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The Relationship between Landscape Metrics and Facial Expressions in 18 Urban Forest Parks of Northern China

Article

The Relationship between Landscape Metrics and Facial

Expressions in 18 Urban Forest Parks of Northern China

Ping Liu 1,†, Mengnan Liu 1,† , Tingting Xia 1, Yutao Wang 1,* and Peng Guo 2,*

1 College of Forestry, Shenyang Agricultural University, Shenyang 110866, China; lp_79@syau.edu.cn (P.L.);

lmndeyouxiang@163.com (M.L.); xtt3939@163.com (T.X.)

2 Environment and Resources College, Dalian Nationalities University, Dalian 116600, China

* Correspondence: ytw730@syau.edu.cn (Y.W.); gp@dlnu.edu.cn (P.G.);

Tel.: +86-024-8848-7150 (Y.W.); +86-158-4086-3291 (P.G.)

† These authors contributed equally to this work.

Citation: Liu, P.; Liu, M.; Xia, T.;

Wang, Y.; Guo, P. The Relationship

between Landscape Metrics and

Facial Expressions in 18 Urban Forest

Parks of Northern China. Forests 2021,

12, 1619. https://doi.org/10.3390/

f12121619

Abstract: Urban forests are an important green infrastructure that positively impacts human well-

being by improving emotions and reducing psychological stress. Questionnaires have been used

frequently to study the inﬂuence of forest experiences on mental health; however, they have poor

controllability and low accuracy for detecting immediate emotions. This study used the alternative

approach of facial reading, detecting the facial expressions of urban forest visitors and their rela-

tionships with the landscape metrics. Using the microblogging site, Sina Weibo, we collected facial

photos of 2031 people visiting 18 different forest parks across Northern China in 2020. We used

satellite imagery analysis to assess the elevation and pattern sizes of green space and blue space

areas. Age and location were taken as independent variables affecting facial expressions, which were

categorized as happy or sad. With increases in green space and intact park areas, people showed a

higher frequency of expressing happy scores. The results showed that the forest experience frequently

elicited positive emotions, suggesting that creating and maintaining urban green spaces enhance

people’s quality of life.

Keywords: urban forest landscape; forest therapy; facial expressions

Academic Editors: Hongxu Wei and

Richard Hauer

Received: 8 November 2021

Accepted: 22 November 2021

Published: 24 November 2021

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1. Introduction

More than half the world’s population lives in urban areas, which studies have shown

can negatively affect mental health [1]. Studies have also shown that contact with beauty

and nature has physical and mental health beneﬁts, including stress reduction [2–4]. Forest

Park sustainability and visitors’ park experiences and well-being depend on park managers’

correlative decisions and the parks’ locations [5]. People living in forest-rich areas have

increased opportunities for stress-reducing experiences in natural settings [6]. Research

has also shown that while age can affect park visitors’ emotions and forest experiencers [7],

comfortable forest environments induce positive emotions and reduce anxiety in young

adults [8], middle-aged and elderly visitors, and visitors with dementia [9]. Furthermore,

short-term exposure to an urban forest environment has been shown to signiﬁcantly lower

the heart rates of middle-aged men with hypertension and older adults [10,11]. Most previ-

ous research has used quantitative physiological measures or qualitative descriptions (e.g.,

questionnaires, interviews) to analyse urban parks’ effects on mental health. This study

tested the objectivity of previous studies’ results by using an alternative measure, facial

expressions, detecting the emotional responses of different types of visitors to different

urban forest environments.

Optimal combinations of natural landscape elements (e.g., water, plants, topography),

characteristics (e.g., location, appearance, art, and cultural elements), and park dimensions

provide restorative effects that induce resilience [12] and beneﬁt physical and mental

health [13–15]. Studies have found that urban residents with ready access to higher

Forests 2021, 12, 1619. https://doi.org/10.3390/f12121619

https://www.mdpi.com/journal/forests

Forests 2021, 12, 1619

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densities of green space and vegetation were less likely to suffer from heart disease [16]

and mental illness [17]. The number, size, accessibility, vegetation, and features of public

green spaces can dramatically affect residents’ mental health [18]. Especially conducive

to relaxation are blue spaces—surface waterbodies (e.g., lakes, ponds, wetlands), surface

watercourses (e.g., rivers, streams, estuaries), and other aquatic landscapes (e.g., beaches,

coasts) [19]. A Finnish study found that people’s favourite restorative experiences involved

exercise and activity in outdoor areas, waterside environments, and extensively managed

natural settings [20]. Experiencing blue spaces can improve mental health and promote

physical activity [21]. Natural environments with aquatic elements evoke more positive

emotions than those without blue spaces [22]. To ﬁgure the mechanism for impacts of

green space and blue space on public mental health is a requisite to compare their positive

effects on mental well-being and are beneﬁcial for better choices, such as the experience to

touch the nature.

This study focused on urban parks, which provide opportunities for urban residents

and visitors to engage in physical activity in managed natural settings in cities and enjoy

the restorative effects of nature [23]. Parks located close to residences generally have

more visitors [24]. Urban parks’ accessibility, area, size, and landscape style can inﬂuence

visitors’ impressions, activities, and mental well-being [25,26]. Brown et al. [27] found that

more frequent park use was associated with park size and suitability for physical activity.

Even small parks and pocket parks can positively impact visitors’ mental health [28].

Parks’ terrain and altitude can affect visitors’ experiences. Shukitt and Bandaret reported

that people’s behaviours and moods differ at different altitudes; they can become more

argumentative and irritable or more euphoric at high altitudes [29]. Dudek found that the

terrain’s slope was closely related to the aesthetics of forest landscapes; for example, tree

species with high aesthetic landscape values mainly grow on high, concentrated slopes

of 8–12◦ [30]. Thus, park visitors’ emotional states can vary depending on the parks’

accessibility, area, size, scale, landscape style, design features, elevation, and topography.

Geographic information systems (GIS) provide spatial decision support for forestry

and park management and planning [31]. GIS data can provide valuable quantitative and

timely information for mapping and assessing changes in landscapes and green spaces [32].

Stored satellite imagery can provide decades of consistent, precise, high-quality images [33]

and detailed landscape pattern records at different temporal and spatial scales [34]. Data

from the joint National Aeronautics and Space Administration (NASA) and United States

Geological Survey (USGS) Landsat program have become increasingly critical of extensive

studies on landscape dynamics [35]. The quantitative approach at a global scale relies on

GIS, high-resolution digital elevation models (DEMs), and digital aerial imagery [36], which

require neither public preferences nor large-scale research or measures [37] to identify and

monitor precise locations over time.

Emotions can be observed through external physical expressions and monitored by

measuring physiological changes and internal sensations [38]. Palermo and Rhodes wrote

that “faces are probably the most biologically and socially signiﬁcant visual stimuli in the

human environment” [39] (p. 75), requiring us to rapidly detect, categorize, and interpret

facial expressions as clues about others’ emotions, behaviours, and intentions. People’s

facial expressions vary widely and can depend on many factors, including their emotions,

social setting, environment, health, cultural background, and personality [40]. Emotional

perception is usually expressed through multiple sensory channels, with crosstalk between

the channels (e.g., sight, smell, touch, taste, hearing) [41]. For example, music can affect

emotions (and thus facial expressions) through auditory, visual, and touch sensations [42].

Responses can be subjective, but pleasant smells induce positive emotions, and unpleasant

smells induce negative emotions [43]. Therefore, perceptions of urban parks can be affected

by numerous sensory perceptions, such as sights, sounds, smells, proprioception (body

position), temperature, and humidity [44]. We posited that assessing park visitors’ facial

expressions could better clarify the inﬂuence of urban forest environments on mental health

and well-being.

Forests 2021, 12, 1619

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Traditional research has relied primarily on self-reported scores to assess visitors’

emotions about the forest experience [45]. However, surveys require a signiﬁcant time

commitment from participants, and it can be challenging to recruit enough participants

to validate the survey. We proposed an alternative method to test urban forest park

visitors’ psychological responses: facial reading. This relatively new technique that uses a

software algorithm trained to assess emotion expressions using visual records of human

faces [46]. Our method of facial emotion recognition followed four steps: (1) detecting facial

images; (2) processing the photos to ensure that they were clear and without excessive

modiﬁcation; (3) extracting facial features; and (4) classifying the facial expressions and

scoring the expressions [47]. Previous studies have demonstrated that facial reading

techniques can be used to assess people’s emotional states in urban forests. For example,

Wei et al. [44] used facial reading technology to investigate the impact of forest experiences

on visitors’ emotional states. Current facial recognition technology has achieved 87%

accuracy in categorizing human facial expressions by emotion [48]. The use of facial

reading technology signiﬁcantly contributes to the real-time detection of park visitors’

facial expressions, providing a measure of their emotional responses to the park.

Our study analysed the relationship between visitors’ facial expressions and urban

forest landscape metrics in Northern China. We calculated the metrics of green spaces,

blue spaces, park features, and park elevations to test the relationship between three

expressions—happy, sad, and neutral—and the positive response index. We hypothesized

that an increase in the combined green space area, blue space area, overall park area, and

park elevation would arouse higher positive emotions in visitors. This study’s ﬁndings

will provide a useful reference for park visitors’ emotional responses to urban forests to

inform the design and construction of future urban forest parks.

2. Materials and Methods

2.1. Study Sites

This study focused on nine cities located in Northern China (Table 1). As the centres

of China’s provinces, provincial capitals have a high status and momentous inﬂuence on

regional economic development, driving and promoting the development of local and

neighbouring areas [49]. Hence, we selected two forest parks in nine provincial capital

cities in Northern China as the research sites, randomly selecting two forest parks with

different landscapes in each city. Figure 1 shows the speciﬁc geographical distribution of

the studied forest locations.

Table 1. Summary of information about forest parks and the number of photos in northern China in 2020.

City

Huhhot

Taiyuan

Shijiazhuang

Beijing

Tianjin

Urumqi

Shenyang

Changchun

Harbin

Forest Park

1. Hadamen National Forest Park

2. Daqingshan Wildlife Park

3. Taiyuan Forest Park

4. Wenying Park

5. Century Park

6. Xiushui Park

7. Olympic Forest Park

8. Grand Canal Forest Park

9. Pak Ning Park

10. Tanggu Forest Park

11. People’s park

12. Tianshan Canyon

13. Beiling Park

14. Changbai Island Forest Park

15. Jingyuetan Scentic Spot

16. South Lake Park

17. Heilongjiang Forest Botanical Garden

18. Sun Island Park

Coordinate

41◦01 N, 111◦58 E

40◦88 N, 111◦62 E

37◦91 N, 112◦54 E

37◦87 N, 112◦57 E

38◦02 N, 114◦54 E

38◦09 N, 114◦39 E

40◦02 N, 116◦39 E

39◦88 N, 116◦74 E

39◦17 N, 117◦22 E

39◦10 N, 117◦67 E

43◦80 N, 87◦61 E

43◦49 N, 87◦44 E

41◦85 N, 123◦43 E

41◦75 N, 123◦39 E

43◦78 N, 125◦48 E

43◦86 N, 125◦31 E

45◦71 N, 126◦65 E

45◦79 N, 126◦60 E

Number of Photos

100

178

111

142

125

134

133

141

180

160

113

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Figure 1. Distributions of study areas (A) in northern China. Forest park locations are labelled by green dots and red dots in

north China (B), northwest China (C), and northeast China (D).

2.2. Data Source

2.2.1. Photo Download and Treatment

We used the microblogging site Sina Weibo (Sina Corporation, Beijing, China) as the

photo data source since the information was completely accessible to the public and the

photos contained geolocation data [50]. Weibo is the largest social network service (SNS)

platform in China, publishing the largest number of microblogs by Chinese users [51].

The study focused on visitors with typical oriental facial features, collecting 2031 photos

to test the facial emotional expression of those visitors from 1 January to 31 December

2020, from 18 different urban forest parks. Our procedures and requirements for collecting

photos for academic use were in full accordance with the ethical standards of the College

of Forestry, Shenyang Agricultural University, China (CF-EC-2021-001). We used the data

only to categorize the facial expressions as happy, sad, or neutral; this data could only be

used for academic research, not any business initiative.

We downloaded and processed the photographs following these steps. First, we

collected all the microblog photographs that showed oriental faces and contained check-in

(geolocation) information relevant to one of the 18 urban forest parks between 1 January to

31 December 2020. Second, we screened the collected photos to select only photos where

people’s facial elements (e.g., eyes, eyebrows, nose, mouth, ears) were clear and not overly

decorated or covered. Third, we cropped the photos so that each photo contained only one

face, with the nose axis perpendicular to the horizontal plane. Finally, we marked all the

processed photos with the geolocation data, the date they were taken, and the person’s age

(i.e., toddler, youth, adult, old).

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2.2.2. Landscape Metric Collection and Treatment

We used the ArcGIS software to accurately outline the green spaces, blues spaces, and

park boundaries in the Landsat image of each forest park. We then calculated the average

elevation of the park. These processes required ﬁrst performing projection conversion on

the Landsat data so we could accurately outline of the green spaces, blue spaces, and park

boundaries. We used DEM images to randomly select multiple points on the cropped park

layer to calculate the average elevation of each forest park. Finally, we studied the green

space, blue space, park area, and average park height as landscape metrics.

2.3. Facial Expression Analysis

We analysed the processed photos using the FireFACETM facial recognition software,

Version 1.0 (Zhilunpudao Agricultural Science & Technique Inc., Changchun, China),

assigning each face a happy, sad, or neutral expression score. We also used the positive

response index (PRI) as a score to assess visitors’ net positive emotions [48]. We calculated

this variable as the happy score minus the sad score, the visitors’ immediate net positive

emotions [44]. We calibrated the FireFACETM software by training it with oriental faces

with deliberately posed happy, sad, and neutral facial expressions. We considered the

training complete when the software could correctly identify 80% of the happy or sad faces

and 85% of the neutral faces [52]. These three expressions were the only expressions used

in the study’s analyses because they had the highest accuracy after testing and could be

matched reliably to the landscape metrics for analysis.

2.4. Statistical Analysis

We used IBM® SPSS® Statistics for Windows, Version 26.0 (IBM Corp., Armonk,

NY, USA) for our data analysis. In the analysis of variance (ANOVA) on the expression

scores, the following variables were ﬁxed effects: age (toddler, youth, middle, old) and

city (Huhhot, Taiyuan, Shijiazhuang, Beijing, Tianjin, Ürümqi, Shenyang, Changchun, and

Harbin). When the data did not follow a normal distribution, we ranked the expression

scores according to the order of variance to make them distribution-free. In the ANOVA

analysis of the landscape metric, we tested the differences in the forest parks’ landscape

metrics in the different cities, using the mean value ± standard deviation (SD) to express

the results. When a signiﬁcant effect was shown, we used the least signiﬁcant difference

(LSD) test with a signiﬁcance level of 0.05 for comparison to prevent unevenness in the

number of repetitions between the data groups. The original data used Spearman’s rank

correlation coefﬁcient analysis to evaluate the relationship between the landscape metrics

(independent variables) and the happy, sad, and neutral expression scores and the well as

PRI (dependent variables).

3. Results

3.1. Landscape Metric among Different Northern Cities Analysis

Table 2 shows the landscape metrics of the 18 forest parks in the nine capital cities in

Northern China, Figure 2 shows the mean distribution of the green spaces, blue spaces,

park area, and average park elevation of each urban forest park.

We found signiﬁcant differences in the green spaces, blue spaces, park area, and

average park elevation among the nine cities (Table 3). The total green space in Ürümqi

(53,073.25 ± 43,820.81 ha) was signiﬁcantly higher than in the other cities. In terms of

blue spaces, Changchun (307.94 ± 235.87 ha), Beijing (123.50 ± 59.13 ha), and Harbin

(75.66 ± 45.48 ha) differed signiﬁcantly from the other ﬁve cities. Changchun had the

largest total blue space area, and Huhhot had the smallest (none). Ürümqi had the

largest forest park area (61,895.76 ± 51,066.03 ha), and Shijiazhuang had the smallest

area (45.35 ± 18.83 ha). Our calculations of the forest parks’ average elevation revealed

that the average park elevations in Harbin (122.25 ± 11.37 m), Changchun (233.98 ± 22.96

m), Taiyuan (781.83 ± 4.68 m), Huhhot (1378.31 ± 331.90 m), and Ürümqi (1588.03 ± 592.87

Forests 2021, 12, 1619

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m) differed from the other four cities, but there was no signiﬁcant difference between

Tianjin (4.94 ± 0.30 m), Beijing (17.60 ± 13.34 m), and Shenyang (35.00 ± 2.26 m).

Table 2. Summary of information about forest parks landscape metrics in north of China in 2020.

City

Huhhot

Taiyuan

Shijiazhuang

Beijing

Tianjin

Urumqi

Shenyang

Changchun

Harbin

Forest Park

Hadamen National

Forest Park

Daqingshan Wildlife

Park

Taiyuan Forest Park

Wenying Park

Century Park

Xiushui Park

Olympic Forest Park

Grand Canal Forest

Park

Pak Ning Park

Tanggu Forest Park

People’s park

Tianshan Canyon

Beiling Park

Changbai Island

Forest Park

Jingyuetan Scentic

Spot

South Lake Park

Heilongjiang Forest

Botanical Garden

Sun Island Park

Green Area (ha)

2970.00

521.73

95.90

3.37

11.29

38.29

404.22

546.66

7.74

117.73

15.98

89,104.68

233.99

25.95

6033.38

86.59

86.34

2126.38

Water Area (ha)

none

25.73

3.96

3.53

10.90

42.86

166.66

9.55

33.39

1.22

none

26.70

4.19

530.00

58.13

2.16

103.63

Forest Park

Area (ha)

3600.00

820.00

224.00

11.90

28.82

66.70

680.00

713.33

57.87

460.00

30.15

103,848.54

356.74

40.25

9638.00

222.34

136.00

3800.76

Forest Park

Elevation (m)

1832.73

1137.47

778.59

788.83

62.26

84.20

35.81

7.86

4.58

5.20

870.20

2075.52

37.33

32.81

255.61

209.66

140.63

115.26

Figure 2. Cont.

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Figure 2. Mean distribution of the green area (A), water area (B), park area (C), and park average elevation (D) in northwest,

north and northeast cities in 2020.

Table 3. Analysis of variance (ANOVA) of landscape metrics of green area, water area, park area and park average elevation

among different northern cities.

Variable

Sum of Squares

DF 1

Mean Square

Green area

Water area

Forest-park area

Forest-park

elevation

City Inter-group

City Intra-group

Total

City Intergroup

City Intra-group

Total

City Inter-group

City Intra-group

Total

City Inter-group

City Intra-group

Total

545,980,466,666.19

433,477,010,268.79

979,457,476,934.99

24,308,520.10

20,032,288.71

44,340,808.81

734,483,266,261.66

592,347,112,110.87

1,326,830,378,372.53

642,193,245.86

95,747,708.43

737,940,954.30

8.00

2022.00

2030.00

8.00

2022.00

2030.00

8.00

2022.00

2030.00

8.00

2022.00

2030.00

68,247,558,333.27

214,380,321.60

3,038,565.01

9907.17

91,810,408,282.71

292,951,094.02

80,274,155.73

47,352.97

Note: 1 DF, degree of freedom; 2 Sig, signiﬁcance, the same below.

Sig. 2

0.000

3.2. Visitors’ Facial Expressions Analysis

3.2.1. Different Ages of Visitors on Facial Expressions Analysis

We categorized the visitors as toddlers (0–5 years), youth (15–25 years), adult

(30–60 years), and old (60+ years) based on their appearance and, when it was avail-

able, data were acquired from the Sina Weibo site. Table 4 shows the results of the ANOVA

of all the visitors’ happy, sad, and neutral expressions. The happy expression scores of

the adult and old visitors were signiﬁcantly higher than for toddlers and youths. There

were signiﬁcantly more neutral facial expressions among the toddlers and youth than the

adult and old visitors. There were signiﬁcantly more sad facial expressions among the old

visitors than the toddler, youth, and adult visitors (Figure 3).

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Table 4. Analysis of variance (ANOVA) of toddler, youth, middle-aged and old visitors on happy, sad, neutral facial

expressions and positive response index in forest parks.

Source of Variance

Sum of Squares

DF 1

Mean Square

Happy

Sad

Neutral

PRI 3

Age Inter-group

Age Intra-group

Total

Age Inter-group

Age Intra-group

Total

Age Inter-group

Age Intra-group

Total

Age Inter-group

Age Intra-group

Total

45,251.894

3,360,420.586

3,405,672.48

2938.082

518,526.228

521,464.31

43,757.541

2,437,688.961

2,481,446.502

51,707.131

5,315,552.956

5,367,260.086

2027

2030

2027

2030

2027

2030

2027

2030

15,083.965

1657.83

979.361

255.81

14,585.847

1202.609

17,235.71

2622.374

Note: 1 DF, degree of freedom; 2 Sig, signiﬁcance; 3 PRI, positive response index, the same below.

Sig. 2

0.000

0.009

0.000

Figure 3. The neutral, sad, and happy expression scores of toddlers (A), youth (B), middle-aged (C) and old (D) visitors in

northern forest parks.

Forests 2021, 12, 1619

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3.2.2. Different Cities of Visitors on Facial Expressions Analysis

We found signiﬁcant differences in the happy and neutral scores of visitors’ expres-

sions in China (Table 5). There were signiﬁcantly higher happy expression scores in Tianjin

than in Taiyuan, Harbin, Changchun, Ürümqi, Shenyang, and Shijiazhuang (Figure 4A).

There were signiﬁcantly higher neutral scores in Shijiazhuang than in Taiyuan, Beijing,

Huhhot, and Tianjin (Figure 4B). There were no differences in the sadness scores between

cities.

Table 5. Analysis of variance (ANOVA) of cities on happy, sad, neutral facial expressions and positive response index in

forest parks.

Source of Variance

Sum of Squares

DF 1

Mean Square

Happy

Sad

Neutral

PRI 3

City Inter-group

City Intra-group

Total

City Inter-group

City Intra-group

Total

City Inter-group

City Intra-group

Total

City Inter-group

City Intra-group

Total

55,801.983

3,349,870.497

3,405,672.48

2669.126

518,795.184

521,464.31

40,370.52

2,441,075.982

2,481,446.502

74,121.697

5,293,138.389

5,367,260.086

2022

2030

2022

2030

2022

2030

2022

2030

6975.248

1656.711

333.641

256.575

5046.315

1207.258

9265.212

2617.774

Note: 1 DF, degree of freedom; 2 Sig, signiﬁcance; 3 PRI, positive response index, the same below.

Sig. 2

0.000

0.239

0.000

Figure 4. Happy (A), and neutral (B) scores on visitors’ face in northern cities. Error bars stand for standard errors that

stared from the columns (means). Different letters of a, b and c indicate signiﬁcant differences of ranked scores according to

LSD test at the 0.05 level.

3.2.3. Cities and Ages Interaction Analysis

We found signiﬁcant differences between the variables city and age and the visitors’

happy, sad, and neutral facial expressions. When the city variable interacted with age, only

happiness and sadness were signiﬁcantly different; however, there was no difference in the

neutral expression (Table 6).

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Table 6. Analysis of variance (ANOVA) with the mixed model of city, age, and their interaction on repeated measures of

ranked scores about happy, sad, and neutral facial expression scores and the positive response index.

Source

Model

City

Age

City × Age

Variable

III Sum of Squares

DF 1

Mean Squares

Happy

Sad

Neutral

PRI 3

Happy

Sad

Neutral

PRI 3

Happy

Sad

Neutral

PRI 3

Happy

Sad

Neutral

PRI 3

147,640.354

19,413.101

111,873.975

219,572.676

38,955.391

9424.833

20,564.952

75,966.925

26,088.062

2551.57

21,006.118

36,141.331

55,217.161

13,185.125

36,484.372

100,695.677

4762.592

626.229

3608.838

7082.99

4869.424

1178.104

2570.619

9495.866

8696.021

850.523

7002.039

12,047.11

2760.858

659.256

1824.219

5034.784

Note: 1 DF, degree of freedom; 2 Sig, signiﬁcance; 3 PRI, positive response index, the same below.

Sig. 2

0.000

0.002

0.000

0.027

0.000

0.001

0.017

0.001

0.003

0.028

0.000

0.060

0.007

3.2.4. Positive Response Index Analysis

Adult visitors had higher PRI scores than toddlers and youth visitors (Figure 5A).

The PRI scores of visitors in Tianjin were signiﬁcantly higher than in Harbin, Changchun,

Shenyang, Shijiazhuang, and Ürümqi (Figure 5B). The visitors’ PRI scores were positively

correlated with park size (p = 0.011), indicating that people’s net positive emotion levels

were signiﬁcantly related to the size of the park (Table 7).

Figure 5. PRI of different ages visitors (A) and PRI of visitors from different cities in China (B). Error bars stand for standard

errors that stared from the columns (means). Different letters of a and b indicate signiﬁcant differences of ranked scores

according to an LSD test at the 0.05 level.

3.3. Landscape Metrics and Facial Expressions Correlation Analysis

The visitors’ happy scores were positively correlated with the green spaces (p = 0.019)

and the park area (p = 0.001); we found the highest number of happy faces in the parks

with the greenest space and overall park area (Table 7). However, there was no correlation

between sad expressions and the four-landscape metrics, suggesting that visitors were not

sadder because a park had less green space, blue space, or overall park area or a speciﬁc

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average park elevation. However, visitors’ neutral expressions were negatively correlated

with a green space area (p = 0.017) and overall park area (p = 0.002). This suggested

that increasing vegetation coverage would not necessarily cause the number of neutral

expressions to have happy (or sad) expressions.

Table 7. Coefﬁcients from Spearman correlation between the landscape metric and three facial expressions and the positive

response index of visitors therein in target forest park.

Index

Happy

Sad

Neutral PRI 1 Green Area Water Area Park Area Park Elevation

Happy

Sad

−0.731 **

Neutral

−0.886 ** 0.522 **

PRI 1

0.936 ** −0.882 ** −0.768 **

Green area

0.052 *

−0.001 −0.053 * 0.039

Water area

0.014

0.012

−0.02

0.006

0.324 **

Park area

0.071 ** −0.011 −0.070 ** 0.056 *

0.984 **

0.319 **

Park elevation −0.026

0.015

0.028

−0.027

0.286 **

−0.422 **

0.261 **

Note: * and ** mean signiﬁcant correlation at p < 0.05 and extremely signiﬁcant correlation at p < 0.01, respectively. 1 PRI, positive response

index.

4. Discussion

4.1. The Age Effect on Facial Expressions

Thanks to the popularity of mobile devices with high-quality cameras, visitors upload

more selﬁes than in the past [53]. More of our facial photographs originated with youths’

selﬁes than other visitors’ selﬁes. Artiﬁcial age assessments have also been used in studies

of public sentiment during pandemics and to provide large-scale data [54]. Toddlers and

youths scored relatively lower than adults and old people on happy expressions. The

sadness scores were lower among adults than among the toddler, youth, and old visitors.

These results suggest that the forest environments had a positive psychological effect

on adult and old visitors, which is consistent with previous research [2]. Therefore, we

concluded that the restorative effects of the urban forest experience extended to the elderly.

The urban park activities provided opportunities for older adults to interact socially while

enjoying the natural environment, enabling them to start and maintain friendships, which

is known to beneﬁt mental health [9]. Because we assessed the emotional expressions

of different age groups during their urban forest park experiences using many sample

photographs, our results provide empirical evidence for the improvement of mental health

through urban parks.

4.2. The Discrepancy of Facial Expressions among Cities

Urbanization is the product of a combination of social, economic, and resource factors.

The rapid development of cities should be based on the science of the human settlement

environment to balance the environmental carrying capacity and urban development

intensity [55]. In 1984, the World Health Organization (WHO) proposed establishing

healthy cities to promote the healthy urban development [56]. The Healthy Cities program

links cities’ living conditions to citizens’ health. Health and well-being are interrelated,

and the environment of a healthy city can inﬂuence residents’ and visitors’ health and well-

being. Therefore, psychological variables might be more signiﬁcant for health than external

variables [57]. Our results showed that parks in Tianjin, Beijing, and Harbin were strongly

correlated with happy expressions; they were the highest in Tianjin. Shijiazhuang had the

lowest happy expression scores. Our results suggest that visitors to northern urban forest

parks showed a higher frequency of happy expressions than visitors to southern urban

forest parks, which is consistent with Wei et al. [46]. Although we found no signiﬁcant

differences in sad expressions among the nine cities, we found the greatest number in

Shijiazhuang was the most prominent.

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4.3. Relationship between Landscape Metrics and Facial Expressions

Other studies have focused on speciﬁc forest landscape metrics [58]. According to

Chen et al. [59], changes in the landscape scale can affect the visual quality of the visitor

photos. People establish connections with the environment through psychological reac-

tions [60]. Psychophysics quantitatively investigates the relationship between stimuli, the

sensations they elicit, and the magnitude of changes in perceivable physical stimuli [12].

This study explored the impact of various urban forest park stimuli on visitors’ facial

expressions from the perspective of landscape metrics. We found a signiﬁcant positive

correlation between green spaces and happy expressions. Similarly, Gozalo et al. [61] found

that large green spaces were positively correlated with frequent strolling and relaxation

among urban residents. Therefore, people’s urban forest park experiences might be inﬂu-

enced by lower normalized difference vegetation index (NDVI) scores; NVDI is a graphical

indicator of the live green vegetation and tree cover density. Residents of communities

with abundant green spaces tend to enjoy better mental health. One study in the Nether-

lands found that the positive correlation was most pronounced among senior citizens,

homemakers, and those from lower socioeconomic groups [62]. More research is needed to

verify the ﬁndings that green spaces induce positive emotions in visitors. Studies show

that experiencing blue spaces helps reduce stress and promote social contact among the

elderly [63]. Other studies support the health beneﬁts of blue space environments [19,64].

In fact, Sonntag-Ostrom et al. [65] reported that blue spaces were the most restorative

landscapes. Similarly, Nutsford et al. [66] indicated that people’s psychological stress

decreased when they were within 15 km of visible outdoor blue spaces. In our study, there

were three forest environments without water element landscape, which may lead to less

happiness of visitors.

The area and average elevation of forest parks are also crucial factors affecting park

visitors’ expressions. People usually prefer to visit large parks with speciﬁc facilities [67].

Another study found that a three-day trek in the highlands could improve children’s

negative moods [68]. We found that the park scale was positively correlated with the PRI

scores. Other studies have shown that humidity, terrain slope, and the vegetation are

also important factors that affect visitors’ experiences [30]. Hence, when planning and

constructing an urban forest park, designers should ensure that the size of the park meets

the residents’ needs to stay active and exercise regularly. They should also consider the

aesthetic value of the park to optimize the beneﬁts of natural surroundings.

4.4. Limitation

We studied the facial expressions of visitors in urban forest parks in nine of China’s

provincial capitals. Future studies should expand the scope of the target city selections and

park locations to eliminate the inﬂuence of neighbourhood and inner group differences.

Second, we addressed the effect of the park environments on visitors’ facial expres-

sions, but we did not set up any other urban environment as a control group for comparison.

Future studies should consider additional urban variables to allow comparisons.

Third, people generally make happy expressions when they take selﬁes or have their

photos taken by others, which might have affected the accuracy of our assessments; visitors

might have worn sad or neutral faces until they were being photographed, which could

have led to inaccuracies in our analyses. Therefore, future research should try to choose

randomly captured candid photos for testing rather than selﬁes because the expressions

would be more representative than posed photos.

Fourth, our age categorizations were guesses based primarily on people’s appearances.

Liu et al.’s study [51] used visual assessments with the same age ranges: toddlers 0–5,

youths 15–25, adults 30–60, and old people 60+; people who seemed to fall into the gaps

were added to the nearest age group (e.g., someone seven years old would be classiﬁed

with the toddlers). We might have misclassiﬁed the facial expressions because of the

person’s age or the ﬂuidity of expression; for example, a snapshot of what we classiﬁed as

a toddler’s neutral expression might have been a transitional expression between happy

Forests 2021, 12, 1619

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and sad. However, there was no accurate indicator for distinguishing the visitors’ ages

without additional information.

Finally, we selected only green spaces, blue spaces, park area, and average park

elevation as the landscape metrics; we did not include hardscapes or structures. Future

studies might follow the lead of Su [69], who divided forest landscapes into coniferous

forest, broad-leaved forest, mixed forest, and static water landscape spaces. Su’s study

found that people’s expressions of excitement and relaxation were more frequent in the

mixed forests than in the other three groups. Therefore, our future studies will consider

the inﬂuence of the different landscape elements on visitors’ facial expressions to conﬁrm

the connection between nature and beneﬁts to mental health.

5. Conclusions

We screened and downloaded 2031 facial photographs from Sina Weibo of urban park

visitors in nine provincial capitals in Northern China. We performed facial recognition

on the processed photos using the FireFACETM ver 1.0 software to categorize the faces’

expressions as happy, sad, or neutral. The results showed that the urban parks with the

greatest number of visitors with happy expressions were the parks with the greenest space

and overall area. Thus, we recommend that future urban parks should feature a high

percentage of vegetation to maximize residents’ experiences with managed natural spaces

to improve their health and well-being.

Author Contributions: Conceptualization, P.L. and M.L.; methodology, P.L. and M.L.; software,

M.L.; validation, P.L., M.L. and T.X.; formal analysis, P.L., M.L. and Y.W.; investigation, M.L. and

T.X.; resources, P.L.; data curation, M.L. and T.X.; writing—original draft preparation, P.L. and

M.L.; writing—review and editing, P.L. and M.L.; visualization, P.G.; supervision, Y.W.; project

administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published

version of the manuscript.

Funding: This research was funded by the Liaoning Key Research and Development Program

(grant number: 2020JH2/10200033, 2021JH2/10200007), the National Natural Science Foundation of

China (grant number: 31771695), and the Fundamental Research Funds for the Central Universities

(Program for ecology research group) (grant number: 0901-110109).

Institutional Review Board Statement: The study was conducted according to the guidelines of

the Declaration of Helsinki, and approved by The Ethic Committee of Human Studies, College of

Forestry, Shenyang Agricultural University (protocol code: CF-EC-2021-001; date: 2 November 2021).

Informed Consent Statement: The study was approved by The Ethnic Committee of Human Studies,

College of Forestry, Shenyang Agricultural University (protocol code: CF-EC-2021-001). All photos

data for this study were obtained through Sina Weibo open source. Humans were informed when

they uploaded photos on this open source platform. The Ethical Approval for Human Studies was

submitted to the Forests submission system.

Data Availability Statement: Not applicable.

Acknowledgments: Authors acknowledge Lingquan Meng for photos treatment.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

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