基于PSR模型和景观格局指数的滇池流域生态系统健康评价

Ecosystem Health Assessment of the Dianchi Lake Basin Based on PSR Model and Landscape Pattern Indexes

  • 摘要: 以“压力-状态-响应”(PSR)概念模型为基础,构建了包括景观格局指数的3个子系统17个指标的评价指标体系,采用层次分析法测定权重,综合评价模型计算生态系统健康综合指数,确定2010—2017年滇池流域生态系统健康等级,并运用灰色关联度分析法确定主要影响因素。结果表明:①2010年、2015年、2017年生态系统健康指数分别为58.57、58.17、64.89,流域发生了亚健康状态向健康状态的转变;②3个子系统中,状态子系统权重为0.5,对整个流域生态系统健康评价的作用最为显著;③17个指标中,关联度>0.9的影响流域生态系统健康的主要因素包括城镇化率、土地垦殖系数、人口干扰度指数以及分离度指数、复杂度指数、均匀度指数、破碎度指数等景观格局指数,共计13个;其中,城镇化率是关联度最高的影响因子。将滇池流域划分为湖滨区、平坝区和山地区,根据不同区域影响因子的差异性提出相应的生态系统健康的管理建议。

     

    Abstract: The evaluation index system including 3 subsystems and 17 indicators of landscape pattern indexes was established based on the Pressure-State-Response(PSR) evaluation model. The index weight was determined by the analytic hierarchy process(AHP). The comprehensive health assessment index was calculated using the comprehensive evaluation model, and the ecosystem health grade of the Dianchi Lake Basin from 2010 to 2017 was assessed. Finally, the main influencing factors were determined by grey correlation analysis. The result showed that the ecosystem health index was 58.57, 58.17 and 64.89, respectively in 2010, 2015and 2017, which indicated that the sub-health state had changed to the healthy state. Among the three subsystems, the weight of the state subsystem was 0.5, which indicated that the state subsystem had the most significant effect on the ecosystem health assessment of the whole Dianchi Lake Basin. Among the 17 indicators, the main factors affecting the ecosystem health of the basin whose correlation degree was greater than 0.9 covering 13 factors of urbanization rate, land reclamation coefficient, population interference index, separation index, complexity index, evenness index and fragmentation index etc.. The urbanization rate was the most relevant factor. The Dianchi Lake Basin was divided into lakeside area, flat dam area and mountain area, and the management suggestions of ecosystem health were put forward according to the differences of impact factors in different areas.

     

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