Abstract
How does the urban spatial landscape (USL) pattern affect the land surface urban heat islands (SUHIs) and canopy urban heat islands (CUHIs)? Based on satellite and meteorological observations, this case study compares the impacts of the USL pattern on SUHI and CUHI in the central urban area (CUA) of Beijing using the satellite land-surface-temperature product and hourly temperature data from automatic meteorological stations from 2009 to 2018. Eleven USL metrics—building height (BH), building density (BD), standard deviation of building height (BSD), floor area ratio (FAR), frontal area index (FAI), roughness length (RL), sky view factor (SVF), urban fractal dimension (FD), vegetation coverage (VC), impervious coverage (IC), and albedo (AB)—with a 500-m spatial resolution in the CUA are extracted for comparative analysis. The results show that SUHI is higher than CUHI at night, and SUHI is only consistent with CUHI at spatial—temporal scales at night, particularly in winter. Spatially, all 11 metrics are strongly correlated with both the SUHI and CUHI at night, with stronger correlation between most metrics and SUHI. VC, AB, and SVF have the greatest impact on both the SUHI and CUHI. High SUHI and CUHI values tend to appear in areas with BD ⩾ 0.26, VC ⩽ 0.09, AB ⩽ 0.09, and SVF ⩽ 0.67. In summer, most metrics have a greater impact on the SUHI than CUHI; the opposite is observed in winter. SUHI variation is affected primarily by VC in summer and by VC and AB in winter, which is different for the CUHI variation. The collective contribution of all 11 metrics to SUHI spatial variation in summer (61.8%) is higher than that to CUHI; however, the opposite holds in winter and for the entire year, where the cumulative contribution of the factors accounts for 66.6% and 49.6%, respectively, of the SUHI variation.
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References
Anniballe, R., S. Bonafoni, and M. Pichierri, 2014: Spatial and temporal trends of the surface and air heat island over Milan using MODIS data. Remote Sens. Environ., 150, 163–171, doi: https://doi.org/10.1016/j.rse.2014.05.005.
Arnfield, A. J., 2003: Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol., 23, 1–26, doi: https://doi.org/10.1002/joc.859.
Azevedo, J. A., L. Chapman, and C. L. Muller, 2016: Quantifying the daytime and night-time urban heat island in Birmingham, UK: A comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sens., 8, 153, doi: https://doi.org/10.3390/rs8020153.
Bokwa, A., M. J. Hajto, J. P. Walawender, et al., 2015: Influence of diversified relief on the urban heat island in the city of Krakow, Poland. Theor. Appl. Climatol., 122, 365–382, doi: https://doi.org/10.1007/s00704-015-1577-9.
Cai, E. L., B. C. Dou, S. Peng, et al., 2016: A study of albedo product downscaling method based on image fusion. Remote Sens. Technol. Appl., 31, 724–730, doi: https://doi.org/10.11873/j.issn.1004-0323.2016.4.0724. (in Chinese)
Chen, L., and E. Ng, 2011: Quantitative urban climate mapping based on a geographical database: A simulation approach using Hong Kong as a case study. Int. J. Appl. Earth Obs. Geoinf., 13, 586–594, doi: https://doi.org/10.1016/j.jag.2011.03.003.
Chen, X. G., L. S. Tang, C. M. Li, et al., 2014: Technical guidelines for climate feasibility demonstration series—technical guidelines for Urban Heat Island effect assessment (Version 1). Available online at https://www.renrendoc.com/paper/164131234.html. Accessing on 9 November 2022. (in Chinese)
Cheval, S., A. Dumitrescu, and A. Bell, 2009: The urban heat island of Bucharest during the extreme high temperatures of July 2007. Theor. Appl. Climatol., 97, 391–401, doi: https://doi.org/10.11007/s00704-008-0088-3.
CMA (China Meteorological Administration), 2018: Meteorological Industry Standard of the People’s Republic of China: Specifications for Climatic Feasibility Demonstration—Urban Ventilation Corridor: QX/T 437–2018. Beijing, Meteorological Press, 1–12.
Connors, J. P., C. S. Galletti, and W. T. L. Chow, 2013: Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc. Ecol., 28, 271–283, doi: https://doi.org/10.1007/s10980-012-9833-1.
de Lucena, A. J., O. C. Rotunno Filho, J. R. de Almeida França, 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: https://doi.org/10.1007/s00704-012-0668-0.
Du, H. Y., D. D. Wang, Y. Y. Wang, 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 Environ., 571, 461–470, doi: https://doi.org/10.1016/j.scitotenv.2016.07.012.
Gál, T., and J. Unger, 2009: Detection of ventilation paths using high-resolution roughness parameter mapping in a large urban area. Build. Environ., 44, 198–206, doi: https://doi.org/10.1016/j.buildenv.2008.02.008.
Gál, T., F. Lindberg, and J. Unger, 2009: Computing continuous sky view factors using 3D urban raster and vector databases: Comparison and application to urban climate. Theor. Appl. Climatol., 95, 111–123, doi: https://doi.org/10.1007/s00704-007-0362-9.
Grimmond, C. S. B., and T. R. Oke, 1999: Aerodynamic properties of urban areas derived from analysis of surface form. J. Appl. Meteor., 38, 1262–1292, doi: https://doi.org/10.1175/1520-0450(1999)038<1262:APOUAD>2.0.CO;2.
He, J. F., J. Y. Liu, D. F. Zhuang, et al., 2007: Assessing the effect of land use/land cover change on the change of urban heat island intensity. Theor. Appl. Climatol., 90, 217–226, doi: https://doi.org/10.1007/s00704-006-0273-1.
Imhoff, M. L., P. Zhang, R. E. Wolfe, et al., 2010: Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens. Environ., 114, 504–513, doi: https://doi.org/10.1016/j.rse.2009.10.008.
Jamei, E., D. R. Ossen, and P. Rajagopalan, 2017: Investigating the effect of urban configurations on the variation of air temperature. Int. J. Sustain. Built Environ., 6, 389–399, doi: https://doi.org/10.1016/j.ijsbe.2017.07.001.
Jia, W. Q., G. Y. Ren, X. J. Yu, et al., 2021: Difference of urban heat island effect among representative cities of different climatic zones over eastern China monsoon region. Climatic Environ. Res., 26, 569–582, doi: https://doi.org/10.3878/j.issn.1006-9585.2021.20149. (in Chinese)
Jiang, L., W. F. Zhan, J. Voogt, et al., 2018: Remote estimation of complete urban surface temperature using only directional radiometric temperatures. Build. Environ., 135, 224–236, doi: https://doi.org/10.1016/j.buildenv.2018.03.005.
Li, N. N., and X. M. Li, 2020: The impact of building thermal anisotropy on surface urban heat island intensity estimation: An observational case study in Beijing. IEEE Geosci. Remote Sensing Lett., 17, 2030–2034, doi: https://doi.org/10.1199/LGRS.0019.2962383.
Li, Y. H., S. G. Miao, F. Chen, et al., 2017: Introducing and evaluating a new building-height categorization based on the fractal dimension into the coupled WRF/urban model. Int. J. Climatol., 37, 3111–3122, doi: https://doi.org/10.1002/joc.4903.
Liu, K., H. B. Su, X. K. Li, et al., 2016: Quantifying spatial—temporal pattern of urban heat island in Beijing: An improved assessment using land surface temperature (LST) time series observations from LANDSAT, MODIS, and Chinese new satellite GaoFen-1. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 9, 2028–2042, doi: https://doi.org/10.1109/jstars.2015.2513598.
Liu, Y. H., Y. M. Xu, J. J. Ma, et al., 2014: Quantitative assessment and planning simulation of Beijing urban heat island. Ecol. Environ. Sci., 23, 1156–1163, doi: https://doi.org/10.3969/j.issn.1674-5906.2014.07.010. (in Chinese)
Liu, Y. H., P. F. Cheng, P. Chen, et al., 2020a: Detection of wind corridors based on “Climatopes”: A study in central Ji’nan. Theor. Appl. Climatol., 142, 869–884, doi: https://doi.org/10.1007/s00704-020-03323-z.
Liu, Y. H., Y. M. Xu, F. M. Zhang, et al., 2020b: A preliminary study on the influence of Beijing urban spatial morphology on near-surface wind speed. Urban Climate, 34, 100703, doi: https://doi.org/10.1016/j.uclim.2020.100703.
Liu, Y. H., Y. M. Xu, F. M. Zhang, et al., 2021a: Influence of Beijing spatial morphology on the distribution of urban heat island. Acta Geogr. Sinica, 76, 1662–1679, doi: https://doi.org/10.11821/dlxb202107007. (in Chinese)
Liu, Y. H., Y. M. Xu, F. Z. Weng, et al., 2021b: Impacts of urban spatial layout and scale on local climate: A case study in Beijing. Sustain. Cities Soc., 68, 102767, doi: doi: https://doi.org/10.1016/j.scs.2021.102767.
Martin, P., Y. Baudouin, and P. Gachon, 2015: An alternative method to characterize the surface urban heat island. Int. J. Biometeorol., 59, 849–861, doi: https://doi.org/10.1007/s00484-014-0902-9.
Mohan, M., Y. Kikegawa, B. R. Gurjar, 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: https://doi.org/10.1007/s00704-012-0758-z.
Oke, T. R., and H. A. Cleugh, 1987: Urban heat storage derived as energy balance residuals. Bound.-Layer Meteor., 39, 233–245, doi: https://doi.org/10.1007/bf00116120.
Oke, T. R., G. Mills, A. Christen, et al., 2021: Urban Climates. Miao, S. G., X. M. Wang, C. G. Wang, et al., Eds., China Meteorological Press, Beijing, 207–215.
Peng, S. S., S. L. Piao, P. Ciais, et al., 2012: Surface urban heat island across 419 global big cities. Environ. Sci. Technol., 46, 696–703, doi: https://doi.org/10.1021/es2030438.
Ridd, M. K., 1995: Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. Int. J. Remote Sens., 16, 2165–2185, doi: https://doi.org/10.1080/01431169508954549.
Rizwan, A. M., L. Y. C. Dennis, and C. H. Liu, 2008: A review on the generation, determination and mitigation of urban heat island. J. Environ. Sci., 20, 120–128, doi: https://doi.org/10.1016/S1001-0742(08)60019-4.
Roth, M., T. R. Oke, and W. J. Emery, 1989: Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. Int. J. Remote Sens., 10, 1699–1720, doi: https://doi.org/10.1080/01431168908904002.
Santamouris, M., C. Cartalis, A. Synnefa, et al., 2015: On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review. Energy Build., 98, 119–124, doi: https://doi.org/10.1016/j.enbuild.2014.09.052.
Scarano, M., and J. A. Sobrino, 2015: On the relationship between the sky view factor and the land surface temperature derived by Landsat-8 images in Bari, Italy. Int. J. Remote Sens., 36, 4820–4835, doi: https://doi.org/10.1080/01431161.2015.1070325.
Schwarz, N., S. Lautenbach, and R. Seppelt, 2011: Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens. Environ., 115, 3175–3186, doi: https://doi.org/10.1016/j.rse.2011.07.003.
Schwarz, N., U. Schlink, U. Franck, 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). Ecol. Indic., 18, 693–704, doi: https://doi.org/10.1016/j.ecolind.2012.01.001.
Sun, H., Y. H. Chen, and W. F. Zhan, 2015: Comparing surface-and canopy-layer urban heat islands over Beijing using MOD-IS data. Int. J. Remote Sens., 36, 5448–5465, doi: https://doi.org/10.1080/01431161.2015.1101504.
Touchaei, A. G., and Y. Wang, 2015: Characterizing urban heat island in Montreal (Canada)—Effect of urban morphology. Sustain. Cities Soc., 19, 395–402, doi: https://doi.org/10.1016/j.scs.2015.03.005.
Tsoka, S., K. Tsikaloudaki, and T. Theodosiou, 2017: Urban space’s morphology and microclimatic analysis: A study for a typical urban district in the Mediterranean city of Thessaloniki, Greece. Energy Build., 156, 96–108, doi: https://doi.org/10.1016/j.enbuild.2017.09.066.
Van Hove, L. W. A., C. M. J. Jacobs, B. G. Heusinkveld, et al., 2015: Temporal and spatial variability of urban heat island and thermal comfort within the Rotterdam agglomeration. Build. Environ., 83, 91–103, doi: https://doi.org/10.1016/j.buildenv.2014.08.029.
Voogt, J. A., 2002: Urban heat island. Encyclopedia of Global Environmental Change, Volume 3: Causes and Consequences of Global Environmental Change, I. Douglas, Ed., John Wiley & Sons, New York, 660–666.
Voogt, J. A., and T. R. Oke, 1997: Complete urban surface temperatures. J. Appl. Meteor., 36, 1117–1132, doi: https://doi.org/10.1175/1520-0450(1997)036<1117:CUST>2.0.CO;2.
Voogt, J. A., and T. R. Oke, 1998: Effects of urban surface geometry on remotely-sensed surface temperature. Int. J. Remote Sens., 19, 895–920, doi: https://doi.org/10.1080/014311698215784.
Voogt, J. A., and T. R. Oke, 2003: Thermal remote sensing of urban climates. Remote Sens. Environ., 86, 370–384, doi: https://doi.org/10.1016/S0034-4257(03)00079-8.
Walawender, J. P., M. Szymanowski, M. J. Hajto, et al., 2014: Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+ Data. Pure Appl. Geophys., 171, 913–940, doi: https://doi.org/10.1007/s00024-013-0685-7.
Wang, K. C., S. J. Jiang, J. K. Wang, et al., 2017: Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network. J. Geophys. Res. Atmos., 122, 2131–2154, doi: https://doi.org/10.1002/2016jd025304.
Wang, T., D. W. Zhou, X. J. Shen, et al., 2020: Koppen’s climate classification map for China. J. Meteor. Sci., 40, 752–760, doi: https://doi.org/10.3969/2019jms.0040. (in Chinese)
Xu, Y. M., and Y. H. Liu, 2013: Study on the thermal environment and its relationship with impervious surface in Beijing city using TM image. Ecology Environ. Sci., 22, 639–643, doi: https://doi.org/10.3969/j.issn.1674-5906.2013.04.015. (in Chinese)
Yang, F., S. S. Y. Lau, and F. Qian, 2011: Urban design to lower summertime outdoor temperatures: An empirical study on high-rise housing in Shanghai. Build. Environ., 46, 769–785, doi: https://doi.org/10.1016/j.buildenv.2010.10.010.
Yang, J. X., M. Menenti, Z. F. Wu, et al., 2021: Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures. Int. J. Climatol., 41, E3219–E3238, doi: https://doi.org/10.1002/joc.6919.
Yin, C. H., M. Yuan, Y. P. Lu, et al., 2018: Effects of urban form on the urban heat island effect based on spatial regression model. Sci. Total Environ., 634, 696–704, doi: https://doi.org/10.1016/j.scitotenv.2018.03.350.
Zakšek, K., K. Oštir, and Ž. Kokalj, 2011: Sky-view factor as a relief visualization technique. Remote Sens., 3, 398–415, doi: https://doi.org/10.3390/rs3020398.
Zhang, P., L. Bounoua, M. L. Imhoff, et al., 2014: Comparison of MODIS land surface temperature and air temperature over the continental USA meteorological stations. Can. J. Remote Sens., 40, 110–122, doi: https://doi.org/10.1080/07038992.2014.935934.
Zhao, C. J., G. B. Fu, X. M. Liu, et al., 2011: Urban planning indicators, morphology and climate indicators: A case study for a north-south transect of Beijing, China. Build. Environ., 46, 1174–1183, doi: https://doi.org/10.1016/j.buildenv.2010.12.009.
Zhou, D. C., S. Q. Zhao, L. X. Zhang, et al., 2016: Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities. Remote Sens. Environ., 176, 272–281, doi: https://doi.org/10.1016/j.rse.2016.02.010.
Zhou, D. C., J. F. Xiao, S. Bonafoni, et al., 2018: Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sens., 11, 48, doi: https://doi.org/10.3390/rs11010048.
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Supported by the National Natural Science Foundation of China (41871028), Opening Fund of National Data Center for Earth Observation Science (NODAOP2021004), and Beijing Natural Science Fund (8192020).
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Liu, Y., Xu, Y., Zhang, Y. et al. Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands. J Meteorol Res 36, 882–899 (2022). https://doi.org/10.1007/s13351-022-2045-y
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DOI: https://doi.org/10.1007/s13351-022-2045-y