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

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.

Keywords: Data manipulation; Forest structure; Highway tree management; Machine learning; Quantitative monitoring techniques; Tree risk assessmentSome qualitative variables.