作用不稳定。在人文景观指标方面,GDP和POP与EH的交互方向一致。在自然景观指标中,TA、MAP、HLI、NDVI和NPP与EH的相互作用方向不一致。③GCCM模型对驱动力排序如下:归一化植被指数>;国内生产总值比;夜间灯光指数>;年降水量>;年平均气温;人口密度;净初级生产力。归一化植被指数是最重要的驱动因子,GDP、夜间光照指数、年降水量和年平均气温是主要驱动因子,人口密度和净初级生产力对生态系统健康的影响较小。本研究分析了长江中游城市群生态系统功能及其变化,为未来生态系统管理提供科学依据,对城市生态系统可持续健康治理和预防性政策制定具有重要的理论和实践意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Remote Sensing Assessment of Ecosystem Health and Its Key Driving Factors: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River].

The Yangtze River Middle Reaches Urban Agglomeration, is a crucial city cluster in central China and plays an important role in promoting urban biodiversity conservation and sustainable development through its ecosystem health. Based on multisource remote sensing data, a comprehensive "Vigor-Organization-Resilience" model was used to systematically evaluate urban ecosystem health. Further, the geographic convergence cross-mapping (GCCM) model was employed to identify key driving factors of ecosystem health and reveal the causal relationships between ecosystem health and its drivers. The study showed that: ① Over 20 years, the ecosystem health level of the Yangtze River Middle Reaches Urban Agglomeration improved, increasing from 0.784 in 2010 to 0.801 in 2020. The overall ecosystem health was better in the northeastern and western regions compared to that in the central and southern regions, with notable differentiation. ② Based on the GCCM model, human activities and ecosystem health had a relatively stable mutual influence, while the interaction between the natural environment and ecosystem health was unstable. For human landscape indicators, GDP and POP had consistent interaction directions with EH. For natural landscape indicators, TA, MAP, HLI, NDVI, and NPP had inconsistent interaction directions with EH. ③ The GCCM model ranked the driving forces as follows: normalized vegetation index > GDP > nighttime light index > annual precipitation > average annual temperature > population density > net primary productivity. The normalized vegetation index was the most important driving factor, with GDP, nighttime light index, annual precipitation, and average annual temperature being the main driving factors, while population density and net primary productivity contributed less to ecosystem health. This study analyzes the ecosystem functions and changes in the Yangtze River Middle Reaches Urban Agglomeration, providing a scientific basis for future ecosystem management, and has significant theoretical and practical implications for sustainable urban ecosystem health governance and preventive policy formulation.

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来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
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