Abstract
This article proposes a method for estimating the surface urban heat island intensity (SUHI) of urban areas, which addresses prior difficulties in the determination of rural contexts that may be used as a point of comparison. Based on indexes produced using this method, as well as remotely sensed datasets, the article compares the temporal and spatial characteristics of SUHIs within three major urban agglomerations (the Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta) and six typical metropolises. The article also examines the influence of socioeconomic factors on SUHI. The study revealed that this method is able to objectively monitor regional-scale SUHIs. The climate of the area studied is probably a determining factor in the seasonal variation of SUHIs. Research from the last 5 years (2010–2014) demonstrates that the urban heat island effect within the three urban agglomerations and five metropolises is serious. From 1994 to 2014, the average SUHI value for central urban areas rose from 0.4 to 2.3 K, while the total area where the SUHI value was >3.0 K increased from 1938 to 29,690 km2. The morphology of heat islands is significantly influenced by urbanization, meaning that heat islands within the areas studied will only continue to grow. Urban population and electricity consumption are the socioeconomic factors that exerted the greatest influence on the size of heat islands in China’s major urban agglomerations. However, it is likely that economic measures designed to mitigate the UHI effect will differ in effectiveness from one urban agglomeration to another.
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References
Balling RC, Idso SB (1989) Historical temperature trends in the United-States and the effect of urban-population growth. J Geophy Res Atmo 94(94):3359–3363
Bokwa AM, Hajto J, Walawender JP et al (2015) Influence of diversified relief on the urban heat island in the city of Kraków, Poland. Theor Appl Climatol 122(1):365–382
Cao X, Chen J, Imura H et al (2009) A svm-based method to extract urban areas from DMSP-OLS and spot VGT data. Remote Sens Enviro 113(10):2205–2209
Cheval S, Dumitrescu A (2015) The summer surface urban heat island of Bucharest (Romania) retrieved from MODIS images. Theor Appl Climatol 121:631–640. doi:10.1007/s00704-014-1250-8
Cheval S, Dumitrescu A, Bell A (2009) The urban heat island of Bucharest during the extreme high temperatures of July 2007. Theor Appl Climatol 97:391–401. doi:10.1007/s00704-008-0088-3
Clinton N, Gong P (2013) MODIS detected surface urban heat islands and sinks: global locations and controls. Remote Sens Enviro 134:294–304
Connors JP, Galletti CS, Chow WTL (2013) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Eco 28(2):271–283
Cui YP, Liu JY, Zhang XZ et al (2015) Modeling urban sprawl effects on regional warming in Beijing-Tianjing-Tangshan urban agglomeration. Acta Eco Sin 35(4):993–1003
Ding SY, Qiao GJ, Guo YY et al (2015) Study on the urban heat islands and meteorological elements over the Pearl river delta. J Tropi Meteorol 31(5):681–690 (in Chinese)
Dong LP, Jiang ZH, Shen SH (2014) Urban heat island change and its relationship with urbanization of urban agglomerations in Yangtze River Delta in past decade. Tran Atmo Sci 37(2):146–154 (in Chinese)
Du H, Wang D, Wang Y et al (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. Sci Total Enviro 571:461–470
Gaffin SR, Rosenzweig C, Khanbilvardi R et al (2008) Variations in New York city’s urban heat island strength over time and space. Theor Appl Climatol 94(1):1–11. doi:10.1007/s00704-007-0368-3
Gallo KP, Tarpley JD, Mcnab AL et al (1995) Assessment of urban heat islands—a satellite perspective. Atmo Res 37(1–3):37–43
He JF, Liu JY, Zhuang DF et al (2007) Assessing the effect of land use/land cover change on the change of urban heat island intensity. Theor Appl Climatol 90(3):217–226. doi:10.1007/s00704-006-0273-1
Henderson M, Yeh ET, Gong P et al (2003) Validation of urban boundaries derived from global night-time satellite imagery. Int J Remote Sens 24(3):595–609
Hove LWAV, Jacobs CMJ, Heusinkveld BG et al (2015) Temporal and spatial variability of urban heat island and thermal comfort within the Rotterdam agglomeration. Build Environ 83:91–103
Hu L, Brunsell NA (2013) The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring. Remote Sens Enviro 134(5):162–174
Hu SQ, Hu DY, Li XJ et al (2009) An remote sensing-based analysis of the thermal environment spatial pattern of Beijing-Tianjin-Hebei metropolitan circle. Remote Sens Land Resourc 81:94–99
Imhoff ML, Lawrence WT, Stutzer DC et al (1997) A technique for using composite DMSP/ OLS’ city lights’ satellite data to accurately map urban areas. Remote Sens Enviro 61(3):361–370
Li CF, Yin JY (2013) Study on urban thermal field of Shanghai using multi-source remote sensing data. J Indian Soc Remote Sens 41(4):1009–1019
Li Q, Zhang H, Liu X et al (2004) Urban heat island effect on annual mean temperature during the last 50 years in China. Theor Appl Climatol 79(3):165–174
Lin YT, Ye JF, Lin KP et al (2014) Remote sensing research of heat island effect in Nanning. J Catastroph 29(4):192–197 (in Chinese)
Liu WD, Ji CP, Zhong X et al (2007) Temporal characteristics of the Beijing urban heat island. Theor Appl Climatol 87:213–221
Liu YH, Xu YM, Ma JJ et al (2014) Quantitative assessment and planning simulation of Beijing urban heat island. Eco Enviro Sci 23(7):1156–1163 (in Chinese)
Liu YH, Luan QZ, Quan WJ et al (2015) Research on heat environment of Beijing-Tianjin-Tangshan urban group based on multisource satellite data. Eco Enviro Sci 24(7):1150–1158 (in Chinese)
Lucena AJD, Rotunno FOC, França JRA et al (2013) Urban climate and clues of heat island events in the metropolitan area of Rio de Janeiro. Theor Appl Climatol 111:497–511. doi:10.1007/s00704-012-0668-0
Martin P, Baudouin Y, Gachon P (2015) An alternative method to characterize the surface urban heat island. Int J Biometeorol 59(7):849–861. doi:10.1007/s00484-014-0902-9
Mihalakakou G, Flocas HA, Santamouris M et al (2002) Application of neural networks to the simulation of the heat island over Athens, Greece, using synoptic types as a predictor. J Appl Meteorol 41(5):519–527
Mohan MY, Kikegawa BR, Gurjar B et al (2013) Assessment of urban heat island effect for different land use-land cover from micrometeorological measurements and remote sensing data for megacity Delhi. Theor Appl Climatol 112:647–658. doi:10.1007/s00704-012-0758-z
Mohsin T, Gough WA (2012) Characterization and estimation of urban heat island at Toronto: impact of the choice of rural sites. Theor Appl Climatol 108(1):105–117
Morris CJG, Simmonds I, Plummer N (2001) Quantification of the influences of wind and cloud on the nocturnal urban heat island of a large city. J Appl Meteorol 40(2):169–182
Nina S, Sven L, Ralf S (2011) Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens Enviro 115:3175–3186
Oke TR (1973) City size and the urban heat island. Atmo Enviro 7:769–779
Peng S, Piao S, Ciais P et al (2012) Surface urban heat island across 419 global big cities. Enviro Sci Techno 46:696–703
Prata AJ (1993) Land surface temperature derived from the advanced very high resolution radiometer and along-track scanning radiometer. J Geophy Res 981(D9):16689–16702
Quan WJ (2014) Retrieval of land surface parameters from AVHRR and analysis of their temporal-spatial variations over Tibetan Plateau. Dissertation. University of Chinese Academy of Sciences (in Chinese)
Quan WJ, Chen HB, Han XZ et al (2012) A modified Becker’s split-window approach for retrieving land surface temperature from AVHRR and VIRR. Acta Meteorol Sin 26(2):229–240
Rao S, Zhang HY, Jin TT et al (2010) The spatial character of regional heat island in Pearl River Delta using MODIS remote sensing data. Geogr Res 29(1):127–136
Ren Y, Ren G (2011) A remote-sensing method of selecting reference stations for evaluating urbanization effect on surface air temperature trends. J Clim 24(13):3179–3189
Ren ZB, Zheng HF, He XY et al (2015) Estimation of the relationship between urban vegetation configuration and land surface temperature with remote sensing. J Indian Soc Remote Sens 43(1):89–100. doi:10.1007/s12524-014-0373-9
Rizwan AM, Dennis LYC, Liu C (2008) A review on the generation determination and mitigation of urban heat island. J Enviro Sci 20(1):120–128. doi:10.1016/S1001-0742(08)60019-4
Roth M (2007) Review of urban climate research in (sub)tropical regions. Int J Climatol 27:1859–1873
Sarkar A, Ridder KD (2011) The urban heat island intensity of Paris: a case study based on a simple urban surface parameterization. Bound-Lay Meterol 138:511–520. doi:10.1007/s10546-010-9568-y
Schwarz N, Lautenbach S, Seppelt R (2011) Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens Enviro 115:3175–3186
Stewart ID (2011) A systematic review and scientific critique of methodology in modern urban heat island literature. Int J Climatol 31(2):200–217
Voogt JA (2002) Urban heat island, causes and consequences of global environmental change. Encycl Global Enviro Change 3:660–666
Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Enviro 86:370–384. doi:10.1016/S0034-4257(03)00079-8
Walawender JP, Szymanowski M, Hajto MJ et al (2014) Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+data. Pure Appl Geophy 171(6):913–940
Wan ZM (2008) New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sens Enviro 112(1):59–74
Wang JK, Wang KC, Wang PC (2007) Urban heat (or cool) island over Beijing from MODIS land surface temperature. J Remote Sens 11(3):330–339
Wang W, Liang S, Meyers T (2008) Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sens Enviro 112:623–635
Wang J, Huang B, Fu DJ et al (2015) Spatiotemporal variation in surface urban heat island intensity and associated determinants across major Chinese cities. Remote Sens 7(4):3670–3689
Weng Q (2009) Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS J Photo Remote Sens 64(4):335–344
Weng Q, Yang S (2004) Managing the adverse thermal effects of urban development in a densely populated Chinese city. J Enviro Manag 70:145–156
Xu YM, Liu YH (2014) Monitoring the near-surface urban heat island in Beijing, China by satellite remote sensing. Geogr Res 53(1):16–25
Yan F, Qin ZH, Li MS et al (2007) On urban heat island of Shanghai city from MODIS data. Geo Inform Sci Wuhan Uni 32(7):576–580
Ye CH, Liu YH, Liu WD et al (2011) Research on ground surface heat environment monitoring index and application. Meteo Sci Tech 39(1):95–101
Zhang H, Sato N, Izumi TK et al (2008) Modified RAMS-Urban canopy model for heat island simulation in Chongqing, China. J Appl Meteoroll Climato 47(2):509–524
Zhang P, Imhoff ML, Wolfe RE et al (2010) Urban heat island effect across biomes in the continental USA. Remote Sens Enviro 114(3):1920–1923
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The authors thank NASA Reverb for providing the MODIS data and ASTER GDEM data.
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This research is sponsored by the Program of the Research and Innovation Team on Urban Climate Assessment of Beijing Meteorological Bureau, Innovation Team on Climate Change of CMA, the Climate Change Project (CCSF201733), and FY-3(02) Meteorological Satellite Ground Application System Engineering (FY-3(02)-UDS-1.12.2).
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Liu, Y., Fang, X., Xu, Y. et al. Assessment of surface urban heat island across China’s three main urban agglomerations. Theor Appl Climatol 133, 473–488 (2018). https://doi.org/10.1007/s00704-017-2197-3
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DOI: https://doi.org/10.1007/s00704-017-2197-3