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10 August 2024

Effects of Tree Leaf Color on Human Physical and Mental Recovery from a Looking-Up Perspective

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College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China
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Sichuan Academy of Forestry Sciences, Chengdu 610081, China
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Author to whom correspondence should be addressed.
Forests2024, 15(8), 1404;https://doi.org/10.3390/f15081404
This article belongs to the Special Issue Forests and Human Health: Effects on Acute and Chronic Illness, and Public Health
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Abstract

Numerous studies have demonstrated the benefits of understory spaces and plants on human well-being, but most spatial research has focused on a horizontal perspective. Additionally, there is a lack of research on the effects of plant color on human recovery, especially with respect to color proportions. This study classifies the leaf colors of trees in autumn, which are observed from a looking-up perspective, into green, red, and yellow. On this basis, we created monochromatic, two-color, and three-color groups with varying color proportions to assess their recovery effects and preferences. A total of 30 participants participated in this experiment, and their physiological, psychological, and preference-related indicators were evaluated. The results revealed that the following. (1) The two-color groups had the greatest reduction effect on systolic blood pressure. Monochromatic groups were most effective at reducing diastolic blood pressure. The three-color groups had the greatest effect on lowering the pulse rate. (2) EEG responses varied by color type. (3) The three-color groups had the best recovery effects on the psychological measures. (4) The three-color groups were most favored by participants, with a red–yellow–green ratio of 0.2/0.4/0.4 being the most preferred. These findings demonstrate the differing recovery potentials of various leaf color proportions from a looking-up perspective. This study can provide valuable references for the planning and design of urban forest parks, supplementing the theoretical foundation and research framework for evaluating and creating environments that meet people’s restorative needs.

1. Introduction

Cities provide high-quality educational resources and diverse cultural experiences, thereby attracting many young people who pursue career development and settle in urban areas. However, the urban lifestyle also brings a series of physical and mental health challenges. Factors such as high population density, traffic congestion, noise pollution, and social pressure often lead to severe psychological and physiological health problems. Arnberger et al. found that the younger and more educated the respondents were, the greater the mental stress they felt []. MacAskill concluded that the number of psychological issues among college students increased by 11% from the first year to the second year of university []. From this, it can be understood that the probability of physical and mental health problems occurring among young people during this period is greatly increased. Therefore, creating a healthy environment becomes crucial.
Urban green spaces are essential areas in the daily lives of city dwellers, with many residents using these spaces to relax and rejuvenate. Studies have shown that walking or sitting in green spaces are important activities that benefit health []. Tree canopies are significant elements in outdoor environments, providing natural shelters where people often stop to rest or chat. The view from beneath the canopy, looking up, offers a stark contrast to the horizontal perspective. Moreover, the upward-looking view contains fewer visual distractions, allowing people to focus more on the beauty of nature and experiencing relaxation and pleasure, thereby enhancing their interaction with the natural environment.
Currently, two mainstream theories suggest that exposure to nature has restorative effects on human health: attention restoration theory (ART) and stress reduction theory (SRT) []. Kaplan’s attention restoration theory emphasizes that to function efficiently in daily life, people must maintain cognitive clarity, which requires directed attention. A decline in the ability to concentrate can lead to many negative effects []. However, certain environments, known as restorative environments, can significantly restore this ability. Ulrich proposed stress reduction theory, which posits that stress is the response process of individuals, psychologically, physiologically, and behaviorally, to challenging or frightening environments. According to this theory, the natural environment positively affects emotions and physiology, significantly alleviating mental stress []. On the basis of these theories, many scholars have explored the relationship between plant color and public health. Research indicates that plant color can help reduce stress, improve work efficiency [], increase vitality [], and stimulate changes in heart rate and brainwaves []. Exposure to certain plant colors can increase alpha waves, increasing creativity, and theta waves, increasing concentration []. At the same time, plant colors can also have certain psychological effects on people. Bright vivid colors such as pure red and blue are energizing and outward-focused; dark shades such as deep forest green and navy blue are dignified and professional []. However, not all plant colors promote human recovery, as the restorative effects vary with plant color.
Plants are a major component of urban landscapes, and city residents have the most direct perception of changes in plant color, especially in autumn. Therefore, research on the restorative effects related to leaf color is particularly important. This study is located in Chengdu, southwestern China, where both deciduous and evergreen trees are abundant. Field surveys revealed that the leaf colors of deciduous trees change throughout the year, particularly in autumn. The purposes of this study are as follows: (1) to explore the differences in physical and mental recovery associated with different leaf colors from a looking-up perspective and (2) to determine which leaf color proportions best promote physical and mental recovery.
In this study, we focus on the relationship between leaf colors and physical and mental restoration. Previous research has shown that leaf colors have a significant impact on physical and mental restoration. For example, Zheng et al. found that changes in leaf color proportions from an eye-level perspective lead to variations in physical and mental restoration []. Based on the discussion of the relationship between variables, this study proposes the following hypotheses: (1) compared to single-color and two-color leaves, three-color leaves provide the best physiological restoration for the human body; (2) compared to single-color and two-color leaves, three-color leaves provide the best psychological restoration for the human body; (3) compared to leaves of other colors, participants prefer three-color leaves from an upward viewing angle.

2. Materials and Methods

2.1. Experimental Images

An image, as a visual replica of a real landscape, is widely applied in research because this method can save time and costs while also allowing better control of external variables during experiments. Therefore, photography and selection of photos are particularly important as stimulus materials in this study.
The survey for this study was conducted from October to December on sunny days, during which a total of 212 photos were taken. The photos were taken vertically upwards from a height of 160 cm above the ground. After the images were captured, the aerial leaf color photos were organized. The autumn tree leaf colors were divided into three types (green, red, and yellow) with different ratios of these three colors. Therefore, the experiment was divided into three groups: single-color, two-color, and three-color groups. The single-color group consisted of single red, yellow, and green colors. The two-color group consisted of two combinations of the three colors, and the three-color group consisted of combinations of the three colors with different proportions. After unclear and dimly lit photos were removed, nine typical photos were selected as objects for extracting leaf color proportions (three for each leaf color type) according to the advice of landscape architecture experts.
According to previous studies by scholars, the ratio of sky–leaf–branch (0.1/0.8/0.1) is most favored by individuals []. Therefore, the sky–leaf–branch ratio in this experiment is based on this ratio. Software (Adobe Photoshop 2020) was used to precisely control the proportions of the three colors in the experimental pictures (Figure 1).
Figure 1. Leaf colors and corresponding color proportions from an upward view.

2.2. Participants

A total of 30 volunteers aged 19–26 years participated in this experiment. Posters were placed on university campuses and nearby areas, and volunteers were recruited through mobile group chat applications to broaden the sample range. Participants were selected based on the following criteria: (1) self-reported normal vision and (2) no history of cardiovascular or mental disorders. Participants were instructed not to smoke, drink alcohol, or engage in vigorous physical activities before they participated in the study. All potential participants were informed about the experimental procedures and related risks and provided informed consent before the experiment began. The study was conducted with the approval of the Academic Ethics Committee of the College of Landscape Architecture, Sichuan Agricultural University, China.

2.3. Experimental Procedure

The experiment was completed by 30 participants over the course of two days. It was conducted in a university classroom, with the room temperature and air humidity maintained at 16 °C and 65%, respectively. The room remained quiet during the experiment, and all windows were covered with blackout curtains to prevent light interference. We aimed to provide the same environmental conditions for each participant to minimize errors.
The experimental procedure for each participant is illustrated in Figure 2. The steps are as follows: (1) In the preparation and introduction phase, participants were informed about the experimental process and the use of the experimental equipment. They also completed a personal information form; (2) In the stress induction phase, participants completed a series of mathematical problems within 2 min. Afterward, their blood pressure and pulse were measured, and they completed the Positive and Negative Affect Schedule (PANAS); (3) In the recovery phase, participants viewed a set of photos while their EEGs were recorded for 2 min. Following this, their blood pressure and pulse rate were measured again, and they completed the PANAS and Perceived Restorativeness Scale (PRS) tests. After a 5-min rest, they proceeded to the next set of experiments. To eliminate the effects of order, the sequence of images viewed by different groups of volunteers varied; and (4) in the preference testing phase, participants selected their favorite image from all the photos shown. The length of the experiment was kept within 45 min to reduce the impact of fatigue on the results. Participants’ demographic characteristics were collected before the experiment.
Figure 2. Experimental procedure diagram.

2.4. Measurements

Electroencephalography (EEG) can be used to measure brain responses to external visual stimuli, and portable EEG devices can accommodate a large number of subjects (Hui Dong Brain Instrument, Sichiray03, Wuxi, China). Throughout the experiment, a wearable EEG device continuously recorded participants’ delta, theta, low alpha, high alpha, low beta, high beta, low gamma, and middle gamma data.
Blood pressure is a key indicator of physical health and reflects the body’s physiological state of arousal or relaxation [,]. Blood pressure measurement is highly portable, meets experimental needs, and imposes a minimal burden on participants. In this experiment, a portable electronic blood pressure monitor (Omron, HEM-7011, Shanghai, China) was used to measure participants’ systolic blood pressure (SBP; mmHg), diastolic blood pressure (DBP; mmHg), and pulse rate (bpm) on their left arm.
For emotional recovery, we selected the PANAS. Participants rated their subjective feelings on a scale from 1 (very slightly or not at all) to 5 (extremely), with higher scores indicating more intense emotional experiences.
Regarding perceived restoration, the primary measurement tool is the PRS, which assesses the quality of environmental restoration based on four characteristics: away, fascination, compatibility, and extent. To prevent participant fatigue, we used the short version of the PRS, revised by Huang et al., which consists of 18 items [].

2.5. Statistical Analysis

First, the data were subjected to reliability and validity tests, as well as normality tests. One-way ANOVA was used to analyze participants’ demographic characteristics. Paired t-tests and Wilcoxon signed-rank tests were used to analyze physiological and psychological data. The Kruskal–Wallis test was employed to analyze the EEG data. Statistical analyses were performed via SPSS 27.0 (SPSS Inc., Chicago, IL, USA), with the significance level set at p < 0.05.

3. Results

3.1. Analysis of Demographic Characteristics

A total of 30 participants were recruited for the experiment. Seven participants had education levels of a master’s degree or higher, and the majority (11 participants) were aged between 18 and 21 years. Participants had diverse academic backgrounds, predominantly in landscape architecture, agronomy, and resource science.
As shown in Table S1, demographic characteristics such as major, and age significantly impacted the perceived restoration assessment. Participants with a background in landscape architecture had significantly greater perceived restoration abilities than those from other fields (F = 4.75, p < 0.001). Age also significantly affected perceived restoration ability (F = 5.55, p < 0.001); specifically, the 24-year-old participants had the best-perceived restoration effect (5.93 ± 0.35), followed by the 25-year-olds (5.54 ± 0.67) and then the 23-year-olds (5.11 ± 0.64). Overall, older participants, and those with a background in landscape architecture experienced more significant perceived restoration benefits. This finding indicates that age, gender, and academic background can influence an individual’s perceived restoration ability to some extent.

3.2. Emotional Restoration Ability

Using paired t-tests and Wilcoxon signed-rank tests (Figure 3), it was found that participants experienced a certain degree of restoration after viewing images from the single-color, two-color, and three-color groups. The positive scores significantly increased across all three groups, indicating that the restoration effects were similar. The negative scores also significantly decreased, with the greatest reduction in the three-color group, followed by the single-color group, and finally the two-color group.
Figure 3. Changes in PANAS scores before and after participants viewed the three color types. PA: positive affect; NA: negative affect. N = 30; ** p < 0.01.
In the single-color group (Figure S1), the positive scores significantly increased for “interested”, “high energy”, “enthusiastic”, “proud”, “inspired”, “determined”, “attentive”, and “active”; the negative scores significantly decreased for “distressed”, “upset”, “guilty”, “ashamed”, “afraid”, “hostile”, “irritable”, “nervous”, “jittery”, and “scared”; and the score for “alert” did not significantly change between the pre-test and post-test.
In the two-color group (Figure S2), the positive scores significantly increased for “interested”, “high energy”, “enthusiastic”, “proud”, “inspired”, “determined”, “attentive”, and “active”; the negative scores significantly decreased for “distressed”, “upset”, “irritable”, “nervous”, “jittery”, “afraid”, and “hostile”; and the scores for “guilty”, “alert”, and “ashamed” did not significantly change between the pre-test and post-test.
In the three-color group (Figure S3), the positive scores significantly increased for “interested”, “high energy”, “enthusiastic”, “proud”, “inspired”, “determined”, “attentive”, and “active”; the negative scores significantly decreased for “distressed”, “upset”, “guilty”, “afraid”, “hostile”, “irritable”, “alert”, “nervous”, “jittery”, and “scared”; and the score for “ashamed” did not significantly change between the pre-test and post-test.

3.3. PRS Analysis

One-way ANOVA was used to analyze the PRS results. Table 1 shows the perceived restorativeness scores for the color types. The overall PRS score and its four dimensions were analyzed via one-way ANOVA. The results revealed significant differences among the three groups in all PRS dimensions: being sway (d.f = 2, F = 6.84, p = 0.002), extent (d.f = 2, F = 7.65, p < 0.001), fascination (d.f = 2, F = 9.10, p < 0.001), and compatibility (d.f = 2, F = 10.09, p < 0.001).
Table 1. Summary table of physiological and psychological indicators.
One-way ANOVA revealed that the three-color group scored higher in all dimensions of perceived restoration and overall restoration potential than the single-color and two-color groups. In the dimensions of being away, fascination, and compatibility, the three-color group scored the highest and significantly higher than the two-color group but not significantly different from the single-color group. In the extent dimension, the three-color group scored the highest and significantly higher than the two-color group but not significantly different from the single-color group, which scored significantly higher than the two-color group. In terms of the overall PRS score, the three-color group scored significantly higher than the two-color group, although the difference between the three-color and single-color groups was not significant.
In summary, the three-color group demonstrated the best restorative potential, followed by the single-color group and finally the two-color group.

3.4. Blood Pressure and Pulse Analysis

The collected data were analyzed via paired t-tests (Figure 4). Compared with the pre-test measurements, the monochromatic group exhibited significant reductions in DBP (65.57 ± 6.94 vs. 62.00 ± 7.87, T = 3.82, p = 0.001), SBP (101.83 ± 10.76 vs. 97.30 ± 9.96, T = 5.60, p < 0.001), and pulse (77.77 ± 10.92 vs. 75.20 ± 9.85, T = 2.89, p = 0.007).
Figure 4. Changes in blood pressure and pulse before and after participants viewed the three color. types. n = 30; mean ± SD; ** p < 0.01, *** p < 0.001; verified via paired t-tests.
In the dichromatic group, SBP (103.87 ± 12.47 vs. 98.93 ± 9.90, T = 4.47, p < 0.001) was significantly lower than in the pre-test, whereas DBP (64.93 ± 7.69 vs. 64.27 ± 8.46, T = 0.64, p = 0.526) and pulse (75.90 ± 10.86 vs. 74.37 ± 10.57, T = 1.48, p = 0.15) were not significantly lower.
In the trichromatic group, only pulse (76.13 ± 10.31 vs. 72.5 ± 9.22, T = 3.92, p < 0.001) was significantly lower than in the pre-test, whereas DBP (63.80 ± 9.74 vs. 62.73 ± 8.17, T = 0.84, p = 0.409) and SBP (101.60 ± 11.55 vs. 99.40 ± 10.54, T = 1.63, p = 0.113) were not significantly lower.
Overall, there was a certain degree of reduction in the physiological data across all three groups compared with the pre-test measurements. Specifically, the dichromatic group showed the most significant reduction effect on SBP, followed by the monochromatic group and finally the trichromatic group. The monochromatic group exhibited the greatest reduction effect on DBP, followed by the trichromatic group and finally the dichromatic group. The trichromatic group showed the most significant impact on pulse, followed by the monochromatic group and finally the dichromatic group.

3.5. EEG Analysis

EEG data were analyzed via the Kruskal–Wallis test (Table 2). The results revealed no significant differences among the groups for low beta, high beta, or low gamma (p > 0.05), indicating no significant variability. However, there were significant differences among the groups in the delta, theta, low alpha, high alpha, and middle gamma indices (p < 0.05), suggesting that these parameters varied across groups and warranted pairwise comparisons (Figure 5).
Table 2. Differences in PRS Dimension Scores for Each Color Type (N = 30).
Figure 5. Changes in theta, delta, high beta, low beta, high alpha, low alpha, middle gamma, and low-gamma data of participants viewing the three color types. n = 30; ** p < 0.01, *** p < 0.001. G1: single-color group, G2: two-color group, G3: three-color group.
Although the nonparametric test results for delta were less than 0.05, pairwise comparisons revealed no significant differences among the three groups.
Theta waves typically occur when a person is deeply thoughtful, focused, or inspired. This band is associated with inner focus, relaxation, meditation, and achieving a flow state, representing physical and mental relaxation, enhanced creativity, and improved learning ability. Compared with the monochromatic group, the trichromatic group presented significantly greater theta EEG signals. These findings suggest that trichromatic plants have a more pronounced effect on the subjects’ relaxation, effectively reducing stress levels and promoting physical and mental relaxation.
Research suggests that low alpha may be associated with creative thinking, self-reflection, and emotional stability, whereas high alpha may be related to focused attention, logical analysis, and positive emotional experiences. The nonparametric test results for low alpha were less than 0.05. In pairwise comparisons, the dichromatic group presented significantly higher low alpha values than the trichromatic and monochromatic groups. This finding indicates that the dichromatic group had significantly enhanced low-alpha EEG signals compared with the other two groups, implying that dichromatic plants have a more significant effect on emotional stability, relaxation, and fostering creative thinking.
For high alpha, the nonparametric test results were also less than 0.05. Pairwise comparisons revealed that the monochromatic group presented significantly higher high alpha values than the trichromatic group. These findings suggest that monochromatic plants enhance high alpha EEG signals more effectively than the trichromatic group does, indicating a more substantial effect on focused attention.
The nonparametric test results for the middle gamma were less than 0.05, but pairwise comparisons revealed no significant differences among the monochromatic, dichromatic, and trichromatic groups. High-beta, low-beta, and low-gamma data had nonparametric test results greater than 0.05, indicating no significant differences from a statistical standpoint.
The theta/beta ratio is commonly used as an indicator of cognitive ability. In this study, theta/beta was divided into theta/H-beta and theta/L-beta for analysis. The beta/alpha ratio, often used to measure relaxation, represents the reduction in stress during the testing process. This study further divided the beta/alpha ratio into H-beta/H-alpha and L-beta/L-alpha for analysis (Figure 6).
Figure 6. Changes in the theta/H-beta, theta/L-beta, H-beta/H-alpha, and L-beta/L-alpha data of. participants viewing the three color types. n = 30; * p < 0.05, ** p < 0.01, *** p < 0.001. G1: single-color group, G2: two-color group, G3: three-color group.
The results for theta/H-beta revealed that the theta/H-beta values of the trichromatic group were significantly higher than those of the monochromatic and dichromatic groups, with those of the dichromatic group slightly higher than those of the monochromatic group. These findings indicate that, compared with the other two groups, the trichromatic group presented significantly greater cognitive ability.
ANOVA was used to analyze theta/L-beta ratios (Table 3). The results revealed significant differences among the monochromatic, dichromatic, and trichromatic groups (p < 0.05). However, pairwise comparisons revealed no significant differences among the groups. Overall, the theta/L-beta values in the trichromatic group were higher than those in the monochromatic and dichromatic groups, with the latter two groups showing nearly identical values.
Table 3. Kruskal–Wallis test for EEG data.
For H-beta/H-alpha, the intergroup differences among the monochromatic, dichromatic, and trichromatic groups were not significant (p > 0.05), making the analysis not meaningful.
ANOVA was used to analyze the L-beta/L-alpha ratio (Table 4, Table 5 and Table 6). The results revealed significant differences among the monochromatic, dichromatic, and trichromatic groups (p < 0.05). Pairwise comparisons revealed that the monochromatic group presented significantly higher L-beta/L-alpha values than the dichromatic group. These findings suggest that the monochromatic group is more effective at promoting relaxation and alleviating stress-induced emotions than the dichromatic group.
Table 4. One-way ANOVA for theta/L-beta.
Table 5. One-way ANOVA for L-beta/L-alpha.
Table 6. Summary table of experimental results.

4. Discussion

In the physiological data, three-color leaves did not show the best physiological restorative effect on diastolic and systolic blood pressure; however, for pulse rate, three-color leaves showed the best physiological restorative effect. In terms of pulse, three-color leaves performed the best; for other physiological indicators, single-color or two-color leaves performed better. Therefore, hypothesis 1 was not fully supported. In the psychological data, three-color leaves showed the best psychological restorative effect on the negative scores of the PANAS scale; on the PRS scale, three-color leaves also showed the best psychological restorative effect, supporting hypothesis 2. In terms of preference, three-color leaves were the most favored by the participants, confirming their visual preference advantage, thus supporting hypothesis 3.

4.1. Demographics

Compared with those without such a background, those with a background in landscape architecture had significantly greater recovery abilities. Each profession corresponds to its own knowledge field, which can act as a variable in preference formation, affecting individual differences among participants from different disciplines [,]. Volunteers from the landscape architecture field have more frequent contact with nature than those from non-landscape architecture fields, leading to a better understanding and appreciation of ecosystems and the benefits of interacting with nature.

4.2. Physiological Recovery

In terms of physiological indicators, all three groups showed some degree of reduction in their post-test data compared with the pre-test data, which aligns with previous findings [,,,,]. In addition, exposure to all color plant landscape treatments resulted in physiological improvements [], indicating that plant color can promote human recovery. Specifically, the monochromatic group exhibited the greatest reduction effect on diastolic blood pressure, followed by the trichromatic group and finally the dichromatic group. A single color might provide a more consistent and predictable emotional response, reducing mental burden and stress, thereby lowering diastolic blood pressure. The dichromatic group showed the greatest reduction effect on systolic blood pressure, whereas the trichromatic group exhibited the most significant reduction effect on pulse rate, followed by the monochromatic group and finally the dichromatic group. This result is similar to Hoyle et al.’s findings, indicating that high color diversity is more conducive to human perceptual recovery [].
For the EEG indicators, the monochromatic group presented significantly lower theta waves than the trichromatic group. An increase in theta wave signals reflects increased comfort, reduced pain perception, and improved cognitive ability, aiding individuals in controlling various learning disorders and alleviating headaches []. These findings suggest that, compared with monochromatic exposure, trichromatic exposure significantly reduces stress levels and promotes physical and mental relaxation. The alpha band reflects the subject’s physical relaxation and alertness. Subhani et al. reported that the alpha band in EEGs is highly correlated with mental stress [], and under high-stress conditions, the alpha band level significantly decreases []. According to the low alpha data, the dichromatic group presented significantly higher values than the trichromatic and monochromatic groups, indicating that the dichromatic group presented significantly greater emotional stability and a more relaxed state, promoting creative thinking. For the high alpha data, the monochromatic group presented significantly higher values than the trichromatic group, suggesting significantly increased attention. There were no significant differences in the delta or middle gamma data in subsequent pairwise comparisons; similarly, the high beta, low beta, and low gamma data were not significantly different between the groups.
Theta/beta waves are used to describe neurophysiological performance related to attention [,,]; studies have shown a negative correlation between theta/beta power and attention control. Higher theta/beta power indicates lower attention control ability []. In terms of the theta/H-beta data, those of the trichromatic group were significantly greater than those of the monochromatic and dichromatic groups, suggesting that, compared with the trichromatic group, the monochromatic and dichromatic groups presented significantly greater cognitive ability. Beta/alpha waves describe the impact of stress on the nervous system [,]. Higher beta/alpha powers indicate higher stress levels []. In the L-beta/L-alpha data, the monochromatic group presented significantly higher values than the dichromatic group, indicating that the dichromatic group was more effective at promoting relaxation and soothing emotions under stress than the monochromatic group. The theta/L-beta data showed no significant differences in subsequent pairwise comparisons, and the H-beta/H-alpha data revealed no significant differences between groups.
Few studies have investigated the impact of plant color combinations on human recovery. However, existing research suggests that using a single color over a large area in a plant community may make the scene appear less vibrant, whereas adding other colors can be advantageous [,,,]. Zheng et al. reported that college students experienced stronger relaxation effects when viewing images of plant communities with two or three colors than when viewing images of monochromatic plant communities []. The conclusions of this study are similar; in this experiment, except for the high alpha and theta/H-beta EEG data, where the monochromatic group had the best recovery, the other EEG data indicated that the dichromatic and trichromatic groups had the best recovery effects. High alpha and theta/H-beta are associated with relaxation and attention states; monochromatic visual input reduces information processing complexity, promoting relaxation and recovery.

4.3. Psychological Recovery

Plant color is an important indicator for improving public mental health, and different plant color landscapes can affect a person’s overall psychological and physiological state [,]. In the experiment, each color group significantly improved the recovery scores on the positive and negative emotion scales. All three groups presented the same recovery ability in terms of increasing positive scores, but the trichromatic group presented the greatest reduction in negative scores, followed by the monochromatic group and finally the dichromatic group. This conclusion is similar to that of previous research [,,], indicating that different plant colors can significantly improve mental health and elicit different responses.
The PRS results revealed that the trichromatic group had the best recovery, followed by the monochromatic group and finally the dichromatic group. Specifically, the trichromatic group scored highest in the dimensions of being away, fascination, and compatibility, significantly higher than the dichromatic group, but not significantly different from the monochromatic group. In terms of extent, the trichromatic group had the highest score, which was significantly higher than that of the dichromatic group but not significantly different from that of the monochromatic group; that of the monochromatic group was significantly greater than that of the dichromatic group. These results are consistent with previous findings that diverse plant species colors have strong restorative effects []. Leaf images with two or three color combinations elicited stronger reactions than single-color images [].

4.4. Preferences

In terms of preferences, the trichromatic group was the most favored by the subjects, followed by the monochromatic group. Specifically, the red/yellow/green ratio of 0.2/0.4/0.4 was the most preferred ratio, followed by the green group. This conclusion is similar to those of previous studies, where the most restorative proportion in trichromatic plant communities had the most green and yellow, followed by red []. Green tones have calming and stress-relieving effects, leading to focused and stable actions [,]. Previous studies have shown that green and yellow plant communities induce stronger relaxation effects than red plant communities in monochromatic settings []. Green is also the most common color in nature, which explains why the green proportion is highest in the most preferred images. Yellow and red are called “bonus colors”, eliciting positive emotions []. Previous research has suggested that red can make people tense and focus their attention on the external environment to some extent, but in terms of the ratio of colors in this study, red has the smallest area, so most subjects focused on green and yellow.
In conclusion, the trichromatic group exhibited the best recovery effect on psychological indicators and preferences. In terms of physiological indicators, dichromatic group showed the greatest reduction effect on systolic blood pressure, the monochromatic group exhibited the greatest reduction effect on diastolic blood pressure, and the trichromatic group showed the most significant reduction effect on pulse rate. For the EEG data, the trichromatic group presented significantly reduced stress levels; the dichromatic group presented significantly increased emotional stability and a relaxed state; and the monochromatic group presented significantly improved attention.

4.5. Research Limitations

This study has several limitations: (1) Considering the sample population, the experiment included only university students. Other researchers suggest that respondents’ variability (e.g., gender, age, education level, occupation, and/or living environment) has a considerable influence on aesthetic preference judgment [,]. Future research should increase the sample size and include participants of varying ages, genders, occupations, cultures, and religious backgrounds. This will increase the generalizability of the conclusions drawn under more diverse and objective contexts; (2) Considering the experimental materials, the images used as experimental materials might have limited the subjects’ perceptual abilities. Future studies should be conducted in outdoor environments or utilize virtual reality technology. The incorporation of multiple sensory experiences and the use of a variety of physiological and psychological measurement methods for quantitative research would provide a more comprehensive understanding; and (3) tree species, seasonality or topography would likely have an effect on preference or restoration [,]. This study focused primarily on the impact of leaf color on human recovery. Future research should integrate various leaf shapes, species, and the gradual color changes of leaves over time to enrich the research in this field. This would provide a more holistic view of how various plant characteristics contribute to human health and well-being.

5. Conclusions

Although numerous studies have demonstrated the positive impact of plants on human well-being, to our knowledge, this study is the first to compare the restorative potential of autumn tree foliage colors from an upward-looking perspective. We found that different leaf colors and proportions have varying effects on individuals. By quantifying the proportions of leaf colors, we created canopy landscape images with different color ratios; evaluated 30 participants; and combined data measurements, questionnaire surveys, and preference analysis to derive more precise conclusions about the impact of these ratios on human recovery and preferences, thus supplementing existing theories.
First, in terms of physiological indicators, the dichromatic group exhibited the most significant reduction effect on systolic blood pressure; the monochromatic group showed the greatest reduction effect on diastolic blood pressure; and the trichromatic group exhibited the most significant reduction effect on pulse rates. Second, different color types induced varying EEG responses. Moreover, in terms of the psychological indicators, the trichromatic group presented the greatest restorative effect. Finally, in terms of preference, the trichromatic group was the most favored by participants.
This study has potential implications for the planning and design of urban forest parks. In plant landscapes, it is essential to allocate the proportions of evergreen and deciduous tree species reasonably. For example, the use of evergreen trees such as camphor and magnolia, combined in appropriate proportions with deciduous trees such as ginkgo and maple, whose leaves turn yellow and red in autumn, can have positive effects on residents’ health and improve the urban landscape.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15081404/s1, Figure S1: Changes in PANAS scale scores before and after the experiment for the single-color group; Figure S2: Changes in PANAS scale scores before and after the experiment for the two-color group; Figure S3: Changes in PANAS scale scores before and after the experiment for the three-color group; Table S1: Demographic characteristics, physiological restoration, and psychological restoration.

Author Contributions

Conceptualization, Y.Y.; methodology, Y.Y.; software, Y.Y.; formal analysis, Y.Y.; investigation, Y.Y.; writing—original draft preparation, Y.Y.; writing—review and editing, Y.Y., S.C., N.L., B.L., K.L., P.Z., Y.Z., X.L. and J.C.; visualization, Y.Y.; supervision, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 31870703.

Data Availability Statement

We do not provide public access to the dataset so as to protect the privacy of the participants. Regarding the details of the data, please contact the corresponding author.

Acknowledgments

The authors thank the 30 individuals who participated in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Arnberger, A.; Eder, R. Are urban visitors’ general preferences for green-spaces similar to their preferences when seeking stress relief? Urban For. Urban Green. 2015, 14, 872–882. [Google Scholar] [CrossRef]
  2. Macaskill, A. The mental health of university students in the United Kingdom. Br. J. Guid. Couns. 2013, 41, 426–441. [Google Scholar] [CrossRef]
  3. Lin, W.; Chen, Q.; Jiang, M.; Zhang, X.; Liu, Z.; Tao, J.; Wu, L.; Xu, S.; Kang, Y.; Zeng, Q. The effect of green space behaviour and per capita area in small urban green spaces on psychophysiological responses. Landsc. Urban Plan. 2019, 192, 103637. [Google Scholar] [CrossRef]
  4. Cao, S.; Song, C.; Jiang, S.; Luo, H.; Zhang, P.; Huang, Y.; Yu, J.; Li, K.; Li, N.; Guo, B. Effects of Urban Greenway Environmental Types and Landscape Characteristics on Physical and Mental Health Restoration. Forests 2024, 15, 679. [Google Scholar] [CrossRef]
  5. Su, Q.; Xin, Z.-Q. The Research on Restorative Environments: Theories, Methods and Advances. Adv. Psychol. Sci. 2010, 18, 177. [Google Scholar]
  6. Hansmann, R.; Hug, S.-M.; Seeland, K. Restoration and stress relief through physical activities in forests and parks. Urban For. Urban Green. 2007, 6, 213–225. [Google Scholar] [CrossRef]
  7. Li, K.; Zhai, Y.; Zhang, Y.; Song, Q.; Luo, X.; Liu, J.-J. Effects of indoor foliage plants on college students’ work attention recovery: An experiment focusing on plant characteristics. Eur. J. Hortic. Sci 2021, 86, 642–650. [Google Scholar] [CrossRef]
  8. Elsadek, M.; Sun, M.; Fujii, E. Psycho-physiological responses to plant variegation as measured through eye movement, self-reported emotion and cerebral activity. Indoor Built Environ. 2017, 26, 758–770. [Google Scholar] [CrossRef]
  9. Haigh, C.A.; Witham, G.; Thompson, J.; Wood, H. Green environments and their effect upon hospital users. Int. J. Res. Nurs. 2014, 5, 37. [Google Scholar] [CrossRef][Green Version]
  10. Jang, H.S.; Kim, J.; Kim, K.S.; Pak, C.H. Human brain activity and emotional responses to plant color stimuli. Color Res. Appl. 2014, 39, 307–316. [Google Scholar] [CrossRef]
  11. Singh, N.; Srivastava, S. Impact of colors on the psychology of marketing—A Comprehensive over view. Manag. Labour Stud. 2011, 36, 199–209. [Google Scholar] [CrossRef]
  12. Zheng, S.; Zhou, Y.; Qu, H. Physiological and psychological responses to tended plant communities with varying color characteristics. J. For. Res. 2024, 35, 32. [Google Scholar] [CrossRef]
  13. Lin, W.; Zeng, C.; Bao, Z.; Jin, H. The therapeutic look up: Stress reduction and attention restoration vary according to the sky-leaf-trunk (SLT) ratio in canopy landscapes. Landsc. Urban Plan. 2023, 234, 104730. [Google Scholar] [CrossRef]
  14. Chang, C.-Y.; Hammitt, W.E.; Chen, P.-K.; Machnik, L.; Su, W.-C. Psychophysiological responses and restorative values of natural environments in Taiwan. Landsc. Urban Plan. 2008, 85, 79–84. [Google Scholar] [CrossRef]
  15. Gordon, A.M.; Mendes, W.B. A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform. Proc. Natl. Acad. Sci. USA 2021, 118, e2105573118. [Google Scholar] [CrossRef] [PubMed]
  16. Huang, Y.; Fu, W.; Weng, Y.; Wang, M. A study on the relationship between individual landscape preference, perceived Restorativeness scale and health benefits assessment of urban Forest pathway—A case study of Fudao. Landsc. Arch. 2020, 36, 73–78. [Google Scholar]
  17. Yau, K.K.-Y.; Loke, A.Y. Effects of forest bathing on pre-hypertensive and hypertensive adults: A review of the literature. Environ. Health Prev. Med. 2020, 25, 23. [Google Scholar] [CrossRef] [PubMed]
  18. Rajoo, K.S.; Karam, D.S.; Aziz, N.A.A. Developing an effective forest therapy program to manage academic stress in conservative societies: A multi-disciplinary approach. Urban For. Urban Green. 2019, 43, 126353. [Google Scholar] [CrossRef]
  19. Ismail, L.E.; Karwowski, W. Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis. PLoS ONE 2020, 15, e0242857. [Google Scholar] [CrossRef]
  20. Jiang, S.; Deng, L.; Luo, H.; Li, X.; Guo, B.; Jiang, M.; Jia, Y.; Ma, J.; Sun, L.; Huang, Z. Effect of fragrant primula flowers on physiology and psychology in female college students: An empirical study. Front. Psychol. 2021, 12, 607876. [Google Scholar] [CrossRef]
  21. Tilley, S.; Neale, C.; Patuano, A.; Cinderby, S. Older people’s experiences of mobility and mood in an urban environment: A mixed methods approach using electroencephalography (EEG) and interviews. Int. J. Environ. Res. Public Health 2017, 14, 151. [Google Scholar] [CrossRef] [PubMed]
  22. Suh, Y.-A.; Yim, M.-S. “High risk non-initiating insider” identification based on EEG analysis for enhancing nuclear security. Ann. Nucl. Energy 2018, 113, 308–318. [Google Scholar] [CrossRef]
  23. Neale, C.; Aspinall, P.; Roe, J.; Tilley, S.; Mavros, P.; Cinderby, S.; Coyne, R.; Thin, N.; Bennett, G.; Thompson, C.W. The aging urban brain: Analyzing outdoor physical activity using the emotiv affectiv suite in older people. J. Urban Health 2017, 94, 869–880. [Google Scholar] [CrossRef] [PubMed]
  24. Watson, D.; Clark, L.A.; Carey, G. Positive and negative affectivity and their relation to anxiety and depressive disorders. J. Abnorm. Psychol. 1988, 97, 346. [Google Scholar] [CrossRef] [PubMed]
  25. Watson, D.; Clark, L.A.; Tellegen, A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J. Personal. Soc. Psychol. 1988, 54, 1063. [Google Scholar] [CrossRef]
  26. Wang, X.; Wu, C.; Yan, J. Experimental study of the perceived restorative scale (PRS) in Chinese by evaluating the restorative qualities of urban park scenes. Chin. Landsc. Arch. 2019, 35. [Google Scholar]
  27. Zheng, B.; Zhang, Y.; Chen, J. Preference to home landscape: Wildness or neatness? Landsc. Urban Plan. 2011, 99, 1–8. [Google Scholar] [CrossRef]
  28. Richardson, E.A.; Mitchell, R. Gender differences in relationships between urban green space and health in the United Kingdom. Soc. Sci. Med. 2010, 71, 568–575. [Google Scholar] [CrossRef]
  29. Elsadek, M.; Liu, B. Effects of viewing flowering plants on employees’ wellbeing in an office-like environment. Indoor Built Environ. 2021, 30, 1429–1440. [Google Scholar] [CrossRef]
  30. Gu, M.; Li, X.; Lv, Y.; Zhang, Q. Effects of plantscape colors on psycho-physiological responses of university students. J. Food Agric. Environ. 2012, 10, 702–708. [Google Scholar]
  31. Hoyle, H.; Norton, B.; Dunnett, N.; Richards, J.P.; Russell, J.M.; Warren, P. Plant species or flower colour diversity? Identifying the drivers of public and invertebrate response to designed annual meadows. Landsc. Urban Plan. 2018, 180, 103–113. [Google Scholar] [CrossRef]
  32. Chavan, D.R.; Kumbhar, M.; Chavan, R.R. Effect of Music Therapy on A Brainwave for Stress Recognition. Int. J. Innov. Eng. Res. Technol. 2016, 1–3. [Google Scholar]
  33. Subhani, A.R.; Mumtaz, W.; Saad, M.N.B.M.; Kamel, N.; Malik, A.S. Machine learning framework for the detection of mental stress at multiple levels. IEEE Access 2017, 5, 13545–13556. [Google Scholar] [CrossRef]
  34. Al-Shargie, F.; Kiguchi, M.; Badruddin, N.; Dass, S.C.; Hani, A.F.M.; Tang, T.B. Mental stress assessment using simultaneous measurement of EEG and fNIRS. Biomed. Opt. Express 2016, 7, 3882–3898. [Google Scholar] [CrossRef] [PubMed]
  35. Sangal, R.B.; Sangal, J.M. Use of EEG beta-1 power and theta/beta ratio over Broca’s area to confirm diagnosis of attention deficit/hyperactivity disorder in children. Clin. EEG Neurosci. 2015, 46, 177–182. [Google Scholar] [CrossRef] [PubMed]
  36. van Son, D.; De Blasio, F.M.; Fogarty, J.S.; Angelidis, A.; Barry, R.J.; Putman, P. Frontal EEG theta/beta ratio during mind wandering episodes. Biol. Psychol. 2019, 140, 19–27. [Google Scholar] [CrossRef] [PubMed]
  37. Song, D.H.; Shin, D.W.; Jon, D.I.; Ha, E.H. Effects of methylphenidate on quantitative EEG of boys with attention-deficit hyperactivity disorder in continuous performance test. Yonsei Med. J. 2005, 46, 34. [Google Scholar] [CrossRef] [PubMed][Green Version]
  38. Moon, S.A.; Bae, J.; Kim, K.; Cho, S.Y.; Kwon, G.; Lee, R.; Ko, S.H.; Lim, S.; Moon, C. Eeg revealed that fragrances positively affect menopausal symptoms in mid-life women. Exp. Neurobiol. 2020, 29, 389. [Google Scholar] [CrossRef]
  39. Franco, D.; Franco, D.; Mannino, I.; Zanetto, G. The impact of agroforestry networks on scenic beauty estimation: The role of a landscape ecological network on a socio-cultural process. Landsc. Urban Plan. 2003, 62, 119–138. [Google Scholar] [CrossRef]
  40. Ribe, R.G. In-stand scenic beauty of variable retention harvests and mature forests in the US Pacific Northwest: The effects of basal area, density, retention pattern and down wood. J. Environ. Manag. 2009, 91, 245–260. [Google Scholar] [CrossRef]
  41. Frank, S.; Fürst, C.; Koschke, L.; Witt, A.; Makeschin, F. Assessment of landscape aesthetics—Validation of a landscape metrics-based assessment by visual estimation of the scenic beauty. Ecol. Indic. 2013, 32, 222–231. [Google Scholar] [CrossRef]
  42. Schirpke, U.; Tasser, E.; Tappeiner, U. Predicting scenic beauty of mountain regions. Landsc. Urban Plan. 2013, 111, 1–12. [Google Scholar] [CrossRef]
  43. Jin, H.Y.; Zhu, Z.W. Design of household multi-function vacuum cleaner. Adv. Mater. Res. 2014, 945, 266–269. [Google Scholar] [CrossRef]
  44. Liu, Z. Study on the Impact of Campus Plant Landscape Colors on College Students’ Physical and Mental Health. Master’s Thesis, Shandong Jianzhu University, Jinan, China, 2020. [Google Scholar]
  45. Sadek, M.; Sayaka, S.; Fujii, E.; Koriesh, E.; Moghazy, E.; El Fatah, Y. Human emotional and psycho-physiological responses to plant color stimuli. J. Food Agric. Environ. 2013, 11, 1584–1591. [Google Scholar]
  46. Wang, Z. Study on the Effect of Plant Campus Plant Color on College Students’ Physical and Mental Health. Master’s Thesis, Northwest A&F University, Yangling, China, 2018. [Google Scholar]
  47. Xing, J. Research on the Influence of Landscape leaf Color Restorative in Northern Medical Environment. Master’s Thesis, Northeast Agricultural University, Harbin, China, 2019. [Google Scholar]
  48. Jin, Y. Landscape Evaluation and Optimization of Jinjiang Greenway in Chengdu Based on Restorative Environmental Theory. Master’s Thesis, Shenyang Agricultural University, Shenyang, China, 2023. [Google Scholar]
  49. Jalil, N.A.; Yunus, R.M.; Said, N.S. Environmental colour impact upon human behaviour: A review. Procedia-Soc. Behav. Sci. 2012, 35, 54–62. [Google Scholar] [CrossRef]
  50. Elliot, A.J.; Maier, M.A. Color psychology: Effects of perceiving color on psychological functioning in humans. Annu. Rev. Psychol. 2014, 65, 95–120. [Google Scholar] [CrossRef] [PubMed]
  51. Wang, R.; Zhao, J.; Meitner, M.J. Urban woodland understory characteristics in relation to aesthetic and recreational preference. Urban For. Urban Green. 2017, 24, 55–61. [Google Scholar] [CrossRef]
  52. Nordh, H.; Alalouch, C.; Hartig, T. Assessing restorative components of small urban parks using conjoint methodology. Urban For. Urban Green. 2011, 10, 95–103. [Google Scholar] [CrossRef]
  53. Paddle, E.; Gilliland, J. Orange is the new green: Exploring the restorative capacity of seasonal foliage in schoolyard trees. Int. J. Environ. Res. Public Health 2016, 13, 497. [Google Scholar] [CrossRef]
  54. Zhao, J.; Xu, W.; Li, R. Visual preference of trees: The effects of tree attributes and seasons. Urban For. Urban Green. 2017, 25, 19–25. [Google Scholar] [CrossRef]
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