Landscape Characteristics in Mountain Parks across Different Urban Gradients and Their Relationship with Public Response
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».