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Information on the distribution of trees with different diameters at breast height (DBH) is needed to inform management programs aimed at achieving conservation objectives in high stem density forest stands. This article explored the feasibility of mapping the density of trees with different DBHs using airborne LiDAR data. Experiments were conducted in the largest river red gum forest in the world, located in the southeast of Australia. Field measured data on trees with different DBHs were used for the supervised learning of airborne LiDAR scans with a pulse density of 5.92 pulses/m2. Specifically, the hyperparameters of gradient boosting and random forest regressors were tuned to produce a viable solution for mapping the density of different sized trees at the plot level. Our results indicate that the total tree density (DBH > 0 cm; height > 1.37 m) can be mapped using airborne LiDAR data with the coefficient of determination R2 of up to 0.67, with gradient boosting outperforming random forest. However, the accuracy of mapping the density of saplings (DBH ≤ 10 cm), small trees (10 cm <; DBH ≤ 50 cm), and large trees (DBH > 50 cm) differed with R2 of 0.65, 0.60, and 0.42, respectively. These results show that the airborne LiDAR data can provide a viable solution for mapping the density of small trees (DBH ≤ 50 cm) over large areas and has the potential for mapping the density of large trees (DBH > 50 cm).

Leveraging Airborne LiDAR Data and Gradient Boosting for Mapping the Density of Different Sized Trees