生态环境学报2024，Vol.33Issue(5)：812-823,12.DOI:10.16258/j.cnki.1674-5906.2024.05.014
耦合ER和GWR的福州市生态环境质量的驱动力分析
Analysis of the Driving Forces of Ecological Environment Quality in Fuzhou City Coupled with ER and GWR
摘要
Abstract
The ecological quality of urban environments is jointly influenced by multiple factors,with road construction being the most significant.Objectively analyzing the driving mechanisms of road networks and other factors on ecological quality is of great importance for minimizing the negative impacts on ecosystems.Based on multi-source datasets including road networks,Landsat series remote sensing images,nighttime lights,digital elevation models,meteorology,and land use,the road density in Fuzhou City in 2015 and 2020 was calculated by using incremental spatial autocorrelation and kernel density estimation(KDE)supported by 3S technology.Then,the remote sensing ecological index(RSEI)for Fuzhou City in 2000,2009,and 2020 was constructed using principal component analysis.On this basis,the spatiotemporal dynamic changes of road density and RSEI were analyzed.Exploratory regression(ER)was then used to identify key influencing factors.Finally,a geographically weighted regression model(GWR)was applied to reveal the driving mechanisms of key influencing factors on the ecological environment quality in Fuzhou City.The results indicate that 1)the range of KDE under the optimal bandwidth in 2015 and 2020 was 0-4.090 km∙km-2 and 0-3.765 km∙km-2,respectively.In 2015,high KDE values were mainly concentrated around the urban area of Fuzhou and the centers of various districts and counties.However,by 2020,the range of high KDE values had gradually expanded,showing a tendency to spread towards coastal areas.2)From a temporal perspective,the RSEI of Fuzhou City showed an upward trend followed by a downward trend from 2000 to 2020,indicating a relatively stable ecological condition overall.Spatially,areas with good ecological environment quality were mainly distributed in mountainous regions such as Yongtai County and Minqing County.Areas with poorer quality were primarily concentrated in the central urban area,central areas of various districts and counties,eastern coastal regions,and along both sides of the river.3)The GWR model,fitted with the best factors selected through ER,outperformed the OLS model.The GWR results showed that the Euclidean distance,elevation,slope,forest proportion,and grassland proportion were positively correlated with RSEI.Road density,nighttime lights,and urban-rural land ratio were negatively correlated with RSEI.The distribution of regression coefficients exhibited significant spatial variations.These findings can provide meaningful implications for road network planning and improvement of ecological environment quality in Fuzhou and other cities.关键词
道路核密度/遥感生态指数/时空变化/探索性回归/地理加权回归Key words
road kernel density/remote sensing ecological index/spatio-temporal changes/exploratory regression/geographically weighted regression分类
资源环境引用本文复制引用
陈晓辉,胡喜生..耦合ER和GWR的福州市生态环境质量的驱动力分析[J].生态环境学报,2024,33(5):812-823,12.基金项目
国家自然科学基金项目(31971639) （31971639）
福建省自然科学基金项目(2023J01477) （2023J01477）
漳州职业技术学院智能装备与关键技术科研创新团队项目 （）
