Evaluation of Rural Healing Landscape DESIGN Based on Virtual Reality and Electroencephalography

Abstract

From the user’s perspective, emotional elements are increasingly being used in design. Researchers have indicated that healing landscapes in rural areas play a positive role in soothing human emotions. In this study, a landscape with healing functions was designed, and 32 subjects experienced emotions in a virtual reality (VR) scene while their 32-channel electroencephalography (EEG) signals were collected. This study compared the brain responses with and without the presence of healing landscape elements and conducted correlation coefficient analysis using eight different regression prediction models to examine the relationship between security, comfort, positivity, and corresponding healing landscape elements. The results show significant improvements in emotions of security, comfort, and positivity post-exposure to the landscape design, especially with certain elements, such as seating, shrubs, and tree pools. EEG data indicate enhanced emotional and cognitive states, particularly relaxation, with increased activity in specific brain regions. The decision tree regression model is the most suitable for our data. It reveals strong correlations between specific healing landscape elements and emotional responses. In the comfort category, “shrubs” show the highest correlation (R² = 0.82), while in the security category, “trees” have the highest correlation (R² = 0.77). Similarly, in the positivity category, “trees” again exhibit the highest correlation (R² = 0.71) with EEG data, indicating their significant impact on these emotional dimensions. This study demonstrates the importance of using scientific methods, such as EEG technology, to validate the principles of emotional design and also underscores the role of green environments in enhancing psychological health and emotional comfort.

1. Introduction

Urban modernization not only brings new technology, new industries, and new services, but it also brings stress, anxiety, mental tension, and mental subhealth. “Healing” has become a hot word for social groups to receive relief and decompression. The green landscape is the most acceptable color for people, and it has a special effect on relieving eye fatigue. Therefore, more and more people start to go to the countryside to get close to nature and seek spiritual care. Many scholars have explored and researched the healing landscape from different aspects. A healing landscape is defined as a material means of utilizing the landscape environment and the natural atmosphere created to relieve people’s psychological and spiritual pressure. Based on the efficacy of “healing”, special landscapes are selected and scientifically configured to meet the requirements of the human psyche so as to give people the physiological benefits of feedback.

This study examines the impact of healing landscapes on human emotions, particularly findings that rural healing landscapes have a positive effect on soothing human emotions, and a growing trend of incorporating emotional aspects in design, particularly within the context of user experience. Therefore, we created a healing landscape in virtual reality (VR), which was experienced by 32 subjects. During this experience, their 32-channel electroencephalography (EEG) signals were recorded to monitor brain responses. The analysis compared brain responses to scenarios with and without healing landscape elements to determine their effect. Two landscape elements and tree pools were found to elicit strong positive emotions and had the most significant healing effects. The EEG signals support the subjective report by showing an increase in alpha-wave activation over beta-waves at the F8 electrode in the frontal region and the POz electrode in the parietal region when subjects were exposed to elements of the therapeutic landscape.

1.1. Design and Emotion

In the evolving landscape of design, there is a burgeoning recognition of the user’s perspective as a pivotal aspect, particularly in the application of emotional elements [1,2,3,4]. This user-centric approach not only aligns with ergonomic and aesthetic considerations but also delves into the psychological impact of design on human emotions and well-being [4,5,6]. The use of emotional elements in design transcends mere functionality, aiming to evoke specific feelings and create a profound user experience. Recent studies underscore the positive influence of healing landscapes. The “Stress Reduction Theory” proposed by Ulrich [7], along with the “Attention Restoration Theory” by Kaplans [8,9], provides strong support for the healing effects of natural environments. Bratman et al. found improved performance in memory and attention tasks in subjects who walked through an arboretum compared to an urban setting [10]. Taylor et al.’s study linked views of nature from home with improved concentration, impulse inhibition, and the delay of gratification in children [11]. Ulrich’s exploratory studies showed that viewing natural scenes can increase positive effects and decrease negative feelings such as fear, anxiety, and sadness. This was contrasted with increased negative emotions in subjects viewing urban landscapes [12,13].

1.2. Healing Landscapes Design and Emotions

In the study of healing landscape design, Saeedi designed a sanatorium using the rehabilitation landscape method, focusing on providing natural landscapes or naturalistic strategies, horticultural therapy, and aromatherapy to enhance the healing level of the sanatorium [14]. Saeedi by analyzing the deficiencies in the existing literature, introduced the importance of native healing landscapes in elderly care facilities, exploring the role of gardens in Australian elder care facilities. He advocates that future designs should pay attention to and incorporate residents’ native healing landscapes into elderly care gardens [14]. Zhenhong Yang used the Mindwave monitor and recovery scale to evaluate the healing function of urban parks in Chengdu, exploring the impact of audio–visual combined scenes in urban parks on healing effects, providing empirical evidence for park design and planning that considers audio–visual healing effects [15]. Xu Leiqing introduced the concept of healing space into street space design against the backdrop of urban renewal. Through a literature review, he defined healing streets and established a healing street model, analyzed its association with healthy streets, and proposed some ideas for the healing direction of community street renewal [16]. Currently, there are many studies on healing environments with diverse methodologies, yet there is a lack of a unified research framework, which poses difficulties for further research [17]. At the same time, research on healing environments is mostly limited to medical and healthcare facilities. In fact, various other living environments that people come into contact with daily, such as certain buildings, parks, and green spaces, are also important places for enhancing human physical health and emotional well-being [18]. Additionally, there is a lack of standards and mechanisms for evaluating healing environments as well as a long-term lack of experimentation.

1.3. Healing Landscape Design and VR

In the design of rural healing landscapes, the application of virtual reality (VR) technology provides a new dimension to design. VR can be used to create and simulate natural environments, allowing designers to present and test their design ideas in a highly realistic manner before actual construction. This approach makes the design process more efficient and precise, as it enables designers to identify and resolve potential issues before they are manifested in the real world [19]. It evaluates the effectiveness of gardens as a therapeutic intervention to enhance clinical outcomes in patients with Alzheimer’s disease (AD) and dementia. The paper also reviews the innovative application of technologies such as virtual reality (VR) alongside nature to aid cognitive rehabilitation in these patients. Technological advancements, including VR and 3D simulation technologies, have been used to reduce anxiety and agitation in AD patients [20]. Lin et al.’s research aims to build a healing environment using VR to reduce stress levels caused by the prolonged period of the COVID-19 pandemic. The healing environment is created based on several theories about healing principles [21]. Juliantino et al. researched the importance of VR technology for the innovation of landscape design, especially in the context of the convergence of three networks and the construction of the Internet of Things, promoting the rapid development of a digital landscape garden design in China [22]. Sun et al.’s study focuses on landscape healing for subhealth individuals in the National High-Tech Zone. It involves creating a digital roaming landscape using Unity 2019 and inviting 91 subjects with a history of mental subhealth diseases to participate in an immersive experience [23]. It discusses the importance of studying and exploring the application of VR technology in landscape garden design, especially in the current environment of triple network integration and the Internet of Things, to promote and facilitate the rapid development of a digital landscape garden design in China [24].

1.4. Landscape Design and EEG

One study used EEG physiological signal monitoring techniques to gain new insights into how people perceive architectural environment features and design [25]. Olszewska et al. [26] used EEG to examine how different levels of green cover visible from various floors affected 29 healthy residents. The results indicated that more green cover in their view led to brainwave patterns associated with positive emotions, motivation, and attention, particularly on higher floors. Mavros et al. [27] study examines the psychological effects of physical (outdoor/indoor) and social (crowded/uncrowded) environments on healthy young adults using a lab experiment with mobile EEG and EDA measurements during active walking. Participants watched videos of different environments, and the results showed that green spaces were perceived as calmer and more positive, reducing attentional demands. Shemesh et al. [28] investigated human reactions to spaces with different geometries, examining affective responses to architecture. Shin et al. [29] studied the impact of direct/indirect lighting in residential environments on emotions and brain activity. Vecchiato et al. [30] explored the EEG correlations of sensorimotor integration and embodiment during the appreciation of virtual architectural environments. Vaquero-Blasco et al. [31] examined virtual reality as an alternative to chromotherapy rooms for stress relief. Chang and Chen [32] focused on human responses to window views and indoor plants in the workplace. Ergan et al. [33] quantified human experience in architectural spaces using virtual reality and body sensor networks. Ha and Park’s [34] study evaluates the impact of color scheme and illuminance changes on color preference and prefrontal EEG alpha and beta signals. The results showed that illuminance significantly influenced psychological responses. Wen and Aris [35] analyze stress features using the power ratio of EEG frequency bands, specifically alpha to beta and theta to beta. Results revealed that alpha/beta and theta/beta ratios are negatively correlated with stress.

2. Materials and Methods

2.1. Experimental Design and Sample

The experiment used a within-subjects approach, where the healing landscape design emotional state was manipulated into two conditions (before the healing design conditions and after the healing condition). Participants were 32 students (13 male; age = 25 ± 5 years 19 female; age = 25 ± 5 years) recruited through our student subject pool; 115 participants were restricted to people determined to have healthy central nervous and autonomic nervous system status. Sensitivity analysis for this sample size was conducted using G*power. This analysis was performed considering a t-test for within-subjects design—with two conditions (before the healing design conditions and after the healing condition) of 0.05 and power of 0.8. Thus, our sample can detect the effects of medium or large size. This study was approved by the Institutional Review Board of Hebei Engineering University (protocol code BER-YXY-2023031, approved on 10 June 2023), and the participants read and provided written informed consent.

2.2. Landscape Elements Design

In the landscape design of rural public space, we analogized to the urban “landscape design of residential areas” (Liu Yali) [36], which divides the spatial landscape elements into hard landscape elements, soft landscape elements, and landscape facilities. The hard landscape elements include road pavement, tree pools, landscape structures, etc.; the soft landscape elements include water bodies and vegetation; and the landscape facilities elements include service facilities, lighting facilities, and play and fitness facilities. The existing landscape elements in the plaza were extracted based on the above definition, including entrance signage, road paving, landscape vignettes, tree pools, low wall, trees, shrubs, seating, and lighting, totaling 9 items. The above extracted landscape elements were designed and transformed in terms of quantity, material, color, form, and richness [37]. Then, the current status of the village center square and adjacent residential buildings were restored and modeled with 1:1 scale reduction [38], then imported into Mars software (Software Version No: 4.2.1.0, Software Manufacturer: Chongqing, China, Glory City Technology Co.) to refine the materials and the natural environment to ensure that it was close to the actual situation. Next, the nine landscape elements before and after the design were placed in the square according to the initial position as scene 1 and scene 2 of the comparison experiment, respectively (as shown in

Table 1. Experimental scenario information.

Sample Information on Experimental Scenarios	Experimental Field
	Scenario 1	Scenario 2
Road Paving
Entrance Signage
Landscape Vignettes
Tree Pools
Low Wall
Shrubs
Trees
Seating
Lighting

and Figure 1 and Figure 2). Finally, high-definition experimental videos were recorded at a walking speed of 1.5 m per second along the same walking route in both scenarios to serve as the final stimuli for the experiment. It is important to note that, apart from the landscape elements, all other external environments were kept.

2.3. Experiment Environment

At the start of the experiment, participants were assisted in donning the experimental equipment, EEG, and head-mounted display (HMD) and briefed on the experimental procedure and precautions to ensure the smooth progression of the experiment. The experimental environment and the participant’s location were displayed, with Figure 3 showing that the participant’s physical experimental environment was in a closed, quiet setting, and data collection was conducted wirelessly.

3. Procedure

The experiment consisted of three steps (as shown in Figure 4). The first step involved closed-eye rest and scene adaptation. Participants’ perception of the environment was diverse, multifaceted, and multilayered. To immerse participants in the virtual simulated space, the study included a scene adaptation phase, with official data collection starting after a 1 min adaptive experience. The second step was scene experience. Participants were asked to watch a 2 min scene video, minimize bodily movement, and remain silent during this time, while the experimenter collected data on the pre-stimulus ratio of alpha wave-to-beta wave (RAB) values extracted from the EEG. The third step was a subjective questionnaire. Sixty seconds after the video playback, participants completed a subjective landscape comfort assessment questionnaire based on the semantic differential (SD) method, with the experimenter asking questions and recording scores and participants providing timely subjective evaluations. This process was carried out twice, with stimulation before and after the healing landscape design. The total duration of the experiment was 8 min.

The American psychologist Maslow [39] proposed the “hierarchy of needs” theory, which divides individual needs into five levels, from basic needs to advanced needs, namely physiological needs, safety needs, social needs, self-esteem needs, and self-actualization needs. Luo Yunhu [40] reorganized Maslow’s theory into three levels of physiological, psychological, and social needs based on the holistic medicine model. In this study, we formulated the elemental layers of healing landscapes based on these three types of needs, i.e., healing physiological needs, healing safety needs, and healing social needs, and the nine landscape elements extracted from the previous section were used as indicator layers, with each elemental layer containing nine evaluation indicators. The subjective questionnaire of this study was made by combining the Likert five-level scale rating method (as shown in

Table 2. Subjective questionnaire on landscape elements.

Test Item	Evaluation Type	Test Sub-Item	Evaluation Scale	Parameter Unit
subjective perception	Healing Security Needs Healing Physical Needs Healing Social Needs	Entrance Signage Road Paving Landscape Vignettes Tree Pools Low Wall Trees Shrubs Seating Lighting	Dangerous—Safe Uncomfortable—Comfortable Single—Various	Score [1,2,3,4,5]

).

4. Analysis

The study is primarily divided into three main parts (as shown in Figure 5): subjective evaluation, EEG data analysis, and correlation analysis. The subjective evaluation is conducted through statistical significance testing to identify the top three healing elements corresponding to the three emotions with higher scores. In the EEG data analysis, the data are first pre-processed, which includes filtering, artifact removal, re-referencing, and feature extraction. Then, statistical significance testing is used to identify characteristic values in brain regions involved in emotional regulation, cognition, and visual analysis. Finally, the study conducts correlation and multiple regression analyses between the three healing elements corresponding to the three emotions and the brain characteristic values.

4.1. EEG Data Processing and Analysis

EEG signals received from 32 channels by the participants were converted to digital format using a 16-bit AD (analog-to-digital) converter at a sampling rate of 512 Hz and stored on a computer within a frequency band of 0.003 150 Hz, totaling up to 4.6 million data points. The raw EEG data were recorded using the real-time data acquisition SAGA system and were processed using a band-pass filter (BPF) of 1–50 Hz, and the EEG spectrum was analyzed using the fast Fourier transform (FFT) method. The EEG spectrum was divided into the following ranges according to the frequency band: delta 1–4 Hz; theta 4–8 Hz; alpha 8–13 Hz; and beta 13–20 Hz [41,42,43]. The EEG channel features were calculated using the ratio between the alpha and beta band power, as shown in Equation (1).

RAB = Power(alpha)/Power(beta)

(1)

4.2. Statistical Analysis

We first need to analyze the impact of healing landscape elements in different designs on participants’ subjective feelings. Initially, a statistical analysis of the subjective questionnaires was conducted, followed by a series of t-test analyses to determine the influence of each healing landscape element on participants’ emotions of security, comfort, and positivity, both before and after the design. The alpha level for each test was set at 0.05 to test the pre-established hypotheses.

4.3. Correlation Analysis

A correlation analysis was conducted to identify the causes of changes in EEG RAB activation. The degree of correlation was analyzed between the scores of each of the three elements of emotional expression and EEG RAB activity. We used a multiple regression analysis to examine the following hypotheses: H1: There is a correlation between the average value of areas with differential EEG RAB after the design for each individual and the sense of security after the design. H2: There is a correlation between the average value of areas with differential EEG RAB after the design for each individual and the three elements of comfort after the design. H3: There is a correlation between the average value of areas with differential EEG RAB after the design for each individual and the three elements of positivity after the design.

5. Results

5.1. Subject Evaluation Results

We performed t-test analyses on the subjective questionnaires to assess the impact of various healing landscape elements (such as entrance signage, road paving, landscape vignettes, tree pools, low wall, trees, shrubs, seating, lighting, and fitness equipment) on participants’ feelings of security, comfort, and positive emotions, both before and after the design. The results indicated significant differences in all three emotional dimensions for most landscape elements, before and after the design. Additionally, the after-design healing landscape elements enhanced feelings of security, comfort, and positivity compared to their before-design state.

• Security

For the design of a sense of security, the seating, lighting, and trees in the healing landscape received higher scores after the design. As shown in Figure 6, the seating after the design gave people a higher sense of security (M = 3.40; SD = 0.38; p < 0.01) than before the design (M = 1.76; SD = 0.23; p < 0.01). The lighting after the design gives people a higher sense of security (M = 3.26; SD = 0.61; p < 0.05) than before the design (M = 1.70; SD = 0.49; p < 0.05). The arbor after the design gives people a higher sense of security (M = 3.17; SD = 0.97; p < 0.01) than before the design (M = 1.76; SD = 0.32; p < 0.01).

2.

Comfort

For the design of comfort, the shrubs, entrance signage, and tree pools in the healing landscape after the design received higher scores. As shown in Figure 7, the shrubs after the design gave people a higher sense of comfort (M = 3.70; SD = 0.42; p < 0.01) than before the design (M = 1.83; SD = 0.28; p < 0.01). The entrance sign after the design gives people a higher sense of comfort (M = 3.60; SD = 0.52; p < 0.05) than before the design (M = 1.86; SD = 0.53; p < 0.05). The tree pools after the design give people a higher sense of comfort (M = 3.60; SD = 0.59; p < 0.01) than before the design (M = 2.40; SD = 0.73; p < 0.01).

3.

Positive

For the positive design, the shrubs, tree pools, and trees in the healing landscape after the design received higher scores. As shown in Figure 8, the shrubs after the design gave people higher positives (M = 3.57; SD = 0.39; p < 0.01) than the ones before the design (M = 1.70; SD = 0.36; p < 0.01). The tree pools after the design give people more positives (M = 3.43; SD = 0.53; p < 0.01) than the ones before the design (M = 1.76; SD = 0.32; p < 0.01). The arbor after the design gives people a higher positive (M = 3.40; SD = 0.38; p < 0.05) than the one before the design (M = 1.60; SD = 0.25; p < 0.05).

The results from the subjective evaluation indicate that improvements in the design of healing landscapes significantly enhance people’s sense of security, comfort, and positive emotions. Specific landscape elements, such as seating, shrubs, and tree pools, particularly stand out in enhancing specific emotional dimensions. These findings can provide valuable guidance and recommendations for landscape designers, aiding in the creation of environments that positively impact emotional well-being.

5.2. EEG Processing Results

The feature values’ RAB are extracted from the 32-channel EEG signals. Assuming there are significant differences in the feature values’ RAB of each channel before and after the design, statistical t-test analysis are used. The results are shown in the following figure. As shown in Figure 9, prefrontal electrodes were used to statistically analyze the results before and after the design. The RAB value of Fp1 after the design (M = 0.654; SD = 0.171; p < 0.05) was higher than before the design (M = 0.644; SD = 0.170; p < 0.05). The RAB value of Fp2 after the design (M = 0.641; SD = 0.165; p < 0.05) is higher than before the design (M = 0.626; SD = 0.156; p < 0.05). The results show that as the prefrontal cortex is responsible for higher cognitive functions and has some influence on emotional control [44,45,46], it can be seen that the therapeutic landscape designed afterward stimulates the prefrontal cortex more, inducing emotional and cognitive states; the induction of relaxation especially can be confirmed.

F3 (left prefrontal lobe) is often associated with the processing and regulation of positive emotions [47]. The area near frontal electrode pairs (such as F3/F4 or F7/F8) may be related to nonverbal emotional expression and understanding [48]. As shown in Figure 10, the results from the frontal electrodes are as follows: after the design, the RAB values for F8 (M = 0.748; SD = 0.149; p < 0.05), F7 (M = 0.690; SD = 0.143; p < 0.05), and F3 (M = 0.582; SD = 0.102; p < 0.05) were greater than the RAB values of F8 (M = 0.737; SD = 0.146; p < 0.05), F7 (M = 0.681; SD = 0.144; p < 0.05), and F3 (M = 0.573; SD = 0.098; p < 0.05) before the design.

POz plays a role in visual processing, affecting visual perception, visual attention, and visual memory [49]. As shown in Figure 11, the results of this experiment show differences in the RAB values at the POz location before and after the design, with an increased activation of RAB after the design (M = 0.851; SD = 0.112; p < 0.01) compared to before (M = 0.838; SD = 0.107; p < 0.01). This indicates that visual perception is more intense after the design. It can also be observed from the right occipital lobe O2 that the subjects exhibited increased activation in RAB values for the healing landscape after the design (M = 0.741; SD = 0.128; p < 0.05) compared to before the design (M = 0.734; SD = 0.130; p < 0.05). This indicates a stronger analysis and perception of image features received by the left eye.

The analysis of EEG physiological signals reveals that the after-design healing landscape exerts a stronger stimulus on the prefrontal cortex, an area responsible for higher cognitive functions and emotional control. This enhancement not only positively affects individuals emotionally but also shows significant positive effects in cognitive and visual processing. These findings further confirm the importance of a healing landscape design in enhancing psychological health and sensory experiences.

5.3. Correlation Analysis Results

We used eight different regression models, namely ridge regression, lasso regression, elastic net, decision tree regression, random forest regression, SVR (support vector regression), gradient boosting regression, and K-nearest neighbor regression, to conduct regression analyses on EEG data and the top three ranked healing design elements in three types of emotions. As shown in Figure 12, the results reveal that the decision tree regression model best fits our data.

As indicated in

Table 3. The degree of correlation between the emotional evaluation of healing elements and EEG data obtained using the decision tree regression.

Security		Comfort		Positivity
Healing Element	R2	Healing Element	R2	Healing Element	R2
Seating	−0.68	Shrubs	−0.82	Shrubs	−0.65
Lighting	−0.60	Entrance Signage	−0.62	Tree Pools	−0.52
Trees	−0.77	Tree pools	−0.74	Trees	−0.71

, the results from the decision tree regression show that in the comfort category, the healing landscape element ”shrubs” has the highest correlation with EEG data, with an R² value of 0.82. In the security category, the healing landscape element ”trees” exhibits the highest correlation with EEG data, with an R² value of 0.77. Lastly, in the positivity category, the healing landscape element ”trees” again shows the highest correlation with EEG data, achieving an R² value of 0.71.

6. Discussion and Conclusions

The study focused on the emotional impact of healing landscapes in rural settings, incorporating user perspectives in the design. It built upon previous findings that such landscapes positively influence human emotions. A within-subjects design was used, manipulating the emotional state of 32 participants (both male and female) in two conditions: before and after exposure to the healing landscape design. The study employed 32-channel EEG technology to monitor brain responses, ensuring the participants’ healthy neurological and autonomic status. The analysis was threefold: a subjective evaluation through questionnaires, an EEG data analysis (including pre-processing and feature extraction), and a correlation analysis between healing elements and emotional responses.

The result was that significant differences were observed in the emotional dimensions of security, comfort, and positivity for most landscape elements before and after the design. Specific elements such as seating, shrubs, and tree pools showed a marked enhancement in these emotional aspects. The EEG data analysis further substantiated these findings. The analysis of rhythmic arousal brainwave (RAB) values from a 32-channel EEG setup showed notable differences before and after the landscape design intervention. This was particularly evident in the prefrontal cortex, associated with higher cognitive functions and emotional control. The enhanced RAB values post-design suggest that the healing landscape has a more substantial influence on inducing emotional and cognitive states, especially relaxation. Other brain regions, such as the left prefrontal lobe (F3) and the occipital lobe (POz and O2), also exhibited increased activation, indicating enhanced nonverbal emotional expression, understanding, visual perception, and attention. The correlation analysis utilizing various regression models revealed that specific healing landscape elements had high correlations with EEG data, suggesting a strong relationship between the landscape design and its impact on emotional and cognitive responses. For instance, ’shrubs’ in the comfort category and ’trees’ in both the security and positivity categories showed the highest correlations, highlighting their significant role in influencing emotional states.

Overall, the study underscores the profound impact of landscape designs on human emotional and cognitive experiences. The findings demonstrate that specific elements within a healing landscape can significantly enhance feelings of security, comfort, and positivity while also stimulating cognitive and emotional responses, as evidenced by EEG data. This study provides valuable insights for landscape designers, emphasizing the importance of incorporating elements that not only beautify the environment but also contribute to emotional and cognitive well-being. The research predominantly focused on short-term effects; long-term impacts remain unexplored. Future research could expand the demographics of the participants to include different age compositions and backgrounds while also focusing on the different preferences that come with different genders as well as the different levels of needs for healing among healthy, subhealthy, and ill populations. Additionally, investigating the effects of different types of rural landscapes could provide a more comprehensive understanding. Research has shown that landscape architects should consider the impact of emotional designs and that different landscape elements can give different positive emotional feedbacks, especially in the use of greenery. It underscores the potential of using VR and EEG technologies in landscape designs to enhance human emotional well-being.

This conclusion encapsulates the study’s comprehensive approach to understanding the emotional effects of rural healing landscapes, offering valuable insights and directions for future research in landscape designs and emotional well-being.