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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Artis, D.A. and Carnahan, W.H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment,12, 313-329.
Carlson, T.N. and Ripley, D.A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment,62, 241-252.
Chander, G. and Markham, B. (2003). Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. Ieee Transactions on Geoscience and Remote Sensing,41, 2674- 2677.
Cheng, F. and Thiel, K.H. (1995). Delimiting the building heights in a city from the shadow in a panchromatic SPOT-Image - Part 1. Test of forty-two buildings. International Journal of Remote Sensing,16, 409-415.
Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment,37, 35-46.
Dousset, B. and Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS Journal of Photogrammetry and Remote Sensing,58, 43-54.
Liu, R.X., Kuang, J., Gong, Q. and Hou, X.L. (2003). Principal component regression analysis with SPSS. Computer Methods and Programs in Biomedicine,71, 141-147.
Markham, B.L. and Barker, J.K. (1985). Spectral characteristics of the Landsat Thematic Mapper sensors International Journal of Remote Sensing,6, 697-716.
Montgomery, D.C. and Peck, E.A. (1992). Introduction to Linear Regression Analysis. John Wiley & Sons, New York.
Nichol, J. (2005). Remote sensing of urban heat islands by day and night. Photogrammetric Engineering and Remote Sensing,71, 613-621.
Smith, A.J. (2000). Subpixel estimates of impervious surface cover using Landsat TM Imagery. In Geography Department, vol. M.A. Scholarly Paper: University of Maryland, College Park.
Sobrino, J.A., Jimenez-Munoz, J.C. and Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment,90, 434-440.
Song, Y.L. and Zhang, S.Y. (2003). The study on heat island effect in Beijing during last 40 years. Chinese Journal of Eco-Agriculture,11, 126-129.
Streutker, D.R. (2003). Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment,85, 282-289.
Unger, J., Sumeghy, Z., Gulyas, A., Bottyan, Z. and Mucsi, L. (2001). Land-use and meteorological aspects of the urban heat island. Meteorological Applications,8, 189-194.
Voogt, J. A. and Oke, T. R. (1998). Effects of urban surface geometry on remotely-sensed surface temperature. International Journal of Remote Sensing,19, 895-920.
Weng, Q. (2001). A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing,22, 1999-2014.
Weng, Q., Lu, D. and Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment,89, 467-483.
Xiao, R., Ouyang, Z., Li, W., Zhang, Z. and Gregory, T.-J. (2005). A review of the eco-environmental consequences of urban heat islands. Acta Ecologica Sinica, 25, 2055-2060.
Yang, L. M., Xian, G., Klaver, J. M. and Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing,69, 1003-1010.
Yang, X. and Liu, Z. (2005). Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Computers, Environment and Urban Systems,29, 524-540.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Ouyang, Z. et al. (2008). Beijing Urban Spatial Distribution and Resulting Impacts on Heat Islands. In: Hong, SK., Nakagoshi, N., Fu, B., Morimoto, Y. (eds) Landscape Ecological Applications in Man-Influenced Areas. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5488-2_27
Download citation
DOI: https://doi.org/10.1007/1-4020-5488-2_27
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-5487-7
Online ISBN: 978-1-4020-5488-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)