DOI QR코드

DOI QR Code

Analysis of the Effect of Heat Island on the Administrative District Unit in Seoul Using LANDSAT Image

LANDSAT영상을 이용한 서울시 행정구역 단위의 열섬효과 분석

  • 이경일 (고려대학교 환경생태공학과) ;
  • 류지은 (고려대학교 환경GIS/RS센터) ;
  • 전성우 (고려대학교 환경생태공학과) ;
  • 정휘철 (한국 환경정책.평가연구원) ;
  • 강진영 (제주연구원)
  • Received : 2017.07.29
  • Accepted : 2017.10.11
  • Published : 2017.10.30

Abstract

The increase in the rate of industrialization due to urbanization has caused the Urban Heat Island phenomenon where the temperature of the city is higher than the surrounding area, and its intensity is increasing with climate change. Among the cities where heat island phenomenon occurs, Seoul city has different degree of urbanization, green area ratio, energy consumption, and population density in each administrative district, and as a result, the strength of heat island is also different. So It is necessary to analyze the difference of Urban Heat Island Intensity by administrative district and the cause. In this study, the UHI intensity of the administrative gu and the administrative dong were extracted from the Seoul metropolitan area and the differences among the administrative districts were examined. and linear regression analysis were conducted with The variables included in the three categories(weather condition, anthropogenic heat generation, and land use characteristics) to investigate the cause of the difference in heat UHI intensity in each administrative district. As a result of analysis, UHI Intensity was found to be different according to the characteristics of administrative gu, administrative dong, and surrounding environment. The difference in administrative dong was larger than gu unit, and the UHI Intensity of gu and the UHI Intensity distribution of dongs belonging to the gu were also different. Linear regression analysis showed that there was a difference in heat island development intensity according to the average wind speed, development degree, Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) value. Among them, the SAVI and NDBI showed a difference in value up to the dong unit and The creation of a wind route environment for the mitigation of the heat island phenomenon is necessary for the administrative dong unit level. Therefore, it is considered that projects for mitigating heat island phenomenon such as land cover improvement plan, wind route improvement plan, and green wall surface plan for development area need to consider administrative dongs belonging to the gu rather than just considering the difference of administrative gu units. The results of this study are expected to provide the directions for urban thermal environment design and policy development in the future by deriving the necessity of analysis unit and the factors to be considered for the administrative city unit to mitigate the urban heat island phenomenon.

도시화로 인한 산업비율 증가는 도시의 기온이 주변지역보다 높아지는 도시 열섬(Urban Heat Island)현상을 유발하였으며 기후변화와 함께 그 강도가 점점 증가하고 있다. 열섬현상이 발생하는 여러 도시 중에서도 서울시는 각 구 또는 동별로 시가화 정도, 녹지율, 에너지소비량, 인구밀도가 다 다르기 때문에 열섬현상의 강도역시 다르다. 따라서 본 연구에서는 서울특별시를 대상으로 행정구, 행정동 단위 열섬현상강도(UHI Intensity)를 추출하여 행정구역별 차이를 확인하고 세 가지 범주(기상상태, 인위적 열 발생, 토지이용특성)에 포함되는 변수들과 선형회귀분석을 실시하여 각 행정구역의 열섬현상강도 차이의 원인을 살펴보았다. 분석결과 UHI Intensity는 행정구별, 행정동별 특징 및 주변 환경에 따른 차이가 존재하며 행정동 단위에서 차이가 더 크게 나타났고 구의 UHI Intensity와 구에 속한 동의 UHI Intensity분포 또한 차이가 존재하였다. 선형회귀분석결과 평균 풍속, 개발정도, 토양보정식생지수(SAVI), 정규화시가지지수(NDBI) 값이 행정구역별 열섬현상강도 차이를 발생시키는 유의한 변수로 나타났다. 토양보정식생지수와 정규화시가지지수는 행정동단위 까지 그 값의 차이가 존재하는 것으로 나타났으며, 열섬현상 완화를 위한 바람길 환경 조성은 행정동 차원에서의 시행이 필요한 사항이다. 따라서 토지피복 개선 계획, 바람길 조성 계획, 개발지역에 대한 벽면 녹화계획 등 열섬현상 완화를 위한 사업들은 행정구 단위의 차이만을 고려하기보단 구안에 속한 행정동까지 고려할 필요가 있을 것으로 판단된다. 본 연구의 결과는 도시 도시열섬현상 완화를 위해 행정동 단위에서의 분석의 필요성과 고려해야할 변수를 도출하여 향후 도시 열환경 설계 및 정책 개발 시 접근방향을 제공할 것으로 기대한다.

Keywords

References

  1. Kwon, Y. W., S. Y. Kim, and J. H. Park, 2016. Understanding the city, pybook, Seoul, Korea.
  2. Kim, S. W., 2007. Fundamentals of statstics, Hakjisa, Seoul, Korea.
  3. Kim, Y. J., 2011. A Study on the Distribution and Intensity of Urban Heat Island in Seoul, Konkuk University, Seoul, Korea. (in Korean with English abstract).
  4. Kim, Y. H. and J. J. Baik, 2002. Maximum urban heat island intensity in Seoul, Journal of Applied Meteorology, 41(6): 651-659. https://doi.org/10.1175/1520-0450(2002)041<0651:MUHIII>2.0.CO;2
  5. Suh, J. E., B. Y. Park, S. G. Kim, T. Y. Kim, and S. B. Leigh, 2009. An Analysis of Elements that Cause Heat Island in Urban Area -Focused on Seoul Metropolitan Area-, Proc. of Proceeding of Autumn Annual Conference of the Architectural Institute of Korea Planning & Design 2011, Gyeongsan, Korea, Oct. 29, vol. 1, pp. 581-584 (in Korean with English abstract).
  6. Ahn, J. S., J. D. Hwang, M. H. Park, and Y. S. Suh, 2012. Estimation of Urban Heat Island Potential Based on Land Cover Type in Busan Using Lnadsat-7 ETM+ and AWS Data, Journal of the Korean Association of Geographic Information Studies, 15(4): 65-77 (in Korean with English abstract). https://doi.org/10.11108/kagis.2012.15.4.065
  7. Oh, K. S. and J. J. Hong, 2005. The Relationship between Urban Spatial Elements and the Urban Heat Island Effect, Journal of the Urban Design Institute of Korea Urban Design, 6(1): 47-63 (in Korean with English abstract).
  8. Yoon, Y. H., 2002. The Scale of Park Influence Temperature Change, Journal of the Korean Institute of Forest Recreation, 6(1): 1-7 (in Korean with English abstract).
  9. Yoon, S., J. H. Ryu, J. E. Min, Y. H. Ahn, S. Lee, and J. S. Won, 2009. Monitoring of the Sea Surface Temperature in the Saemangeum Sea Area Using the Thermal Infrared Stellite Data, Korean Journal of Remote Sensing, 25(4): 339-357 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2009.25.4.339
  10. Lee, S. H. and J. J. Jeong, 2007. Study of urban extraction using NDVI and NDBI, Proc. of 2007 GIS Joint Spring Conference, Korea, Jun. 15, pp. 156-161.
  11. Lee, S. M., J. Jang, and C. H. Oh, 2009. Characteristics of the Heat Island according to the Land Use Type in Seoul, Proc. of 2009 Korean Society of Environment and Ecology Conference, Jeonju, Korea, Oct. 16, vol. 2, pp. 249-252.
  12. Lee, N. Y., Y. S. Cho, and J. Y. Lim, 2014. Analysis of mortality change in vulnerable classes of climate change due to heat, Health and Social Welfare Review, 34(1): 456-484. https://doi.org/10.15709/hswr.2014.34.1.456
  13. Jo, H. Y., J. S. Lee, S. H. Baek, and H. C. Yun, 2015. Time Series Land Cover Change Detection of Urban using Landsat Satellite Images, Proc. of 2015 Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Conference, Changwon, Korea, Apr. 23-24, pp. 261-262 (in Korean with English abstract).
  14. Jee, J. B. and Y. J. Choi, 2014. Conjugation of Landsat Data for Analysis of the Land Surface Properties in Capital Area, Journal of Korean Earth Science, 35(1): 54-68 (in Korean with English abstract). https://doi.org/10.5467/JKESS.2014.35.1.54
  15. Cho, M. J., L. Zhong, and C. W. Lee, 2013. Timeseries Analysis of Pyroclastic Flow Deposit and Surface Temperature at Merapi Volcano in Indonesia Using Landsat TM and ETM+, Korean Journal of Remote Sensing, 29(5): 443-459 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2013.29.5.1
  16. Cho, C. Y., J. B. Jee, M. S. Park, S. H. Park, and Y. J. Choi, 2016. Comparison of Surface Temperatures between Thermal Infrared Image and Landsat 8 Satellite, Journal of Korean Society for Atmospheric Environment, 32(1): 46-56 (in Korean with English abstract). https://doi.org/10.5572/KOSAE.2016.32.1.046
  17. Choe, Y. J., G. W. Lee, J. Y. Han, and J. H. Yom, 2016. Correlation Analysis of the Distribution of Population Density and Land Surface Temperature - With Emphasis on Seoul Metropolitan -, Proc. of 2016 Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Conference, Suwon, Korea, Apr. 28-29, pp. 210-214.
  18. The Seoul Institute, 2017. Proc. of Direction of Seoul City Policy in response to the heatwave, Seoul, Korea, Jun. 7.
  19. Weather data release portal, 2015. Disaster weather observation data, https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36, Accessed at Apr. 5, 2017.
  20. Ackerman, B., 1985. Temporal march of the Chicago heat island, Journal of Climate and Applied Meteorology, 24(6): 547-554. https://doi.org/10.1175/1520-0450(1985)024<0547:TMOTCH>2.0.CO;2
  21. Artis, D. A. and W. H. Carnahan, 1982. Survey of emissivity variability in thermography of urban areas, Remote Sensing of Environment, 12(4): 313-329. https://doi.org/10.1016/0034-4257(82)90043-8
  22. Du, H., D. Wang, Y. Wang, X. Zhao, F. Qin, H. Jiang, and Y. Cai, 2016. Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration, Science of The Total Environment, 571: 461-470. https://doi.org/10.1016/j.scitotenv.2016.07.012
  23. dos Santos, A. R., F. S. de Oliveira, A. G. da Silva, J. M. Gleriani, W. Goncalves, G. L. Moreira, F. G. Silva, E. R. F. Branco, M. M. Moura, R. G. da Silva, R. S. Juvanhol, K. B. de Souza, C. A. A. S. Ribeiro, V. T. de Queiroz, A. V. Costa, A. S. Lorenzon, G. F. Domingues, G. E. Marcatti, N. L. M. de Castro, R. T. Resende, D. E. Gonzales, L. A. de Almeida Telles, T. R. Teixeira, G. M. A. D. A. dos Santos, and P. H. S. Mota, 2017. Spatial and temporal distribution of urban heat islands, Science of The Total Environment, 605: 946-956.
  24. Elsayed, I. S., 2012. Effects of population density and land management on the intensity of urban heat islands: A case study on the city of Kuala Lumpur, Malaysia, Application of geographic information systems, InTech.
  25. Gedzelman, S. D., S. Austin, R. Cermak, N. Stefano, S. Partridge, S. Quesenberry, and D. A. Robinson, 2003. Mesoscale aspects of the urban heat island around New York City, Theoretical and Applied Climatology, 75(1): 29-42. https://doi.org/10.1007/s00704-002-0724-2
  26. Huete, A. R., 1988. A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25(3): 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  27. Hwang, R. L., C. Y. Lin, and K. T. Huang, 2016. Spatial and temporal analysis of urban heat island and global warming on residential thermal comfort and cooling energy in Taiwan, Energy and Buildings, 152: 804-812. https://doi.org/10.1016/j.enbuild.2016.11.016
  28. Jimenez-Munoz, J. C. and J. A. Sobrino, 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data, Journal of Geophysical Research: Atmospheres, 108(D22): 2-1-2-9.
  29. Lowe, S. A., 2016. An energy and mortality impact assessment of the urban heat island in the US, Environmental Impact Assessment Review, 56: 139-144. https://doi.org/10.1016/j.eiar.2015.10.004
  30. Mahmoudabadi, E., A. Karimi, G. H. Haghnia, and A. Sepehr, 2017. Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran, Environmental Monitoring and Assessment, 189(10): 500. https://doi.org/10.1007/s10661-017-6197-7
  31. Oke, T. R., 1982. The energetic basis of the urban heat island, Quarterly Journal of the Royal Meteorological Society, 108(455): 1-24. https://doi.org/10.1002/qj.49710845502
  32. Offerle, B., C. S. B. Grimmond, K. Fortuniak, K. Klysik, and T. R. Oke, 2006. Temporal variations in heat fluxes over a central European city centre, Theoretical and Applied Climatology, 84(1): 103-115. https://doi.org/10.1007/s00704-005-0148-x
  33. Peng, S., S. Piao, P. Ciais, P. Friedlingstein, C. Ottle, F. M. Breon, and R. B. Myneni, 2012. Surface urban heat island across 419 global big cities, Environmental Science & Technology, 46(2): 696-703. https://doi.org/10.1021/es2030438
  34. Qin, Z., A. Karnieli, and P. Berliner, 2001. A monowindow algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region, International Journal of Remote Sensing, 22(18): 3719-3746. https://doi.org/10.1080/01431160010006971
  35. Rouse Jr, J., R. H. Haas, J. A. Schell, and D. W. Deering, 1974. Monitoring vegetation systems in the Great Plains with ERTS, NASA, Washington, USA.
  36. Streutker, D. R., 2002. A remote sensing study of urban heat island of Houston, texas, International Journal of Remote Sensing, 23(13): 2595-2608. https://doi.org/10.1080/01431160110115023
  37. Soytas, U. and R. Sari, 2009. Energy consumption, economic growth, and carbon emissions: challenges faced by an EU candidate member, Ecological Economics, 68(6): 1667-1675. https://doi.org/10.1016/j.ecolecon.2007.06.014
  38. Stempihar, J., T. Pourshams-Manzouri, K. Kaloush, and M. Rodezno, 2012. Porous asphalt pavement temperature effects for urban heat island analysis. Transportation Research Record, Journal of the Transportation Research Board, (2293): 123-130.
  39. Son, N. T., C. F. Chen, C. R. Chen, B. X. Thanh, and T. H. Vuong, 2017. Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data, Sustainable Cities and Society, 30: 150-161. https://doi.org/10.1016/j.scs.2017.01.009
  40. Taha, H., 1997. Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat, Energy and Buildings, 25(2): 99-103. https://doi.org/10.1016/S0378-7788(96)00999-1
  41. Tan, M. and X. Li, 2015. Quantifying the effects of settlement size on urban heat islands in fairly uniform geographic areas, Habitat International, 49: 100-106. https://doi.org/10.1016/j.habitatint.2015.05.013
  42. Yavuzturk, C., K. Ksaibati, and A. D. Chiasson, 2005. Assessment of temperature fluctuations in asphalt pavements due to thermal environmental conditions using a two-dimensional, transient finite-difference approach, Journal of Materials in Civil Engineering, 17(4): 465-475. https://doi.org/10.1061/(ASCE)0899-1561(2005)17:4(465)
  43. Zanter, K., 2016. Landsat 8 (L8) data users handbook Version 2.0, USGS, Virginia, USA.
  44. Zha, Y., J. Gao, and S. Ni, 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, 24(3): 583-594. https://doi.org/10.1080/01431160304987
  45. Zhang, J., Y. Wang, and Y. Li, 2006. A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6, Computers & Geosciences, 32(10): 1796-1805. https://doi.org/10.1016/j.cageo.2006.05.001
  46. Zhang, H., Z. F. Qi, X. Y. Ye, Y. B. Cai, W. C. Ma, and M. N. Chen, 2013. Analysis of land use/ land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China, Applied Geography, 44: 121-133. https://doi.org/10.1016/j.apgeog.2013.07.021
  47. Zhang, Y., L. Chen, Y. Wang, L. Chen, F. Yao, P. Wu, B. Wang, Y. Li, T. Zhou, and T. Zhang, 2015. Research on the contribution of urban land surface moisture to the alleviation effect of urban land surface heat based on Landsat 8 data, Remote Sensing, 7(8): 10737-10762. https://doi.org/10.3390/rs70810737