Abstract
Urban morphology is a crucial contributor to urban heat island (UHI) effects. However, few studies have explored the complex effect of 2D/3D urban morphology on UHIs from a multiscale perspective. In this study, we chose the central area of Jinan city, which is commonly known as the “furnace,” as the case study area. The 2D/3D urban morphology indexes-building coverage ratio (BCR) (for assessing the 2D building density), building volume density (BVD) (for assessing the 3D building density), and frontal area index (FAI) (for assessing 3D ventilation conditions) were calculated and derived to investigate the complexity of the relationship between 2D/3D urban morphology and the land surface temperature (LST) at different scales using the maximum information coefficient (MIC) and geographically weighted regression (GWR). The results indicated that (1) these 2D/3D urban morphology indexes are essential factors that are responsible for LST variation, and BCR is the most important urban morphology index affecting LST, followed by BVD and FAI. Importantly, the relationship between the BCR, BVD, FAI, and LST was an inverse U-shaped curve. (2) The relationship between 2D/3D urban morphology and LST variation showed a significant scale effect. With increased grid size, the correlation between the BCR, BVD, and FAI and the LST strengthened, “inflection point” of inverse U-shaped curve significantly declined, and their explanation rate of the LST first increased and then decreased, with a maximum value at the 700 m scale. Additionally, the FAI exerted a stronger negative effect, while the BCR and BVD generally had stronger positive effects on the LST as the grid size increased. This study extends our scientific understanding of the complex effect of urban morphology on the LST and is of great practical significance for multiscale urban thermal environment regulation.
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Abbreviations
- 2D:
-
Two-dimensional
- 3D:
-
Three-dimensional
- UHI:
-
Urban heat island
- SUHI:
-
Surface urban heat island
- LST:
-
Land surface temperature
- UBD:
-
Urban building density
- FDI:
-
Building fractal dimension index
- TIRS:
-
Thermal infrared sensor
- BCR:
-
Building coverage ratio
- BVD:
-
Building volume density
- FAI:
-
Frontal area index
- MIC:
-
Maximum information coefficient
- GWR:
-
Geographically weighted regression
- UBH:
-
Urban building height
- SVF:
-
Sky view factor
References
Beck C, Straub A, Breitner S, Cyrys J, Philipp A, Rathmann J, Schneider A, Wolf K, Jacobeit J (2018) Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions. Urban Clim 25:152–166
Carmona PL, Tran DX, Pla F, Myint SW, Kieu HV (2017) Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J Photogramm Remote Sens 124:119–132
Chen XL, Zhao HM, Li PX, Yin ZY (2006) Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ 104:133–146
Chen YC, Lin TP, Shih WY (2017) Modeling the urban thermal environment distributions in Taipei Basin using local climate zone (LCZ). In: 2017 Joint Urban Remote Sensing Event (JURSE). IEEE, 1–4
Coseo P, Larsen L (2019) Accurate characterization of land cover in urban environments: determining the importance of including obscured impervious surfaces in urban heat island models. Atmosphere 10:347
Estoque RC, Murayama Y, Myint SW (2017) Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Sci Total Environ 577:349–359
Giridharan R, Lau SSY, Ganesan S, Givoni B (2007) Urban design factors influencing heat island intensity in high-rise high-density environments of Hong Kong. Build Environ 42:3669–3684
Guo JM, Han GF, Xie YS, Cai Z, Zhao YF (2020) Exploring the relationships between urban spatial form factors and land surface temperature in mountainous area: a case study in Chongqing city, China. Sustainable Cities and Society 61:102286
Han SR, Wei S, Zhou W, Zhang MJ, Xu C (2017) Quantifying the spatial pattern of urban thermal fields based on point of interest data and Landsat images. Acta Ecologica Sinica 37:5305–5312
Hu Y, Dai Z, Guldmann JM (2020) Modeling the impact of 2D/3D urban indicators on the urban heat island over different seasons: a boosted regression tree approach. J Environ Manag 266:110424
Huang X, Wang Y (2019) Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: a case study of Wuhan, Central China. ISPRS J Photogramm Remote Sens 152:119–131
Inouye DW (2015) The next century of ecology. Science 349:565
Jamei E, Rajagopalan P (2017) Urban development and pedestrian thermal comfort in Melbourne. Sol Energy 144:681–698
Kammuang-Lue N, Sakulchangsatjatai P, Sangnum P, Terdtoon P (2015) Influences of population, building, and traffic densities on urban heat island intensity in Chiang Mai City, Thailand. Therm Sci 19:85–85
Koc CB, Osmond P, Peters A, Irger M (2018) Understanding land surface temperature differences of local climate zones based on airborne remote sensing data. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 11:2724–2730
Kolokotroni M, Zhang Y, Watkins R (2007) The London heat island and building cooling design. Sol Energy 81:102–110
Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1967
Li T, Xu Y, Yao L (2021a): Detecting urban landscape factors controlling seasonal land surface temperature: from the perspective of urban function zones. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13695-y
Li X, Yang B, Xu G, Liang F, Jiang T, Dong Z (2021b) Exploring the impact of 2D/3D building morphology on the land surface temperature: a case study of three megacities in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:4933–4945
Liu Y, Li Q, Yang L, Mu K, Zhang M, Liu J (2020) Urban heat island effects of various urban morphologies under regional climate conditions. Sci Total Environ 743:140589
Meng F, Liu M (2013) Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan, China. Int J Remote Sens 34:8838–8853
Meng Q, Zhang L, Sun Z, Meng F, Wang L, Sun Y (2018) Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: a 12-year case study in Beijing, China. Remote Sens Environ 204:826–837
Peng J, Jia J, Liu Y, Li H, Wu J (2018) Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas. Remote Sens Environ 215:255–267
Peng J, Qiao R, Liu Y, Blaschke T, Li S, Wu J, Xu Z, Liu Q (2020) A wavelet coherence approach to prioritizing influencing factors of land surface temperature and associated research scales. Remote Sens Environ 246:111866
Perini K, Magliocco A (2014) Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For Urban Green 13:495–506
Petralli M, Massetti L, Brandani G, Orlandini S (2014) Urban planning indicators: useful tools to measure the effect of urbanization and vegetation on summer air temperatures. Int J Climatol 34:1236–1244
Qiao Z, Han XP, Wu C, Liu L, Xu XL, Sun ZY, Sun W, Cao Q, Li LW (2020) Scale effects of the relationships between 3D building morphology and urban heat island: a case study of provincial capital cities of Mainland China. Complexity 2020:9326793
Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC (2011) Detecting novel associations in large data sets. Science 334:1518–1524
Sadiq Khan M, Ullah S, Sun T, Rehman AUR, Chen L (2020): Land-use/land-cover changes and its contribution to urban heat island: a case study of Islamabad, Pakistan. Sustainability 12:3861
Seto KC, Gueneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Natl Acad Sci U S A 109:16083–16088
Shi L, Luo Z, Matthews W, Wang Z, Li Y, Liu J (2019) Impacts of urban microclimate on summertime sensible and latent energy demand for cooling in residential buildings of Hong Kong. Energy 189:116208
Song J, Du S, Feng X, Guo L (2014) The relationships between landscape compositions and land surface temperature: quantifying their resolution sensitivity with spatial regression models. Landsc Urban Plan 123:145–157
Subedi M, Fullen MA (2009) Temporal changes in soil temperature at the Hilton Experimental Site, Shropshire, UK (1982-2006): Evidence of a warming trend? Arch Agron Soil Sci 55:105–113
Sun F, Liu M, Wang Y, Wang H, Che Y (2020) The effects of 3D architectural patterns on the urban surface temperature at a neighborhood scale: relative contributions and marginal effects. J Clean Prod 258:120706
Taha H (1997) Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and Buildings 25: 99-103
Tiatragul S, Kurniawan A, Kolbe JJ, Warner DA (2017) Embryos of non-native anoles are robust to urban thermal environments. J Therm Biol 65:119–124
Tu L, Qin Z, Li W, Geng J, Yang L, Zhao S, Zhan W, Wang F (2016) Surface urban heat island effect and its relationship with urban expansion in Nanjing, China. J Appl Remote Sens 10:026037
Wang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S (2015) An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens 7:4268–4289
Wang J, Qingming Z, Guo H, Jin Z (2016) Characterizing the spatial dynamics of land surface temperature–impervious surface fraction relationship. Int J Appl Earth Obs Geoinf 45:55–65
Wang X, Zhou T, Tao F, Zang F (2019) Correlation analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery. Applied Sciences 9:5224
Wang Y, Xu M, Li J, Jiang N, Wang D, Yao L, Xu Y (2020) The gradient effect on the relationship between the underlying factor and land surface temperature in large urbanized region. Land 10:20
Weng Q (2009) Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS J Photogramm Remote Sens 64:335–344
Weng QH, Lu DS, Schubring J (2004) Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483
Wheeler DC, Páez A (2010) Geographically weighted regression. Handbook of Applied Spatial Analysis. https://doi.org/10.1007/978-3-642-03647-7_22
Wong MS, Nichol JE (2012) Spatial variability of frontal area index and its relationship with urban heat island intensity. Int J Remote Sens 34:885–896
Wong MS, Nichol J, Lee KH (2010): A satellite view of urban heat island: causative factors and scenario analysis. Korean Journal of Remote Sensing 26:617–627
Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landsc Ecol 19:125–138
Wu Z, Yao L, Ren Y (2020) Characterizing the spatial heterogeneity and controlling factors of land surface temperature clusters: a case study in Beijing. Build Environ 169:106598
Xu Y, Ren C, Ma PF, Ho J, Wang WW, Lau KKL, Lin H, Ng E (2017) Urban morphology detection and computation for urban climate research. Landsc Urban Plan 167:212–224
Yang F, Lau SSY, Qian F (2010) Summertime heat island intensities in three high-rise housing quarters in inner-city Shanghai China: building layout, density and greenery. Build Environ 45:115–134
Yang J, Wang YC, Xiao XM, Jin C, Xia JH, Li XM (2019) Spatial differentiation of urban wind and thermal environment in different grid sizes. Urban Clim 28:100458
Yang SJ, Ran GP, Zhang W, Wang ZH (2020) The cooling effect of an urban lake landscape on the urban heat island: a case study in Jinan, China. Appl Ecol Environ Res 18:2197–2211
Yao L, Xu Y, Zhang B (2019) Effect of urban function and landscape structure on the urban heat island phenomenon in Beijing, China. Landscape and Ecological Engineering 15:379–390
Yao L, Li T, Xu M, Xu Y (2020) How the landscape features of urban green space impact seasonal land surface temperatures at a city-block-scale: an urban heat island study in Beijing, China. Urban For Urban Green 52:126704
Yao L, Sun S, Song CX, Li J, Xu WT, Xu Y (2021) Understanding the spatiotemporal pattern of the urban heat island footprint in the context of urbanization, a case study in Beijing. China, Applied Geography 133:102496
Youpei Hu, Marcus White, Wowo Ding (2016) An urban form experiment on urban heat island effect in high density area. Procedia Engineering 169:166–174
Yu B, Liu H, Wu J, Hu Y, Zhang L (2010) Automated derivation of urban building density information using airborne LiDAR data and object-based method. Landsc Urban Plan 98:210–219
Yuan S, Xia H, Yang L (2020) How changing grain size affects the land surface temperature pattern in rapidly urbanizing area: a case study of the central urban districts of Hangzhou City. China, Environ Sci Pollut Res Int. https://doi.org/10.1007/s11356-020-08672-w
Zhang Q, Chen C, Wang J, Yang D, Zhang Y, Wang Z, Gao M (2020) The spatial granularity effect, changing landscape patterns, and suitable landscape metrics in the Three Gorges Reservoir Area, 1995-2015. Ecol Indic 114:106259
Zhang Y, Balzter H, Li Y (2021) Influence of impervious surface area and fractional vegetation cover on seasonal urban surface heating/cooling rates. Remote Sensing 13:1263
Zhao C, Jensen J, Weng Q, Weaver R (2018) A Geographically weighted regression analysis of the underlying factors related to the surface urban heat island phenomenon. Remote Sens 10:1428
Zhou W, Wang J, Cadenasso ML (2017) Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sens Environ 195:1–12
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This research was supported by the National Social Science Fund of China (No. 18BJY086).
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The manuscript was reviewed and approved for publication by all authors. Yu Liu and Baolei Zhang conceived and designed the experiments. Yu Liu, Zhipeng Wang, and Xuan Liu performed the experiments. Yu Liu, Baolei Zhang, and Xuan Liu analyzed the data. Yu Liu and Baolei Zhang wrote the paper. Zhipeng Wang and Yu Liu reviewed and revised the paper. All authors have read and agreed to the published version of the manuscript.
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Liu, Y., Wang, Z., Liu, X. et al. Complexity of the relationship between 2D/3D urban morphology and the land surface temperature: a multiscale perspective. Environ Sci Pollut Res 28, 66804–66818 (2021). https://doi.org/10.1007/s11356-021-15177-7
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DOI: https://doi.org/10.1007/s11356-021-15177-7