Traditional villages are historically, culturally, scientifically and aesthetically valuable, and a beautiful landscape is the primary embodiment of a traditional village environment. Urbanization and modernization have had a great impact on village landscapes. As an important aspect of traditional village landscapes, creating beautiful public spaces is an effective way to attract tourists and improve the well-being of residents. Landscape aesthetic activities are the result of the interaction between landscape objects and aesthetic subjects. Research on the relationship between the form of traditional village public spaces and subjective aesthetic preferences has long been neglected. This research examined 31 public spaces in traditional villages in the Dongshan and Xishan areas in Lake Taihu, Suzhou. An index system of public spatial forms in traditional villages was created, basic data of spatial forms were collected using a hand-held 3D laser scanner, and the value of the spatial forms index was calculated using R language. The scenic beauty estimation (SBE) method was improved, with the estimation of the beauty of the scenic environment based on VR panorama rather than traditional photo media. Parameter screening was performed using correlation analysis and full subset regression analysis, and four models were used to fit the SBE scores and grades. The results show that the majority of public spaces had lower than average SBE scores, and the four key indicators of average contour upper height, solid-space ratio, vegetation cover, and comprehensive closure predicted SBE. In addition, the linear model (R² = 0.332, RMSE = 64.774) had the most accurate SBE level prediction and the stochastic forest model (R² = 0.405, RMSE = 63.311) was better at predicting specific SBE scores. The model provides managers, designers, and researchers with a method for the quantitative evaluation of visual landscape preferences and quantitative landscape spatial forms and provides a reference for the protection and renewal of traditional village landscapes.

Rapid urbanization has made urban forest scenes scarce resources, leading to a surge in the demand for high-quality rural forest scenes as alternative outdoor recreation spaces. Previous studies mainly applied survey methods, focusing on visitors’ feedback for different types of scenes from the perspective of visual quality evaluation. Nevertheless, the explanations of the relationships between various factors of scenes and visitors’ preferences are relatively superficial. This study sought to explore the distribution and characteristics of preferred rural forest scenes based on visitor reviews from social media, and using Geodetector, a geospatial statistics tool, to quantitatively analyzed the reasons for visitors’ preferences in terms of factors obtained from multi-source geospatial data. The findings are that (1) visitors are already satisfied with the natural environment but expect scenes that reflect the culture of tea; (2) spatial factor has a more robust interpretation of visitors’ preference, and although the Normalized Difference Vegetation Index (NDVI) and non-consumption indicators barely explain visitors’ preference solely when each of them is combined with other indicators, they can produce non-linear enhancement effects. Consequently, this study synthesizes visitors’ feedback and factors in rural forest scenes to understand visitors’ preferences, thus providing insights into human-centered planning.

Rural areas provide ecosystem services (ESs) to urban metropolitan regions. These services are threatened by the constant pressure of urbanisation and new interest in rural development. This has heightened the conflict between environmental concerns and developmental needs, thereby presenting significant land management and rural planning challenges. Employing a quantitative measurement and optimisation framework, we investigate six representative ES variables to assess planning strategies that can address this contradiction. We used a suburban rural area around Nanjing, China, as our study area. We collected spatial data from 2005 to 2020 at two scales (village level and 500 m grid) to map ESs, quantify interactions (trade-offs and synergies among ES bundles), and identify the social, ecological, and landscape drivers of rural change. Based on this, rural planning strategies for optimising ESs at different scales have been proposed. Our findings include (1) spatial heterogeneity in the distribution of ESs, (2) the identification of seven synergistic and eight trade-off pairs among ESs, (3) a spatial scale effect in suburban rural areas, and (4) the spatial trade-offs/synergies of ESs exhibiting a ‘Matthew effect’. The identification of key trade-offs and synergistic ES pairs and the categorisation of ES bundles form the basis for a multi-scale hierarchical management approach for ESs in the region. By examining the commonalities and variations in drivers across diverse scales, we established connections and focal points for spatial planning. We use these findings to propose spatial planning and landscape policy recommendations for rural suburban areas on multiple scales. This study aims to provide a comprehensive and detailed spatial optimisation strategy for rural areas that can help contribute to their revitalisation.