Urban skylines with mountainous backgrounds sustain urban identity and public well-being, yet are challenged by urban vertical growth. Currently, a lack of scientific evidence undermines visual impact controls for such skylines. This study quantified public preferences through a photo-based survey on Fuzhou's Fu Forest Trail (N = 450, 60 skyline samples), integrating correlation, Poisson LASSO and negative binomial regression, and decision tree analysis. The key findings reveal that: (1) visual preference is predominantly pleasure-driven, showing a strong positive correlation with pleasure scores (r = 0. 664, p < 0. 001) and a negative trend with arousal; (2) a core set of six physical parameters was identified and hierarchically validated, with building contour volatility (PVB) as the most influential, followed by the horizontal position of the tallest building (PHSR₂) and mountain contour volatility (PVM) ; and (3) the decision tree model uncovered critical parameter interactions and compensatory pathways, leading to specific, evidence-based threshold recommendations for planning: maintain PVB within 5. 35, 12. 37, PHSR₂ within 0. 85, 1. 62, and PVM within 2. 78, 6. 17. These findings provide a scientific basis for data-driven skyline management, enabling policymakers to implement targeted design guidelines and control measures that balance urban growth with the preservation of natural landscapes and aesthetic quality.
Lin et al. (Thu,) studied this question.
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