Journal of Korean Society for Geospatial Information Science (대한공간정보학회지)

Korea Spatial Information Society (대한공간정보학회)
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Analysis of Urban Heat Island Effect Using Time Series of Landsat Images and Annual Temperature Cycle Model

시계열 Landsat TM 영상과 연간 지표온도순환 모델을 이용한 열섬효과 분석

  • Hong, Seung Hwan (Dept. of Civil & Environmental Engineering, Yonsei University) ;
  • Cho, Han Jin (Dept. of Civil & Environmental Engineering, Yonsei University) ;
  • Kim, Mi Kyeong (Dept. of Civil & Environmental Engineering, Yonsei University) ;
  • Sohn, Hong Gyoo (Dept. of Civil & Environmental Engineering, Yonsei University)
  • 홍승환 (연세대학교 토목환경공학과) ;
  • 조한진 (연세대학교 토목환경공학과) ;
  • 김미경 (연세대학교 토목환경공학과) ;
  • 손홍규 (연세대학교 토목환경공학과)
  • Received : 2015.03.03
  • Published : 2015.03.31
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Abstract

Remote sensing technology using a multi-spectral satellite imagery can be utilized for the analysis of urban heat island effect in large area. However, weather condition of Korea mostly has a lot of clouds and it makes periodical observation using time-series of satellite images difficult. For this reason, we proposed the analysis of urban heat island effect using time-series of Landsat TM images and ATC model. To analyze vegetation condition and urbanization, NDVI and NDBI were calculated from Landsat images. In addition, land surface temperature was calculated from thermal infrared images to estimate the parameters of ATC model. Furthermore, the parameters of ATC model were compared based on the land cover map created by Korean Ministry of Environment to analyze urban heat island effect relating to the pattern of land use and land cover. As a result of a correlation analysis between calculated spectral indices and parameters of ATC model, MAST had high correlation with NDVI and NDBI (-0.76 and 0.69, respectively) and YAST also had correlation with NDVI and NDBI (-0.53 and 0.42, respectively). By comparing the parameters of ATC model based on land cover map, urban area had higher MAST and YAST than agricultural land and grassland. In particular, residential areas, industrial areas, commercial areas and transportation facilities showed higher MAST than cultural facilities and public facilities. Moreover, residential areas, industrial areas and commercial areas had higher YAST than the other urban areas.

다중분광 위성영상을 이용한 원격탐측 기술은 광범위한 지역의 열섬효과 분석에 있어 유용하게 활용될 수 있다. 하지만 우리나라와 같이 구름이 많은 기상조건은 위성영상을 활용한 주기적인 관측을 어렵게 한다. 이에 본 연구에서는 시계열 Landsat 영상과 ATC 모델을 이용한 열섬현상 분석 방법을 제안하였다. 식생상태와 도시화정도를 분석하기 위하여 Landsat 영상으로부터 NDVI와 NDBI를 산출하였으며 ATC 모델의 파라미터 추정을 위하여 Landsat 열적외선 영상으로부터 지표온도를 산출하여 활용하였다. 또한 토지 피복 및 이용형태에 따른 열섬현상 분석을 위해 환경부에서 제공하는 토지피복도를 기반으로 ATC 모델의 파라미터를 비교하였다. 산출한 분광지수와 ATC 모델의 파라미터 간의 상관관계를 분석한 결과 ATC 모델의 MAST는 NDVI 및 NDBI와 각각 -0.76, 0.69 의 강한 상관관계를 보였으며, YAST는 NDVI 및 NDBI와 각각 -0.53, 0.42의 상관관계를 나타냈다. 토지 피복 및 이용형태에 따라 ATC 모델의 파라미터를 비교한 결과 도시 지역에서의 MAST와 YAST가 도시 주변의 농업지역, 초지 등에 비해 높게 나타나는 것을 확인하였다. 또한 도시 지역 내에서 주거지역, 산업지역, 상업지역, 교통지역이 문화 체육 휴양지역, 공공시설지역에 비해 높은 MAST를 나타나며 주거지역, 산업지역, 상업지역이 다른 도시 지역들보다 높은 YAST 값을 지님을 확인할 수 있었다.

Keywords

References

  1. Ackerman, B., 1985, Temporal march of the Chicago heat island, Journal of Climate and Applied Meteorology, Vol. 24, No. 6, pp. 547-554. https://doi.org/10.1175/1520-0450(1985)024<0547:TMOTCH>2.0.CO;2
  2. Bechtel, B., 2012, Robustness of annual cycle parameters to characterize the urban thermal landscapes, Geoscience and Remote Sensing Letters, IEEE, Vol. 9, No. 5, pp. 876-880. https://doi.org/10.1109/LGRS.2012.2185034
  3. Chander, G. and Markham, B., 2003, Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges, Geoscience and Remote Sensing, IEEE Transactions on, Vol. 41, No. 11, pp. 2674-2677. https://doi.org/10.1109/TGRS.2003.818464
  4. Daejeon Metropolitan City, 2015, http://www.daejeon.go.kr/sta/StaStatisticsFldView.do?ntatcSeq=1042691&menuSeq=&colmn1Cont=&colmn2Cont=&pageIndex=1#.
  5. Göttsche, F. M. and Olesen, F. S., 2001, Modelling of diurnal cycles of brightness temperature extracted from METEOSAT data, Remote Sensing of Environment, Vol. 76, No. 3, pp. 337-348. https://doi.org/10.1016/S0034-4257(00)00214-5
  6. Hua, L. and Wang, M., 2012, Temporal and spatial characteristics of urban heat island of an Estuary city, China, Journal of Computers, Vol. 7, No. 12, pp. 3082-3087.
  7. Jensen, J. R. and Cowen, D. C., 1999, Remote sensing of urban/suburban infrastructure and socioeconomic attributes, Photogrammetric engineering and remote sensing, Vol. 65, pp. 611-622.
  8. Jung, G. S., Koo, S. and Yoo, H. H., 2011, Temperature change analysis for land use zoning using landsat satellite imagery, Journal of the Korean Society for Geospatial Information System, Vol. 19, No. 2, pp. 55-61.
  9. Kang, J. M., Ka, M. S., Lee, S. S. and Park, J. K., 2010, Detection of heat change in urban center using Landsat imagery, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 28, No. 2, pp. 197-206.
  10. 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. https://doi.org/10.7780/kjrs.2012.28.2.203
  11. Kim, M. K., Kim, S. P., Kim, N. H. and Sohn, H. G., 2014, Urbanization and urban heat island analysis using LANDSAT imagery: Sejong city as a case study, Journal of the Korean Society of Civil Engineers, Vol. 34, No. 3, pp. 1033-1041. https://doi.org/10.12652/Ksce.2014.34.3.1033
  12. Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F. and Sobrino, J. A., 2013, Satellite-derived land surface temperature: Current status and perspectives, Remote Sensing of Environment, Vol. 131, pp. 14-37. https://doi.org/10.1016/j.rse.2012.12.008
  13. Ministry of Environment, http://egis.me.go.kr.
  14. More, J. J. 1978, The Levenberg-Marquardt algorithm: implementation and theory Numerical analysis (pp. 105-116): Springer.
  15. Myeong, S., Nowak, D., Hopkins, P. and Brock, R., 2001, Urban cover mapping using digital, highspatial resolution aerial imagery, Urban Ecosystems, Vol. 5, No. 4, pp. 243-256. https://doi.org/10.1023/A:1025687711588
  16. Na, S. I. and Park, J. H., 2012, Assessment of the urban heat island effects with LANDSAT and KOMPSAT-2 data in Cheongju, Journal of Agricultural Science, Vol. 39, No. 1, pp. 87-95.
  17. NASA User Handbook, 2013, Landsat 7 science data users handbook, http://landsathandbook.gsfc.nasa.gov/
  18. Oke, T. R., 1973, City size and the urban heat island, Atmospheric Environment (1967), Vol. 7, No. 8, pp. 769-779. https://doi.org/10.1016/0004-6981(73)90140-6
  19. Purevdorj, T., Tateishi, R., Ishiyama, T. and Honda, Y., 1998, Relationships between percent vegetation cover and vegetation indices, International Journal of Remote Sensing, Vol. 19, No. 18, pp. 3519-3535. https://doi.org/10.1080/014311698213795
  20. Quan, H. C. and Lee, B. G., 2009, Analysis of relationship between LST and NDVI using Landsat TM images on the city areas of Jeju island, Journal of the Korean Society for Geospatial Information System, Vol. 17, No. 4, pp. 39-44.
  21. Villa, P., 2012, Mapping urban growth using Soil and Vegetation Index and Landsat data: The Milan (Italy) city area case study, Landscape and Urban Planning, Vol. 107, No. 3, pp. 245-254. https://doi.org/10.1016/j.landurbplan.2012.06.014
  22. Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K., 2010, Probability and statistics for engineers and scientists, 9/E, Pearson, pp. 518-520.
  23. Weng, Q., 2009, Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 64, No. 4, pp. 335-344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
  24. Weng, Q. and Fu, P., 2014, Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data, Remote Sensing of Environment, Vol. 140, pp. 267-278. https://doi.org/10.1016/j.rse.2013.09.002
  25. Zha, Y., Gao, J. and Ni, S., 2003, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, Vol. 24, No. 3, pp. 583-594. https://doi.org/10.1080/01431160304987
  26. Zhu, Z. and Woodcock, C. E., 2012, Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, Vol. 118, pp. 83-94. https://doi.org/10.1016/j.rse.2011.10.028