The assessment of individual tree canopies using drone-based intra-canopy photogrammetry

• First-person view drones capture high-resolution video, enabling accurate 3D photogrammetric reconstruction of trees. • Ray marching quantifies canopy transparency from point clouds, providing a precise alternative to visual estimates. • Drone-based estimates of tree height, DBH, and canopy spread strongly align with ground and lidar measurements across seasons. • Processing time increases with tree size and seasonality because of greater frame count and scene complexity. With many forests experiencing rapidly declining health, effective management requires increasingly accurate and precise tools to measure tree attributes across scales. Tree health, especially in deciduous species, is strongly correlated with crown condition, specifically crown transparency and dieback. Present-day assessment of these attributes is undertaken using ground-based visual approaches, which can be imprecise and subjective. Here we evaluate the feasibility of applying drone-based digital aerial photogrammetry (DAP) below, within, and above the tree canopy to estimate tree height, diameter at breast height, canopy transparency, and canopy spread. Video imagery was acquired across 18 deciduous trees under leaf-off and leaf-on conditions in Metro Vancouver, British Columbia, Canada, using small, lightweight first-person-view drones. Images were extracted and processed into coloured 3D point clouds using digital Structure-from-Motion Multiview-Stereo photogrammetry. Photogrammetry estimates were compared with field measurements and above-canopy drone-based aerial Light Detection and Ranging (lidar) estimates. The DAP estimates explained significant variance in the field observations and were strongly correlated with both ground-based measurements and lidar estimates, with correlations of height (DAP vs. ground: r = 0.93, RMSE = 1.54 m; DAP vs. lidar: r = 0.94), DBH (DAP vs. ground: r = 0.98, RMSE = 2.90 cm), transparency (DAP vs. ground: r = 0.66, RMSE = 12.61 %), and crown spread (DAP vs. ground: r = 0.88, RMSE = 3.35 m; DAP vs. lidar: r = 0.89). The reconstruction time for each tree using the drone footage was strongly correlated with tree size and seasonal condition, with minimal influence from crown form. This work suggests that first-person view drones can provide accurate information on individual tree attributes associated with tree health, offering a reliable alternative or complement to both ground-based methods and lidar for tree-level measurements in ongoing forest health assessment programs.