Land surface temperature variation and major factors in Beijing, China

Land surface temperature (LST) is a significant parameter in urban environmental analysis. Current research mainly focuses on the impact of land-use and land-cover (LULC) on LST. Seldom has research examined LST variations based on the integration of biophysical and demographic variables, especially for a rapidly developing city such as Beijing, China. This study combines the techniques of remote sensing and geographic information system (GIS) to detect the spatial variation of LST and determine its quantitative relationship with several biophysical and demographic variables based on statistical modeling for the central area of Beijing. LST and
LULC data were retrieved from a Landsat Thematic Mapper (TM) image. Building heights were delimited from the shadows identified on a panchromatic SPOT image. The integration of LULC and census data was further applied to retrieve gridbased population density. Results indicate that the LST pattern was non-symmetrical and non-concentric with high temperature zones clustered towards the south of the central axis and within the fourth ring road. The percentage of forest, farmland,
and water per grid cell were found to be most significant factors, which can explain 71.3 percent of LST variance. Principal component regression analysis shows that LST was positively correlated with the percentage of low density builtup, high density built-up, extremely-high buildings, low buildings per grid cell, and population density, but was negatively correlated with the percentage of forest, farmland, and water bodies per grid cell. The findings of this study can be applied as the theoretical basis for improving urban planning for mitigating the effects of urban heat islands.