Advertisement

Beijing Urban Spatial Distribution and Resulting Impacts on Heat Islands

  • Z. Ouyang
  • R.B. Xiao
  • E.W. Schienke
  • W.F. LI
  • X. Wang
  • H. Miao
  • H. Zheng
Chapter

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.

Keywords

Normalize Difference Vegetation Index Geographic Information System Thematic Mapper Land Surface Temperature Urban Heat Island 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Artis, D.A. and Carnahan, W.H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment,12, 313-329.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment,37, 35-46.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.PubMedCrossRefGoogle Scholar
  8. Markham, B.L. and Barker, J.K. (1985). Spectral characteristics of the Landsat Thematic Mapper sensors International Journal of Remote Sensing,6, 697-716.CrossRefGoogle Scholar
  9. Montgomery, D.C. and Peck, E.A. (1992). Introduction to Linear Regression Analysis. John Wiley & Sons, New York.Google Scholar
  10. Nichol, J. (2005). Remote sensing of urban heat islands by day and night. Photogrammetric Engineering and Remote Sensing,71, 613-621.Google Scholar
  11. 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.Google Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.Google Scholar
  14. Streutker, D.R. (2003). Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment,85, 282-289.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.Google Scholar
  20. 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.Google Scholar
  21. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Z. Ouyang
    • 1
  • R.B. Xiao
    • 1
  • E.W. Schienke
    • 2
  • W.F. LI
    • 1
  • X. Wang
    • 1
  • H. Miao
    • 1
  • H. Zheng
    • 1
  1. 1.National Key Lab of Systems EcologyResearch Center for Eco-Environmental Sciences Chinese Academy of SciencesHaidian DistrictChina
  2. 2.Dartment of Science and Technology StudiesRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations