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

Abstract

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