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Research Article

Measuring the cooling effects of green cover on urban heat island effects using Landsat satellite imagery

Ni Naa College of Ecology and Environment, Nanjing Forestry University, Nanjing, People’s Republic of ChinaView further author information
,
Dan Loua College of Ecology and Environment, Nanjing Forestry University, Nanjing, People’s Republic of ChinaView further author information
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Dandan Xua College of Ecology and Environment, Nanjing Forestry University, Nanjing, People’s Republic of China;b School of Agriculture Engineering, Jiangsu University, Zhenjiang, People’s Republic of ChinaCorrespondencedandan.xu@njfu.edu.cn
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Xuhui Nia College of Ecology and Environment, Nanjing Forestry University, Nanjing, People’s Republic of ChinaView further author information
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Yanqing Liua College of Ecology and Environment, Nanjing Forestry University, Nanjing, People’s Republic of ChinaView further author information
&
Haobin Wanga College of Ecology and Environment, Nanjing Forestry University, Nanjing, People’s Republic of China;c Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, People’s Republic of ChinaView further author information
Article: 2358867 | Received 18 Jan 2024, Accepted 17 May 2024, Published online: 28 May 2024

ABSTRACT

Rapid urbanization has resulted in urban heat island (UHI) effects becoming a global environmental issue. Urban green space (UGS) can effectively alleviate UHI effects; however, how their spatial arrangements and the cooling radiation region effects on UGS are relatively poorly understood. To advance our understanding, we analyzed Landsat imagery from 2010 to 2021 for the world’s first fine classification product of surface cover to quantitatively analyze the cooling radiation range of UGS. We used buffer analysis to measure the impact of the spatial distribution of UGS on UHI effects from normalized difference vegetation index (NDVI) and variance mean ratio (VMR). UGS has its greatest cooling effect in summer, among which evergreen coniferous forests contribute the most to cooling and grasslands contribute the least. The cooling radiation range of UGS on UHI effects is 300 m, and the cooling effect is most effective from 0 to 200 m. The more the UGS is dispersed, the more significant the cooling effect of UGS. Our study demonstrates that the cooling mechanism of UGS on UHI includes radiation range, types and spatial distribution of UGS, and the approach provides a basis for evaluating UHI effects for urban planning.

This article is part of the following collections:
Data-Driven Public Health and Urban Sustainability

1. Introduction

The urban heat island (UHI) effect is a phenomenon in which air and land surface temperature (LST) is higher in urban areas than in the surrounding rural areas. This has become an important environmental issue caused by the recent speed of urbanization and only recent attention to greening (Quan et al. Citation2014; Yin et al. Citation2018). As the urban population continues to grow, the area of urban construction and building density are increasing. As a result, natural land cover types in urban regions have been replaced by impervious surfaces, aggravating UHI effects (Voogt and Oke Citation2003) and influencing both the comfort of urban life and the urban microclimate (Herbel et al. Citation2018; Tan et al. Citation2010). How to effectively relieve the UHI effects is prompting greater research interest.

The most common way to characterize UHI trends is to use air temperature data obtained from meteorological stations (Peng et al. Citation2018). However, because of the finite number of meteorological stations they have low spatial resolution, making it difficult to apply in large-scale research (Chen, Shan, and Yu Citation2022b). Advances in remote sensing technology have promoted the wide application of satellite images with high temporal and spatial resolution in more recent UHI studies (Chen, Shan, and Yu Citation2022b). LST, an effective indicator of UHI effects (Chen et al. Citation2022a; Zhou et al. Citation2022), is available from satellite images (e.g. Landsat) and is being increasingly used.

Urban green space (UGS) has been shown to reduce the LST and alleviate UHI effects (Li et al. Citation2011). The cooling effect of UGS is achieved through shading and vegetation evapotranspiration (Wang et al. Citation2020). Branches and leaves block incident solar radiation, reducing the light reaching the ground surface, and lowering LST under the canopy. Vegetation can also remove heat through transpiration, cooling the surrounding environment (Speak et al. Citation2020; Zhang et al. Citation2014). The spatial distribution and composition of UGS have been shown to affect its cooling effects; the area, perimeter and shape index of UGS are positively correlated with cooling (Lemoine-Rodríguez et al. Citation2022; Lu et al. Citation2012; Shah, Garg, and Mishra Citation2021; Wang Citation2019).

Large UGS typically have lower LST and thus significant cooling effects (Cao et al. Citation2010). Adjustment of the area, perimeter and shape of UGS can improve the cooling effect of UGS on UHI (Huang, Cui, and He Citation2018). The higher the vegetation cover, the lower the LST and the better the cooling effect of UGS (Jiang et al. Citation2021; Liao, Tan, and Li Citation2021; Yang et al. Citation2017; Zhang and Dai Citation2022). Urban expansion can result in UGS fragmentation and changes to its spatial distribution, although this has not been well studied. But because UGS cooling effects will affect the surrounding built-up area and regulate the ambient temperature, their distribution in urban settings will contribute to cooling effects beyond their footprint. The cooling effect on the surrounding environment from the edge of the green patch is nevertheless finite, and when the limit range is reached, the cooling effect of the UGS disappears (Chen et al. Citation2014; Lin et al. Citation2015; Shah, Garg, and Mishra Citation2021). Because of differences in the characteristics of urban environments, the cooling range in UGS varies across studies. For example, park green space in Addis Ababa has a cooling distance of 240 m (Feyisa, Dons, and Meilby Citation2014), while urban green spaces in Harbin, China were reported to have a cooling range of just 120 m (Huang, Cui, and He Citation2018).

The spatial extent of vegetation cover of UGS is negatively correlated with the LST (Li et al. Citation2011; Li et al. Citation2012), but vegetation effects are greater than physical area. Cooling effects differ among vegetation communities with comparisons of tree–grass, scrub–grass and grassland green spaces consistently showing that tree–grass green spaces are the most effective (Petri, Wilson, and Koeser Citation2019; Zhang, Lv, and Pan Citation2013). But few studies have examined the cooling effect of single vegetation types (e.g. evergreen broadleaf or needleleaf forests). Most studies have focused on the biophysical parameters of UGS and the landscape pattern index to relate UGS and LST. The biophysical parameters used to study the relationship between UGS and UHI are vegetation cover, LST and normalized difference vegetation index (NDVI). The greater the vegetation cover and NDVI, the lower the surface temperature of the UGS and the more significant the cooling effect of the UGS (Bao et al. Citation2016; Li et al. Citation2011; Yang et al. Citation2017). The landscape pattern parameters used to study the relationship between UGS and UHI include area, perimeter, shape index, patch density and aggregation index of UGS. Area and perimeter of UGS are negatively correlated with LST of UGS (Du et al. Citation2017; Huang, Cui, and He Citation2018; Yu et al. Citation2017). However, when the area and perimeter exceed a certain threshold, the cooling effects of UGS do not increase significantly (Huang, Cui, and He Citation2018). LST of UGS with high patch density is low, and the cooling effect of UGS is more significant (Estoque, Murayama, and Myint Citation2017; Li and Zhou Citation2019). The cooling effect of UGS with complex shapes is weak (Masoudi and Tan Citation2019; Yu et al. Citation2017). However, the relationship between the aggregation index of UGS and the LST is different in different studies. Some studies indicate that the cooling effect of aggregated UGS is significant (Gong et al. Citation2023; Masoudi and Tan Citation2019; Yin et al. Citation2019; Zhou et al., Citation2019), while some studies show that the cooling effect of scattered UGS is more significant (Bao et al. Citation2016; Basu and Das Citation2023; Zhang et al. Citation2017). Therefore, it is important to study the impact of aggregation or scatter degree of UGS on the cooling effects.

Overall, the cooling radiation range and mechanisms (vegetation types and configurations) of the UGS on UHI are not well understood, especially since it is not clear how the spatial arrangement of UGS impacts the cooling effects. Moreover, it lacks comprehensive research focus on different aspects of the cooling effects of UGS, including different types, cooling ranges and the spatial distribution (aggregation or scatter degree) of UGS. Therefore, in order to provide a comprehensive explanation for the cooling mechanism of UGS on UHI, this study will focus on the cooling effect of UGS on UHI from three aspects: vegetation type, the cooling range and the spatial distribution of UGS on the cooling effect of UHI. We studied Landsat 5 and Landsat 8 images of Nanjing City, China to explore the cooling mechanism of UGS on UHI. Our research objectives were to: (1) analyze seasonal variation of LST of different underlying surfaces and different vegetation types, (2) determine the cooling radiation range of UGS on UHI, and (3) evaluate the influence of spatial arrangement of UGS on its cooling effect on UHI.

2. Study area

Nanjing (31°14′−32°37′N, 118°22′−119°14′E) is in the subtropical humid climate region with abundant rain, short climatic spring and autumn and long summer and winter (Tao and Ye Citation2022). The average temperature of Nanjing in 2022 was 17.6°C (1.4°C higher than that in 2010), the extreme maximum temperature was 40.4°C, and the precipitation reached 819.8 mm. Nanjing, a city with a history of more than two thousand years, is one of the core cities in the Yangtze River Delta and an important political economic and cultural center (Shi et al. Citation2019; Tu et al. Citation2016). It has been labeled a ‘furnace’ city in China due apparently to dominant UHI effects. With the intensification of urbanization in Nanjing, it has been divided administratively into 11 districts (Pukou, Qixia, Yuhuatai, Xuanwu, Gaochun, Liuhe, Lishui, Jianye, Qinhuai, Gulou and Jiangning) (). The urban population of Nanjing has increased due to more and more people migrating to the city. The resident population of Nanjing in 2022 was 9.49 million (), and the population urbanization rate was 87.01%. The population urbanization rate has increased by 8.51% compared with 2010 (the population urbanization rate was 78.50%; https://tjj.nanjing.gov.cn/bmfw/njtjnj/). The Gross Domestic Product (GDP) of Nanjing in 2010 and 2022 is 501.264 billion RMB and 1690.785 billion RMB, respectively (https://tjj.nanjing.gov.cn/bmfw/njtjnj/).

Figure 1. Study area: Nanjing, China. Classification of underlying surface in Nanjing (Zhang et al. Citation2021) (a) and LST of Nanjing on 18 May 2017 (b). LST, land surface temperature (the selected UGS refers to selected urban green space, including natural vegetation and artificial vegetation).

Figure 1. Study area: Nanjing, China. Classification of underlying surface in Nanjing (Zhang et al. Citation2021) (a) and LST of Nanjing on 18 May 2017 (b). LST, land surface temperature (the selected UGS refers to selected urban green space, including natural vegetation and artificial vegetation).

Table 1. The population data of each administrative region in Nanjing, China (Nanjing Bureau of Statistics, 2023).

The UHI effect of Nanjing is increasing. This is despite natural green spaces provided by topography (Zijin Mountain, Qixia Mountain, Laoshan Mountain, Fangshan Mountain, Qinglong Mountain, Niushou Mountain, Jiangjun Mountain), and artificial green spaces included in urban construction. Identifying why UHI effects persist makes Nanjing a suitable study area.

3. Materials and methods

3.1. Datasets

3.1.1. Landsat images

A total of 33 cloudless images of Landsat 5 and Landsat 8 (Collection 2, Level 2) of Nanjing from 2010 to 2021 were downloaded from the United States Geological Survey (USGS) website (). We collected 6 Thematic Imager (TM) images (2010–2011) and 27 Operational Land Image (OLI) images (2013–2021).

Table 2. Acquisition dates of Landsat images of Nanjing, China.

The continuous expansion of urban impervious surfaces makes the accumulation of surface energy eventually lead to the increase of LST. Thus, LST is the most direct manifestation of UHI and an important indicator of UHI evaluation (Zhou et al. Citation2022). Therefore, LST was used to characterize UHI and Landsat Collection 2 Level 2 imagery was used to retrieve LST in this study. The images of Landsat Collection 2 Level 2 were preprocessed for radiometric calibration, and atmospheric and geometric correction, including both the surface reflectance of multi-spectral bands (Equation (1): pixel value conversion surface reflectance) and the LST of thermal infrared bands (Equation (2) (Xu et al. Citation2021): pixel value conversion surface temperature; unit: °C). (1) SR=DN×0.0000275−0.2(1) (2) LST=DN×0.00341802+149−273.15(2) where SR is the surface reflectance, LST is the land surface temperature, and DN is the pixel value of Landsat Collection 2 Level 2 images.

3.1.2. Global land cover with fine classification system

The land surface classification data are based on the world's first global 30 m surface coverage fine classification product in 2020 (the dataset is free to download at http://doi.org/10.5281/zenodo.4280923). The dataset is of the surface cover distribution of the global land area (excluding Antarctica) at 30 m spatial resolution in 2020 and provides effective data support for global surface coverage applications. The classification products are significant for global change, sustainable development analysis and geographical condition monitoring. Therefore, we used this product to reclassify the underlying surface and vegetation types in Nanjing ((a)). We divided the underlying surface into four categories (impervious surface, wetland, selected UGS and water). The vegetation types we used were evergreen broad-leaved forest, evergreen coniferous forest, deciduous broad-leaved forest and grassland.

3.2. Methodology

In order to understand the cooling mechanism of UGS on UHI effect, the methodology includes the following three aspects: First, the cooling effect of the UGSwith four different vegetation types were analyzed and compared, including evergreen broad-leaved forest, evergreen coniferous forest, deciduous broad-leaved forest and grassland from the global land cover product (Section 3.1.2); Then, the cooling radiation range of UGS were analyzed based on the 1000 m’s buffer zones of 11 selected green space (Section 3.2.1); Finally, the relationship between VMR (Variance to Mean Ratio of NDVI for UGS; Section 3.2.2) and LST was analyzed to discuss the influence of the cooling effect of UGS on the spatial distribution of UGS ().

Figure 2. Methodology flowchart. NDVI = normalized difference vegetation index; LST = land surface temperature; VMR = variance to mean ratio, UGS = urban green space, UHI = urban heat island.

Figure 2. Methodology flowchart. NDVI = normalized difference vegetation index; LST = land surface temperature; VMR = variance to mean ratio, UGS = urban green space, UHI = urban heat island.

3.2.1. Maximum cooling distance among green patches

Buffer analysis determines the area of defined width (radius) established around the spatial geographical elements. While it is primarily used for transportation and urban planning, buffer analysis is generally useful to study the influencing area of spatial entities on their surroundings (Guo et al. Citation2020). We selected 11 UGS ( and ; 4 natural vegetation areas and 7 urban green patches), and used buffer analysis to determine their maximum cooling distance as a way of understanding UHI effects.

Figure 3. Distribution of selected green patches in Nanjing, China. The background is a Landsat 8 OLI image acquired on 6 June 2018 using standard false color composites (the near- infrared band, red band and green band are shown as red, green and blue, respectively).

Figure 3. Distribution of selected green patches in Nanjing, China. The background is a Landsat 8 OLI image acquired on 6 June 2018 using standard false color composites (the near- infrared band, red band and green band are shown as red, green and blue, respectively).

Figure 4. Eleven green spaces in Nanjing, China. (a). Laoshan Mountain, (b). Niushou Mountain, (c). Qixia Mountain, (d). Zijin Mountain, (e). Green Space 1, (f). Green Space 2, (g). Green Space 3, (h). Green Space 4, (i). Green Space 5, (j). Green Space 6, (k). Green Space 7. The background image is from Google Earth.

Figure 4. Eleven green spaces in Nanjing, China. (a). Laoshan Mountain, (b). Niushou Mountain, (c). Qixia Mountain, (d). Zijin Mountain, (e). Green Space 1, (f). Green Space 2, (g). Green Space 3, (h). Green Space 4, (i). Green Space 5, (j). Green Space 6, (k). Green Space 7. The background image is from Google Earth.

Ten buffer strips (1000 m at 100 m intervals) were set around each UGS with water and green pixels removed. The LST difference between the interior of the UGS and each buffer region is calculated to create an LST difference curve graph from which the cooling range of the UGS was calculated.

3.2.2. Green space spatial distribution pattern

NDVI is the most commonly used vegetation index to evaluate vegetation coverage and growth status (Na et al. Citation2023; Zhang et al. Citation2017). Therefore, this study uses the NDVI value of UGS in each administrative region of Nanjing to illustrate the vegetation coverage of each administrative region. NDVI is calculated from the reflectance of near-infrared and red bands (Equation (3); Xu et al. Citation2021). In this study, Landsat images acquired on 18 May 2011, 9 October 2011, 18 May 2017, 9 October 2017, and 19 April 2018, 6 June 2018, 28 October 2018, 31 October 2019 and 4 October 2021 were used to calculate the NDVI value of vegetation after removing water bodies: (3) NDVI=ρNIR−ρREDρNIR+ρRED(3) where ρNIR and ρRED are the surface reflectance of band 4 (near-infrared) and band 3 (red) in the Landsat 5 image, respectively, and ρNIR and ρRED are the surface reflectance of band 5 (near-infrared) and band 4 (red) in the Landsat 8 image.

The NDVI value calculated from the Landsat images obtained in Nanjing on 18 May 2011, 9 October 2011, 18 May 2017, 9 October 2017, and 19 April 2018, 6 June 2018, 28 October 2018, 31 October 2019 and 4 October 2021 were analyzed using hot spots analysis to identify data concentrations with confidence greater than 95% (P ≤ 0.05, the UGS distribution area), and then converted into green patches. Green patches were defined using point density analysis, selecting the pixels with point densities greater than 0.0002 (i.e. the number of midpoints per square meter), and raster conversion polygon. The polygons with an area less than 30,000 m2 were removed. The remaining polygons were considered UGS areas. The variance to mean ratio (VMR, Equation (4)) (Seri and Shnerb Citation2015) was calculated using the NDVI of UGS. The relationship between VMR values of UGS and LST (excluding the water and wetland areas) was divided into the 11 administrative regions of Nanjing. VMR is suitable for various data structures to characterize the spatial distribution pattern of samples: (4) VMR=s2m(4) where s2 is the variance of NDVI values for the UGS in each region, and m is the mean of NDVI values for the UGS in each administrative unit. The VMR represents the spatial distribution characteristics of UGS in our study. If VMR = 1, the sample distribution is random; if VMR > 1, the sample distribution tends toward clustered; if VMR < 1, the sample distribution tends toward dispersed; when VMR < 1, lower VMR indicates higher dispersion.

4. Results

4.1. Seasonal temperature variation among surface types

The seasonal variation of LST of an impervious surface, wetland, selected UGS and water are significantly different (). Seasonal LST variation of impervious surface is the most obvious ((a)), and generally, LST difference among the four land cover types is largest in summer, and lowest in winter ((a)). The LST of impervious surfaces from April 19 to October 9 is significantly higher than that of selected UGS, indicating a potential UHI source during this period ((b)). This identified the time period to explore the cooling effects of UGS, including the cooling radiation range and the spatial distribution pattern.

Figure 5. (a) Seasonal variation of land surface temperature of different Land cover types in Nanjing during spring (March to mid-May), summer (mid-May to mid-September), autumn (mid-September to late-October) and winter (November to February). Selected UGS are the urban green spaces from , including both natural and artificial vegetation). (b) Annual change of land surface temperature of different land cover types in Nanjing (see for the image acquiring year of land surface temperature). Month is and date in mm-dd format.

Figure 5. (a) Seasonal variation of land surface temperature of different Land cover types in Nanjing during spring (March to mid-May), summer (mid-May to mid-September), autumn (mid-September to late-October) and winter (November to February). Selected UGS are the urban green spaces from Fig. 3, including both natural and artificial vegetation). (b) Annual change of land surface temperature of different land cover types in Nanjing (see Table 1 for the image acquiring year of land surface temperature). Month is and date in mm-dd format.

The cooling effect of UGS is the greatest in summer but is weaker than for water and wetland cover types ((a)). The UGS also cools in autumn, but less so than in summer ((a)). Different vegetation types have different cooling capacities in summer and autumn among the UGS. Evergreen coniferous forest has the greatest cooling effect in summer while grassland has the least (). The cooling effect of the deciduous broad-leaved forest begins to weaken in late-October, but generally, its LST is higher than that of evergreen coniferous forest ().

Figure 6. Land Surface temperature of different vegetation types during April 19th to October 9th.

Figure 6. Land Surface temperature of different vegetation types during April 19th to October 9th.

4.2. Spatial range of UGS cooling

The cooling distance of UGS is 300 m, and the UGS areas have the greatest cooling effect on UHI in a range from 0 to 200 m (; see supplementary materials for the UGS cooling range for other LST images). When the buffer distance of the UGS exceeds a threshold (200 or 300 m), the LST difference between the UGS and the buffer region stabilizes in a buffer zone of 300–1000 m. However, the LST difference between Qixia Mountain and the surrounding area shows a downward trend in the buffer zone of 200–700 m after it reaches a maximum at 200 m (). The LST difference between green space 1 and the surrounding region decreases in the buffer (300–700 m), while the LST difference between green space 7 and the surrounding area decreases in over a greater distance in the buffer range (300–1000 m) after it reaches its maximum at 300 m ().

Figure 7. Cooling radiation range of green spaces based on satellite satellite-measured land surface temperature (LST). The image was acquired on 6 June 2018. All results of cooling radiation range from images are provided in the supplementary material).

Figure 7. Cooling radiation range of green spaces based on satellite satellite-measured land surface temperature (LST). The image was acquired on 6 June 2018. All results of cooling radiation range from images are provided in the supplementary material).

4.3. Cooling effects of green space distribution

The average NDVI value varies among the 11 districts in Nanjing (), signaling vegetation cover differences in the 11 municipal districts. The VMR of the UGS in 11 districts is consistently less than 1, thus the spatial arrangement of UGS is dispersed. Higher VMR is correlated with significantly lower LST up to a VMR threshold (), after which the rate of LST decline slows ((a,d,f)) or tends to be stable ((c–e)). This pattern indicates the spatial distribution of UGS significantly affects its cooling effect on UHI. Within a given range of UGS dispersion, the cooling effects of UGS increase when they are more dispersed (i.e. lower VMR when VMR < 1). However, the cooling effect is either weakened or not significant when the UGS reaches a threshold dispersion ().

Figure 8. The relationship between variance to mean ratio (VMR) and land surface temperature (LST). Panels are images of Nanjing, China captured on different dates during the study: (a) 18 May 2011, (b) 9 October 2011, (c) 18 May 2017, (d) 9 October 2017, (e) 19 April 2018, (f) 6 June 2018, and (g) 4 October 2021.

Figure 8. The relationship between variance to mean ratio (VMR) and land surface temperature (LST). Panels are images of Nanjing, China captured on different dates during the study: (a) 18 May 2011, (b) 9 October 2011, (c) 18 May 2017, (d) 9 October 2017, (e) 19 April 2018, (f) 6 June 2018, and (g) 4 October 2021.

Table 3. The NDVI value of Nanjing area.

5. Discussion

5.1. The influence of underlying surfaces on LST

Strong summer solar radiation shining on impervious surfaces has the thermal characteristics of small specific heat capacity, rapid heating and high thermal conductivity (Shen et al. Citation2016; Zhou et al. Citation2022). Unsurprisingly, the LST of impervious surfaces reaches their highest in summer, and the LST of impervious surfaces remains higher that other land cover types through autumn ((a)). The UHI effect remains strong from April 19 to October 9 ((b)). Water not only has a large specific heat capacity but also can dissipate heat through evaporation. As a result, water temperature varies little among seasons, and its cooling effect is the greatest (Sheng, Xiao, and Wang Citation2022). UGS not only removes heat by transpiration, but also reduces the solar irradiation reaching the surface through canopy shading, reducing LST and effectively cooling the surface below by preventing it from heating (Sheng, Xiao, and Wang Citation2022; Zhang and Dai Citation2022). For the plants we considered, summer is when canopy growth (and NDVI) was the greatest providing the greatest cooling effect. Vegetation canopy structure (height and density) can affect shading (Richards et al. Citation2020), and the cooling effect of UGS among plant types differs (Xiao et al. Citation2018). We found that the cooling effect of grassland was the weakest, and the cooling effect of evergreen coniferous forest was the strongest. Previous studies have shown that the cooling effect of trees is stronger than that of shrubs and grasslands (Wang et al. Citation2020; Zheng et al. Citation2022), indicating the role that overhead canopies play relative to cool surfaces. However, in combination, trees and grass had the strongest cooling effect, while grassland alone is the weakest (Li et al. Citation2020; Zhang and Dai Citation2022; Zhang, Odeh, and Ramadan Citation2013). This holds for the shading and transpiration of evergreen broad-leaved forests, deciduous broad-leaved forests and evergreen coniferous forests (Li et al. Citation2020; Zhang and Dai Citation2022). Because of this, the cooling effect of UGS in summer and autumn is clear ((a)), weakening after October 9 ((b)); LST of all four vegetation types decreased during middle to late October ().

Understory vegetation is also an important part of forest ecosystem with high species diversity (Tuanmu et al. Citation2010; Xi et al. Citation2022). However, the understory vegetation of trees under natural conditions is complex, and the classification method and classification product used in this study cannot identify the understory vegetation under different vegetation types, so this may affect the cooling effect of UGS with different vegetation types, which is a limitation of this study.

5.2. The cooling radiation range of UGS

The cooling effect of UGS extends to the surrounding thermal environment. Of course, the further from the UGS edge the LST difference between the UGS and the surrounding environment increases, but we found a threshold distance at which this change declines rapidly (Lin et al. Citation2015). The maximum cooling distance in our study was 300 m, and the cooling effect was best from 0 to 200 m. The LST of the UGS is low and cool air shrinks and sinks, causing the ground pressure to rise. The cool air moves from within the UGS to the outside, creating a local circulation that reduces the local ambient temperature (Chen et al. Citation2014; Zhang and Dai Citation2022). However, as the buffer distance from the UGS increases, the LST in the buffer zone also increases, and the cooling effect of the UGS disappears at a threshold distance ().

We also compared the cooling range of Nagoya's UGS during the day and night. The cooling range of the UGS during the day is between 300 and 500 m, and between 200 and 300 m at night (Hamada and Ohta Citation2010).

The surrounding natural and built environment of UGS will affect cooling. For example, high-rise buildings will hinder the flow of cold air, weakening the cooling effect of UGS (Park and Cho Citation2016). It is necessary to consider the distance between UGS and surrounding environmental and built conditions when planning urban spaces to maximize the cooling effect of UGS. Different types of UGS (i.e. natural vegetation areas, urban green parks, water) have different degrees of heat exchange with the external environment (Chang, Li, and Chang Citation2007; Giridharan et al. Citation2008). However, the cooling radiation range of different types of UGS to UHI remains consistent (). We attempted to control for the cooling influence of water on UGS in the buffer zone; however, its effects could not be completely eliminated, and we detected these effects in our results. For example, Qixia Mountain is adjacent to the Yangtze River and green spaces – the cooling range of green spaces around Qixia Mountain is 200 m ((a, b)) and with water 300 m ((c)). Therefore, the LST difference between Qixia Mountain and the buffer range tends to increase first and then decrease further into the buffer (). The LST difference between green spaces 1 and 7 also clearly interacted with water and green space. The cooling range of the water around green space 1 extends to 600 m ((d)), but in green space 7 it maximizes at 200 m, with a tendency of first decreasing and then increasing ((e)).

The environment around the UGS will affect the cooling effect of UGS. Although this study has removed water and other UGS in the buffer zone, the interactions of the cooling effect between water and UGS still exist (), which will have a certain impact on the results and affect the accuracy of the determination of the cooling range. Even though the radiation range of the cooling effects of UGS on UHI was quantified, it is still a limitation for this study to evaluate the impact of the interactions of the cooling effects between water bodies and UGS, or between different UGS.

Figure 9. The cooling radiation range of water and other (buffer) green space around urban green space is demonstrated through changes in land surface temperature (LST). (a) and (b) are the cooling radiation ranges of two green spaces in the buffer around Qixia Mountain. (c) is the cooling radiation range of the water body around Qixia Mountain. (d) is the cooling radiation range of the water body around green space 1. (e) Cooling radiation range of green space in the buffer of green space 7.

Figure 9. The cooling radiation range of water and other (buffer) green space around urban green space is demonstrated through changes in land surface temperature (LST). (a) and (b) are the cooling radiation ranges of two green spaces in the buffer around Qixia Mountain. (c) is the cooling radiation range of the water body around Qixia Mountain. (d) is the cooling radiation range of the water body around green space 1. (e) Cooling radiation range of green space in the buffer of green space 7.

5.3. Impact of the spatial distribution of UGS on its cooling effects

We used NDVI to calculate VMR to measure the spatial distribution of UGS (Xu et al. Citation2021). Some studies have shown that in areas with high NDVI (high vegetation cover), transpiration and shading effects of green areas are strong, which effectively reduces LST (Bao et al. Citation2016; Li et al. Citation2011; Yang et al. Citation2017). Perini et al. found that the LST decreased by 5 K in 33% of vegetation cover and by 10 K in 67% of vegetation cover (Perini and Magliocco Citation2014). A study in Hong Kong showed that when the vegetation cover reaches 30%, the LST drops by 1 K (Ng et al. Citation2012).

The spatial distribution of UGS also strongly influences the cooling effect. The more dispersed the spatial distribution of UGS is, the better the cooling effect of UGS on UHI effects (Basu and Das Citation2023; Sun et al. Citation2023). The research of Bao et al. (Citation2016) also shows fragmented UGS improves its cooling effect. However, we found a threshold for the degree of dispersion of UGS beyond which cooling effects decline (). Indeed, other studies have reported that fragmented UGS reduces the cooling effect in comparison to aggregated UGS (Basu and Das Citation2023; Sun et al. Citation2023; Yin et al. Citation2019). An aggregated spatial arrangement of UGS increases the intensity of cooling effects for its surrounding region (Masoudi and Tan Citation2019), however, dispersed spatial arrangement of UGS may improve cooling effects over a whole region (Maimaitiyiming et al. Citation2014).

The influence of vegetation phenology may lead to the seasonal fragmentation of UGS beginning in Autumn. We found the UHI effect is weaker starting in mid-late-October ((b)), so the spatial distribution of the UGS in mid-late-October has no clear effect on cooling UHI, and there is no VMR threshold ((a)) nor significant linear correlation between VMR and LST at this time ((b)).

Figure 10. The relationship between variance to mean ratio (VMR) and land surface temperature (LST). (a) and (b) The relationships between VMR and LST on 28 October 2018 and 31 October 2019, respectively. (c) The relationship between median LST (obtained from the LST data of 18 May 2011, 9 October 2011, 18 May 2017, 9 October 2017, 19 April 2018, 6 June 2018 and 4 October 2021) and VMR threshold.

Figure 10. The relationship between variance to mean ratio (VMR) and land surface temperature (LST). (a) and (b) The relationships between VMR and LST on 28 October 2018 and 31 October 2019, respectively. (c) The relationship between median LST (obtained from the LST data of 18 May 2011, 9 October 2011, 18 May 2017, 9 October 2017, 19 April 2018, 6 June 2018 and 4 October 2021) and VMR threshold.

Urbanization has brought a series of environmental issues, and UGS can play an important role in urban planning (Armson, Stringer, and Ennos Citation2012). Accelerating urban greening in Nanjing from 2010 to 2020 may alleviate the UHI effect (Na et al. Citation2023) with the most significant cooling effect occurring with a specific degree of dispersion that we have identified. More dispersed (i.e. high VMR) ((c)) UGS at higher LST can effectively mitigate the UHI effect. Therefore, it is important to carefully plan the spatial distribution of UGS, especially with limited urban space.

In Nanjing’s subtropical humid climate, higher summer rainfall leads to fewer cloud-free images at that time of year. Therefore, there are relatively few images available to determine the influence of the distribution of summertime UGS on the cooling effect. When extracting green pixels from satellite images, the process of converting hotspots to the UGS range may exclude some small green areas leading to a reduction in extraction accuracy. The method of spatial autocorrelation in this study to extract UGS will bring some error sources, which is related to the spatial resolution of the images and the quality of the images (Wang et al. Citation2022). These errors will lead to the removal of some small UGS, which may weaken the degree of dispersion of UGS, thus affecting the overall spatial distribution pattern of UGS.

6. Conclusions

UGS can provide relief from UHI effects. Our study shows the cooling mechanism of UGS on UHI in Nanjing including the cooling effect radiation range, the types and spatial distribution of UGS. Our work provides a basis for alleviating the UHI effect through mindful urban planning. We found that (1) the cooling effect of UGS is the strongest in summer, and the evergreen coniferous forest has the strongest cooling effect. Grassland has the weakest cooling effect. (2) UGS can affect surface temperatures in the surrounding environment, but the cooling radiation range has a specific distance threshold (300 m in Nanjing) and the cooling effect is generally more apparent in the range of 0–200 m. (3) Within a given range of UGS dispersion of the higher the dispersion, the more significant the cooling effect of UGS on UHI.

The results of this study provide fundamental information on the cooling mechanism of UGS for urban planning to effectively alleviate the UHI effect and improve the living comfort of residents. The suggestions for urban planning drove from the results and discussions of this study are: besides the esthetics of UGS, it is also important to optimize vegetation type and structure of UGS for urban planning in order to mitigate the UHI effect and create a livable environment for residents; due to the radiation range and spatial arrangement of UGS, the distance and distribution pattern between UGS need to be taken into account for urban planning; the more discrete the UGS in a certain range of discrete degree, the more significant the cooling effect of UGS, which can help to achieve the purpose of making full use of urban space and improving the urban thermal environment.

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Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, D. Xu, upon reasonable request.

Additional information

Funding

This work was supported by China Postdoctoral Science Foundation [grant number 2023M741430]; National Natural Science Foundation of China [grant number 41901361].

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