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Article

The Influence of Different Forest Landscapes on Physiological and Psychological Recovery

by
Hui Shi
1,
Han Luo
2,
Yawei Wei
3 and
Won-Sop Shin
4,*
1
Department of Forest Therapy, Chungbuk National University, Cheongju 28644, Republic of Korea
2
School of Journalism and Communications, Henan University of Technology, Zhengzhou 450001, China
3
Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
4
Department of Forest Science, Chungbuk National University, Cheongju 28644, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2024, 15(3), 498; https://doi.org/10.3390/f15030498
Submission received: 11 January 2024 / Revised: 5 March 2024 / Accepted: 6 March 2024 / Published: 8 March 2024
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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Abstract

:
Previous studies have reported that exposure to forest landscapes has many benefits on human physiological and psychological health, as well as effectiveness in reducing stress and improving mood depending on different types of landscape. This study examined the effects of different types of forest landscapes for indirect visual experiences on the physical and mental health of college students (N = 33). Three types of landscape images were selected, in which forest landscapes included vegetated landscapes and water features, and as a control, we set up images of urban landscapes without natural elements. Physiological and psychological assessment was performed before the experiment for each student, followed by each student being exposed consecutively to nine landscape images for 3 min (each type) and assessed after each exposure. The results showed that both forest landscapes decreased stress (p < 0.05 for all) and improved mood and self-esteem (p < 0.01 for all). In contrast, water landscapes showed a slightly higher impact on physical and mental health than vegetated landscapes, but there was no significant difference. Conversely, only for self-esteem, the response after viewing vegetated landscapes (VL, SD = 29.06 ± 3.38) was better than after water views (WL, SD = 28.21 ± 2.48). Despite significant differences between the two types of forest landscapes not being found in our findings, the benefits of forest landscapes were observed through understanding the health-promoting capacities of different forest landscapes.

1. Introduction

Since the previous century, there has been a global shift toward economic reform and expansion, paralleled by rapid industrial growth and extensive urbanization. The World Bank estimated in 2023 that 56% of the world’s population lived in urban areas, a figure projected to increase by 1.5 times to 6 billion by 2045 [1]. The swift pace of urban development has been a catalyst for economic and social advancement, yet it has also given rise to a host of environmental challenges and changes in lifestyle, such as local climate alteration [2,3,4,5], increased air and water pollution [6,7,8], a major reduction in natural vegetation production [9,10,11,12], and decreased ecosystem services [13]. These problems are eroding human physical and mental health and increasing exposure to environmental stressors. Urban environments today, characterized by their unceasing and diverse distractions, may hinder individuals’ ability to concentrate on vital matters or to attain states of relaxation [14,15]. This crisis was particularly pronounced among college students, who face multiple health risk factors [16,17,18,19,20,21]. Previous studies have shown that approximately half of the college student population reports significant levels of stress, anxiety, depression, or their combination [22], underscoring the growing need for effective stress management and healthy living approaches [23,24]. The restorative effect of natural environments is increasingly recognized in environmental psychology and public health domains [25,26,27]. A report in 1974 by M. Lalonde [28] introduced the “Health Field Concept”, which broadly considered environmental factors and identified them as one of the determinants of health promotion. The theory of therapeutic landscapes [29] explored why certain environments contribute to a healing sense of place. And building on this work, the contemplative landscape model by Olszewska-Guizzo et al. [30] focuses on how certain landscapes, such as forests, can be designed or identified to promote mental and physical recovery by providing a space for contemplation and relaxation. Moreover, the Attention Restoration Theory suggested that depleted directed attention caused mental fatigue and that exposure to natural environments facilitated the restoration of the capacity for directed attention [31]. Stress Reduction Theory suggests that the non-threatening natural setting as a restorative environment could evoke positive emotions and block negative emotions [32,33]. More and more studies have corroborated the restorative effects of the natural environment as a medium for people’s physical and mental health [25,26,34,35]. Restorative primarily translates into physiological and psychological advantages of exposure to the natural environment. That is, positive physiological and psychological changes resulting from exposure to forest landscapes have a restorative effect on people’s physical and mental health [36].
Forests have been studied a lot of times as typical restorative environments [37,38,39]. These studies directly proved or inferred that the forest environment was effective in relieving stress and depression, and individuals felt more relaxation, dynamism, and rest when exposed to the forest [31,40,41,42]. Studies focusing on the physiological effects of relaxation exposure to the forest space showed that exposure to the forest could decrease pre-frontal cortex cerebral blood flow, lower blood pressure and heart rate [39,40], enhance parasympathetic activity, inhibit sympathetic activity [42,43,44], decrease salivary cortisol stress hormone concentrations [42,43], and enhance anticancer proteins and natural killer cells activities [45,46], thereby enhancing immune performance. This was mainly due to various environmental elements in the forest, such as sufficient oxygen, phytoncides, sunlight, sound, etc. Additionally, studies have demonstrated that forest settings, as opposed to urban ones, enhance positive mood states, alleviate negative emotions like depression and anxiety [47,48,49], and improve specific psychological responses such as attention recovery, self-esteem, and life quality [50,51,52].
However, the capacity of forest environments to improve human health was influenced by landscape type and characteristics [31,53,54], which meant that some types or characteristics were more effective for health recovery. Green plants were more favored by people than other colored plants [55,56,57]. Nordh et al. [58] applied choice-based conjoint analysis to assess the restorative value of small urban parks, identifying landscape features such as grass, trees, and shrubs as key factors in restoration likelihood. A study of landscape types and landscape elements in urban parks showed that different landscape types elicit varied physiological and emotional reactions; landscapes mimicking natural mountain forests showed the highest restorative impacts [59], coniferous forests provided relaxation for the subjects, and broadleaved forests produced the most stable mood [60]. Furthermore, various specific landscape features, including “open view” [61], “stand density” [60,62], “planting with flower cover” [63], etc., have been identified as significant in creating restorative environments within each study’s specific context.
Similar to green landscapes, blue landscapes are also increasingly recognized for their therapeutic and public health benefits [31]. Bell et al. discovered that physical activity, social interaction, and psychological benefits were evident in people visiting blue spaces [64]. The research by Tang et al. [65] used fMRI to compare the rejuvenating effects of different landscapes (urban, mountain, forest, water), noting that water landscapes, unlike urban ones, increased neural activity in brain regions associated with attention, implying a stimulatory effect on the attention system. They found that in contrast to urban landscapes, viewing water landscapes was associated with increased brain neural activity in the attention area, suggesting that viewing water landscapes might stimulate the rest of the attention system. In addition, a study on seven different forest landscape types for stress relief showed that exposure to forest–water spaces, especially dynamic water landscapes, was more effective for psychological stress relief [66]. Moreover, a study of Han [67] on landscape preferences showed that the highest-ranked forest scenes contained water.
However, there are also differences in the effects of green and blue landscapes on physical and mental health. A study by Li et al. investigated the restorative effects of exposure to water, lawn, and topography landscape environments and showed significant differences between the three landscapes on participants’ blood pressure, brain activity, and mood states [59]. In addition, the effect of urban blue space on users’ health enhancement was prominent [68]. On the contrary, Zhao et al. [69] reported in their study that water features reduced the quality of restoration of vegetated pathway landscapes.
As a result of these studies, various types of landscapes have been found to affect the perceived degree of physical and mental health improvement. However, little evidence has been collected regarding specific landscape types in relation to the sympathetic nervous system and emotional states. In this study, we sought to discover the different restorative effects between vegetated landscapes in forests and water landscapes with vegetated elements. Therefore, diverse landscape settings including broadleaved forests, coniferous forests, mixed forests, topography forest landscapes, flower, open nature landscapes, and a variety of water features with vegetated elements were applied as experimental stimuli. This was carried out to improve our understanding of landscape types and elements using scientific and evidence-based research, aiming to identify the effects of these more resource-intensive natural environments in promoting health.
In recent years, virtual reality (VR) has been used a lot in improving health; nevertheless, VR offers a more realistic experience, but it also faces technical and logistical challenges. Numerous studies have explored and validated the differences between direct interviews and indirect exposures, finding that viewing pictures might, to some extent, substitute for on-site surveys [70,71,72]. Therefore, in this study, we used pictures as experimental stimuli, set the urban landscapes as opposed to restorative environments as negative stimuli, and used images to evaluate the physical and mental restorative effects during exposure to different types of landscape.
The aim of this study is to evaluate the differential impacts of purely vegetated forest landscapes and water landscapes with vegetated elements on stress reduction and mood improvement. We specifically address the following research questions: (1) Do purely vegetated landscapes (VL, included broadleaved, coniferous, mixed forests, and topography-influenced forest landscapes) and water landscapes (WL) featuring vegetative elements differ in their restorative effects on individuals? (2) Among these landscapes, do water features integrated with vegetation more effectively promote health than purely vegetated landscapes? This investigation seeks to enhance understanding of the health-promoting capacities of diverse natural environments through scientific and evidence-based research to inform decisions about landscape design and healthcare interventions.

2. Materials and Methods

2.1. Participants

The sample size was determined using G*Power 3.1 (University of Düsseldorf, Düsseldorf, Germany), considering the effect size was set to 0.25 (large effect size: 0.4, medium effect size: 0.25, small effect size: 0.1), the significance level was set to 0.05, and the β power was set to 0.8, with an anticipated dropout rate of 20%, resulting in a total requirement of 35 participants. Previous research indicates that college students experience a lot of stress, anxiety, and depression, which could lead to insomnia [16], alcoholism [17,18], and substance abuse [19,20]. Therefore, 33 (male 15, female 18, age: 19.82 ± 0.88) college students from Henan Agriculture University and Henan University of Animal Husbandry and Economy participated in the experiment. Each participant consented to the study by signing a research informed consent form prior to involvement. All participants were healthy adults aged 18 and above and had no major cardiovascular diseases or mental disorders. Participants were told to abstain from consuming beverages, such as tea, coffee, or alcohol that may lead to mental excitement for 12 h before the experiment, and were prohibited from strenuous exercise for 24 h before the experiment.

2.2. Experimental Materials

Based on the analysis of the landscape types obtained from the previous studies [55,56,57,58,59,60], we collected photographs of each type of landscape through web portals such as Google, sohu, and photography. Two typical landscape types of forest environment were selected: vegetated landscape and water landscape (contains vegetated elements). This study categorized vegetated landscapes into coniferous, broadleaved, and mixed forest landscapes and other elements like trails, overlooks, and understory based on vegetation type and restorative quality. The water landscape was subdivided into waterfall, river, lake, creek, etc., according to the water flow pattern. Images of the urban landscape without natural elements were selected to be the control landscape. Finally, as shown in Appendix A, a total of three types of landscapes were selected: urban image (buildings, alleyways, dwellings, overpasses, traffic, underground, shopping malls, markets, industrial areas); forest landscape (broadleaf forest, coniferous forest, mixed forest, flowers, forest deck road, forest trail, overlook, understory, grassland); and water landscape (mountain waterfall, forest waterfall, river waterfall, canal, river, lake, brook, stream, creek).

2.3. Measures

2.3.1. Physiological Indices

In this investigation, we aimed to assess the effects of visual exposure to natural environments on two key outcomes: stress levels and autonomic nervous system activity. The outcomes and their respective measures are detailed below:
Stress Levels: To evaluate stress levels, an outcome indicative of the sympathetic nervous system’s response to psychological stress, we measured salivary alpha-amylase (SAA) activity. Increases in SAA activity are directly correlated with heightened sympathetic nervous system activity, serving as a biomarker for stress to quantify stress levels [73,74]. For this purpose, salivary samples were collected using sublingual reagent paper. Participants were instructed to moisten their mouths and place the reagent paper under their tongues for 60 s before it was placed into the monitoring device for analysis. The soaked test paper was then inserted into an SAA monitor (Nipro, Osaka, Japan) to record the SAA activity.
Autonomic Nervous System Activity: Heart rate variability (HRV) was assessed as a physiological marker to measure the activity of the autonomic nervous system, encompassing both its sympathetic and parasympathetic components. High-frequency (HF) power, indicative of parasympathetic nervous system activity, is typically elevated during states of relaxation. Conversely, the Low Frequency-to-High Frequency (LF/HF) ratio reflects the balance between sympathetic and parasympathetic nervous system functions. To measure these indicators, the uBio Macpa (Biosense Creative, Seoul, Republic of Korea), a portable HRV-measuring instrument, was utilized. This device allows for the detection of the autonomic nervous system’s response to various environments through 2 min and 30 s of uninterrupted measurements.
By focusing on these outcomes (stress levels and autonomic nervous system activity), this study underscores the physiological responses to environmental stimuli. The use of SAA activity and HRV as outcome measures provides a nuanced understanding of how visual exposure to natural landscapes can influence psychological and physiological states.

2.3.2. Psychological Indices

In this study, we focused on evaluating several key psychological indices: mood, perceived stress, and self-esteem. To measure these indices, we employed various validated tools.
Mood: The primary index of mood was assessed using the Profile of Mood States (POMS), developed by Grove et al. in 1992 [75] and translated into a Chinese version [76]. POMS is a comprehensive tool that evaluates mood across seven sub-scales: Tension (T), Anxiety (A), Depression (D), Fatigue (F), Confusion (C), Vigor (V), and Esteem-Related Affect (E), encompassing a total of 40 questions [75]. To analyze the overall mood state changes in participants, we used the Total Mood Disturbance (TMD) score, calculated as follows:
TMD = T + A + D + F + C − (V + E) + 100
Perceived Stress: As a secondary outcome, we assessed participants’ perceived stress levels using the Perceived Stress Scale (PSS). This self-report tool, originally developed by Cohen et al. [77] and later translated into Chinese [78], consists of 10 items rated on a 5-point Likert scale from ‘never’ to ‘very often’ with total scores ranging from 0 to 40, with higher scores indicating greater perceived stress. Adequate reliability and validity were demonstrated [77,79].
Self-Esteem: Finally, self-esteem was measured using the Self-Esteem Scale (SES), developed by Rosenberg and translated into Chinese by Yu and Ji [80]. This scale is used to rate an individual’s overall feelings about self-worth and self-acceptance [81]. This assessment comprises 10 items rated on a 4-point Likert scale, featuring both positive and negative statements.

2.4. Experimental Design

The participants received information in a waiting room about the purpose and procedures of the experiment before the experiment. After obtaining informed consent for the experiment, each participant was subsequently sent to the experimental room. To acclimate participants to the experimental setting, they were asked to observe a gray background for 60 s in a calm state. Then, we collected participants’ demographic characteristics and they performed tests for heart rate variability (HRV), salivary α amylase (SAA), and psychological indicators as baseline measurements. Attention Restoration Theory (ART) proposes that exposure to forest landscape images for 3 to 5 min can yield comparable mental and physical restoration effects to direct forest visits, suggesting that even brief visual engagement with nature imagery can facilitate recovery from mental fatigue and stress. Therefore, after a 1 min rest following baseline measurements, each participant was separately exposed to urban landscape images, vegetated landscape images, or water landscape images for 180 s each, while maintaining a sitting position. Instantly following the completion of each viewing, the participants were asked to respond to HRV and SAA measurements and answer the POMS, SES, and PSS scales.
Additionally, salivary amylase shows diurnal variation, with a relatively steady period between 10:30 a.m. and 5 p.m. To mitigate the influence of food intake, participants were instructed not to consume any food one hour prior to testing. The experiment was therefore conducted between 10:30 a.m. and 5:00 p.m. The experimental design and measurement procedures are outlined in Figure 1.

2.5. Analysis and Statistics

Statistical analysis was conducted using SPSS 26.0 (IBM corporation, Armonk, NY, USA). Initially, participants’ demographic characteristics were examined through a descriptive analysis. Second, normality testing was performed using the Shapiro–Wilk test. Then, for variables that satisfied normality, a paired t-test was used to compare baseline and post-tests of each landscape, while the variables not satisfying normality were examined using the Wilcoxon signed-rank test. Third, physiological and psychological differences between the three types of landscapes were further discerned by a one-way repeated-measures ANOVA with a post hoc (Bonferroni) test. As a result, the threshold for statistical significance was established at p < 0.05.

3. Results

3.1. Physiological Measurement

3.1.1. Salivary α Amylase (SAA)

The results between baseline and post-tests of salivary α amylase concentration for each landscape type are presented in Table 1. As shown, there was a significant decrease after viewing vegetated landscapes (VL, baseline: 21.85 ± 11.46, post: 18.06 ± 8.59, t = 2.491, p = 0.018, paired t-test) and water landscapes (WL, baseline: 21.85 ± 11.46, post: 17.55 ± 9.70, t = 2.211, p = 0.034, paired t-test). However, no significant differences were shown before and after viewing urban landscapes (UL, baseline: 21.85 ± 11.46, post: 23.42 ± 13.45, Z = −0.44, p = 0.661, Wilcoxon signed-rank).
Also, one-way repeated-measures ANOVA of salivary α amylase concentration showed that there was a significant difference in salivary α amylase levels across the three types of landscape. The main effect was F = 3.391, p = 0.030, ηp2 = 0.202. Mauchly’s test indicated that χ2 = 11.414, p = 0.003. As illustrated in Table 2, post hoc analyses using Bonferroni correction revealed a significant increase in salivary amylase levels after viewing vegetated landscapes (p = 0.027) and water landscapes (p = 0.039) compared to urban landscapes, while between vegetated (M ± SD = 18.06 ± 8.59) and water (M ± SD = 17.55 ± 9.70), there was a small and non-significant decrease (p = 1.000).

3.1.2. HRV

The results of High-frequency (HF) for each landscape by paired t-tests between baseline and post-tests are presented in Table 3. As shown, there was a significant increase after viewing vegetated landscapes (baseline: 6.90 ± 0.43, post: 7.03 ± 0.53, t = −2.293, p = 0.029) and water landscapes (baseline: 6.90 ± 0.43, post: 7.08 ± 0.49, t = −2.914, p = 0.006). However, no significant differences were shown before and after viewing urban landscapes (baseline: 6.90 ± 0.43, post: 6.89 ± 0.49, t = 0.374, p = 0.711).
Also, one-way repeated-measures ANOVA of HF showed that there was a significant difference across the three types of landscape. The main effect was F = 4.451, p = 0.015, ηp2 = 0.122. Mauchly’s test indicated that χ2 = 2.251, p = 0.324. As illustrated in Table 4, post hoc analyses using Bonferroni correction revealed a significant increase in HF after viewing water landscapes compared to urban landscapes (p = 0.044), while between vegetated (M ± SD = 7.03 ± 0.53) and water (M ± SD = 7.08 ± 0.49), there was a small and non-significant increase (p = 1.000), as well as between vegetated and urban landscapes (p = 0.054).
The results of the Wilcoxon signed-rank test between baseline and post-tests of Low Frequency-to-High Frequency (LF/HF) for each landscape type are presented in Table 5. As shown, there was a significant decrease after viewing water landscapes (Z = −2.353, p = 0.019). However, no significant differences were shown before and after viewing urban landscapes (Z = −1.213, p = 0.225) and vegetated landscapes (Z = −0.269, p = 0.204).
Also, one-way repeated-measures ANOVA of LF/HF showed that there was a significant difference across the three types of landscape. The main effect was F = 6.559, p = 0.003, ηp2 = 0.170. Mauchly’s test indicated that χ2 = 5.479, p = 0.065. As illustrated in Table 6, post hoc analyses using Bonferroni correction revealed a significant increase in LF/HF after viewing vegetated landscapes (p = 0.028) and water landscapes (p = 0.011) compared to urban landscapes, while between vegetated (M ± SD = 1.15 ± 0.10) and water (M ± SD = 1.12 ± 0.08), there was a small and non-significant decrease (p = 0.546).

3.2. Psychological Measurement

3.2.1. Profile of Mood Scale (POMS)

The differences between baseline and post-tests of Total Mood Disturbance scores of POMS for each landscape type are presented in Table 7. As shown, there was a significant decrease after viewing VL (t = 6.508, p < 0.001) and WL (t = 6.505, p < 0.001) than UL. And we can see in the sub-scales of POMS, significant positive changes were also demonstrated. The Tension (VL: t = 3.690, p = 0.001; WL: t = 5.066, p < 0.001), Anger (VL: Z = −3.888, p < 0.001; WL: Z = −3.932, p < 0.001), Confusion (VL: t = 3.516, p = 0.001; WL: t = 4.656, p < 0.001), Depression (VL: Z = −3.058, p = 0.002; WL: Z = −3.699, p < 0.001), Fatigue (VL: Z = −3.118, p = 0.002; WL: Z = −3.683, p < 0.001), and Vigor (VL: t= −2.082, p = 0.45; WL: t = −2.080, p = 0.046) were decreased after viewing VL and WL. And the Esteem (VL: Z = −2.289, p = 0.022) was increased after viewing VL. However, there were no significant differences between baseline and post-test of POMS for viewing ULs.
Also, repeated-measures ANOVA of POMS showed that there were significant differences in all POMS scores across the three types of landscape. The main effect of TMD was F = 13.725, p < 0.001, ηp2 = 0.470; Tension was F = 8.094, p = 0.001, ηp2 = 0.343; Confusion was F = 8.078, p = 0.002, ηp2 = 0.343; Vigor was F = 5.836, p = 0.007, ηp2 = 0.274; Anger was F = 12.929, p = 0.002; Depression was F = 17.5709, p < 0.001; Fatigue was F = 11.574, p = 0.003; Esteem was F = 7.582, p = 0.023. Post hoc analyses using Bonferroni correction revealed a significant difference after viewing VL (TMD: p < 0.001; Tension: p = 0.020; Confusion: p = 0.004; Vigor: p =0.005; Anger: p =0.003; Depression: p = 0.026; Esteem: p = 0.006) and WL (TMD: p < 0.001; Tension: p = 0.001; Confusion: p = 0.001; Vigor: p = 0.012; Anger: p = 0.002; Depression: p = 0.003; Fatigue: p = 0.002; Esteem: p = 0.027) than UL. However, all of the POMS scores were different between vegetated and water landscapes, but there was no significance (Table 8).

3.2.2. SES

The results of SES for each landscape by paired t-tests between baseline and post-tests are presented in Table 9. As shown, there was a significant increase after viewing vegetated landscapes (t = −3.695, p = 0.001). However, no significant differences were shown before and after viewing urban landscapes (t = −1.428, p = 0.163) and water landscapes (t = −1.382, p = 0.177).
Also, one-way repeated-measures ANOVA of SES showed that there was a significant difference in SES across the three types of landscape. The main effect was F = 3.922, p = 0.030, ηp2 = 0.202. Mauchly’s test indicated that χ2 = 0.843, p = 0.656. As illustrated in Table 10, post hoc analyses using Bonferroni correction revealed a significant increase in SES after viewing VL (p = 0.036) compared to UL.

3.2.3. PSS

The results of HF for each landscape by paired t-tests between baseline and post-tests are presented in Table 11. As shown, there was a significant increase after viewing vegetated landscapes (t = 3.115, p = 0.004) and water landscapes (t = 2.487, p = 0.018). However, no significant differences were shown before and after viewing urban landscapes (t = −0.437, p = 0.665).
Also, one-way repeated-measures ANOVA of PSS showed that there was a significant difference in PSS across the three types of landscape. The main effect was F = 7.283, p = 0.003, ηp2 = 0.320. Mauchly’s test indicated that χ2 = 0.806, p = 0.668. As illustrated in Table 12, post hoc analyses using Bonferroni correction revealed a significant decrease in PSS after viewing VL (p = 0.004) and WL(p = 0.007) compared to UL, while this decrease was shown between vegetated (M ± SD = 16.73 ± 6.01) and water (M ± SD = 16.61 ± 5.55) landscapes, but there was no significance (p = 1.000).

4. Discussion

4.1. Physiological Effects

In this study, salivary α amylase (SAA) and heart rate variability (HRV) were employed as biomarkers to evaluate the influence of viewing forest images on the autonomic nervous system of college students. Under experimental conditions, compared to baseline measurements, both paired-sample t-tests and Wilcoxon signed-rank tests revealed that exposure to landscapes featuring forest vegetation and water scenarios, a notable decrease in salivary α amylase and a significant increase in parasympathetic activity post-exposure to these environmental settings. However, there was no significant difference between the vegetated and water landscapes.
Notably, LF/HF ratios declined significantly only after viewing water landscapes, while changes associated with vegetated landscapes were minor and not statistically significant. These outcomes are consistent with extensive prior research [35,54,67,82,83,84,85], suggesting a beneficial influence of forest landscapes on autonomic nervous system function. Furthermore, our study explored the comparative restorative impacts of vegetated versus water views. Water views demonstrated a marginally greater influence on physiological recovery than vegetated landscapes, though the disparity between the two was not statistically notable. This finding echoes other studies [56,86] indicating minimal variance in restorative effects among different natural environments. Patrik and Ulrika’s research [87] supports the notion that not only visual but also other sensory experiences shape the psychological response to green environments. In accordance with Stress Reduction Theory (SRT), humans are innately drawn to safe, natural settings abundant with trees, water, and varied vegetation, eliciting immediate positive responses [32,88]. Additional studies [89,90] have emphasized the stress-relieving properties of water features, which might be potentially linked to a preference for blue spaces [67].

4.2. Psychological Effects

Our study indicated that viewing both vegetated and water landscapes significantly ameliorated negative emotions, improved activity, and reduced stress. However, comparing the three types of landscape, the impact of water landscapes on emotion improvement and stress relief was marginally greater than that of vegetated landscapes, but the difference was not significant. In addition, self-esteem was significantly boosted only after exposure to the vegetated landscapes.
Tyrväinen et al. [85] noted that environments with distinct characteristics, such as densely vegetated forests and natural reserves, can alleviate stress and fatigue. This could be attributed to negative emotions being more responsive to environmental changes, which may swiftly and instinctively mitigate unpleasant emotions [90]. Blue spaces, in particular, are considered superior for mental restoration [91,92], possibly reflecting a general preference for such environments. Notably, only the ‘fatigue’ dimension showed a significant reduction post-exposure to water features. We hypothesize that the influence of water on self-perception may surpass that of green environments [93,94] and that aquatic environments like beaches and rivers are effective in diminishing negative emotions [95]. This observation was corroborated by PSS measurements.
Moreover, improvements in positive mood were significant in both vegetated and water landscapes. An increase in ‘Vigor’ was noted after exposure to water landscapes compared to vegetated ones, although this change was not significant. This could be attributed to the fact that the monotonous green of vegetation might appear unengaging and visually unappealing [96], whereas the diversity in water landscapes may evoke more pleasant feelings [27,31]. Conversely, vegetated landscapes were more effective than water landscapes in enhancing ‘self-esteem’, a finding exclusively noted in SES measurements. Zhang’s research [97] suggested that visual stimuli from forest landscapes, particularly the green color and tree shapes, are influential in boosting self-esteem. This finding underscores the unique contribution of greenery in forest landscapes to self-esteem restoration.

4.3. Limitations

This study has several limitations. Firstly, while it explored the restorative effects of in-forest vegetated and water landscapes, it did not examine the nine landscape elements within each type individually. In real-world settings, different types of environments may vary in their effectiveness for physical and mental health restoration. Not just visual elements but auditory elements and even olfactory elements may also affect recovery. Future research should expand on this aspect to provide a more comprehensive understanding of the restorative effects of forest environments and explore these nuances in more detail.
Secondly, the absence of a control group in this study may affect its internal validity and increase the potential for bias. Future studies would benefit from employing a randomized controlled trial design to mitigate these concerns and enhance the reliability of findings.
Thirdly, despite college students typically being a stressed population, in reality, other populations also experience a variety of stressors. In the future, the long-term effects of such indirect nature exposure on various populations such as seniors, manual laborers, stay-at-home moms, etc., could provide valuable insights into its efficacy as a sustainable intervention.
Lastly, images were selected as visual stimuli for this study, and although many previous studies have demonstrated the effectiveness of forest landscape images for stress relief, results may vary depending on the image accuracy, subject preference, and exposure order.
Future research could build on our findings, exploring the optimal types and qualities of images that most effectively convey the restorative properties of natural environments. This could include investigating aspects such as image resolution, realism, the inclusion of auditory elements, or even the use of emerging technologies like virtual reality (VR) to provide a more immersive and realistic comparison between vegetated landscapes and water-containing forest landscapes. Such investigations are crucial for identifying the visual and sensory characteristics that most significantly contribute to the restorative experience. Moreover, examining the long-term effects of such indirect nature exposure on various populations could provide valuable insights into its efficacy as a sustainable mental health intervention. Simulating realistic nature experiences could revolutionize approaches to mental health care, providing accessible, effective solutions for stress and mood management.

5. Conclusions

In this research, aiming to provide foundational insights into the benefits of visual exposure to different types of landscapes, we explored the physical and psychological effects resulting from visual exposure to different landscape environments. We specifically investigated whether predominantly vegetated landscapes and water features with vegetation elements exhibit distinct restorative effects on individuals and whether there are differences between the two in their capacity to foster physical and psychological recovery. Our findings illuminate the nuanced benefits of visual exposure to diverse landscape environments, particularly emphasizing the restorative potential of forest landscapes that integrate both vegetation and water elements. We observed that both types of landscapes (vegetated and water features with vegetation elements) contribute positively to human health, enhancing both physiological and psychological well-being. Notably, while water features presented a marginally higher restorative advantage, the difference in recovery effects between purely vegetated landscapes and those including water was not statistically significant. This outcome suggests that forest landscapes, regardless of the inclusion of water, are inherently beneficial, though the presence of water elements tends to augment emotional improvement and stress alleviation slightly more.
In the context of forest therapy, our research underscores the potential of forest landscapes, both with and without water features, as therapeutic environments for enhancing human well-being. This insight can guide the management and utilization of forest landscape resources, suggesting that the inclusion of diverse landscape features, including both vegetation and water, can be beneficial in landscape design and healthcare interventions. Urban planners, landscape architects, and public health policymakers can leverage these findings to design green spaces that optimize the health benefits for the community, particularly for stress-prone populations such as college students. Furthermore, this research could inspire the development of more targeted forest therapy programs that capitalize on the specific elements of forest landscapes that are most effective in promoting physical and mental recovery.

Author Contributions

Conceptualization, H.S.; methodology, H.S., H.L. and Y.W.; investigation, H.L.; data curation, H.S. and H.L.; writing-original draft, H.S.; writing-review and editing, H.S., Y.W.; project administration and supervision, W.-S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We would like to thank the participants in the experiment, and also thank Xiangnan Lin from Chungbuk National University for his donation of materials for experiment and assistance during the preparation of this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Forests 15 00498 g0a1
Figure A1. Urban landscape images.
Figure A1. Urban landscape images.
Forests 15 00498 g0a1
Forests 15 00498 g0a2
Figure A2. Vegetated landscape images.
Figure A2. Vegetated landscape images.
Forests 15 00498 g0a2
Forests 15 00498 g0a3
Figure A3. Water landscape images.
Figure A3. Water landscape images.
Forests 15 00498 g0a3

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Forests 15 00498 g001
Figure 1. Experimental process chart. (A): experimental timeline; (B): exposure to visual stimuli; (C): measurement of HRV; (D): measurement of SAA; (E): completion of psychological questionnaires.
Figure 1. Experimental process chart. (A): experimental timeline; (B): exposure to visual stimuli; (C): measurement of HRV; (D): measurement of SAA; (E): completion of psychological questionnaires.
Forests 15 00498 g001
Table 1. Effect of viewing landscapes on salivary α amylase concentration.
Table 1. Effect of viewing landscapes on salivary α amylase concentration.
MeanSDtp
VLbaseline21.8511.462.4910.018 *
post18.068.59
WLbaseline21.8511.462.2110.034 *
post17.559.70
MeanSDZp
ULbaseline21.8511.46−0.44b0.661
post23.4213.45
UL: urban landscape; VL: vegetated landscape; WL: water landscape; b: the value based on negative rank. N = 33. * p < 0.05.
Table 2. A comparison of three landscape types in salivary α amylase concentration.
Table 2. A comparison of three landscape types in salivary α amylase concentration.
M ± SDFpηp2Bonferroni
UL23.42 ± 13.453.931b0.030 *0.202UL > VL,WL
VL17.45 ± 8.27
WL17.09 ± 9.60
UL: urban landscape; VL: vegetated landscape; WL: water landscape; b: exact statistic. N = 33. * p < 0.05.
Table 3. Effect of viewing landscapes on HF.
Table 3. Effect of viewing landscapes on HF.
MeanSDtp
ULbaseline6.900.430.3740.711
post6.890.49
VLbaseline6.900.43−2.2930.029 *
post7.030.53
WLbaseline6.900.43−2.9140.006 **
post7.080.49
UL: urban landscape; VL: vegetated landscape; WL: water landscape. N = 33. * p < 0.05, ** p < 0.01.
Table 4. A comparison of three landscape types in HF.
Table 4. A comparison of three landscape types in HF.
M ± SDFpηp2Bonferroni
UL6.89 ± 0.494.4510.015 *0.122UL < WL
VL7.03 ± 0.53
WL7.08 ± 0.49
UL: urban landscape; VL: vegetated landscape; WL: water landscape. N = 33. * p < 0.05.
Table 5. Effect of viewing landscapes on LF/HF.
Table 5. Effect of viewing landscapes on LF/HF.
MeanSDZp
ULbaseline1.160.09−1.213b0.225
post1.180.09
VLbaseline1.160.09−0.269c0.204
post1.150.10
WLbaseline1.160.09−2.353b0.019 *
post1.120.08
UL: urban landscape; VL: vegetated landscape; WL: water landscape. N = 33; b: the value based on negative rank; c: the value based on positive rank. * p < 0.05.
Table 6. A comparison of three landscape types in LF/HF.
Table 6. A comparison of three landscape types in LF/HF.
M ± SDFpηp2Bonferroni
UL1.18 ± 0.096.5590.003 **0.170UL > VL,WL
VL1.15 ± 0.10
WL1.12 ± 0.08
UL: urban landscape; VL: vegetated landscape; WL: water landscape. N = 33. ** p < 0.01.
Table 7. Effect of viewing landscapes on POMS.
Table 7. Effect of viewing landscapes on POMS.
MeanSDtp
TMDULbaseline111.0615.120.8220.417
post109.3314.58
VLbaseline111.0615.126.508<0.001 ***
post100.2115.38
WLbaseline111.0615.126.505<0.001 ***
post98.1514.70
TensionULbaseline6.672.821.2260.229
post6.062.96
VLbaseline6.672.823.6900.001 **
post4.703.72
WLbaseline6.672.825.066<0.001 ***
post4.153.05
VigorULbaseline10.093.010.7630.451
post9.484.14
VLbaseline10.093.01−2.0820.045 *
post11.362.99
WLbaseline10.093.01−2.0800.046 *
post11.583.50
ConfusionULbaseline6.303.010.8090.425
post5.972.51
VLbaseline6.303.013.5160.001 **
post4.732.88
WLbaseline6.303.014.656<0.001 ***
post4.333.05
MeanSDZp
AngerULbaseline4.633.28−1.090b0.056
post4.003.39
VLbaseline4.633.28−3.888b<0.001 ***
post2.673.27
WLbaseline4.633.283.932b<0.001 ***
post2.452.56
FighterULbaseline6.063.39−1.163b0.245
post5.553.20
VLbaseline6.063.39−3.118b0.002 **
post4.583.32
WLbaseline6.063.39−3.683b<0.001 ***
post3.912.84
DepressionULbaseline4.243.83−0.640b0.522
post3.823.09
VLbaseline4.243.83−3.058b0.002 **
post2.703.04
WLbaseline4.243.83−3.699b<0.001 ***
post2.372.85
EsteemULbaseline6.762.24−0.680b0.497
post5.972.51
VLbaseline6.762.24−2.289c0.022 *
post4.732.88
WLbaseline6.762.24−1.953c0.051
post4.333.05
TMD: Total Mood Disturbance. UL= urban landscape; VL= vegetated landscape; WL= water landscape; b: the value based on negative rank; c: the value based on positive rank. N = 33. *** p < 0.001, ** p < 0.01, * p < 0.05.
Table 8. A comparison of three landscape types in POMS scores.
Table 8. A comparison of three landscape types in POMS scores.
Sub-ScaleTypeM ± SDFpηp2Bonferroni
TMDUL109.33 ± 14.5813.725b<0.001 ***0.470UL > VL,
WL
VL100.21 ± 15.38
WL98.15 ± 14.70
TensionUL6.06 ± 2.968.094b0.001 **0.343UL > VL,
WL
VL4.70 ± 3.72
WL4.15 ± 3.05
ConfusionUL5.97 ± 2.518.078b0.002 **0.343UL > VL,
WL
VL4.73 ± 2.88
WL4.33 ± 3.05
VigorUL9.48 ± 4.415.836b0.007 **0.274UL < VL,
WL
VL11.36 ± 2.99
WL11.58 ± 3.56
AngerUL4.00 ± 3.397.368b0.002 **0.322UL > VL,
WL
VL2.67 ± 3.28
WL2.45 ± 2.56
DepressionUL3.82 ± 3.076.215b0.005 **0.286UL > VL,
WL
VL2.70 ± 3.04
WL2.36 ± 2.85
FatigueUL5.55 ± 3.207.420b0.002 **0.324UL > WL
VL4.58 ± 3.32
WL3.91 ± 2.84
EsteemUL6.58 ± 2.747.3890.001 **0.188UL < VL,
WL
VL7.79 ± 2.07
WL7.48 ± 2.15
UL = urban landscape; VL = vegetated landscape; WL = water landscape; b: exact statistic. ** p < 0.01 *** p < 0.001.
Table 9. Effect of viewing landscapes on SES score.
Table 9. Effect of viewing landscapes on SES score.
MeanSDtp
ULbaseline27.483.58−1.4280.163
post28.123.29
VLbaseline27.483.58−3.6950.001 **
post29.063.38
WLbaseline27.483.58−1.3820.177
post28.212.48
UL: urban landscape; VL: vegetated landscape; WL: water landscape. N = 33. ** p < 0.01.
Table 10. A comparison of three landscape types in SES score.
Table 10. A comparison of three landscape types in SES score.
M ± SDFpηp2Bonferroni
UL28.12 ± 3.293.922b0.030 *0.202UL < VL
VL29.06 ± 3.38
WL28.21 ± 2.48
UL: urban landscape; VL: vegetated landscape; WL: water landscape; b: exact statistic. N = 33. * p < 0.05.
Table 11. Effect of viewing landscapes on PSS score.
Table 11. Effect of viewing landscapes on PSS score.
MeanSDtp
ULbaseline18.185.80−0.4370.665
post18.365.95
VLbaseline18.185.803.1150.004 **
post16.736.01
WLbaseline18.185.802.4870.018 *
post16.615.55
UL: urban landscape; VL: vegetated landscape; WL: water landscape. N = 33. ** p < 0.01. * p < 0.05.
Table 12. A comparison of three landscape types in PSS score.
Table 12. A comparison of three landscape types in PSS score.
M ± SDFpηp2Bonferroni
UL18.36 ± 5.957.283b0.003 **0.320UL > VL WL,
VL16.73 ± 6.01
WL16.61 ± 5.55
UL: urban landscape; VL: vegetated landscape; WL: water landscape; b: exact statistic. N = 33. ** p < 0.01.
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Shi, H.; Luo, H.; Wei, Y.; Shin, W.-S. The Influence of Different Forest Landscapes on Physiological and Psychological Recovery. Forests 2024, 15, 498. https://doi.org/10.3390/f15030498

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Shi H, Luo H, Wei Y, Shin W-S. The Influence of Different Forest Landscapes on Physiological and Psychological Recovery. Forests. 2024; 15(3):498. https://doi.org/10.3390/f15030498

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Shi, Hui, Han Luo, Yawei Wei, and Won-Sop Shin. 2024. "The Influence of Different Forest Landscapes on Physiological and Psychological Recovery" Forests 15, no. 3: 498. https://doi.org/10.3390/f15030498

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