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

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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

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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

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