Research into the Influence Mechanisms of Visual-Comfort and Landscape Indicators of Urban Green Spaces

Yumeng Meng, Jiaxuan Shi, Mei Lyu, Dong Sun and Hiroatsu Fukuda ()
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Yumeng Meng: Department of Architecture, The University of Kitakyushu, Kitakyushu 808-0135, Japan
Jiaxuan Shi: Department of Architecture, The University of Kitakyushu, Kitakyushu 808-0135, Japan
Mei Lyu: School of Design and Art, Shenyang Jianzhu University, Shenyang 110168, China
Dong Sun: School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
Hiroatsu Fukuda: Department of Architecture, The University of Kitakyushu, Kitakyushu 808-0135, Japan

Land, 2024, vol. 13, issue 10, 1-17

Abstract: Urban green spaces play a crucial role in providing social services and enhancing residents’ mental health. It is essential for sustainable urban planning to explore the relationship between urban green spaces and human perceptions, particularly their visual comfort. However, most current research has analyzed green spaces using two-dimensional indicators (remote sensing), which often overlook human visual perceptions. This study combined two-dimensional and three-dimensional methods to evaluate urban green spaces. Additionally, the study employed machine learning to quantify residents’ visual comfort in green-space environments and explored the relationship between green spaces and human visual perceptions. The results indicated that Kitakyushu exhibited a moderate FCV and an extremely low Green View Index (GVI). Yahatanishi-ku was characterized as having the highest visual comfort. Tobata-ku demonstrated the lowest visual comfort. Natural, GVI, openness, enclosure, vegetation diversity, landscape diversity, and NDBI were positively correlated with visual comfort. FCV and ENVI were negatively correlated with visual comfort. Vegetation diversity had the most impact on improving visual comfort. By integrating remote sensing and street-view data, this study introduces a methodology to ensure a more holistic assessment of green spaces. Urban planners could use it to better identify areas with insufficient green space or areas that require improvement in terms of green-space quality. Meanwhile, it could be helpful in providing valuable input for formulating more effective green-space policies and improving overall urban environmental quality. The study provides a scientific foundation for urban planners to improve the planning and construction of healthy and sustainable cities.

Keywords: green spaces; visual comfort; remote sensing; street-view image; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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