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Matching Trees to Streets by Street Type: A Case Study of Street Tree Suitability and Services in a Highly Urbanized City

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  • Heejung Nam

    (Department of Forest Resources, Graduate School of Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea
    Research Center for Endangered Species, National Institute of Ecology, 23 Gowol-gill, Yeongyang-gun 36531, Gyeongsangbuk-do, Republic of Korea)

  • Seunghyun Hong

    (Department of Forest Resources, Graduate School of Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea)

  • Dohyuk Im

    (Department of Forest Resources, Graduate School of Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea)

  • Ayun Maeng

    (Department of Forest Resources, Graduate School of Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea)

  • Sunmi Je

    (Research Planning and Coordination Division, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Republic of Korea)

  • Wanmo Kang

    (Department of Forest Environment and Systems, College of Science and Technology, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea
    These authors contributed equally to this work.)

  • Hanna Chang

    (Livable Urban Forests Research Center, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Republic of Korea
    These authors contributed equally to this work.)

Abstract

Street trees are a representative form of urban green space that play an important role in mitigating the environmental impact of urbanization. Planting the right tree in the right place in urban streetscapes can improve tree health and ecosystem services. Here, we propose a novel approach to selecting appropriate street trees using street type classifications. In the highly urbanized area of Uijeongbu City, South Korea, 221.9 km of streets with 19,717 street trees were classified into 12 types based on road width, aspect ratio, land use, and the presence of power lines. Appropriate tree species were selected for each street type, taking into account tree traits and functions as well as street environments. Then, we analyzed the structure and ecosystem-regulating services of street trees by type, also comparing the services of appropriate and non-appropriate trees. As a result, all 12 street types were identified, but their distribution was uneven. Tree dimension was the key factor in determining appropriate species, and, for the second most common street type, characterized by narrow roads, low aspect ratios, and power lines, only four appropriate species were identified, indicating an urgent need for more options. Additionally, the most dominant species accounted for over 20%, averaging 44% across the 12 street types, further highlighting the necessity of introducing more diverse tree species. Overall, appropriate street trees generally provided higher service efficiency compared to non-appropriate trees across four ecosystem regulating services. These findings emphasize the need for policies and guidelines that promote street tree diversity and enhance the ecological benefits of street trees. This study provides a foundation for developing sustainable street tree management strategies that contribute to healthier and more resilient urban streetscapes.

Suggested Citation

  • Heejung Nam & Seunghyun Hong & Dohyuk Im & Ayun Maeng & Sunmi Je & Wanmo Kang & Hanna Chang, 2024. "Matching Trees to Streets by Street Type: A Case Study of Street Tree Suitability and Services in a Highly Urbanized City," Land, MDPI, vol. 13(12), pages 1-20, December.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2079-:d:1535628
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    References listed on IDEAS

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    1. Bohong Zheng & Komi Bernard BEDRA & Jian Zheng & Guoguang Wang, 2018. "Combination of Tree Configuration with Street Configuration for Thermal Comfort Optimization under Extreme Summer Conditions in the Urban Center of Shantou City, China," Sustainability, MDPI, vol. 10(11), pages 1-23, November.
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