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Chinese Geographical Science

, Volume 29, Issue 6, pp 905–916 | Cite as

Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

  • Yuanzheng Li
  • Lan Wang
  • Liping Zhang
  • Min LiuEmail author
  • Guosong Zhao
Article
  • 5 Downloads

Abstract

This study aimed to accurately study the intra-annual spatiotemporal variation in the surface urban heat island intensities (SUHIIs) in 1449 cities in China. First, China was divided into five environmental regions. Then, the SUHIIs were accurately calculated based on the modified definitions of the city extents and their corresponding nearby rural areas. Finally, we explored the spatiotemporal variation of the mean, maximum, and minimum values, and ranges of SUHIIs from several aspects. The results showed that larger annual mean daytime SUHIIs occurred in hot-humid South China and cold-humid northeastern China, and the smallest occurred in arid and semiarid west China. The seasonal order of the SUHIIs was summer > spring > autumn > winter in all the temperate regions except west China. The SUHIIs were obviously larger during the rainy season than the dry season in the tropical region. Nevertheless, significant differences were not observed between the two seasons within the rainy or dry periods. During the daytime, the maximum SUHIIs mostly occurred in summer in each region, while the minimum occurred in winter. A few cold island phenomena existed during the nighttime. The maximum SUHIIs were generally significantly positively correlated with the minimum SUHIIs during the daytime, nighttime and all-day in all environmental regions throughout the year and the four seasons. Moreover, significant correlation scarcely existed between the daytime and nighttime ranges of the SUHIIs. In addition, the daytime SUHIIs were also insignificantly correlated with the nighttime SUHIIs in half of the cases.

Keywords

surface urban heat island intensities (SUHIIs) land surface temperature (LST) seasonal changes maximum and minimum SUHII cold island China 

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References

  1. Chen L, Zhang M G, Zhu J et al., 2018. Modeling impacts of urbanization and urban heat island mitigation on boundary layer meteorology and air quality in Beijing under different weather conditions. Journal of Geophysical Research-Atmospheres, 123(8): 4323–4344. doi:  https://doi.org/10.1002/2017jd027501 CrossRefGoogle Scholar
  2. Clinton N, Gong P, 2013. MODIS detected surface urban heat islands and sinks: Global locations and controls. Remote Sensing of Environment, 134: 294–304. doi:  https://doi.org/10.1016/j.rse.2013.03.008 CrossRefGoogle Scholar
  3. Fallmann J, Forkel R, Emeis S, 2015. Secondary effects of urban heat island mitigation measures on air quality. Atmospheric Environment, 125(Part A): 199–211. doi:  https://doi.org/10.1016/j.atmosenv.2015.10.094 doiGoogle Scholar
  4. Filho W L, Icaza L E, Emanche V O et al., 2017. An evidence- based review of impacts, strategies and tools to mitigate urban heat islands. International Journal of Environmental Research & Public Health, 14(12): 1600. doi:  https://doi.org/10.3390/ijerph14121600 CrossRefGoogle Scholar
  5. Haashemi S, Weng Q, Darvishi A et al., 2016. Seasonal variations of the surface urban heat island in a semi-arid city. Remote Sensing, 8(4): 352. doi:  https://doi.org/10.3390/rs8040352 CrossRefGoogle Scholar
  6. Howard L, 1833. Climate of London Deduced from Metrological Observations (3rd edition). London: Harvey and Dorton Press.Google Scholar
  7. Imhoff M L, Zhang P, Wolfe R E et al., 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114(3): 504–513. doi:  https://doi.org/10.1016/j.rse.2009.10.008 CrossRefGoogle Scholar
  8. Lai L W, Cheng W L, 2009. Air quality influenced by urban heat island coupled with synoptic weather patterns. Science of the Total Environment, 407(8): 2724–2733. doi:  https://doi.org/10.1016/j.scitotenv.2008.12.002 CrossRefGoogle Scholar
  9. Lazzarini M, Marpu P R, Ghedira H, 2013. Temperature-land cover interactions: the inversion of urban heat island phenomenon in desert city areas. Remote Sensing of Environment, 130: 136–152. doi:  https://doi.org/10.1016/j.rse.2012.11.007 CrossRefGoogle Scholar
  10. Lee Y Y, Din M F M, Ponraj M et al., 2017. Overview of urban heat island (UHI) phenomenon towards human thermal comfort. Environmental Engineering and Management Journal, 16(9): 2097–2111. doi:  https://doi.org/10.30638/eemj.2017.217 CrossRefGoogle Scholar
  11. Li Y, Wang L, Zhang L et al., 2019. Monitoring the interannual spatiotemporal changes in the land surface thermal environment in both urban and rural regions from 2003 to 2013 in China based on remote sensing. Advances in Meteorology, 2019: 8347659. doi:  https://doi.org/10.1155/2019/8347659 Google Scholar
  12. Li Yuanzheng, Yin Ke, Wang Yanting et al., 2017. Studies on influence factors of surface urban heat island: a review. World Sci Tech R & D, 39(1): 56–66. (in Chinese)Google Scholar
  13. Li Yuanzheng, Yin Ke, Zhou Hongxuan et al., 2016. Progress in urban heat island monitoring by remote sensing. Progress in Geography, 35(9): 1062–1074. doi:  https://doi.org/10.18306/dlkxjz.2016.09.002 CrossRefGoogle Scholar
  14. Liu J, Kuang W, Zhang Z et al., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences, 24(2): 195–210. doi:  https://doi.org/10.1007/s11442-014-1082-6 CrossRefGoogle Scholar
  15. Liu J, Liu M, Tian H et al., 2005. Spatial and temporal patterns of China’s cropland during 1990-2000: an analysis based on Landsat TM data. Remote Sensing of Environment, 98(4): 442–456. doi:  https://doi.org/10.1016/j.rse.2005.08.012 CrossRefGoogle Scholar
  16. Liu X, Hu G, Ai B et al., 2015. A normalized urban areas composite index (NUACI) based on combination of DMSP-OLS and MODIS for mapping impervious surface area. Remote Sensing, 7(12): 17168–17189. doi:  https://doi.org/10.3390/rs71215863 CrossRefGoogle Scholar
  17. Memon R A, Leung D Y C, Liu C H, 2009. An investigation of urban heat island intensity (UHII) as an indicator of urban heating. Atmospheric Research, 94(3): 491–500. doi:  https://doi.org/10.1016/j.atmosres.2009.07.006 CrossRefGoogle Scholar
  18. Mostovoy G V, King R L, Reddy K R et al., 2006. Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi. GIScience & Remote Sensing, 43(1): 78–110. doi:  https://doi.org/10.2747/1548-1603.43.1.78 CrossRefGoogle Scholar
  19. United Nations, Department of Economic and Social Affairs, Population Division (UN DESA PD), 2014. World Urbanization Prospects: The 2014 Revision, Highlights. Department of Economic and Social Affairs. Population Division, United Nations.Google Scholar
  20. Peng S, Piao S, Ciais P et al., 2011. Surface urban heat island across 419 global big cities. Environmental Science & Technology, 46(2): 696–703. doi:  https://doi.org/10.1021/es2030438 CrossRefGoogle Scholar
  21. Ren Guoyu, Guo Jun, Xu Mingzhi et al., 2005. Climate changes of China’s mainland over the past half century. Acta Meteorologica Sinica, 63(6): 942–956. (in Chinese)Google Scholar
  22. Richards D R, Edwards P J, 2018. Using water management infrastructure to address both flood risk and the urban heat island. International Journal of Water Resources Development, 34(4): 490–498. doi:  https://doi.org/10.1080/07900627.2017.1357538 CrossRefGoogle Scholar
  23. Schwarz N, Lautenbach S, Seppelt R, 2011. Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sensing of Environment, 115(12): 3175–3186. doi:  https://doi.org/10.1016/j.rse.2011.07.003 CrossRefGoogle Scholar
  24. Schwarz N, Schlink U, Franck U et al., 2012. Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—an application for the city of Leipzig (Germany). Ecological Indicators, 18: 693–704. doi:  https://doi.org/10.1016/j.ecolind.2012.01.001 CrossRefGoogle Scholar
  25. Sfîcã L, Ichim P, Apostol L et al., 2017. The extent and intensity of the urban heat island in Iaşi city, Romania. Theoretical & Applied Climatology, 134(3–4): 777–791. doi:  https://doi.org/10.1007/s00704-017-2305-4 Google Scholar
  26. Shastril H, Barikl B, Ghosh S et al., 2017. Flip flop of day-night and summer-winter surface urban heat island intensity in India. Scientific Reports, 7: 40178. doi:  https://doi.org/10.1038/srep40178 CrossRefGoogle Scholar
  27. Shi B, Tang C S, Gao L et al., 2012. Observation and analysis of the urban heat island effect on soil in Nanjing, China. Environmental Earth Sciences, 67(1): 215–229. doi:  https://doi.org/10.1007/s12665-011-1501-2 CrossRefGoogle Scholar
  28. Tran H, Uchihama D, Ochi S et al., 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8(1): 34–48. doi:  https://doi.org/10.1016/j.jag.2005.05.003 CrossRefGoogle Scholar
  29. Voogt J A, Oke T R, 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370–384. doi:  https://doi.org/10.1016/S0034-4257(03)00079-8 CrossRefGoogle Scholar
  30. Wang J, Huang B, Fu D et al., 2015. Spatiotemporal variation in surface urban heat island intensity and associated determinants across major Chinese cities. Remote Sensing, 7: 3670–3689. doi:  https://doi.org/10.3390/rs70403670 CrossRefGoogle Scholar
  31. Zhang P, Imhoff M L, Wolfe R E et al., 2010. Characterizing urban heat islands of global settlements using MODIS and nighttime lights products. Canadian Journal of Remote Sensing, 36(3): 185–196. doi:  https://doi.org/10.5589/m10-039 CrossRefGoogle Scholar
  32. Zhang Z, Wang X, Zhao X et al., 2014. A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images. Remote Sensing of Environment, 149: 142–154. doi:  https://doi.org/10.1016/j.rse.2014.04.004 CrossRefGoogle Scholar
  33. Zhao L, Lee X, Smith R B et al., 2014. Strong contributions of local background climate to urban heat islands. Nature, 511(7508): 216–219. doi:  https://doi.org/10.1038/nature13462 CrossRefGoogle Scholar
  34. Zhou D, Xiao J, Bonafoni S et al., 2019. Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sensing, 11(1): 48. doi:  https://doi.org/10.3390/rs11010048 CrossRefGoogle Scholar
  35. Zhou D, Zhang L, Li D et al., 2016. Climate-vegetation control on the diurnal and seasonal variations of surface urban heat islands in China. Environmental Research Letters, 11(7): 074009. doi:  https://doi.org/10.1088/1748-9326/11/7/074009 CrossRefGoogle Scholar
  36. Zhou D, Zhao S, Liu S et al., 2014. Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sensing of Environment, 152: 51–61. doi:  https://doi.org/10.1016/j.rse.2014.05.017 CrossRefGoogle Scholar
  37. Zhou D, Zhao S, Zhang L et al., 2015. The footprint of urban heat island effect in China. Scientific Reports, 5: 11160. doi:  https://doi.org/10.1038/srep11160 CrossRefGoogle Scholar
  38. Zhou J, Chen Y, Zhang X et al., 2013. Modelling the diurnal variations of urban heat islands with multi-source satellite data. International Journal for Remote Sensing, 34(21): 7568–7588. doi:  https://doi.org/10.1080/01431161.2013.821576 CrossRefGoogle Scholar
  39. Zinzi M, Carnielo E, Mattoni B, 2018. On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy. Applied Energy, 221: 148–160. doi:  https://doi.org/10.1016/j.apenergy.2018.03.192 CrossRefGoogle Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yuanzheng Li
    • 1
    • 2
    • 3
  • Lan Wang
    • 4
  • Liping Zhang
    • 5
  • Min Liu
    • 1
    • 3
    Email author
  • Guosong Zhao
    • 6
  1. 1.School of Resources and EnvironmentHenan University of Economics and LawZhengzhouChina
  2. 2.Academician Laboratory for Urban and Rural Spatial Data Mining of Henan ProvinceHenan University of Economics and LawZhengzhouChina
  3. 3.State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina
  4. 4.Key Laboratory of Urban Environment and Health, Institute of Urban EnvironmentChinese Academy of SciencesXiamenChina
  5. 5.Center for Environmental Zoning, Chinese Academy for Environmental PlanningMinistry of Environmental Protection of ChinaBeijingChina
  6. 6.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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