Tree species richness and diversity predicts the magnitude of urban heat island mitigation effects of greenspaces
1
Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing, Jiangsu 210042, China
|
5
Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China
|
Publication type: Journal Article
Publication date: 2021-05-01
scimago Q1
wos Q1
SJR: 2.137
CiteScore: 16.4
Impact factor: 8.0
ISSN: 00489697, 18791026
PubMed ID:
33513510
Environmental Chemistry
Environmental Engineering
Pollution
Waste Management and Disposal
Abstract
The Urban Heat Island Effect (UHIE) is a widely recognised phenomenon that profoundly affects the quality of life for urban citizens. Urban greenspace can help mitigate the UHIE, but the characteristics that determine the extent to which any given greenspace can cool an urban area are not well understood. A key characteristic is likely to be the properties of trees that are found in a greenspace. Here, we explore the sensitivity of the strength of the cooling effect to tree community structure for greenspaces in Changzhou, China. Land surface temperatures were retrieved from Landsat 7 ETM+ and Landsat 8 TIRS and were used to evaluate the temperature drop amplitude (TDA) and cooling range (CR) of 15 greenspaces across each of the four seasons. Tree community structure of the greenspaces was investigated using 156 sample plots across the 15 greenspaces. We found that a number of plant community structure indicators of greenspaces have a significant impact on the strength of the cooling effect. The Shannon-Wiener diversity index, tree species richness and tree canopy coverage of greenspaces are all positively correlated with the magnitude of the temperature drop amplitude, with the strength of their influence varying seasonally. We also find that mean crown width is positively correlated with cooling range in summer and autumn, while greenspace tree density is negatively correlated with cooling range in winter. Our findings improve understanding of the relationship between plant community structure and the cooling effect of greenspaces. In particular, we highlight the important role that tree species diversity provides for mitigating the UHIE, and suggest that if planners wish to improve the role of urban greenspaces in cooling cities, they should include a higher diversity of trees species. • Cooling effect of greenspaces varies seasonally • Tree diversity and tree coverage positively correlated with temperature drop amplitude • Tree crown width positively correlated with cooling range, and tree density negatively correlated with the cooling range. • Optimizing the plant community structure improves the cooling effect of greenspaces without increasing their size.
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123
Total citations:
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Citations from 2024:
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Sun N. et al. Tree species richness and diversity predicts the magnitude of urban heat island mitigation effects of greenspaces // Science of the Total Environment. 2021. Vol. 770. p. 145211.
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Sun N., Guenat S., Scott C. A., Shi W., Gao J. Tree species richness and diversity predicts the magnitude of urban heat island mitigation effects of greenspaces // Science of the Total Environment. 2021. Vol. 770. p. 145211.
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TY - JOUR
DO - 10.1016/j.scitotenv.2021.145211
UR - https://doi.org/10.1016/j.scitotenv.2021.145211
TI - Tree species richness and diversity predicts the magnitude of urban heat island mitigation effects of greenspaces
T2 - Science of the Total Environment
AU - Sun, Nian-Xiang
AU - Guenat, Solène
AU - Scott, Catherine A.
AU - Shi, Weiting
AU - Gao, Jixi
PY - 2021
DA - 2021/05/01
PB - Elsevier
SP - 145211
VL - 770
PMID - 33513510
SN - 0048-9697
SN - 1879-1026
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2021_Sun,
author = {Nian-Xiang Sun and Solène Guenat and Catherine A. Scott and Weiting Shi and Jixi Gao},
title = {Tree species richness and diversity predicts the magnitude of urban heat island mitigation effects of greenspaces},
journal = {Science of the Total Environment},
year = {2021},
volume = {770},
publisher = {Elsevier},
month = {may},
url = {https://doi.org/10.1016/j.scitotenv.2021.145211},
pages = {145211},
doi = {10.1016/j.scitotenv.2021.145211}
}