Effects of building density on land surface temperature in China: Spatial patterns and determinants

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Effects of building density on land surface temperature in China : Spatial patterns and determinants. / Song, Jinchao; Chen, Wei; Zhang, Jianjun; Huang, Ke; Hou, Boyan; Prishchepov, Alexander V.

I: Landscape and Urban Planning, Bind 198, 103794, 2020.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Song, J, Chen, W, Zhang, J, Huang, K, Hou, B & Prishchepov, AV 2020, 'Effects of building density on land surface temperature in China: Spatial patterns and determinants', Landscape and Urban Planning, bind 198, 103794. https://doi.org/10.1016/j.landurbplan.2020.103794

APA

Song, J., Chen, W., Zhang, J., Huang, K., Hou, B., & Prishchepov, A. V. (2020). Effects of building density on land surface temperature in China: Spatial patterns and determinants. Landscape and Urban Planning, 198, [103794]. https://doi.org/10.1016/j.landurbplan.2020.103794

Vancouver

Song J, Chen W, Zhang J, Huang K, Hou B, Prishchepov AV. Effects of building density on land surface temperature in China: Spatial patterns and determinants. Landscape and Urban Planning. 2020;198. 103794. https://doi.org/10.1016/j.landurbplan.2020.103794

Author

Song, Jinchao ; Chen, Wei ; Zhang, Jianjun ; Huang, Ke ; Hou, Boyan ; Prishchepov, Alexander V. / Effects of building density on land surface temperature in China : Spatial patterns and determinants. I: Landscape and Urban Planning. 2020 ; Bind 198.

Bibtex

@article{b3bce13b6dca48ebb10e29ffcc60265a,
title = "Effects of building density on land surface temperature in China: Spatial patterns and determinants",
abstract = "The effects of building density on land surface temperature (LST) and its spatial patterns remain poorly understood over large areas. Using Landsat 8 satellite imagery, we quantified the effects of building density on land surface temperature (K) across 21 cities in China and analysed their spatial patterns, possible factors, and mechanisms. Results showed that the effects of building density on LST were more significant in areas with dry climates compared to humid climates. The spatial variability in the effects of building density on LST was closely related to climate conditions, soil type, and vegetation. The results from stepwise regression analysis showed that precipitation (climate) controlled the spatial variability, indicating that there is a complex mechanism underlying these potential factors. Furthermore, the results from climatic zoning statistics revealed that the K-values of northern Chinese cities were positively correlated with the areas of local water bodies. However, the K-values of southern Chinese cities were significantly and positively correlated with the mean annual temperature and aridity and were negatively correlated with population density. Stepwise regression results further indicated that the mean annual temperature may be the most influential factor for southern cities. These results highlight the spatial variance and different determinants of K and suggest that climate-adapted urban design and planning standards are needed in different climate zones.",
keywords = "Building density, Climate zone, Land surface temperature, Remote sensing, Urban planning",
author = "Jinchao Song and Wei Chen and Jianjun Zhang and Ke Huang and Boyan Hou and Prishchepov, {Alexander V.}",
year = "2020",
doi = "10.1016/j.landurbplan.2020.103794",
language = "English",
volume = "198",
journal = "Landscape and Urban Planning",
issn = "0169-2046",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Effects of building density on land surface temperature in China

T2 - Spatial patterns and determinants

AU - Song, Jinchao

AU - Chen, Wei

AU - Zhang, Jianjun

AU - Huang, Ke

AU - Hou, Boyan

AU - Prishchepov, Alexander V.

PY - 2020

Y1 - 2020

N2 - The effects of building density on land surface temperature (LST) and its spatial patterns remain poorly understood over large areas. Using Landsat 8 satellite imagery, we quantified the effects of building density on land surface temperature (K) across 21 cities in China and analysed their spatial patterns, possible factors, and mechanisms. Results showed that the effects of building density on LST were more significant in areas with dry climates compared to humid climates. The spatial variability in the effects of building density on LST was closely related to climate conditions, soil type, and vegetation. The results from stepwise regression analysis showed that precipitation (climate) controlled the spatial variability, indicating that there is a complex mechanism underlying these potential factors. Furthermore, the results from climatic zoning statistics revealed that the K-values of northern Chinese cities were positively correlated with the areas of local water bodies. However, the K-values of southern Chinese cities were significantly and positively correlated with the mean annual temperature and aridity and were negatively correlated with population density. Stepwise regression results further indicated that the mean annual temperature may be the most influential factor for southern cities. These results highlight the spatial variance and different determinants of K and suggest that climate-adapted urban design and planning standards are needed in different climate zones.

AB - The effects of building density on land surface temperature (LST) and its spatial patterns remain poorly understood over large areas. Using Landsat 8 satellite imagery, we quantified the effects of building density on land surface temperature (K) across 21 cities in China and analysed their spatial patterns, possible factors, and mechanisms. Results showed that the effects of building density on LST were more significant in areas with dry climates compared to humid climates. The spatial variability in the effects of building density on LST was closely related to climate conditions, soil type, and vegetation. The results from stepwise regression analysis showed that precipitation (climate) controlled the spatial variability, indicating that there is a complex mechanism underlying these potential factors. Furthermore, the results from climatic zoning statistics revealed that the K-values of northern Chinese cities were positively correlated with the areas of local water bodies. However, the K-values of southern Chinese cities were significantly and positively correlated with the mean annual temperature and aridity and were negatively correlated with population density. Stepwise regression results further indicated that the mean annual temperature may be the most influential factor for southern cities. These results highlight the spatial variance and different determinants of K and suggest that climate-adapted urban design and planning standards are needed in different climate zones.

KW - Building density

KW - Climate zone

KW - Land surface temperature

KW - Remote sensing

KW - Urban planning

UR - http://www.scopus.com/inward/record.url?scp=85081201797&partnerID=8YFLogxK

U2 - 10.1016/j.landurbplan.2020.103794

DO - 10.1016/j.landurbplan.2020.103794

M3 - Journal article

AN - SCOPUS:85081201797

VL - 198

JO - Landscape and Urban Planning

JF - Landscape and Urban Planning

SN - 0169-2046

M1 - 103794

ER -

ID: 237997456