Utilizing the green view index to improve the urban street greenery index system: A statistical study using road patterns and vegetation structures as entry points

Zhu, Huaizhen; Nan, Xinge; Yang, Fan; Bao, Zhiyi

doi:10.1016/j.landurbplan.2023.104780

Cited by 19 publications

(10 citation statements)

References 64 publications

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“…Hangzhou, as a highly developed location in China in terms of its socioeconomic status, is also one of the earliest cities to be selected as a "National Garden City of China", which represents an exemplary level of urban greening in China [42]. Our research selected several major districts in Hangzhou, Zhejiang Province, China, as research areas, including the Xihu, Gongshu, and Binjiang Districts.…”

Section: Study Areamentioning

confidence: 99%

“…The proposed method effectively analyses the global structural information of picture pixels as well as the local spectral information of pixels, enabling reliable identification of varied shapes and even partially deformed photographs. Past research has demonstrated impressive outcomes in the semantic segmentation of street view images using DeepLabV3+, and the approach has also exhibited strong practical performance [42,[44][45][46]. In terms of the dataset, we selected the ADE20K dataset [47], which contains 150 categories of objects.…”

Section: Preprocessing Of Street View Imagesmentioning

confidence: 99%

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Quantifying the Impact of Street Greening during Full-Leaf Seasons on Emotional Perception: Guidelines for Resident Well-Being

Hao,

Li,

Han

et al. 2024

Forests

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Quantifying the emotional impact of street greening during the full-leaf seasons in spring, summer, and fall is important for well-being-focused urban construction. Current emotional perception models usually focus on the influence of objects identified through semantic segmentation of street view images and lack explanation. Therefore, interpretability models that quantify street greening’s emotional effects are needed. This study aims to measure and explain the influence of street greening on emotions to help urban planners make decisions. This would improve the living environment, foster positive emotions, and help residents recover from negative emotions. In Hangzhou, China, we used the Baidu Map API to obtain street view images when plants were in the full-leaf state. Semantic segmentation was used to separate plant parts from street view images, enabling the calculation of the Green View Index, Plant Level Diversity, Plant Color Richness, and Tree–Sky View Factor. We created a dataset specifically designed for the purpose of emotional perception, including four distinct categories: pleasure, relaxation, boredom, and anxiety. This dataset was generated through a combination of machine learning algorithms and human evaluation. Scores range from 1 to 5, with higher values indicating stronger emotions and lower values indicating less intense ones. The random forest model and Shapley Additive Explanation (SHAP) algorithm were employed to identify the key indicators that affect emotions. Emotions were most affected by the Plant Level Diversity and Green View Index. These indicators and emotions have an intricate non-linear relationship. Specifically, a higher Green View Index (often indicating the presence of 20–35 fully grown trees within a 200 m range in street view images) and a greater Plant Level Diversity significantly promoted positive emotional responses. Our study provided local planning departments with support for well-being-focused urban planning and renewal decisions. Based on our research, we recommend the following actions: (1) increase the amount of visible green in areas with a low Green View Index; (2) plant seasonal and flowering plants like camellia, ginkgo, and goldenrain trees to enhance the diversity and colors; (3) trim plants in areas with low safety perception to improve visibility; (4) introduce evergreen plants like cinnamomum camphor, osmanthus, and pine.

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Section: Study Areamentioning

confidence: 99%

Section: Preprocessing Of Street View Imagesmentioning

confidence: 99%

Quantifying the Impact of Street Greening during Full-Leaf Seasons on Emotional Perception: Guidelines for Resident Well-Being

Hao,

Li,

Han

et al. 2024

Forests

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“…It allows for a comprehensive assessment of green metrics in cities, such as the structure and quantity of urban greenery, through the detailed distribution of trees, shrubs, and herbs identified within the images. This imagery provides constructive suggestions for urban greening projects [25]. When combined with computerised object detection technologies, street view imagery also finds multifaceted applications in measuring the sky, including assessing sky openness [26], calculating the solar reflectivity of building façades [27], and measuring solar radiation.…”

Section: Time Series Street View Researchmentioning

confidence: 99%

Analysing the Spatio-Temporal Variations of Urban Street Summer Solar Radiation through Historical Street View Images: A Case Study of Shanghai, China

Wang,

Zhang,

2024

IJGI

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Understanding solar radiation in urban street spaces is crucial for comprehending residents’ environmental experiences and enhancing their quality of life. However, existing studies rarely focus on the patterns of urban street solar radiation over time and across different urban and suburban areas. In this study, street view images from the summers of 2013 and 2019 in Shanghai were used to calculate solar radiation in urban street spaces. The results show a general decrease in street solar radiation in 2019 compared to 2013, with an average drop of 12.34%. The decrease was most significant in October (13.47%) and least in May (11.71%). In terms of solar radiation data gathered from street view sampling points, 76.57% showed a decrease, while 23.43% showed an increase. Spatially, solar radiation decreased by 79.66% for every additional 1.5 km from the city centre. In summary, solar radiation generally shows a decreasing trend, with significant variations between different areas. These findings are vitally important for guiding urban planning, optimising green infrastructure, and enhancing the urban ecological environment, further promoting sustainable urban development and improving residents’ quality of life.

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“…The green view index (GVI) is the ratio of the vegetation area in the human view to the total human view area [31,32]. The research area of the GVI is widely used in evaluating the greening effect of cities [33][34][35][36][37][38] as well as landscape justice and equity [39][40][41][42][43] and in combining environmental psychology with human well-being and landscape preferences to provide feedback on environmental benefits [44][45][46]. However, most studies about GVI use street view images as the calculation methods, which are shot in the roadway, but there is a serious lack of GVI data from the pedestrian perspective, which directly affects residents' perception of green volume in the city.…”

Section: Quantifying the Visible Greenery From Street Tree Canopies C...mentioning

confidence: 99%

“…However, most studies about GVI use street view images as the calculation methods, which are shot in the roadway, but there is a serious lack of GVI data from the pedestrian perspective, which directly affects residents' perception of green volume in the city. The academic community has also suggested that GVI lacks data reference in the early stages of urban construction and planning [38]; that is, how can we obtain and evaluate the greening levels at the beginning? Which plants and how many plants in specific cities should be planted to obtain what amount of GVI?…”

Section: Quantifying the Visible Greenery From Street Tree Canopies C...mentioning

confidence: 99%

How Much Visual Greenery Can Street Trees Generate from a Humanistic Perspective? An Attempt to Quantify the Canopy Green View Index Based on Tree Morphology

Zhu,

Nan,

Kang

et al. 2024

Forests

Self Cite

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Street trees are essential to urban ecological benefits and human well-being. The canopy morphology relates to the green view index (GVI), which needs to be calculated based on specific tree species. This study conducts a field study on 760 street trees of 3 species from the pedestrian perspective, and explores the differences in canopy GVI and its factors of different specifications of street trees using DBH grading. The results indicate that (1) street trees can provide 20% of the GVI in unilateral streets, with 13% of the GVI in a single canopy. (2) A flat oval crown with a wider canopy width is more effective in providing GVI than a long oval crown, and a CW with a higher canopy GVI highlights this advantage. (3) DBHs of 30 and 40 can be used as grading indicators for Cinnamomum camphora, and the specification requirements can be reduced for Southern magnolia and Chinese privet. (4) The concept of DE is introduced, and new parameters related to the central crown have significant impacts on GVI. The conclusions can improve the GVI application in urban greenery planning as well as have certain implications for the cost management of future seedling cultivation.

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Utilizing the green view index to improve the urban street greenery index system: A statistical study using road patterns and vegetation structures as entry points

Cited by 19 publications

References 64 publications

Quantifying the Impact of Street Greening during Full-Leaf Seasons on Emotional Perception: Guidelines for Resident Well-Being

Quantifying the Impact of Street Greening during Full-Leaf Seasons on Emotional Perception: Guidelines for Resident Well-Being

Analysing the Spatio-Temporal Variations of Urban Street Summer Solar Radiation through Historical Street View Images: A Case Study of Shanghai, China

How Much Visual Greenery Can Street Trees Generate from a Humanistic Perspective? An Attempt to Quantify the Canopy Green View Index Based on Tree Morphology

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