OPTIMIZING UAV SURVEYS FOR COASTAL MORPHODYNAMICS: ESTIMATION OF SPATIAL UNCERTAINTY AS A FUNCTION OF FLIGHT ACQUISITION AND POST PROCESSING FACTORS
To address the methodological differences amongst research groups, this study uses various collection parameters to investigate the error of drone-derived spatial data. A controlled field test (of 1.2 hectares) was performed to determine SfM’s sensitivity to the following flight parameters: flight altitude (60 m, 80 m, 120 m), photo overlap (70%, 75%, 80%), drone model (DJI Phantom quadcopter, senseFly eBee RTK fixed-wing), SfM software (PhotoScan, Pix4D), number of GCPs (4-34), and spacing scheme of GCPs (even, random). Through comparisons of the root mean squared error (RMSE) relative to the GCPs, flight altitude affected error significantly (>1 cm RMSE difference between 60 m and 120 m) while photo overlap was the least significant parameter (only 4 mm RMSE difference between 70% and 80% overlap). Different drone models, and thereby different cameras, along with varying photogrammetry software, affected RMSE significantly (>3 cm RMSE differences). Surprisingly, GCP spacing schemes were insignificant to error sensitivity (<1 mm RMSE differences). The final results determined six GCPs per hectare of land surveyed to be the most efficient, while flight altitudes of 80 meters with 70% overlap were the most efficient for flight time (~4 min), ground resolution (3.42-cm/pixel), and RMSE (4-cm). This study can be immediately referenced in future studies for its insight on conducting efficient and low-error UAV surveys.