How Did Plant Communities Impact Microclimate and Thermal Comfort in City Green Space: A Case Study in Zhejiang Province, China

Abstract

Urban green spaces play a crucial role in mitigating the effects of urban microclimates. This study quantitatively explored how the spatial structural parameters of plant communities regulate microclimates during the hot summer in Zhuji City, Zhejiang Province. Field measurements and ENVI-met simulations were conducted to evaluate the microclimatic effects of different plant communities, including broadleaf and coniferous tree communities. Microclimatic variables, such as air temperature, relative humidity, and solar radiation, were analyzed. The results revealed that spatial structural parameters, such as Acanopy/H, sky view factor (SVF), and canopy density, significantly affected temperature reduction and humidity increase. Among these, the canopy-to-height ratio (Acanopy/H) was a promising potential factor influencing cooling. Simulations revealed that with a constant tree height, cooling and humidification benefits increased as Acanopy/H increased. However, with a constant canopy area, these benefits were greater when Acanopy/H ratio decreased. This study emphasizes the importance of spatial structural parameters in optimizing summer microclimatic regulation, providing key insights into urban green space design to enhance thermal comfort. These findings can guide the planning of climate-resilient plant landscapes in subtropical cities.

1. Introduction

As global urbanization accelerates, urban climate challenges, particularly the urban heat island (UHI) effect, have emerged as key concerns impacting environmental quality and human health [1]. The UHI effect is driven by a combination of factors, such as land cover changes, increased building density, and reduced green space, all of which contribute to significant urban temperature rises. These shifts not only undermine thermal comfort and air quality but also intensify energy consumption [2]. Consequently, mitigating the UHI effect and enhancing urban thermal environments have become critical research priorities for advancing sustainable urban development.

Urban green spaces are integral to mitigating the UHI effect, particularly in regulating microclimates. Research has shown that plant communities within these green spaces significantly reduce UHI by modulating local temperatures, increasing humidity, and enhancing air circulation [3,4]. Trees play a central role in two key mechanisms: canopy transpiration and shading [5,6]. Transpiration cools the canopy air by evaporating heat, while shading reduces solar radiation, preventing excessive surface warming [7,8]. However, these effects are strongly influenced by structural parameters, including tree density, canopy density, canopy area, vegetation height, vertical stratification, and sky view factor (SVF) [9,10,11,12,13,14,15]. The SVF, which reflects the proportion of the visible sky, plays a critical role in shading and microclimate regulation [12]. Canopy area, a key determinant of shading, reduces solar radiation and surface temperature, thereby improving the microclimate [9]. Tree height also significantly influences microclimate regulation. Taller trees enhance air circulation, provide longer shading, and amplify evaporative cooling, thereby reducing temperature [16].

While individual canopy attributes, such as tree height, canopy area, and canopy density, have been widely studied, they often fail to fully capture the integrated effects of plant community structure on microclimate regulation [9,10,11,12,13,14,15]. For instance, taller trees generally provide greater microclimate benefits [9], but their effectiveness is closely associated with canopy spread. The Acanopy/H ratio, with Acanopy representing the horizontal canopy projection and H denoting tree height, which combines both horizontal and vertical canopy dimensions, offers a more holistic metric for evaluating the cooling potential of plant communities. Therefore, the ratio of canopy area to tree height (Acanopy/H) has emerged as a more comprehensive indicator of a plant community’s potential to mitigate UHI.

Despite the growing recognition of the role of plant canopy structure in alleviating the UHI [9,10,11,12,13,14,15], limited attention has been paid to the combined effects of canopy area and tree height on microclimatic changes. The Acanopy/H ratio addresses this gap by reflecting the interplay between tree architecture and microclimate regulation, offering a more integrated perspective of plant community cooling capacity. This study proposes the Acanopy/H ratio as an innovative spatial structure indicator for assessing the microclimate regulation of plant communities. Unlike traditional metrics that focus solely on horizontal or vertical canopy attributes, this ratio integrates both dimensions to better quantify the vegetation cooling potential. By examining the relationship between the Acanopy/H ratio and temperature, humidity, and wind speed, this study fills a critical research gap in the literature. These findings provide new insights into how plant community structure influences microclimate, offering valuable guidance for optimizing urban green space design to mitigate the urban heat island effect.

This study explored the relationship between plant community canopy structures—including tree height, canopy density, SVF, and Acanopy/H ratio—and urban microclimate variations. Field surveys of several representative plant communities in urban parks in Zhuji City, Zhejiang Province, coupled with meteorological measurements and ENVI-met simulations (temperature, humidity, and solar radiation), highlighted Acanopy/H as a promising potential indicator of microclimate regulation. These findings underscore the potential of Acanopy/H in mitigating the UHI effect, offering valuable insights for urban planning and landscape design aimed at enhancing urban thermal environments.

2. Materials and Methods

This study adopted a combined approach of field measurements and ENVI-met simulations to assess the regulatory effects of plant communities on microclimatic conditions. Field measurements captured key microclimatic variables, such as air temperature, relative humidity, and solar radiation, within the plant communities. ENVI-met simulations quantified the impact of various spatial structural parameters on the microclimate. The high spatial resolution of the ENVI-met model allowed for precise predictions of the microclimatic effects of different plant community structures [17,18]. In turn, the field measurements provided essential empirical data for validating the simulation results.

2.1. Study Area

Zhuji City, situated in the northern-central region of Zhejiang Province (119°53′ E to 120°32′ E, 29°21′ N to 29°59′ N), falls within the subtropical monsoon climate zone [19]. This region is distinguished by marked seasonal variations, moderate temperatures, abundant sunlight, and rich precipitation, all contributing to a humid climate where rainfall and heat coincide [20]. The city’s annual average temperature fluctuates between 15 °C and 18 °C, with total annual precipitation ranging from 980 mm to 2000 mm. Additionally, Zhuji enjoys between 1710 and 2100 h of sunshine annually [21].

Field measurements were conducted with precision in the plant communities of the Sports Park in Zhuji City, which was chosen as the focal point of this study. Situated in the urban core, this park is surrounded by a dense residential network, witnessing frequent public engagement and daily activity. A total of seven distinct sample points (B1–B7) were carefully selected, one of which served as a control. The study area’s geographical locations are shown in Figure 1, and the details of the sample points are shown in Figure 2.

2.2. Canopy Structural Characteristics of Plant Communities

To ensure the representativeness of the sampling and the reliability of the findings, this study focused on communities with both trees and herbaceous plants. Several typical evergreen plant communities, including broadleaf and coniferous species, were selected to ensure comparability in terms of canopy density, species composition and height. To analyze the canopy structural characteristics of plant communities, this study conducted field surveys to document the plant species, plant height, canopy area, SVF, canopy density, and ratio of canopy area to tree height (Acanopy/H) at each sampling point. The SVF, an indicator of the proportion of visible sky from the ground, was measured using fisheye lens photography to capture full-sky images of the target area [22]. The images were first processed using Adobe Photoshop CC 2018 to enhance the contrast and perform binarization, ensuring a clear distinction between the sky and non-sky areas. The processed images were then imported into RayMan, where the SVF was computed by analyzing the proportion of sky pixels relative to the total image area for each ground measurement point [23,24]. Canopy density, which reflects the extent of canopy coverage within a given area, was determined using the ratio of the canopy projection area to the ground area. This was estimated through both plot sampling and photographic methods: multiple sample areas were selected, and either images of the ground were taken or transparent grids were laid over the ground, with the proportion of the area shaded by the canopy used to estimate canopy density. Plant heights were measured using LiDAR scanning technology, which allowed for the precise capture of tree heights [25]. The canopy area, representing the horizontal projection of the tree crown, was calculated for individual trees by measuring the crown’s maximum horizontal diameters (D1 and D2) using the following formula:

Acanopy = π × (D1/2) × (D2/2),

(1)

The canopy structural characteristics of each plant community are presented in

Table 1. Measure point features.

Sample Settings	Vegetation Composition Type	SVF	Average Height of Vegetation	Acanopy/H	Crown Density	Plant Species
B1	/	0.991	/	/	/	/
B2	Arbor + herb	0.187	25.1 m	0.8 m	82%	Cinnamomum camphora; Zoysia japonica.
B3	Arbor + herb	0.305	6.9 m	1.3 m	85%	Osmanthus fragrans; Ophiopogon japonicus.
B4	Arbor + herb	0.103	15.9 m	3.2 m	95%	Cinnamomum camphora; Ophiopogon japonicus.
B5	Arbor + herb	0.270	28.1 m	0.8 m	75%	Pinus massoniana; Ophiopogon japonicus.
B6	Arbor + herb	0.281	20.8 m	0.5 m	75%	Cedrus deodara; Zoysia japonica.
B7	Arbor + herb	0.129	19.9 m	2.4 m	89%	Cinnamomum camphora; Osmanthus fragrans; Cynodon dactylon.

. (The plant species listed do not represent the complete species composition of all plots but rather indicate the unique species specific to each sampling site. To distinguish entries clearly, a period is placed after each sample’s species name.)

2.3. Field Microclimate Data

2.3.1. Data Collection

From June to August 2024, 15 field measurements were conducted under clear, windless conditions using high-precision portable meteorological instruments, namely a hygrometer (Model LWS-2, Greenbo Instruments Co., Ltd, Fuhua Building, Xihu Technology Park, Xihu District, Hangzhou, Zhejiang Province, China) and a light intensity meter (Model LBJ-22, Greenbo Instruments Co., Ltd, Fuhua Building, Xihu Technology Park, Xihu District, Hangzhou, Zhejiang Province, China). Field measurements monitored air temperature, relative humidity, and solar radiation within plant communities, providing essential empirical data for validating the accuracy of the ENVI-met simulation results. The measurement parameters and instrument specifications are listed in

Table 2. Measurement parameters and instrument specifications.

Sensors	Measurement Range	Accuracy	Resolution
Air temperature sensor	−40 to 80 °C	±0.2 °C	0.1 °C
Air humidity sensor	0~100% RH	±0.5%	0.1%
Light intensity sensor	0~200 klx	±4 klx	0.001 klx

. For consistency, data collection spanned 15 days, each characterized by typical clear, windless weather conditions. The maximum and minimum temperatures for each day are presented in

Table 3. Selected typical days.

Date	Minimum Temperature	Highest Temperature
4 June 2024	21 °C	34 °C
9 June 2024	20 °C	35 °C
10 June 2024	22 °C	36 °C
12 June 2024	20 °C	33 °C
14 June 2024	20 °C	32 °C
28 June 2024	23 °C	35 °C
29 June 2024	24 °C	36 °C
4 July 2024	25 °C	34 °C
7 July 2024	26 °C	36 °C
8 July 2024	26 °C	36 °C
12 July 2024	25 °C	39 °C
13 July 2024	26 °C	38 °C
14 July 2024	26 °C	39 °C
25 July 2024	23 °C	36 °C
26 July 2024	25 °C	34 °C

. Data were recorded at ten-minute intervals from 7:00 AM to 7:00 AM the following day, with all readings taken at a height of 1.5 m above the ground. Figure 3 shows a schematic of the meteorological instruments and field measurement procedures.

2.3.2. Microclimate Data Analysis

In order to facilitate a comprehensive and comparative analysis of microclimate parameters across different plant communities, air temperature, relative humidity, and light intensity data were standardized following the approach proposed by Yan (2014), as outlined in Equations (2)–(7) [4].

Cooling intensity: ∆Tem = Tem_sun − Tem_shade,

(2)

Cooling rate: ∆Tem% = (Tem_sun − Tem_shade)/Tem_sun × 100%,

(3)

Humidification intensity: ∆RH = RH_sun − RH_shade,

(4)

Humidification rate: ∆RH% = (RH_sun − RH_shade)/RH_sun × 100%,

(5)

Shading intensity: ∆LI = LI_sun − LI_shade,

(6)

Shading rate: ∆LI% = (LI_sun − LI_shade)/LI_sun × 100%,

(7)

where: Temsun represents the air temperature at the reference point; Temshade denotes the air temperature within the plant community,

where RHsun refers to the relative humidity at the reference point and RHshade refers to the relative humidity within the plant community,

where LIsun indicates the light intensity at the reference point and LIshade refers to the light intensity within the plant community.

2.4. Validation of Numerical Simulation Methods

ENVI-met, a comprehensive tool for simulating dynamic changes in site climate factors, integrates principles from physics and ecology, including thermodynamics, fluid dynamics, and meteorology. It evaluates the influence of various factors, such as soil, paving materials, building structures, and vegetation on the microclimate [26,27]. The ENVI-met model was selected for its efficiency in simulating small-scale microclimatic variations in urban environments, making it particularly suitable for evaluating the impact of green spaces and plant communities on the surrounding environment [28]. In this study, ENVI-met 5.6.1 version was employed to simulate the microclimate of the study site, generating spatial distribution maps of key climatic parameters. To assess the model accuracy, a linear regression analysis was performed between the simulated results and field measurements. The ENVI-met simulation process consists of three key stages: model configuration, input of environmental parameters, and output of results [28,29]. The summer simulations incorporated parameters such as the initial values, time, ground materials, air temperature, and relative humidity. The overall numerical simulation framework is depicted in Figure 4.

2.5. The ENVI-Met Simulation Setup

2.5.1. Impact of Acanopy/H on Microclimate with Consistent Canopy Area and Under-Canopy Height

In the context of plant community-driven microclimate regulation, the canopy area to tree height ratio (Acanopy/H) emerges as a structural metric, offering a comprehensive assessment of the community’s potential to modulate local climate conditions. To investigate the effects of varying Acanopy/H on the microclimate under a fixed canopy area, we implemented a controlled experimental setup following the validation of the ENVI-met simulation model. The simulations were designed with tree heights set at 3 m, 6 m, 9 m, 12 m, 15 m, 18 m, and 21 m, corresponding to Acanopy/H ratios of 25/3, 25/6, 25/9, 25/12, 25/15, 25/18, and 25/21, while maintaining a constant canopy height of 2 m, a canopy width of 5 m, an upright crown form, and a leaf area density (LAD) of 1 (see

Table 4. Simulation setup for the impact of different Acanopy/H ratios (25/21–25/3) on the microclimates of plant communities with equal canopy areas.

Acanopy/H	Understory Height	Crown Width	Canopy Shape	LAD	3D Model
25/3	2 m	5 m	Upright	1
25/6	2 m	5 m	Upright	1
25/9	2 m	5 m	Upright	1
25/12	2 m	5 m	Upright	1
25/15	2 m	5 m	Upright	1
25/18	2 m	5 m	Upright	1
25/21	2 m	5 m	Upright	1

). This configuration ensured a robust comparison of the microclimate mitigation effects across the seven Acanopy/H ratios. The community model was set within a 100 m × 100 m grid, employing a row-column planting pattern of 15 × 15 trees, and adopting a tree-grass composite vertical structure with 5 m inter-tree spacing. The climate simulation covered the period from 07:00 to 22:00 on 1 July 2024. The simulated microclimatic parameters were air temperature, relative humidity, and wind speed.

To further explore the impact of varying Acanopy/H ratios on microclimate enhancement under a fixed canopy width, a series of tree heights—4 m, 5 m, 7 m, and 8 m—were introduced, corresponding to Acanopy/H values of 25/4, 25/5, 25/7, and 25/8. These conditions were maintained with an under-canopy height of 2 m, a canopy width of 5 m, an upright canopy form, and a LAD value of 1. Building on previous simulations for Acanopy/H ratios of 25/3, 25/6, and 25/9, a comparative analysis was conducted across seven distinct Acanopy/H ratios to evaluate the microclimate benefits of the plant communities. The simulated canopy coverage, planting spacing, and vertical structure remained unchanged (see

Table 5. Simulation setup for the impact of different Acanopy/H ratios (25/9–25/3) on the microclimates of plant communities with equal canopy area.

Acanopy/H	Understory Height	Crown Width	Canopy Shape	LAD	3D Model
25/3	2 m	5 m	Upright	1
25/4	2 m	5 m	Upright	1
25/5	2 m	5 m	Upright	1
25/6	2 m	5 m	Upright	1
25/7	2 m	5 m	Upright	1
25/8	2 m	5 m	Upright	1
25/9	2 m	5 m	Upright	1

).

2.5.2. Impact of Acanopy/H on Microclimate with Consistent Tree Height and Under-Canopy Height

To further explore the influence of varying Acanopy/H ratios on the microclimate of plant communities with a fixed tree height, a controlled variable approach was employed to establish a reference model. In this study, simulation scenarios were meticulously designed (as outlined in

Table 6. Simulation setup for the impact of different Acanopy/H ratios (4/6–64/6) on the microclimates of plant communities with equal tree height.

Acanopy/H	Tree Height	Crown Width	Canopy Shape	LAD	3D Model
4/6	6 m	2 m	Upright	1
9/6	6 m	2 m	Upright	1
16/6	6 m	2 m	Upright	1
25/6	6 m	2 m	Upright	1
36/6	6 m	2 m	Upright	1
49/6	6 m	2 m	Upright	1
64/6	6 m	2 m	Upright	1

), with tree canopy widths set at 2 m, 3 m, 4 m, 5 m, 6 m, 7 m, and 8 m, yielding Acanopy/H ratios of 4/6, 9/6, 16/6, 25/6, 36/6, 49/6, and 64/6. The tree height was consistently maintained at 6 m, with an under-canopy height of 2 m, spherical canopy shape, and LAD value of 1. The experimental setup included a 100 m × 100 m community model area with a grid layout, tree-grass composite vertical structure, and planting density where the inter-tree spacing was equal to the canopy width. The duration of the climate simulation was consistent with the previous settings.

3. Results

3.1. Impact of Diverse Vegetation Canopy Features on Microclimate

3.1.1. Variation in Light Intensity Across Diverse Vegetation Communities

Light intensity, a crucial microclimate variable, is significantly reduced by vegetation canopies, which attenuate solar radiation, thereby regulating local temperature and humidity and improving thermal comfort. The instantaneous light intensity at 14:00 and the average light intensity throughout the measurement period for each sampling point are shown in

Table 7. The instantaneous light intensity at 14:00 and the average light intensity throughout the measurement period (24 h) for each sampling point.

	B1-CK	B2	B3	B4	B5	B6	B7
LI14:00	50.109 klx	9.444 klx	9.896 klx	0.972 klx	10.853 klx	13.598 klx	3.115 klx
LIAverage	37.259 klx	8.092 klx	7.698 klx	0.758 klx	6.108 klx	9.236 klx	2.091 klx

. The variation in light intensity and the corresponding shading intensity effects across the sampling points are shown in Figure 5. At B1-CK (SVF = 0.991, no vegetation cover), the instantaneous light intensity (LI_14:00) reached 50.109 klx, with a daily average of 37.259 klx, which was substantially higher than at the other sites, indicating the marked light-shading effect of vegetation. At the other sites (B2–B7), LI_14:00 ranged from 0.972 klx to 13.598 klx, with average daily intensities between 0.758 klx and 9.236 klx, revealing significant variations in light attenuation due to differences in canopy characteristics.

Vegetation communities with higher canopy densities demonstrated a stronger capacity for light attenuation. At B4 (canopy density 95%), the instantaneous light intensity at 14:00 (LI_14:00) was 0.972 klx, with an average light intensity (LI_Average) of 0.758 klx, which exhibited the most significant reduction in light intensity. At B3 (canopy density 85%) and B2 (canopy density 82%), the LI_14:00 values were 9.896 klx and 9.444 klx, respectively, with the average light intensities notably lower than those at the control site. In contrast, B6, with a lower canopy density of 75%, exhibited an LI_14:00 of 13.598 klx, indicating a weaker light reduction effect. A significant negative correlation between canopy density and light intensity was observed (p < 0.05), with a higher canopy density leading to a more pronounced reduction in surface light intensity.

The higher the SVF value, the greater the light transmittance. B1-CK (SVF = 0.991) exhibited the highest light transmittance, with an instantaneous light intensity at 14:00 (LI_14:00) of 50.109 klx and an average light intensity (LI_Average) of 37.259 klx, significantly surpassing all other sampling points, indicating that light transmittance was maximized when the SVF was close to 1. Conversely, B4 (SVF = 0.103) and B7 (SVF = 0.129) recorded LI_14:00 values of 0.972 klx and 3.115 klx, respectively, with LI_Average values of 0.758 klx and 2.091 klx, indicating a marked reduction in light transmittance. B6 (SVF = 0.281), representing a moderate SVF, exhibited an LI_14:00 of 13.598 klx, placing its light transmittance in the intermediate range. A significant positive correlation between the SVF and light intensity was established (p < 0.05), with communities possessing lower SVF values significantly attenuating surface light intensity by reducing the influx of sky radiation.

Plant communities with higher Acanopy/H ratios exhibited a more pronounced ability to attenuate light intensity, highlighting the pivotal role of canopy coverage and tree height ratios in modulating the light transmittance. For instance, the LI14:00 values for B4 (Acanopy/H = 3.2 m) and B7 (Acanopy/H = 2.4 m) were 0.972 klx and 3.115 klx, respectively, demonstrating significantly greater light reduction compared to other sample points. In contrast, B6 (Acanopy/H = 0.5 m) exhibited an LI14:00 of 13.598 klx, indicating a marked decline in its light attenuation capacity. The medium Acanopy/H values for B3 (Acanopy/H = 1.3 m) and B2 (Acanopy/H = 0.8 m) resulted in LI14:00 values of 9.896 klx and 9.444 klx, respectively, yielding moderate reductions in light intensity. A significant positive correlation (p < 0.05) was observed between Acanopy/H and light attenuation efficiency, with higher Acanopy/H values enhancing canopy-shading effects and substantially mitigating the surface light intensity. Linear regression scatter plots depicting the relationships between SVF, Acanopy/H, canopy density, and shading intensity effects are shown in Figure 6.

The attenuation of light intensity not only directly curtailed the influx of solar radiation at the surface but also likely exerted indirect effects by influencing local temperature and humidity dynamics. The reduction in light levels led to a decrease in surface evaporation and thermal radiation, which, in turn, may have further modulated the variations in air humidity and temperature. These changes provided crucial insights into the underlying mechanisms of microclimate regulation, particularly with regard to the alterations in humidity and temperature.

3.1.2. Variations in Relative Humidity Across Diverse Vegetation Communities

Relative humidity (RH), a key metric reflecting atmospheric moisture content, is intrinsically linked to factors such as light intensity, surface temperature, and plant transpiration. A comparative analysis revealed that vegetation cover exerted a profound influence on enhancing local humidity. The instantaneous relative humidity at 14:00 and the average relative humidity throughout the measurement period for each sampling point are shown in

Table 8. The instantaneous relative humidity at 14:00 and the average relative humidity throughout the measurement period (24 h) at each sampling point.

	B1	B2	B3	B4	B5	B6	B7
RH14:00	32.0%	56.3%	57.2%	61.5%	57.7%	54.2%	59.2%
RHAverage	41.0%	77.1%	77.4%	84.3%	76.3%	77.8%	82.1%

. The variations in the relative humidity and corresponding humidification intensity effects across the sampling points are shown in Figure 7. At the control site (B1-CK), devoid of vegetation, both instantaneous RH (RH_14:00) and average RH (RH_Average) were significantly lower, with values of 32.0% and 40.9%, respectively. In contrast, at vegetated sites (B2–B7), RH_14:00 ranged from 54.2% to 61.5%, and RH_Average varied between 76.3% and 84.3%. These results underscore the crucial role of vegetation in modulating humidity, primarily through transpiration and attenuation of solar radiation [5,6].

The effect of canopy density on the enhancement of relative humidity was particularly significant. The data from the sampling sites clearly indicated that higher canopy densities were associated with higher relative humidity levels. Specifically, the site with the highest canopy density, B4 (95%), recorded an RH at 14:00 (RH14:00) of 61.5%, with an average relative humidity (RHAverage) of 84.3%, showing the most significant increase in humidity. In contrast, the site with a lower canopy density, B6 (75%), exhibited an RH14:00 of 54.2% and an RHAverage of 77.8%, reflecting a noticeably diminished effect on humidity. These results clearly demonstrate that canopy density, by enhancing both the interception of solar radiation and plant transpiration, plays a crucial role in elevating local humidity levels. A statistically significant positive correlation (p < 0.05) was observed between canopy density and the relative humidity.

Furthermore, the SVF was a significant determinant of humidity regulation. Lower SVF values enhanced the ability to block solar radiation, leading to increased humidity. Specifically, B4 (SVF = 0.103) and B7 (SVF = 0.129) had RH14:00 values of 61.5% and 59.2%, with an RHAverage of 84.3% and 82.1%, respectively. In contrast, B6 (SVF = 0.281) recorded an RH14:00 of 54.2% and an RHAverage of 77.8%. A significant negative correlation was found between SVF and humidity, highlighting the role of a lower SVF in enhancing humidity by reducing sky radiation.

Similarly, the Canopy Area-to-Height ratio (Acanopy/H) significantly influenced humidity. Higher Acanopy/H values were associated with increased humidity levels. B4 (Acanopy/H = 3.2 m) showed the highest RH14:00 (61.5%) and RHAverage (84.3%), whereas B6 (Acanopy/H = 0.5 m) exhibited a RH14:00 of 54.2% and RHAverage of 77.8%. A positive correlation (p < 0.05) was found between Acanopy/H and relative humidity, confirming that a higher Acanopy/H enhanced humidity through greater canopy coverage and transpiration. Linear regression scatter plots depicting the relationships between SVF, Acanopy/H, canopy density, and humidification intensity effects are shown in Figure 8.

In conclusion, canopy density, SVF, and Acanopy/H were all key factors in humidity regulation. Vegetation communities with higher canopy density and Acanopy/H and lower SVF synergistically enhanced humidity by reducing solar radiation and boosting transpiration. This increase in humidity also indirectly impacted air temperature by lowering the evaporative cooling potential, laying a crucial foundation for follow-up analyses of the plant community effects on temperature regulation.

3.1.3. Variations in Air Temperature Across Diverse Vegetation Communities

The shading effect of plant communities played a central role in temperature regulation. The instantaneous air temperature at 14:00 and the average air temperature throughout the measurement period for each sampling point are shown in

Table 9. The instantaneous air temperature at 14:00 and the average air temperature throughout the measurement period (24 h) at each sampling point.

	B1	B2	B3	B4	B5	B6	B7
Tem14:00	38.5 °C	36.3 °C	36.5 °C	34.7 °C	36.2 °C	36.6 °C	35.2 °C
TemAverage	32.9 °C	29.9 °C	30.2 °C	29.8 °C	30.0 °C	30.4 °C	29.8 °C

. The variation in air temperature and the corresponding cooling intensity effects across the sampling points are shown in Figure 9. A detailed analysis of the temperature data from various sampling points revealed that areas with higher vegetation coverage (such as B4 and B3) exhibited lower temperatures at 14:00 and throughout the measurement period. Specifically, the temperatures were 34.7 °C and 36.5 °C, respectively, which were notably lower than the control point B1, where the temperature reached 38.5 °C. This clearly demonstrated that plant communities, through effective canopy cover, reduced direct solar radiation, thereby significantly lowering both surface and air temperatures. Notably, B4, with its high canopy density (95%) and low SVF (0.103), exhibited the most substantial temperature-regulating effect, highlighting the combined influence of these factors on microclimate moderation.

Initially, both canopy density and SVF played pivotal roles in temperature regulation. A higher canopy density generally implied a stronger shading effect, which effectively reduced direct solar radiation, thereby lowering both surface and air temperatures. For example, at B3, where the canopy density was 85%, the temperature at 14:00 was 36.5 °C, which was significantly lower than the 38.5 °C observed at the control point B1-CK. In contrast, at sites B4 and B7, with canopy densities of 95% and 89%, the temperatures were 34.7 °C and 35.2 °C, respectively, indicating that a lower canopy density diminished the temperature-moderating effect. Furthermore, SVF values were also closely correlated with temperature changes. Lower SVF values corresponded to higher vegetation cover and more effective shading, which in turn contributed to a significant reduction in surface temperatures. For instance, at B2, with an SVF of 0.187, the temperature was 36.3 °C, which was higher than that at B7 (SVF = 0.129), where the temperature was 35.2 °C. This suggested that lower SVF values were associated with a stronger shading effect, thereby enhancing temperature reduction.

Furthermore, the Acanopy/H ratio played a critical role in temperature regulation. A higher Acanopy/H ratio generally indicated a broader canopy, which provided more substantial shading, thereby effectively reducing both surface and air temperatures. For instance, in the case of B4, where the Acanopy/H ratio reached its maximum at 3.2 m, the temperature was the lowest at 34.7 °C, demonstrating the pivotal role that larger tree crowns and greater tree heights played in temperature regulation.

The analysis of temperature data and plant community characteristics revealed that canopy attributes—such as canopy density, SVF, and the Acanopy/H ratio—played a pivotal role in regulating microclimate conditions, particularly temperature. Linear regression scatter plots depicting the relationships between SVF, Acanopy/H, canopy density, and cooling intensity effects are shown in Figure 10.

The interplay between shading intensity, humidity regulation, and temperature modulation beneath plant canopies collectively contributes to the regulation of the local microclimate. Firstly, plants, through their shading effect, diminish the direct solar radiation, thereby reducing surface heat absorption and, consequently, lowering the temperature [7,8]. A positive correlation exists between light intensity and surface temperature, with higher light intensities correlating with higher surface temperatures. Conversely, the process of transpiration plays a pivotal role in regulating air humidity. By releasing water vapor from plant leaves into the atmosphere, transpiration effectively increases air moisture [5,6]. Humidity, in turn, exhibits a negative correlation with temperature—higher humidity generally corresponds to lower air temperature. Elevated humidity helps alleviate heat, as moist air acts to mitigate rapid temperature increases, particularly in warmer environments. An increase in humidity provides a cooling effect. Therefore, plant communities regulate local temperatures through the combined effects of reduced light intensity and enhanced humidity, working synergistically to optimize the microclimate.

The interrelationships among the various factors, as illustrated in the correlation matrix heatmap (see Figure 11), underscore the pivotal role of spatial structural parameters—namely Acanopy/H, SVF, and canopy density—in modulating the microclimate. Specifically, the SVF exhibited a pronounced negative correlation with both light reduction intensity and cooling intensity, suggesting that lower SVF values are typically associated with enhanced shading and cooling effects. Acanopy/H, in contrast, demonstrated a robust positive correlation with all variables, with the most salient associations observed with light reduction and cooling intensities, thereby affirming its dominant influence on microclimate regulation. Moreover, canopy density was also positively correlated with both light reduction and cooling intensity, further corroborating the beneficial role of a higher canopy density in local temperature and humidity regulation. The high correlation between canopy density and Acanopy/H indicates a synergistic effect between these two parameters on microclimate modulation.

Taken together, these findings provide compelling scientific evidence supporting the optimization of plant community layout through spatial structural parameters as a means to enhance urban microclimates. Consequently, the ensuing analysis will leverage ENVI-met simulations to examine the influence of varying Acanopy/H ratios on microclimate conditions, thus further substantiating the potential of Acanopy/H in the regulation of local temperature and humidity.

3.2. The Influence of Acanopy/H on the Summer Microclimate of Plant Comunities: Validation via ENVI-Met Numerical Simulations

3.2.1. Validation of ENVI-Met Model Effectiveness

Before using the ENVI-met model for the simulation, its accuracy and applicability were verified. The calibration process adjusted key parameters—albedo, thermal conductivity, and vegetation physiology—to reduce discrepancies between the observed and simulated results. The daily average measured and simulated values for each sample point are listed in

Table 10. Daily average measured and simulated values for each sample point (7:00–22:00).

	B2	B3	B4	B5	B6	B7
Measured temperature	33.7 °C	33.8 °C	33.6 °C	33.7 °C	34.4 °C	33.5 °C
Simulate temperature	33.0 °C	34.2 °C	33.7 °C	34.0 °C	35.5 °C	33.9 °C
Measured humidity	67.1%	67.4%	74.3%	66.3%	67.8%	72.1%
Simulate humidity	66.0%	66.2%	72.0%	64.6%	67.0%	69.3%

. The measured and simulated data were analyzed using linear regression. Figure 12 presents the linear regression results between the observed and simulated values for summer air temperature and relative humidity in the study area. The data revealed a strong correlation between the observed and simulated microclimate parameters (R²-air temperature = 0.861; R²-relative humidity = 0.946), highlighting the robustness of ENVI-met in accurately modeling the microclimates of plant communities, particularly in East China, as exemplified by Zhejiang Province.

3.2.2. The Impact of Acanopy/H on Microclimate Based on Equal Canopy Area and Under-Canopy Height

At noon, under a constant canopy area, the variation in tree Acanopy/H induced distinct and differential trends in air temperature, relative humidity, and wind speed at 1.4 m (

Table 11. At 12:00, the influence of varying Acanopy/H on microclimate at 1.4 m height with equal canopy area.

Acanopy/H	25/3	25/6	25/9	25/12	25/15	25/18	25/21
Temperature	42.5 °C	40.3 °C	39.6 °C	39.4 °C	39.1 °C	39.1 °C	38.8 °C
Humidity	32.3%	35.5%	36.5%	36.9%	37.3%	37.4%	37.9%
Wind speed	0.948 m/s	0.930 m/s	0.938 m/s	0.967 m/s	0.975 m/s	0.997 m/s	1.005 m/s

). The distribution of air temperature, relative humidity, and wind speed at 1.4 m at 12:00 for plant communities with varying Acanopy/H values based on equal canopy area is shown in Figure 13. Initially, the air temperature beneath the canopy showed a progressive decline with a reduction in tree Acanopy/H, although the rate of decrease was not uniform across the different levels. The cooling effect of Acanopy/H on the microclimate gradually weakened as tree height increased. Specifically, for each decrement in Acanopy/H (equivalent to an increase of 3 m in tree height), the air temperature experienced reductions of 2.2 °C, 0.7 °C, 0.2 °C, 0.3 °C, 0.0 °C, and 0.3 °C, respectively. In contrast, the relative humidity beneath the canopy exhibited an upward trend with increasing tree height, albeit at a nonlinear rate. The increase in relative humidity for each stepwise reduction in Acanopy/H was 3.2%, 1.0%, 0.4%, 0.4%, 0.1%, and 0.5%, respectively. Wind speed, on the other hand, displayed a divergent response: initially decreasing and then increasing as tree height increased. Specifically, as Acanopy/H decreased from 25/3 to 25/6, wind speed was reduced, whereas when Acanopy/H further decreased from 25/6 to 25/21, wind speed gradually increased.

Between 7:00 and 22:00, the trends in the understory microclimate were similar (

Table 12. From 7:00 to 22:00, the influence of varying Acanopy/H on microclimate at 1.4 m height with equal canopy area.

Acanopy/H	25/3	25/6	25/9	25/12	25/15	25/18	25/21
Temperatureave	35.4 °C	32.3 °C	31.3 °C	31.2 °C	31.0 °C	31.2 °C	31.0 °C
Humidityave	50.0%	57.3%	60.3%	60.4%	60.8%	60.1%	60.6%
Wind speedave	0.954 m/s	0.932 m/s	0.939 m/s	0.968 m/s	0.976 m/s	0.999 m/s	1.009 m/s

, Figure 14). However, significant differences in microclimate levels were observed at specific time points. Specifically, the daily average air temperature (Ta-avg) in the understory progressively decreased as Acanopy/H reduced, although the decrease was not uniform. The cooling effect of Acanopy/H on Ta-avg diminished as Acanopy/H decreased. For each decrease in Acanopy/H (i.e., an increase in tree height by 3 m), the reductions in Ta-avg were 3.1 °C, 1.0 °C, 0.1 °C, 0.2 °C, −0.2 °C, and 0.2 °C, respectively. Conversely, the daily average relative humidity (RH-avg) increased as Acanopy/H decreased, although the rate of increase was uneven. For each step decrease in Acanopy/H, the increases in RH-avg were 7.3%, 3.0%, 0.1%, 0.4%, −0.7%, and 0.5%, respectively. Wind speed displayed a contrasting trend: it initially decreased as Acanopy/H decreased, but then increased. Specifically, when Acanopy/H dropped from 25/3 to 25/6, wind speed decreased, whereas as Acanopy/H further reduced from 25/6 to 25/21, wind speed gradually increased.

Considering the overall effects of Acanopy/H on the microclimate, the cooling and humidification benefits varied under different Acanopy/H ratios (25/3, 25/6, 25/9, 25/12, 25/15, 25/18, and 25/21). The most pronounced cooling and humidification effects were observed when Acanopy/H decreased from 25/3 to 25/6. Therefore, it can be preliminarily concluded that the microclimatic benefits of the plant community are most optimal when Acanopy/H is 25/6. To further refine this conclusion, follow-up studies should investigate tree-grass communities with Acanopy/H ratios of 25/4, 25/5, 25/7, and 25/8 to validate the optimal Acanopy/H ratio for microclimate regulation.

At 12:00, the air temperature, relative humidity, and wind speed at 1.4 m for plant communities with different Acanopy/H values (25/9–25/3) based on equal canopy area are presented in Figure 15. The influence of varying Acanopy/H on the microclimate at 1.4 m at 12:00 is detailed in

Table 13. At 12:00, the influence of varying Acanopy/H (25/9–25/3) on the microclimate at 1.4 m height with equal canopy area.

Acanopy/H	25/3	25/4	25/5	25/6	25/7	25/8	25/9
Temperature	42.5 °C	40.6 °C	40.4 °C	40.3 °C	40.0 °C	39.7 °C	39.6 °C
Humidity	32.3%	35.2%	35.3%	35.5%	36.0%	36.3%	36.5%
Wind speed	0.948 m/s	0.923 m/s	0.926 m/s	0.930 m/s	0.937 m/s	0.950 m/s	0.938 m/s

, while the effects from 7:00 to 22:00 are presented in

Table 14. From 7:00 to 22:00, the influence of varying Acanopy/H (25/9–25/3) on the microclimate at 1.4 m height with equal canopy area.

Acanopy/H	25/3	25/4	25/5	25/6	25/7	25/8	25/9
Temperatureave	35.4 °C	32.7 °C	32.6 °C	32.3 °C	31.9 °C	31.6 °C	31.3 °C
Humidityave	50.0%	56.4%	56.5%	57.3%	58.5%	59.6%	60.2%
Wind speedave	0.954 m/s	0.924 m/s	0.929 m/s	0.932 m/s	0.937 m/s	0.949 m/s	0.939 m/s

. Figure 16 shows the impact of varying Acanopy/H ratios on the microclimate at 1.4 m from 7:00 to 22:00. The simulation results demonstrated that the understory air temperature varied with changes in the ratio of Acanopy/H. When the canopy area remained constant, a reduction in Acanopy/H (within the range of 25/9 to 25/3) led to a gradual decrease in understory temperature, exhibiting a nonlinear trend characterized by an initial decrease, followed by an increase, and then another decrease. Specifically, for each decrement in Acanopy/H (i.e., an increase in tree height by 1 m), the temperature drop at noon was 1.9 °C, 0.2 °C, 0.1 °C, 0.3 °C, 0.3 °C, and 0.1 °C, respectively. The daily average air temperature followed a similar trend to the instantaneous temperature at noon, but the reduction was slightly greater. For each decrease in Acanopy/H, the daily average air temperature decrease was 2.7 °C, 0.1 °C, 0.3 °C, 0.4 °C, 0.3 °C, and 0.3 °C, respectively. As Acanopy/H decreased, the shading effect of the canopy on solar radiation increased, leading to a decrease in the temperature. However, as Acanopy/H continued to decline, the marginal contribution of the shading effect appeared to diminish [30]. This phenomenon was attributed to the saturation effect of canopy radiation interception and the dispersion of airflow. Although there were slight fluctuations in the temperature decrease across Acanopy/H gradients between 25/9 and 25/4, the change was consistently below 0.5 °C, indicating that the marginal contribution of Acanopy/H to microclimate regulation remained stable during this period.

The relative humidity in the understory was similarly influenced by variations in the ratio of Acanopy/H. Specifically, for each decrement in Acanopy/H (i.e., for every 1-m increase in tree height), the midday relative humidity increased by 2.9%, 0.1%, 0.2%, 0.5%, 0.3%, and 0.2%, respectively. Similarly, for each decrement in Acanopy/H, the daily average relative humidity increased by 6.4%, 0.1%, 0.8%, 1.2%, 1.1%, and 0.6%. As Acanopy/H decreased, the understory relative humidity increased at an inconsistent rate. With a constant canopy area, a lower Acanopy/H expanded vertical crown volume, enhancing transpiration and moisture release, thereby increasing relative humidity. However, the uneven increase in humidity likely stemmed from the influence of tree height on transpiration. At lower heights (3 m–4 m), stronger transpiration drove a more pronounced humidity rise, whereas in taller stands (4 m–9 m), canopy structure and density led to saturation, slowing the increase.

The understory wind speed displayed a distinct “decrease-increase-decrease” trend in response to changes in the Acanopy/H ratio. Specifically, when the Acanopy/H ratio decreased from 25/3 to 25/4, the wind speed in the understory decreased due to the canopy’s obstruction of air movement. However, as the Acanopy/H ratio further decreased to 25/8, the larger canopy may have improved the convective conditions in the air, resulting in a gradual increase in the wind speed. Conversely, when the Acanopy/H ratio dropped from 25/8 to 25/9, the denser canopy likely restricted air circulation again, causing wind speed to decrease.

In conclusion, the nonlinear relationship between the plant structural parameter Acanopy/H and microclimate regulation provides valuable insights into optimizing urban vegetation configurations. Specifically, within the range of Acanopy/H from 25/9 to 25/3, fine-tuning the tree Acanopy/H ratio can maximize the climate regulation benefits. Based on the incremental maximization of the microclimate adjustment effects, the findings reveal that an Acanopy/H ratio of 25/4 represents the optimal tree structure. This configuration effectively blocks solar radiation while simultaneously regulating airflow through an appropriate canopy structure, thus significantly enhancing microclimatic conditions beneath the trees.

3.2.3. The Impact of Acanopy/H on Microclimate Based on Equal Tree Height and Under-Canopy Height

The distribution of air temperature, relative humidity, and wind speed at 1.4 m at 12:00 for plant communities with different Acanopy/H values based on equal tree height is shown in Figure 17. The influence of varying Acanopy/H on microclimate at 1.4 m at 12:00 with equal tree height is shown in

Table 15. At 12:00, the influence of varying Acanopy/H on microclimate at 1.4 m height with equal tree height.

Acanopy/H	4/6	9/6	16/6	25/6	36/6	49/6	64/6
Temperature	43.2 °C	41.5 °C	41.1 °C	38.4 °C	33.5 °C	31.2 °C	30.3 °C
Humidity	26.5%	28.8%	29.2%	33.3%	41.0%	45.1%	46.9%
Wind speed	1.017 m/s	1.039 m/s	1.023 m/s	0.942 m/s	0.846 m/s	0.822 m/s	0.813 m/s

, while the influence of varying Acanopy/H (4/6–64/6) on microclimate at 1.4 m with equal tree height from 7:00 to 22:00 is shown in

Table 16. From 7:00 to 22:00, the influence of varying Acanopy/H (4/6–64/6) on the microclimate at 1.4 m height with equal tree height.

Acanopy/H	4/6	9/6	16/6	25/6	36/6	49/6	64/6
Temperatureave	38.6 °C	37.0 °C	36.4 °C	32.9 °C	25.8 °C	22.3 °C	20.8 °C
Humidityave	43.8%	46.6%	47.8%	55.5%	66.8%	74.2%	77.1%
Wind speedave	1.117 m/s	1.046 m/s	1.027 m/s	0.943 m/s	0.853 m/s	0.823 m/s	0.815 m/s

. The effects of varying Acanopy/H ratios at 1.4 m on the microclimate of plant communities based on equal tree height from 7:00 to 22:00, including changes in air temperature, relative humidity, and wind speed in that order, are shown in Figure 18. The simulation results demonstrated that, under identical tree heights, varying the Acanopy/H ratio had a significant impact on the regulation of understory air temperature (p < 0.05). As Acanopy/H increased, air temperature consistently decreased; however, the temperature reduction was not uniform with each incremental change in crown width (i.e., each 1-m increase in crown spread). At midday, for each step change in Acanopy/H, the temperature reductions were 1.7 °C, 0.4 °C, 2.7 °C, 4.9 °C, 2.3 °C, and 0.9 °C, respectively, showing a nonlinear pattern of “decrease, followed by increase, and then decrease again”. The cooling effect peaked when Acanopy/H ranged between 25/5 and 25/6, where the combined benefits of shading and transpiration were most effectively achieved. The daily mean temperature reductions followed a similar trend to those observed at noon but were generally more pronounced, with reductions of 1.6 °C, 0.6 °C, 3.5 °C, 7.1 °C, 3.5 °C, and 1.5 °C, respectively. This highlights that the overall daily cooling benefit was predominantly driven by the cumulative effects of both shading and transpiration.

The simulation results revealed that variations in the Acanopy/H ratio significantly impacted understory relative humidity (p < 0.05), with humidity increasing as Acanopy/H increased, although the rate of increase was not uniform. At midday, for each unit increase in Acanopy/H, the humidity increased by 2.3%, 0.4%, 4.1%, 7.7%, 4.1%, and 1.8%, respectively. The cumulative daily increase in humidity was even more pronounced, with increments of 2.8%, 1.2%, 7.7%, 11.3%, 7.4%, and 2.9%. These changes followed a nonlinear pattern of fluctuation—initially diminishing, followed by an increase, and ultimately tapering off. The peak increment in humidity was observed within the Acanopy/H range of 25/6 to 36/6, where the combined effects of shading and transpiration were maximized. This dynamic was attributed to the complex interaction between the canopy area and its modulation of transpiration and airflow diffusion. Within lower Acanopy/H ratios (e.g., 4/6 to 16/6), the enhancement of transpiration was gradual, but the limited shading effect resulted in relatively minor increases in the humidity. However, when Acanopy/H expanded within the range of 25/6 to 36/6, both the shading and transpiration effects reached their optimal balance, leading to a significant increase in humidity. In contrast, when Acanopy/H exceeded 36/6, the obstructive effect of the canopyon airflow became more pronounced, thereby impeding the diffusion of water vapor, which led to a subsequent reduction in the increase in humidity. Furthermore, the increase in the daily average relative humidity was more substantial, predominantly driven by the cumulative effect of transpiration during the morning and evening periods.

The simulation results revealed a clear and progressive reduction in the daily average wind speed beneath the canopy as the Acanopy/H ratio increased. This phenomenon was primarily attributed to the enhanced air circulation under smaller canopy areas and the inhibitory effect of larger canopy widths on airflow. With narrower canopy widths (ranging from 2 m to 3 m), the canopy did not fully obstruct airflow, thereby fostering a stronger ventilation effect. Conversely, with broader canopy widths (>6 m), the canopy impedes airflow and generates turbulence, ultimately leading to a decline in wind speed [31].

The results of this study indicated that, with tree height held constant, air temperature decreased progressively as the Acanopy/H ratio increased, while relative humidity exhibited a corresponding rise. However, the benefits of microclimatic regulation varied significantly with each incremental change in the Acanopy/H ratio. Notably, an Acanopy/H ratio of 36/6 yielded optimal cooling and humidifying effects. This was primarily due to the superior balance between canopy shading and transpiration that was achieved within this range. Additionally, the variation in wind speed was influenced by the combined effects of canopy-induced airflow obstruction and turbulence generated by the canopy.

3.2.4. Multiple Regression Models

Building upon a multiple linear regression model, this study comprehensively examined the effects of the Acanopy/H ratio and tree height (H) on microclimate parameters (see

Table 17. Results of multiple linear regression analysis for the effect of Acanopy/H and H on air temperature (Tem) and relative humidity (RH).

Dependent Variable	Independent Variables	B	Standard Error	t-Value	Sig.	Beta	R²
Temperature	Constant (β0)	31.932	0.693	46.087	0.000		0.937
	Acanopy/H (β1)	0.086	0.037	2.362	0.046	0.407
	Tree height (β2)	0.735	0.098	7.513	0.000	1.295
Humidity	Constant (β0)	67.117	1.572	42.694	0.000		0.950
	Acanopy/H (β1)	−0.220	0.083	−2.659	0.029	−0.411
	Tree height (β2)	−1.872	0.222	−8.435	0.000	−1.304

). The results unequivocally demonstrated that the Acanopy/H ratio exerted a far more pronounced regulatory influence on the microclimate than did tree height. Specifically, regression analysis revealed a strong association between Acanopy/H and temperature modulation, with Beta-Acanopy/H (1.295) substantially exceeding Beta-H (0.407). This finding highlights that, under conditions of a fixed canopy area and planting density, Acanopy/H plays a significantly more pivotal role in temperature regulation. Additionally, Acanopy/H was shown to have a more substantial effect on relative humidity (RH) than tree height. In the regression model, Beta-Acanopy/H (−1.304) indicated that the negative influence of Acanopy/Hon humidity surpassed that of tree height (Beta-H = −0.411). This negative correlation suggests that lower Acanopy/H ratios lead to higher humidity levels, primarily due to increased transpiration. While taller trees may increase the overall canopy volume, height alone does not significantly affect the transpiration efficiency per unit canopy area. In contrast, the ratio of canopy area to tree height determines the Leaf Area Index (LAI), a key parameter that directly influences the transpiration process. Research by Alam et al. (2021) showed that higher LAI values can substantially enhance the cooling effects of transpiration [32]. The regression model results are presented as follows:

The temperature regulation model:

Tem = 31.932 + 0.086 × Acanopy/H + 0.735 * H (R2 = 0.937, sig. = 0.000)

The humidity regulation model:

RH = 67.117 + −0.220 × Acanopy/H + −1.872 * H (R2 = 0.950, sig. = 0.000)

Furthermore, this study examined the effects of the Acanopy/H ratio and crown width on microclimatic variables. Linear regression (

Table 18. Results of multiple linear regression analysis for the effect of Acanopy/H and crown width on air temperature (Tem) and relative humidity (RH).

Dependent Variable	Independent Variables	B	Standard Error	t-Value	Sig.	Beta	R²
Temperature	Constant (β0)	45.162	4.807	9.396	0.001		0.956
	Acanopy/H (β1)	−1.596	1.255	−1.271	0.009	−0.784
	Crown width (β2)	−0.676	2.213	−0.319	0.026	−0.196
Humidity	Constant (β0)	37.511	8.140	4.608	0.010		0.963
	Acanopy/H (β1)	2.786	2.126	1.311	0.015	0.738
	Crown width (β2)	1.571	3.595	0.437	0.034	0.246

) showed that Acanopy/H had a stronger influence, particularly on air temperature and humidity. Under conditions of uniform planting density and fixed tree height, the results of the multiple linear regression analysis further substantiated the significant role of the Acanopy/H ratio in shaping microclimatic adjustments.

The regression analysis results revealed that the temperature model (Tem = 45.162 + (−1.596) * Acanopy/H + (−0.676) * crown width) demonstrated a significant impact of Acanopy/H on air temperature (R² = 0.956, sig. = 0.001). Specifically, the standardized coefficient for Acanopy/H (Beta = −0.784) was markedly higher than that for crown width (Beta = −0.196), indicating that, under conditions of identical planting density and fixed tree height, Acanopy/H had a more pronounced regulatory effect on air temperature than crown width. Similarly, the regression model for humidity (RH = 37.511 + 2.786 * Acanopy/H + 1.571 * crown width) further substantiated that Acanopy/H had a more significant influence on humidity compared to crown width (R² = 0.963, sig. = 0.01). In this case, the standardized coefficient for Acanopy/H (Beta = 0.738) substantially exceeded that of crown width (Beta = 0.246), thereby reinforcing Acanopy/H’s dominant role in regulating relative humidity. These findings highlight the critical contribution of Acanopy/H to humidity regulation.

The temperature regulation model:

Tem = 45.162 + −1.596 * Acanopy/H + −0.676*crown width (R2 = 0.956, sig. = 0.001)

The humidity regulation model:

RH = 37.511 + 2.786 * Acanopy/H + 1.571*crown width (R2 = 0.963, sig. = 0.01)

Linear regression showed that Acanopy/H had a stronger and more direct impact on temperature and humidity than crown width or tree height. This underscores the importance of prioritizing urban green space planning for optimal microclimate regulation.

4. Discussion

This study investigated the microclimate regulation of urban green space plant communities, introducing the Acanopy/H ratio as a new indicator for characterizing their spatial and structural features. As urban heat island effects intensify, optimizing local thermal environments through plant configuration has become a key issue in urban ecological planning. Existing research often focuses on individual plant traits (e.g., crown density or tree height) without addressing the comprehensive analysis of canopy horizontal expansion and vertical height ratio [9,10,11,12,13,14,15]. This study fills this gap by adopting a holistic approach to spatial structure and advancing the quantification of microclimate regulation by urban plant communities.

The results highlight the significant role of the Acanopy/H ratio in regulating the temperature, humidity, and wind speed. This indicator not only reflects the microclimate regulation potential of plant communities but also reveals its key role in multidimensional regulation. A higher Acanopy/H ratio enhances shading, thereby reducing solar radiation and surface temperature. It also increases air humidity through expanded transpiration areas and significantly slows the wind speed. Notably, wind speed regulation by the Acanopy/H ratio exhibited a nonlinear pattern, reflecting the complex impact of canopy spatial configuration on the local airflow dynamics.

The primary objective of this study was to quantify the manner in which plant community spatial structure regulates the summer microclimate, exploring the combined influence of this parameter on temperature, humidity, and wind speed. The findings validate the effectiveness of this indicator in explaining the multidimensional regulation of microclimates by plant communities and offer scientific guidance for urban green space design.

4.1. Light Attenuation Mechanism of Diverse Vegetation Canopy Features

Vegetation canopies play a pivotal role in regulating urban microclimate by attenuating solar radiation and mitigating heat accumulation [33,34,35,36,37]. This light attenuation mechanism is primarily achieved through the direct interception and shading of incoming solar radiation, which significantly reduces light intensity beneath the canopy [38]. The extent of this attenuation is governed by structural properties, including canopy density, sky view factor (SVF), and the Acanopy/H ratio, which collectively influence solar radiation blockage and energy redistribution within plant communities [39,40,41,42].

Canopy density is a fundamental parameter for controlling light penetration. Higher canopy density enhances shading by increasing the interception of solar radiation by leaves, thereby reducing the light transmittance to the understory. SVF, which quantifies the proportion of the visible sky from a specific observation point, and reflects canopy openness. A lower SVF indicates a more enclosed canopy, resulting in greater light interception and lower light intensity at the pedestrian level.

The Acanopy/H ratio, an integrated parameter that expresses the horizontal extent of the canopy relative to its vertical height, provides a comprehensive metric for assessing the light-blocking capacity of vegetation canopies. Higher Acanopy/H values typically represent broader, more expansive canopies, which not only intercept direct solar radiation but also extend shading coverage across a larger surface area. This ratio encapsulates the geometric characteristics of the canopy, offering a robust indicator for quantifying light attenuation performance in urban plant communities. Additionally, the Acanopy/H ratio influences plant species diversity and growth by modulating light availability and soil moisture retention [43,44,45,46]. Lower Acanopy/H ratios create more open canopies, favoring heliophilic species and promoting diversity, albeit at the potential cost of increased soil moisture competition.

The synergistic interaction among canopy density, SVF, and Acanopy/H collectively enhances the light attenuation capacity of vegetation canopies, playing a critical role in lowering surface temperatures and improving human thermal comfort in urban green spaces. Understanding the causal mechanisms underlying light attenuation provides a theoretical foundation for optimizing plant community configurations in urban green infrastructure planning, contributing to the development of more sustainable and thermally comfortable urban environments.

In this study, representative plant communities commonly observed in urban green spaces across Zhejiang Province were selected to reflect the microclimate regulation capacity of tree-dominated vegetation, including both broadleaf and coniferous species. However, the limited sample size may restrict the generalizability of these findings. Additionally, this study focused solely on microclimate regulation during summer high-temperature periods, while the regulatory performance of plant communities may vary significantly across seasons. In winter or drought seasons, canopy structures may exert weaker regulatory effects due to reduced solar radiation exposure or suppressed transpiration. Future studies should increase the sample size and incorporate year-round observations or multi-season simulations to better assess the seasonal variability of plant community microclimate regulation.

4.2. Causal Mechanisms of Acanopy/H Ratio in Urban Microclimate Regulation

The results of this study underscore the promising role of the Acanopy/H ratio in urban microclimate regulation, although its underlying mechanisms require further investigation. The ratio’s impact on light attenuation stems from its influence on canopy density and spatial configuration. Denser canopies with higher Acanopy/H ratios block more solar radiation, reducing surface light intensity and limiting heat absorption, which ultimately lowers the surface temperature [47]. For humidity regulation, the Acanopy/H ratio enhances transpiration by increasing the leaf area available for moisture release and raising humidity levels. Higher ratios also improve the canopy’s ability to absorb and reflect surface radiation, further moderating air temperature and humidity [3]. This interaction highlights the ratio’s dual role in balancing temperature reduction and humidity enhancement. The wind speed effects observed in this study can be understood as a complex function of the canopy structure. Higher Acanopy/H ratios increase airflow resistance, slowing wind speeds. However, denser canopies may induce turbulence, causing wind speed fluctuations. This nonlinear relationship depends on the canopy spatial configuration and density [31].

In conclusion, the Acanopy/H ratio is a comprehensive factor that regulates urban microclimates by influencing temperature, humidity, and wind speed. Its ability to integrate both the horizontal and vertical aspects of the canopy structure makes it a more comprehensive and effective indicator of microclimate regulation than single measures, such as crown width or tree height.

Although multiple linear regression analysis demonstrated the dominant role of the Acanopy/H ratio in urban microclimate regulation, this study recognizes that the underlying causal mechanisms are multifaceted and require further validation. The Acanopy/H ratio primarily reflects the geometric characteristics of tree canopies but overlooks the influence of leaf morphological and physiological traits (e.g., transpiration rate and stomatal conductance) on microclimate regulation [48,49]. Trees with similar canopy densities may differ in light transmittance and transpiration capacity, resulting in varying cooling and humidifying effects. Future studies should incorporate vegetation physiological properties into the Acanopy/H ratio to develop a structure-function composite index, offering a more comprehensive evaluation of plant communities’ microclimate regulation potential. Moreover, this study did not consider the cooperative effects of urban geometric features—such as building arrangement, street orientation, and height—along with the wind speed effect on the microclimate regulation of plant communities. These factors can play a crucial role in influencing the overall climate dynamics in urban settings. Notably, excessively tall trees may have adverse effects on air circulation in certain urban environments, particularly in dense areas or narrow streets, where they can obstruct natural ventilation and hinder airflow. Future research could expand on these aspects by incorporating both urban geometry and wind dynamics, thus enhancing the applicability of the Acanopy/H ratio across a wider range of urban contexts.

Furthermore, we need to explicitly outline the potential model limitations, including boundary conditions, vegetation parameterization, and uncertainties in urban microclimate simulations, to ensure a more balanced interpretation of the findings. Notably, ENVI-met does not incorporate human factors into its simulations. Additionally, in terms of physiological parameters, the model tends to overestimate the leaf surface temperature while underestimating the vapor flux, particularly at midday in summer [50].

These research findings offer a scientific basis for designing and optimizing plant communities in Zhuji City. They provide essential guidance for decision-makers in selecting suitable plant species to improve air quality, regulate temperature and humidity, and enhance the city’s ecological services.

5. Conclusions

This study highlights the crucial role of vegetation canopy characteristics in regulating urban microclimates, with a particular focus on the Acanopy/H ratio as a promising potential indicator of microclimate modulation. The key findings are as follows:

• Vegetation canopies significantly reduce light intensity and play a vital role in regulating local temperature, humidity, and thermal comfort. Sites with higher canopy density, lower SVF, and greater Acanopy/H ratios showed stronger light attenuation effects.
• Light attenuation indirectly influences temperature and humidity dynamics, providing critical insights into the causal mechanisms of microclimate regulation. Higher Acanopy/H ratios enhance light reduction, lower temperatures and improved humidity retention. The relationship between Acanopy/H and microclimate regulation follows a nonlinear pattern, with optimal effects observed within specific ranges (e.g., 25/4 and 36/6). The combined influence of a high canopy density, low SVF, and elevated Acanopy/H ratios maximizes the microclimate benefits.

These findings provide valuable guidance for optimizing urban green space planning and improving climate resilience. Future research should explore the effectiveness of Acanopy/H under different climatic conditions, incorporate vegetation physiological properties into the Acanopy/H ratio, and consider the synergistic effects of urban geometric features, such as building arrangement and street orientation, in order to further refine strategies for sustainable urban vegetation management.