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Effects of changing spatial extent on the relationship between urban forest patterns and land surface temperature
Ecological Indicators 109 (2020) 105778
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Effects of changing spatial extent on the relationship between urban forest patterns and land surface temperature Wen Zhou, Fuliang Cao
T
⁎
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Landscape metrics Urban forest pattern Scale effect Land surface temperature Shanghai
The effects of spatial pattern of urban greenspace on land surface temperature (LST) have been extensively documented. Previous studies have confirmed that the relationship between urban green spatial pattern and LST is sensitive to spatial resolution and remote sensing imagery with higher resolution could be more accurate on quantifying the urban green patterns, but little is known about another scaling issue—spatial extent. This paper examined whether the spatial extent applied to derive landscape metrics affect the relationship between LST and spatial pattern of urban forested areas of highly urbanized Shanghai and the seasonal variations using correlation analyses and regression analyses. Spatial pattern of forested areas was measured with eight class-level 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 moving-window approach based on a land-use and land cover (LULC) map derived from SPOT 6 datasets. Results demonstrated that changing spatial extent had significant impacts on the relationship between spatial pattern of urban forested areas and LST. The responses of correlations between spatial pattern metrics and LST to changing extent fell into three categories: correlation decreases with extent increases, correlation increases with extent increases and unpredictable pattern. In general, the amount of forested cover accounts for greater variability in LST than its spatial arrangement. This study extended our scientific understanding of the effects of spatial pattern of urban forested area on LST. In addition, it can provide insights for urban forest planning and management.
1. Introduction Urban heat island (UHI), refers to the phenomenon of higher temperatures occurring in urban areas than in surrounding rural areas (Weng et al., 2004). Urban warming increases air pollution, and has negative impacts on human well-being, particularly those who are poor, vulnerable and lacking the resources to adapt to the climate change (Hajat et al., 2006; Jacob and Winner, 2009; Patz et al., 2005). Moreover, evidence is mounting that many prevalent human diseases such as cardiovascular and respiratory illnesses are associated with heatwaves (Patz et al., 2005). The risks arising from the UHI effect are greatest in large metropolitan areas around the world (Department of Health, 2008). Therefore, UHI mitigation is urgent in order to improve urban living environment (Opdam et al., 2009). Generally, UHI studies are based on two types of temperature data source: air/atmospheric temperature and land surface temperature (LST). Atmospheric temperature data has high temporal resolution with extensive time coverage, which always be applied to describe the temporal variation of the cooling effect of greenspaces (Li et al., 2013). Meanwhile, LST derived from
⁎
infrared remote sensing imagery has been widely applied to study its relationship with landscape patterns (Cao et al., 2010; Weng, 2009; Zhou et al., 2011). This study focused on surface temperature. The cooling effect of urban vegetation has been consistently demonstrated using remote sensing at all scales from a greenspace patch to a city, and even beyond (Mackey et al., 2012; Oliveira et al., 2011; Park et al., 2017; Yan et al., 2018; Zhang et al., 2017). Previous studies have suggested that the urban green patterns significantly affect LST in urban areas (Chen et al., 2014; Shih, 2017). It has become widely accepted that increasing the area of vegetation cover can better mitigate the UHI effect (Bowler Buyung-Ali et al., 2010; Xiao et al., 2018). However, land for urban greening is normally limited, especially for compact urbanized areas. Meanwhile, the optimization of configuration of urban greenspaces can help better achieve reductions of high urban temperatures (Norton et al., 2015). As a result, there is an increased interest in the effect of spatial configuration of urban greenspace on LST (Kong et al., 2014; Lin and Lin, 2016). Considerable researches have demonstrated that the spatial configuration of urban vegetated space affect the magnitude of LST,
Corresponding author at: Nanjing Forestry University, NO. 159 Longpan Road, Xuanwu District, Nanjing, Jiangsu 210037, China. E-mail address: [email protected] (F. Cao).
https://doi.org/10.1016/j.ecolind.2019.105778 Received 17 January 2019; Received in revised form 24 July 2019; Accepted 27 September 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
Ecological Indicators 109 (2020) 105778
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one panchronmatic band (1.5 m resolution), four cloud-free Landsat 8 Thermal Infrared Sensor (TIRS) images (30 m resolution) from the United States Geological Survey (https://glovis.usgs.gov/) and meteorological data. Spot 6 images were used for urban land classification and urban green pattern quantification, and Landsat 8 TIRS images were applied to derive LST and detailed imagery information was shown in Table 1. Based on spot 6 fused image (combined multispectral and panchromatic bands), an urban LULC map was created by manual interpretation supported by the ArcMap platform (Environmental Systems Research Institute, Inc., Redlands, CA, USA), combined with field surveys and ground-truthing as necessary. Green areas were classified into forest vegetation (trees with shrubs and grasses) and other vegetation (shrubs and grasses). As a result, the study area was classified into five land-use types including: forest vegetation, other vegetation, water, impervious surface (roads and buildings) and barren land (land without vegetation cover, mainly including exposed soil and landfill sites) (Fig. 1d). Next, 30 samples for each land use type, totally 150 samples were selected using random stratified method to examine the accuracy of the classified map. Land use survey data derived from historical aerial photos, and a 1:250,000 digitalized land cover map acquired in 2016 were applied as the reference data. As a result, the overall accuracy of LULC classification was 94%. Next, the LULC map was converted to a grid format with a 5 m × 5 m grain size in order to conduct the moving-window analysis in FRAGSTATS (3.3). The grain size chosen was based on the resolution of Spot 6 images and Landsat 8 TIRS imagery (30 m). This study focused on forest vegetation. Four Landsat 8 TIRS images were used to retrieve LST. The TIRS images were rectified to a common UTM coordinate system based on the spot 6 images. LST was retrieved from thermal band of Landsat TIRS images mainly according to the Mono-Window Algorithm (MWA) (Qin et al., 2001) with the following equation:
however, its relationship with LST was inconsistent (Li et al., 2012). For example, the study by Chen et al. (2014) suggested that irregular and elongated green patches have stronger cooling intensity compared to regular and compact shaped green patches. However, Feyisa et al. (2014) demonstrated that shape index is negatively correlated with temperature reduction, indicating that urban parks with regular and compact shape perform better in reducing temperature. Moreover, the study of Kong et al. (2014) indicated that the shape index explains around 20% of the temperature reduction (R2 = 0.187). Inconsistent conclusions from previous studies suggested that the correlation between urban green patterns and LST may be scale dependent. Previous studies either applied a variety of remotely sensed data with different spatial resolutions to derive LULC map based on which the urban green patterns were calculated, or conducted under different spatial extents (geographical scales). Several studies have confirmed that remotely sensed image data with different spatial resolutions would influence the quantification of landscape metrics which have been developed to characterize the landscape patterns, and thus affect the relationship to LST (Townsend et al., 2009; Vannier et al., 2011; Weng et al., 2004). For example, Liu and Weng (2009) reported that the relationships between LST and landscape patterns were strongest when the resolution of 30 m and 90 m remote sensed data were used at class and landscape level, respectively. The study by Li et al. (2013) confirmed that the correlation between urban green patterns and LST varied by spatial resolution, and remotely sensed imagery with higher resolution could more accurately quantify the spatial pattern of greenspace. In general, previous studies have primarily focused on the impacts of spatial resolution on the statistical relationship between urban green patterns and LST, however, seldom studies reported the influence of spatial extent. The main purpose of this study is to examine the effects of spatial extent on the relationship between LST and spatial pattern of urban forested areas and the seasonal variations. Since composition and configuration are the two aspects of spatial patterns, this study mainly addresses two questions: (1) how does spatial extent affect the relationship between PLAND of forest vegetation and LST? and (2) it the relationship between configuration metrics and LST consistent across spatial extents?
Ts = [a10 (1 − C10 − D10) + (b10 (1 − C10 − D10) + C10 + D10) T10 − D10 Ta] / C10
(1)
where a10 is the coefficient equals −63.1885, −67.9542, −63.1885 and −60.3263 for spring, summer, autumn and winter, respectively; and b10 is the coefficient and constant with values of 0.44411, 0.45987, 0.44411 and 0.43436 for spring, summer, autumn and winter, respectively (according to Qin et al. 2001), T10 represents the at-sensor brightness temperature, and Ta is the mean atmospheric temperature. C10 and D10 are defined, respectively, by Eqs. (2), (3):
2. Methods 2.1. Study area Shanghai, the largest city and the economic center of China, is located on the coast of East China Sea. The Shanghai metropolitan region, covers a total area of approximately 6340.5 km2, and had a permanent resident population of about 24.1527 million in 2015 (Shanghai Municipal Bureau Statistics, 2016) It is largely situated on a broad flat alluvial plain with a few remnant hills in the southwest, and the average elevation is about 4 m above the sea level (Huang et al., 2017). Shanghai has a subtropical monsoon climate with a mean annual temperature of 17.1 °C and a mean annual precipitation of 1166.1 mm (Qiu et al., 2017). The regional vegetation consists of evergreen broadleaved forest, evergreen broadleaved-deciduous broadleaved mixed forest and shrub-grass communities (Li et al., 2011). Since the 1990s, rapid economic growth of Shanghai was accompanied by enormous urbanization in both scope and degree (Li et al., 2009). Currently, Shanghai is one of the most urbanized cities in China (Du et al., 2016). Our study focused on the center area of Shanghai City, an area of about 290 km2 with diverse land-use and land cover (LULC) types as can be seen in Fig. 1.
C10 = τ10 ε10,
(2)
D10 = (1 − τ10)[1 + (1 − ε10) τ10],
(3)
where τ10 and ε10 represent the total atmospheric transmissivity and the land surface emissivity, respectively. The result of spatial distribution of LST for four seasons was shown in Fig. 2. 2.3. Urban green pattern metrics Numerous landscape metrics have been developed and widely applied to quantify landscape patterns (McGarial and Marks, 1995; O’Neill et al., 1988). These metrics were generally classified into two categories: composition and configuration metrics. Composition refers to the amount of area and diversity of land cover features without considering their spatial arrangement, and configuration refers to the spatial arrangement or distribution of land cover features (Gustafson, 1998). In this study, eight commonly used landscape metrics were selected to quantify the urban forest patterns and to analyze the effects of spatial extents/scales on the relationship between these patterns and LST (Table 2): Percentage of Landscape (PLAND), mean patch size (MPS), largest patch index (LPI), patch density (PD), area-weighted mean perimeter area ratio (PARA_AM), mean shape index (Shape_MN), cohesion index (Cohesion) and aggregation index (AI) (McGarigal et al.,
2.2. Data descriptions and pre-processing The data used in this research include Spot 6 images (10:08 a.m., July 25th, 2016) with four multiple spectral bands (6 m resolution) and 2
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Fig. 1. (a) Location of Shanghai in China; (b) Administrative boundary of Shanghai; (c) The study area in the 25 July 2016 Spot image; (d) LULC map of the study area based on the Spot 6 image.
respectively applied to characterize urban green pattern and explore its relationship with LST. As the resolution of TIRS band is 30 m, the tested window sizes were chosen as integer multiples of 30 m. Moreover, the mean patch size of forest vegetation in the study area was 3.8 × 103 m2 (about the same size as 60 m × 60 m window), and the statistical analysis showed that 91.2% of the forest vegetation patches was smaller than 8.1 × 103 m2 (same size as 90 m × 90 m window). Therefore, four window sizes that all greater than 60 m × 60 m were chosen and tested to avoid dividing vegetation patches into small parts by moving window.
Table 1 Descriptions of the Landsat-8 TIRS imagery used. Seasons
Acquisition dates
Acquisition time (BJT)
Mean LST (°C)
Standard deviation
Spring Summer Autumn
02 April 2017 20 July 2016 04 November 2014 26 January 2016
10:24:35 am 10:24:59 am 10:25:13 am
23.49 43.17 20.43
1.40 2.55 1.46
10:25:05 am
5.34
1.20
Winter
BJT, Beijing time.
2002). PLAND is the only composition metric, and others are all configuration metrics. Landscape metrics were conducted by movingwindow analysis in FRAGSTATS (3.3) to deal with the relationship between urban forest patterns and LST in four spatial scales.
2.5. Statistical analysis Statistical analyses were performed using the SPSS 23.0. Bivariate correlation analysis and scatter plots were carried out to examine the relationship between landscape metrics and LST of four seasons at four spatial extents respectively. Regression analyses were conducted to quantify the relationship between the percentage of landscape (PLAND) of forest vegetation and LST across four seasons and spatial extents. As configuration metrics were highly correlated with composition metric (PLAND) (Table 3), it may obtain spurious results of relationship between configuration metrics and LST. Therefore, partial Pearson correlation analysis was conducted to examine the relationship between configuration metrics and LST, after controlling the influence of
2.4. Moving-window analysis and window sizes/spatial extents chosen Moving-window analysis, a method of computing and returning value to the focal cell to output a new complete and continuous grid image for each selected metric at the class and landscape level (McGarigal, 2002), was conducted over the entire study area. In this study, four spatial extents, equals window sizes 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m (Fig. 3) were 3
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Fig. 2. Land surface temperature (°C) of the four dates, (a) April 02, (b) July 20, (c) November 04 and (d) January 26.
(p < 0.01), but no correlation was established between LST and PLAND of forest vegetation in scale MV720 in spring (Table 4). In winter, slightly negative correlation was only reported under scale MV90, and none correlations were established under scale MV180, MV360 and MV720. The magnitude of correlations between LST and PLAND of forest vegetation varied by spatial extents and seasons. Specifically, the correlation between PLAND of forest vegetation and LST decreased as the spatial extent increased in spring, summer and autumn. Based on the same spatial extent, summer all had the strongest correlation relationship between PLAND of forest vegetation and LST, and followed by autumn and spring, and winter had the lowest. Based
composition metric (i.e. using PLAND of forest vegetation as the controlled variable). 3. Results 3.1. Effects of spatial extent on the relationship between LST and PLAND of forest vegetation and the seasonal variations As shown in Table 4, PLAND of forest vegetation mainly showed negative correlation with LST across four different spatial extents (MV90, MV180, MV360, MV720) in spring, summer and autumn Table 2 Definitions of landscape metrics (based on McGarigal et al., 2002). Landscape metrics
Abbreviation
Description
Range
Percentage of Landscape
PLAND
0 < PLAND < 100
Mean patch size
MPS
Largest patch index
LPI
Patch density Area-weighted perimeter area ratio Mean patch shape index Patch cohesion index
PD PARA_AM
Aggregation index
AI
The proportion of total area occupied by a particular patch type; a measure of landscape composition and dominance of patch types (%) The sum of area across all patches of the corresponding patch type divided by the number of patches of the same type (ha) The area (m2) of the largest patch of the corresponding patch type divided by total landscape area (m2), multiplied by 100 (to convert to a percentage) (%) The number of patches in the landscape for patch type The sum, across all patches of the corresponding patch type, of the corresponding perimeter area ratio multiplied by the value of patch area (m2) divided by the sum of patch areas Mean value of shape index 1 minus the sum of patch perimeter (in terms of number of cell surfaces) divided by the sum of patch perimeter times the square root of patch area (in terms of number of cells) for patches of the corresponding patch type, divided by 1 minus 1 over the square root of the total number of cells in the landscape, multiplied by 100 to convert to a percentage The number of like adjacencies involving the corresponding class, divided by the maximum possible number of like adjacencies involving the corresponding class, which is achieved when the class is maximally clumped into a single, compact patch; multiplied by 100 (to convert to a percentage) (%)
Shape_MN Cohesion
4
MPS > 0, no limit 0 < LPI < 100 PD > 0 PARA_AM > 0 Shape_MN ≥ 1, no limit 0 ≤ Cohesion < 100
0 ≤ AI ≤ 100
Ecological Indicators 109 (2020) 105778
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Fig. 3. Four spatial extents/moving-windows consist of 5 m × 5 m grains. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively.
3.2. Effects of spatial extent on the relationship between LST and urban green configuration metrics and the seasonal variations
Table 3 Pearson correlation between PLAND of forest vegetation and configuration metrics.
MV90 MV180 MV360 MV720
MPS
LPI
PD
PARA_AM
Shape_MN
Cohesion
AI
0.71** 0.74** 0.76** 0.92**
0.93** 0.92** 0.88** 0.88**
0.31** 0.25** 0.17** 0.09*
−0.60** −0.58** −0.73** −0.80**
0.36** 0.19** 0.39** 0.26**
0.20** 0.26** 0.78** 0.87**
0.15 0.04 0.69** 0.79**
Pearson correlations between LST and configuration metrics varied by spatial extents (Table 4). In spring, summer and autumn, the correlations between most configuration metrics and LST differed by spatial extents in magnitude and significance, except Shape_MN which also in direction. Similar to composition metric, the magnitude of correlations between configuration metrics and LST decreased as spatial extents increased. In winter, correlations between configuration metrics and LST varied by spatial extent in magnitude, significance and direction. Table 5 listed the partial Pearson correlation coefficients between configuration metrics and LST, after controlling for the effect of PLAND of forest vegetation. Since no significant correlation between LST and configuration metrics was observed in winter, only analytical results from spring, summer, and autumn were displayed in Table 5. Compared to the results in Table 4, the relationships between configuration metrics and LST differed greatly. Specifically, the correlation between area metrics (i.e. MPS and LPI in this study) and LST was only observed in smaller scales and it decreased as the spatial extent increased. Contrarily, results demonstrated that Cohesion, AI and PD were significantly
MPS, LPI, PD, PARA_AM, Shape_MN, Cohesion and AI are mean patch size, largest patch index, patch density, area-weighted perimeter area ratio, mean patch shape index, patch cohesion index and aggregation index respectively. Other details are as in Fig. 3. * p < 0.05. ** p < 0.01.
on linear regression models, the slope values varied by spatial extents among seasons, but were only outstanding in summer (Fig. 4). Specifically, the slope values were −0.746, −0.71, −0.846 and −0.532 at MV90, MV180, MV360 and MV720 scales in summer, respectively. Moreover, a 10% increase in PLAND of forest vegetation led to a reduction in LST of 1.02 °C, 0.98 °C, 0.98 °C, and 0.47 °C at MV90, MV180, MV360 and MV720 in summer, respectively. Table 4 Pearson correlation between LST and landscape metrics. Dates
Window Sizes
PLAND
MPS
LPI
PD
PARA_AM
Shape_MN
Cohesion
AI
LST0402
MV90 MV180 MV360 MV720
−0.34** −0.14** −0.10** −0.05
−0.33** −0.11** −0.09* −0.03
−0.31** −0.12** −0.07* −0.02
−0.21** −0.05 0.01 −0.01
0.28** 0.17** 0.05 0.01
−0.18** −0.15** −0.03 0.05
−0.17** −0.06 −0.03 −0.02
−0.02 0.03 −0.13** −0.01
LST0720
MV90 MV180 MV360 MV720
−0.59** −0.38** −0.30** −0.15**
−0.40** −0.29** −0.24** −0.12**
−0.54** −0.36** −0.27** −0.17**
−0.26** −0.04 −0.15* −0.09*
0.32** 0.18** 0.19** 0.02
−0.19** −0.15** −0.13** −0.11**
−0.15** −0.10** −0.20** −0.09*
0.01 0.01 −0.18** −0.02
LST1104
MV90 MV180 MV360 MV720
−0.47** −0.18** −0.16** −0.14**
−0.30** −0.13** −0.13** −0.04
−0.42** −0.15** −0.14** −0.12**
−0.25** −0.10** −0.08* −0.24**
0.31** 0.08* 0.07* 0.04
−0.13** −0.08* −0.04 0.19**
−0.18** −0.03 −0.16** −0.05
−0.15** −0.01 −0.16** −0.03
LST0126
MV90 MV180 MV360 MV720
−0.25** 0.03 −0.02 0.02
−0.16** 0.07* −0.02 0.08*
−0.23** 0.08* −0.02 −0.00
−0.11** −0.03 −0.02 −0.15**
0.17** −0.03 −0.01 −0.08*
−0.08* −0.07* 0.02 0.21**
−0.06 −0.02 0.03 0.07*
−0.03 −0.01 0.01 0.08*
The digits 0402, 0720, 1104 and 0126 after LST here represent the dates April 02, July 20, November 04 and January 0126. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively. * p < 0.05 and **p < 0.01. 5
Ecological Indicators 109 (2020) 105778
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Fig. 4. Relationship between the sqrt of PLAND of forest vegetation and LST across four seasons and spatial extents. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively.
4. Discussion
correlated with LST only in larger scales and the correlation increased as the spatial extent increased. Moreover, correlations between LST and shape metrics (i.e. PARA_AM and Shape_MN) varied by spatial scale in magnitude in summer, but both in magnitude and direction in spring and autumn.
It is widely acknowledged that landscape pattern analysis, for example the area estimation of forest is sensitive to the spatial resolution of the remote sensing data applied for landscape mapping (Li et al., 2013; Nelson et al., 2009; Shao and Wu, 2008). However, the influence of spatial extent on the relationship between urban forest pattern and LST is not yet known. Linking the method of landscape ecology and
Table 5 Partial Pearson correlation between LST and configuration metrics after controlling for PLAND of forest vegetation. Dates
Window Sizes
MPS
LPI
LST0402
MV90 MV180 MV360 MV720
0.24** 0.09*
0.10**
MV90 MV180 MV360 MV720
−0.39** −0.18**
MV90 MV180 MV360 MV720
0.31** 0.16**
LST0720
LST1104
PD
PARA_AM
−0.08* −0.15** −0.25**
−0.13** −0.19**
−0.15** −0.27**
−0.23** −0.15**
0.20** −0.15** −0.33**
Shape_MN
Cohesion
AI
0.31**
0.18** 0.35**
0.23** 0.32**
−0.30** −0.28** −0.35** −0.29**
−0.28** −0.33** −0.25** −0.20**
0.09* 0.29** 0.40**
0.10** 0.34** 0.45**
−0.15**
0.13** −0.16** 0.22** 0.39**
0.19** 0.32**
−0.25**
−0.17** −0.21**
0.33**
The digits 0402, 0720, 1104 and 0126 after LST here represent the dates April 02, July 20, November 04 and January 0126. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively. * p < 0.05 and **p < 0.01. 6
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suggested that the spatial configuration of vegetated areas was irrelevant to the magnitude of LST in winter.
remote sensing, this study demonstrated that the relationship between urban forest pattern and LST was sensitive to spatial extents. In another word, the geographical scale of research area would influence the relationship between LST and landscape pattern metrics and it might be one of reasons that contributed to the inconsistent results from previous studies. By studying the quantitative relationships of LST and forest pattern metrics under different seasons and geographical scales, the scientific understanding of the effects of urban forest patterns on LST can be expanded. The results have important theoretical and management implications and can provide insights for urban forest planning and management.
4.3. Implications for urban forest planning Similar to the findings of Kong et al. (2014), the spatial pattern of vegetated areas was strongly correlated with summer LST. Therefore, the result has important implications for urban forest planning and design to better mitigate the UHI effect, particularly for areas where urbanization is still in process. It suggests that by increasing the amount of vegetated areas, the cooling effect can be greatly enhanced. Since land for urban forest is usually limited and the removal of paved surfaces or buildings is expensive and impractical, increasing tree vegetation canopy cover and the greening of roofs, and adding vegetation cover on parking lots and even building walls are all effective means of UHI mitigation. The findings in this study indicated that the optimizing of spatial arrangement of vegetated areas can also be an effective and practical measure to alleviate the UHI effect rather than increasing the vegetation cover. Therefore, spatial pattern of vegetated areas should not be ignored when making urban forest planning decisions. When the amount of vegetation is fixed, scattered and even distribution across the urban landscape is more effective to reduce the LST than aggregated distribution. The results also suggested that vegetation patches with irregular and elongated shapes performed better in cooling the surrounding urban areas, which is similar to the results of Chen et al. (2014). This is probably because when vegetation patch area is fixed, irregular patch is in contact with larger area of surrounding landscape compared to a relatively compact one. In general, this research suggested that reasonable spatial arrangement of vegetated areas can be an effective measure to regulate the local LST.
4.1. Spatial extent affects the relationship between LST and percent cover of forest The relationship between PLAND of forest vegetation and LST was negative across spatial extents. This is consistent with previous findings that the amount of vegetation cover is an important factor mitigating UHI effects (Jenerette et al., 2007; Larsen, 2015; Shiflett et al., 2017). The significance of correlation between PLAND of forest vegetation and LST decreased as the spatial extent increased (Table 4). Besides, the magnitudes of the reduction of LST by increasing percent cover of vegetation were higher at small scales (Fig. 4). This may because smaller scales normally contain less diversified land cover features and simpler combination of those features than larger scales, therefore, are more sensitive to the response of changing percent cover of vegetation on LST. Seasonal variations in correlations between PLAND of forest vegetation and LST suggested the strongest coupling in summer, and followed by autumn and spring, which is similar to the results of previous studies (Buyantuyev and Wu, 2010; Jenerette et al., 2007; Myint et al., 2013). Data across four spatial extents suggested that increase vegetation cover can effectively mitigate urban warming, and the cooling effect was strongest in summer (Fig. 4). The results suggested that the amount of vegetation cover does not have significant effects on winter LST. This might be related to reduced evapotranspiration and shading by deciduous trees in vegetated surfaces in winter.
4.4. Limitations and recommendations for further studies This study has its limitations. This research examined the effects of only four spatial extents (90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m) on the relationship between spatial pattern of urban forest and LST. Results showed that the relationship between urban forest patterns and LST were sensitive to spatial extent. The findings in this study suggested that multi-scale analysis is needed in order to fully explore the relationship between urban forest pattern and LST. However, gradients in the correlation coefficient across scales were not fully explored with only four spatial extents examined in this study. Therefore, future studies involving more spatial extents and other cities need to be undertaken.
4.2. Spatial extent affects the relationship between LST and spatial configuration of forest After controlling the vegetation cover percentage, the relationship between configuration metrics and LST varied in both magnitudes, significances and directions across different scales and seasons. The responses of correlations between configuration metrics and LST to changing extent fell into three categories: (1) correlation decreased as the spatial extent increased; (2) correlation increased as the spatial extent increased; and (3) unpredictable pattern. Specifically, area metrics (i.e. MPS and LPI) were correlated with LST in smaller scales and the correlations between LST with MPS (i.e. partial r = −0.39 and −0.18 under MV90 and MV180, respectively, P < 0.01, for summer) and with LPI (i.e. partial r = −0.23 and −0.15 under MV90 and MV180, respectively, P < 0.01, for summer) decreased as the spatial extent increased. It suggested that larger vegetation patch performs better in urban cooling than smaller ones in summer. Contrarily, significant correlations were only observed between LST with PD (i.e. partial r = −0.15 and −0.27 under MV360 and MV720, respectively, P < 0.01, for summer), and Cohesion (i.e. partial r = 0.09 under MV180, P < 0.05; partial r = 0.29 and 0.40 under MV360 and MV720, respectively, P < 0.01, for summer), and AI (i.e. partial r = 0.10, 0.34 and 0.45 under MV180, MV360 and MV720, respectively, P < 0.01, for summer) under larger extents. Shape metrics, including PARA_AM and Shape_MN in this study, showed inconsistent correlations with LST in different spatial extents and seasons, suggesting the relationship between LST and shape metrics is not as sensitive as other landscape metrics to spatial extent. The results also
5. Summary and conclusions This research examined the effects of spatial extent on the relationship between urban forest patterns and LST using Landsat 8 TIRS and Spot 6 imagery data. The results confirmed that changing spatial extent had significant effects on the relationship between urban forest patterns and LST, and the effects varied considerably among the spatial extents and seasons. Among seasons, the correlation between LST and urban green patterns was strongest in summer, which indicated that the optimization of the spatial pattern of vegetated areas could be adopted to better mitigate the UHI effect. In general, the effects of changing extent on the correlation between LST and composition metric is more predictable than with configuration metrics. The results also showed that the amount of vegetation cover is more important in determining LST than its spatial arrangement. The findings in this study, in some extent, explained the inconsistent results between spatial pattern metrics and LST from current literature. Results also suggested that there is no single correct or optimal scale to quantify the relationship between urban forest patterns and LST. The study highlights the need for multiscale analysis in order to fully explore the relationship between urban 7
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forest patterns and regional temperature.
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Shanghai statistical yearbook 2016 [WWW Document]. URL www.stats-sh.gov.cn/tjnj/nj16.htm?d1=2016tjnj/C0201.htm. Shao, G., Wu, J., 2008. On the accuracy of landscape pattern analysis using remote sensing data. Landsc. Ecol. https://doi.org/10.1007/s10980-008-9215-x. Shiflett, S.A., Liang, L.L., Crum, S.M., Feyisa, G.L., Wang, J., Jenerette, G.D., 2017. Variation in the urban vegetation, surface temperature, air temperature nexus. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2016.11.069. Shih, W., 2017. Greenspace patterns and the mitigation of land surface temperature in Taipei metropolis. Habitat Int. https://doi.org/10.1016/j.habitatint.2016.12.006. Townsend, P.A., Lookingbill, T.R., Kingdon, C.C., Gardner, R.H., 2009. Spatial pattern analysis for monitoring protected areas. Remote Sens. Environ. 113, 1410–1420. https://doi.org/10.1016/j.rse.2008.05.023. Vannier, C., Vasseur, C., Hubert-Moy, L., Baudry, J., 2011. Multiscale ecological assessment of remote sensing images. 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Acknowledgements The work was supported by the National Key Research and Development Program of China (Project No. 2017YFD0600701) and the Doctorate Fellowship Foundation of Nanjing Forestry University. References Bowler Buyung-Ali, L., Knight, T.M., Pullin, A.S., 2010. Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc. Urban Plan in press, doi: Yes. Buyantuyev, A., Wu, J., 2010. Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landsc. Ecol. https://doi.org/10.1007/s10980-009-9402-4. Cao, X., Onishi, A., Chen, J., Imura, H., 2010. Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landsc. Urban Plan. 96, 224–231. https://doi.org/10.1016/j.landurbplan.2010.03.008. Chen, A., Yao, X.A., Sun, R., Chen, L., 2014. Effect of urban green patterns on surface urban cool islands and its seasonal variations. Urban For. Urban Green. 13, 646–654. https://doi.org/10.1016/j.ufug.2014.07.006. Department of Health, 2008. Heatwave Plan for England. Protecting health and reducing from extreme heat and heatwaves, NHS Best Practice Guidance. NHS Best Pract. Guid. 32. Du, H., Song, X., Jiang, H., Kan, Z., Wang, Z., Cai, Y., 2016. Research on the cooling island effects of water body: a case study of Shanghai, China. Ecol. Indic. 67, 31–38. https:// doi.org/10.1016/j.ecolind.2016.02.040. Feyisa, G.L., Dons, K., Meilby, H., 2014. Efficiency of parks in mitigating urban heat island effect: an example from Addis Ababa. Landsc. Urban Plan. 123, 87–95. https:// doi.org/10.1016/j.landurbplan.2013.12.008. Gustafson, E.J., 1998. Quantifying landscape spatial pattern: what is the state of the art? Ecosystems. https://doi.org/10.1007/s100219900011. Hajat, S., Kovats, R.S., Lachowycz, K., 2006. Heat-related and cold-related deaths in England and Wales: who is at risk? Occup. Environ. Med. 64, 93–100. https://doi. org/10.1136/oem.2006.029017. Huang, W., Li, J., Guo, Q., Mansaray, L.R., Li, X., Huang, J., 2017. A satellite-derived climatological analysis of urban heat Island over Shanghai during 2000–2013. Remote Sens. 9. https://doi.org/10.3390/rs9070641. Jacob, D.J., Winner, D.A., 2009. Effect of climate change on air quality. Atmos. Environ. 43, 51–63. https://doi.org/10.1016/j.atmosenv.2008.09.051. Jenerette, G.D., Harlan, S.L., Brazel, A., Jones, N., Larsen, L., Stefanov, W.L., 2007. Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landsc. Ecol. https://doi.org/10.1007/ s10980-006-9032-z. Kong, F., Yin, H., James, P., Hutyra, L.R., He, H.S., 2014. Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landsc. Urban Plan. 128, 35–47. https://doi.org/10.1016/j.landurbplan.2014.04.018. Larsen, L., 2015. Urban climate and adaptation strategies. Front. Ecol. Environ doi: 10.1890/150103. Li, J., Song, C., Cao, L., Zhu, F., Meng, X., Wu, J., 2011. Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens. Environ. 115, 3249–3263. https://doi.org/10.1016/j.rse.2011.07.008. Li, J., Wang, Xiang-rong, Wang, Xin-jun, Ma, W., Zhang, H., 2009. Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecol. Complex 6, 413–420. https://doi.org/10.1016/j.ecocom.2009.02.002. Li, X., Zhou, W., Ouyang, Z., 2013. Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution? Landsc. Urban Plan. 114, 1–8. https://doi.org/10.1016/j.landurbplan.2013.02.005. Li, X., Zhou, W., Ouyang, Z., Xu, W., Zheng, H., 2012. Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landsc. Ecol. 27, 887–898. https://doi.org/10.1007/s10980-0129731-6. Lin, B.S., Lin, C.T., 2016. Preliminary study of the influence of the spatial arrangement of urban parks on local temperature reduction. Urban For. Urban Green. https://doi. org/10.1016/j.ufug.2016.10.003. Liu, H., Weng, Q.H., 2009. Scaling effect on the relationship between landscape pattern and land surface temperature: a case study of Indianapolis, United States. Photogramm. Eng. Remote Sensing 75, 291–304. https://doi.org/10.14358/PERS.75. 3.291. Mackey, C.W., Lee, X., Smith, R.B., 2012. Remotely sensing the cooling effects of city scale efforts to reduce urban heat island. Build. Environ. 49, 348–358. https://doi. org/10.1016/j.buildenv.2011.08.004. McGarial, K., Marks, B., 1995. FRAGSTAT: Spatial pattern analysis program for
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Effects of changing spatial extent on the relationship between urban forest patterns and land surface temperature Wen Zhou, Fuliang Cao
T
⁎
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Landscape metrics Urban forest pattern Scale effect Land surface temperature Shanghai
The effects of spatial pattern of urban greenspace on land surface temperature (LST) have been extensively documented. Previous studies have confirmed that the relationship between urban green spatial pattern and LST is sensitive to spatial resolution and remote sensing imagery with higher resolution could be more accurate on quantifying the urban green patterns, but little is known about another scaling issue—spatial extent. This paper examined whether the spatial extent applied to derive landscape metrics affect the relationship between LST and spatial pattern of urban forested areas of highly urbanized Shanghai and the seasonal variations using correlation analyses and regression analyses. Spatial pattern of forested areas was measured with eight class-level 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 moving-window approach based on a land-use and land cover (LULC) map derived from SPOT 6 datasets. Results demonstrated that changing spatial extent had significant impacts on the relationship between spatial pattern of urban forested areas and LST. The responses of correlations between spatial pattern metrics and LST to changing extent fell into three categories: correlation decreases with extent increases, correlation increases with extent increases and unpredictable pattern. In general, the amount of forested cover accounts for greater variability in LST than its spatial arrangement. This study extended our scientific understanding of the effects of spatial pattern of urban forested area on LST. In addition, it can provide insights for urban forest planning and management.
1. Introduction Urban heat island (UHI), refers to the phenomenon of higher temperatures occurring in urban areas than in surrounding rural areas (Weng et al., 2004). Urban warming increases air pollution, and has negative impacts on human well-being, particularly those who are poor, vulnerable and lacking the resources to adapt to the climate change (Hajat et al., 2006; Jacob and Winner, 2009; Patz et al., 2005). Moreover, evidence is mounting that many prevalent human diseases such as cardiovascular and respiratory illnesses are associated with heatwaves (Patz et al., 2005). The risks arising from the UHI effect are greatest in large metropolitan areas around the world (Department of Health, 2008). Therefore, UHI mitigation is urgent in order to improve urban living environment (Opdam et al., 2009). Generally, UHI studies are based on two types of temperature data source: air/atmospheric temperature and land surface temperature (LST). Atmospheric temperature data has high temporal resolution with extensive time coverage, which always be applied to describe the temporal variation of the cooling effect of greenspaces (Li et al., 2013). Meanwhile, LST derived from
⁎
infrared remote sensing imagery has been widely applied to study its relationship with landscape patterns (Cao et al., 2010; Weng, 2009; Zhou et al., 2011). This study focused on surface temperature. The cooling effect of urban vegetation has been consistently demonstrated using remote sensing at all scales from a greenspace patch to a city, and even beyond (Mackey et al., 2012; Oliveira et al., 2011; Park et al., 2017; Yan et al., 2018; Zhang et al., 2017). Previous studies have suggested that the urban green patterns significantly affect LST in urban areas (Chen et al., 2014; Shih, 2017). It has become widely accepted that increasing the area of vegetation cover can better mitigate the UHI effect (Bowler Buyung-Ali et al., 2010; Xiao et al., 2018). However, land for urban greening is normally limited, especially for compact urbanized areas. Meanwhile, the optimization of configuration of urban greenspaces can help better achieve reductions of high urban temperatures (Norton et al., 2015). As a result, there is an increased interest in the effect of spatial configuration of urban greenspace on LST (Kong et al., 2014; Lin and Lin, 2016). Considerable researches have demonstrated that the spatial configuration of urban vegetated space affect the magnitude of LST,
Corresponding author at: Nanjing Forestry University, NO. 159 Longpan Road, Xuanwu District, Nanjing, Jiangsu 210037, China. E-mail address: [email protected] (F. Cao).
https://doi.org/10.1016/j.ecolind.2019.105778 Received 17 January 2019; Received in revised form 24 July 2019; Accepted 27 September 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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W. Zhou and F. Cao
one panchronmatic band (1.5 m resolution), four cloud-free Landsat 8 Thermal Infrared Sensor (TIRS) images (30 m resolution) from the United States Geological Survey (https://glovis.usgs.gov/) and meteorological data. Spot 6 images were used for urban land classification and urban green pattern quantification, and Landsat 8 TIRS images were applied to derive LST and detailed imagery information was shown in Table 1. Based on spot 6 fused image (combined multispectral and panchromatic bands), an urban LULC map was created by manual interpretation supported by the ArcMap platform (Environmental Systems Research Institute, Inc., Redlands, CA, USA), combined with field surveys and ground-truthing as necessary. Green areas were classified into forest vegetation (trees with shrubs and grasses) and other vegetation (shrubs and grasses). As a result, the study area was classified into five land-use types including: forest vegetation, other vegetation, water, impervious surface (roads and buildings) and barren land (land without vegetation cover, mainly including exposed soil and landfill sites) (Fig. 1d). Next, 30 samples for each land use type, totally 150 samples were selected using random stratified method to examine the accuracy of the classified map. Land use survey data derived from historical aerial photos, and a 1:250,000 digitalized land cover map acquired in 2016 were applied as the reference data. As a result, the overall accuracy of LULC classification was 94%. Next, the LULC map was converted to a grid format with a 5 m × 5 m grain size in order to conduct the moving-window analysis in FRAGSTATS (3.3). The grain size chosen was based on the resolution of Spot 6 images and Landsat 8 TIRS imagery (30 m). This study focused on forest vegetation. Four Landsat 8 TIRS images were used to retrieve LST. The TIRS images were rectified to a common UTM coordinate system based on the spot 6 images. LST was retrieved from thermal band of Landsat TIRS images mainly according to the Mono-Window Algorithm (MWA) (Qin et al., 2001) with the following equation:
however, its relationship with LST was inconsistent (Li et al., 2012). For example, the study by Chen et al. (2014) suggested that irregular and elongated green patches have stronger cooling intensity compared to regular and compact shaped green patches. However, Feyisa et al. (2014) demonstrated that shape index is negatively correlated with temperature reduction, indicating that urban parks with regular and compact shape perform better in reducing temperature. Moreover, the study of Kong et al. (2014) indicated that the shape index explains around 20% of the temperature reduction (R2 = 0.187). Inconsistent conclusions from previous studies suggested that the correlation between urban green patterns and LST may be scale dependent. Previous studies either applied a variety of remotely sensed data with different spatial resolutions to derive LULC map based on which the urban green patterns were calculated, or conducted under different spatial extents (geographical scales). Several studies have confirmed that remotely sensed image data with different spatial resolutions would influence the quantification of landscape metrics which have been developed to characterize the landscape patterns, and thus affect the relationship to LST (Townsend et al., 2009; Vannier et al., 2011; Weng et al., 2004). For example, Liu and Weng (2009) reported that the relationships between LST and landscape patterns were strongest when the resolution of 30 m and 90 m remote sensed data were used at class and landscape level, respectively. The study by Li et al. (2013) confirmed that the correlation between urban green patterns and LST varied by spatial resolution, and remotely sensed imagery with higher resolution could more accurately quantify the spatial pattern of greenspace. In general, previous studies have primarily focused on the impacts of spatial resolution on the statistical relationship between urban green patterns and LST, however, seldom studies reported the influence of spatial extent. The main purpose of this study is to examine the effects of spatial extent on the relationship between LST and spatial pattern of urban forested areas and the seasonal variations. Since composition and configuration are the two aspects of spatial patterns, this study mainly addresses two questions: (1) how does spatial extent affect the relationship between PLAND of forest vegetation and LST? and (2) it the relationship between configuration metrics and LST consistent across spatial extents?
Ts = [a10 (1 − C10 − D10) + (b10 (1 − C10 − D10) + C10 + D10) T10 − D10 Ta] / C10
(1)
where a10 is the coefficient equals −63.1885, −67.9542, −63.1885 and −60.3263 for spring, summer, autumn and winter, respectively; and b10 is the coefficient and constant with values of 0.44411, 0.45987, 0.44411 and 0.43436 for spring, summer, autumn and winter, respectively (according to Qin et al. 2001), T10 represents the at-sensor brightness temperature, and Ta is the mean atmospheric temperature. C10 and D10 are defined, respectively, by Eqs. (2), (3):
2. Methods 2.1. Study area Shanghai, the largest city and the economic center of China, is located on the coast of East China Sea. The Shanghai metropolitan region, covers a total area of approximately 6340.5 km2, and had a permanent resident population of about 24.1527 million in 2015 (Shanghai Municipal Bureau Statistics, 2016) It is largely situated on a broad flat alluvial plain with a few remnant hills in the southwest, and the average elevation is about 4 m above the sea level (Huang et al., 2017). Shanghai has a subtropical monsoon climate with a mean annual temperature of 17.1 °C and a mean annual precipitation of 1166.1 mm (Qiu et al., 2017). The regional vegetation consists of evergreen broadleaved forest, evergreen broadleaved-deciduous broadleaved mixed forest and shrub-grass communities (Li et al., 2011). Since the 1990s, rapid economic growth of Shanghai was accompanied by enormous urbanization in both scope and degree (Li et al., 2009). Currently, Shanghai is one of the most urbanized cities in China (Du et al., 2016). Our study focused on the center area of Shanghai City, an area of about 290 km2 with diverse land-use and land cover (LULC) types as can be seen in Fig. 1.
C10 = τ10 ε10,
(2)
D10 = (1 − τ10)[1 + (1 − ε10) τ10],
(3)
where τ10 and ε10 represent the total atmospheric transmissivity and the land surface emissivity, respectively. The result of spatial distribution of LST for four seasons was shown in Fig. 2. 2.3. Urban green pattern metrics Numerous landscape metrics have been developed and widely applied to quantify landscape patterns (McGarial and Marks, 1995; O’Neill et al., 1988). These metrics were generally classified into two categories: composition and configuration metrics. Composition refers to the amount of area and diversity of land cover features without considering their spatial arrangement, and configuration refers to the spatial arrangement or distribution of land cover features (Gustafson, 1998). In this study, eight commonly used landscape metrics were selected to quantify the urban forest patterns and to analyze the effects of spatial extents/scales on the relationship between these patterns and LST (Table 2): Percentage of Landscape (PLAND), mean patch size (MPS), largest patch index (LPI), patch density (PD), area-weighted mean perimeter area ratio (PARA_AM), mean shape index (Shape_MN), cohesion index (Cohesion) and aggregation index (AI) (McGarigal et al.,
2.2. Data descriptions and pre-processing The data used in this research include Spot 6 images (10:08 a.m., July 25th, 2016) with four multiple spectral bands (6 m resolution) and 2
Ecological Indicators 109 (2020) 105778
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Fig. 1. (a) Location of Shanghai in China; (b) Administrative boundary of Shanghai; (c) The study area in the 25 July 2016 Spot image; (d) LULC map of the study area based on the Spot 6 image.
respectively applied to characterize urban green pattern and explore its relationship with LST. As the resolution of TIRS band is 30 m, the tested window sizes were chosen as integer multiples of 30 m. Moreover, the mean patch size of forest vegetation in the study area was 3.8 × 103 m2 (about the same size as 60 m × 60 m window), and the statistical analysis showed that 91.2% of the forest vegetation patches was smaller than 8.1 × 103 m2 (same size as 90 m × 90 m window). Therefore, four window sizes that all greater than 60 m × 60 m were chosen and tested to avoid dividing vegetation patches into small parts by moving window.
Table 1 Descriptions of the Landsat-8 TIRS imagery used. Seasons
Acquisition dates
Acquisition time (BJT)
Mean LST (°C)
Standard deviation
Spring Summer Autumn
02 April 2017 20 July 2016 04 November 2014 26 January 2016
10:24:35 am 10:24:59 am 10:25:13 am
23.49 43.17 20.43
1.40 2.55 1.46
10:25:05 am
5.34
1.20
Winter
BJT, Beijing time.
2002). PLAND is the only composition metric, and others are all configuration metrics. Landscape metrics were conducted by movingwindow analysis in FRAGSTATS (3.3) to deal with the relationship between urban forest patterns and LST in four spatial scales.
2.5. Statistical analysis Statistical analyses were performed using the SPSS 23.0. Bivariate correlation analysis and scatter plots were carried out to examine the relationship between landscape metrics and LST of four seasons at four spatial extents respectively. Regression analyses were conducted to quantify the relationship between the percentage of landscape (PLAND) of forest vegetation and LST across four seasons and spatial extents. As configuration metrics were highly correlated with composition metric (PLAND) (Table 3), it may obtain spurious results of relationship between configuration metrics and LST. Therefore, partial Pearson correlation analysis was conducted to examine the relationship between configuration metrics and LST, after controlling the influence of
2.4. Moving-window analysis and window sizes/spatial extents chosen Moving-window analysis, a method of computing and returning value to the focal cell to output a new complete and continuous grid image for each selected metric at the class and landscape level (McGarigal, 2002), was conducted over the entire study area. In this study, four spatial extents, equals window sizes 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m (Fig. 3) were 3
Ecological Indicators 109 (2020) 105778
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Fig. 2. Land surface temperature (°C) of the four dates, (a) April 02, (b) July 20, (c) November 04 and (d) January 26.
(p < 0.01), but no correlation was established between LST and PLAND of forest vegetation in scale MV720 in spring (Table 4). In winter, slightly negative correlation was only reported under scale MV90, and none correlations were established under scale MV180, MV360 and MV720. The magnitude of correlations between LST and PLAND of forest vegetation varied by spatial extents and seasons. Specifically, the correlation between PLAND of forest vegetation and LST decreased as the spatial extent increased in spring, summer and autumn. Based on the same spatial extent, summer all had the strongest correlation relationship between PLAND of forest vegetation and LST, and followed by autumn and spring, and winter had the lowest. Based
composition metric (i.e. using PLAND of forest vegetation as the controlled variable). 3. Results 3.1. Effects of spatial extent on the relationship between LST and PLAND of forest vegetation and the seasonal variations As shown in Table 4, PLAND of forest vegetation mainly showed negative correlation with LST across four different spatial extents (MV90, MV180, MV360, MV720) in spring, summer and autumn Table 2 Definitions of landscape metrics (based on McGarigal et al., 2002). Landscape metrics
Abbreviation
Description
Range
Percentage of Landscape
PLAND
0 < PLAND < 100
Mean patch size
MPS
Largest patch index
LPI
Patch density Area-weighted perimeter area ratio Mean patch shape index Patch cohesion index
PD PARA_AM
Aggregation index
AI
The proportion of total area occupied by a particular patch type; a measure of landscape composition and dominance of patch types (%) The sum of area across all patches of the corresponding patch type divided by the number of patches of the same type (ha) The area (m2) of the largest patch of the corresponding patch type divided by total landscape area (m2), multiplied by 100 (to convert to a percentage) (%) The number of patches in the landscape for patch type The sum, across all patches of the corresponding patch type, of the corresponding perimeter area ratio multiplied by the value of patch area (m2) divided by the sum of patch areas Mean value of shape index 1 minus the sum of patch perimeter (in terms of number of cell surfaces) divided by the sum of patch perimeter times the square root of patch area (in terms of number of cells) for patches of the corresponding patch type, divided by 1 minus 1 over the square root of the total number of cells in the landscape, multiplied by 100 to convert to a percentage The number of like adjacencies involving the corresponding class, divided by the maximum possible number of like adjacencies involving the corresponding class, which is achieved when the class is maximally clumped into a single, compact patch; multiplied by 100 (to convert to a percentage) (%)
Shape_MN Cohesion
4
MPS > 0, no limit 0 < LPI < 100 PD > 0 PARA_AM > 0 Shape_MN ≥ 1, no limit 0 ≤ Cohesion < 100
0 ≤ AI ≤ 100
Ecological Indicators 109 (2020) 105778
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Fig. 3. Four spatial extents/moving-windows consist of 5 m × 5 m grains. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively.
3.2. Effects of spatial extent on the relationship between LST and urban green configuration metrics and the seasonal variations
Table 3 Pearson correlation between PLAND of forest vegetation and configuration metrics.
MV90 MV180 MV360 MV720
MPS
LPI
PD
PARA_AM
Shape_MN
Cohesion
AI
0.71** 0.74** 0.76** 0.92**
0.93** 0.92** 0.88** 0.88**
0.31** 0.25** 0.17** 0.09*
−0.60** −0.58** −0.73** −0.80**
0.36** 0.19** 0.39** 0.26**
0.20** 0.26** 0.78** 0.87**
0.15 0.04 0.69** 0.79**
Pearson correlations between LST and configuration metrics varied by spatial extents (Table 4). In spring, summer and autumn, the correlations between most configuration metrics and LST differed by spatial extents in magnitude and significance, except Shape_MN which also in direction. Similar to composition metric, the magnitude of correlations between configuration metrics and LST decreased as spatial extents increased. In winter, correlations between configuration metrics and LST varied by spatial extent in magnitude, significance and direction. Table 5 listed the partial Pearson correlation coefficients between configuration metrics and LST, after controlling for the effect of PLAND of forest vegetation. Since no significant correlation between LST and configuration metrics was observed in winter, only analytical results from spring, summer, and autumn were displayed in Table 5. Compared to the results in Table 4, the relationships between configuration metrics and LST differed greatly. Specifically, the correlation between area metrics (i.e. MPS and LPI in this study) and LST was only observed in smaller scales and it decreased as the spatial extent increased. Contrarily, results demonstrated that Cohesion, AI and PD were significantly
MPS, LPI, PD, PARA_AM, Shape_MN, Cohesion and AI are mean patch size, largest patch index, patch density, area-weighted perimeter area ratio, mean patch shape index, patch cohesion index and aggregation index respectively. Other details are as in Fig. 3. * p < 0.05. ** p < 0.01.
on linear regression models, the slope values varied by spatial extents among seasons, but were only outstanding in summer (Fig. 4). Specifically, the slope values were −0.746, −0.71, −0.846 and −0.532 at MV90, MV180, MV360 and MV720 scales in summer, respectively. Moreover, a 10% increase in PLAND of forest vegetation led to a reduction in LST of 1.02 °C, 0.98 °C, 0.98 °C, and 0.47 °C at MV90, MV180, MV360 and MV720 in summer, respectively. Table 4 Pearson correlation between LST and landscape metrics. Dates
Window Sizes
PLAND
MPS
LPI
PD
PARA_AM
Shape_MN
Cohesion
AI
LST0402
MV90 MV180 MV360 MV720
−0.34** −0.14** −0.10** −0.05
−0.33** −0.11** −0.09* −0.03
−0.31** −0.12** −0.07* −0.02
−0.21** −0.05 0.01 −0.01
0.28** 0.17** 0.05 0.01
−0.18** −0.15** −0.03 0.05
−0.17** −0.06 −0.03 −0.02
−0.02 0.03 −0.13** −0.01
LST0720
MV90 MV180 MV360 MV720
−0.59** −0.38** −0.30** −0.15**
−0.40** −0.29** −0.24** −0.12**
−0.54** −0.36** −0.27** −0.17**
−0.26** −0.04 −0.15* −0.09*
0.32** 0.18** 0.19** 0.02
−0.19** −0.15** −0.13** −0.11**
−0.15** −0.10** −0.20** −0.09*
0.01 0.01 −0.18** −0.02
LST1104
MV90 MV180 MV360 MV720
−0.47** −0.18** −0.16** −0.14**
−0.30** −0.13** −0.13** −0.04
−0.42** −0.15** −0.14** −0.12**
−0.25** −0.10** −0.08* −0.24**
0.31** 0.08* 0.07* 0.04
−0.13** −0.08* −0.04 0.19**
−0.18** −0.03 −0.16** −0.05
−0.15** −0.01 −0.16** −0.03
LST0126
MV90 MV180 MV360 MV720
−0.25** 0.03 −0.02 0.02
−0.16** 0.07* −0.02 0.08*
−0.23** 0.08* −0.02 −0.00
−0.11** −0.03 −0.02 −0.15**
0.17** −0.03 −0.01 −0.08*
−0.08* −0.07* 0.02 0.21**
−0.06 −0.02 0.03 0.07*
−0.03 −0.01 0.01 0.08*
The digits 0402, 0720, 1104 and 0126 after LST here represent the dates April 02, July 20, November 04 and January 0126. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively. * p < 0.05 and **p < 0.01. 5
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Fig. 4. Relationship between the sqrt of PLAND of forest vegetation and LST across four seasons and spatial extents. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively.
4. Discussion
correlated with LST only in larger scales and the correlation increased as the spatial extent increased. Moreover, correlations between LST and shape metrics (i.e. PARA_AM and Shape_MN) varied by spatial scale in magnitude in summer, but both in magnitude and direction in spring and autumn.
It is widely acknowledged that landscape pattern analysis, for example the area estimation of forest is sensitive to the spatial resolution of the remote sensing data applied for landscape mapping (Li et al., 2013; Nelson et al., 2009; Shao and Wu, 2008). However, the influence of spatial extent on the relationship between urban forest pattern and LST is not yet known. Linking the method of landscape ecology and
Table 5 Partial Pearson correlation between LST and configuration metrics after controlling for PLAND of forest vegetation. Dates
Window Sizes
MPS
LPI
LST0402
MV90 MV180 MV360 MV720
0.24** 0.09*
0.10**
MV90 MV180 MV360 MV720
−0.39** −0.18**
MV90 MV180 MV360 MV720
0.31** 0.16**
LST0720
LST1104
PD
PARA_AM
−0.08* −0.15** −0.25**
−0.13** −0.19**
−0.15** −0.27**
−0.23** −0.15**
0.20** −0.15** −0.33**
Shape_MN
Cohesion
AI
0.31**
0.18** 0.35**
0.23** 0.32**
−0.30** −0.28** −0.35** −0.29**
−0.28** −0.33** −0.25** −0.20**
0.09* 0.29** 0.40**
0.10** 0.34** 0.45**
−0.15**
0.13** −0.16** 0.22** 0.39**
0.19** 0.32**
−0.25**
−0.17** −0.21**
0.33**
The digits 0402, 0720, 1104 and 0126 after LST here represent the dates April 02, July 20, November 04 and January 0126. MV90, MV180, MV360, MV720 represent 90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m moving window sizes, respectively. * p < 0.05 and **p < 0.01. 6
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suggested that the spatial configuration of vegetated areas was irrelevant to the magnitude of LST in winter.
remote sensing, this study demonstrated that the relationship between urban forest pattern and LST was sensitive to spatial extents. In another word, the geographical scale of research area would influence the relationship between LST and landscape pattern metrics and it might be one of reasons that contributed to the inconsistent results from previous studies. By studying the quantitative relationships of LST and forest pattern metrics under different seasons and geographical scales, the scientific understanding of the effects of urban forest patterns on LST can be expanded. The results have important theoretical and management implications and can provide insights for urban forest planning and management.
4.3. Implications for urban forest planning Similar to the findings of Kong et al. (2014), the spatial pattern of vegetated areas was strongly correlated with summer LST. Therefore, the result has important implications for urban forest planning and design to better mitigate the UHI effect, particularly for areas where urbanization is still in process. It suggests that by increasing the amount of vegetated areas, the cooling effect can be greatly enhanced. Since land for urban forest is usually limited and the removal of paved surfaces or buildings is expensive and impractical, increasing tree vegetation canopy cover and the greening of roofs, and adding vegetation cover on parking lots and even building walls are all effective means of UHI mitigation. The findings in this study indicated that the optimizing of spatial arrangement of vegetated areas can also be an effective and practical measure to alleviate the UHI effect rather than increasing the vegetation cover. Therefore, spatial pattern of vegetated areas should not be ignored when making urban forest planning decisions. When the amount of vegetation is fixed, scattered and even distribution across the urban landscape is more effective to reduce the LST than aggregated distribution. The results also suggested that vegetation patches with irregular and elongated shapes performed better in cooling the surrounding urban areas, which is similar to the results of Chen et al. (2014). This is probably because when vegetation patch area is fixed, irregular patch is in contact with larger area of surrounding landscape compared to a relatively compact one. In general, this research suggested that reasonable spatial arrangement of vegetated areas can be an effective measure to regulate the local LST.
4.1. Spatial extent affects the relationship between LST and percent cover of forest The relationship between PLAND of forest vegetation and LST was negative across spatial extents. This is consistent with previous findings that the amount of vegetation cover is an important factor mitigating UHI effects (Jenerette et al., 2007; Larsen, 2015; Shiflett et al., 2017). The significance of correlation between PLAND of forest vegetation and LST decreased as the spatial extent increased (Table 4). Besides, the magnitudes of the reduction of LST by increasing percent cover of vegetation were higher at small scales (Fig. 4). This may because smaller scales normally contain less diversified land cover features and simpler combination of those features than larger scales, therefore, are more sensitive to the response of changing percent cover of vegetation on LST. Seasonal variations in correlations between PLAND of forest vegetation and LST suggested the strongest coupling in summer, and followed by autumn and spring, which is similar to the results of previous studies (Buyantuyev and Wu, 2010; Jenerette et al., 2007; Myint et al., 2013). Data across four spatial extents suggested that increase vegetation cover can effectively mitigate urban warming, and the cooling effect was strongest in summer (Fig. 4). The results suggested that the amount of vegetation cover does not have significant effects on winter LST. This might be related to reduced evapotranspiration and shading by deciduous trees in vegetated surfaces in winter.
4.4. Limitations and recommendations for further studies This study has its limitations. This research examined the effects of only four spatial extents (90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m) on the relationship between spatial pattern of urban forest and LST. Results showed that the relationship between urban forest patterns and LST were sensitive to spatial extent. The findings in this study suggested that multi-scale analysis is needed in order to fully explore the relationship between urban forest pattern and LST. However, gradients in the correlation coefficient across scales were not fully explored with only four spatial extents examined in this study. Therefore, future studies involving more spatial extents and other cities need to be undertaken.
4.2. Spatial extent affects the relationship between LST and spatial configuration of forest After controlling the vegetation cover percentage, the relationship between configuration metrics and LST varied in both magnitudes, significances and directions across different scales and seasons. The responses of correlations between configuration metrics and LST to changing extent fell into three categories: (1) correlation decreased as the spatial extent increased; (2) correlation increased as the spatial extent increased; and (3) unpredictable pattern. Specifically, area metrics (i.e. MPS and LPI) were correlated with LST in smaller scales and the correlations between LST with MPS (i.e. partial r = −0.39 and −0.18 under MV90 and MV180, respectively, P < 0.01, for summer) and with LPI (i.e. partial r = −0.23 and −0.15 under MV90 and MV180, respectively, P < 0.01, for summer) decreased as the spatial extent increased. It suggested that larger vegetation patch performs better in urban cooling than smaller ones in summer. Contrarily, significant correlations were only observed between LST with PD (i.e. partial r = −0.15 and −0.27 under MV360 and MV720, respectively, P < 0.01, for summer), and Cohesion (i.e. partial r = 0.09 under MV180, P < 0.05; partial r = 0.29 and 0.40 under MV360 and MV720, respectively, P < 0.01, for summer), and AI (i.e. partial r = 0.10, 0.34 and 0.45 under MV180, MV360 and MV720, respectively, P < 0.01, for summer) under larger extents. Shape metrics, including PARA_AM and Shape_MN in this study, showed inconsistent correlations with LST in different spatial extents and seasons, suggesting the relationship between LST and shape metrics is not as sensitive as other landscape metrics to spatial extent. The results also
5. Summary and conclusions This research examined the effects of spatial extent on the relationship between urban forest patterns and LST using Landsat 8 TIRS and Spot 6 imagery data. The results confirmed that changing spatial extent had significant effects on the relationship between urban forest patterns and LST, and the effects varied considerably among the spatial extents and seasons. Among seasons, the correlation between LST and urban green patterns was strongest in summer, which indicated that the optimization of the spatial pattern of vegetated areas could be adopted to better mitigate the UHI effect. In general, the effects of changing extent on the correlation between LST and composition metric is more predictable than with configuration metrics. The results also showed that the amount of vegetation cover is more important in determining LST than its spatial arrangement. The findings in this study, in some extent, explained the inconsistent results between spatial pattern metrics and LST from current literature. Results also suggested that there is no single correct or optimal scale to quantify the relationship between urban forest patterns and LST. The study highlights the need for multiscale analysis in order to fully explore the relationship between urban 7
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forest patterns and regional temperature.
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