Spatial and Temporal Variation Patterns of Urban Habitat Quality and Its Influencing Factors Based on Land Use Change
Habitat quality,as an important characterization of regional biodiversity capacity,is of great significance for maintaining the stability of the regional ecological pattern and promoting the coordinated economic and ecological development.The study takes the land use data in 2000,2010,and 2020 in Suzhou City,Jiangsu Province to simulate the land uses in 2030,and analyze and predict the land use change using the INVEST and CA-Markov models.Spatial analysis methods such as standard deviation ellipse,center of gravity analysis,and random forest model are used to reveal the characteristics of spatial and temporal differentiation of habitat quality in Suzhou and analyze its influencing factors.The results show that:1)The changes in land use in Suzhou from 2000 to 2030 present the significant increase in the area of cultivated land,construction land and un-utilized land,and the gradual shrinkage in the area of water bodies and forest land;2)The spatial pattern of habitat quality generally presents a patchy layout,high in the center and low in the surrounding areas.The water bodies and mountains in the region have the habitat quality of relatively high and high grade,while the urban construction land has a low-grade habitat quality.With the change of time,the habitat quality showed a downward trend,and the center of gravity of the habitat quality showed a"C"type migration trajectory of"northeast-southwest";and 3)The spatial and temporal differentiation characteristics of habitat quality result from the combined action of various factors including spatial pattern,socioeconomy,land use,land cover,and natural environment.Among them,land use and land cover have the greatest impact.In the sense,the future planning should place an importance on the optimization and adjustment of land use structure and the mountainous areas and water bodies with the best habitat quality.
land use changehabitat qualityCA-Markov modelINVEST modelspatial and temporal variation
NETL
NSTL
万方数据
