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Streamlining urban forest monitoring based on a large-scale tree survey: a case study of highway vegetation in Hong Kong

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Abstract

Through the analysis of an urban tree inventory with the aid of machine learning, this study brought together different aspects of urban forestry. Urban tree monitoring is essential to successful urban forestry. Transport land use accommodates huge tree stock which requires substantial monitoring efforts. In Hong Kong, more research is needed to take into consideration how monitoring works can be improved in response to variations in tree stand characteristics. This case study aimed to illustrate the usefulness of a large-scale tree survey in mainstreaming future tree monitoring and management in transport land use. A total of 7209 trees were found in a large-scale tree survey conducted in 53 slopes and 52 verges along San Tin Highway in Hong Kong. Dominance by Corymbia citriodora (72%) was observed, especially for the highway verges. Using chi-square tests, significant associations were found between monospecific stands, habitat type, and tree risk rating. A logistic regression model was constructed to predict the occurrence of monoculture. Every metre increase in maximum tree height, the odds of a stand being monospecific would be 1.22 times greater. Stands on verges had 5.26 times greater odds of being monospecific against the slope. The associations and relationships were attributed to the dominance of C. citriodora. By boosting the logistic model, model reliability increased as kappa rose from 0.51 to 0.63, while balanced accuracy improved from 0.72 to 0.85. The occurrence of monospecific stands could be reliably predicted using maximum tree height and habitat type of tree stands. These quantitative findings monitoring can guide urban forest monitoring. Through a better understanding of urban forest structure and composition, future monitoring can aid the mainstreaming of urban forestry in transport planning.

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Data availability

The data that support the findings of this study are available from the Highways Department of the Hong Kong Special Administrative Region Government but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Highways Department of the Hong Kong Special Administrative Region Government.

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Funding

This study was funded by the Highways Department of the Hong Kong Special Administrative Region Government.

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Authors and Affiliations

  1. Faculty of Design and Environment, Technological and Higher Education Institute of Hong Kong, Shing Tai Road, Chai Wan, Hong Kong, China

    Louis Shing Him Lee & Hao Zhang

  2. Landscape Division, Highways Department, Spectrum Tower, 53 Hung To Road, Kwun Tong, Kowloon, Hong Kong, China

    Kathy Tze Kwun Ng, Shun Cheong Lo & Alan Siu Lun Yu

  3. Greening, Landscape and Tree Management Section, Development Bureau, 2 Tim Mei Avenue, Tamar, Hong Kong, China

    Kathy Tze Kwun Ng

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  1. Louis Shing Him Lee
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  2. Hao Zhang
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Contributions

All authors contributed to the study conception and design. Data analysis was performed by the first author. The manuscript was written by the first author and all authors commented on, read, and approved the manuscript. We would like to express our gratitude to Gabriella Leung and Marcus Leung for their efforts in data collection.

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Correspondence to Hao Zhang.

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The third, fourth, and fifth authors were employed by the Highways Department of the Hong Kong Special Administrative Region Government, which funded this research.

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Lee, L.S.H., Zhang, H., Ng, K.T.K. et al. Streamlining urban forest monitoring based on a large-scale tree survey: a case study of highway vegetation in Hong Kong. Environ Monit Assess 195, 198 (2023). https://doi.org/10.1007/s10661-022-10803-4

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