【摘要】 科学评价生态环境的脆弱性,对合理利用土地资源和促进区域生态安全具有重要意义。以公里格网为基本评价单元,以快速城市化区域典型代表南昌市为研究区,通过SRP概念模型构建评价指标体系并选取17个评价指标,基于空间主成分分析法、全局Moran′s I指数以及LISA聚类图,量化分析了2000—2015年南昌市生态环境脆弱性的时空分布特征及驱动力。结果表明:(1)2000—2015年南昌市生态环境脆弱性呈现由轻度脆弱向中度脆弱演变的趋势;(2)2000—2015年南昌市各县(区)生态环境脆弱性差异明显,东湖区、西湖区、青山湖区、青云谱区、湾里区脆弱性程度最高,其他县(区)脆弱性增幅较大;(3)2000和2015年南昌市生态环境脆弱性具有明显的空间自相关性,且为正相关,高高聚集区主要分布在极度脆弱区,低低聚集区与微度和轻度脆弱区相关;(4)研究时段内,南昌市生态环境脆弱性主要驱动力为人口密度、人均GDP、人均耕地、坡度、地形起伏度、年均气温、极端最高温、极端最低温、年均降水量。更多还原

【Abstract】 Scientific evaluation of the fragility of the ecological environment is of great significance for the rational use of land resources and regional ecological security. Taking the Nanchang City as a case study area, we developed an index-based system for eco-environmental vulnerability assessment based on the ecological sensitivity-ecological recovery-ecological pressure model. Utilizing the spatial principal component analysis method, the global Moran′ I index and LISA clustering map, seventeen indicators were selected and used to analyze the spatial and temporal distribution and driving factors of the eco-environmental vulnerability of Nanchang City from 2000 to 2015. Our analyses show there was an increasing trend from light to medium vulnerability in Nanchang City from 2000 to 2015. The spatial heterogeneity of eco-environmental vulnerability in Nanchang City is obvious in both 2000 and 2015. Among the administrative divisions in Nanchang City, the areas with the highest environmental vulnerability were mainly distributed in Donghu, West Lake, Qingshan Lake and Wanli districts. In 2000 and 2015, there was significant global spatial autocorrelation of the eco-environmental vulnerability. High-high(HH) areas were mainly distributed in extremely vulnerable regions, and low-low(LL) regions were associated with negligible and lightly vulnerable areas. From 2000 to 2015, the drivers of eco-environmental vulnerability in Nanchang City were population density, per capita GDP, per capita arable land, slope, topographic relief, average annual temperature, extreme maximum temperature, extreme minimum temperature, and average annual precipitation.更多还原