Landscape Ecology

Elements and Element Components of the Rural Landscape in Linpan of Western Sichuan in Relation to Perception, Preference and Stress Recovery

  • LUO Hao ,
  • DENG Li ,
  • JIANG Songlin ,
  • FU Erkang ,
  • MA Jun ,
  • SUN Lingxia ,
  • JIANG Mingyan ,
  • CAI Shizhen ,
  • JIA Yin ,
  • CAI Jun , * ,
  • LI Xi , *
  • College of Landscape Architecture, Sichuan Agricultural University, Chengdu 611130, China
*CAI Jun, E-mail: ;
LI Xi, E-mail:

LUO Hao, E-mail:

Received date: 2020-06-26

  Accepted date: 2020-10-21

  Online published: 2021-07-30

Supported by

The National Natural Science Foundation of China(31870703)


Natural environments contribute to people’s perception, preference and health restoration. Many researchers have focused either on the positive effects of overall rural environments on stress recovery or concentrated on the perception and preference aspects of the rural landscape, but few have integrated perception, preference and stress recovery simultaneously. This paper developed a framework which includes 11 elements and 38 element components related to Linpan, China, and distributed it online as part of a survey. As a result, a total of 324 valid questionnaires were collected. The questionnaire included demographic details, perception and preference degree for Linpan, as well as self-estimations of stress recovery. Stepwise multiple linear regression was employed, and revealed 16 significant predictors for the perception, preference and stress recovery in rural environments. In terms of elements, atmosphere and imagery were the two elements that could be best perceived, while woodland, farmland, water, residence and road were the five most important elements for the preference. Moreover, seven elements were also identified as significant predictors for stress recovery. Among the element components, tranquility as well as road and water proximity were the two significant predictors for perception, while wide visibility as well as woodland and residence blending contributed significantly to stress recovery. The five element components of woodland interior, vegetable field, stream, courtyard space and branch road each had a significantly predictive ability for preference and stress recovery. These findings extend the understanding of the perception, preference and restorative properties of rural environments through the combination of elements and element components in Linpan of Western Sichuan, helping to improve the quality and characteristics of rural external and internal environments and create health-promoting environments.

Cite this article

LUO Hao , DENG Li , JIANG Songlin , FU Erkang , MA Jun , SUN Lingxia , JIANG Mingyan , CAI Shizhen , JIA Yin , CAI Jun , LI Xi . Elements and Element Components of the Rural Landscape in Linpan of Western Sichuan in Relation to Perception, Preference and Stress Recovery[J]. Journal of Resources and Ecology, 2021 , 12(3) : 384 -396 . DOI: 10.5814/j.issn.1674-764x.2021.03.008

1 Introduction

With the high density of cities, the residents are increasingly removed from nature and are living a sedentary, physically inactive lifestyle (Bratman et al., 2019), which tends to induce great stress. Stress-induced illnesses have become a tremendous global problem (Nielsen et al., 2008). Prolonged stress can cause various adverse physiological and psychological symptoms (Danielsson et al., 2012). Many psychiatric diseases in particular are strongly associated with prolonged and incorrect stress reactions (Aldwin and Levenson, 2013). Studies show that people tend to favor natural environments such as nature reserves, woodlands and urban parks for recovering from stress (Nilsson, 2006). However, in order to understand which qualities in natural environment can relieve stress for their inhabitants, we need to improve our knowledge and understanding of these natural environments.

1.1 Natural environment and stress recovery

Restoration from stress and mental fatigue relates to contact with nature (Ulrich et al., 1991; Hartig et al., 2003), and a large body of research has shown that urban green spaces (Yang et al., 2020) and forests (Wang et al., 2019) have significant positive effects on human health by reducing the stress of individuals. There is a positive connection between how often or how long people stay in urban parks and restoration from stress (Nielsen and Hansen, 2007; Mitchell and Popham, 2008). There are also significant relationships between self-reported stress, diurnal patterns of cortisol secretion, and quantity of green space in the community (Thompson et al., 2012). In addition, the workplace outdoor environment is another asset for employees' wellbeing and level of stress (Lottrup et al., 2013). Studies have demonstrated that greening of the schoolyard significantly improves students' physiological well-being and reduces physiological stress (Kelz et al., 2015). For people suffering from exhaustion disorder, visits to forest environments are believed to be significantly more restorative, more mood- enhancing and better for restoring attention capacity than city visits (Sonntag-Öström et al., 2014).
Compared to urban environments, the rural environment can provide more positive effects for stress recovery. Exposure to rural environments can more effectively reduce physiological stress and enhance psychological well-being than exposure to urban environments (Lee et al., 2015). Besides, a rural walk is more beneficial to adult groups with poor and more-stressed health than an urban walk (Roe and Aspinall, 2011). The above experimental studies found that rural environments were associated with stress reduction, but they were limited to comparative studies of the rural and urban environments. Therefore, there is a need to focus on the rural environment itself, such as its elements and element components, and further improve our understanding of the relationship between rural environments and stress.

1.2 Perception and preference of rural landscape

The perception and preference of rural landscape have already been the subject of many studies. In rural areas, all of the elements that are perceived as natural improve the landscape (Cook and Cable, 1995; Franco et al., 2003; Arriaza et al., 2004). It is now widely recognized that the general public has a preference for traditional, extensive farming landscapes (Kline and Wichelns, 1996; Hietala-Koivu, 1999). Most of the above studies were established in Europe and the United States, but these aspects of rural perception and preference have not been explored in the Chengdu area of Western Sichuan Plain in China thus far.

1.3 Hypotheses and goals

Most previous studies have either focused on the positive effects of overall rural environments on stress recovery or concentrated on the perception and preference of the rural landscape, but few have linked perception and preference with stress recovery simultaneously. Therefore, the aims of this study included developing a framework which combines the perceptions and preferences of 11 elements and 38 element components based on the rural landscape characteristics of the Western Sichuan Plain, and using this framework to explore the effects of those elements and element components on people's perception, preference and stress recovery.

2 Materials and methods

2.1 Study sites

As one of the traditional villages in Chengdu, Linpan was based on a Dujiangyan Water Conservancy Project with farmland, woodland, water, residences and roads as the constituent elements, and reflected the cultural characteristics and unique ecological value of the rural residential space in Western Sichuan Plain (Sun, 2011). Of the total population of Linpan, that of Chengdu City accounted for about 70% (Fang, 2012). According to the list of restored Linpan sites in Chengdu, 12 Linpan sites were initially selected, and then through a field investigation of them, eight Linpan sites were finally selected as the study sites: Zhang Yard, Zhou Yard, Yang Yard, Liang Yard, the yard of Juechen Temple, Yan yard, Caipengzi Linpan, and Mottled Bamboo Grove. These eight typical sites had different locations and sizes, and they all contained important elements such as farmland, woodland, water, residences and roads, which can reflect the general characteristics of Linpan in Western Sichuan comprehensively (Fig. 1).
Fig. 1 Basic information for the eight Linpan sites

2.2 Elements and element components of Linpan

Referring to the evaluation of the perception and preference of rural landscape as well as the stress recovery from nature, and combining these concepts with the characteristics of Linpan landscape, the elements and element components of Linpan were preliminarily selected and then adjusted with expert opinions. Finally, it was determined to include the two parts of perception elements and preference elements, and each element had two, three or four finer element components (Appendix A).
Perception elements and perception element components included ecology (fresh air and pleasant climate), atmosphere (wide visibility, full of vitality, nature and comfort, and tranquility), imagery (woodland and residence blending, road and water proximity as well as residence and gardenbeing complementary), sanitation (centralized waste processing, poultry manure deodorization, mosquito control and sewage treatment), culture (farming culture experience, traditional skill experience, folk culture experience and traditional residence experience) and stewardship (coordinated architectural style, flat pavement and free-range dog control).
The preference elements and preference element components included farmland (rice field, wheat field, rape field and vegetable field of traditional plantation in Linpan), woodland (exterior form, edge form and interior form of woodland), water (lateral canal and sublateral canal outside Linpan as well as river and stream inside Linpan), residences (architectural form, roof shape, building façade and courtyard space) and roads (main road, branch road and path).

2.3 Materials

In the current study, each preference element, including farmland, woodland, water, residences and roads, was represented by three or four color photos of the element components (Table 1). Photos were used in place of actual landscapes in a manner that has been widely used in previous studies of perceptions and preferences in rural environments (Franco et al., 2003). All photos were taken at eye-level on clear or less cloudy days to control for similar lighting conditions. The equipment was a Canon EOS 500D camera with focal length 35 mm and aspect ratio 3:2. The camera was positioned horizontally to capture the principal characteristics of the landscape elements and element components.
During 22-29 April 2019, field research and photography were carried out on eight traditional Linpan sites selected from Wenjiang District and Xinjin County of Chengdu, and a total of 761 photos were obtained. In order to fit the theme, context and scope, 305 photos (about 15-18 photos for each element component) were preliminarily selected after screening and sorting. In addition, in order to improve the reliability of photos, experts from the college of Landscape Architecture at Sichuan Agricultural University were organized to select the most representative photo for each element component. Their criteria were good photographic quality and a high level of representation of the landscape element component. The resulting 18 photos shown in Table 1 became the stimuli for the visual assessments.

2.4 Measurements

The test tool was a three-part questionnaire. In the first part, demographic details were collected, including gender, age, education level, occupation and dwelling place. The second part aimed to assess the perception and preference degree of Linpan. Respondents were asked how strongly they perceived each component of the perception elements, and they could score it on a five-point scale ranging from 1 (totally not strong) to 5 (very strong). Guided by the color photos, respondents were asked to rate each component of the preference elements on a scale ranging from 1 (totally dislike it) to 5 (totally like it). In the last part, participants needed to assess the effects of various element components on stress recovery on a five-point scale, from 1 (totally not obvious) to 5 (very obvious).
Table 1 Five preference elements and eighteen preference element components
Elements Element components
Rice field

Wheat field

Rape field

Vegetable field
Exterior form

Edge form

Interior form
Lateral canal

Sublateral canal


Architectural form

Roof shape

Building façade

Courtyard space
Main road

Branch road


2.5 Data collection and analysis

Using a snowball approach, the questionnaire was sent online in May 2019. The exclusion criterion was screened by asking respondents whether they had visited the Linpan after they read the related introduction of Linpan and viewed a photo presentation on Linpan. For example, if the respondents chose the option of “have not been to”, the online questionnaire survey would be regarded as invalid. In the end, a total of 654 people participated in the questionnaire survey, with 324 valid questionnaires completed (response rate of 49.5%).The statistical analysis was carried out with SPSS 19.0 software. A descriptive statistical analysis was conducted on the demographic characteristics of the respondents. Stepwise multiple linear regression, using perception, preference and stress recovery as dependent variables, and using the elements and element components as independent variables, was employed to explore the predictive effects of the elements and element components on perception, preference and stress recovery. The stress recovery potential was divided into two parts: a stress recovery score for perception and a stress recovery score for preference. Overall perception and preference, as well as the two parts of stress recovery, were determined by the average score of the 324 respondents who rated it. The perception score of each element component (20 perception element components) and preference score of each element component (18 preference element components) were determined by the average scores of respondents who judged each of them. The perception score of each element (six perception elements) and preference score of each element (five preference elements) were determined by the average scores of corresponding perception element components and preference element components, respectively.

3 Results

3.1 Demographic characteristics of participants

Table 2 shows that females were slightly overrepresented (66.7%) among the 324 participants and their perception and preference scores were slightly higher than those of males, but their stress recovery score was the opposite. Most participants were aged 18 to 34 (79.9%) and had a relatively high stress recovery score. Overall, the older the participant, the greater their perception and preference scores. The majority of participants held a Master's degree (48.7%), but participants with a bachelor's degree (36.7%) scored highest on perception, preference and stress recovery. Among the participants, 53.4% were students while other professionals (6.8%) had the highest scores in perception and stress recovery. In addition, participants who lived in the urban center (35.5%) or the suburbs (34.0%) scored higher on perception, preference, and stress recovery than those who lived in the urban fringe (26.5%) or rural area (4.0%).
Table 2 Descriptive statistics of the participants
Survey items Category Number of
Perception score of perception elements Preference score of preference elements Stress recovery score for
Stress recovery score for
Gender Male 108 33.3 3.563 3.850 3.557 3.688
Female 216 66.7 3.598 3.864 3.548 3.651
Age <18 2 0.65 2.400 2.275 2.442 2.308
18-34 259 79.9 3.601 3.852 3.566 3.657
35-60 61 18.8 3.559 3.932 3.528 3.736
>60 2 0.65 3.625 4.225 3.390 3.658
Education level Primary and junior secondary 8 2.6 3.069 3.319 3.082 3.192
High school 15 4.6 3.501 3.800 3.344 3.454
Technical college 24 7.4 3.505 3.823 3.555 3.651
Undergraduate college 119 36.7 3.622 3.903 3.594 3.735
Graduate school 158 48.7 3.619 3.879 3.571 3.657
Occupation Student 173 53.4 3.588 3.827 3.537 3.619
Teacher 28 8.6 3.498 3.948 3.524 3.782
Enterprise and public
institution staff
53 16.4 3.632 3.914 3.640 3.731
Other professionals 22 6.8 3.696 3.981 3.681 3.888
Service sales and trade
6 1.8 3.505 4.036 3.430 3.881
Retired 5 1.5 3.550 3.820 3.308 3.372
Liberal professions 18 5.6 3.575 3.891 3.502 3.711
Others 19 5.9 3.488 3.654 3.462 3.410
Dwelling place Urban center 115 35.5 3.623 3.892 3.610 3.714
Suburb 110 34.0 3.582 3.905 3.550 3.701
Urban fringe 86 26.5 3.564 3.770 3.474 3.556
Rural area 13 4.0 3.440 3.771 3.541 3.608

3.2 Reliability of perception and preference scores

The interclass reliabilities of perception and preference element scores were calculated (Table 3). All perception elements had good reliabilities among participants, with Cronbach's Alpha ranging from 0.810 to 0.917. Cronbach's Alpha values for all preference element scores were more than 0.8. Consequently, the results showed very good internal reliabilities of the perception and preference elements.
Table 3 The interclass reliability of perception and preference elements
Perception element Cronbach's Alpha Preference element Cronbach's Alpha
Ecology 0.858 Farmland 0.843
Atmosphere 0.870 Woodland 0.807
Imagery 0.867 Water 0.849
Sanitation 0.810 Residence 0.891
Culture 0.917 Road 0.876
Stewardship 0.837

3.3 Predictive characteristics of elements and element components for perception and stress recovery

In terms of perception elements, atmosphere and imagery explained 69.8% and 65.6% of the variance, respectively. The variances of ecology (51.1%) and culture (49.1%) were slightly lower. Among the atmosphere element components, tranquility was a significant predictor for the perception. Its Beta coefficient was 0.333, which means that tranquility makes the strongest contribution to explaining perception. Among the imagery element components, the Beta coefficient of road and water proximity was similar to woodland and residence blending, and both of them had strong predictive effects on perception. Fresh air among the ecology elements and traditional residence experience among the cultural elements were significant predictors for the perception. Centralized waste processing among the sanitation elements and flat pavement among the stewardship elements also predicted the perception to some extent (Table 4).
Table 4 Significant predictors for the perception
Dependent and independent Standardized beta t Sig. Collinearity statistics
Tolerance VIF
Ecology (Adj. R2 = 0.511; Sig. < 0.001)
Fresh air 0.434 7.355 < 0.001 0.436 2.294
Pleasant climate 0.331 5.623 < 0.001 0.436 2.294
Atmosphere (Adj. R2 = 0.698; Sig. < 0.001)
Tranquility 0.333 7.174 < 0.001 0.435 2.297
Wide visibility 0.270 6.574 < 0.001 0.552 1.181
Full of vitality 0.231 4.752 < 0.001 0.397 2.518
Nature and comfort 0.154 2.904 0.004 0.332 3.013
Imagery (Adj. R2 = 0.656; Sig. < 0.001)
Road and water proximity 0.341 6.475 < 0.001 0.385 2.599
Woodland and residence blending 0.317 6.711 < 0.001 0.479 2.088
Residence and garden complementary 0.254 5.178 < 0.001 0.443 2.258
Sanitation (Adj. R2 = 0.411; Sig. < 0.001)
Centralized waste processing 0.473 8.715 < 0.001 0.618 1.617
Sewage treatment 0.332 5.846 < 0.001 0.565 1.769
Mosquito control 0.266 5.449 < 0.001 0.766 1.306
Culture (Adj. R2 = 0.491; Sig. < 0.001)
Traditional residence experience 0.290 4.649 < 0.001 0.406 2.463
Farming culture experience 0.266 4.587 < 0.001 0.467 2.142
Folk culture experience 0.233 3.728 < 0.001 0.404 2.474
Stewardship (Adj. R2 = 0.297; Sig. < 0.001)
Flat pavement 0.417 7.072 < 0.001 0.625 1.600
Coordinated architectural style 0.263 4.274 < 0.001 0.577 1.733
Free-range dog control 0.135 2.383 < 0.001 0.682 1.466

Note: Dependent variable is perception mean. Correlation is significant at the P < 0.05 level.

Atmosphere and imagery were significant predictors for the stress recovery among the perception elements, explaining 73.9% and 65.6% of the variance, respectively. The variances of ecology and culture were, respectively, 59.3% and 60.6%. Among the atmosphere element components, wide visibility was the most significant predictor of stress recovery. Also, woodland and residence blending was a reliable predictor for the stress recovery. Fresh air among the ecology elements and folk culture experience among the cultural elements were significant predictors for the stress recovery. There were predictive effects on stress recovery of centralized waste processing among the sanitation elements and coordinated architectural style among the stewardship elements (Table 5).
Table 5 Significant perception predictors for the stress recovery
Dependent and independent Standardized beta t Sig. Collinearity statistics
Tolerance VIF
Ecology (Adj. R2 = 0.593; Sig. < 0.001) `
Fresh air 0.473 8.934 < 0.001 0.450 2.221
Pleasant climate 0.353 6.678 < 0.001 0.450 2.221
Atmosphere (Adj. R2 = 0.739; Sig. < 0.001)
Wide visibility 0.346 8.003 < 0.001 0.434 2.305
Tranquility 0.239 5.131 < 0.001 0.373 2.684
Full of vitality 0.249 5.218 < 0.001 0.356 2.811
Nature and comfort 0.144 2.843 < 0.001 0.316 3.165
Imagery (Adj. R2 = 0.656; Sig. < 0.001)
Woodland and residence blending 0.417 6.093 < 0.001 0.270 3.705
Residence and garden complementary 0.242 3.573 < 0.001 0.276 3.625
Road and water proximity 0.166 2.822 0.005 0.366 2.732
Sanitation (Adj. R2 = 0.427; Sig. < 0.001)
Centralized waste processing 0.521 8.314 < 0.001 0.451 2.216
Sewage treatment 0.399 6.581 < 0.001 0.484 2.067
Poultry manure deodorization 0.159 2.343 0.020 0.384 2.603
Mosquito control 0.140 2.225 0.027 0.449 2.226
Culture (Adj. R2 = 0.606; Sig. < 0.001)
Folk culture experience 0.314 4.545 < 0.001 0.255 3.920
Farming culture experience 0.310 4.928 < 0.001 0.309 3.234
Traditional residence experience 0.216 3.499 0.001 0.322 3.110
Stewardship (Adj. R2 = 0.431; Sig. < 0.001)
Coordinated architectural style 0.411 7.286 < 0.001 0.553 1.809
Flat pavement 0.216 3.802 < 0.001 0.547 1.829
Free-range dog control 0.150 3.118 0.002 0.760 1.316

Note: Dependent variable is stress recovery mean on perception. Correlation is significant at the P < 0.05 level.

3.4 Predictive characteristics of elements and element components for preference and stress recovery

Five reliable predictors for the preference were woodland, farmland, water, residence and road, which respectively explained 69.6%, 68.1%, 67.6%, 66.0% and 63.3% of the variance. Among the five elements, the corresponding element components that had predictive effects on preference were woodland interior, vegetable field, stream, courtyard space and branch road (Table 6).
Table 6 Significant predictors for the preference
Dependent and independent Standardized beta t Sig. Collinearity statistics
Tolerance VIF
Farmland (Adj. R2 = 0.681; Sig. < 0.001)
Wheat field 0.248 5.110 < 0.001 0.419 2.385
Vegetable field 0.373 9.744 < 0.001 0.672 1.488
Rape field 0.182 4.261 < 0.001 0.539 1.854
Rice field 0.202 4.059 < 0.001 0.399 2.507
Woodland (Adj. R2 = 0.696; Sig. < 0.001)
Edge form 0.346 8.147 < 0.001 0.521 1.919
Interior form 0.362 9.316 < 0.001 0.623 1.605
Exterior form 0.275 6.599 < 0.001 0.544 1.839
Water (Adj. R2 = 0.676; Sig. < 0.001)
Stream 0.413 9.722 < 0.001 0.555 1.802
Sublateral canal 0.199 4.034 < 0.001 0.413 2.422
Lateral canal 0.176 4.268 < 0.001 0.586 1.707
River 0.199 4.138 < 0.001 0.432 2.313
Residence (Adj. R2 = 0.660; Sig. < 0.001)
Building façade 0.245 4.618 < 0.001 0.373 2.681
Courtyard space 0.304 6.467 < 0.001 0.476 2.099
Architectural form 0.214 4.321 < 0.001 0.431 2.322
Roof shape 0.175 3.261 0.001 0.365 2.742
Road (Adj. R2 = 0.633; Sig. < 0.001)
Branch road 0.342 6.329 < 0.001 0.388 2.574
Path 0.318 6.285 < 0.001 0.443 2.260
Main road 0.229 4.419 < 0.001 0.425 2.355

Note: Dependent variable is preference mean. Correlation is significant at the P < 0.05 level.

Regarding preference elements for the stress recovery, woodland (70.9%) and farmland (70.4%) had relatively high variances, followed by water (66.2%), residence (66.9%) and road (62.5%). The element components that had significant predictive effects on stress recovery were the same as those that had significant predictive effects on preferences (Table 7).
Table 7 Significant preference predictors for the stress recovery
Dependent and independent Standardized beta t Sig. Collinearity statistics
Tolerance VIF
Farmland (Adj. R2 = 0.704; Sig. < 0.001)
Vegetable field 0.409 10.443 < 0.001 0.598 1.672
Rape field 0.255 6.088 < 0.001 0.520 1.922
Wheat field 0.197 4.293 < 0.001 0.436 2.294
Rice field 0.146 2.984 0.003 0.385 2.598
Woodland (Adj. R2 = 0.709; Sig. < 0.001)
Edge form 0.348 8.094 < 0.001 0.488 2.051
Interior form 0.353 9.265 < 0.001 0.622 1.608
Exterior form 0.286 6.804 < 0.001 0.511 1.957
Water (Adj. R2 = 0.662; Sig. < 0.001)
River 0.225 4.308 < 0.001 0.382 2.620
Stream 0.373 9.153 < 0.001 0.628 1.592
Lateral canal 0.213 4.881 < 0.001 0.550 1.819
Sublateral canal 0.172 3.184 0.002 0.360 2.780
Residence (Adj. R2 = 0.669; Sig. < 0.001)
Architectural form 0.237 4.216 < 0.001 0.323 3.092
Courtyard space 0.310 7.130 < 0.001 0.542 1.846
Building façade 0.207 3.880 < 0.001 0.362 2.765
Roof shape 0.188 3.185 0.002 0.295 3.395
Road (Adj. R2 = 0.625; Sig. < 0.001)
Branch road 0.385 7.526 < 0.001 0.444 2.250
Path 0.262 4.959 < 0.001 0.415 2.411
Main road 0.236 4.177 < 0.001 0.364 2.748

Note: Dependent variable is stress recovery mean on preference. Correlation is significant at the P < 0.05 level.

4 Discussion

4.1 Predictive elements for perception, preference and stress recovery

Our results showed that atmosphere and imagery were not only the two elements that could be best perceived, but also significant predictors for the stress recovery. As a combination of what is communicated by the destination and what is understood by the tourist (Royo-Vela, 2009), atmosphere and imagery were related not just to the separate components but to the place or landscape as a whole (Arler, 2000). It is based on the totality of landscape experience that the positive response to the landscape is obtained (Appleton, 1994). In addition, when perceiving the atmosphere and imagery, people may experience the nostalgia consciousness which is a stress-relieving psychological activity that lets people be reminded of the past, relax their mood and adjust their mentality (Zhou and Liu, 2014). It is clear from numerous studies that culture plays a very important role in the perception of landscape (Lyons, 1983) and is often perceived as having a high level of restorative attributes (Packer and Bond, 2010). However, the influence of culture on perception and stress recovery was not as high as atmosphere or imagery in our study. One possible explanation for this might be that the full demonstration and effective spread of cultural values need to be realized by interaction, participation and experience with the actual environment (Zhang et al., 2019). Therefore, a future study can be designed to take the participants to the sites in order to enhance their impact on perception and stress recovery.
Our results broadly support the findings of other research in identifying the positive correlations between nature-related landscape elements (e.g., woodland, farmland, and water), preference and stress recovery (Wherrett, 2000; Rogge et al., 2007; Völker and Kistemann, 2011; Jiang et al., 2014; Arnberger and Eder, 2015; Lee et al., 2015; Al-Akl et al., 2018). However, the artificial elements in the present research (e.g., residence and road) also contributed significantly to preference and stress recovery. Although these results differed from some published studies (Arriaza et al., 2004; Acar et al., 2006), they are consistent with other studies, such as those which showed that well-preserved traditional farm buildings could improve the visual quality of the landscape (Tempesta, 2010) and a well-maintained trail environment could also absorb more visitors (Reichhart and Arnberger, 2010; Arnberger and Eder, 2011). What's more, good maintenance is regarded as an important part of the properties of successful restorative environments (Memari et al., 2017). Generally, our study found that preference and stress recovery were likely to be associated not only with nature-related landscape elements, but also with well managed and maintained artificial elements.
In addition, the current study suggested that well-perceived elements and preferred elements can result in stress recovery. For example, there was a positively relationship between perception of the surrounding environment and human health (Kaplan, 2001; Bucci, 2003). Moreover, a broad functional significance of environmental preference includes not only restoration from mental fatigue but also restoration from anxiety-based stress (Van den Berg et al., 2003). Actually, the combination of well-perceived elements (e.g., experience-related elements) and preferred elements (e.g., nature-related elements and well-maintained artificial elements) forms an environment with good landscape quality, which serves as the important center of attraction for various activities and positively contributes to the psychological and physical wellbeing of individuals (Düzgüneş and Demirel, 2015). Therefore, paying attention to the sense of experience, naturalness and good maintenance presented by the Linpan elements can enhance the perception and preference for the rural environment, which will make it is easier to attract people to relieve pressure in the rural environment.

4.2 Predictive element components for perception, preference and stress recovery

Among all the element components of perception, tranquility as well as road and water proximity were the two significant predictors for perception, while wide visibility as well as woodland and residence blending contributed significantly to stress recovery. Regarding the element components of atmosphere, tranquility is identified as the most valued quality in the opinion of visitors (Tyrväinen et al., 2007). In addition, the motivation which makes people want to go out in natural areas is to find a place far away from densely built-up and populated areas (Jensen, 1998), while the imagery of road and water proximity in rural environments may be able to satisfy this need. Also, woodland and residence blending can provide a secluded place for participants, and coupled with wide visibility, it is easier to watch people being active. The combination of wide visibility and woodland and residence blending complies with the concept of refuge, which is mostly related to a sheltered and safe supportive location surrounded by trees (Lückmann et al., 2013) and allows a broad view to look at the surroundings (Pálsdóttir et al., 2014). Refuge is considered to be an important predictor of recovery (Bengtsson and Grahn, 2014), and as one of eight different perceived sensory dimensions (PSDs) used to describe the characteristics of different landscape environments, refuge is most preferred by people reporting the highest levels of stress (Grahn and Stigsdotter, 2010).
The five element components of woodland interior, vegetable field, stream, courtyard space and branch road all had significantly predictive ability for preference and stress recovery. Among the element components of woodland, interior form, with the presence of tall canopy cover and moderate plant density which could enhance public preference (Misgav, 2000), received more positive evaluations than the exterior and edge forms. Vegetable field was found to be preferred over the crops of rape, wheat and rice, maybe because the vegetable field is a mixture of several plant types rather than a homogeneous texture and such richness can increase preference ratings of agrarian landscapes (Stilma et al., 2009; Junge et al., 2011). As the most preferred element component in the water element, stream was more hydrophilic than the other three water element components, while hydrophilia is the preference as a visible feature (Herzog, 1985). Also, respondents preferred the courtyard space over the residence itself since a medium-sized area is seen to provide the best quality compared to a small area (Arnberger and Eder, 2015). In addition, a broader branch road induced a higher preference than a narrow path, due to the fact that dense vegetation on both sides of a narrow path makes the visual accessibility poor and prevents people from understanding their surroundings (Herzog and Kutzli, 2002). In terms of the effects of element components on stress recovery, our findings showed that the interior form of woodland, by allowing people to experience vegetation in close proximity and strengthening the psychological and physiological benefit (Jorgensen et al., 2002), offered significant stress recovery compared to the exterior and edge forms. Similarly, vegetable fields, which can provide participants with gardening activities that could not be carried out in their daily life, might incur the feeling of being away and bring more attention to restoration (Kaplan, 1984). As a reliable predictor for stress recovery, the stream has a more natural revetment than the other types of waterways, which may result in potentially restorative effects. Moreover, the courtyard space is a semi-open and well-maintained area which is the preferred area for stress relief (Tyrväinen et al., 2014). Besides, respondents may fear that the likelihood of user conflicts and social interactions is higher on small trails, which makes them less suitable when seeking stress relief (Arnberger and Eder, 2015). Thus, the broader branch road had the more obvious effect on stress recovery compared to the narrow path.
As indicated by the findings described above, in addition to focusing on the predictive ability of different element components for perception, preference and stress recovery, their landscape characteristics, such as naturalness, openness, maintenance, variety and tranquility (Rogge et al., 2007), as well as the PSD combination of Nature and Refuge together with the absence of Rich in species and Social (Memari et al., 2017), also seem to be powerful predictor variables. Based on the predictive ability of perception elements, we can obtain a stronger perception and stress recovery by strengthening the perception of the external environment of the countryside (such as tranquility, road and water proximity, wide visibility as well as woodland and residence blending). Moreover, in order to increase preference and stress recovery, the element components of the internal environment of the countryside (woodland interior, vegetable field, stream, courtyard space and branch road) require more attention.

4.3 Limitations and future research

Some limitations of our study should be addressed in future research. The present study focused on the effects of elements and element components on people's perception, preference and stress recovery from the perspective of public consensus, but little attention has been given to the differences in landscape perception and preference that may result among participants with different backgrounds. Thus, further examination of different demographic characteristics is necessary to improve our understanding of how individual landscape perceptions and preferences would be affected. Moreover, the measurement of stress recovery, which was only based on self-rated values here, should be strengthened by physiological indicators or narrative interviews. In addition, as this research was cross-sectional, it cannot be concluded that the elements and element components lead to stress recovery. Further experimental studies, establishing the cause-effect relationships, may be useful in advancing the knowledge regarding elements, element components and stress recovery. Photo-based methods have the advantage of controlling for severe weather and extraneous conditions such as passers-by (Grahn and Stigsdotter, 2010), so they are extensively used in landscape perception and preference inquiries and have proven to be very helpful. However, the method of rating the environment based on photos remains relatively subjective. Therefore, some quantitative methods, such as VR panorama, eye tracking and choice-based conjoint analysis, can be used in future research to help us gain more knowledge about people's perception, preference and stress recovery.

5 Conclusions and practical application

This study focused on the rural landscape in Linpan of western Sichuan to investigate the effects of elements and element components on participant's perception, preference, and stress recovery, and ultimately 16 significant predictors were revealed. In terms of landscape elements, atmosphere and imagery were the two elements that could be best perceived; and the five elements of woodland, farmland, water, residence and road, were all significant predictors for the preference. Additionally, these seven elements were also recognized as the important predictors influencing stress recovery. Among all element components, tranquility as well as road and water proximity were the two significant predictors for perception, while wide visibility as well as woodland and residence blending contributed significantly to stress recovery. The five element components of woodland interior, vegetable field, stream, courtyard space and branch road all had significant predictive abilities for preference and stress recovery. Thus, these findings not only add to existing knowledge regarding people's perceptions and preferences for the rural environment, but also provide some more specific indicators for understanding the effect of the rural environment on stress recovery.
Improving the qualities and characteristics of urban green spaces is a good way to increase citizens' use of them and promote their health and well-being (Stigsdotter and Grahn, 2011), although new building within city limits and densification of the city may result in the disappearance of the health-promoting qualities in parks and other natural areas (Burgess et al., 1988). However, by analyzing and redesigning the rural environment in relation to citizens' needs, perceptions and preferences, the rural environment can also be seen as another resource for promoting public health. In view of this, in the overall planning of Linpan, the spatial structure of farmland, woodland, water, residence and road should be emphasized in order to strengthen the perceived atmosphere and imagery, and enhance the attraction of participants. Meanwhile, in the specific design of Linpan, it is necessary to pay more attention to its internal environment, such as the woodland interior, vegetable field, stream, courtyard space and branch road, in order to isolate participants from the external environment, provide a rich sense of experience and elicit a greater sense of relaxation and restoration. Therefore, we should improve the quality of landscape elements to enhance the attractiveness of the rural external environment, and make full use of the characteristics of element components to provide the rich experience of the rural internal environment. This is not only conducive to improving people's perception and preference for the rural environment, but can also provide inspiration for the construction of a restorative landscape in the rural setting.
Table 8 Appendix AElements and element components describing the Linpan
Type Element Element component Description References
Perception elements Ecology Fresh air Not polluted and containing a high level of negative oxygen ions Yang and Li, 2007;
Li et al., 2018; Chen et al., 2019
Pleasant climate Not cold and not hot, and suitable for people's living
Atmosphere Wide visibility Can see a wide area without many obstacles Che et al., 2008; Tang et al., 2013; Chen et al., 2019
Full of vitality Describing a state of exuberant vitality
Nature and comfort A natural and comfortable living environment
Tranquility Quiet and not noisy
Imagery Woodland and residence blending The characteristics of integration of woodland and residence Sun, 2011; Chen et al., 2019; Wang, 2019
Road and water proximity Road surrounded by river, stream or canal
Residence and garden complementary Residence and garden setting each other off beautifully
Sanitation Centralized waste processing Dealing with piles of rubbish dumped randomly in front of and behind residence Che et al., 2008; Fan, 2009; Van Zanten et al., 2014; Chen et al., 2019
Poultry manure deodorization Reducing the direct impact on air quality of poultry manure piled up in the open
Mosquito control Cleaning the surrounding environment of the house and managing the stagnant water to prevent mosquito breeding
Sewage treatment Effective and proper treatment of domestic sewage discharged on both sides of the pond or ditch
Culture Farming culture experience Cultivation of various crops, including farming, watering, fertilizing, weeding and other farming experience Scott, 2002; Yang and Li, 2007; Zhang, 2017;
Chen et al., 2019
Traditional skill experience Bamboo-weaving skills and Sichuan Potted Landscape Art
Folk culture experience Rowing dragon boats, guessing lantern riddles, visiting the temple fair and burning the dragon and lion lamps
Traditional residence experience Experience of the construction process of traditional wooden structure residence
Stewardship Coordinated architectural style Coordinating the style of new houses and traditional residences to form an orderly village style Scott, 2002; Fan, 2009;
Fang, 2012
Flat pavement Leveling the road surface to reduce the inconvenience of muddy roads in rainy days
Free-range dog control Tethering the free-range dogs to prevent random bites
Preference elements Farmland Rice field A traditional crop planted in patches in the external environment of Linpan, appearing green in the middle of June Natori and Chenoweth, 2008; Rechtman, 2013;
Li and Zhang, 2017;
Ren et al., 2018
Wheat field A traditional crop planted in patches in the external environment of Linpan, ripening and turning golden in early May
Rape field A traditional crop planted in patches in the external environment of Linpan, flowering around March
Vegetable field Including the vegetable gardens located in the external environment of Linpan and the vegetable patches in front and back of the residence
Woodland Exterior form Showing the natural “mountain” type that can be seen from far away Nijnik and Mather, 2008; Chiang et al., 2017; Li and Zhang, 2017;
Ren et al., 2018
Edge form Roughly showing the hierarchical structure of bamboo forest and trees
Interior form Clearly showing the plant types inside the forestland
Water Lateral canal Made of cement masonry and bringing water to irrigated areas White et al., 2010; Faggi et al., 2013; Li and Zhang, 2017; Ren et al., 2018
Sublateral canal Made of natural stone and bringing water from the lateral canal to the fields
River Made of pebbles or natural stones and convenient for farmers to irrigate and use in daily life
Stream Made of pebbles and good for rainwater drainage
Residence Architectural form Showing the L-shaped plane layout of the traditional residence Tempesta, 2010; Torreggiani and Tassinari, 2012; Li and Zhang, 2017;
Ren et al., 2018
Roof shape The roof with a herringbone hanging peak, the wall whitened with lime and three small stacks of green decorated tiles in the middle of the roof for decoration
Building façade Showing the residence details including wood carvings of flowers, birds, fish and insects, and wooden windows of chess lattice
Courtyard space Mostly enclosed by bamboo fence or low wall, wells and fruit trees existing inside of it
Road Main road 3.0-5.0 meters wide road which is cement pavement, and for agricultural vehicles, cars to pass Janowsky and Becker, 2003; Arnberger and Eder, 2011, 2015; Li and Zhang, 2017; Ren et al., 2018
Branch road 1.5-2.0 meters wide road which is cement or gravel pavement, connecting each residence and leading farmers to the farmland outside the Linpan
Path 0.5-1.0 meters wide road which is gravel pavement, and connecting various functional plots of trees, flowers, vegetables, or fruits inside the Linpan
Acar C, Kurdoglu B C, Kurdoglu O , et al. 2006. Public preferences for visual quality and management in the Kackar Mountains National Park (Turkey). International Journal of Sustainable Development & World Ecology, 13(6):499-512.

Aldwin C M, Levenson M R. 2013. Stress. In: Runehov A L C, Oviedo L (eds.). Encyclopedia of sciences and religions. Dordrecht, the Netherlands: Springer.

Al-Akl N M, Karaan E N, Al-Zein M S , et al. 2018. The landscape of urban cemeteries in Beirut: Perceptions and preferences. Urban Forestry & Urban Greening, 33:66-74.

Appleton J . 1994. Running before we can walk: Are we ready to map ‘beauty’. Landscape Research, 19(3):112-119.


Arler F . 2000. Aspects of landscape or nature quality. Landscape Ecology, 15(3):291-302.


Arnberger A, Eder R . 2011. The influence of age on recreational trail preferences of urban green-space visitors: A discrete choice experiment with digitally calibrated images. Journal of Environmental Planning and Management, 54(7):891-908.


Arnberger A, Eder R . 2015. Are urban visitors’ general preferences for green-spaces similar to their preferences when seeking stress relief. Urban Forestry & Urban Greening, 14(4):872-882.

Arriaza M, Cañas-Ortega J F, Cañas-Madueño J A , et al. 2004. Assessing the visual quality of rural landscapes. Landscape and Urban Planning, 69(1):115-125.


Bengtsson A, Grahn P . 2014. Outdoor environments in healthcare settings: A quality evaluation tool for use in designing healthcare gardens. Urban Forestry & Urban Greening, 13(4):878-891.

Bratman G N, Anderson C B, Berman M G , et al. 2019. Nature and mental health: An ecosystem service perspective. Science Advances, 5(7): eaax0903. DOI: 10.1126/sciadv.aax0903.


Bucci W . 2003. Varieties of dissociative experiences: A multiple code account and a discussion of Bromberg’s case of “William”. Psychoanalytic Psychology, 20(3):542-557.


Burgess J, Harrison C M, Limb M . 1988. People, parks and the urban green: A study of popular meanings and values for open spaces in the city. Urban Studies, 25(6):455-473.


Che S Q, Yang Z J, Ni J X , et al. 2008. Investigation of landscape patterns and study on landscape element design patterns of Shanghai countryside. Chinese Landscape Architecture, 8:21-27. (in Chinese)

Chen Q Y, Yang J X, Luo S X , et al. 2019. Identification and extraction of the Linpan culture landscape gene. Tropical Geography, 39(2):254-266. (in Chinese)

Chiang Y C, Li D Y, Jane H A . 2017. Wild or tended nature? The effects of landscape location and vegetation density on physiological and psychological responses. Landscape and Urban Planning, 167:72-83.


Cook P S, Cable T T . 1995. The scenic beauty of shelterbelts on the Great Plains. Landscape and Urban Planning, 32(1):63-69.


Danielsson M, Heimerson I, Lundberg U , et al. 2012. Psychosocial stress and health problems: Health in Sweden: The national public health report 2012. Scandinavian Journal of Public Health, 40(9S):121-134.


Düzgüneş E, Demirel Ö . 2015. Evaluation of rural areas in terms of landscape quality: Salacik Village (Trabzon/Turkey) example. Environmental Monitoring and Assessment, 187(6):310. DOI: 10.1007/s10661-015-4544-0.


Faggi A, Breuste J, Madanes N , et al. 2013. Water as an appreciated feature in the landscape: A comparison of residents’ and visitors’ preferences in Buenos Aires. Journal of Cleaner Production, 60:182-187.


Fan Y Z . 2009. Research on environmental landscape plan and design with safety action of Linpan in Chengdu Plain. Diss., Chengdu, China: Sichuan Agriculture University.

Fang Z R . 2012. Basic study on the Linpan culture at Western Sichuan plain. Diss., Chongqing, China: Chongqing University.

Gesler W M . 1992. Therapeutic landscapes: Medical issues in light of the new cultural geography. Social Science & Medicine, 34(7):735-746.


Grahn P, Stigsdotter U K . 2010. The relation between perceived sensory dimensions of urban green space and stress restoration. Landscape and Urban Planning, 94(3-4):264-275.


Hartig T, Evans G W, Jamner L D , et al. 2003. Tracking restoration in natural and urban field settings. Journal of Environmental Psychology, 23(2):109-123.


Herzog T R . 1985. A cognitive analysis of preference for waterscapes. Journal of Environmental Psychology, 5(3):225-241.


Herzog T R, Kutzli G E . 2002. Preference and perceived danger in field/forest settings. Environment and Behavior, 34(6):819-835.


Hietala-Koivu R . 1999. Agricultural landscape change: A case study in Yläne, southwest Finland. Landscape and Urban Planning, 46(1-3):103-108.


Janowsky D V, Becker G . 2003. Characteristics and needs of different user groups in the urban forest of Stuttgart. Journal for Nature Conservation, 11(4):251-259.


Jensen F S . 1998. Outdoor recreation in the public landscape 1994/95. Diss., Copenhagen, Denmark: The Royal Veterinary and Agricultural University.

Jiang B, Chang C Y, Sullivan W C . 2014. A dose of nature: Tree cover, stress reduction, and gender differences. Landscape and Urban Planning, 132:26-36.


Jorgensen A, Hitchmough J, Calvert T . 2002. Woodland spaces and edges: Their impact on perception of safety and preference. Landscape and Urban Planning, 60(3):135-150.


Junge X, Lindemann-Matthies P, Hunziker M , et al. 2011. Aesthetic preferences of non-farmers and farmers for different land-use types and proportions of ecological compensation areas in the Swiss lowlands. Biological Conservation, 144(5):1430-1440.


Kaplan R . 1984. Impact of urban nature: A theoretical analysis. Urban Ecology, 8(3):189-197.


Kaplan S . 2001. Meditation, restoration, and the management of mental fatigue. Environment and Behavior, 33(4):480-506.


Kelz C, Evans G W, Röderer K . 2015. The restorative effects of redesigning the schoolyard: A multi-methodological, quasi-experimental study in rural Austrian middle schools. Environment and Behavior, 47(2):119-139.


Kline J, Wichelns D . 1996. Measuring public preferences for the environmental amenities provided by farmland. European Review of Agricultural Economics, 23(4):421-436.


Lee J, Park B J, Ohira T , et al. 2015. Acute effects of exposure to a traditional rural environment on urban dwellers: A crossover field study in terraced farmland. International Journal of Environmental Research and Public Health, 12(2):1874-1893.


Li L, Zhang Y J . 2017. Rural ecotourism: An effective way of maintaining indigenous character in rapidly urbanizing rural areas: A case study of Five Golden Flowers in Chengdu, China. Journal of Resources and Ecology, 8(5):485-493.


Li Y Q, Luo Y S, Li Y Q , et al. 2018. Establishment of the evaluation system for rural landscape quality based on analytic hierarchy process: Taking Western Sichuan Linpan as an example. Journal of Northwest Forestry University, 33(2):263-268. (in Chinese)

Lottrup L, Grahn P, Stigsdotter U K . 2013. Workplace greenery and perceived level of stress: Benefits of access to a green outdoor environment at the workplace. Landscape and Urban Planning, 110:5-11.


Lückmann K, Lagemann V, Menzel S . 2013. Landscape assessment and evaluation of young people: Comparing nature-orientated habitat and engineered habitat preferences. Environment and Behavior, 45(1):86-112.


Lyons E . 1983. Demographic correlates of landscape preference. Environment and Behavior, 15(4):487-511.


Memari S, Pazhouhanfar M, Nourtaghani A . 2017. Relationship between perceived sensory dimensions and stress restoration in care settings. Urban Forestry & Urban Greening, 26:104-113.

Misgav A . 2000. Visual preference of the public for vegetation groups in Israel. Landscape and Urban Planning, 48(3-4):143-159.


Mitchell R, Popham F . 2008. Effect of exposure to natural environment on health inequalities: An observational population study. The Lancet, 372(9650):1655-1660.


Natori Y, Chenoweth R . 2008. Differences in rural landscape perceptions and preferences between farmers and naturalists. Journal of Environmental Psychology, 28(3):250-267.


Nielsen L, Curtis T, Kristensen T S , et al. 2008. What characterizes persons with high levels of perceived stress in Denmark? A national representative study. Scandinavian Journal of Public Health, 36(4):369-379.


Nielsen T S, Hansen K B . 2007. Do green areas affect health? Results from a Danish survey on the use of green areas and health indicators. Health & Place, 13(4):839-850.

Nijnik M, Mather A . 2008. Analyzing public preferences concerning woodland development in rural landscapes in Scotland. Landscape and Urban Planning, 86(3-4):267-275.


Nilsson K . 2006. Forests, trees and human health and wellbeing. Urban Forestry & Urban Greening, 5(3):109. DOI: 10.1016/j.ufug.2006.07.002.


Packer J, Bond N . 2010. Museums as restorative environments. Curator: the Museum Journal, 53(4):421-436.


Pálsdóttir A M, Persson D, Persson B , et al. 2014. The journey of recovery and empowerment embraced by nature—Clients’ perspectives on nature-based rehabilitation in relation to the role of the natural environment. International Journal of Environmental Research and Public Health, 11(7):7094-7115.


Rechtman O . 2013. Visual perception of agricultural cultivated landscapes: Key components as predictors for landscape preferences. Landscape Research, 38(3):273-294.


Reichhart T, Arnberger A . 2010. Exploring the influence of speed, social, managerial and physical factors on shared trail preferences using a 3D computer animated choice experiment. Landscape and Urban Planning, 96(1):1-11.


Ren X X, Kang J, Zhu P S , et al. 2018. Effects of soundscape on rural landscape evaluations. Environmental Impact Assessment Review, 70:45-56.


Roe J, Aspinall P . 2011. The restorative benefits of walking in urban and rural settings in adults with good and poor mental health. Health & Place, 17(1):103-113.

Rogge E, Nevens F, Gulinck H . 2007. Perception of rural landscapes in Flanders: Looking beyond aesthetics. Landscape and Urban Planning, 82(4):159-174.


Royo-Vela M . 2009. Rural-cultural excursion conceptualization: A local tourism marketing management model based on tourist destination image measurement. Tourism Management, 30(3):419-428.


Scott A . 2002. Assessing public perception of landscape: The LANDMAP experience. Landscape Research, 27(3):271-295.


Sonntag-Öström E, Nordin M, Lundell Y , et al. 2014. Restorative effects of visits to urban and forest environments in patients with exhaustion disorder. Urban Forestry & Urban Greening, 13(2):344-354.

Stigsdotter U K, Grahn P . 2011. Stressed individuals’ preferences for activities and environmental characteristics in green spaces. Urban Forestry & Urban Greening, 10(4):295-304.

Stilma E S C, Smit A B, Geerling-Eiff F A , et al. 2009. Perception of biodiversity in arable production systems in the Netherlands. NJAS: Wageningen Journal of Life Sciences, 56(4):391-404.

Sun D Y . 2011. Researches on the modes about conservation and development of Linpan in Chengdu Plain. Diss., Chengdu, China: Sichuan Agriculture University.

Tempesta T . 2010. The perception of agrarian historical landscapes: A study of the Veneto Plain in Italy. Landscape and Urban Planning, 97(4):258-272.


Torreggiani D, Tassinari P . 2012. Landscape quality of farm buildings: The evolution of the design approach in Italy. Journal of Cultural Heritage, 13(1):59-68.


Thompson C W, Roe J, Aspinall P , et al. 2012. More green space is linked to less stress in deprived communities: Evidence from salivary cortisol patterns. Landscape and Urban Planning, 105(3):221-229.


Tyrväinen L, Ojala A, Korpela K , et al. 2014. The influence of urban green environments on stress relief measures: A field experiment. Journal of Environmental Psychology, 38:1-9.


Tyrväinen L, Mäkinen K, Schipperijn J . 2007. Tools for mapping social values of urban woodlands and other green areas. Landscape and Urban Planning, 79(1):5-19.


Ulrich R S, Simons R F, Losito B D , et al. 1991. Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology, 11(3):201-230.


Van den Berg A E, Koole S L, Van der Wulp N Y. 2003. Environmental preference and restoration: (How) are they related. Journal of Environmental Psychology, 23(2):135-146.


Van Zanten B T, Verburg P H, Koetse M J , et al. 2014. Preferences for European agrarian landscapes: A meta-analysis of case studies. Landscape and Urban Planning, 132:89-101.


Völker S, Kistemann T . 2011. The impact of blue space on human health and well-being—Salutogenetic health effects of inland surface waters: A review. International Journal of Hygiene and Environmental Health, 214(6):449-460.


Wang H B . 2019. Study on spatial morphology of forest pan settlements in Western Sichuan. Diss., Chengdu, China: Southwest Jiaotong University.

Wang X B, Shi Y X, Zhang B , et al. 2019. The influence of forest resting environments on stress using virtual reality. International Journal of Environmental Research and Public Health, 16(18):3263. DOI: 10.3390/ijerph16183263.


Wherrett J R . 2000. Creating landscape preference models using internet survey techniques. Landscape Research, 25(1):79-96.


White M, Smith A, Humphryes K , et al. 2010. Blue space: The importance of water for preference, affect, and restorativeness ratings of natural and built scenes. Journal of Environmental Psychology, 30(4):482-493.


Yang L, Ho J Y S, Wong F K Y , et al. 2020. Neighbourhood green space, perceived stress and sleep quality in an urban population. Urban Forestry & Urban Greening, 54:126763. DOI: 10.1016/j.ufug.2020.126763.


Yang Y B, Li T S . 2007. A study on the development of rural tourist destinations in Xi’an based on the evaluation of tourists’ psychological perception. Tourism Tribune, 22(11):32-37. (in Chinese)

Zhang L . 2017. Tourism rural landscape features and landscape planning—A case study of Azheke Village of Yuanyang County in Yunnan Province. Landscape Architecture, 33(2):87-93. (in Chinese)

Zhang L, Yang K, Liu B Y , et al. 2019. A study on the regional characteristics perception of ancient towns in the South of the Yangtze River based on different perspectives of tourists and residents: A case study of Tongli ancient town. Chinese Landscape Architecture, 35(1):16-22. (in Chinese)

Zhou Y T, Liu H M . 2014. Discussing of creating a nostalgic atmosphere in landscape planning and design. Advanced Materials Research, 919-921:1649-1654.