Forest understory trees revealed using sufficiently dense airborne laser scanning point clouds

Airborne laser scanning (lidar) point clouds can be process to extract tree-level information over large forested landscapes. Existing procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of reduced number of lidar points penetrating the top canopy layer. Although understory trees provide limited financial value, they offer habitat for numerous wildlife species and are important for stand development. Here we model tree identification accuracy according to point cloud density by decomposing lidar point cloud into overstory and multiple understory canopy layers, estimating the fraction of points representing the different layers, and inspecting tree identification accuracy as a function of point density. We show at a density of about 170 pt/m2 understory tree identification accuracy likely plateaus, which we regard as the required point density for reasonable identification of understory trees. Given the advancements of lidar sensor technology, point clouds can feasibly reach the required density to enable effective identification of individual understory trees, ultimately making remote quantification of forest resources more accurate. The layer decomposition methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.