Authors: J. Jones, S. Popescu, J. Perkin, S. Webb
Urban trees provide numerous environmental benefits in built environments. To understand and measure ecosystem services provided by urban trees, we must be able to map, identify, and measure individual trees as accurately and efficiently as possible. Automating this approach using lidar and aerial photography can improve efficiencies and affordability of urban tree inventories. In addition to tree biometrics, identifying and measuring trees in urban areas is important for city planning, locating hazardous trees, and assessing tree influence on carbon and water cycling. The overall goal of this study was to map and model urban trees and estimate their biophysical parameters (i.e., tree height, diameter at breast height, and crown width), using aerial and mobile lidar with high-resolution aerial photography. The specific objectives were to: 1) develop the methodologies of mobile lidar data collection, 2) create a process to remotely measure trees from on-screen lidar point clouds to provide an efficient method of “ground truthing” data as a surrogate for field inventory, and 3) establish and incorporate processes to automate the tree measuring and validation workflow. Two open-source automated tree detection R packages were auditioned to automate the digital tree measuring process: lidR and TreeLS. The lidR package generated heights for large trees (R2 = 0.74, RMSE = 1.07 m, p-value < 0.001, y = 0.90 x + 1.62) and shorter trees (R2 = 0.82, RMSE = 0.45 m, p-value < 0.001, y = 1.02 x – 0.16). The TreeLS package generated dbh (R2 = 0.91, RMSE = 5.25, p-value < 0.001, y = 1.06 x – 4.78). We demonstrated that mobile lidar data collection was an accurate and viable alternative to traditional forest inventory methods.
Suggested Citation
Jones, J. J., S. C. Popescu, J. S. Perkin, and S. L. Webb. 2026. Toward fully automated urban tree inventory: Integrating mobile lidar and open-source tools. Urban Forestry & Urban Greening 118:129362.
