Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China

小气候 城市热岛 植被(病理学) 大都市区 环境科学 自然地理学 城市森林 共同空间格局 城市气候 空间生态学 特大城市 城市化 地理 生态学 林业 气象学 生物 病理 医学 考古
作者
Fanhua Kong,Haiwei Yin,Philip James,Lucy R. Hutyra,Hong S. He
出处
期刊:Landscape and Urban Planning [Elsevier BV]
卷期号:128: 35-47 被引量:452
标识
DOI:10.1016/j.landurbplan.2014.04.018
摘要

Urban areas will experience the greatest increases in temperature resulting from climate change due to the urban heat island (UHI) effect. Urban greenspace mitigates the UHI and provides cooler microclimates. Field research has established that temperatures within parks or beneath trees can be cooler than in non-greenspaces, but little is known about the effects of the spatial pattern of greenspace on urban temperatures or the optimal spatial patterns needed to cool an urban environment. Here, urban cool islands (UCIs) and greenspace in Nanjing, China were identified from satellite data and the relationship between them analyzed using correlation analyses. The results indicate the following: (1) Areas with a higher percentage of forest-vegetation experience a greater cooling effect and a 10% increase in forest-vegetation area resulted in a decrease of about 0.83 °C in surface temperature; (2) A correlation analysis between mean patch size, patch density, and an aggregation index of forest vegetation with temperature reduction showed that for a fixed amount of forest vegetation, fragmented greenspaces also provide effective cooling; (3) The spatial pattern of UCIs was strongly correlated with greenspace patterns; a mainland-island greenspace spatial configuration provided an efficient means of enhancing the cooling effects; and (4) the intensity of the cooling effect was reflected in cool island characteristics. These findings will support better prediction of the effects of specific amounts and spatial arrangements of greenspace, helping city managers and planners mitigate increasing temperatures associated with climate change.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
阔达的苑睐完成签到,获得积分10
1秒前
高贵电源发布了新的文献求助10
2秒前
4秒前
李爱国应助tangtang采纳,获得10
5秒前
科研通AI6.4应助可乐必妥采纳,获得10
6秒前
6秒前
7秒前
Xikyi完成签到,获得积分20
10秒前
完美世界应助甜美的忻采纳,获得10
11秒前
温壶老酒完成签到 ,获得积分10
12秒前
12秒前
ding应助高贵电源采纳,获得10
13秒前
cc2713206发布了新的文献求助60
13秒前
scl发布了新的文献求助30
13秒前
han发布了新的文献求助10
13秒前
13秒前
wanci应助过时的猫咪采纳,获得10
14秒前
愉快的真发布了新的文献求助10
14秒前
Xikyi发布了新的文献求助10
14秒前
16秒前
16秒前
Orange应助无私的若南采纳,获得10
16秒前
17秒前
lalalal发布了新的文献求助10
18秒前
18秒前
杏仁饼干完成签到 ,获得积分10
19秒前
20秒前
zzzxxx发布了新的文献求助10
21秒前
嘉月拾完成签到,获得积分10
21秒前
jjjj完成签到,获得积分10
22秒前
tangtang发布了新的文献求助10
23秒前
华仔应助lalalal采纳,获得10
24秒前
shs完成签到 ,获得积分10
24秒前
25秒前
26秒前
彭于晏应助典雅青槐采纳,获得10
27秒前
Owen应助梦蝶采纳,获得10
27秒前
wanci应助恣意采纳,获得10
28秒前
七点完成签到,获得积分10
28秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Reducing Compassion Fatigue, Secondary Traumatic Stress and Burnout 600
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Mammalian Synthetic Biology 500
Auslegungsgeschichte 500
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7638481
求助须知:如何正确求助?哪些是违规求助? 9211737
关于积分的说明 19759776
捐赠科研通 7205450
什么是DOI,文献DOI怎么找? 3275880
关于科研通互助平台的介绍 2437447
邀请新用户注册赠送积分活动 2273082