Explore the vast range of titles available.

ContextIt is essential for policy-making and planning that we understand landscapes not only in terms of landscape ecological patterns, but also in terms of their contribution to people's quality of life.ObjectivesIn this study our objective is to test relationships between landscape ecology and social science indicators, by investigating how landscape patterns are linked to people’s perception of landscape quality.MethodsTo assess public views on landscapes we conducted a survey among 858 respondents in Switzerland. We combined this survey data on perceived landscape quality and place attachment with landscape metrics (e.g. diversity, naturalness of land cover, urban sprawl, fragmentation) in a statistical model to test hypotheses about the relationships between the different variables of interest.ResultsOur results illustrate the contribution of both landscape composition metrics and social science indicators to understanding variation in people’s perception and assessment of landscape. For example, we found the landscape ecology metrics on urban sprawl and fragmentation to be a negative predictor of overall satisfaction with landscape, and that perceived landscape quality positively predicted place attachment and satisfaction with the municipality landscape.ConclusionsThis study highlights the importance and feasibility of combining landscape ecology metrics and public survey data on how people perceive, value and relate to landscape in an integrated manner. Our approach has the potential for implementation across a variety of settings and can contribute to holistic and integrated landscape assessments that combine ecological and socio-cultural aspects.

Urban parks are an important component of public urban spaces, which directly impact the living experiences of residents and the urban image. High-quality urban parks are crucial for enhancing the well-being of residents. This study selected Fukuoka, Japan, as the study site. Five urban parks were chosen to evaluate landscape visual quality by using the Scenic Beauty Estimation (SBE) method. The Semantic Differential (SD) method was used to get sample subjective landscape features. Meanwhile, sample objective landscape features were obtained by using semantic segmentation techniques in deep learning and combined with spatial analysis to understand their distribution. A regression model was established, which used the SBE values as the dependent variable and subjective landscape features as the independent variables to analyze the relationship between urban park landscape visual quality and subjective landscape features. The regression analysis revealed that sense of layering, harmony, interestingness, sense of order, and vitality were the core factors influencing visual quality. All five features had a significant positive impact on landscape visual quality. The sense of order was the most influential factor, which would be the key to enhancing the landscape perception experience. Moreover, the XGBoost model and SHAP value from machine learning were used to reveal the nonlinear relationships and significant threshold effects between urban park visual quality and five objective landscape features: openness, greenness, enclosure, vegetation diversity, and Shannon–Wiener diversity index. This study showed that when openness exceeded 0.27, the positive effect was significant. The optimal threshold for the greenness was 0.38. Vegetation diversity and enclosure had to be below 0.82 and 0.58, respectively, to have a positive impact. Meanwhile, the positive influence of the Shannon–Wiener diversity index reached its maximum at a value of 1.37. This study not only establishes a systematic method for diagnosing landscape problems and evaluating landscape visual quality but also provides both theoretical support and practical guidance for urban park landscape optimization.

Traditional visual quality assessments of rural landscapes rely on subjective methods. This study integrates eye-tracking technology with subjective perception evaluation to construct a visual quality assessment model for rural landscapes, aiming to reveal the intrinsic relationship between objective visual behavior and subjective perception, with the aim of providing scientific guidance for rural landscape planning to promote sustainable rural development. Using landscape photographs from nine rural sampling sites in Guangzhou, eye-tracking experiments were conducted to collect participants’ eye movement data, combined with online questionnaires to obtain scenic beauty ratings and landscape characteristic factor evaluations. The findings reveal the following: (1) Eye-tracking experiments and subjective evaluation results showed high consistency, with samples having higher scenic beauty ratings demonstrating more prominent performance in core eye movement indicators such as total fixation duration and count, and total saccade duration, and typically possessing higher landscape characteristic factor values. (2) Urban–suburban-integrated rural landscapes exhibited poorer visual quality, characteristic-preservation rural landscapes elicited more in-depth and sustained visual exploration, and clustered-improvement rural landscapes possessed higher scenic beauty ratings and landscape characteristic factor values. (3) Total saccade duration was the key eye movement indicator for predicting scenic beauty ratings. (4) Multiple landscape characteristic factors significantly influence eye movement behavior.

The assessment of landscape visual quality (LVQ) holds significant importance in the preservation and advancement of traditional villages. One challenge in measuring human perception lies in establishing a connection between public preferences and landscape characteristics. This study conducted an analysis of social media data from Anyi traditional villages in China to address this issue and identified eight human perceptions: naturalness, ancientness, colorfulness, variety, uniqueness, ingenuity, vividness, and pleasantness. A total of thirty characteristic indicators with potential explanations for LVQ were determined by research group through field investigations. A questionnaire survey was developed to assess human’s preferences using 82 traditional village photos, and scores for the eight perceptions were obtained. The logistic regression was employed to establish distinct perception models, with perceptions serving as the dependent variables and characteristic indicators as the independent variables. Nomograms were subsequently utilized to visualize regression results and display the correlation between these two factors. The findings suggest that nomograms facilitate intuitive determination of the weights assigned to characteristic indicators in perceptual models, as well as their influence on LVQ. This work provides a reference for decision-making related to the adaptive protection and development of traditional villages, thereby helping to enhance the competitiveness of tourist destinations.

The rural road landscape is crucial in forming rural areas’ landscape character (LC). As a platform for portraying the rural landscape, the rural roads demonstrate the area’s unique natural and cultural characteristics to visitors. However, with the continuous development of rural areas, the rural LC has been severely impacted, thus impacting visitors’ visual experience. In order to preserve and protect the rural landscape, this study aims to assess the visual quality of rural road landscapes based on public preference and heatmap analysis. The results indicated that most of the participants had a higher level of preference for rural landscapes with open horizontal views represented by agricultural areas, such as paddy fields. It was also found that different paddy field characters based on their planting stages can also positively affect the visual quality of rural road landscapes. The study also revealed that rural LCs with roadside settlements, commercial structures, mixed agricultural crops, and vegetation received low preference ratings. These characters negatively impact the visual quality of the rural road landscape. These findings provide significant insight for planners and decision-makers regarding protecting and preserving the essential rural road landscapes for the rural tourism experience.

Wetland parks are an important part of urban ecosystems, characterized by artificial and natural features. While providing outdoor recreational space for urban residents, the aesthetics of the landscape also affect residents’ willingness to visit the wetland park. Previous studies of wetland parks focused on the ecological benefit. However, fewer systematic studies analyzed the aesthetic qualities and influencing elements of wetland parks. In this study, the Wuping Qianlu Lake Wetland Park in Fujian Province, China was used to evaluate the landscape visual quality of the wetland park using the the Scenic Beauty Estimation method (SBE), and the landscape features of the wetland park were obtained through the Semantic Differential (SD) method and semantic segmentation technique. The regression model of landscape visual quality and landscape features in wetland parks was established using SBE values as the dependent variable, and 23 landscape features as independent variables. The SBE values and accessibility were also combined to create four quadrants, which were combined with K-means clustering to diagnose existing problems. The results of the study showed that two landscape features, approach and proportion of water area were weaker among the four types of wetland park landscapes. The SBE values of the man-made landscape type were the most significant. Meanwhile, the high enclosure landscape type had a significant sense of rusticity, accentuating the wildness of the plants. It was less aesthetically pleasing and had the poorest landscape visual quality of the four types. This study provides a systematic method for diagnosing landscape problems in wetland parks, evaluating landscape visual quality assessment, and providing theoretical and technical support for urban wetland park renewal and construction.

Rural landscapes serve as important platforms to determine the landscape characteristics (LCs) of rural areas, demonstrating the landscape characteristics specific to certain regions to the public. However, the development trend of urban and rural areas is continuous and impacts the characteristics of rural landscapes, which directly affects the public’s visual experience and landscape perception. In order to improve the characteristics of rural landscapes, this study evaluates and analyzes their visual quality based on public preferences and eye movement heat maps. The results show that most subjects have a high preference for horizontal, open-view rural landscapes with fields and landform features as the dominant landscape elements. This study also found that the combination of strip-like or planar settlement buildings with regional characteristics and landform features has an active impact on the visual quality of rural landscapes. These results show that rural landscapes characterized by scattered settlement buildings without significant regional characteristics, horizontally curved roads, bridges, and other human-made landscape elements, and mixed and disorderly vegetation have low landscape preference, which degrades their visual quality. These research results provide crucial suggestions for landscape managers to protect and renew rural landscape features.

Visual perception is one of the main elements that shape the visitor experience in tourism and recreation activities. The Cappadocia Region, where natural and cultural landscape elements form a unique integrity, has a strong visual appeal at the universal level in this respect. In this study, the perceptual effects of different landscape types of Cappadocia on individuals were examined; the role of individual variables such as spatial familiarity, settlement and visit history on perception was evaluated. Methodologically, the study consisted of (1) photographing the characteristic landscapes of the region, (2) selecting photographs with diverse content, (3) administering a questionnaire to 120 participants in order to evaluate these images in terms of scenic beauty, tranquility, attractiveness and excitement, and (4) statistically analyzing the data. The findings revealed that Cappadocia has a strong aesthetic and emotional impact on the participants with its natural landscapes as well as its cultural landscape elements. However, it was also found that locals evaluated certain landscapes more positively and that spatial familiarity and emotional connection created significant differences in the perception of \"tranquility\" and \"beauty\". Moreover, individuals who had visited the region before, developed a deeper and more meaningful perception of some images. The results point to the importance of developing holistic planning strategies that are sensitive to user profiles and consider perceptual differences in areas with unique landscapes such as Cappadocia. They also emphasize the need to integrate visual perception into tourism planning and promotion processes.

National parks play a vital role in safeguarding natural scenery, maintaining ecological integrity, and preserving cultural heritage, while simultaneously offering valuable opportunities for recreation and education. Among the diverse resources provided by national parks, visual landscape resources hold particular significance due to their capacity to inspire, educate, and enhance aesthetic appreciation. However, assessing and managing these resources remain challenging, as they span both the physical attributes of the landscape and the human visual perception process. This study aims to develop a theoretical and practical framework for evaluating the “landscape visual affordance” of national parks. Grounded in ecological psychology’s affordance theory, the proposed approach integrates physical affordance and sensory affordance, encompassing both the objective physical attributes of the landscape and the subjective processes of human perception. Drawing on a multi-dimensional set of indicators, the research quantifies physical features—such as topography, land use, vegetation cover, and landscape structure—as well as sensory dimensions, including visibility, visual prominence, and viewing frequency. These elements are synthesized into a landscape visibility assessment model built upon the affordance theory framework. The results demonstrate that landscape visual affordance effectively identifies landscape patches with varying degrees of visual quality and importance within national parks and other protected areas. By providing robust support for management decisions—such as zoned protection, optimizing recreational facilities, and evaluating visitor carrying capacity—this model offers new insights and practical guidance for the sustainable planning and management of landscapes in national parks and other ecologically critical regions.

El objetivo fue evaluar la influencia de la contaminación acústica en los paisajes visuales y sonoros del Jardín Botánico y del Parque São Lourenço en Curitiba, Brasil. Los datos se recolectaron en tres transectos (A, B y C), que representan diferentes densidades de vegetación. La calidad visual del paisaje se caracterizó mediante un método directo de valoración de los elementos del paisaje. La calidad visual se clasificó como buena, media o mala. Para el análisis de sonido se utilizaron dos decibelímetros INSTRUTHERM, modelo DEC-470, con precisión de 1,5 decibelios. Los paisajes evaluados en el sitio C mostraron variación en la calidad del paisaje de 1 a 21.57, con las mejores cualidades de atenuación visual y de ruido. El análisis de correlación entre la calidad visual del paisaje y la cantidad de ruido bloqueado mostró un fuerte coeficiente de correlación (0,65; p = 0,0001). Los paisajes con mejor calidad visual dan como resultado una mayor cantidad de ruido atenuado, principalmente debido a la presencia de vegetación. The objective of this study was to evaluate both the landscape’s visual and sound quality influence on propagating noise pollution in the Botanical Garden and the São Lourenço Park in Curitiba, Brazil. Data were collected in three transects (A, B, and C), representing different vegetation densities. Landscape visual quality was characterized through a direct method for valuing landscape elements. The visual quality was classified as Good, Average, or Bad. We used two INSTRUTHERM DEC-470 decibel meters with 1.5-decibel precision for the sound analysis. The landscapes evaluated at site C showed variation in the landscape visual quality from 1 to 21.57, with the best visual and noise attenuation qualities. Correlation analysis between the landscape visual quality and the amount of blocked noise showed a strong correlation coefficient (0.65; p = 0.0001). Landscapes with better visual quality resulted in a greater amount of attenuated noise, mainly due to the presence of vegetation.