Spatio-temporal scale evaluation of landscape visual quality in urban near-natural plant communities: A nomogram visualization approach

• Spatiotemporal analysis reveals key factors influencing landscape visual quality in plant communities. • Nomogram enhances interpretability of landscape quality evaluation and visualization. • Seasonal and spatial factors differently influence landscape visual quality aspects. • Medium levels of qualitative indicators optimize landscape visual quality better than high levels. • Nomograms support visual decision-making for Nature-based Solutions prediction. Quantifying factors influencing landscape visual quality (LVQ) in urban near-natural plant communities (NNPCs) is essential for promoting the mainstreaming of Nature-based Solutions (NbS) and revealing connections between human perception and natural environments. However, existing research often relies on complex formulations that hinder practical application of the models constructed. This study systematically evaluated the LVQ of plant communities across spatiotemporal scales by surveying 65 NNPCs in Hefei city, capturing landscape photographs covering four seasons from two viewpoints (interior and exterior landscapes), and employing the Scenic Beauty Estimation (SBE) method. The research identified 25 landscape characteristic indicators across four dimensions: structural, phenological, environmental, and morphological. Logistic regression was used for indicator analysis, while nomograms were employed to illustrate relationships between key factors and LVQ. Unlike other models, the nomogram visualization model intuitively displays the contribution weight of each indicator to LVQ, making complex statistical relationships easier to understand and apply. The study found that background clarity (OR interior “clear” = 5.53, OR exterior “clear” = 3.63), green view ratio (OR interior = 1.62, OR exterior = 2.23), and permeability (OR interior = 1.84, OR exterior = 1.70) had significant impacts across different seasons and spatial scales, while other factors exhibited obvious seasonal or spatial specificity. Some qualitative indicators showed higher influence levels when at “medium” states, indicating that maintaining reasonable indicator levels plays an important role in enhancing LVQ. The research results provide intuitive, quantifiable scientific evidence for urban designers and planners, helping to optimize plant configuration in urban green spaces and improve overall landscape quality. Meanwhile, this study has significant implications for promoting the application and evaluation of NNPC as NbS in human-dominated landscapes.