Urban park waterfront green spaces provide positive mental health benefits to the public. In order to further explore the specific influence mechanism between landscape elements and public psychological response, 36 typical waterfront green areas in Xihu Park and Zuohai Park in Gulou District, Fuzhou City, Fujian Province, China, were selected for this study. We used semantic segmentation technology to quantitatively decompose the 36 scenes of landscape elements and obtained a public psychological response evaluation using virtual reality technology combined with questionnaire interviews. The main results showed that: (1) the Pyramid Scene Parsing Network (PSPNet) is a model suitable for quantitative decomposition of landscape elements of urban park waterfront green space; (2) the public’s overall evaluation of psychological responses to the 36 scenes was relatively high, with the psychological dimension scoring the highest; (3) different landscape elements showed significant differences in four dimensions. Among the elements, plant layer, pavement proportion, and commercial facilities all have an impact on the four dimensions; and (4) the contribution rate of the four element types to the public’s psychological response is shown as spatial element (37.9%) > facility element (35.1%) > natural element (25.0%) > construction element (2.0%). The obtained results reveal the influence of different landscape elements in urban park waterfront green spaces on public psychology and behavior. Meanwhile, it provides links and methods that can be involved in the planning and design of urban park waterfront green space, and also provides emerging technical support and objective data reference for subsequent research.

We explored the spatial and temporal characteristics of the urban forest area soundscape by setting up monitoring points (70 × 70 m grid) covering the study area, recorded a total of 52 sound sources, and the results showed that: (1) The soundscape composition of the park is dominated by natural sounds and recreational sounds. (2) The diurnal variation of sound sources is opposite to that of temperature, 6:00–9:00 is the best time for the public to perceive birdsong, and after 18:00, the park is dominated by insect chirps. (3) The PSD (power spectral density) and the SDI (soundscape diversity index) of the park are greatly affected by public recreation behaviors, and some recreation behaviors may affect the vocal behavior of organisms such as birds. (4) Spaces with high canopy density can attract more birdsong and recreational sounds in summer, and the combination of “tree + lake” can attract more birdsong. Vegetation has a significant dampening effect on traffic sound. (5) Landscape spatial elements, such as the proportion of hard ground, sky, trees, and shrubs, have a significant impact on changes in the PSD, the SDI and different kinds of sound sources. The research results provide effective data support for improving the soundscape of urban forests.

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Butterflies are key indicators of urban biodiversity and one of the most vulnerable organism groups to environmental changes. Studying how butterflies are distributed and what factors might influence them in urban green spaces is crucial. In this study, from July 2022 to September 2022, we examined and analyzed the butterfly diversity in nine parks in Fuzhou, China, along three different levels of urbanization (urban, peri-urban, and suburban). We investigated how butterfly communities respond to increasing urbanization. The findings revealed that: (1) A total of 427 butterfly individuals from 4 families and 13 species were observed; (2) Shannon diversity, richness, and abundance of the overall butterfly community were lower in the more urbanized parks. Urbanization had significant effects on Shannon diversity (p = 0.003) and abundance (p = 0.007) but no significant effects on the whole butterfly community richness (p = 0.241); (3) non-metric multidimensional scaling revealed that there were differences in the overall number of butterfly species in urban parks among different geographic regions.

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