Multi⁃scale analysis of the effects of green space pattern on the urban surface thermal environment

doi:10.3969/j.issn.1000-2006.201811056

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2020, Vol. 44 ›› Issue (3): 133-141.doi: 10.3969/j.issn.1000-2006.201811056

Previous Articles Next Articles

Multi⁃scale analysis of the effects of green space pattern on the urban surface thermal environment

ZHOU Wen¹(), CAO Fuliang¹(), ZHANG Rui², WANG Guibin¹

^1.College of Forestry, Nanjing Forestry University, Nanjing 210037, China
^2.College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China

Received:2018-11-28 Revised:2019-04-23 Online:2020-05-30 Published:2020-06-11
Contact: CAO Fuliang E-mail:wenzhou0305@hotmail.com;fuliangcaonjfu@163.com

Abstract

Abstract: Objective

Urban greenspace can be an effective contributor to mitigation of the urban heat island (UHI) effect and adaption to urban climate change. Previous studies have confirmed that the relationship between urban green pattern and land surface temperature (LST) is sensitive to spatial resolution of applied remote sensing imagery; however, little is known about spatial extent, another scaling issue. This study examined the effects of greenspace pattern on urban cooling and the influence of spatial extent when applied to derive landscape metrics. Understanding how the spatial pattern of urban greenspace affects the cooling intensity at different spatial extents is essential for creating a more scientific urban green network to better counteract the UHI effect.

Method

The study applied Landsat?8 TIRS imagery to derive LST data and Spot 6 imagery to retrieve the land-use and land-cover (LULC) map. The spatial pattern of woodland was measured by landscape metrics over four spatial extents/scales (90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m) using a moving-window approach based on the LULC map. The relationship between landscape metrics and LST was established using correlation analyses and regression analyses.

Result

At patch level, the size, shape and connectivity with neighboring greenspaces all affect the cooling intensity of woodland. Meanwhile, patch area (PA) is the main factor influencing the LST of grassland. An increase in size and shape complexity can effectively reduce the LST within the greenspace. Compared with different LULC types, water performs best in urban cooling, followed by woodland and grassland, respectively. At class level, areas with a higher percentage of woodland cover experience a greater cooling effect, and a 10% increase of woodland resulted in a decrease in LST of 1.03 ℃. When given a fixed amount of woodland cover, aggregated distribution provides a stronger cooling effect than relative fragmented distribution. This study has suggested that landscape composition is more important than spatial configuration in determining the magnitude of LST. Moreover, results also demonstrated that changing spatial extent had significant impacts on the relationship between spatial pattern of urban greenspace and LST. Specifically, the significance of correlation between percentage of landscape (PLAND), mean patch size (MPS), largest patch index (LPI), and LST decreased as the spatial extent increased, and increased as the spatial extent increased between number of patches (NP), aggregation index (AI) and LST. The relationship between mean patch shape index (Shape_MN) and LST is not as sensitive as other landscape metrics to spatial extent. The findings in this study indicate that multi-scale analysis is required to fully explore the relationship between urban green pattern and LST.

Conclusion

Quantifying the relationship between spatial patterns of greenspaces and cooling intensity will provide a better prediction of the optimal pattern required to cool the urban environment, thus providing practical suggestions for urban greenspace planning and sustainable development.

Key words: urban greenspace, multi-scale analysis, landscape pattern, cooling effect, land surface temperature (LST)

CLC Number:

S731.2

ZHOU Wen, CAO Fuliang, ZHANG Rui, WANG Guibin. Multi⁃scale analysis of the effects of green space pattern on the urban surface thermal environment[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 133-141.

Figures/Tables 9

Fig. 1

Table 1

Landscape pattern metrics used to describe urban green patterns"

景观格局指数 landscape pattern metrics	公式 formula	描述 description
斑块面积 PA(P_A)	$P A = a i j (1 10 ? 000)$	指斑块的面积大小
斑块形状 SI( $I S)$	$I S = p i j (2 π A i j)$	指斑块形状的复杂程度， $I S$ 接近1表示斑块形状越接近圆形， $I S$ 越大，形状越复杂。其中，P_ij为斑块周长，A_ij为斑块面积
周长面积比 PARA(P_ARA)	$P A R A = P i j A i j$	形容斑块形状的复杂程度，P_ARA越大斑块形状越复杂
邻接绿地面积百分比NGP(P_NG)	$P N G = a i j A i j × 100 %$	指邻近绿地斑块的面积占比，P_NG越大，目标斑块与周边绿地的连接度越高。其中，a_ij为目标斑块缓冲区内的绿地面积，A_ij指建立的缓冲区的面积
景观类型面积 CA(S_CA)	$S C A = ∑ j = 1 n a i j (1 10 ? 000)$	是景观中各个类型斑块所占面积
景观面积百分比PLAND(P_LAND)	$P L A N D = ∑ j = 1 n a i j / A × 100 %$	景观中某类斑块的面积占整个景观面积的百分比。其中A是整个景观的面积
平均斑块面积 MPS(S_MP)	$S M P = A i N$	A_i为某类型景观的面积，N为该类型景观中的斑块数。S_MP可用来对比不同景观的聚集或破碎度，也可指示景观各类型之间的差异
斑块个数 NP(N_P)	N_P=n_i	n_i为景观中某类型斑块的个数，其值大小与景观的破碎度也有很好的正相关性，一般规律是N_P越大，破碎度越高，反之亦然
最大斑块指数 LPI(I_LP)	$I L P = m a x j = 1 a (a i j) A$	斑块类型中最大斑块面积占总景观面积的百分数，是斑块水平上优势度的量度
平均形状指数Shape?MN(I_MS)	$I M S = ∑ i = 1 m ∑ j = 1 m 0.25 P i j a i j / N$	在类型等级上反映同类斑块的平均形状复杂程度，当值为1时，同类所有斑块的形状接近为方形（圆形）
聚集度 AI(A_I)	$A I = 100 g i i m a x → g i i$	在类型等级上反映同类斑块的聚合程度。其中，g_ii为相应景观类型的相似邻接斑块数量

Table 1

Table 2

Table 3

Table 4

Table 5

Fig. 4

Table 6

References 33

1	WARD K，LAUF S，KLEINSCHMIT B，et al．Heat waves and urban heat islands in Europe：a review of relevant drivers[J]．Sci Total Environ，2016（569/570）：527-539．DOI:10.1016/j.scitotenv.2016.06.119.
2	KERAMITSOGLOU I，KIRANOUDIS C T，CERIOLA G，et al．Identification and analysis of urban surface temperature patterns in Greater Athens，Greece，using MODIS imagery[J]. Remote Sens Environ，2011，115(12)：3080-3090. DOI:10.1016/j.rse.2011.06.014.
3	GEORGI J N，DIMITRIOU D. The contribution of urban green spaces to the improvement of environment in cities：case study of Chania，Greece[J]. Build Environ，2010，45(6)：1401-1414．DOI:10.1016/j.buildenv.2009.12.003.
4	HAMADA S，OHTA T. Seasonal variations in the cooling effect of urban green areas on surrounding urban areas[J]. Urban For Urban Green,2010,9(1)：15-24. DOI:10.1016/j.ufug. 2009.10.002.
5	LI J X，SONG C H，CAO L，et al. Impacts of landscape structure on surface urban heat islands：a case study of Shanghai，China[J]. Remote Sens Environ，2011，115(12)：3249-3263．DOI:10.1016/j.rse.2011.07.008.
6	孔繁花，尹海伟，刘金勇，等. 城市绿地降温效应研究进展与展望[J]. 自然资源学报，2013，28(1)：171-181．
	KONG F H，YIN H W，LIU J Y，et al. A review of research on the urban green space cooling effect[J]. J Nat Resour，2013，28(1)：171-181．
7	WENG Q H. Thermal infrared remote sensing for urban climate and environmental studies：methods，applications，and trends[J]. ISPRS J Photogramm Remote Sens，2009，64(4)：335-344．DOI:10.1016/j.isprsjprs.2009.03.007.
8	CAO X，ONISHI A，CHEN J，et al．Quantifying the cool island intensity of urban parks using Aster and IKONOS data[J]．Landsc Urban Plan，2010，96(4)：224-231．DOI:10.1016/j.landurbplan.2010.03.008.
9	ZHOU W Q，HUANG G L，CADENASSO M L. Does spatial configuration matter?‍Understanding the effects of land cover pattern on land surface temperature in urban landscapes[J]．Landsc Urban Plan，2011，102(1)：54-63．DOI:10.1016/j.landurbplan.2011.03.009.
10	熊春妮，魏虹，兰明娟. 重庆市主城区城市景观动态的多尺度分析[J]. 西南大学学报(自然科学版)，2008，30(5)：128-134.
	XIONG C N，WEI H，LAN M J. Multi⁃scale analysis of the dynamics of urban landscape in the downtown area of Chongqing[J]. J Southwest Univ (Nat Sci Ed)，2008，30(5)：128-134．DOI:10.13718/j.cnki.xdzk.2008.05.016.
11	邬建国.景观生态学：概念与理论[J]. 生态学杂志，2000，19(1)：42-52.
	WU J G. Landscape ecology⁃concepts and theories[J]. Chin J Ecol，2000，19(1)：42-52．DOI:10.13292/j.1000-4890.2000.0008.
12	WENG Q H，LU D S，SCHUBRING J．Estimation of land surface temperature‍⁃‍vegetation abundance relationship for urban heat island studies[J]. Remote Sens Environ，2004，89(4)：467-483．DOI:10.1016/j.rse.2003.11.005.
13	VANNIER C，VASSEUR C，HUBERT⁃MOY L，et al. Multiscale ecological assessment of remote sensing images[J]. Landscape Ecol，2011，26(8)：1053-1069. DOI:10.1007/s10980-011-9626-y.
14	TOWNSEND P A，LOOKINGBILL T R，KINGDON C C，et al．Spatial pattern analysis for monitoring protected areas[J]. Remote Sens Environ，2009，113(7)：1410-1420．DOI:10.1016/j.rse.2008.05.023.
15	LI X M，ZHOU W Q，OUYANG Z Y. Relationship between land surface temperature and spatial pattern of greenspace：what are the effects of spatial resolution?[J]. Landsc Urban Plan，2013，114：1-8．DOI:10.1016/j.landurbplan.2013.02.005.
16	QIU T，SONG C H，LI J X. Impacts of urbanization on vegetation phenology over the past three decades in Shanghai，China[J]. Remote Sens，2017，9(9)：970．DOI:10.3390/rs9090970.
17	LI J J，WANG X R，WANG X J，et al. Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area，China[J]．Ecol Complex，2009，6(4)：413-420．DOI:10.1016/j.ecocom.2009.02.002.
18	邬建国. 景观生态学——格局、过程、尺度与等级 [M].2版. 北京：高等教育出版社，2007:106. WU J G. Landscape ecology: pattern, process, scale and hierarchy [M]. 2nd ed. Beijing: Higher Education Press, 2007: 106.
19	QIN Z，KARNIELI A，BERLINER P. A mono⁃window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel⁃Egypt border region[J]. Int J Remote Sens，2001，22(18)：3719-3746．DOI:10.1080/01431160010006971.
20	陈利顶，刘洋，吕一河，等. 景观生态学中的格局分析：现状、困境与未来[J]. 生态学报，2008，28(11)：5521-5531．
	CHEN L D，LIU Y，LÜ Y H，et al．Landscape pattern analysis in landscape ecology：current，challenges and future[J]. Acta Ecol Sin，2008，28(11)：5521-5531．DOI:10.3321/j.issn:1000-0933.2008.11.037.
21	CHEN A L，YAO X A，SUN R H，et al. Effect of urban green patterns on surface urban cool islands and its seasonal variations[J]. Urban For Urban Green，2014，13(4)：646-654．DOI:10.1016/j.ufug.2014.07.006.
22	MCGARIGAL K，MARKS B J. FRAGSTATS:spatial pattern analysis program for quantifying landscape structure[R]. U S Department of Agriculture,Forest Service,Pacific Northwest Research Station,1995.DOI:10.2737/pnw-gtr-351.
23	KONG F H，YIN H W，JAMES P，et al. Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of Eastern China[J]. Landsc Urban Plan，2014，128：35-47．DOI:10.1016/j.landurbplan.2014.04.018.
24	王彦超，朱一丹，徐丹丹. 基于Landsat8影像的南京市热岛效应对植物物候的影响[J]. 南京林业大学学报(自然科学版)，2018，42(6)：99-105．
	WANG Y C，ZHU Y D，XU D D．The impact of urban heat island of Landsat 8 OLI on plant phenology in Nanjing[J]. J Nanjing For Univ (Nat Sci Ed)， 2018，42(6)：99-105．DOI:10.3969/j.issn.1000-2006.201710024.
25	武文昊，王新杰，黄瑞芬. 基于遥感数据的常州市热岛效应分析[J]. 南京林业大学学报(自然科学版)，2017，41(5)：185-190.
	WU W H，WANG X J，HUANG R F．Study on urban heat islands in Changzhou City based on remote sensing data[J]. J Nanjing For Univ （Nat Sci Ed），2017，41(5)：185-190．DOI:10.3969/j.issn.1000-2006.201607005.
26	薛申亮，刘滨谊.上海市苏州河滨水带不同类型绿地和非绿地夏季小气候因子及人体热舒适度分析[J].植物资源与环境学报，2018,27（2）：108-116.
	XUE S L, LIU B Y.Analyses on microclimatic factors and human thermal comfort of different types of greenbelt and non⁃greenbelt in riparian zone of Suzhou River in Shanghai City in summer[J]. Journal of Plant Resources and Environment,2018,27（2）：108-116.DOI: 10.3969/j.issn.1674-7895.2018.02.14.
27	BOWLER D E，BUYUNG⁃ALI L，KNIGHT T M，et al. Urban greening to cool towns and cities：a systematic review of the empirical evidence[J]. Landsc Urban Plan，2010，97(3)：147-155. DOI:10.1016/j.landurbplan.2010.05.006.
28	KUANG W H，DOU Y Y，ZHANG C，et al. Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement[J]. J Geophys Res Atmos，2015，120(1)：113-130．DOI:10.1002/2014jd022249.
29	彭保发，石忆邵，王贺封，等. 城市热岛效应的影响机理及其作用规律：以上海市为例[J]. 地理学报，2013，68(11)：1461-1471.
	PENG B F，SHI Y S，WANG H F，et al. The impacting mechanism and laws of function of urban heat islands effect：a case study of Shanghai[J]. Acta Geogr Sin，2013，68(11)：1461-1471. DOI:10.11821/dlxb201311002.
30	刘越，SHINTARO GOTO，庄大方，等. 城市地表热通量遥感反演及与下垫面关系分析[J]. 地理学报，2012，67(1)：101-112．
	LIU Y，GOTO S，ZHUANG D F，et al. Urban surface heat flux inversion based on infrared remote sensing and the relationship with land cover[J]. Acta Geogr Sin，2012，67(1)：101-112．
31	GUSTAFSON E J. Minireview：quantifying landscape spatial pattern：what is the state of the art?[J]. Ecosystems，1998，1(2)：143-156. DOI:10.1007/s100219900011.
32	TURNER M G. Landscape ecology：what is the state of the science?[J]. Annu Rev Ecol Evol Syst，2005，36(1)：319-344．DOI:10.1146/annurev.ecolsys.36.102003.152614.
33	WU J G. Effects of changing scale on landscape pattern analysis：scaling relations[J]. Landsc Ecol，2004，19(2)：125-138．DOI:10.1023/b:land.0000021711.40074.ae.

Metrics

Comments

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

用地类型 landuse type	S_CA/hm²	P_LAND/%	N_P	S_MP/hm²	A_I
乔木林地woodland	8 134.83	18.97	11 896	0.32	82.97
草地grassland	3 897.25	9.09	4 453	0.16	80.59
水体water	1147.39	2.68	1 250	0.92	91.84
裸地barren land	2 055.85	4.80	495	0.19	89.09
建设用地construction land	27 640.06	64.46	782	3.53	93.42

用地类型 landuse type	地表温度 land surface temperature	降温强度 cooling intensity
乔木林地woodland	39.03	-1.99
草地grassland	40.26	-0.76
水体water	36.19	-4.83
裸地barren land	42.52	1.50
建设用地construction land	46.29	5.27

格局指数 pattern metrics	相关系数correlation
格局指数 pattern metrics	乔木林地 woodland (N=5 300)	草地 grassland （N=2 700）
PA	0.51^**	0.35^**
SI	0.23^**	0.04
PARA	0.33^**	0.08
NGP	0.46^**	0.18^*

尺度scale	景观构型指数 configuration metrics
尺度scale	MSP	LPI	NP	Shape?MN	AI
1	0.71^**	0.93^**	0.31^**	0.36^**	0.15^*
2	0.74^**	0.92^**	0.25^**	0.19^**	0.14^*
3	0.76^**	0.88^**	0.17^**	0.39^**	0.69^**
4	0.92^**	0.88^**	0.09^*	0.26^**	0.79^**

构型指数 configuration metrics	相关系数 correlation
构型指数 configuration metrics	尺度1 scale 1	尺度2 scale 2	尺度3 scale 3	尺度4 scale 4
MPS	0.30^**	0.21^**	0.09^*	0.07
LPI	0.23^**	0.11^*	0.03	0.00
NP	-0.02	-0.00	-0.15^**	-0.27^**
Shape_MN	0.38^**	0.33^**	0.35^**	0.29^**
AI	0.04	0.10^*	0.34^**	0.45^**

Multi⁃scale analysis of the effects of green space pattern on the urban surface thermal environment

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 33

Related Articles 15

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer

[1]	ZHAO Zhiqiang, XU Xiaolong, YUAN Qing, WU Yan. Landscape pattern evolution and driving factors of Songhua River wetland in Harbin [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(2): 219-226.
[2]	ZENG Zheli, SHE Jiyun, TANG Zichao, LUO Chuying. Dynamic change of wetland landscape in Changsha based on JRC global surface water data [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(2): 9-18.
[3]	XU Hao, JIN Ting, LIU Wei. Study on the scale and landscape pattern evolution characteristics of blue-green space in Suzhou-Wuxi-Changzhou metropolitan area, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(1): 219-226.
[4]	MA Yingyi, LIU Zhifeng. Assessment of landscape ecological risk and its dynamic response with urbanization development of Jiangsu Province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(5): 185-194.
[5]	LU Hongwang, HU Wenmin, SHE Jiyun, ZENG Wen, SONG Yabin. Study on the influence of Ecological Poplar Withdrawal on the landscape pattern of Dongting Lake wetland [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 171-178.
[6]	JIA Yanyan, TANG Xiaolan, TANG Fanglin, YANG Yang. Spatial⁃temporal evolution of landscape pattern in the middle and lower reaches of the Yangtze River basin from 1995 to 2015 [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(3): 185-194.
[7]	LIU Zhili, YUE Depeng, YANG Hui, ZHANG Qibin, YU Qiang, XU Xiaodong. Evolvement and prediction of landscape pattern in Yixing City based on urbanization process [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 43(6): 105-112.
[8]	XU Haishun, DU Hongyu, CAI Chaolin. Spatial characteristics of landscape pattern based on ecological restoration in the riparian zone of Huangpu River, Shanghai City [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2019, 62(04): 125-131.
[9]	DAI Tingting, XU Ming, XU Yannan. Spatio-temporal distribution charasteristics and driving factors of rural settlements: a case study in Yixian, Anhui Province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 61(05): 155-162.
[10]	LYU Guoping, LIAO Chengrui, XU Yannan, ZHANG Ting, LI Haidong. Dynamic simulation of future ecosystem service value in Puding County, Guizhou Province based on CA-Markov model [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 60(05): 49-56.
[11]	CHEN Yanfei, TANG Chendong, MA Qiang, XUE Jianhui. Landscape pattern change of Chongming Dongtan Nature Reserve of Shanghai from 2011 to 2015 [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2017, 60(01): 1-8.
[12]	REN Qiong,TONG Guangchen, ZHANG Jinchi. Dynamics of Poyang Lake watershed landscape pattern [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2016, 59(03): 94-100.
[13]	YANG Qiang, WANG Tao. Spatial-temporal variation of land use/cover and the analysis of its effect on ecological vulnerability in Yulin City of Shaanxi Province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2015, 58(02): 79-83.
[14]	WANG Zhi,ZHUANG Changwei,XU Wanggu. Spatio-temporal variation and driving forces of natural landscape patterns in the coast zone of Yancheng [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2014, 57(05): 91-96.
[15]	OU Guanglong, WANG Junfeng,XU Hui, MA Huancheng,LU Hui, YANG Caihua. Impacts of Jatropha curcas energy forest afforestation on landscape pattern in Shuangjiang county of Yunnan province [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2013, 56(02): 159-163.