 This research focuses on developing algorithms for automated urban forest inventory at the individual tree level by utilizing lidar point cloud data. These algorithms allow for precise measurement of tree height, base height, crown depth, and crown diameter, which can then be used to create accurate inventories of urban forests. Additionally, these algorithms work directly with the lidar point cloud data rather than relying on raster surfaces as other methods do. Tests have been conducted in typical urban forests, and the results are promising. Future work will involve combining lidar data with optical imagery to further improve characterizations of urban trees. This article was authored by Kaiyun Zhang, Yu Hongzhou, and Fengqiu.