Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

Yonghong LIU; Yongming XU; Yeping ZHANG; Xiuzhen HAN; Fuzhong WENG; Chunyi XUAN; Wenjun SHU

doi:10.1007/s13351-022-2045-y

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Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

北京城市空间结构对陆表热岛和冠层热岛影响对比研究

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Corresponding author: Xiuzhen HAN, hanxz@cma.gov.cn;

Funds:

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)

DOI: 10.1007/s13351-022-2045-y

Abstract

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.
- surface urban heat island,
- canopy urban heat island,
- vegetation coverage,
- albedo,
- sky view factor,
- building density,
- Beijing
摘要

城市空间结构对于基于卫星观测的陆表热岛（SUHI）和基于气象观测的冠层热岛（CUHI）的影响是否存在明显差异？本文以北京中心城为例，利用2009–2018年MODIS卫星陆表温度产品和高密度自动气象站逐小时气温资料，对比分析了11种城市空间结构特征参数（建筑高度BH、建筑密度BD、建筑高度标准差BSD、容积率FAR、迎风截面积指数FAI、粗糙度长度RL、天空开阔度SVF、城市分数维FD、植被覆盖度VC、不透水盖度IC、反照率AB）对SUHI和CUHI时空分布影响的定量差异。结果显示：北京中心城区SUHI和CUHI分布形态与季节变化存在明显差异，两者在夏季白天差异最大；SUHI在夜晚虽然高于CUHI，但两者在夜晚尤其是冬季夜晚具有很好的时空一致性。11种参数与SUHI和CUHI在夜晚均存在明显空间相关性，且大部分参数与SUHI相关性较CUHI更为明显；影响SUHI和CUHI最为明显的3个参数均为VC、AB和SVF，两者高值区均容易出现在BD ≥ 0.26、VC ≤ 0.09、AB ≤ 0.09、SVF ≤ 0.67等区域。大部分参数在夏季对SUHI变化的贡献超过了其对CUHI变化的贡献，而在冬季则相反；夏季和冬季影响SUHI变化的主要因素分别为VC以及VC和AB，这与影响CUHI的主要因素不同；各参数在夏季和冬季对SUHI和CUHI变化的综合贡献存在明显差异，这些可以作为未来北京城市规划指标如VC、BH、BD、FAI、AB等设计考虑的重要参考。
- 陆表热岛,
- 冠层热岛,
- 植被覆盖度,
- 反照率,
- 天空开阔度,
- 建筑密度,
- 北京

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Fig. 1. (a) Topography and automatic meteorological observation stations (AMSs) in Beijing and (b) land-use types in the study area.

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Fig. 2. (a) Annual mean nighttime light intensity in Beijing in 2018 extracted from NPP VIIRS satellite data and (b) distribution of the rural background areas and rural and urban stations in Beijing.

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Fig. 3. (a) Annual, (b) spring, summer, autumn, and winter mean AB values for the CUA in 2017.

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Fig. 4. Spatial distribution patterns (resolution: 5 m) of the BH and SVF and means of the USL metrics (BH, BD, FAR, BSD, RL, FAI, SVF, FD, IC, VC, and annual AB) for the (a) Ganjiakou and (b) Jianwai blocks in Beijing.

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Fig. 5. Spatial distributions of the mean SUHI with 1-km spatial resolution for the 115 block areas in Beijing’s CUA during different periods of 2009–2018.

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Fig. 6. Spatial distributions of ΔUHI with 1-km spatial resolution for Beijing’s CUA during different periods of 2009–2018.

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Fig. 7. Distributions of R of the (a) SUHI and (b) CUHI and the USL metrics (i.e., BH, BD, FAR, BSD, RL, FAI, SVF, FD, IC, VC, and AB) for Beijing’s CUA during different periods of 2009–2018.

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Fig. 8. Comparison of R_max of the SUHI and CUHI with each USL metric (i.e., BH, BD, FAR, BSD, RL, FAI, SVF, FD, IC, VC, and AB) for Beijing’s CUA during different periods of 2009–2018.

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Fig. 9. Distributions of the land-use types, BH, SVF, VC, and AB in the SHI areas and their surrounding areas in Beijing. The black framed areas are the SHI areas.

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Table 1 Meanings and calculation methods for the 11 USL metrics selected in this study

USL metrics	Definition or calculation method
BH	Area-weighted mean height of all the buildings in the area
BD	The percentage of building projection per unit area
FAR	The ratio of the building’s total floor area to the area of land in the area
RL	The roughness of ground surface and urban ventilation (Gál and Unger, 2009). It can be estimated by using the urban morphology model (Grimmond and Oke, 1999)
FAI	The ratio of the frontal area of the buildings to the entire land area. It reflects the permeability of urban buildings to wind (Chen and Ng, 2011) and can be estimated through BD and BH (Liu et al., 2020a)
SVF	Describes the three-dimensional urban morphology and reflects the degree of closure of urban space (Gál et al, 2009). It can be estimated through a high-resolution gridded digital elevation model (Zakšek et al., 2011)
FD	Describes the spatial heterogeneity of urban surface (Li et al., 2017). The higher the value is, the higher the complexity and heterogeneity of the underlying surface. It can be estimated by Landsat 8 satellite data (Liu et al., 2020b)
VC	The percentage of urban area with vegetation. It can be estimated from the vegetation-impervious surface-soil (VIS) model (Ridd, 1995) based on the 30-m resolution Landsat 8 satellite datasets (Xu and Liu, 2013)
IC	The percentage of the urban area with impervious surface. The estimation method is similar to that for VC
AB	A key land surface parameter affects urban land surface radiation budget and it can be estimated by using MODIS black space and white space satellite albedo products (MCD43A3) and radiation observation data of meteorological stations (Cai et al., 2016)

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Table 2 Statistical information of 115 block spatial units in the study area

Parameter	Mean	Minimum	Maximum	Standard deviation
Area (km²)	8.98	1.18	50.1	9.68
BH (m)	12.6	1.7	25.2	5.9
BD	0.20	0.05	0.42	0.07
FAR	0.99	0.08	2.13	0.49
BSD (m)	10.86	0.74	28.56	5.60
RL (m)	1.33	0.12	2.73	0.66
SVF	0.73	0.55	0.95	0.10
FAI	0.15	0.02	0.27	0.06
FD	2.56	2.31	2.78	0.11
IC	0.725	0.469	0.858	0.083
VC	0.202	0.082	0.440	0.080
Annual AB	0.119	0.099	0.162	0.013
AB in spring	0.120	0.100	0.160	0.013
AB in summer	0.119	0.100	0.163	0.013
AB in autumn	0.115	0.093	0.163	0.013
AB in winter	0.121	0.101	0.163	0.013

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Table 3 Mean SUHI and CUHI within the Fifth Ring Road and R values for the correlation between the SUHI and CUHI in the 115 blocks in Beijing’s CUA during different periods of 2009–2018

Period	SUHI (°C)	CUHI (°C)	R
Annual	2.24	1.51	0.62
Spring	2.13	1.15	0.55
Summer	3.53	1.04	0.69
Autumn	2.78	1.67	0.64
Winter	2.11	2.07	0.76
Annual day	0.47	0.29	0.34
Spring day	−0.25	0.14	0.29
Summer day	3.75	0.51	0.50
Autumn day	2.03	0.16	0.42
Winter day	−1.15	0.26	−0.02
Annual night	4.05	2.1	0.71
Spring night	4.47	1.99	0.65
Summer night	3.40	1.63	0.59
Autumn night	3.55	2.33	0.67
Winter night	5.47	2.86	0.80

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Table 4 Single-factor regression and all-factor stepwise regression models between the summer, winter, and annual mean SUHI values and the USL metrics (BH, BD, FAR, BSD, RL, FAI, SVF, FD, VC, IC, and AB) for Beijing’s CUA in 2009–2018

Metrics	Summer	Winter	Annual
BH	SUHI = 0.076BH + 2.252 R² = 0.268, p < 0.01	SUHI = 0.059BH + 1.357 R² = 0.337, p < 0.01	SUHI = 0.058BH + 1.433 R² = 0.184, p < 0.01
BD	SUHI = 7.870BD + 1.870 R² = 0.460, p < 0.01	SUHI = 5.672BD + 0.939 R² = 0.500, p < 0.01	SUHI = 6.440BD + 0.852 R² = 0.357, p < 0.01
FAR	SUHI = 1.094FAR + 2.390 R² = 0.386, p < 0.01	SUHI = 0.854FAR + 1.248 R² = 0.493, p < 0.01	SUHI = 0.848FAR + 1.324 R² = 0.269, p < 0.01
BSD	SUHI = 0.074BSD + 2.673 R² = 0.228, p < 0.01	SUHI = 0.058BSD + 1.468 R² = 0.291, p < 0.01	SUHI = 0.058BSD + 1.531 R² = 0.165, p < 0.01
RL	SUHI = 0.713RL + 2.524 R² = 0.295, p < 0.01	SUHI = 0.551RL + 1.360 R² = 0.370, p < 0.01	SUHI = 0.542RL + 1.442 R² = 0.198, p < 0.01
FAI	SUHI = 8.358FAI + 2.239 R² = 0.390, p < 0.01	SUHI = 6.326FAI + 1.160 R² = 0.468, p < 0.01	SUHI = 6.384FAI + 1.221 R² = 0.264, p < 0.01
SVF	SUHI = −5.623SVF + 7.574 R² = 0.464, p<0.01	SUHI = −4.219SVF + 5.170 R² = 0.548, p < 0.01	SUHI = −4.35SVF + 5.336 R² = 0.322, p < 0.01
FD	SUHI = 4.606FD − 8.309 R² = 0.364, p < 0.01	SUHI = 3.51FD − 6.883 R² = 0.443, p < 0.01	SUHI = 3.451FD − 6.663 R² = 0.237, p < 0.01
VC	SUHI = −8.507VC + 5.191 R² = 0.618, p < 0.01	SUHI = −5.945VC + 3.294 R² = 0.633, p < 0.01	SUHI = −6.944VC + 3.566 R² = 0.478, p < 0.01
IC	SUHI = 7.593IC − 2.031 R² = 0.534, p < 0.01	SUHI = 4.958IC − 1.500 R² = 0.478, p < 0.01	SUHI = 5.938IC − 2.140 R² = 0.379, p < 0.01
AB	SUHI = −35.083AB + 7.655 R² = 0.279, p < 0.01	SUHI = −31.783AB + 5.929 R² = 0.462, p < 0.01	SUHI = −25.394AB + 5.174 R² = 0.162, p < 0.01
All	SUHI = −8.507VC + 5.191 R² = 0.618, p < 0.01	SUHI = 4.446 − 4.657VC − 11.7AB R² = 0.666, p < 0.01	SUHI = 2.385 − 8.365VC + 12.379AB R² = 0.496, p < 0.01
Note: p < 0.01 indicates that the linear fitting model reaches the 0.01 significance level.

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References (60)

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: 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: 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: 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 Kraków, Poland. Theor. Appl. Climatol., 122, 365–382. doi: 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. (in Chinese) doi: 10.11873/j.issn.1004-0323.2016.4.0724

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: 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: 10.1007/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: 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: 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: 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: 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: 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: 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: 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: 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: 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. (in Chinese) doi: 10.3878/j.issn.1006-9585.2021.20149

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: 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: 10.1109/LGRS.2019.2962383

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: 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: 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. (in Chinese) doi: 10.3969/j.issn.1674-5906.2014.07.010

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: 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: 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. (in Chinese) doi: 10.11821/dlxb202107007

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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 MODIS data. Int. J. Remote Sens., 36, 5448–5465. doi: 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: 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: 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: 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: 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: 10.1080/014311698215784

Voogt, J. A., and T. R. Oke, 2003: Thermal remote sensing of urban climates. Remote Sens. Environ., 86, 370–384. doi: 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: 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: 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. (in Chinese) doi: 10.3969/2019jms.0040

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. (in Chinese) doi: 10.3969/j.issn.1674-5906.2013.04.015

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: 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: 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: 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: 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: 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: 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: 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: 10.3390/rs11010048

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Liu, Y. H., Y. M. Xu, Y. P. Zhang, et al., 2022: Impacts of the urban spatial landscape in Beijing on surface and canopy urban heat islands. J. Meteor. Res., 36(6), 882–899, doi: 10.1007/s13351-022-2045-y.

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Manuscript History

Received: 04 March 2022

Revised: 13 June 2022

Accepted: 07 July 2022

Available online: 11 July 2022

Final form: 25 July 2022

Typeset Proofs: 19 August 2022

Issue in Progress: 19 August 2022

Published online: 29 December 2022

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Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

北京城市空间结构对陆表热岛和冠层热岛影响对比研究

Abstract