Mangrove restoration is challenging within protected coastal habitats. Predicting the dominant species distributions in mangrove communities is essential for appropriate species selection and spatial planning for restoration. We explored the spatial distributions of six mangrove species, including their related environmental factors, thereby identifying potentially suitable habitats for mangrove protection and restoration. Based on six dominant mangrove species present in the Beibu Gulf, Guangxi, China, we used a linear correlation analysis to screen environmental factors. In addition, we used the maximum entropy model to analyze the spatial distributions of potential mangrove afforestation areas. Based on the spatial superposition analysis, we identified mangrove conservation and restoration hot spots. The findings indicate that topographic and bioclimatic factors affect the distribution of suitable mangrove habitats in the Beibu Gulf, followed by land use type, salinity, and substrate type. We identified 13,816 hm² of prime mangrove habitat in the Beibu Gulf that is primarily distributed in protected areas. The protection rate for existing mangroves was 42.62%. According to the predicted spatial distributions of the mangrove plants, the findings suggest that mangrove restoration should be based on suitable species and site selection.

The intensifying pressures of urbanization and climate change on coastal zones necessitate a holistic understanding of the interplay between human activity and ecological integrity for sustainable development. However, prevailing methods for assessing coastal vibrancy often overlook direct measures of human presence and fail to quantitatively capture its complex relationship with ecological vulnerability. To address these gaps, this study develops a novel multi-dimensional assessment framework for Coastal Landscape Vibrancy (CLV) and empirically examines its interaction with ecological vulnerability factors in Beihai, China. Moving beyond built-environment-centric approaches, our framework integrates the ‘Crowd’ dimension, directly quantified using Baidu Heat Index data, with the ‘Place’ dimension, characterized by urban features, natural attributes, and visual experience. Principal Component Analysis (PCA) was employed to objectively weight these indicators and construct a composite CLV index. We then applied multiple linear regression to analyze the influence of ecological factors constructed based on the Sensitivity-Resilience-Pressure (SRP) model. The results revealed that vibrancy was highly concentrated in urban cores and exhibited significant spatiotemporal variations. Regression analysis revealed that while ecological quality factors like green coverage (β = 0.236, p < 0.001) positively influenced vibrancy, anthropogenic stressors such as slope (β = −0.457, p < 0.001) and the impervious surface index (β = −0.092, p < 0.001) had significant negative impacts, highlighting a critical trade-off between human activity and ecological conditions. The findings provide a quantitative, evidence-based foundation for spatial planning, demonstrating that sustainable coastal vibrancy is achieved through a balanced integration of human activity and ecological conservation, rather than through unchecked development. This framework offers critical insights for formulating strategies that simultaneously enhance ecological resilience and optimize human service facilities.

Urban forest walkways provide accessible forest recreation opportunities within cities, but little is known about how different walkway landscape environments are associated with older adults’ perceived psychological restoration. This study examined three representative urban forest walkways in Fuzhou, China, by integrating image-based landscape classification with photo-elicitation questionnaires. A total of 1188 field photographs were collected, of which 963 valid images were used for landscape analysis. A SegNet-based semantic segmentation model was used to extract major landscape elements, and the images were subsequently classified into four landscape environment types. Questionnaire data were obtained from 415 older walkway users, generating 4980 respondent–image evaluation records. The SegNet model achieved a mean IoU of 79.4%. The high-naturalness canopy-immersive type received the highest scores for tranquillity, refuge, naturalness, landscape preference, and perceived psychological restoration, whereas the semi-open viewing-platform type showed the highest spaciousness and prospect scores. Landscape preference was positively associated with perceived psychological restoration in the mixed-effects model and partially mediated the associations between selected perceived landscape attributes and perceived psychological restoration. These findings suggest that age-friendly urban forest walkway design should maintain canopy immersion, provide semi-open viewing nodes, improve resting and interaction spaces, and refine forest-edge urban-interface sections.