The objective of this study is to develop new algorithms for automated urban
forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data
contain three-dimensional structure information that can be used to estimate tree height, base
height, crown depth, and crown diameter. This allows precision urban forest inventory down
to individual trees. Unlike most of the published algorithms that detect individual trees from
a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to
separate individual trees and estimate tree metrics. Testing results in typical urban forests
are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery
for urban tree characterization through data fusion techniques.