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Article

Greenery Effects: Comparing the Associations Between Multi-Dimensional Measurements of Urban Green Space Greenery and Engagement in Health-Related Activities Across Age Groups

by
Chongxiao Wang
1,
Shuolei Chen
1,*,
Yang Chen
2,3 and
Zhongzhe Shen
4
1
College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
2
School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
3
Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China
4
College of Environment and Design, University of Georgia, Athens, GA 30602, USA
*
Author to whom correspondence should be addressed.
Land 2025, 14(6), 1128; https://doi.org/10.3390/land14061128
Submission received: 2 May 2025 / Revised: 19 May 2025 / Accepted: 20 May 2025 / Published: 22 May 2025
(This article belongs to the Topic Human–Environmental Relations: Ecotourism and Sustainability)
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Abstract

:
With the progression of global urbanization, UGS greenery plays an increasingly important role in encouraging engagement in various health-related activities among sedentary residents, and its quality improvement is widely recognized as a promising strategy for achieving public health benefits. However, existing studies have not reached an agreement on the associations between UGS greenery and engagement in health-related activities, largely due to limited dimensions of greenery measurement and insufficient evidence on health-related activities. To address these gaps, this study proposes a holistic analytical framework that integrates multi-dimensional greenery measurements (measured by the NDVI, GVI, and LVV metrics) with systematic observations of engagement in physical activity and social interaction across the general population, children, and seniors, allowing for a more comprehensive understanding of their varied associations. Conducting empirical research in the Xuanwu Lake Scenic Area, the results showed that (1) UGS greenery had stronger explanatory power for activity participant numbers than activity intensity across age groups; (2) top–down greenery (NDVI) was positively associated with engagement in health-related activities (although not statistically significant), while excessive eye-level (GVI) and spatial greenery (LVV) demonstrated negative effects; (3) UGS greenery alone did not sufficiently predict children’s engagement in health-related activities; and (4) greenery conditions in adjacent UGS samples also significantly impacted local health-related activities. These findings suggest that UGS greenery measured from a single dimension may not reliably predict engagement in health-related activities across age groups, thereby calling for balanced and context-sensitive greenery design in future UGS planning to support inclusive public health.

1. Introduction

1.1. UGS and Public Health

With worldwide urbanization, more than half of people have settled in high-density urban areas [1]. This trend has been accompanied by prevailing sedentary lifestyles, with the majority of urban residents lacking sufficient engagement in healthy activities [2], which has raised significant public health concerns [3,4,5]. The role of Urban Green Spaces (UGSs) in addressing related health issues has gained growing recognition [6,7]. In addition to the direct health benefits of exposure to UGSs [8,9,10,11], evidence from various disciplines has confirmed another crucial health pathway of UGSs, mediated by encouraging people to participate in various health-related activities [3,12,13,14,15,16,17]. In the post-pandemic period, UGSs have continued to play a vital role in promoting public health by helping to mitigate the long-term impacts of lockdowns, such as physical inactivity and mental health challenges [18,19,20,21]. Recent statistics indicate that residents’ visitation patterns to UGSs have witnessed significant changes, particularly among children and seniors, who are vulnerable populations in terms of equitable access to UGS resources [22,23,24,25]. For instance, a study conducted in the metropolitan area of Vienna, Austria found that while young people reconnected with nature and increased the time that they spent outdoors, older urban residents reduced the time that they spent in UGSs on average [25]. This highlights the need for UGS planning strategies that actively promote inclusive visitation. However, merely increasing the amount and accessibility of UGSs was found to be insufficient in encouraging urban residents to engage in health-related activities [11,26,27]. In response, increasing numbers of urban planners and policy-makers have recognized that enhancing the greenery quality of existing UGSs presents a more efficient and sustainable strategy for achieving their associated health benefits [28,29,30,31].

1.2. Dimensions of UGS Greenery Measurement

With increasing attention to the health benefits of UGS greenery, it has typically been measured through two primary dimensions: the top–down dimension, and the eye-level dimension [32,33]. The Normalized Difference Vegetation Index (NDVI) is the most common and accessible metric for measuring UGS greenery from the top–down dimension [30,34], as it is available worldwide based on satellite images to reflect the vegetation coverage and living status [35,36]. For measuring eye-level dimension greenery, the Green View Index (GVI) metric is commonly applied to assess the visibility of upper and lower greenery from a pedestrian perspective [37,38]. Through field surveys and photograph interpretation, the GVI captures vertical forms of greenery, such as lawns, shrubs, trees, green walls, etc. [39,40]. However, the NDVI and GVI metrics are inherently constrained by fixed observation angles [35,36,41,42]. To overcome this limitation, recent studies gvhave proposed the Living Vegetation Volume (LVV) metric, which leverages terrestrial laser scanning or airborne remote sensing to measure greenery from a spatial dimension, thereby enabling more flexible and multidirectional observation angles, showing great potential for spatial greenery measurement [43,44,45]. Moreover, LVV may better align with visitors’ perceptions of UGS greenery [14,46]. Nevertheless, more empirical evidence is needed to validate its effectiveness in research on UGS–health relationships [46].
Despite these advancements in individual measurement dimensions, studies integrating multiple dimensions of UGS greenery are limited, and the associations between UGS greenery and engagement in health-related activities are yet to be clearly established [14,31,37,47]. Several studies have reported that UGS greenery measured by the GVI was positively associated with health activities [37,48], whereas other studies found no significant relationship when using the NDVI for greenery measurement [42,49]. This discrepancy may be due to the different greenery characteristics that each metric captures—the NDVI is more sensitive to horizontal canopy cover, whereas the GVI primarily reflects vertical visibility [50,51,52]. Moreover, inconsistencies persist even within studies from the same measurement dimension [14,35,41,42], implying the need for an integrated framework that combines top–down, eye-level, and spatial dimensions to comprehensively examine the associations between UGS greenery and engagement in health-related activities.

1.3. Evidence on Engagement in Health-Related Activities

Additionally, the limited and incomplete evidence on engagement in health-related activities also hinders the establishment of a consensus on UGS–health associations. Existing studies have predominantly focused on physical activity [37,42,48,49], while another prevalent activity within UGSs, social interaction, has received comparatively less attention [16,26,53]. Preliminary findings suggest that engaging in social interaction can also contribute to public health in UGS settings [54,55,56]. Beyond the general population, an increasing number of studies have begun to specifically investigate the activity patterns of children or seniors, highlighting that engagement in health-related activities varies across age groups [14,54,57,58,59,60]. However, few studies have adopted a holistic approach by simultaneously examining engagement in health-related activities across all age groups.
In terms of data collection, most studies rely on questionnaires to assess engagement in health-related activities [47,48,49], which typically fail to capture all concurrent participants and are prone to subjective bias due to their reliance on self-reported preferences. Although some studies have also employed objective sensor devices, such as accelerometers and GPS trackers, to record participants’ movement, their high costs make them less feasible for large-scale studies [59,61,62]. Additionally, systematic observational tools such as the System for Observing Play and Recreation in Communities (SOPARC) and Systematically Observing Social Interaction in Parks (SOSIP) offer promising alternatives for collecting comprehensive evidence on engagement in health-related activities in UGS–health research. By adopting a third-person perspective to comprehensively scan UGSs, these tools can capture both physical activity and social interaction across various age groups, providing a more objective and inclusive understanding of engagement in health-related activities in UGSs [53,63].

1.4. Research Objectives

Based on the above literature review, three main gaps were identified: First, existing studies have not sufficiently integrated multiple measurement dimensions of UGS greenery. Second, prior research on engagement in health-related activities in UGSs has primarily focused on physical activity or a specific age group, with limited consideration of social interaction and inclusive participation across all age groups. Moreover, the commonly used data collection methods also compromise the reliability and comprehensiveness of the evidence on engagement in health-related activities in UGSs. Third, as a result of the above limitations, the associations between UGS greenery and engagement in health-related activities remain unclear and inconclusive.
To address these research gaps, this study aims to (1) integrate top–down (using the NDVI metric), eye-level (using the GVI metric), and spatial (using the LVV metric) dimensions to comprehensively measure UGS greenery; (2) employ systematic observational tools (the SOPARC and SOSIP) to objectively capture both physical activity and social interaction across all age groups, thereby offering a holistic perspective on engagement in health-related activities in UGSs; and (3) explore the associations between multi-dimensional greenery measurement and multi-age health-related activities, thereby informing effective planning strategies to improve the quality of UGS greenery for promoting inclusive public health.

2. Materials and Methods

2.1. Study Settings and Sample

This study was conducted in the Xuanwu Lake Scenic Area, which is the biggest park cluster in the central built-up area of Nanjing, China, supporting an active lifestyle for local residents as well as tourists. This scenic area covers an area of 5.02 km2, and 35 urban parks from it were selected as the UGS samples, based on a previous on-site survey (Figure 1). These selected UGS samples included the gamut of greenery forms, such as lawns, street trees, flower beds, green walls, etc. Accordingly, the study conducted using these UGS samples is representative.

2.2. Measuring UGS Greenery from Multiple Dimensions

According to our research objectives, the UGS greenery was measured by multi-dimensional metrics, including the NDVI from the top–down dimension, the GVI from the eye-level dimension, and LVV from the three-dimensional perspective (Figure 2). With the approval of the Nanjing Forestry University Institutional Review Board, the measurement fieldwork was carried out in the summer months of June to July, so as to avoid the significant variations in greenery across different seasons [64].

2.2.1. Top–Down Greenery Measurement Using the NDVI

While the NDVI is typically measured based on satellite images, Unmanned Aerial Vehicle (UAV) remote sensing technology made it more convenient to obtain the high-resolution images at fine scales [65,66,67]. This study utilized UAV-based images from a height of 120 m to calculate the NDVI metric, and the formula is as follows [68]:
N D V I = N I R − R e d N I R + R e d
where N I R represents the pixel values from the near-infrared band (which vegetation strongly reflects), while R e d represents the pixel values from the red band (which vegetation absorbs). The NDVI values were computed and distributed to each UGS sample using ArcGIS Pro software (version 3.0.1).

2.2.2. Eye-Level Greenery Measurement Using the GVI

This study measured the GVI based on panoramic images taken from a height of 1.6 m in the UGS samples [69]. The outside portion of the panoramic images was cropped out because of severe distortion caused by the camera lens [70], and the center portion was consistent with the view of a pedestrian in the UGS [37,71,72]. This study employed a deep learning algorithm (DeepLabv3, based on the Cityscapes dataset) to facilitate the measurement process by automatically identifying greenery elements, including both upper and lower greenery [73,74]. Accordingly, the GVI can be calculated as follows [37,38]:
G V I = N u m b e r   o f   G r e e n e r y   P i x e l s N u m b e r   o f   T o t a l   p i x e l s × 100 %
where N u m b e r   o f   G r e e n e r y   P i x e l s is the sum of the upper and lower greenery, while N u m b e r   o f   T o t a l   p i x e l s is the number of pixels in the center portion of a panoramic image. For each UGS sample, the GVI was determined by averaging all of the panoramic images that it contained.

2.2.3. Spatial Greenery Measurement Using LVV

We applied a terrestrial laser scanner (GEO SLAM ZEB-HORIZON) to collect the point cloud data, capturing the spatial information of the UGS samples [44]. Based on these data, we applied a combination of automated classification and manual processing in Trimble RealWorks software (Version 12.0) to accurately segment the vegetation point clouds [43]. The segmented data were then voxelized in Grasshopper (Version 6.0) to calculate the LVV values, using a voxel cell size of 0.3 m × 0.3 m × 0.3 m [75].

2.3. Capturing Engagement in Health-Related Activities Across All Age Groups

Two types of health-related activities in the UGS samples, including physical activity (PA) and social interaction (SI), were captured via systematic observational tools: the System for Observing Play and Recreation in Communities (SOPARC), and Systematically Observing Social Interaction in Parks (SOSIP) [53,76]. Using the SOPARC and SOSIP, both the number of participants and their corresponding activity levels for all targeted populations (general population, children, and seniors) were captured through systematic observation. The intensity of PA and SI was quantified by multiplying the number of participants by their respective activity levels. Simultaneously, the field videos were recorded for verification of the on-site observations. To minimize measurement bias caused by random variability, we conducted a total of 16 observations under varying weather conditions, on both weekdays and weekends, and during both morning and afternoon periods, and the date of each measurement was recorded for further analysis [77].

2.4. Statistical Analysis

This study fitted a series of regression models to examine the associations between UGS greenery and engagement in health-related activities. The independent variables (IVs) included multi-dimensional greenery measurement based on the NDVI, GVI, and LVV metrics. The dependent variables (DVs) comprised the intensity of PA and SI among the general population, children, and seniors. In addition, the total number of participants in PA and SI were also considered as DVs. Park size was included as a control variable (CV) in the regressions, given its positive contribution to engagement in health-related activities [54,78]. Spearman correlation analysis was conducted to assess the relationships between each DV and the CV, aiming to detect potential multicollinearity. To account for the data characteristics, the Generalized Linear Model (GLM) with a negative binomial distribution was employed to fit the regression models [79].
Before the formal GLM analysis, this study preliminarily tested the variations in multi-age health-related activities across dates using analysis of variance (ANOVA), and we compared the performance of the GLM with and without the data as random terms through likelihood ratio tests (LRTs). Based on this, this study determined whether the unobserved factors across measurement dates need to be controlled for when establishing models [80].
The UGS samples in this study were from the same scenic area, and the measurement results may have been influenced by their spatial adjacency, meaning that adjacent UGS samples may be more similar than distant ones in terms of engagement in health-related activities [81,82]. This study analyzed the residuals of the regression model using Moran’s I to test the potential spatial autocorrelation and decide whether the spatial lag term (using the k-nearest neighbors approach with k = 4, where all of the neighbors are equally weighted) should be included when establishing regression models. The GLM analysis and tests can be conducted using the glmmTMB, lmtest, and spdep packages in R (version 4,4.0) [83,84,85].

3. Results

3.1. Sample Characteristics

Table 1 depicts the sample characteristics of all variables, including the mean, standard deviation (SD), coefficient of variation (CoV), and range. To account for the multi-dimensional nature of the variables, we standardized the DVs and CV to the same scale through min–max normalization. This standardization ensured comparability across variables with different measurement units and value ranges, reduced the influence of scale differences on the subsequent model estimation, and improved the robustness and interpretability of the regression results [86].
Table 2 depicts the Spearman correlation analysis results for the IVs and CV. The NDVI demonstrated a significantly positive correlation with both the GVI and LVV. LVV was also significantly and positively correlated with park size. These significant coefficients were around 0.5, indicating a moderate degree of correlation between related variables, which did not induce serious multicollinearity concerns in the regression analysis [87].

3.2. The Results of Preliminary Tests

As shown in Table 3, the ANOVA results indicated that, except for the intensity of children’s physical activity and children’s social interaction, all other DVs showed significant differences across the 16 measurements (p < 0.01), suggesting that unobserved factors influenced the measurement results of engagement in health-related activities. The LRT (Table 4) results further confirmed that excluding the intensity of children’s social interaction, along with controlling for unobserved factors by introducing the dates as a random term in the GLM, improved the model’s fit and explanatory power.
The Moran’s I statistics for the residuals (Table 5) indicated significant spatial autocorrelation in the regression models (p < 0.001), except for the model for children’s physical activity levels. Accordingly, it was necessary to add the spatial lag in these spatially significant regression models to account for spatial dependencies and improve the models’ accuracy.

3.3. Results of GLM Analysis

The coefficients of the GLM analysis shown in Table 6 and Table 7 demonstrate varied associations between the multi-dimensional measurements of UGS greenery and engagement in health-related activities across age groups.
Among the greenery metrics, the NDVI showed positive associations, while the GVI and LVV generally exhibited negative effects on engagement in health-related activities across age groups. The NDVI exhibited the weakest explanatory power, with a significant positive association observed only for the intensity of seniors’ physical activity (β = 0.636, p < 0.05). In contrast, the GVI showed a consistent and significant negative association with different types of engagement in health-related activities (e.g., β = −0.829 p < 0.01 for the number of physical activity participants; β = −0.998 p < 0.01 for the intensity of seniors’ social interaction). Regarding LVV, significant negative associations were observed for the number of physical activity participants (β = –0.440, p < 0.05), the number of social interaction participants (β = –0.484, p < 0.05), and the intensity of seniors’ physical activity (β = –1.216, p < 0.01).
In addition, park size served as a control variable, and its positive contributions to both the number of activity participants and the intensity of health-related activities were consistently confirmed across models (e.g., β = 0.560, p < 0.01 for the number of social interaction participants; β = 0.997, p < 0.05 for the intensity of seniors’ social interaction). The effects of spatial lag terms were significantly positive in all models except for the model of the intensity of children’s physical activity (e.g., β = 0.073, p < 0.01), highlighting the importance of the greenery in adjacent UGSs in affecting engagement in health-related activities.

4. Discussion

This study expands the UGS greenery measurement dimensions by combining two well-established dimensions (the top–down dimension, measured by the NDVI, and the eye-level dimension, measured by the GVI) with a newly introduced dimension (the spatial dimension, measured by LVV). Compared with other top–down metrics like land use and land cover, which are subject to variations in spatial resolution and vegetation classification [35], the NDVI offers a standardized and representative measure of vegetation growth conditions and distribution by differentiating the light bands that are reflected and absorbed by vegetation. Traditional NDVI data, derived from satellite imagery, are typically captured at a single point in time and updated annually or less frequently, potentially compromising accuracy in rapidly evolving UGS environments [35]. To overcome this, our study employed UAV-based remote sensing, which provides high-resolution and up-to-date NDVI data, offering practical advantages in UGS management and improvement [65,66,67].
For eye-level greenery measurement using the GVI metric, this study employed self-captured panoramic images and selected the least distorted sections aligned with pedestrians’ visual field [37,72]. Compared with traditional photos, panoramic images capture more on-site information with fewer shooting efforts [70]. Based on panoramic images, this study applied deep learning algorithms to automatically identify greenery elements, significantly reducing labor costs in the GVI measurement process [71,73,74,88]. Although an increasing number of studies have relied on panoramic images derived from online street map platforms (e.g., Google Street View, Baidu Street View), limitations in data availability and outdated images reduce their reliability for accurate greenery assessment [88,89]. Taking the Xuanwu Lake Scenic Area as an example, Google Street View images were only available in a few locations, while the coverage of Baidu Street View was mainly limited to the exterior driveway, with most images captured several years ago.
In addition, this study incorporated the LVV metric to capture spatial greenery, which is an emerging and underexplored dimension in UGS–health research. Given the limited empirical evidence on LVV’s role, this study provides additional insights and empirical support [14,46]. Unlike traditional two-dimensional metrics, such as the NDVI and GVI, which rely on a fixed observation perspective, LVV considers both horizontal and vertical observation of greenery, thereby reflecting more realistic greenery conditions in UGSs [44,46]. Moreover, the NDVI and GVI are subject to limitations in complex urban park environments, where factors such as building obstructions and heterogeneity of the greenery structure can compromise measurement accuracy [37,46,88]. We acknowledge that capturing LVV using technologies such as laser scanners involves high equipment and operational costs. However, its enhanced greenery measurement enables new possibilities for UGS–health analysis [14,46].
This study further holistically captured engagement in health-related activities by observing both physical activity and social interaction across different age groups. The intensity of health-related activities was recorded separately for the general population, children, and seniors. In the meantime, the number of participants engaged in each activity type was also recorded. This inclusive approach offers a more comprehensive understanding of age-diverse activity patterns and provides robust empirical evidence on engagement in health-related activities in UGSs.
On this basis, this study disentangles how multi-dimensional UGS greenery contributes to engagement in health-related activities, highlighting the importance of comprehensive greenery measurement as well as the variations across different types of health-related activities and age groups. In general, compared to the intensity of health-related activities, all dimensions of greenery measurement (using the NDVI, GVI, and LVV metrics) exhibited stronger explanatory power in accounting for the number of activity participants across different age groups, as verified in previous research [26,31]. This suggests that greenery has a more direct influence on whether people participate, rather than on how intensely they engage. This is likely because the intensity of such activities is influenced by more complex factors. For example, engaging in high-intensity physical activity often requires the availability of supportive facilities, such as sports courts or running tracks, while high-intensity social interaction may depend on conducive social settings, such as plazas suitable for dancing or seating areas that encourage conversation [13,21,54,90].
From an age-specific perspective, the associations between greenery and engagement in health-related activities were generally less robust for children compared to the general population and seniors. This may be attributable to the high variation observed in the intensity of children’s physical activity (CoV = 5.163) and social interaction (CoV = 4.545) across different UGS samples and observation dates. Compared with the influence of UGS greenery, children’s engagement in health-related activities is more likely shaped by family-related factors, school schedules, and the availability of parental or guardian supervision [91,92,93]. Moreover, previous studies have found that children tended to congregate in the areas with facilities and amenities specifically designed for them, such as playgrounds, sandpits, slides, etc., further reducing the predictive power of greenery alone in explaining their activity patterns [54,56,90]. In addition, the specific needs and vulnerabilities of each age group, such as seniors’ reliance on nearby and restorative spaces, or children’s need for stimulation and mobility, may also contribute to the varied associations across age groups [94]. Further age-specific research is needed to provide additional empirical evidence on these associations.
In contrast, greenery demonstrated stronger associations with engagement in health-related activities among the general population and seniors. These groups typically have greater autonomy in selecting their activity locations and tend to exhibit more stable outdoor routines, such as regular exercise and social gathering, which allows greenery to exert a more consistent influence on their activity patterns [21,63]. Compared with the general population, the strength of the associations between greenery and engagement in health-related activities was even greater among seniors, indicating that older adults are more sensitive to greenery [95,96]. This could be because seniors prefer the greenery-related spaces, such as shady trees, a peaceful and relaxed setting, and walking paths [21,60], while the general population engages in a wider range of health-related activities that may occur in various types of spaces, such as sports courts, skate areas, or pavement [26,54,97].
Among the greenery metrics, only the NDVI was positively associated with engagement in health-related activities, whereas the GVI and LVV demonstrated negative associations in this study. These negative associations can be interpreted in the light of previous findings on the inverted U-shaped relationship between eye-level greenery (GVI) and the walking propensity of older adults. L. Yang et al. identified a GVI threshold of 0.24, beyond which further increases in eye-level greenery started to demonstrate a negative effect [60]. In our study, the mean GVI across the 35 UGS samples was 0.468, indicating that the observed negative association may be attributed to the excessive level of eye-level greenery. Our findings provide further empirical evidence suggesting that this threshold effect may also apply to physical activity and social interaction across all age groups. Moreover, the negative association was not limited to eye-level greenery (GVI) but was also observed for spatial greenery measured by LVV. This may be due to the fact that excessive eye-level greenery can obstruct visual permeability, while overly dense spatial greenery may reduce the availability of usable spaces for physical and social activities [98]. Additionally, excessive eye-level and spatial greenery may also be closely associated with an increased sense of enclosure and lower levels of perceived safety, which can act as negative factors influencing activity engagement in UGSs [99,100,101]. In contrast, the positive association observed for the NDVI suggests that, when visual and spatial constraints are minimal, people still tend to prefer more densely vegetated environments [36]. Nonetheless, the NDVI’s influence in our study was not statistically significant, highlighting the need for further research to validate this finding. In addition, the GVI showed significant associations with engagement in health-related activities across most age groups and activity types, whereas the influence of LVV was relatively limited. This validates the intuitive and readily perceived nature of eye-level greenery, which may have a more direct impact on individuals’ activity decisions [14,46].
Furthermore, our findings revealed that the greenery standards in surrounding UGSs also significantly influenced engagement in health-related activities, underscoring the importance of external environments. While previous studies mainly relied on buffer-based approaches to examine surrounding contexts [14,102], our study employed a spatial lag term established by the k-nearest neighbors approach (k = 4) to incorporate adjacent UGS samples into the analysis. This method captured inter-UGS clustering effects, which are particularly relevant in the Xuanwu Lake Scenic Area, where UGSs are densely distributed and located in a high-density urban area. In other settings, the incorporated UGS neighbors could be adjusted based on local UGS distribution patterns [81]. Overall, this approach enhances spatial precision and highlights the cluster-level effects of UGS environments on public engagement in health-related activities.
Additionally, this study was conducted in the Xuanwu Lake Scenic Area during the summer months, a period chosen to minimize the influence of unobserved factors on the greenery measurement results. While the effects of unobserved factors, such as seasonality, accessibility, and maintenance, were effectively controlled in this context, we acknowledge that these factors can influence greenery metrics in other settings [64,103,104]. Therefore, we encourage future research to further explore how greenery measurements may vary under different seasonal conditions, accessibility levels, and maintenance conditions. Such investigations would contribute to a more robust and comprehensive foundation for developing effective and adaptive UGS planning strategies.

5. Conclusions

This study proposed an integrated framework to measure UGS greenery from multiple dimensions (NDVI, GVI, and LVV) and examined their associations with engagement in health-related activities across age groups. Our findings suggested that UGS greenery had stronger explanatory power for activity participant numbers than activity intensity across age groups. While top–down greenery (NDVI) showed a positive but non-significant association, excessive eye-level greenery (GVI) and spatial greenery (LVV) were negatively associated with engagement in health-related activities, indicating a possible threshold effect of UGS greenery. Among the three metrics, the GVI had the strongest and most consistent influence, particularly among adults and seniors, highlighting its intuitive and perceptual impact on activity decisions. Children’s engagement in health-related activities, however, appeared to be less tied to greenery, indicating the need to account for their unique activity patterns and preferences in greenery improvement. Additionally, the surrounding UGS greenery significantly shaped local activity patterns, emphasizing the importance of spatial context. These insights suggest that UGS greenery measured from a single dimension may not reliably predict engagement in health-related activities across age groups, reinforcing the need for balanced, context-sensitive UGS design to support inclusive public health.

Author Contributions

Conceptualization, C.W. and S.C.; methodology, C.W.; software, C.W.; validation, C.W., S.C. and Y.C.; formal analysis, C.W.; investigation, C.W. and S.C.; resources, S.C.; data curation, S.C.; writing—original draft preparation, C.W.; writing—review and editing, C.W., S.C., Y.C. and Z.S.; visualization, C.W.; supervision, S.C.; project administration, C.W. and S.C.; funding acquisition, C.W. and S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Postgraduate Research and Practice Innovation Program of Jiangsu Province, grant number KYCX23_1205; the Jiangsu Province Natural Science Foundation, grant number BK20230401; the National Natural Science Foundation of China, grant number 52308066; and the China Postdoctoral Science Foundation, grant number 2023M741720.

Data Availability Statement

The data presented in this study are available upon request from the authors. The data are not publicly available due to privacy concerns. The images employed for this study will be made available online for readers.

Acknowledgments

The authors have reviewed and edited the output and take full responsibility for the contents of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
UGSsUrban Green Spaces
NDVINormalized Difference Vegetation Index
GVIGreen View Index
LVVLiving Vegetation Volume
UAVUnmanned Aerial Vehicle
PAPhysical activity
SISocial interaction
IVsIndependent variables
DVsDependent variables
CVControl variable
GLMGeneralized Linear Model
ANOVAAnalysis of variance
LRTLikelihood ratio test
SDStandard deviation
CoVCoefficient of variation

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Figure 1. Distribution of UGS samples in Xuanwu Lake Scenic Area.
Figure 1. Distribution of UGS samples in Xuanwu Lake Scenic Area.
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Figure 2. Greenery measurement process utilizing multi-dimensional metrics.
Figure 2. Greenery measurement process utilizing multi-dimensional metrics.
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Table 1. Descriptive statistics of the variables.
Table 1. Descriptive statistics of the variables.
Mean SDCoVRange
Independent variables (IVs)
NDVI0.414 0.053 0.128(0.301, 0.517)
(standardized *)0.523 0.246 0.470(0, 1)
GVI0.468 0.172 0.368(0.138, 0.873)
(standardized *)0.450 0.233 0.518(0, 1)
LVV14,732.930 8644.416 0.587(6148.143, 46,776.290)
(standardized *)0.211 0.213 1.009(0, 1)
Control variable (CV)
Park Size9921.995 5272.066 0.531(1111.820, 25,665.020)
(standardized *)0.359 0.215 0.599(0, 1)
Dependent variables (DVs)
Number of PA participants16.5339319.183761.160(0, 173)
Intensity of general PA28.2267934.173561.211(0, 377)
Intensity of children’s PA0.49285712.5447595.163(0, 45)
Intensity of seniors’ PA6.16428610.610091.721(0, 110)
Number of SI participants16.8428623.267911.381(0, 282)
Intensity of general SI37.4696454.299631.449(0, 673)
Intensity of children’s SI0.55357142.5159224.545(0, 30)
Intensity of seniors’ SI7.18392912.84281.788(0, 102)
* Min–max normalization, PA: physical activity, SI: social interaction.
Table 2. Correlation matrix for independent variables and control variable.
Table 2. Correlation matrix for independent variables and control variable.
Park SizeNDVIGVILVV
Park size10.269−0.2390.587 ***
NDVI0.26910.308 *0.400 **
GVI−0.2390.308 *10.152
LVV0.587 ***0.400 **0.1521
Note: *** represents significance at the 0.01 level; ** represents significance at the 0.05 level; * represents significance at the 0.10 level.
Table 3. Summary of the ANOVA results of dependent variables.
Table 3. Summary of the ANOVA results of dependent variables.
Dependent Variables (DVs)F Valuep Value
Number of PA participants12.26<0.001 ***
Intensity of general PA14.17<0.001 ***
Intensity of children’s PA0.0620.804
Intensity of seniors’ PA15.16<0.001 ***
Number of SI participants12.3<0.001 ***
Intensity of general SI12.71<0.001 ***
Intensity of children’s SI0.8120.368
Intensity of seniors’ SI7.4480.007 ***
Note: *** represents significance at the 0.01 level. PA: physical activity, SI: social interaction.
Table 4. Summary of the likelihood ratio test results.
Table 4. Summary of the likelihood ratio test results.
Dependent Variables (DVs)LogLik of Models
Considering Dates		LogLik of Models
Not Considering Dates		Chi-Squaredp Value
Number of PA participants−2043.9−2084.781.555<0.001
***
Intensity of general PA−2340.0−2379.779.348<0.001
***
Intensity of children’s PA−312.03−315.146.2300.013
**
Intensity of seniors’ PA−1476.8−1489.425.188<0.001
***
Number of SI participants−2052.1−2094.183.905<0.001
***
Intensity of general SI−2490.7−2530.479.389<0.001
***
Intensity of children’s SI−323.84−324.150.6170.432
Intensity of seniors’ SI−1512.4−1522.219.706<0.001
***
Note: *** represents significance at the 0.01 level; ** represents significance at the 0.05 level. PA: physical activity, SI: social interaction.
Table 5. Summary of Moran’s I statistics for the residuals of LME.
Table 5. Summary of Moran’s I statistics for the residuals of LME.
Dependent Variables (DVs)Moran’s I Statisticsp Value
Number of PA participants0.589<0.001 ***
Intensity of general PA0.399<0.001 ***
Intensity of children’s PA0.0030.402
Intensity of seniors’ PA0.172<0.001 ***
Number of SI participants0.565<0.001 ***
Intensity of general SI0.534<0.001 ***
Intensity of children’s SI0.140<0.001 ***
Intensity of seniors’ SI0.238<0.001 ***
Note: *** represents significance at the 0.01 level. PA: physical activity, SI: social interaction.
Table 6. GLM results for physical activity (PA).
Table 6. GLM results for physical activity (PA).
VariablesRegression Models for Physical Activity (PA)
Number of PA ParticipantsIntensity of General PAIntensity of Children’s PAIntensity of Seniors’ PA
(Intercept)2.370 ***2.885 ***0.4510.857 ***
NDVI0.1920.060−0.2890.636 **
GVI−0.829 ***−0.772 ***−2.368 *−0.785 **
LVV−0.440 **−0.343−1.932−1.216 ***
Park size0.495 **0.518 **−0.4061.056 **
Spatial lag0.020 ***0.014 ***/0.073 ***
Note: *** represents significance at the 0.01 level; ** represents significance at the 0.05 level; * represents significance at the 0.10 level.
Table 7. GLM results for social interaction (SI).
Table 7. GLM results for social interaction (SI).
VariablesRegression Models for Social Interaction (SI)
Number of SI ParticipantsIntensity of General SIIntensity of Children’s SIIntensity of Seniors’ SI
(Intercept)2.451 ***3.292 ***−0.5581.112 ***
NDVI0.2360.1900.5360.320
GVI−0.926 ***−0.952 ***−0.676−0.998 ***
LVV−0.484 **−0.359−2.865 *−0.631
Park size0.560 ***0.501 **0.2850.997 **
Spatial lag0.015 ***0.006 ***0.278 **0.063 ***
Note: *** represents significance at the 0.01 level; ** represents significance at the 0.05 level; * represents significance at the 0.10 level.
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Wang, C.; Chen, S.; Chen, Y.; Shen, Z. Greenery Effects: Comparing the Associations Between Multi-Dimensional Measurements of Urban Green Space Greenery and Engagement in Health-Related Activities Across Age Groups. Land 2025, 14, 1128. https://doi.org/10.3390/land14061128

AMA Style

Wang C, Chen S, Chen Y, Shen Z. Greenery Effects: Comparing the Associations Between Multi-Dimensional Measurements of Urban Green Space Greenery and Engagement in Health-Related Activities Across Age Groups. Land. 2025; 14(6):1128. https://doi.org/10.3390/land14061128

Chicago/Turabian Style

Wang, Chongxiao, Shuolei Chen, Yang Chen, and Zhongzhe Shen. 2025. "Greenery Effects: Comparing the Associations Between Multi-Dimensional Measurements of Urban Green Space Greenery and Engagement in Health-Related Activities Across Age Groups" Land 14, no. 6: 1128. https://doi.org/10.3390/land14061128

APA Style

Wang, C., Chen, S., Chen, Y., & Shen, Z. (2025). Greenery Effects: Comparing the Associations Between Multi-Dimensional Measurements of Urban Green Space Greenery and Engagement in Health-Related Activities Across Age Groups. Land, 14(6), 1128. https://doi.org/10.3390/land14061128

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