摘要
针对生态敏感性评价中指标选择、权重计算步骤存在的主观性,片面性的问题,以乐山市为例,基于压力-状态-响应模型构建评价指标体系,运用主成分分析(PCA)-熵权法组合权重模型对选取的20个评价指标因子赋权。结合层次分析法对乐山市2018年生态环境进行评价研究。结果表明:乐山市生态敏感性指数分布存在明显空间差异,乐山市大部分地区属于不敏感、较不敏感等级,占研究区总面积的67.18%;其次为极敏感等级,占研究区面积的14.4%;一般敏感和较敏感等级所占面积最少,分别是9.84%和8.58%。压力-状态-响应模型全面考虑了生态影响因素,PCA-熵权法组合权重计算也弥补了层次分析法主观性强的缺陷,为生态敏感性分析提供了一种可行的思路。
Aimed at the subjectivity and one-sidedness of indexs selection and weight calculation steps in eco-environmental sensitivity evaluation,in this paper,Leshan City is taken as example,by constructing an indicator system based on the pressure-state-response(PSR)model,and principal component analysis(PCA)-entropy weight method combined model is used to evaluate 20 indicators.Analytic hierarchy process(AHP)is applied to analysis eco-environmental sensitivity of Leshan City,2018.The results indicate that:The distribution of eco-environmental sensitivity index of Leshan City manifest obvious spatial distinction.Most areas of Leshan City belong to insensitive and less sensitive grades,accounting for 67.18%of the total area of the study area,following by extremely sensitive grade,accounting for 14.4%of the study area,and general sensitive and more sensitive grades account for the least area,9.84%and 8.58%respectively.The Pressure-State-Response model comprehensively considers the ecological influence factors,and the PCA-entropy weight method combined weight calculation also makes up for the strong subjectivity of AHP,which provides a practicable thinking for ecological sensitivity analysis.
作者
黎家琦
武雪玲
唐诗怡
LI Jiaqi;WU Xueling;TANG Shiyi(Key Laboratory of Urban Land Resources Monitoring and Simulation,MNR,Shenzhen,Guangdong 518034,China;Institute of Geophysics&Geomatics,China University of Geosciences,Wuhan 430074,China)
出处
《测绘科学》
CSCD
北大核心
2020年第11期75-83,106,共10页
Science of Surveying and Mapping
基金
国家自然科学基金项目(41871355)
国土资源部城市土地资源监测与仿真重点实验室开放基金资助课题项目(KF-2019-04-045)
中央高校基本科研业务费专项资金资助项目(CUGQY1936)。
关键词
乐山市
生态敏感性
PSR模型
PCA-熵权法组合权重
Leshan city
eco-environmental sensitivity
PSR model
PCA-entropy weight method combined model
