Estimating 3D individual tree crown growth using multi-temporal LiDAR

Predictions of individual tree crown growth provide key insights into future crown size and condition, and are important for sustainable forest management. This study presents a novel approach for forecasting three-dimensional (3D) crown growth using crown structural metrics derived from multi-temporal airborne lidar (ALS) at two-time intervals. Model development consisted of segmenting tree crowns from ALS point clouds, and producing convex hulls with matching vertex datums created for each crown pair (n = 110). A machine learning approach was then used to model the vertex shift (∆-xyz) between time-points, estimating growth for 33 independent crowns. Predictions of crown height (H), volume (V), and area (A2D) showed good to strong correlations (H R2 = 0.97, V R2 = 0.62, A2D R2 = 0.6). All metrics showed negative bias, with V and A2D to a greater extent than H, aligning with ∆-z being 5.6 times greater than ∆-xy. Future iterations of this model should be investigated at plot scale, incorporating model variables such as surrounding crown structure and competition.