Identifying the integrated visual characteristics of greenway landscape: A focus on human perception

Liu, Wenping; Hu, Xuyu; Song, Ziliang; Yuan, Xionggang

doi:10.1016/j.scs.2023.104937

Cited by 13 publications

(1 citation statement)

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“…The pixel ratio of visual elements within a given scene, defined as an objective visual index, is a pivotal metric for representing the salience of visual elements from an eye-level perspective, particularly in research concerning physical activity [61]. Several studies indicated that, beyond the physical landscape attributes like vegetation and water, a holistic amalgamation of spatial factors, colors, and various elements equally exerts a profound impact on public perception [62,63]. Therefore, this research identified and grouped 23 landscape elements into 4 categories (vegetation, water, spatial, and construction elements).…”

Section: Classification and Calculation Of Physical Featuresmentioning

confidence: 99%

Landscape Characteristics in Mountain Parks across Different Urban Gradients and Their Relationship with Public Response

Chen,

Sheng,

Luo

et al. 2023

Forests

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Numerous researchers have demonstrated the positive impacts of urban green spaces on human physiology and psychology. In mountainous urban regions, mountains have often been preserved as green spaces during urban sprawl, owing to the limited costs associated with development. While the landscape elements of these mountain parks exhibit differences depending on their locations, the nature and effects of such differences on the public’s physiological and psychological perceptions remain unclear. Therefore, we employed panoramic cameras and semantic segmentation (PSPNet-based training algorithm) to analyze the composition of landscape elements in mountain parks along an urban gradient (i.e., urban areas [UA], suburban areas [SA], and exurban areas [EA]). Concurrently, open-ended questionnaires and portable physiological monitors (ErgoLAB 3.0 Portable physiological monitoring equipment) were utilized to examine relationships between specific landscape elements and the public’s physiological and psychological responses. Our findings revealed that: (1) Urban park landscapes possessed high proportions of paved areas, humanistic vibe, vegetation hierarchy, and vegetation color richness, alongside lower scene clutter; suburban mountain park landscapes were characterized by heightened contemporary ambiance and wide viewshed area; and exurban mountain park landscapes exhibited high green view indices, expansive water surfaces, broad view area, and low scene clutter. (2) HRV and EMG differed significantly between mountain parks situated across the urban gradient. EMG also significantly varied across landscape types. All four psychological perception metrics showed significant distinctions across the three urban gradients and three green space categories. It further highlighted the importance of naturalness perception in urban mountain parks. (3) Viewshed area, average sight distance, architecture, enclosure, humanistic vibe, contemporary elements, vegetation color richness, trees and shrubs, distant hills, and scene clutter showed significant effects on both physiological and psychological outcomes. However, the application of these findings needs additional refinement tailored to the typology of the landscape. (4) To provide practical insights for constructing diverse green space typologies, we employed partial correlation modeling to eliminate covarying factors and developed a perception feedback model for public physiological and psychological indicators. Our findings elucidate relationships between landscape elements and the benefits of urban forests for public physiology and psychology. By shedding light on these connections, we further understand how landscape elements shape human perceptions of mountainous urban forests. These results offer valuable insights for shaping policies that promote favorable urban forest landscapes while also advancing landscape perception research through the use of semantic segmentation and portable physiological monitoring.

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Section: Classification and Calculation Of Physical Featuresmentioning

confidence: 99%