1. Introduction
Urban environments increase the risk of adverse effects on mental health [
1]. Studies have found that contact with greenery in urban nature benefits psychological health [
2,
3]. Green space is positively related to active, positive emotions [
4,
5]. People stroll in metropolitan settings with greeneries that affect physical and mental relaxation [
6,
7]. Specifically, seeing green nature can lower blood pressure and the heart rate [
8], reduce negative emotions (depression, anxiety, and stress) [
9], and induce positive emotions [
6]. It is also argued that contact with greening does not consistently promote positive emotions [
10]. It is necessary to identify the correspondence of the greening effect with mental well-being as more detailed determinants. More needs to be known about the explicit mechanism that drives the perception of positive emotions.
The remotely detected multispectral image is a common data source for measuring the quantification of greening, which can also be applied to an extensive area of studies [
11,
12]. Previous researchers have usually used conventional greening variables to describe the natural probability of human exposure, such as the Normalized Difference Vegetation Index (NDVI) [
13,
14], the green area [
5,
15], and the green ratio [
16]. Conventional greening variables are used to evaluate the largeness of green spaces from a top-down view, which cannot assess the frequency of seeing visual greening. For example, in mountains, there is a high level of green cover, but it is difficult to encounter exact greenery from an experience therein if no path is reachable [
17]. Green areas do not represent the actual exposure of visitors to greening. In addition, traditional greening indicators have the disadvantage of not being able to measure vertical greening and sub-canopy vegetation due to the tree canopy shading [
18]. Therefore, it is necessary to quantify the amount of green area and the greening of human contact from visual perspectives [
19] to explore the impact on emotional perception.
The horizontal view of the Green View Index (GVI) is closer to actual human exposure because visual greeneries can be accurately measured [
20]. GVI data can be collected through field research photography [
21,
22]. They can also be obtained from online map images [
23,
24]. Field surveys were usually used to measure greenness for the GVI, which, however, relies on a large labor investment of a long time trapped in forests and is easily interrupted by weather [
20] and wild animals [
25]. Open Street Maps (OSM) is a real-view mapping service that provides users with panoramic street-view images. With the increasing requirement for big data and machine learning in recent years, OSM has been widely used as a dataset for social perception and the city environment [
26,
27]. Using these maps, users can get an actual browsing experience. Yu et al. used OSM to obtain the GVI to quantify street greening, in response to which visual greening was effectively distinguished [
23]. Cheng et al. used OSM with eight orientations to composite panoramic photos and described accessible greening with the Panoramic Green View Index (PGVI) [
28]. A panoramic street-view image can cover an angle of 360° of surrounding environments [
20]. It is essential to add the PGVI to conventional greening variables, such as the NDVI and the green ratio, for assessing the nature of greening that people can actually experience along streets.
Recent studies have explored the effect of the largeness of green spaces on emotional health [
29]. In large-scale planning of urban parks, people generally trust the sense that large green spaces may result in a higher frequency of inducing positive emotions through contact with nature, such as the green ratio [
16,
26] and the NDVI [
13]. For example, Zhu [
13] showed that a higher NDVI in green areas leads to a higher emotional probability and closer association with happy emotions. In addition, larger green spaces of the NDVI can alleviate negative emotions, such as fear and anger [
13]. The NDVI can be classified using thresholds [
30] and Jenks natural breaks classification [
31,
32]. Jenks natural breaks classification has the distinct advantage of being better suited to geo-mapping than the average threshold method [
32]. We can use the Jenks’ classification of landscape metrics to evaluate the impact of accessible greening on emotional health. Currently, a separate indication of greening does not fully describe the exposed greening [
33]. Emotions are influenced not only by the largeness of green space but also by its elevation [
16] and location [
34,
35]. Because the association of multiple space factors with emotions is not fully known, we emphasize landscape metrics, such as the NDVI, green ratio, PGVI, and elevation, to quantify greening from different perspectives and explore the effects on facial emotions.
The conventional method of assessing emotional perception is limited to questionnaires and self-reported scores [
36,
37]. These would lead to the uncertainty of the impact of urban forests on emotions due to subjective choices influenced by respondents [
38]. In addition, the conventional way is limited by the amount of data and is labor intensive and time-consuming [
39]. In recent years, some scholars have used wearable devices to investigate human emotions [
40]. This method has high accuracy and can accurately measure mood changes but is limited by the device and sample size [
41]. With the development of the internet and social media platforms, social network services (SNSs) have been widely used as a source of data. SNSs have a considerable number of users who can upload data voluntarily anytime and anywhere [
13]. Global SNSs include Facebook, Twitter [
42], Instagram [
43], Flickr [
44], and, in China, mainly Sina Microblog [
15,
45]. With the increasing use of technology and software [
46], researchers can extract and analyze public sentiment [
15,
44]. Facial expressions reflect the perception of human emotions, which can be analyzed by modern facial recognition technology [
15]. There are several advantages of obtaining facial expression data from SNS photos, including more realistically exposing human emotions.
We focused on the impact of visual greeneries (PGVI) and green space areas (NDVI) on emotional expression. In this study, we tested the relationship between actually perceived greening and visitor sentiment in different green space areas in Nanchang City, China. The quantification of multidimensional greening allows for a better understanding of the emotional impact of green spaces, which can enhance the mental health of residents in the future. In this study, we came up with the following hypotheses: (1) Increasing the NDVI does not always promote positive effects. (2) Emotional expressions of visitors in public green spaces are related to the PGVI.
4. Discussion
Scholars have used the PGVI to describe human visual greening and explored its correlation with traditional remotely greening metrics, such as the NDVI [
61,
62]. However, our results were unable to demonstrate that greenness of the NDVI and PGVI were not correlated with each other. The green ratio was significantly associated with the PGVI and NDVI. Although this study focused on the NDVI and PGVI, the findings may well have a bearing on the green ratio. Furthermore, elevation had a significant positive correlation with the NDVI. The higher the elevation, the larger the area of green in the study area. However, the PGVI did not increase with the NDVI and elevation. High-value NDVIs do not mean high-value PGVIs. It can therefore be assumed that the correlation between the green space area and visual greening was related to geographic location. Bigger green space areas do not mean more greening is accessible. An implication of this is the possibility that the PGVI plays an irreplaceable role in greening quantification.
This research, conducted in the same city, excluded other factors, such as city location [
33,
34] and climate [
4,
34], from influencing sentiment perception. Different degrees of the NDVI have diverse influences on sentiment. To be specific, distinct values of the NDVI had a significant negative effect on the neutral expression. Meanwhile, NDVI groups had positive effects on happy and PRI scores. We did not find any significant difference in sadness among different green space areas. These results are in accord with recent studies indicating that experience of urban greening is not enough to cause sad expressions [
63]. Neutral emotion had the most significant effect, which was higher than happy and PRI scores. A possible explanation for this might be that neutral emotion may be more responsive than sadness in public green spaces.
We also found that the PGVI had a significant effect on positive emotions. Positive sentiment increases with increasing PGVI, and neutral sentiment decreases with increasing PGVI. The photos used for the emotion perception measurement were obtained from the photo data uploaded by users on Sina Microblog in our research. Photographs are more accurate and realistic in interpreting emotions compared to traditional questionnaire methods [
38]. Due to the impact of the COVID-19 epidemic in 2020, many people were wearing face masks [
33]. The chances of taking pictures of facial expressions were reduced in green spaces. To avoid data collection restrictions, we chose to analyze facial expressions from 2020 to 2021. Consistent with previous traditional methods [
3], the experience in the streetscape in green spaces can promote positive emotions. These results further support the idea that green space is positively associated with positive emotions [
5,
16] and relieves negative emotions [
64].
Another important finding was that the PGVI of green space not only promoted happy and PRI scores but also suppressed the presentation of indifferent sentiment (neutral emotions). Several reports have shown that facial expressions contain a combination of multiple emotions [
65]. The PRI is different from happy scores, which stands for positive emotions and negative emotions are eliminated [
62]. Two positive sentiment factors, happy and PRI, showed similar increases with PGVI increase, according to the MLR results. According to these data, we can infer that happy and PRI scores are consistent and that results of positive sentiments are reliable.
However, the results of this study were different from those of previous studies. The impact of the NDVI [
13,
34] on emotional perception was not significant in the study area. This interpretation differs from that of Zhu (2021), who argued that residents’ positive mood usually increases with the green area and green coverage [
13]. Low-green-space areas with a high PGVI promoted the presentation of positive emotions and inhibited neutral ones. The results were concordant with our hypothesis that experiencing an environment with more green areas does not always promote more positive emotions. The PGVI in urban landscaping makes a significant contribution to positive attitudes and is a strong motivator in facial emotions. Therefore, we need to focus more on enhancing the quantity and quality of the PGVI, that is, to fully optimize the positive benefits of the natural environment. In future urban green space planning and design, we need to focus on enhancing the amount of visual greenery to obtain a more positive emotional impact on urban residents.
We acknowledge several limitations. Because of the limitations of data sources for emotional expression, most emotional photos uploaded to SNSs were consciously presented. SNS messages may over-evaluate positive emotions and suppress negative ones [
13]. Since people prefer to share optimistic images, users tend to upload their happy messages on SNSs [
66]. We should increase the number of photos collected so that negative emotions can also be captured [
16]. Emotional scores can be validated by other methods, such as field surveys [
13], words, or voice expressions [
67].
Machine learning semantic segmentation can better quantify the scale of green spaces from a human visual perspective. Segmented photos were collected from OSM data sources, covering a wide range and a large amount of data, but there were also several disadvantages. A camera usually takes panoramic photo collections from OSM on a mobile vehicle, and the collection time is different in different locations. Therefore, it is impossible to guarantee that the time of all PGVI sampling points is the same as that of the NDVI and expression photos. However, despite its limitations, the study certainly adds to our understanding of visual greening and emotional perception. A cross-sectional study in this paper cannot draw inferences of causal conclusions, and longitudinal studies can be conducted on their greenfield long-term effects in the future.