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Article

Exploring the Relationship between Forest Scenic Beauty with Color Index and Ecological Integrity: Case Study of Jiuzhaigou and Giant Panda National Park in Sichuan, China

by
Honghong Jia
1,2,
Peng Luo
1,2,*,
Hao Yang
1,
Chuan Luo
1,2,
Honglin Li
1,2,
Sujuan Wu
1,2,
Yue Cheng
1,2,
Yu Huang
1,2 and
Wenwen Xie
1,2
1
CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(11), 1883; https://doi.org/10.3390/f13111883
Submission received: 11 August 2022 / Revised: 15 September 2022 / Accepted: 28 October 2022 / Published: 10 November 2022
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
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Abstract

:
Aesthetics of natural landscapes and the conservation of forest ecological integrity have received much attention because of the increasing public demand for aesthetic quality and the shift of forest conservation management objectives from single elements to multiple ecosystem services. However, existing research has not adequately addressed the relationship between forest scenic beauty with the color index and ecological integrity. This study aimed to evaluate and quantify the scenic beauty of forests and the vegetation color index using web questionnaires and Python color interpretation, with Jiuzhaigou World Natural Heritage and Giant Panda National Park in western Sichuan, China, as examples. The relationships between forest scenic beauty with vegetation color and ecological integrity were explored via correlation and linear regression analyses, respectively. The results showed that: (1) The overall scenic beauty of the autumn forests in the study area was at a “medium” level; (2) the scenic beauty of the autumn forest was significantly positively correlated with the saturation and value ratio; and (3) the scenic beauty was not significantly negatively correlated with ecological integrity. This research provides an important reference for the renovation of autumn foliage forests and ecotourism planning. This study also provides a theoretical basis for the transformation of forest conservation management objectives, offering the possibility for nature reserves to achieve the “win-win” management goal of ecological protection and local economic development.

1. Introduction

The aesthetic and recreational values of natural landscapes, such as national parks and world natural heritage locations [1], have received much attention [2] due to urbanization [3] and the increasing public demand for aesthetic quality of landscapes [4]. This is particularly common in developing countries [5], such as China, where national parks are positioned as areas that merit the highest priority of protection for nature conservation and implementation of the most stringent conservation management policies [6]. Ecotourism can effectively alleviate the conflict of interests between the construction of national parks and local residents, since it can create many alternative employment opportunities [7,8] for most residents who rely heavily on natural resources for their livelihoods [6]. Terrestrial protected areas receive approximately 8 billion visits yearly [9,10], with European national parks receiving more than 2 billion visits annually [11]. Moreover, outdoor recreation activities are expected to significantly increase in some countries, such as the USA, over the next 50 years [12]. Natural landscapes promote ecotourism and recreational activities worldwide [13].
In summary, although the importance of natural landscapes for human well-being has been widely recognized [14], forest ecosystems are complex and diverse and can provide multiple ecosystem services for human production, supply, and regulation [14,15]. However, landscape aesthetics are just part of forest cultural ecosystem services. Therefore, some researchers have proposed that forest ecological conservation or landscape forest construction [16] should not only focus on landscape aesthetics but also on ecological aesthetics [17]. However, traditional forest conservation management or ecological restoration has primarily focused on timber production [18,19], limiting consideration of cultural ecosystem services, such as the scenic beauty and recreation of forests [20,21], and thus neglecting the overarching objectives of ecologically sustainable forest management [22]. In addition, this value is difficult to quantify [21,23,24], since cultural ecosystem services or landscape aesthetics are evaluated subjectively [25] or intangibly [26]. As a result, relatively few studies have evaluated scenic beauty in nature reserves, such as national parks or world natural heritage sites [27,28]. Future conservation management and planning for natural resources [27] should consider the cultural ecosystem services provided by forests [29], and strive to achieve a balance between forest conservation and multiple ecosystem services [17,30,31].
Aesthetic evaluation is a value judgment that involves the comprehensive reflection of observers on aesthetic objects at different spatial and temporal scales [28]. There are currently two main evaluation methods of landscape aesthetics worldwide. One is based on expert opinion [32], while the other is based on the aesthetics of the general public [33]. The scenic beauty estimation (SBE) [34], which is based on the aesthetics of the general public, is currently the most commonly used international psychophysical method [35]. This value is primarily obtained by evaluations based on site experiences or photographic observations combined with questionnaires [36,37]. Moreover, previous studies have validated the reliability [38], efficiency [39], and objectivity [40] of this method. For example, Nijboer et al. showed that the evaluation of scenic beauty derived from landscape photographs is similar to the judgments based on on-site landscape experience [39,41]. They also showed that the personal background factors of the judges, such as gender, age, occupation, and level of education [42], cannot significantly affect visual assessment results [24]. Therefore, the SBE has been used to evaluate the aesthetic quality of the forested fall landscape.
Scenic forests are an essential part of national parks. Particularly, the changing colors of the autumn foliage forests attract tourists and promote local economic development [16,42]. For example, the “Momijigari” period in Japan [43] and the “color forest” in the Santai Mountain Forest Park in Suqian, China, attract many tourists every year and drive the local economy. This is because color, as one of the important physical characteristics of forest landscape appearance, is the first information received visually and directly affects the human viewing experience [44]. Evaluating and quantifying the beauty of forest color improves the visual quality of the landscape [16], thus mitigating the conflicts between ecological conservation and local economics [45,46]. Particularly, the human activities in some nature reserves, such as the Giant Panda National Park, are strictly limited [47], and thus vegetation color is a crucial visual element for appreciating the natural landscape from a distance [42]. However, most of the existing forest color studies have focused on the effects of climate change on the phenology of foliage species [43,46] and the spatial configuration of the landscape [24]. To the best of our knowledge, no study has comprehensively assessed the relationship between vegetation color and forest beauty [48]. Moreover, there are no unified and common standards for landscape color extraction, quantification, and evaluation [16]. In this study, the forest vegetation color was extracted and quantified using the common “photo acquisition + software tools” method to explore the relationship between forest beauty and vegetation color.
Many studies have evaluated forest scenic beauty and forest characteristic attributes. Philip S. assessed the relationship between forest landscape variables and publicly perceived landscape aesthetics using multiple linear regression [49,50]. Rahmandari et al. also showed that scenic beauty evaluation can be used to assess the characteristics of the forest [49]. They also showed that tree quality is important for forest landscape aesthetics and forest management [51]. Moreover, many current studies have shown that scenic beauty is associated with the characteristic properties of the forest [33,46,47]. Human preferences are influenced by stand quality [51] or stand structure [52], such as tree size, diameter at breast height, stand age [16], canopy, stand density, and base area [17]. These indicators characterizing the aesthetic features of forests are often used to reflect the ecological integrity of forests. Therefore, forest ecological integrity or ecological health could be a key parameter of forest visual landscape quality [53], or there may be a strong correlation between the two [54]. However, the relationship between forest scenic beauty and ecological integrity [55] is unknown [52]. In this research, a set of ecological integrity evaluation index systems (Supplementary Table S1) were constructed from the perspectives of species composition, stand structure, and ecological processes [56] to explore the interrelationship between autumn beauty and forest ecological integrity.
Herein, the autumn forest vegetation landscapes of Jiuzhaigou World Natural Heritage site and Baoxing Giant Panda National Park in Sichuan Province were used as the research objects to (1) evaluate the autumn landscape scenic beauty of different types of vegetation; and (2) explore the relationship between scenic beauty with the vegetation color index and ecological integrity.

2. Materials and Methods

2.1. Study Area

This research was conducted in two areas: Jiuzhaigou World Natural Heritage site and the Baoxing National Park for Giant Pandas in Sichuan, China (102°39′59″–104°13′21″ E, 30°13′56″–33°19′37″ N) (Figure 1). The study area has the largest area of autumn color-leaved sightseeing forests in China, and it is characterized by high biodiversity and rich resources for tourism [57]. Moreover, the study area has typical vertical zonal vegetation characteristics. The dominant tree species in the study area include Abies fabri (Mast.) Craib, Picea asperata (Mast.), Betula albosinensis (Burke), Larix kaempferi (Lamb.) Carr, Japanese cedar (Cryptomeria japonica D. Don), Oriental white oak (Quercus aliena Blume), and Mongolian oak (Q. mongolica Blume). The vegetation in the study area was divided into seven types based on the composition of tree species: a planted evergreen coniferous pure forest (PECP), planted broadleaf deciduous pure forest (PBDP), planted coniferous deciduous pure forest (PCDP), natural birch mixed forest (NBMF), natural mixed cycad forests (NCMF), natural coniferous broad-leaved mixed forest (NCBM), and natural coniferous pure forest (NCPF).

2.2. Data Collection

2.2.1. Collection and Screening of Photos

The photographs of autumn landscapes of different types of vegetation were collected in October 2020 for the evaluation of landscapes beauty. The photos were strictly obtained using the following principles to reflect the true beauty of the forest vegetation and enhance the contrast between the landscape photos: (1) The images were shot in bright, high visibility weather between 9:00 and 11:00 and 15:00 and 17:00; (2) shooting was conducted with the same person, same camera, and same parameters to ensure consistency of the photos. For example, the shooting height was approximately 1.6 m; a Canon EOS 6D Mark II camera was used for the photoshoot with an F6.3 aperture and autofocus mode; and no flash was used. Finally, (3) the influence of non-forest factors was avoided [33].
To avoid aesthetic fatigue during the judging process, the 238 photos were treated as follows: (1) The photos were representative and could fully reflect the characteristics of a certain vegetation type; (2) the photos were high-quality and did not have any backlighting. Finally, 35 photos were used to evaluate and quantify the color of beauty of autumn forests among the seven vegetation types in the study area.

2.2.2. Collection of Ecological Integrity Data

In addition to the 35 landscape photographs screened above, a sample survey of the forest vegetation characteristics of the landscape photographs was also conducted.
Vegetation survey: The size of the survey plot was 20 m × 20 m. The tree survey involved measuring each tree and recording the tree canopy density, tree species, tree individual number, tree height, DBH, crown width, tree death, and the status of pests and diseases. The shrubs were sampled at 5 m × 5 m, with three replicates established evenly along the diagonal of a 20 m × 20 m sample. The types of species, number, height, cover, and growth status of the shrubs were recorded. Herbaceous plants were sampled at 1 m × 1 m, with three replicates established along the other diagonal, while recording species name, number, and total herbaceous cover of the plot (Supplementary Table S2).
Soil collection: First, the soil surface litter was removed, then the topsoil (0–20 cm) in the middle of three shrub samples was collected using a soil auger. The debris in the soil was also removed. The soil samples were mixed and sealed in plastic bags for indoor soil index determination as described in the “Methods of Agricultural Chemical Analysis of Soil”.

2.3. Scenic Beauty Estimation (SBE) Method

2.3.1. Questionnaire Survey

To avoid the influence of photo backgrounds on the evaluation results, we processed the 35 landscape photos by initially screening them for background removal [42] and pixel uniformity (2249 × 1632) using Adobe Photoshop (San Jose, CA, USA).
Combined with the current relevant research [33,48,58], this questionnaire was divided into three main parts of the scenic beauty questionnaire. The first part included the basic information about the judges (Table 1). In the second part, the judges ranked the beauty of each group of type of vegetation. The judges then subjectively evaluated the beauty of each photo in the third part. The web questionnaire was used to evaluate the subjects by showing them seven representative photographs of forest vegetation landscapes (stimuli). The judges were also asked to evaluate the photos based on their first intuitive perception after observing each photo for about 5–10 s [51]. The rating was assigned to each photograph on a scale of beauty from −3 (disliked very much) to 3 (liked very much) [28] using Liptak’s 7-point scale.
The questionnaires (https://www.wjx.cn/vj/exV2Rn8.aspx (accessed on 30 March 2021)) were randomly distributed through the Questionnaire web platform, and the judges were drawn from the Internet. The questionnaires were returned in July 2021, and 256 of 268 returned questionnaires were valid. Many studies have shown that demographic factors, such as gender, age, major, and education level of the judges, do not significantly affect the results of the visual assessment of the landscape [24,42]. Therefore, the relationship between the individual backgrounds of the judges and the evaluation results was not discussed in this study. The validity and reliability of the questionnaire results were analyzed using Cronbach’s test and Pearson’s correlation analysis. The number of valid questionnaire in this research was reasonable (between 105 [40] and 331 [58]) based on the literature review and the related questionnaire results of the key references [48,57,59].

2.3.2. Score Standardization

A direct calculation could affect the evaluation results since the beauty value could be affected by the characteristics of landscape photos and individual differences in evaluation [42]. Therefore, the original questionnaire results were standardized to eliminate this bias as follows:
Z i j = R i j − R j / S j
S B E i = ∑ j 0 Z i j / N j
where Zij represents the standardized value of the jth valuator’s beauty rating for the ith landscape; Rij represents the value of the jth valuator’s beauty rating for the ith landscape; Rj represents the average of the jth valuator’s beauty ratings for all the landscapes; Sj represents the standard deviation of the jth valuator’s beauty ratings for all the landscapes; Zi represents the standardized value of the ith landscape; and Nj represents the total number of evaluators. SBEi represents the final standardized scenic beauty evaluation value of the ith landscape [39].

2.3.3. Rank Order Method

The ranking method was used to rank each judge’s rating of each image in the group (the highest rating was the first). Ratings with equal scores were tied. Secondly, the average ranking of each picture was obtained by summing the rankings of each judge for each picture, then used to rank the photos [60].

2.4. Extraction and Quantification of the Color Index

2.4.1. Color Quantization Based on the HSV Model

The hue, saturation, and value (HSV) models best describe the color perception of human eyes and the psychophysiological responses to color [42]. The distance between the two HSV color points in the color space can be calculated using the Euclidean distance. In this study, the HSV values of each pixel point of 35 landscape photographs (2249 × 1632 pixels) were first extracted from the perspective of pixel color decoding using Python 3.9. The HSV color space was divided into 256 colors using the Chen et al. quantization method [61] (H:S:V = 16:4:4 [48]). However, some quantified colors could not be distinguished since the human eye tends to simplify similar colors [48]. In addition, some of the colors were affected by the extreme values of color saturation and lightness, and thus some pixel points in the image showed three shades: black, white, and gray. Therefore, similar colors were normalized to black, white, and gray after non-uniform quantization and removed from the quantification of the color metrics. Finally, the H, S, and V of the remaining pixels were saved in Microsoft Excel (Redmond, WA, USA), and the color component indices containing three categories of hue, saturation, and value were calculated using R 4.1.1 software (Vienna, Austria).

2.4.2. Selection of Color Component Indicators

Previous research showed that the ratio of main color, hue, saturation, and value are related to color [62]. Herein, the ratio of primary color, hue, saturation, and value were selected as quantitative indicators of the color characteristics of forest vegetation (Table 2).

2.5. Ecological Integrity Calculation

A structural equation model was used to establish a system of indicators to evaluate the ecological integrity of forest vegetation (Supplementary Figure S1 and Table S3). The weights of each indicator were obtained based on a factor analysis (Supplementary Table S4).

3. Results

3.1. Scenic Beauty Evaluation

3.1.1. Reliability of the Questionnaires

The Cronbach’s test Alpha [63] value was 0.958 (>0.9) indicating that the questionnaire data were credible (Figure 2). In addition, the beauty of both judging methods passed the Kolmogorov-Smirnov test (p = 0.2 > 0.05) and conformed to a normal distribution. Pearson’s correlation analysis achieved a correlation coefficient of R = 0.809 ** (R > 0.8), further verifying the reliability and validity of the results of the degree of beauty questionnaire.

3.1.2. SBE in Different Types of Vegetation

The beauty value of the autumn forest vegetation landscape was between −0.93 and 1.27 (Figure 2 and Figure 3). The planted coniferous deciduous pure forest had the highest beauty value (dominant tree species were L. kaempferi and L. mastersiana Rehd. El Wils). It is a typical colorful leaf ornamental forest in the autumn. A one-way analysis of variance (ANOVA) showed that the SBE was significantly different among the various types of vegetation (Figure 3). PCDP was rated the most beautiful, followed by NBMF and NCMF. PECP, PBDP, and NCPF were third place, and the NCBM was rated as the least attractive.

3.2. SBE and Color Indices

3.2.1. Correlation Analysis

Pearson’s correlation analysis, SBE was positively correlated with the maximum hue index, a cool to warm color ratio, and a complementary color ratio (Table 3). Moreover, SBE was significantly positively correlated with the value index (p < 0.01) and saturation index (p < 0.05). However, SBE was negatively correlated with the hue ratio, adjacent color ratio, and hue diversity. These results indicate that the beauty of vegetation in the study area in autumn could be primarily influenced by color saturation and color value indices, and the maximum hue index is secondary. In addition, there were significant correlations among the color indices. For example, the maximum hue index was positively correlated with the saturation ratio (p < 0.01) but negatively correlated with the other color indices except for the value ratio. The saturation ratio was negatively correlated with the other color indices except for the maximum hue index (p < 0.01). The hue diversity was significantly and positively correlated with hue uniformity (correlation coefficient R = 0.998 **). Furthermore, hue diversity and hue uniformity were significantly and positively correlated with the other color indices (p < 0.01). However, they were significantly negatively correlated with the maximum hue index (p < 0.01).

3.2.2. The Difference between Beauty and the Color Index

ANOVA showed that there were no significant differences among the types of vegetation in the cool to warm color ratio, complementary color ratio, and adjacent color ratio. However, there were significant differences among the types of vegetation, maximum hue index, hue diversity, hue uniformity, hue ratio, saturation ratio, and value ratio (Figure 4). The PCDP, rated as the most beautiful, had a significantly higher maximum hue index, value ratio, and saturation ratio than the other types of vegetation. However, PCDP had significantly lower hue diversity, hue uniformity and hue ratio than the other types of vegetation. The hue diversity, hue ratio, and hue uniformity were significantly higher in the NBMF and NCMF (ranked as the second most beautiful) than in the other types of vegetation. However, the maximum hue index of NBMF and NCMF were significantly lower than those of the other types of vegetation.

3.3. SBE and Ecological Integrity

The SBE of different types of vegetation was ranked as follows: PCDP > NCMF > NBMF > NCPF > PECP > PBDP > NCBM (Figure 5). The order of ecological integrity was as follows: NCPF > NCMF > NCBM > PECP > PBDP > PCDP > NBMF. The PCDP and NBMF (rated as the most beautiful) had the lowest ecological integrity, while the NCBM was rated as the least attractive and had a relatively high level of ecological integrity. The linear fitting of the relationship between the vegetation beauty degree and ecological integrity had a Pearson correlation coefficient of p = −0.247 (Figure 6). Moreover, the degree of beauty was negatively correlated with ecological integrity.

4. Discussion

4.1. SBE among Different Types of Vegetation

SBE was used to evaluate the autumn beauty of different types of types of vegetation in the study area. The results showed that the average score of the autumn vegetation landscape in the study area was 0.063, which is in the “moderate” level of the questionnaire scoring rank. This result was inconsistent with the conclusions of Zhang et al. [44], probably because the evaluation of visual aesthetic quality in this study was based on various types of vegetation that are common and typical in the study area rather than evaluating a targeted selection of autumn landscape forests. Therefore, the beauty of the autumn vegetation landscape in this study was slightly lower than that determined by previous studies that evaluated the autumn vegetation landscape in this area.
A comparison of the differences In scenic beauty values of different types of vegetation provided the following ranking in the study area: PCDF > NCMF > NBMF > NCPF > PECP > PBDP > NCBM. The results of this ranking are consistent with existing studies [44,64] and the general perception of the public. However, there were exceptions, including the coniferous deciduous forests, which had significantly higher ratings than the other types of vegetation, and natural mixed coniferous forests, which had significantly lower ratings than the other types of vegetation. PCDP had a high aesthetic value probably due to the planting of L. kaempferi and L. mastersiana, which present the orange-red or orange-yellow colors [16,42] in the autumn foliage viewing season that is strongly preferred by most individuals. In addition, they contrasted with the surrounding dark evergreen coniferous forest, which gives a bright and warm feeling. Therefore, its aesthetic value was much higher than that of the other types of vegetation. The lowest aesthetic value of the NCBM could be because most of the natural mixed coniferous forests in the study area were at a later stage of vegetation succession, and the sites had some trees that were dead or had fallen [65]. In addition, the broad-leaved deciduous species are affected by interspecific competition. As a result, the leaves may have withered, or only a few scattered leaves were present [57] during the time that the photos were collected, resulting in a more disorganized appearance for the vegetation landscape, thus, reducing the aesthetic value of this vegetation landscape.

4.2. Influence of the Color Index on Vegetation Beauty

In this study, the primary color shades of vegetation that was the most beautiful in the autumn were mostly orange, yellow, red, and green. Similarly, Wang et al. [16,42] concluded that “forests with high visual aesthetic quality usually have vegetation colors that are green, red, yellow, etc. with high color brightness and saturation”. Correlation analysis showed that the visual aesthetic quality of the autumn vegetation landscape is significantly positively correlated with the saturation ratio and the value ratio. However, it was not significantly correlated with the rest of color indices. Similarly, Mu et al. [42] showed that a higher vegetation color saturation and value index are associated with brighter vegetation color, which effectively meets the aesthetic preferences and emotional needs of the public [59].
Furthermore, there were significant differences among the various types of vegetation based on their maximum hue index, hue ratio, saturation ratio, value ratio, hue diversity, and hue uniformity. For example, the primary color comparison, saturation, and value indices were significantly higher in PCDP than in the other types of vegetation. However, hue diversity and hue ratio were significantly lower in PCDP than in the other types of vegetation. This could be because this vegetation type is an artificially pure forest, and its autumn vegetation color is a single pure color (orange-yellow or orange-red), resulting in the lowest hue ratio and hue diversity. Gong et al. showed that “the higher the saturation and value of vegetation landscape color, the greater the proportion of color (red, orange, yellow), and the higher the ornamental value of the autumn landscape forest” [4].
The hue diversity, hue uniformity, and hue ratio were significantly higher in NBMF and NCMF than in the other types of vegetation. However, the dominant color and saturation ratio were significantly lower in NBMF and NCMF than in the other types of vegetation. Similarly, Zhang et al. showed that “the more uniform and fragmented the color patches are, the greater the beauty value” [44]. This could be because large and concentrated single-color patches result in a single color composition that is rated as less attractive. In contrast, the distribution of color patches and green as mosaics results in sharper color contrast that provides a rich viewing experience for the public. However, the PECP, PBDP, and NCPF were rated as relatively less beautiful, probably because these types of vegetation are mostly composed of several dark or cool single species or a few colored species, with low color contrast and almost no visual appeal to tourists [4]. Zhuang et al. also showed that “a high proportion of cool and green vegetation can significantly weaken the aesthetic preferences and emotions of the public” [59].

4.3. Relationship between SBE and Ecological Integrity

In this study, the ecological integrity of forest vegetation was higher than the beauty of the vegetation landscape in autumn as a whole, probably because of the conservation management objectives of the forests and the selection of the research samples in this study. The study areas Jiuzhaigou and Baoxing Giant Panda National Park are hotspots for biodiversity conservation in southwest China, with rich plant and animal resources and expertly managed forest vegetation conservation. Particularly, Jiuzhaigou is the first nature reserve in China to focus on the conservation of the natural landscape [59]. Therefore, the ecological integrity of the forest vegetation in the study area was in good condition.
The linear fit and correlation analysis showed that the beauty of vegetative landscape was not significantly negatively correlated with ecological integrity during autumn. For example, NCBM was rated as the least beautiful (SBE = −0.33) and had a relatively high level of ecological integrity (EI = 0.46), while the PCDP that was ranked among the most beautiful (SBE = 0.71) and had a relatively low level of ecological integrity (EI = 0.26). This could be because the evaluation of scenic beauty targeted the autumn vegetation landscape. The PCDP had a large area of orange (red) color with the brightest color during that season, while the natural mixed coniferous forests had a grayish dull green and bare stand. Therefore, they have a significantly different scenic beauty.
Ecological integrity refers to “the ability of an ecosystem to support and maintain the species composition, stand structure, ecological processes, and to keep the resilience and resistance of the system stable in a region” [66]. The natural mixed coniferous forests have a significantly higher integrity than the planted coniferous deciduous forests based on species diversity, hierarchical changes in stand structure, and the ability of systems to adapt to external disturbances. These results are consistent with the current situation of forest ecotourism in China. Most existing scenic forests have relatively poor landscape aesthetic values and ecological quality [67] and thus do not meet the development concept of sustainable forest ecotourism [68]. Moreover, current studies have shown that the characteristic attributes of forests, such as species composition and stand structure [16], significantly impact their aesthetic quality. The public may be more concerned with “ecological beauty” rather than visual aesthetics [69].

5. Conclusions

5.1. Recommendations

The following recommendations can be made based on the current research and the findings of this study.
In terms of vegetation color configuration, colors with high saturation and value, such as red, yellow, and other warm colors, should be prioritized, followed by colors that can be color-considered with large differences or complementary color combinations.
In the creation of colorful foliage landscape forests and the transformation of planted forests, priority should be given to local native colorful foliage species, and the higher density planted forest can be moderately harvested. The dead and fallen wood should be appropriately cleared in the colorful foliage landscape forests accessible to pedestrians.
In the management of the beauty and ecological integrity of types of vegetation, natural forests are preferred to planted forests, and mixed forests are preferred to pure forests.

5.2. Prospects and Limitations

This research used Python color interpretation to investigate the interrelationship between forest beauty and vegetation color index, which provides a new methodological reference for the quantitative study of forest vegetation color. This is the first study to investigate the interrelationship between vegetation beauty and forest ecological integrity, providing a theoretical basis for the transformation of forest conservation management objectives from a single element to multiple ecosystem services. This study also provides a possibility for nature reserves to achieve a “win-win” situation between ecological conservation and local economic development.
This study has the following limitations: (1) It lacks a comprehensive comparison of color changes in the forest throughout the seasons, which could limit the applicability of the results. (2) Moreover, 35 vegetation landscape photographs representing 7 different types of vegetation were selected in this study. This study only explored the interrelationship between the scenic beauty of vegetation and the ecological integrity of the forest due to the relatively small sample size. Therefore, further studies should evaluate the synergistic or trade-off relationship between the scenic beauty of different vegetation types and forest ecological integrity from the perspectives of: (1) increasing the beauty of vegetation color in three seasons other than autumn; (2) increasing the sample size of different vegetation types, and expanding the scope of the beauty questionnaire survey.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f13111883/s1, Figure S1: Validation of a framework system of ecological integrity indicators based on structural equation modeling(SEM); Table S1: Ecological integrity evaluation indicators and sources of indicators; Table S2: Raw data of ecological integrity indicators in the study area; Table S3: Model fitting criteria and results; Table S4: Forest ecological integrity evaluation hierarchy and index weight.

Author Contributions

Conceptualization, H.J., P.L. and H.Y.; methodology, H.J. and C.L.; software, H.J. and S.W.; validation, H.J., H.L. and Y.C.; formal analysis, H.J.; investigation, H.J., W.X., Y.H. and S.W.; resources, H.J.; data curation, H.J.; writing—original draft preparation, H.J.; writing—review and editing, H.J., P.L. and H.Y.; visualization, H.J.; supervision, C.L. and H.L.; project administration, P.L.; funding acquisition, P.L. and H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [the Investigation and evaluation of traditional knowledge related to diversity in the Daxueshan-Xiaoxiangling mountain-Liangshan Mountains], grant number: [Y9Q2070001]; [Key Technology and Demonstration of Diversity Conservation in Giant Panda National Park], grant number [Y8D2191100]; [Development of Technical Guidelines for Assessing the Impact of Post-Disaster Restoration and Reconstruction Projects on Outstanding Universal Value of World Natural Heritage], grant number: [Y8D2081100]; [Important Species Habitat Class Heritage Site Monitoring and Conservation Demonstration], grant number [Y6K201100].

Data Availability Statement

The data presented in this study are available in the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Arif, U.; Hussain, A.; Nizami, M.; Ullah, H. Potential of forest landscape restoration in selected geo-heritage sites in district Haripur, Khyber Pakhtunkhwa, Pakistan. Environ. Dev. Sustain. 2022. [Google Scholar] [CrossRef]
  2. Tahvanainen, L.; Tyrväinen, L.; Ihalainen, M.; Vuorela, N.; Kolehmainen, O. Forest management and public perceptions—Visual versus verbal information. Landsc. Urban Plan. 2001, 53, 53–70. [Google Scholar] [CrossRef]
  3. An, C.; Liu, J.; Liu, Q.; Liu, Y.; Fan, X.; Hu, Y. How Perceived Sensory Dimensions of Forest Park Are Associated with Stress Restoration in Beijing? Int. J. Environ. Res. Public Health 2022, 19, 883. [Google Scholar] [CrossRef] [PubMed]
  4. Gong, L.; Zhang, Z.D.; Xu, C.Y. Developing a Quality Assessment Index System for Scenic Forest Management: A Case Study from Xishan Mountain, Suburban Beijing. Forests 2015, 6, 225–243. [Google Scholar] [CrossRef] [Green Version]
  5. Saha, N.; Mukul, S.A. Visitor’s Willingness to Pay for Cultural Ecosystem Services in Bangladesh: An Assessment for Lawachara National Park, a Biodiversity Hotspot. Small-Scale For. 2021, 21, 185–201. [Google Scholar] [CrossRef]
  6. Peng, J.; Xiao, H.; Wang, R.; Qi, Y. The Impacts of Establishing Pilot National Parks on Local Residents’ Livelihoods and Their Coping Strategies in China: A Case Study of Qilianshan National Park. Sustainability 2022, 14, 3537. [Google Scholar] [CrossRef]
  7. Walpole, M.J.; Goodwin, H.J. Local attitudes towards conservation and tourism around Komodo National Park, Indonesia. Environ. Conserv. 2001, 28, 160–166. [Google Scholar] [CrossRef]
  8. Mukul, S.; Rashid, A.Z.M.M.; Quazi, S.; Uddin, M.; Fox, J. Local peoples’ response to co-management in protected areas: A case study from Satchari National Park, Bangladesh. For. Trees Livelihoods 2012, 21, 16–29. [Google Scholar] [CrossRef]
  9. Balmford, A.; Green, J.M.H.; Anderson, M.; Beresford, J.; Huang, C.; Naidoo, R.; Walpole, M.; Manica, A. Walk on the Wild Side: Estimating the Global Magnitude of Visits to Protected Areas. PLoS Biol. 2015, 13, e1002074. [Google Scholar] [CrossRef] [Green Version]
  10. Le Thanh, A.; Markowski, J.; Bartos, M.; Rzeńca, A.; Namiecinski, P. An evaluation of destination attractiveness for nature-based tourism: Recommendations for the management of national parks in Vietnam. Nat. Conserv.-Bulg. 2019, 32, 51–80. [Google Scholar] [CrossRef]
  11. Schägner, J.P.; Brander, L.; Maes, J.; Paracchini, M.L.; Hartje, V. Mapping recreational visits and values of European National Parks by combining statistical modelling and unit value transfer. J. Nat. Conserv. 2016, 31, 71–84. [Google Scholar] [CrossRef]
  12. Sherrouse, B.C.; Semmens, D.J.; Ancona, Z.H.; Brunner, N.M. Analyzing land-use change scenarios for trade-offs among cultural ecosystem services in the Southern Rocky Mountains. Ecosyst. Serv. 2017, 26, 431–444. [Google Scholar] [CrossRef]
  13. Pérez-Hernández, E.; Peña-Alonso, C.; Fernández-Cabrera, E.; Hernández-Calvento, L. Assessing the scenic quality of transgressive dune systems on volcanic islands. The case of Corralejo (Fuerteventura island, Spain). Sci. Total Environ. 2021, 784, 147050. [Google Scholar] [CrossRef] [PubMed]
  14. Alvarez-Codoceo, S.; Cerda, C.; Perez-Quezada, J.F. Mapping the provision of cultural ecosystem services in large cities: The case of The Andean piedmont in Santiago, Chile. Urban For. Urban Green. 2021, 66, 127390. [Google Scholar] [CrossRef]
  15. Balzan, M.V.; Caruana, J.; Zammit, A. Assessing the capacity and flow of ecosystem services in multifunctional landscapes: Evidence of a rural-urban gradient in a Mediterranean small island state. Land Use Policy 2018, 75, 711–725. [Google Scholar] [CrossRef]
  16. Wang, Z.; Li, M.Y.; Zhang, X.H.; Song, L.Y. Modeling the scenic beauty of autumnal tree color at the landscape scale: A case study of Purple Mountain, Nanjing, China. Urban For. Urban Green. 2020, 47, 126526. [Google Scholar] [CrossRef]
  17. Ribe, R.G. In-stand scenic beauty of variable retention harvests and mature forests in the US Pacific Northwest: The effects of basal area, density, retention pattern and down wood. J. Environ. Manag. 2009, 91, 245–260. [Google Scholar] [CrossRef]
  18. Tadesse, T.; Teklay, G.; Mulatu, D.W.; Rannestad, M.M.; Meresa, T.M.; Woldelibanos, D. Forest benefits and willingness to pay for sustainable forest management. FOREST Policy Econ. 2022, 138, 102721. [Google Scholar] [CrossRef]
  19. Lindenmayer, D.; Taylor, C. Diversifying Forest Landscape Management—A Case Study of a Shift from Native Forest Logging to Plantations in Australian Wet Forests. Land 2022, 11, 407. [Google Scholar] [CrossRef]
  20. Tribot, A.-S.; Mouquet, N.; Villéger, S.; Raymond, M.; Hoff, F.; Boissery, P.; Holon, F.; Deter, J. Taxonomic and functional diversity increase the aesthetic value of coralligenous reefs. Sci. Rep. 2016, 6, srep34229. [Google Scholar] [CrossRef]
  21. Baumeister, C.F.; Gerstenberg, T.; Plieninger, T.; Schraml, U. Exploring cultural ecosystem service hotspots: Linking multiple urban forest features with public participation mapping data. Urban For. Urban Green. 2020, 48, 126561. [Google Scholar] [CrossRef]
  22. Taye, F.A.; Folkersen, M.V.; Fleming, C.M.; Buckwell, A.; Mackey, B.; Diwakar, K.C.; Le, D.; Hasan, S.; Saint Ange, C. The economic values of global forest ecosystem services: A meta-analysis. Ecol. Econ. 2021, 189, 107145. [Google Scholar] [CrossRef]
  23. Gosal, A.S.; Ziv, G. Landscape aesthetics: Spatial modelling and mapping using social media images and machine learning. Ecol. Indic. 2020, 117, 106638. [Google Scholar] [CrossRef]
  24. Frank, S.; Fürst, C.; Koschke, L.; Witt, A.; Makeschin, F. Assessment of landscape aesthetics—Validation of a landscape metrics-based assessment by visual estimation of the scenic beauty. Ecol. Indic. 2013, 32, 222–231. [Google Scholar] [CrossRef]
  25. Levering, A.; Marcos, D.; Tuia, D. On the relation between landscape beauty and land cover: A case study in the UK at Sentinel-2 resolution with interpretable AI. Isprs J. Photogramm. Remote Sens. 2021, 177, 194–203. [Google Scholar] [CrossRef]
  26. Costanza, R.; dArge, R.; de Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; Oneill, R.V.; Paruelo, J.; et al. The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 253–260. [Google Scholar] [CrossRef]
  27. Chidakel, A.; Child, B.; Muyengwa, S. Evaluating the economics of park-tourism from the ground-up: Leakage, multiplier effects, and the enabling environment at South Luangwa National Park, Zambia. Ecol. Econ. 2021, 182, 106960. [Google Scholar] [CrossRef]
  28. Di, F.; Yang, Z.; Liu, X.; Wu, J.; Ma, Z. Estimation on Aesthetic Value of Tourist Landscapes in a Natural Heritage Site: Kanas National Nature Reserve, Xinjiang, China. J. Chin. Geogr. Sci. 2010, 20, 59–65. [Google Scholar] [CrossRef]
  29. Bachi, L.; Ribeiro, S.C.; Hermes, J.; Saadi, A. Cultural Ecosystem Services (CES) in landscapes with a tourist vocation: Mapping and modeling the physical landscape components that bring benefits to people in a mountain tourist destination in southeastern Brazil. Tour. Manag. 2020, 77, 104017. [Google Scholar] [CrossRef]
  30. Duan, X.; Yang, S. Progress of ecological environment protection and restoration in China’s Giant Panda National Park. IOP Conf. Ser. Earth Environ. Sci. 2020, 446, 032027. [Google Scholar] [CrossRef]
  31. Marques, M.; Juerges, N.; Borges, J.G. Appraisal framework for actor interest and power analysis in forest management—Insights from Northern Portugal. For. Policy Econ. 2020, 111, 102049. [Google Scholar] [CrossRef]
  32. Daniel, T.C.; Vining, J. Methodological Issues in the Assessment of Landscape Quality. In Behavior and the Natural Environment; Springer: Boston, MA, USA, 1983; pp. 39–84. [Google Scholar] [CrossRef]
  33. Mo, L.; Chen, J.; Xie, Y. Assessment of landscape resource using the scenic beauty estimation method at compound ecological system. Environ. Sci. Pollut. Res. 2021, 28, 5892–5899. [Google Scholar] [CrossRef] [PubMed]
  34. Daniel, T.C.; Wheeler, L.; Boster, R.S.; Best, P.R. Quantitative evaluation of landscapes: An application of signal detection analysis to forest management alternatives. Man-Environ. Syst. 1973, 3, 330–344. [Google Scholar]
  35. Patsfall, M.R.; Feimer, N.R.; Buhyoff, G.J.; Wellman, J.D. The prediction of scenic beauty from landscape content and composition. J. Environ. Psychol. 1984, 4, 7–26. [Google Scholar] [CrossRef]
  36. Sulistyantara, B.; Sesara, R. Evaluation of Aesthetic Function and Thermal Modification of Vertical Greenery at Bogor City, Indonesia. In Proceedings of the 2nd International Symposium on Sustainable Landscape Development (ISSLD), Bogor, Indonesia, 9–10 November 2017. [Google Scholar] [CrossRef]
  37. Urbis, A.; Povilanskas, R.; Šimanauskienė, R.; Taminskas, J. Key Aesthetic Appeal Concepts of Coastal Dunes and Forests on the Example of the Curonian Spit (Lithuania). Water 2019, 11, 1193. [Google Scholar] [CrossRef] [Green Version]
  38. Iv, R.J.L. Sensitivity of scenic beauty assessments. Landsc. Urban Plan. 1986, 13, 319–321. [Google Scholar]
  39. Li, H.; Shi, K.; Wang, Y.; Li, Y.; Feng, Y. Research on scenic beauty estimation of plant landscape on the roof on SBE method. Arab. J. Geosci. 2021, 14, 882. [Google Scholar] [CrossRef]
  40. Peng, S.-H.; Han, K.-T. Assessment of Aesthetic Quality on Soil and Water Conservation Engineering Using the Scenic Beauty Estimation Method. Water 2018, 10, 407. [Google Scholar] [CrossRef] [Green Version]
  41. Nijboer, T.C.; Kanai, R.; de Haan, E.H.; van der Smagt, M.J. Recognising the forest, but not the trees: An effect of colour on scene perception and recognition. Conscious Cogn. 2008, 17, 741–752. [Google Scholar] [CrossRef] [Green Version]
  42. Mu, Y.; Lin, W.; Diao, X.; Zhang, Z.; Wang, J.; Lu, Z.; Guo, W.; Wang, Y.; Hu, C.; Zhao, C. Implementation of the visual aesthetic quality of slope forest autumn color change into the configuration of tree species. Sci. Rep. 2022, 12, 1034. [Google Scholar] [CrossRef]
  43. Liu, J.; Cheng, H.; Jiang, D.; Huang, L. Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan. Tour. Manag. 2019, 70, 262–272. [Google Scholar] [CrossRef]
  44. Zhang, X. Study on Color Characteristics Changes and Landscape Aesthetic Quality Evaluation of Landscape Forest in Subalpine Region of Western Sichuan, China. Master Thesis, Southwest University, Chongqing, China, 2020. (In Chinese). [Google Scholar]
  45. Nurfaida; Arifin, H.S.; Sitorus, S.R.P.; Eriyatno. Assessing scenic beauty of culture-based landscapes in North Toraja Regency. IOP Conf. Ser. Earth Environ. Sci. 2019, 399, 012040. [Google Scholar] [CrossRef]
  46. Ye, Y.; Zhang, X. Exploration of global spatiotemporal changes of fall foliage coloration in deciduous forests and shrubs using the VIIRS land surface phenology product. Sci. Remote Sens. 2021, 4, 100030. [Google Scholar] [CrossRef]
  47. Halstead, B.J.; Ray, A.M.; Muths, E.; Grant, E.H.C.; Grasso, R.; Adams, M.J.; Delaney, K.S.; Carlson, J.; Hossack, B.R. Looking ahead, guided by the past: The role of US national parks in amphibian research and conservation. Ecol. Indic. 2022, 136, 108631. [Google Scholar] [CrossRef]
  48. Zhang, Z.; Qie, G.; Wang, C.; Jiang, S.; Li, X.; Li, M. Relationship between Forest Color Characteristics and Scenic Beauty: Case Study Analyzing Pictures of Mountainous Forests at Sloped Positions in Jiuzhai Valley, China. Forests 2017, 8, 63. [Google Scholar] [CrossRef]
  49. Cook, P.S.; Cable, T.T. The scenic beauty of shelterbelts on the Great Plains. Landsc. Urban Plan. 1995, 32, 63–69. [Google Scholar] [CrossRef]
  50. Bishop, I.D. Comparing regression and neural net based approaches to modelling of scenic beauty. Landsc. Urban Plan. 1996, 34, 125–134. [Google Scholar] [CrossRef]
  51. Rahmandari, A.V.; Gunawan, A.; Mugnisjah, W.Q. An evaluation of visual aesthetic quality of pedestrian pathways based on ecological network corridor within campus landscape. IOP Conf. Ser. Earth Environ. Sci. 2018, 179, 012010. [Google Scholar] [CrossRef]
  52. Ebenberger, M.; Arnberger, A. Exploring visual preferences for structural attributes of urban forest stands for restoration and heat relief. Urban For. Urban Green. 2019, 41, 272–282. [Google Scholar] [CrossRef]
  53. Bogaert, J. Forests and landscapes—Linking ecology, sustainability and aesthetics (S.R.J. Sheppard, H.W. Harshaw). Landsc. Urban Plan. 2002, 59, 125–127. [Google Scholar] [CrossRef]
  54. Parrish, J.D.; Braun, D.P.; Unnasch, R.S. Are We Conserving What We Say We Are? Measuring Ecological Integrity within Protected Areas. BioScience 2003, 53, 851–860. [Google Scholar] [CrossRef] [Green Version]
  55. Rissman, A.R.; Burke, K.D.; Kramer, H.A.C.; Radeloff, V.C.; Schilke, P.R.; Selles, O.A.; Toczydlowski, R.H.; Wardropper, C.B.; Barrow, L.A.; Chandler, J.L.; et al. Forest management for novelty, persistence, and restoration influenced by policy and society. Front. Ecol. Environ. 2018, 16, 454–462. [Google Scholar] [CrossRef]
  56. Hansen, M.M.; Jones, R.; Tocchini, K. Shinrin-Yoku (Forest Bathing) and Nature Therapy: A State-of-the-Art Review. Int. J. Environ. Res. Public Health 2017, 14, 851. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Li, Q.; Du, Y.; Liu, Y.; Chen, J.; Zhang, X.; Liu, J.; Tao, J. Canopy Gaps Improve Landscape Aesthetic Service by Promoting Autumn Color-Leaved Tree Species Diversity and Color-Leaved Patch Properties in Subalpine Forests of Southwestern China. Forests 2021, 12, 199. [Google Scholar] [CrossRef]
  58. Ribe, R.G. Aesthetic perceptions of green-tree retention harvests in vista views: The interaction of cut level, retention pattern and harvest shape. Landsc. Urban Plan. 2005, 73, 277–293. [Google Scholar] [CrossRef]
  59. Zhuang, J.; Qiao, L.; Zhang, X.; Su, Y.; Xia, Y. Effects of Visual Attributes of Flower Borders in Urban Vegetation Landscapes on Aesthetic Preference and Emotional Perception. Int. J. Environ. Res. Public Health 2021, 18, 9318. [Google Scholar] [CrossRef]
  60. Buhyoff, G.J.; Arndt, L.K.; Propst, D.B. Interval scaling of landscape preference by direct-measurement and indirect-measurement methods. Landsc. Urban Plan. 1981, 8, 257–267. [Google Scholar] [CrossRef]
  61. Chen, X.; Jia, K. Application of three-dimensional quantized color histogram in color image retrieval. Comput. Appl. Softw. 2012, 29, 31–32+40. (In Chinese) [Google Scholar]
  62. Moretti, G.; Marsland, S.; Lyons, P. Computational production of colour harmony. Part 2: Experimental evaluation of a tool for gui colour scheme creation. J. Color Res. Appl. 2013, 3, 8218–8228. [Google Scholar] [CrossRef]
  63. Zijlema, W.L.; Triguero-Mas, M.; Cirach, M.; Gidlow, C.; Kruize, H.; Grazuleviciene, R.; Nieuwenhuijsen, M.J.; Litt, J.S. Understanding correlates of neighborhood aesthetic ratings: A European-based Four City comparison. Urban For. Urban Green. 2020, 47, 126523. [Google Scholar] [CrossRef]
  64. Deng, S.Q.; Yan, J.F.; Guan, Q.W.; Katoh, M. Short-term effects of thinning intensity on scenic beauty values of different stands. J. For. Res. 2013, 18, 209–219. [Google Scholar] [CrossRef]
  65. Janeczko, E.; Bielinis, E.; Tiarasari, U.; Woźnicka, M.; Kędziora, W.; Przygodzki, S.; Janeczko, K. How Dead Wood in the Forest Decreases Relaxation? The Effects of Viewing of Dead Wood in the Forest Environment on Psychological Responses of Young Adults. Forests 2021, 12, 871. [Google Scholar] [CrossRef]
  66. Brown, E.D.; Williams, B.K. Ecological integrity assessment as a metric of biodiversity: Are we measuring what we say we are? Biodivers. Conserv. 2016, 25, 1011–1035. [Google Scholar] [CrossRef] [Green Version]
  67. Deng, S.Q.; Yin, N.; Guan, Q.W.; Katoh, M. Dynamic response of the scenic beauty value of different forests to various thinning intensities in central eastern China. Environ. Monit. Assess. 2014, 186, 7413–7429. [Google Scholar] [CrossRef]
  68. Chen, H.J. Complementing conventional environmental impact assessments of tourism with ecosystem service valuation: A case study of the Wulingyuan Scenic Area, China. Ecosyst. Serv. 2020, 43, 101100. [Google Scholar] [CrossRef]
  69. Gobster, P.H.; Arnberger, A.; Schneider, I.E.; Floress, K.M.; Haines, A.L.; Dockry, M.J.; Benton, C. Restoring a “scenically challenged” landscape: Landowner preferences for pine barrens treatment practices. Landsc. Urban Plan. 2021, 211, 104104. [Google Scholar] [CrossRef]
Forests 13 01883 g001 550
Figure 1. Location of the study region.
Figure 1. Location of the study region.
Forests 13 01883 g001
Forests 13 01883 g002 550
Figure 2. SBE and comprehensive ranking scores of vegetation photos. Note: The asterisk ** (0.01 level; two-tailed) represents significant correlations.
Figure 2. SBE and comprehensive ranking scores of vegetation photos. Note: The asterisk ** (0.01 level; two-tailed) represents significant correlations.
Forests 13 01883 g002
Forests 13 01883 g003 550
Figure 3. SBE in different types of vegetation. Different lowercase letters (a, b, ab) in the figure indicate significant differences in SBE of different vegetation types (p < 0.05).
Figure 3. SBE in different types of vegetation. Different lowercase letters (a, b, ab) in the figure indicate significant differences in SBE of different vegetation types (p < 0.05).
Forests 13 01883 g003
Forests 13 01883 g004 550
Figure 4. Vegetation color index and SBE. Different lowercase letters (a, b, ab) in the figure indi-cate significant differences in SBE of different vegetation types (p < 0.05).
Figure 4. Vegetation color index and SBE. Different lowercase letters (a, b, ab) in the figure indi-cate significant differences in SBE of different vegetation types (p < 0.05).
Forests 13 01883 g004
Forests 13 01883 g005 550
Figure 5. SBE and ecological integrity. Different lowercase letters (a, b, ab) in the figure indicate significant differences in SBE of different vegetation types (p < 0.05).
Figure 5. SBE and ecological integrity. Different lowercase letters (a, b, ab) in the figure indicate significant differences in SBE of different vegetation types (p < 0.05).
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Forests 13 01883 g006 550
Figure 6. Linear fit of SBE and ecological integrity.
Figure 6. Linear fit of SBE and ecological integrity.
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Table
Table 1. Basic demographics of questionnaire subjects.
Table 1. Basic demographics of questionnaire subjects.
Basic InformationCategoryNumberPercent(%)Basic InformationCategoryNumberPercent(%)
GenderMen12245.52EducationPrimary education62.24
Women14654.48Junior education114.10
AgeUnder 20 years old5018.66College11241.79
20–40 years old19472.38Graduate13951.87
40–60 years old228.21Work or Major about forestsYes8431.34
60 years old or older20.75No18468.66
Table
Table 2. Quantification of color indicators.
Table 2. Quantification of color indicators.
Color IndexFormulaParameter Meaning
Hue Ratio (HR)H = Hi/AnHi represents the sum of pixels of the ith hue (i = 1…16); An represents the total number of pixels
Saturation Ratio (SR)S = Si/AnSi represents the sum of pixels of the ith saturation (i = 1…16); An represents the total number of pixels
Value Ratio (VR)V = Vi/AnVi represents the sum of pixels of the ith value (i = 1…16); An represents the total number of pixels
Maximum Hue Index (MH)MH = SM/AnSM represents the maximum hue pixel; An represents the total number of pixels
Adjacent color ratio (AC)AC = SA/AnSA represents the adjacent color pixel; An represents the total number of pixels
Complementary color ratio (CC)CC = SC/AnSC represents the complementary color pixel; An represents the total number of pixels
Cool to warm color ratio (CW)CW = SW/AnSW represents the cool to warm color pixel; An represents the total number of pixels
Hue diversity (HD) HD = ∑ P i l n P i Pi represents the ratio of the ith hue pixel to the total number of pixels, i = 1, 2,…, 16
Hue uniformity (HU)HU = HD/ln(SL)HD represents hue diversity; An represents the total number of pixels
Note: (1) Ratio of maximum hue (MH): The maximum hue is the largest proportion of the area of the forest division hue; (2) adjacent color ratio (AC): the two colors in the hue ring that are 60 o apart or five or six digits apart are the neighboring colors, and the neighboring colors of the main hue were used in this study; (3) complementary color ratio (CC): the complementary colors are the two colors that are 180° apart in the hue ring, and the complementary colors of the main hue were used in this study; (4) cold and warm hue ratio (CW): the values of the cold and warm hue pixel points in this study were 108–290° and 316–80° respectively.
Table
Table 3. Correlation analysis between the SBE and color indicators.
Table 3. Correlation analysis between the SBE and color indicators.
SBEMHCWCCACHDHUHRSRVR
SBE1
MH0.2291
CW0.082−0.536 **1
CC0.019−0.535 **0.650 **1
AC−0.203−0.487 **0.3090.2711
HD−0.141−0.910 **0.664 **0.676 **0.508 **1
HU−0.153−0.897 **0.661**0.671 **0.498 **0.998 **1
HR−0.041−0.626 **0.888 **0.759 **0.343 *0.786 **0.791 **1
SR0.373 *0.554 **−0.416 *−0.368 *−0.542 **−0.663 **−0.661 **−0.561 **1
VR0.491 **0.033−0.0180.1150.1390.0240.016−0.0390.1481
Note: The asterisks * (0.05 level; two-tailed) and ** (0.01 level; two-tailed) represent significant correlations.
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Jia, H.; Luo, P.; Yang, H.; Luo, C.; Li, H.; Wu, S.; Cheng, Y.; Huang, Y.; Xie, W. Exploring the Relationship between Forest Scenic Beauty with Color Index and Ecological Integrity: Case Study of Jiuzhaigou and Giant Panda National Park in Sichuan, China. Forests 2022, 13, 1883. https://doi.org/10.3390/f13111883

AMA Style

Jia H, Luo P, Yang H, Luo C, Li H, Wu S, Cheng Y, Huang Y, Xie W. Exploring the Relationship between Forest Scenic Beauty with Color Index and Ecological Integrity: Case Study of Jiuzhaigou and Giant Panda National Park in Sichuan, China. Forests. 2022; 13(11):1883. https://doi.org/10.3390/f13111883

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Jia, Honghong, Peng Luo, Hao Yang, Chuan Luo, Honglin Li, Sujuan Wu, Yue Cheng, Yu Huang, and Wenwen Xie. 2022. "Exploring the Relationship between Forest Scenic Beauty with Color Index and Ecological Integrity: Case Study of Jiuzhaigou and Giant Panda National Park in Sichuan, China" Forests 13, no. 11: 1883. https://doi.org/10.3390/f13111883

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