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Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation

Author

Listed:
  • Yusuke Kumakoshi

    (Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan)

  • Sau Yee Chan

    (Independent Engineer, Tokyo 113-8656, Japan)

  • Hideki Koizumi

    (Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
    Department of Urban Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Xiaojiang Li

    (Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA)

  • Yuji Yoshimura

    (Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan)

Abstract

Urban greenery is considered an important factor in sustainable development and people’s quality of life in the city. To account for urban green vegetation, Green View Index (GVI), which captures the visibility of greenery at street level, has been used. However, as GVI is point-based estimation, when aggregated at an area-level by mean or median, it is sensitive to the location of sampled sites, overweighing the values of densely located sites. To make estimation at area-level more robust, this study aims to (1) propose an improved indicator of greenery visibility (standardized GVI; sGVI), and (2) quantify the relation between sGVI and other green metrics. Experiment on an hypothetical setting confirmed that bias from site location can be mitigated by sGVI. Furthermore, comparing sGVI and Normalized Difference Vegetation Index (NDVI) at the city block level in Yokohama city, Japan, we found that sGVI captures the presence of vegetation better in the city center, whereas NDVI is better at capturing vegetation in parks and forests, principally due to the different viewpoints (eye-level perception and top-down eyesight). These tools provide a foundation for accessing the effect of vegetation in urban landscapes in a more robust matter, enabling comparison on any arbitrary geographical scale.

Suggested Citation

  • Yusuke Kumakoshi & Sau Yee Chan & Hideki Koizumi & Xiaojiang Li & Yuji Yoshimura, 2020. "Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7434-:d:411403
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    References listed on IDEAS

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    1. Lu, Yi & Sarkar, Chinmoy & Xiao, Yang, 2018. "The effect of street-level greenery on walking behavior: Evidence from Hong Kong," Social Science & Medicine, Elsevier, vol. 208(C), pages 41-49.
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    Cited by:

    1. Chao Xiao & Qian Shi & Chen-Jie Gu, 2021. "Assessing the Spatial Distribution Pattern of Street Greenery and Its Relationship with Socioeconomic Status and the Built Environment in Shanghai, China," Land, MDPI, vol. 10(8), pages 1-19, August.
    2. Yixing Chen & Qilin Zhang & Zhang Deng & Xinran Fan & Zimu Xu & Xudong Kang & Kailing Pan & Zihao Guo, 2022. "Research on Green View Index of Urban Roads Based on Street View Image Recognition: A Case Study of Changsha Downtown Areas," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    3. Shan Lu & Wonseok Oh & Ryozo Ooka & Lijun Wang, 2022. "Effects of Environmental Features in Small Public Urban Green Spaces on Older Adults’ Mental Restoration: Evidence from Tokyo," IJERPH, MDPI, vol. 19(9), pages 1-22, April.
    4. Zahra Nourmohammadi & Tanapon Lilasathapornkit & Mudabber Ashfaq & Ziyuan Gu & Meead Saberi, 2021. "Mapping Urban Environmental Performance with Emerging Data Sources: A Case of Urban Greenery and Traffic Noise in Sydney, Australia," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    5. Jiancheng Lu & Xiaolong Luo & Ningning Yang & Yang Shen, 2021. "Multiple Pathways: The Influence Mechanism of Greenspace Exposure on Mental Health—A Case Study of Hangzhou, China," Land, MDPI, vol. 10(4), pages 1-17, March.
    6. Yanyan Zhang & Meng Wang & Junyi Li & Jianxia Chang & Huan Lu, 2022. "Do Greener Urban Streets Provide Better Emotional Experiences? An Experimental Study on Chinese Tourists," IJERPH, MDPI, vol. 19(24), pages 1-21, December.

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