Evaluating the Impacts of Flying Height and Forward Overlap on Tree Height Estimates Using Unmanned Aerial Systems

Unmanned aerial systems (UASs) and structure-from-motion (SfM) image processing are promising tools for sustainable forest management as they allow the generation of photogrammetrically derived point clouds from UAS images that can be used to estimate structure, a fraction cost LiDAR. The SfM process quality products produced, however, sensitive chosen flight parameters. An understanding effect parameter choice has on accuracy will improve operational feasibility UASs in forestry. This study investigated change plot-level top-of-canopy height (TCH) across three levels flying (80 m, 100 120 m) four forward overlap (80%, 85%, 90%, 95%). A SenseFly eBee X with an Aeria DSLR camera was collect imagery which then run through derive photogrammetric clouds. Estimates TCH were extracted all combinations compared estimated ground data. generalized linear model statistically assess accuracy. RMSE (root-mean-square error) estimates (RMSETCH) ranged between 1.75 m (RMSETCH % = 5.94%) 3.20m 10.1%) missions. Flying found have no significant RMSETCH, while increasing significantly decrease RMSETCH; minor at 4 mm per 1% increase overlap. results this suggest users fly higher lower without sacrificing accuracy, substantial time-saving benefits both field collecting data office