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Evolution and Simulation of Spatial-temporal Heterogeneities in Ecological Background Quality of Changsha City

Evolution and Simulation of Spatial-temporal Heterogeneities in Ecological Background Quality of Changsha City
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摘要 By GIS and ENVI,TM/ETM remote sensing images of five districts(Yuelu District,Furong District,Yuhua District,Tianxin District and Kaifu District) in Changsha City center in 2005,2010 and 2015 were interpreted.Moreover,distribution chart for ecological background factors in 2020 was simulated by using CA-Markov module in IDRISI.Using principal component analysis,evaluation model for ecological background quality of the city was established.Via circle analysi s,GS+semi variance function analysis,hot spot area analysis and grey correlation analysis,integrated analysis and evaluation on spatial heterogeneity evolution of ecological background quality in research region were conducted.Results showed that firstly Changsha overall has formed ecological pattern of landscape island city,but ecological background started to show the evolution trend of high heterogeneity and fragmentation under the construction land expansion,and ecological background quality of the city declined from 0.300,6 to 0.257,1 during 2005-2020.Secondly,ecological background quality of Changsha City had typical circle and axial gradient structure,and "eco tone" had the most violent evolution.Thirdly,spatial structure of ecological background quality had region,time and direction heterogeneities,and spatial heterogeneity of region was the most important.Fourthly,hot spot area distribution of ecological background quality evolution showed the "frog jump" trend of gathering in marginal zone and diffusing to peripheral zone.Fifthly,in driving factors of ecological background quality,industrialization rate had the highest grey correlation degree(0.842,1),and grey absolute correlation degree between ecological background quality in Yuelu District and industrialization rate was the highest(0.603,1). By GIS and ENVI,TM/ETM remote sensing images of five districts(Yuelu District,Furong District,Yuhua District,Tianxin District and Kaifu District) in Changsha City center in 2005,2010 and 2015 were interpreted.Moreover,distribution chart for ecological background factors in 2020 was simulated by using CA-Markov module in IDRISI.Using principal component analysis,evaluation model for ecological background quality of the city was established.Via circle analysi s,GS+semi variance function analysis,hot spot area analysis and grey correlation analysis,integrated analysis and evaluation on spatial heterogeneity evolution of ecological background quality in research region were conducted.Results showed that firstly Changsha overall has formed ecological pattern of landscape island city,but ecological background started to show the evolution trend of high heterogeneity and fragmentation under the construction land expansion,and ecological background quality of the city declined from 0.300,6 to 0.257,1 during 2005-2020.Secondly,ecological background quality of Changsha City had typical circle and axial gradient structure,and 'eco tone' had the most violent evolution.Thirdly,spatial structure of ecological background quality had region,time and direction heterogeneities,and spatial heterogeneity of region was the most important.Fourthly,hot spot area distribution of ecological background quality evolution showed the 'frog jump' trend of gathering in marginal zone and diffusing to peripheral zone.Fifthly,in driving factors of ecological background quality,industrialization rate had the highest grey correlation degree(0.842,1),and grey absolute correlation degree between ecological background quality in Yuelu District and industrialization rate was the highest(0.603,1).
出处 《Journal of Landscape Research》 2016年第6期46-52,共7页 景观研究(英文版)
基金 Sponsored by National Natural Science Fund(51578454)
关键词 Changsha City Ecological background quality Spatial heterogeneity Simulation prediction Grey correlation Changsha City Ecological background quality Spatial heterogeneity Simulation prediction Grey correlation
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