基于PSRM模型的宁波市生态脆弱性时空演变及其驱动因素  

Spatio-temporal evolution of ecological vulnerability and its driving factors in Ningbo City based on PSRM model

作　　者：陈伟峰 苏雷 CHEN Weifeng;SU Lei(Faculty of Science and Engineering,University of Nottingham Ningbo China,Ningbo 315100,China;College of Art and Design,Zhongshan Institute,University of Electronic Science and Technology of China,Zhongshan 528402,China)

机构地区：[1]宁波诺丁汉大学理工学院,浙江宁波315100 [2]电子科技大学中山学院艺术设计学院,广东中山528402

出　　处：《宁波大学学报(理工版)》2025年第1期91-99,共9页Journal of Ningbo University:Natural Science and Engineering Edition

基　　金：广东省教育厅特色创新项目(420N50)。

摘　　要：生态脆弱性是衡量区域生态水平的核心议题.为助力国家实现“十三五”生态质量总体改善的目标,利用PSRM模型,选取PM2.5、高程、NDVI等12个指标,结合综合赋权法、热点分析、CA-Markov模型、地理探测器等方法,探究宁波市生态脆弱性时空演变特征及其驱动因子.结果表明:宁波市的生态脆弱性呈中等水平,在2000—2020年逐渐降低,但在预测的时间点(2030年)有小幅度升高;冷热点区域与脆弱性格局基本一致;PM2.5、年平均降水、年平均气温、生境质量、归一化植被指数、植被净初级生产力为宁波市生态脆弱性的主要驱动因子.研究结果可为区域生态保护和可持续发展提供科学依据.Ecological vulnerability is the core issue in measuring regional ecological level.In order to help achieve the goal of overall improvement of ecological quality in the 13th National Five-Year Plan,the PSRM model was used,and twelve indicators,including PM2.5,elevation,NDVI,etc.,were selected,together with the integrated weighting method,hotspot analysis,CA-Markov model,geo-detector,etc.,to explore the spatial and temporal evolution of ecological vulnerability of Ningbo and the driving factors of ecological vulnerability.The results showed that the ecological vulnerability of Ningbo was at a medium level,gradually decreasing from 2000 to 2020,but will be slightly increasing at the predicted time point of 2030;the cold and hotspot areas were basically consistent with the vulnerability pattern;PM2.5,annual average precipitation,annual average temperature,habitat quality,normalized vegetation index,and net primary productivity of vegetation were the main drivers of ecological vulnerability in Ningbo.The results of the study can provide a scientific basis for regional ecological protection and sustainable development.

关 键 词：生态脆弱性 PSRM模型 地理探测器 CA-Markov模型 

分 类 号：X826[环境科学与工程—环境工程]