Abstract
The physical characteristics of the ground surface are regarded as the main factors in the urban heat island phenomena. Over two seasons, this study spatially and quantitatively examines the influence of urban surface features on land surface temperature in Beijing, China through the use of remote sensing (RS) combined with geographic information systems (GIS). Primary data sources include: Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), SPOT, QuickBird and Beijing Road vector map. Variables extracted and considered in the study are: (1) percent (surface) imperviousness, (2) Normalized Difference Vegetation Index (NDVI), (3) ratio of water bodies, (4) ratio of tall-building areas, and (5) road density. Results indicate that Beijing’s urban spatial pattern presents a typical concentric distribution: NDVI values increase, but impervious surface and tall-building area decrease from inner city to outskirts. The land surface temperature (LST) pattern is non-symmetrical and nonconcentric, with relatively higher temperature zones clustered towards the south of the central axis and within the fourth ring road. Principal component regressions indicate that a strong linear relationship exists between LST and the studied urban parameters, such as percent imperviousness, NDVI, ratio of water cover, tall building and road density, though they do exhibit seasonal variations. In the August image, the percentage of impervious surfaces exhibits the largest positive correlation with LST, which is able to explain 81.7% of LST variance. NDVI follows in impact with a strong negative correlation. For analysis in May, with an R2 of 0.720, NDVI and water are the two features, which most negatively correlate with LST. As a practical result, these findings can be used to inform future design measures for mitigating urban heat island effects.
Published Version
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