DOI QR코드

DOI QR Code

Identification of the Anthropogenic Land Surface Temperature Distribution by Land Use Using Satellite Images: A Case Study for Seoul, Korea

  • Bhang, Kon Joon (Dept. of Civil Engineering, Kumoh National Institute of Technology) ;
  • Lee, Jin-Duk (Dept. of Civil Engineering, Kumoh National Institute of Technology)
  • Received : 2017.07.18
  • Accepted : 2017.08.30
  • Published : 2017.08.31

Abstract

UHI (Urban Heat Island) is an important environmental issue occurring in highly developed (or urbanized) area such as Seoul Metropolitan City of Korea due to modification of the land surface by man-made structures. With the advance of the remote sensing technique, land cover types and LST (Land Surface Temperature) influencing UHI were frequently investigated describing that they have a positive relationship. However, the concept of land cover considers material characteristics of the urban cover in a comprehensive way and does not provide information on how human activities influence on LST in detail. Instead, land use reflects ways of land use management and human life patterns and behaviors, and explains the relationship with human activities in more details. Using this concept, LST was segmented according to land use types from the Landsat imagery to identify the human-induced heat from the surface and interannual and seasonal variation of LST with GIS. The result showed that the LST intensity of Seoul was greatest in the industrial area and followed by the commercial and residential areas. In terms of size, the residential area could be defined as the major contributor among six urban land use types (i.e., residential, industrial, commercial, transportation, etc.) affecting UHI during daytime in Seoul. For temperature, the industrial area was highest and could be defined as a major contributor. It was found that land use type was more appropriate to understand the human-induced effect on LST rather than land cover. Also, there was no significant change in the interannual pattern of LST in Seoul but the seasonal difference provided a trigger that the human life pattern could be identified from the satellite-derived LST.

Keywords

References

  1. Atwater, M.A. (1972), Thermal effects of urbanization and industrialization in the boundary layer: a numerical study, Boundary-Layer Meteorology, Vol, 3, pp. 229-245. https://doi.org/10.1007/BF02033921
  2. Bhang, K.J. and Park, S.S. (2009), Evaluation of the surface temperature variation with surface settings on the urban heat island in Seoul, South Korea using Landsat-7 ETM+ and SPOT, IEEE Geoscience and Remote Sensing Letters, Vol. 6, pp. 708-712. https://doi.org/10.1109/LGRS.2009.2023825
  3. Bornstein, R.D. (1968), Observation of the urban heat island effect in New York City, Journal of Applied Meteorology, Vol. 7, pp. 575-582. https://doi.org/10.1175/1520-0450(1968)007<0575:OOTUHI>2.0.CO;2
  4. Fu, P. and Weng, Q. (2017), Responses of urban heat island in Atlanta to different land-use scenarios, Theoretical and Applied Climatology, https://doi.org/10.1007/s00704-017- 2160-3.
  5. Gallo, K.P., Mcnab, A.L., Karl, T.R., Brown, J.F., Hood, J.J., and Tarpley, J.D. (1993), The use of NOAA AVHRR data for assessment of the urban heat island effect, Journal of Applied Meteorology, Vol. 32, pp. 899-908. https://doi.org/10.1175/1520-0450(1993)032<0899:TUONAD>2.0.CO;2
  6. Gallo, K.P. and Owen, T.W. (1998), Assessment of urban heat islands: A multi-sensor perspective for the Dallas-Ft. Worth, USA region, Geocarto International, Vol. 13, pp. 35-41.
  7. Gallo, K.P. and Owen, T.W. (1999), Satellite-based adjustments for the urban heat island temperature bias, Journal of Applied Meteorology, Vol. 38, pp. 806-813. https://doi.org/10.1175/1520-0450(1999)038<0806:SBAFTU>2.0.CO;2
  8. Jensen, J.R. (2015), Instroductory Digital Image Processing: A Remote Sensing Perspective, Pearson, New Jersey.
  9. Kim, H.H. (1992), Urban heat island, International Journal of Remote Sensing, Vol. 12, pp. 2319-2336.
  10. Kim, Y. and Baik, J. (2002), Maximum urban heat island intensity in Seoul, Journal of Applied Meteorology, Vol. 41, pp. 651-659. https://doi.org/10.1175/1520-0450(2002)041<0651:MUHIII>2.0.CO;2
  11. Konopacki, S. and Akbari, H. (2002), Energy Savings for Heat Island Reduction Strategies in Chicago and Houston (Including Updates for Baton Rouge, Sacramento, and Salt Lake City), Draft Final Report, LBNL-49638, University of California, Berkeley.
  12. Landsberg, H.E. (1956), The Climate of Towns. Man's Role in Changing the Face of the Earth, The University of Chicago Press, Chicago, Illinois.
  13. Li, L., Li, W., Middel, A., Harland, S.L., Brazel, A.J., and Turner II, B.L. (2016), Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral-demographic-economic factors, Remote Sensing of Environment, Vol. 174, pp. 233-243. https://doi.org/10.1016/j.rse.2015.12.022
  14. Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2008), Remote Sensing and Image Interpretation, John Wiley & Sons.
  15. Lo, C.P., Quattrochi, D.A., and Luvall, J.C. (1997), Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect, International Journal of Remote Sensing, Vol. 18, pp. 287- 304. https://doi.org/10.1080/014311697219079
  16. Oke, T.R. (1982), The energetic basis of the urban heat island, Quarterly Journal of the Royal Meteorological Society, Vol. 108, pp. 1-24.
  17. Oke, T.R., Johnson, G.T., Steyn, D.G., and Watson, I.D. (1991), Simulation of surface urban heat island, Boundary Layer Meteorology, Vol. 56, pp. 339-358. https://doi.org/10.1007/BF00119211
  18. Owen, T.W., Carlson, T.N., and Gillies, R.R. (1998), An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization, International Journal of Remote Sensing, Vol 19, pp. 1663-1681. https://doi.org/10.1080/014311698215171
  19. Qin, Z. and Karnieli, A. (1999), Progress in the remote sensing of land surface temperature and ground emissivity using NOAA$\pm$ AVHRR data, International Journal of Remote Sensing, Vol. 20, pp. 2367-2383. https://doi.org/10.1080/014311699212074
  20. Rao, P.K. (1972), Remote sensing of urban "heat islands" from and environmental satellite, Bulletin of the American Meteorological Society, Vol. 53, pp. 647-648.
  21. Roth, M., Oke, T.R., and Emery, W.J. (1989), Satellitederived urban heat islands from three coastal cities and the utilization of such data in urban climatology, International Journal of Remote Sensing, Vol 10, pp. 1699-1720. https://doi.org/10.1080/01431168908904002
  22. Seo, K.H. and Park, K.J. (2017), Analysis of urban heat island intensity among administrative district using GIS and MODIS Imagery, Journal of the Korean Association of Geographic Information Studies, Vol. 20, No. 2, pp. 1-16. https://doi.org/10.11108/KAGIS.2017.20.2.001
  23. SDI, Seoul Development Institute (2007), Urban heat island mitigation plan of Seoul city, Seoul Policy Focus, Vol. 23.
  24. Sidique, P., Huete, A., and Devadas, R. (2016), Spatiaotemporal mapping and monitoring of urban heat island patterns over Sydney, Australia using MODIS and Landsat-8, Procedings of 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 4-6 July, Guangzhou, China, pp. 217-221.
  25. Stanhill, G. and Kalma, J.D. (1995), Solar dimming and urban heating at Hong Kong, International Journal of Climatology, Vol. 15, pp. 933-941. https://doi.org/10.1002/joc.3370150807
  26. Streutker, D.R. (2002), A remote sensing study of the urban heat island of Houston, Texas, International Journal of Remote Sensing, Vol. 23, pp. 2595-5608. https://doi.org/10.1080/01431160110115023
  27. U.S. Geological Survey (1998), Landsat 7 Science Data Users Handbook, U.S. Geological Survey, https://pubs.er.usgs. gov/publication/7000070 (last date accessed: 18 July 2017).
  28. U.S. Geological Survey (2003), Preliminary assessment of the value of Landsat 7 ETM+ data following Scan Line Corrector malfunction, USGS, NASA, and Landsat 7 Sceince Team.
  29. U.S. Gelological Survey (2017), What are the acquisition schedules for the Landsat satellites? U.S. Department of the Interior, https://landsat.usgs.gov/what-acquisitionschedule-landsat (laste date accessed: 15 July 2017).
  30. 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, Vol. 89, pp. 467-483. https://doi.org/10.1016/j.rse.2003.11.005

Cited by

  1. Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review vol.8, pp.10, 2021, https://doi.org/10.3390/environments8100105