Spatial non-stationarity analysis of thermal environment driving factors in main urban area of Hefei City
To alleviate the problem of urban heat exposure,it is necessary to explore the spatial characteristics of urban thermal environment and its driving factors.This article uses multi-source data such as Landsat-9 remote sensing images of the main urban area of Hefei in the summer of 2022,Baidu architecture,and urban heat,and constructs a regression model between surface temperature and driving factors based on analysis platforms such as ENVI and ArcGIS.Research has found that:① Surface temperature(LST)in the study area has strong spatial clustering characteristics,with high-high clustering mainly distributed in industrial areas,high-density commercial housing and mixed residential areas,as well as large-scale hard paved areas;Low-low clustering is mainly located in urban fringe areas,represented by large parks,water bodies,and areas with high vegetation coverage.The heat risk areas are widely distributed in the main urban area,with the Economic Development Zone accounting for the largest proportion of heat risk areas in the administrative area,followed by Yaohai District,and Luyang District accounting for the smallest proportion of heat risk areas.②Through Pearson correlation analysis,it was found that the selected 9 driving factors were significantly correlated with LST,while the model without normalized building index(NDBI)passed the collinearity test(VIF＜7.5)and had the highest explanatory power.Due to the complexity of urban surface coverage,regional functional characterization,and three-dimensional orientation of land development,the spatial distribution differences of various driving factors are significant.③The spatial feature relationship shows a negative correlation between all factors and surface temperature,except for nighttime population(NP).The water body index(MNDWI)and vegetation index(NDVI)have significant thermal environment mitigation effects,and the regional difference maps of MNDWI and NDVI on LST are similar.The global contribution is that MNDWI has the greatest impact on LST,followed by NDVI,and NP has the smallest impact.Compared to areas with concentrated thermal environments,the local effect efficiency of MNDWI and NDVI on LST is weakened in areas dominated by medium and low temperatures,while the local effect efficiency of FAR and NP on LST is exactly the opposite.
land surface temperaturethermal environmentdriving factorsspatial non-stationarityHefei
NETL
NSTL
万方数据
