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

• Published in: Progress in natural science Vol. 19; no. 12; pp. 1757 - 1763
• Main Authors: Meng, Dan, Li, Xiaojuan, Zhao, Wenji, Gong, Huili
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 10.12.2009; 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
• Subjects: Beijing; Drive mechanism; Spatial regression; Urban thermal environment; 个人电脑; 回归模型; 城市热环境; 归一化植被指数; 空间误差; 陆地表面温度; 驱动机制; Beijing; Drive mechanism; Spatial regression; Urban thermal environment
• ISSN: 1002-0071
• DOI: 10.1016/j.pnsc.2009.07.005

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.