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Article

Visual Satisfaction of Urban Park Waterfront Environment and Its Landscape Element Characteristics

by
Mei Lyu
1,
Shujiao Wang
2,
Jiaxuan Shi
3,
Dong Sun
2,*,
Kangting Cong
2 and
Yi Tian
1,4
1
School of Art and Design, Shenyang Jianzhu University, Shenyang 110168, China
2
School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
3
Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
4
College of Management, Maharishi International University, Fairfield, IA 52557, USA
*
Author to whom correspondence should be addressed.
Water 2025, 17(6), 772; https://doi.org/10.3390/w17060772
Submission received: 10 February 2025 / Revised: 1 March 2025 / Accepted: 5 March 2025 / Published: 7 March 2025
(This article belongs to the Special Issue Ecohydrological Processes, Environmental Effects, and Integrated Regulation of Wetland Ecosystems, 3rd Edition)
Browse Figures
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Abstract

:
Close contact with nature helps moderate public emotions and enhance happiness. As an important space for the public to connect with nature, the urban park waterfront environment plays a significant role. Studying the characteristics of landscape elements contributes to the optimization of the urban park natural environment. In this study, the waterfront spaces of 23 urban parks in Shenyang were selected in order to categorize urban park waterfront spaces from the perspective of landscape elements and to explore the relationship between the characteristics of landscape elements in different types of waterfront spaces and public visual satisfaction. Using qualitative analysis, typical spatial types were identified based on differences in landscape elements. Content analysis was used to extract and quantify the characteristics of landscape elements for various waterfront spaces. Through orthogonal experimental design, virtual scenarios were created to evaluate public satisfaction. Methods such as the least significant difference multiple comparison analysis (LSD) were applied to explore the effects of landscape element characteristics on satisfaction in different types and differences within groups. Among the four types of waterfront spaces identified in the experiment, the landscape elements that influenced spatial satisfaction were primarily concentrated in plant characteristics and pavement characteristics. In different types of spaces, the impact of landscape element factors at different levels varied. The study introduced virtual experiments to analyze the characteristics of landscape elements in waterfront spaces, which provided a new method for the satisfaction research of waterfront spaces. The results are a valuable guidance for the scientific classification of urban park waterfront spaces. A new perspective for enhancing the urban park waterfront landscape was supplied.

1. Introduction

The urban park is not only the main content of urban construction but also an important part of the urban ecosystem and urban landscape, with many functions such as beautifying the urban landscape environment, purifying the air, and improving the microclimate [1,2]. Urban waterfront space quality affects the style and features of the urban landscape, public behavior [3], and physical and mental health [4]. As an integral part of urban waterfront space, urban park waterfront space has natural attributes and humanistic attributes, ecological functions, and social functions [5,6,7]. It is a characteristic landscape space formed by planning and designing the water body, integrating ecological environmental protection, leisure and entertainment, garden art exhibition, and other characteristics, which can provide a comfortable rest space for urban residents to have close contact with water [8]. The urban park waterfront plays a significant role in regulating the public mood and enhancing well-being [9].
Most of the current research on urban park waterfront space is based on the study of urban waterfront space, which is categorized and discussed from the perspectives of form and function [10,11,12]. The scholars mainly focus on the revitalization mode, ecosystem, and renewal strategies of waterfront space around the world [13,14]. Research perspectives have gradually shifted from the macro to the micro level, showing a cross-disciplinary trend and focusing more on the study of human feelings [15,16,17,18,19]. The relationship between the visual environment and landscape features has become a research hotspot in recent years [20,21,22,23,24]. The urban visual environment is a resource type, which is as important as natural resources, such as water and soil. The urban visual environment quality affects aesthetic experience and is directly related to public physical and mental health [25], especially dynamic water surfaces. It is conducive to the alleviation of mental illness and emotional well-being [26,27]. The differences in the prominence of a feature and composition in the landscape space lead to different emotional changes and psychological feelings among the public [23]. As the public’s main leisure and recreational activities place, the importance of the urban park visual environment is increasingly evident [28]. Meanwhile, the study of urban park waterfront space, especially about spatial quality evaluation, has increased [29], involving the evaluation of satisfaction, botanical landscape evaluation, health recovery evaluation, after-use evaluation, landscape comprehensive evaluation, and other related research from multiple perspectives [30,31,32,33,34,35,36,37].
Previous urban landscape quality evaluation studies showed that the visual factor was the first thing that affected public visual satisfaction [38,39], dominated the public’s psychological feelings, and improved the visual aesthetics quality. It could directly and effectively enhance the urban landscape quality [40,41,42]. Accordingly, the visual perception of the waterfront spatial landscape is the primary factor that affects the waterfront space quality and the resident traveling quality [43]. The landscape visual satisfaction evaluation can effectively help to explore the relationship between public preferences and the characteristics of the landscape elements [44]. Currently, images as a source of visual stimuli are the most common way of acquiring data in landscape visual satisfaction studies [39,45]. However, the traditional way of acquiring images by smartphone or camera photography has some limitations. Some scholars suggested that creating or reproducing virtual scenes is identical to real environments for assessment appropriateness and meaningfulness [46]. Usually, computer technology is used to construct scenarios for research by building models and other methods [47,48]. It provides the viewer with a more complete sense of the environment. Previous studies have mostly used more subjective methods, such as the SBE method, SD method, AHP-IPA method, or image semantic segmentation techniques, to quantitatively analyze landscape elements [49,50,51,52,53]. Nowadays, the popularization and application of technological tools, such as virtual reality technology, electroencephalographic instruments, and ocular dynamometry, have opened up new possibilities for the study of visual satisfaction in landscapes [54,55,56].
At present, there are few studies for the classification research of urban park waterfront space landscapes, which start from the level of specific landscape elements and carry out in-depth discussion. They fail to fully reveal the influence of different landscape elements on the overall landscape visual effect. The research of waterfront space landscape visual satisfaction lacks more objective data support, and the scientific nature of the research methods needs to be improved.
Based on this, this study selected the urban park waterfront space in Shenyang and used NVivo11 software to conduct qualitative analysis. According to the probability of occurrence of landscape elements and the frequency of co-occurrence of different landscape elements combined with field survey, four typical landscape types of waterfront spaces were extracted. Virtual scenario experiments, orthogonal experimental methods, box plot analysis, and least significant difference multiple comparison analysis were used to explore the influence of landscape element characteristics on the satisfaction of different types. By constructing a virtual scene to refine the landscape elements in different types of urban park waterfront spaces in Shenyang, the results can provide reference guidelines for waterfront space landscape design. The method proposed can provide a new perspective for the classification of urban park space and the quantitative study of the spatial landscape.

2. Materials and Methods

2.1. Samples Acquisition and Selection

Shenyang is located in the southern part of Northeast China. The Hun River is the largest natural river flowing along the southern edge of Shenyang. There are many urban parks in the region. The existing urban parks show a cohesive pattern in spatial distribution, with uneven distribution and differences between regions [57]. Most of the urban parks containing waterfront spaces are located along the Hun River, the South Canal, and the Xinkai River. The study selected a total of 23 urban comprehensive parks within Shenyang Fourth Ring Road that contained water bodies. The distribution is shown in Figure 1.
In order to obtain a more comprehensive spatial type of landscape, field survey and field acquisition methods were used to take sample pictures at different landscape nodes along the waterfronts of the 23 parks. The requirements for the pictures of the sample points were: (1) Determine one sample point every 50 m along the water in the parks for the pictures. (2) Ensure that the pictures included the water and all landscape elements in the node, such as different types of paved paths and different forms of planting, garden vignettes, structures, etc.
Examples of sample pictures are shown in Figure 2.
A total of 276 pictures were obtained, and the sample pictures with continuous homogenized landscapes were excluded. Finally, 181 sample pictures were obtained.

2.2. Data Acquisition and Processing

This study mainly contained two parts of experiments. The first part was the waterfront space classification experiment, which realized the urban park waterfront space classification in Shenyang based on landscape elements through the qualitative analysis method. The second part was the virtual scene experiment. Based on the spatial classification and the characteristics of the landscape elements contained in the space obtained in Experiment 1, the virtual scene was set up. Then, the scene was evaluated for satisfaction in order to explore the influence of the characteristics of each element of each space type on satisfaction. The experimental flow chart is shown in Figure 3.

2.2.1. Experiments in Waterfront Space Categorization

NVivo11, a software for the qualitative analysis of acquired pictures, was used to analyze the content of landscape elements in the acquired pictures of urban parks waterfront spaces. The key step was to code the landscape elements identified in the pictures and associate the landscape element keywords as free nodes with the contents of the pictures. In the coding process, the landscape elements identified in the pictures were encoded into several free nodes, with a maximum of four free nodes per picture [58]. In this study, water as a constant element was not involved in coding.
After obtaining the free nodes, the representative types of urban park waterfront spaces in Shenyang were extracted through co-occurrence analysis combined with the subjective analysis of field research.

2.2.2. Virtual Scene Experiments

The experimental characterization factors were selected based on the waterfront spatial landscape elements included in the spatial types obtained from Experiment 1.
The virtual experimental scenarios were set up using the orthogonal experimental design method. Orthogonal experimental design, referred to as orthogonal design, is a scientific method of arranging and analyzing multifactor, multilevel experiments using orthogonal tables. It can select a representative combination of elements to represent all experimental combinations and efficiently and scientifically explore the influence relationship between variables [59].
In this experiment, three experimental factors were selected as independent variables for each spatial type, and each variable was categorized into three different levels: low, medium, and high. The standard L9.3.3 orthogonal table was adopted as the basic basis for the experimental scenario design (Table 1), and nine representative scenarios were selected as the experimental samples from the twenty-seven possible scenarios that could be formed in each spatial type. The representative photos of each spatial type were used as the base for the element design, and the landscape element characterization factors of each scene were combined at different levels to form the final experimental scenes.
Each spatial scene image was used as an experimental sample. Forty observers were recruited, all with architecture and landscape design-related backgrounds. The details are shown in Figure 4.
The experiment was conducted with 36 sample virtual scene pictures as the evaluation object, using the form of large-screen projection, and each picture was displayed for 20 s; a 5-level Likert scale was used to evaluate the sample pictures on a scale of 1–5 in terms of satisfaction. The evaluation criteria are shown in Table 2.
The high and low levels of each experimental factor were defined as 3, 2, and 1 points, and the main effect analysis was conducted with the standardized satisfaction scores to obtain the landscape element characteristics, with significant main effects in each spatial type. Then, two-by-two comparisons between groups of this feature were conducted by box plot analysis and least significant difference (LSD) multiple comparison analysis to explore the effects of different levels of landscape element features on satisfaction in each space.

3. Result and Analysis

3.1. Results of Waterfront Space Classification

Through the content analysis of the pictures and quantitative statistics by using NVivo11 software, 26 free nodes (26 waterfront spatial landscape elements) were finally obtained. The node categories and the frequency of occurrence are shown in Figure 5a.
The landscape elements of the waterfront space were analyzed by keyword co-occurrence (Figure 5b), and the existing samples were clustered into eight types. Combined with the frequency of landscape elements appearing (Figure 5a) and the subjective analysis of field research, four representative types of urban park waterfront space in Shenyang were extracted, respectively, including lawns with the woodland type, waterfront plaza type, recreational and ornamental spaces, and waterfront runway-type spaces.
The lawn with the woodland-type space used a masonry-paved garden road combined with sparse forest and grassland, and rest seats were set up along the road. The waterfront plaza-type space was set up with a waterfront plaza, tree pools were distributed on the plaza, and there were boats and other amusement facilities on the water surface. The recreational and ornamental spaces were equipped with landscape pavilions and paved trestles, and ornamental aquatic plants were planted in the water. The waterfront runway-type space was set up with waterfront paths and jogging paths at the same time, with street trees planted along the perimeter and the use of plastic shrubs for plant landscaping.
The combinations of landscape elements of each type of waterfront space and their representative pictures are shown in Figure 6.

3.2. Experimental Results of the Virtual Scene

3.2.1. Experimental Scene Setting

The experimental characterization factors were selected based on the waterfront spatial landscape elements included in the spatial types obtained in the previous section.
In the lawn with the woodland-type space, the landscape elements were mainly sparse forest meadow and masonry-paved garden road. The experimental characterization factors of this scene were defined as tree size, tree density, and pavement richness. Similarly, the landscape characteristic factors of the waterfront plaza-type space were defined as tree size, waterfront height difference, and pavement richness. For the recreational and ornamental type, they were aquatic plant density, pavement type, and shoreline liveliness; the characteristic factors of the waterfront runway type were shrub-planting naturalness, path richness, and shrub species richness.
The low, medium, and high levels of control for each variable in the experiment are shown in Table 3.
Representative pictures of the four spatial types were used as the elemental design substrate, and different levels of landscape elemental characterization factors for each scene were combined according to an orthogonal experimental table to form the final experimental scene.

3.2.2. Effects on Satisfaction Due to the Characteristics of Landscape Elements for Each Spatial Type

Lawn with Woodland-Type Space

The scenario with the highest average satisfaction scores in the lawn with the woodland-type space was the scene with high tree size, high tree density, and high pavement richness, and the lowest scoring scenario was the scene with low tree size, low tree density, and low pavement richness (Figure 7).
The tree size factor had a significant effect on the spatial satisfaction of the type (Table 4), which differed significantly between high and low and medium and high levels (Table 5). The spatial satisfaction scores showed a clear upward trend with the gradual increase in tree size (Figure 8).
The tree density factor also had a significant effect on sparse forest and grassland-type spaces (Table 4), significant only between high and low levels (Table 5). Lawns with woodland scenarios with high tree density had higher satisfaction scores but a lower influence on satisfaction than the tree size (Figure 8).
There were no significant differences in satisfaction scores for high, medium, and low levels of the pavement richness factor. Changes in pavement richness had essentially no effect on satisfaction in the lawn with woodland-type space (Figure 8).

Waterfront Plaza-Type Space

The scenario with the highest average satisfaction scores in the waterfront plaza-type space was the scene with high tree size, a waterfront height difference of 0.5 m, and high pavement richness, and the lowest-scoring scenario was the scene with low tree size, a waterfront height difference of 0.1 m, and low pavement richness (Figure 9).
The tree size factor had no significant effect on the spatial satisfaction of this type. Scene satisfaction scores for medium-sized trees were more stable, with intermediate scores. The scene satisfaction scores with low- and high-sized trees were more variable (Figure 10).
The effect of the waterfront height difference factor on spatial satisfaction was also not significant. However, the box plots showed an overall increase in the mean satisfaction score as the waterfront height difference increased, which had a certain positive effect (Figure 10).
The pavement richness factor had a significant effect on the spatial satisfaction of the waterfront plaza type (Table 6), and only the differences between its groups were significant between high and low levels, while the rest were not significant (Table 7). As the pavement richness increased, the spatial satisfaction scores increased significantly. From the box plot, it can be seen that the satisfaction scores of medium-level pavement richness were more concentrated (Figure 10).

Recreational and Ornamental Space

The scenario with the highest average satisfaction scores in the recreational and ornamental space was the scene with low aquatic plant density, timber-paved roads, and low shoreline liveliness, and the lowest scoring scenario was the scene with high aquatic plant density, gravel-paved roads, and high shoreline liveliness (Figure 11).
The aquatic plant density factor had a significant effect on recreational and ornamental space satisfaction (Table 8), with significant differences between groups at all levels (Table 9). As the density of aquatic plants increased, the space satisfaction scores showed a significant decrease, and the satisfaction scores of aquatic plant density were more concentrated at the medium level (Figure 12).
The pavement type factor also had a significant effect on satisfaction with this type of space (Table 8). The differences between pavement types were significant between timber paving and stone paving and between timber paving and gravel paving, while the difference in satisfaction between stone paving and gravel paving was not significant (Table 9). Satisfaction scores were highest when the paving type was timber paving, followed by masonry paving and, finally, stone paving (Figure 12).
The shoreline liveliness factor did not have a significant effect on spatial satisfaction, and as the shoreline liveliness increased, the satisfaction scores decreased overall, showing a negative trend in satisfaction (Figure 12).

Waterfront Runway-Type Space

The scenario with the highest average satisfaction scores in the waterfront runway-type space was the scene with shrubs planted naturally, high path richness, and high shrub species richness, and the lowest scoring scenario was the scene with shrubs planted regularly, low garden path richness, and low shrub species richness (Figure 13).
The shrub-planting naturalness factor had no significant effect on the waterfront runway-type spatial satisfaction, and scenario satisfaction scores were similar for shaping regular, semi-natural, and natural shrub-planting forms (Figure 14).
The path richness also had a non-significant effect on satisfaction with this type of space, with more stable satisfaction scores for scenes with two paving types and more fluctuating satisfaction scores for scenes with one and three paving types (Figure 14).
The shrub species richness factor had a significant effect on waterfront runway-type spatial satisfaction (Table 10), with significant differences between its high and low levels, and non-significant levels of differences between the medium-low and medium-high groups (Table 11). As the shrub species richness increased, the scene satisfaction scores increased significantly, and the satisfaction scores of all three levels of this scene were more concentrated (Figure 14).

3.2.3. Comparative Analysis of the Four Types of Spaces

By analyzing the scenarios of waterfront spaces in urban parks of different types (Figure 15), the study found that the landscape features of plants and paving had a significant impact on user satisfaction, while the impact of environmental factors was relatively small. Specifically, a higher percentage of plants had a positive effect on space satisfaction, but an increase in the percentage of aquatic plants showed the opposite effect, with large areas of aquatic plants negatively affecting satisfaction.
In addition, increasing the richness of pavement types usually enhanced user satisfaction; however, in spaces where plants had a significant effect on satisfaction, paving changes had a diminished effect on satisfaction.

4. Discussion

In the four types of waterfront space landscapes identified in the experiment, the landscape elements influencing spatial landscape satisfaction varied. Different kinds of spaces and levels of landscape element characteristic factors had different impacts on satisfaction. The features that primarily affected visual landscape satisfaction were mainly vegetation and paving.

4.1. The Impact of Vegetation Features on Visual Satisfaction in Waterfront Landscapes of Urban Parks

Vegetation is the most popular factor in visual satisfaction assessments [60,61,62]. The tree size, tree density, and aquatic plant density significantly influence visual landscape satisfaction in sparse forest and grassland-type spaces and recreational and ornamental spaces. Therefore, designers should prioritize the scale of the trees in the open forest when designing and enhancing the spatial quality of the open forest meadow type and ensure line-of-sight permeability to create a sense of a space with sheltered shade. In the leisure and ornamental space, the density of aquatic plants should not be too high and should be distributed in a balanced manner, and priority can be given to reeds and other local varieties that have both ecological functions and seasonal changes. It should appropriately expose water surfaces to maintain spatial openness and transparency, which positively impacts landscape satisfaction [63]. This is consistent with the findings of Bulut, etc. [64].
In the waterfront runway-type space, shrub species richness has a significant impact, and complexity is one of the key landscape characteristics influencing people’s preferences [65]. A diverse range of shrub species can significantly enhance spatial satisfaction. An appropriately increasing contrast can help improve the attractiveness of the landscape [66]. Layered focal areas should be created to enhance visual tension.
In waterfront plaza-type spaces, tree size has a moderate impact on satisfaction. The research findings suggest that selecting medium-sized trees in near-water spaces achieves better results, which is consistent with the findings of Dong Sun, etc. [67,68].

4.2. The Impact of Pavement Features on Visual Satisfaction in Waterfront Landscapes of Urban Parks

In waterfront plaza-type spaces and recreational and ornamental spaces, the pavement richness significantly influences visual landscape satisfaction [69,70]. Therefore, special attention should be paid to the paving styles in waterfront plazas. Through research, we found that when the plaza paving was relatively single, increasing the variety of paving patterns could effectively enhance spatial satisfaction [71]. However, when the variety of paving patterns reached a certain level, adding more diversity in paving had a limited impact on improving satisfaction [72]. Other dimensions should be taken into consideration. When designing pathways in recreational and ornamental spaces, timber paving should be prioritized, followed by brick or stone paving. Timber paving not only offers an excellent sense of reality and comfort but also integrates well with the natural environment, enhancing the overall quality of the space. Overly playful shoreline designs may lead to spatial disorder and incoherence. Thus, it is advisable to keep the shoreline simple and natural, avoiding excessive curvature and complexity.
In sparse forest and grassland-type spaces, although the pavement richness has little impact on satisfaction, it still requires moderate attention. In the process of the design, functional needs and aesthetic requirements should be considered, and appropriate paving materials and colors should be selected [73,74]. Future research could further explore the combined effects of paving and other landscape elements.

4.3. Limitations

This study still has some limitations.
Firstly, the sample photos of the experimental scenes in this study were all on-site and fixed-point, without considering the effects of seasonal variations. Secondly, the study focused on the main effects of experimental factors, which might have led to precision biases in the results.
In future research, the introduction of dynamic panoramic experimental scenes will be further considered to ensure the diversity of the scenes, covering different seasonal changes and exploring the impact of seasonal changes on visual satisfaction. The selection of experimental factors will be further expanded to consider the interaction effect between factors, and the role of landscape element characteristics on the visual satisfaction of the urban park waterfront space landscape will be further studied. Meanwhile, we will actively introduce new technologies, such as eye tracking and electroencephalography (EEG), to discuss the effects of changes in landscape element characteristics on observers’ physiology so as to make the experimental results more accurate.

5. Conclusions

This study focused on the urban park waterfront space in Shenyang. NVivo11 software was used for qualitative analysis, which identified four typical types of waterfront space landscapes based on the probability of occurrence of landscape elements, co-occurrence frequencies of different elements, and field surveys. Through virtual scene experiments, orthogonal experimental methods, box plot analysis, and least significant difference (LSD) multiple comparison analysis, the study explored the impact of landscape element features on satisfaction across different types of waterfront spaces.
The experiment showed that the significant factors influencing satisfaction in sparse forest and grassland-type spaces were tree size and tree density. In waterfront plaza-type spaces, pavement richness significantly influenced satisfaction. In recreational and ornamental spaces, aquatic plant density and pavement type had a significant impact on satisfaction. In waterfront runway-type spaces, shrub species richness significantly influenced satisfaction. Additionally, intra-group differences for each feature were analyzed. The findings provide a reference for the design of waterfront landscapes in Shenyang and offer a new perspective for space classification and the quantitative study of urban park landscapes spaces.

Author Contributions

Conceptualization, M.L.; Methodology, M.L. and D.S.; Validation, D.S.; Formal analysis, M.L., S.W. and J.S.; Investigation, K.C. and Y.T.; Resources, J.S.; Data curation, S.W.; Writing—original draft, M.L. and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by the Project of Liaoning Provincial Department of Education Service Local Project (JYTMS20231560).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We would like to thank the reviewers for their careful reading of the manuscript and look forward to their comments on the problems and corrections in the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
LSDLeast Significant Difference

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Figure 1. Sample park distribution map.
Figure 1. Sample park distribution map.
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Figure 2. Examples of sample pictures.
Figure 2. Examples of sample pictures.
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Figure 3. Experimental flow chart.
Figure 3. Experimental flow chart.
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Figure 4. Observer context. Gender (a); age (b); specialty (c).
Figure 4. Observer context. Gender (a); age (b); specialty (c).
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Figure 5. Categories and numbers of nodes (a); keyword co-occurrence (b).
Figure 5. Categories and numbers of nodes (a); keyword co-occurrence (b).
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Figure 6. Representative scenes of the four space types.
Figure 6. Representative scenes of the four space types.
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Figure 7. Highest (a) and lowest (b) satisfaction score samples in the lawn with woodland-type space.
Figure 7. Highest (a) and lowest (b) satisfaction score samples in the lawn with woodland-type space.
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Figure 8. Box plot analysis—Lawn with Woodland-Type Space.
Figure 8. Box plot analysis—Lawn with Woodland-Type Space.
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Figure 9. Highest (a) and lowest (b) satisfaction score samples in the waterfront plaza-type space.
Figure 9. Highest (a) and lowest (b) satisfaction score samples in the waterfront plaza-type space.
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Figure 10. Box plot analysis—Waterfront Plaza-Type Space.
Figure 10. Box plot analysis—Waterfront Plaza-Type Space.
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Figure 11. Highest (a) and lowest (b) satisfaction score samples in the recreational and ornamental space.
Figure 11. Highest (a) and lowest (b) satisfaction score samples in the recreational and ornamental space.
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Figure 12. Box plot analysis—Recreational and Ornamental Space.
Figure 12. Box plot analysis—Recreational and Ornamental Space.
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Figure 13. Highest (a) and lowest (b) satisfaction score samples in the waterfront runway-type space.
Figure 13. Highest (a) and lowest (b) satisfaction score samples in the waterfront runway-type space.
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Figure 14. Box plot analysis—Waterfront Runway-Type Space.
Figure 14. Box plot analysis—Waterfront Runway-Type Space.
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Figure 15. Box plots of satisfaction with the characteristics of landscape elements within the four spaces.
Figure 15. Box plots of satisfaction with the characteristics of landscape elements within the four spaces.
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Table 1. Orthogonal experiments.
Table 1. Orthogonal experiments.
Scene NumberExperimental Factor
Feature 1Feature 2Feature 3
1LowLowLow
2LowMediumHigh
3LowHighMedium
4MediumLowHigh
5MediumMediumMedium
6MediumHighLow
7HighLowMedium
8HighMediumLow
9HighHighHigh
Table 2. Satisfaction evaluation criteria.
Table 2. Satisfaction evaluation criteria.
PointSatisfaction Evaluation Criteria
1Observers had a very poor overall impression of the landscape scene and felt that the landscape design did not meet expectations and did not fulfill basic needs.
2Observers had a poor overall impression of the landscape scene and felt that there were some problems with the landscaping and that it failed to achieve the desired effect.
3Observers’ overall feelings about the landscape scene were average, and they thought that the landscape design basically met expectations, but there is still room for improvement.
4Observers had a better overall perception of the landscape scene and believed that the landscape design met expectations and fulfilled needs.
5Observers had a very good overall impression of the landscape scene and felt that the landscape design exceeded expectations and was able to adequately meet needs.
Table 3. Characterization level control table.
Table 3. Characterization level control table.
Type of SpaceCharacterization of Landscape ElementsControl Method
LowMediumHigh
Atree sizeZoom ratio 0.5Zoom ratio 1.0Zoom ratio 1.5
tree densityLow densityMedium density High density
pavement richnessType of pavement = 1Type of pavement = 2Type of pavement = 3
Btree sizeZoom ratio 0.5Zoom ratio1.0Zoom ratio 1.5
waterfront height differenceHeight difference of about 0.1 mHeight difference of about 0.3 mHeight difference of about 0.5 m
pavement richnessType of pavement = 1Type of pavement = 2Type of pavement = 3
Caquatic plant densityPlants account for about 30% of waterPlants account for about 50% of waterPlants account for about 70% of water
pavement typetimber pavingstone pavinggravel paving
shoreline livelinessLAn/LSn ≈ 1.2LAn/LSn ≈ 2LAn/LSn ≈ 3
Dshrub-planting naturalnessRegular plantingSemi-natural plantingNatural planting
path richnessType of pavement = 1Type of pavement = 2Type of pavement = 3
shrub species richnessShrub species = 1Shrub species = 2Shrub species = 3
Note: lawn with woodland-type space A, waterfront plaza-type space B, recreational and ornamental space C, waterfront runway-type space D. LSn is the straight-line distance between the ends of the waterfront space unit, and LAn is the actual distance of the waterfront space.
Table 4. Main effects analysis- Lawn with Woodland-Type Space.
Table 4. Main effects analysis- Lawn with Woodland-Type Space.
VariableType III Sum of SquaresdfMSFp
tree size1.17920.590141.4100.007 *
tree density0.19520.09723.3520.041 *
pavement richness0.02520.0122.9810.251
Dependent Variable: satisfaction; R-squared = 0.994 (Adj R-squared = 0.976)
Note: * indicates p < 0.05, reaching the significance level.
Table 5. LSD—Lawn with Woodland-Type Space.
Table 5. LSD—Lawn with Woodland-Type Space.
(I) tree sizeMD (I-J)Std. Err.p95% Conf. Interval
lower limitupper limit
Among groupsLowMedium−0.2020.0530.062−0.4290.025
High−0.8490.0530.004 *−1.075−0.622
MediumLow0.2020.0530.062−0.0250.429
High−0.6470.0530.007 *−0.874−0.420
HighLow0.8490.0530.004 *0.6221.075
Medium0.6470.0530.007 *0.4200.874
(I) tree densityMD (I-J)Std. Err.p95% Conf. Interval
lower limitupper limit
Among groupsLowMedium−0.1710.0530.083−0.3980.056
High−0.3600.0530.021 *−0.587−0.133
MediumLow0.1710.0530.083−0.0560.398
High−0.1890.0530.070−0.4160.038
HighLow0.3600.0530.021 *0.1330.587
Medium0.1890.0530.070−0.0380.416
Note: * indicates p < 0.05, reaching the significance level.
Table 6. Main effects analysis—Waterfront Plaza-Type Space.
Table 6. Main effects analysis—Waterfront Plaza-Type Space.
VariableType III Sum of SquaresdfMSFp
tree size0.07520.0370.9780.506
waterfront height difference0.37820.1894.9640.168
pavement richness2.02221.01126.5310.036 *
Dependent Variable: satisfaction; R-squared = 0.970 (Adj R-squared = 0.880)
Note: * indicates p < 0.05, reaching the significance level.
Table 7. LSD—Waterfront Plaza-Type Space.
Table 7. LSD—Waterfront Plaza-Type Space.
(I) Pavement RichnessMD (I-J)Std. Err.p95% Conf. Interval
Lower LimitUpper Limit
Among groupsLowMedium−0.6650.1590.053−1.3500.021
High−1.1570.1590.018 *−1.842−0.471
MediumLow0.6650.1590.053−0.0211.350
High−0.4920.1590.091−1.1780.194
HighLow1.1570.1590.018 *0.4711.842
Medium0.4920.1590.091−0.1941.178
Note: * indicates p < 0.05, reaching the significance level.
Table 8. Main effects analysis—Recreational and Ornamental Space.
Table 8. Main effects analysis—Recreational and Ornamental Space.
VariableType III Sum of SquaresdfMSFp
aquatic plant density3.90021.950165.5880.006 *
pavement type0.87020.43536.9440.026 *
shoreline liveliness0.35620.17815.1090.032
Dependent Variable: satisfaction; R-squared = 0.995 (Adj R-squared = 0.982)
Note: * indicates p < 0.05, reaching the significance level.
Table 9. LSD—Recreational and Ornamental Space.
Table 9. LSD—Recreational and Ornamental Space.
(I) aquatic plant densityMD (I-J)Std. Err.p95% Conf. Interval
lower limitupper limit
Among groupsLowMedium1.0330.0890.007 *0.6521.415
High1.5890.0890.003 *1.2071.970
MediumLow−1.0330.0890.007 *−1.415−0.652
High0.5550.0890.025 *0.1740.937
HighLow−1.5890.0890.003 *−1.970−1.207
Medium−0.5550.0890.025 *−0.937−0.174
(I) pavement typeMD (I-J)Std. Err.p95% Conf. Interval
lower limitupper limit
Among groupsLowMedium0.4190.0890.042 *0.0370.800
High0.7600.0890.013 *0.3791.142
MediumLow−0.4190.0890.042 *−0.800−0.037
High0.3420.0890.061−0.0400.723
HighLow−0.7600.0890.013 *−1.142−0.379
Medium−0.3420.0890.061−0.7230.040
Note: * indicates p < 0.05, reaching the significance level.
Table 10. Main effects analysis—Waterfront Runway-Type Space.
Table 10. Main effects analysis—Waterfront Runway-Type Space.
VariableType III Sum of SquaresdfMSFp
shrub-planting naturalness0.20820.1042.6930.271
path richness0.01820.0090.2350.810
shrub species richness1.77220.88622.9120.041 *
Dependent Variable: satisfaction; R-squared = 0.963 (Adj R-squared = 0.851)
Note: * indicates p < 0.05, reaching the significance level.
Table 11. LSD—Waterfront Runway-Type Space.
Table 11. LSD—Waterfront Runway-Type Space.
(I) Pavement RichnessMD (I-J)Std. Err.p95% Conf. Interval
Lower LimitUpper Limit
Among groupsLowMedium−0.6750.1610.052−1.3660.016
High−1.0750.1610.022 *−1.766−0.384
MediumLow0.6750.1610.052−0.0161.366
High−0.4010.1610.130−1.0920.290
HighLow1.0750.1610.022 *0.3841.766
Medium0.4010.1610.130−0.2901.092
Note: * indicates p < 0.05, reaching the significance level.
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Lyu, M.; Wang, S.; Shi, J.; Sun, D.; Cong, K.; Tian, Y. Visual Satisfaction of Urban Park Waterfront Environment and Its Landscape Element Characteristics. Water 2025, 17, 772. https://doi.org/10.3390/w17060772

AMA Style

Lyu M, Wang S, Shi J, Sun D, Cong K, Tian Y. Visual Satisfaction of Urban Park Waterfront Environment and Its Landscape Element Characteristics. Water. 2025; 17(6):772. https://doi.org/10.3390/w17060772

Chicago/Turabian Style

Lyu, Mei, Shujiao Wang, Jiaxuan Shi, Dong Sun, Kangting Cong, and Yi Tian. 2025. "Visual Satisfaction of Urban Park Waterfront Environment and Its Landscape Element Characteristics" Water 17, no. 6: 772. https://doi.org/10.3390/w17060772

APA Style

Lyu, M., Wang, S., Shi, J., Sun, D., Cong, K., & Tian, Y. (2025). Visual Satisfaction of Urban Park Waterfront Environment and Its Landscape Element Characteristics. Water, 17(6), 772. https://doi.org/10.3390/w17060772

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