How to Accurately Detect and Assess the Street Trees Risk in Mega Cities: A Tree Risk Assessment Method and Its Application

Abstract

Traditional visual tree assessment based on rapid diagnosis is suitable for the rapid, large-scale assessment of urban tree risks, but it cannot accurately determine internal decay in trunks and underground root systems, may leading to highly subjective evaluation results. A risk matrix-based street tree risk assessment system was established by combining a visual tree assessment method, nondestructive detection techniques, information acquisition, and data analysis using a geographic information system (GIS). The method was used to conduct risk detection and assessment on 1,001 street trees with a diameter at breast height greater than 40 cm on 14 streets in a Historic Features Protection Area of Shanghai. The result showed that: 1) The branch and the trunk risk possibility of the vast majority of street trees were at level 2 or below, while the root risk possibility of more than one third of the trees was at level 3 or above. 2) The risk level of 23% of the trees in the protected area was moderate or above, while that of most of the other trees was at a negligible or acceptable level. The risk consequence severity level was high for trees on Tianping Road but low for trees on Donghu Road and Taojiang Road. 3) The street tree risk level shows a strong correlation with the presence of tree cavities, diseases and insect pests, the depth and range of root distribution, leaning, and internal decay in trunks, and the risk points are concentrated in the trunk and root system. Conducting a risk assessment of street trees based on precision diagnosis techniques can improve the scientific rigor of the assessment. The results of this work can provide a basis for the accurate assessment of street trees risk in some large cities, which provides a new assessment system for the trees risk assessment in important urban areas, and the approach is conducive to the refined management of urban greening trees.