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Planning Strategies of Wind Corridor Forests Utilizing the Properties of Cold Air

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  • Uk-Je Sung

    (Department of Landscape Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea)

  • Jeong-Hee Eum

    (Department of Landscape Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea)

  • Jeong-Min Son

    (Department of Landscape Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea)

  • Jeong-Hak Oh

    (Division of Urban Forests, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Korea)

Abstract

A wind corridor forest is defined as an urban forest for utilizing the functions of a wind corridor that allow “cool and fresh air (cold air)” generated in forests at night to flow to urban development areas. This study aims to provide planning strategies for implementing a wind corridor forest by analyzing current conditions in Haengbok City (HBC region), Sejong, South Korea. The HBC region had many wind-generating forests (WGF), wind-spreading forests (WSF), and wind-connecting forests (WCF), and secured the connections among the target areas of each wind corridor forest. Despite the favorable conditions for a wind corridor forest, cold air flow showed that there are regions with unfavorable wind conditions in the HBC region. In order to strengthen the functions of a wind corridor forests in the HBC region, four zones were distinguished according to the functional characteristics. Additionally, the planning strategies of a wind corridor forests suitable for each zone were provided, and the strategies for establishing a wind corridor forest were proposed. The results of this study can be used as the fundamental data for establishing guidelines for a wind corridor forest and utilized as resources for selecting regions suitable for a wind corridor forest.

Suggested Citation

  • Uk-Je Sung & Jeong-Hee Eum & Jeong-Min Son & Jeong-Hak Oh, 2021. "Planning Strategies of Wind Corridor Forests Utilizing the Properties of Cold Air," Land, MDPI, vol. 10(6), pages 1-17, June.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:6:p:607-:d:570154
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    References listed on IDEAS

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    Cited by:

    1. Chen Zuo & Chengcheng Liang & Jing Chen & Rui Xi & Junfei Zhang, 2023. "Machine Learning-Based Urban Renovation Design for Improving Wind Environment: A Case Study in Xi’an, China," Land, MDPI, vol. 12(4), pages 1-18, March.

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