邹桃红, 常雅轩, 陈鹏, 刘家福. 基于AHP-PCA熵权组合模型的吉林省生态环境脆弱性动态评价[J]. 中国生态农业学报(中英文), 2023, 31(9): 1511−1524. DOI: 10.12357/cjea.20230115
引用本文: 邹桃红, 常雅轩, 陈鹏, 刘家福. 基于AHP-PCA熵权组合模型的吉林省生态环境脆弱性动态评价[J]. 中国生态农业学报(中英文), 2023, 31(9): 1511−1524. DOI: 10.12357/cjea.20230115
ZOU T H, CHANG Y X, CHEN P, LIU J F. Evaluation of eco-environmental vulnerability in Jilin Province based on an AHP-PCA entropy weight model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(9): 1511−1524. DOI: 10.12357/cjea.20230115
Citation: ZOU T H, CHANG Y X, CHEN P, LIU J F. Evaluation of eco-environmental vulnerability in Jilin Province based on an AHP-PCA entropy weight model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(9): 1511−1524. DOI: 10.12357/cjea.20230115

基于AHP-PCA熵权组合模型的吉林省生态环境脆弱性动态评价

Evaluation of eco-environmental vulnerability in Jilin Province based on an AHP-PCA entropy weight model

  • 摘要: 吉林省不仅是我国粮食主产区之一, 也是东北地区重要的生态屏障和功能区, 科学掌握其生态环境脆弱性的空间分布和时空变化特征, 对合理利用土地资源及实现区域环境保护起着重要作用。本研究参考SRP (Sensitivity–Resilience–Pressure)模型, 从地形、气候、植被覆盖、景观格局及人类活动5个方面选取13个评价指标, 综合运用AHP-PCA (analytic hierarchy process-principal component analysis)熵权模型及空间自相关方法, 分析2000—2020年不同时期吉林省生态环境脆弱性的时空格局, 并探讨其空间关联关系。结果表明: 1)研究区生态环境以轻度以下脆弱为主, 且整体呈现明显的地域差异, 由东向西脆弱度逐渐增加, 重度脆弱区主要出现在吉林省西部平原, 潜在脆弱区主要出现在吉林省东部山区。2)将脆弱性指数分为5个等级, 各等级间面积比例差异明显, 以2020年为例,表现为轻度>中度>微度>潜在>重度, 且轻度及以下脆弱区面积占比均达到67.9%以上, 这表明吉林省整体处于中等脆弱水平。3)时间变化上, 2000年至2020年间, 吉林省生态环境脆弱性呈逐年向好的趋势, 重度、微度脆弱性区域面积占比与2000年相比分别下降2.78%和9.20%, 轻度和中度脆弱性区域面积占比同研究初期相比分别增加7.45%和5.24%, 潜在脆弱性区域面积占比与2000年基本持平。4) 2000年研究区生态环境脆弱性指数的Moran I值为0.2335, 表明其在空间上呈集聚现象, 高聚集区主要分布在吉林省西部地区; 至2020年, Moran I值增加至0.3841, 空间集聚更为显著。根据脆弱性等级的分区及其影响因素, 对不同脆弱性等级区域给出了不同生态保护建议: 潜在脆弱和微度脆弱区继续实行现有生态环境保护政策; 轻度脆弱和中度脆弱区要坚持以黑土地资源保护为前提进行合理的农用地资源开发; 重度脆弱性区要加大生态环境保护投入, 特别要有针对性地实施盐碱地治理等策略。

     

    Abstract: Jilin Province is the main grain-producing area in China and has a considerable ecological function in Northeast China. Understanding the spatial and temporal characteristics of ecological vulnerability can aid effectively managing environmental change, guiding the rational use of land resources, and developing strategies for regional environmental protection. Based on the Sensitivity–Resilience–Pressure model of ecological vulnerability, a comprehensive evaluation indexes system for ecological vulnerability was established from the perspectives of human activities and natural environment in Jilin Province using meteorological, remote sensing, and statistical data. Thereafter, an entropy weight model constructed by using an analytic hierarchy process and principal component analysis was employed to analyze the geospatial and temporal dynamics of ecological vulnerability from 2000 to 2020 in the study area. Spatial autocorrelation analysis was used to probe spatial relationships between the different ecological vulnerability levels. The results revealed that 1) the overall environment was suitable with a light vulnerability level and below in the study area; however, ecological vulnerability varied among different regions and increased gradually from east to west. High vulnerability areas were mainly distributed in the western region, characterized by less rainfall and lower vegetation cover, and displayed a considerable global spatial autocorrelation with high-high aggregation. Potentially vulnerable areas were concentrated in the mountainous regions of eastern Jilin Province. 2) The ecological vulnerability index was divided into five levels as potential, slight, light, moderate and heavy. The area proportion was varied significantly among different levels. Taking 2020 as an example, the proportion in descending order is light>moderate>slight>potential>heavy, moreover, the area of light and below vulnerable area accounts for about 67.9%, indicating that Jilin Province is at a medium level of vulnerability in the whole. 3) Temporally, the vulnerability of the ecological environment in Jilin Province improved from 2000 to 2020. Compared to 2000, the proportions of heavyly and slightly vulnerable areas in 2020 decreased by 2.78% and 9.20%, respectively; whereas the proportions of light and moderately vulnerable areas in 2020 increased by 7.45% and 5.24%, respectively. The potentially vulnerable areas in 2020 were the same as those in 2000. 4) The value of Moran’s I index increased from 0.2335 to 0.3841 from 2000 to 2020, implying that spatial agglomeration was more pronounced, and high-high aggregation was distributed in the western region of the study area, whereas low-low aggregation was concentrated in the eastern region of Jilin Province. Relevant suggestions for environmental protection were proposed based on vulnerability assessments and impact factors. Existing ecological protection strategies should be continued in zones with potential and slight vulnerabilities. Zones with light and moderate vulnerability should prioritize black soil protection to ensure reasonable development of agricultural land resources. For heavyly vulnerable zones, investment in environmental protection should increase.ince. Relevant suggestions for environmental protection were proposed based on vulnerability assessments and impact factors. Existing ecological protection strategies should be continued in zones with potential and slight vulnerabilities. Zones with light and moderate vulnerability should prioritize black soil protection to ensure reasonable development of agricultural land resources. For heavyly vulnerable zones, investment in environmental protection should increase.

     

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