Quantitative exploration of the mechanisms behind the urban thermal environment in Beijing  

作　　者：Dan Meng Xiaojuan Li Wenji Zhao Huili Gong 

机构地区：[1]Key Laboratory of 3D Information Acquisition and Application,Ministry of Education,Key Laboratory of Resource,Environment and GIS in Beijing,College of Resources Environment and Tourism,Capital Normal University,Beijing 100048,China

出　　处：《Progress in Natural Science:Materials International》2009年第12期1757-1763,共7页自然科学进展·国际材料(英文版)

基　　金：supported by the National High Technology Research and Development Program of China (No.2006AA12Z111);the National Key Technology R&D Program(2007BAH15B02).

摘　　要：The driving mechanism behind the formation of urban thermal environments is the result of a combination of factors. Beijing was chosen as the study area, and the technique of principal component analysis (PCA) was used. A spatial regression method was also applied for quantitative explanation of the thermal mechanism. Multiple Landsat thematic mapper images were used to quantify potential causing factors. Considering the eigenvalues of each factor and its relationship with land surface temperature, the first three principal components (PCs) are regarded as the main causative factors explaining the mechanism as independent variables. The first three PCs mainly reflect urban construction, road density and the normalized difference vegetation index (NDVI), respectively. Ordinary least squares, spatial lag and spatial error regression models were established separately for the relationships between the first three PCs and land surface temperature (LST). In the two spatial regression models, z-statistics for both the spatial lag parameter (p) and spatial residual parameter (2) are significant, indicating the necessity of using spatial regression to replace the OLS regression model, as well as indicating that the spatial error regression model is superior to the spatial lag regression model. Overall, the normalized difference builtup index (NDBI) and road density are the most significant positive contributions to LST.The driving mechanism behind the formation of urban thermal environments is the result of a combination of factors.Beijing was chosen as the study area,and the technique of principal component analysis(PCA) was used.A spatial regression method was also applied for quantitative explanation of the thermal mechanism.Multiple Landsat thematic mapper images were used to quantify potential causing factors.Considering the eigenvalues of each factor and its relationship with land surface temperature,the first three principal components(PCs) are regarded as the main causative factors explaining the mechanism as independent variables.The first three PCs mainly reflect urban construction,road density and the normalized difference vegetation index(NDVI),respectively.Ordinary least squares,spatial lag and spatial error regression models were established separately for the relationships between the first three PCs and land surface temperature(LST).In the two spatial regression models,z-statistics for both the spatial lag parameter(ρ) and spatial residual parameter(λ) are significant,indicating the necessity of using spatial regression to replace the OLS regression model,as well as indicating that the spatial error regression model is superior to the spatial lag regression model.Overall,the normalized difference built-up index(NDBI) and road density are the most significant positive contributions to LST.

关 键 词：Urban thermal environment Drive mechanism Spatial regression BEIJING 

分 类 号：P463.3[天文地球—大气科学及气象学]