Mapping of urban roadside trees - A case study in the tree register update process in Helsinki City

This case study describes a method for utilizing leaf-off airborne laser scanning (ALS) data for mapping characteristics of urban trees. ALS data were utilized to detect and update all street trees in the tree inventory of the City of Helsinki, Finland. The inventory consists of roughly 20,000 street trees with mean diameter at breast height (DBH) of 24 cm and mean height of 10.6 m. The large number of trees makes the manual updating process very laborious. The automatic mapping procedure presented in this paper detected 88.8% of all trees in the inventory. Tree height was predicted with root mean square error (RMSE) of 1.27 meters and tree DBH with RMSE of 6.9 cm. The presented method provides a practical and cost-effective tool for the mapping of urban tree characteristics. The cost-efficiency was further enhanced because the used ALS data were originally collected for other urban planning purposes.