DOI QR코드

DOI QR Code

Analysis of Thermal Heat Island Potential by Urbanization Using Landsat-8 Time-series Satellite Imagery

Landsat-8 시계열 위성영상을 활용한 도심지 확장에 따른 열섬포텐셜 분석

  • Kim, Taeheon (Dept. of Geospatial Information, Kyungpook National University) ;
  • Lee, Won Hee (School of Convergence & Fusion System Engineering, Kyungpook National University) ;
  • Han, Youkyung (School of Convergence & Fusion System Engineering, Kyungpook National University)
  • Received : 2018.07.27
  • Accepted : 2018.08.29
  • Published : 2018.08.31

Abstract

As the urbanization ratio increases, the heat environment in cities is becoming more important due to the urban heat island. In this study, the heat island spatial analysis was calculated and conducted for analysis of urban thermal environment of Sejong city, which was launched in 2012 and has been developed rapidly. To analyze the ratio and change rate of urban area, a multi temporal land cover map (2013 to 2015 and 2017) of study area is generated based on Landsat-8 OLI/TIRS (Operational Land Imager / Thermal Infrared Sensor) satellite imagery. Then, we select an TIR (Thermal Infrared) band from the two TIR bands provided by the Landsat-8, which is used for calculating the heat island potential, through the accuracy evaluation of the brightness temperature and AWS (Automatic Weathering Station) data. Based on the selected band and surface emissivity, land surface temperature is calculated and the estimated heat island potential change is analyzed. As a result, the land surface temperature of the high ratio and change rate of urban area was significantly higher than the surrounding area around $3^{\circ}C$ to $4^{\circ}C$, and the heat island potential was also higher around $4^{\circ}C$ to $5^{\circ}C$. However, the heat island phenomenon was alleviated in urban areas with high rate of change that also show high green area ratio. Therefore, we demonstrated that dense urban area increases the possibility of inducing heat island, but it can mitigate the heat island through green areas.

우리나라의 도시화 비율이 증가함에 따라 도시열섬으로 인한 도시 열 환경의 중요성이 증대되고 있다. 본 연구에서는 2012년에 출범하여 급속도로 발전을 이룬 세종특별자치시의 도시 열 환경 분석을 위해 열섬포텐셜을 이용하였다. 우선 도심지의 비율 및 변화율을 분석하기 위해 Landsat-8 OLI/TIRS 위성영상을 기반으로 연구지역의 시계열 토지피복도(2013년~2015년, 2017년)를 생성하였다. 그리고 취득된 위성영상에서 제공하는 두 가지 열적외선 밴드에서 산출된 밝기온도와 자동기상관측망 자료와의 정확도 평가를 통해 연구에 활용할 밴드를 선정하였다. 선정된 밴드와 지표면 방사율을 고려하여 지표면온도를 산출하였으며, 이를 기반으로 산출된 열섬포텐셜 변화분석을 수행하였다. 분석결과, 연구지역의 행정구역별 도심지 변화율이 크게 관측되는 지역의 지표면온도는 주변지역 보다 $3^{\circ}C{\sim}4^{\circ}C$ 높고, 열섬포텐셜 또한 $4^{\circ}C{\sim}5^{\circ}C$ 높게 관측되었다. 하지만 도심지 변화율이 크고 녹지의 비율이 높은 지역에서는 열섬현상이 완화되는 경향을 보였다. 이를 통해 면적대비 도심지가 차지하는 비율이 높아지면 열섬을 유발할 가능성이 증가하지만 녹지를 통해 열섬을 완화 시킬 수 있다는 것을 알 수 있었다.

Keywords

References

  1. Aflaki, A., Mirnezhad, M., Ghaffarianhoseini, A., Ghaffarianhoseini, A., Omrany, H., Wang, Z.H., and Akbari, H. (2017), Urban heat island mitigation strategies: A state-of-the-art review on Kuala Lumpur, Singapore and Hong kong, Cities, Vol. 62, pp. 131-145. https://doi.org/10.1016/j.cities.2016.09.003
  2. Ahn, J.S., Hwang, J.D., Park, M.H., and Suh, Y.S. (2012), Estimation of urban heat island potential based on land cover type in Busan using Landsat-7ETM+ and AWS Data, Journal of the Korean Association of Geographic Information Studies, Vol. 15, No. 4, pp. 65-77. (in Korean with English abstract) https://doi.org/10.11108/kagis.2012.15.4.065
  3. Ahn, J.S., Kim, H.D., and Kim, S.W. (2007), Estimation of urban heat island potential based on land use in summertime of Daegu, Journal of the Environmental Sciences, Vol. 16, No. 1, pp. 65-71. (in Korean with English abstract) https://doi.org/10.5322/JES.2007.16.1.065
  4. Chen, X.L., Zhao, H.M., Li, P.X., and Yin, Z.Y. (2006), Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Remote Sensing of Environment, Vol. 104, No. 2, pp. 133-146. https://doi.org/10.1016/j.rse.2005.11.016
  5. Choi, J.W., Byun. Y.G., Kim, Y.I., and Yu, K.Y. (2006), Support vector machine classification of hyperspectral image using spectral similarity kernel, Journal of Korean Society for Geospatial Information System, Vol. 14, No. 4, pp. 71-77. (in Korean with English abstract)
  6. Hashida, S., Omori, H., and AtsumasaYoshida, S.K. (2017), Heat island mitigation effects of various ground cover materials in and around Yokohama campus, tokyo city university, Journal of Heat Island Institute International, Vol. 12, pp. 54-60.
  7. Huang, C., Davis, L.S., and Townshend, J.R.G. (2002), An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, Vol. 23, No. 4, pp. 725-749. https://doi.org/10.1080/01431160110040323
  8. Ichinose, T., Hanaki, K., and Matsuo, T. (1994), Analyses on geographical distribution of urban anthropogenic heat based on very precise geographical information, Environmental Engineering Research, Vol. 31, pp. 263-273.
  9. Iino, A. and Hoyano, A. (1996), Development of a method to predict the heat island potential using remote sensing and GIS data, Energy and Buildings, Vol. 23, No. 3, pp. 199-205. https://doi.org/10.1016/0378-7788(95)00945-0
  10. Sejong City (2018), Monthly statistics of Sejong city on May 2018, Sejong Special Self-governing City, Republic of Korea, http://www.sejong.go.kr/stat.do (last date accessed: 24 June 2018).
  11. Kim, G.H., Lee, Y.G., Kim, J.H., Choi, H.W., and Kim, B.J. (2018), Analysis of the cooling effects in urban green areas using the Landsat 8 satellite data, Korean Journal of Remote Sensing, Vol. 34, No. 2, pp. 167-178. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2018.34.2.1.1
  12. Kim, H.O. and Yeom, J.M. (2012), Effect of the urban land cover types on the surface temperature: case study of Ilsan new city, Korean Journal of Remote Sensing, Vol. 28, No. 2, pp. 203-214. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2012.28.2.203
  13. Kim, H.S., Seok, H.B., and Kim, Y.K. (2014a), A study on the change of the urban heat island structure in Busan metropolitan area Korea, Journal of Environmental Science International, Vol. 23, No. 11, pp. pp. 1807-1820. (in Korean with English abstract) https://doi.org/10.5322/JESI.2014.23.11.1807
  14. Kim, M.K., Kim, S.P., Kim, N.H., and Sohn, H.G. (2014b), Urbanization and urban heat island analysis using Landsat imagery: Sejong city as a case study, Jounal of the Korean Society of Civil Engineers, Vol. 34, No. 3, pp. 1033-1041. (in Korean with English abstract) https://doi.org/10.12652/Ksce.2014.34.3.1033
  15. Ku, C.Y. (2014), Development of land surface temperature map generation method with Landsat 8 TIRS imagery and automatic weather system data, Journal of the Korean Cartographic Association, Vol. 14, No. 1, pp. 17-27. (in Korean with English abstract)
  16. Lee, S.H., Ahn, J.S., Kim, H.D., and Hwang, S.J. (2009), Comparison study on the estimation algorithm of land surface temperature for MODIS data at the Korean peninsula, Journal of Environmental Science International, Vol. 18, No. 4, pp. 355-367. (in Korean with English abstract) https://doi.org/10.5322/JES.2009.18.4.355
  17. Maclachlan, A., Biggs, E., Roberts, G., and Boruff, B. (2017), Urbanisation-induced land cover temperature dynamics for sustainable future urban heat island mitigation, Urban Science, Vol. 1, No. 4, pp. 38. https://doi.org/10.3390/urbansci1040038
  18. Onishi, A., Cao, X., Ito, T., Shi, F., and Imura, H. (2010), Evaluating the potential for urban heat-island mitigation by greening parking lots, Urban forestry & Urban greening, Vol. 9, No. 4, pp. 323-332. https://doi.org/10.1016/j.ufug.2010.06.002
  19. Park, H.M. and Baek, T.K. (2009), Progress and land-use characteristics of urban sprawl in Busan metropolitan city using remote sensing and GIS, Journal of the Korean Association of Geographic Information Studies, Vol. 12, No. 2, pp. 23-33. (in Korean with English abstract)
  20. Park, J.C. and Kim, J.S. (2014), Application and limitation of national land cover change detection-a case study on Chungcheongnam-do, Journal of the Association of Korean Photo-Geographers, Vol. 24, No. 1, pp. 19-34. (in Korean with English abstract)
  21. USGS (2016), Landsat 8 data users handbook section 5, United States Geological Survey, http://landsat.usgs.gov/landsat-8-l8-data-users-handbook/ (last date accessed: 1 May 2018).
  22. Van de Griend, A.A. and Owe, M. (1993), On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces, International Journal of Remote Sensing, Vol. 14, No.6, pp. 1119-1131. https://doi.org/10.1080/01431169308904400
  23. 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, No. 4, pp. 467-483. https://doi.org/10.1016/j.rse.2003.11.005
  24. Yale (2016), Yale guide to Landsat 8 image processing, Yale University, http://surfaceheat.sites.yale.edu/understanding-landsat-8/ (last date accessed: 16 May 2018).
  25. Zhang, J., Wang, Y., and Li, Y. (2006), A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6, Computers & Geosciences, Vol. 32, No. 10, pp. 1796-1805. https://doi.org/10.1016/j.cageo.2006.05.001