The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing

Zheng, Zhong; Zhou, Weiqi; Yan, Jingli; Qian, Yuguo; Wang, Jia; Liu, Weifeng

doi:10.1016/j.pce.2019.01.008

Cited by 90 publications

(62 citation statements)

References 55 publications

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“…The LST value showed gradients from low-density to high-density areas. Similarly, the previous study by Zheng et al [78] revealed that residential neighborhoods in Beijing city, China have low LST effects compared to low-height buildings. Their study also found that low-height buildings have higher LST than tall buildings.…”

Section: Three-dimensional (3d) Surface Plotmentioning

confidence: 55%

“…The findings presented in this paper suggest that vertical development is better than horizontal development, which provides enough open spaces, green spaces, and preserves natural features. A study by Zheng et al [78] revealed that higher residential buildings have low LST effects in a neighborhood-scale residential area mixed with residential buildings and enough vegetation, which results in a significant cool island effect. In this context, urban sprawl must be controlled to improve the microclimate environment of DMA.…”

Section: Discussionmentioning

confidence: 99%

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Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

et al. 2020

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Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

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Section: Three-dimensional (3d) Surface Plotmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

et al. 2020

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“…Regardless of climate context, spatial variation of heat intensity amongst urban neighbourhoods is closely related to the proportion and spatial pattern of different land cover ( Connors, Galletti, & Chow, 2013 ; Kong, Yin, James, Hutyra, & He, 2014 ; Li, Zhou, & Ouyang, 2013 ; Zhou, Huang, & Cadenasso, 2011 ; Zhou, Wang, & Cadenasso, 2017 ); land use ( Bechtel et al, 2019 ; Myint et al, 2015 ); built-up geometry ( Bechtel et al, 2019 ; Giridharan & Emmanuel, 2018 ); radiative properties of objects, and anthropogenic heat release ( Bechtel et al, 2019 ; Oke, 1986 ) in both the horizontal and vertical dimensions of cities ( Alavipanah et al, 2018 ; Tian, Zhou, Qian, Zheng, & Yan, 2019 ; Zheng et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…There is limited agreement on how the efficiency of greenspace cooling effect may vary in cities depending on climate type ( Zhou et al, 2017 ). However, greenspace cooling effect is often influenced by types of vegetation through the difference in shading and evapotranspiration rate ( Skelhorn, Lindley, & Levermore, 2014 ); spatial configuration of greenspaces ( Du et al, 2017 ; Shih, 2017a ; Yu, Xu, Zhang, Jørgensen, & Vejre, 2018 ; Zhou et al, 2017 ); and interrelation with surrounding built environments ( Li et al, 2013 ; Shih, 2017b ; Zhao, Sailor, & Wentz, 2018 ; Zheng et al, 2019 ). Spatially, greater tree coverage, larger greenery area and higher coherence of greenspaces are a preferable structure for delivering cooling to a local scale ( Li et al, 2013 ; Maimaitiyiming et al, 2014 ; Zhang, Murray, & Turner Ii, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, both greenspace cooling intensity and extension are modifiable by the thermal state of the adjacent non-green areas ( Li et al, 2013 ; Middel, Häb, Brazel, Martin, & Guhathakurta, 2014 ) and the relative location of greenery to buildings ( Zhao et al, 2018 ). A more comprehensive cooling strategy should consider the interplay between grey and green environments ( 2017b , Shih, 2017b ; Zhao et al, 2018 ; Zheng et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Spatial relationship between land development pattern and intra-urban thermal variations in Taipei

Shih

Ahmad

Chen

et al. 2020

Sustainable Cities and Society

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Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants

Shan

Chen

et al. 2023

Environ Monit Assess

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Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area, and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) The effect of each spatial distribution index of building on the concentration distribution of the four gaseous pollutants was significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations.(2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

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The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing

Cited by 90 publications

References 55 publications

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Spatial relationship between land development pattern and intra-urban thermal variations in Taipei

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants

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