OPTIMIZING UAV SURVEYS FOR COASTAL MORPHODYNAMICS: ESTIMATION OF SPATIAL UNCERTAINTY AS A FUNCTION OF FLIGHT ACQUISITION AND POST PROCESSING FACTORS

With recent developments in unmanned aerial vehicles (UAVs) and photogrammetry software, the rapid collection of aerial photography and video over study areas of varying sizes and accessibility has risen dramatically within coastal research groups. Utilizing structure from motion (SfM) algorithms, aerial photos are stitched into orthomosaics, point clouds, meshes, and digital elevation models. Monitoring morphological changes, vegetation densities, ecosystem changes, structure conditions, and countless more applications are possible using the aforementioned outputs. However, there remains uncertainty over UAV survey techniques, with disagreement on specific flight patterns, flight altitudes, photograph amounts, ground control point (GCP) amounts, GCP spacing schemes, drone models, and which SfM software to use, amongst other study-specific parameters.

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.

Session No. 354--Booth# 68

T144. Drones in Geoscience (Posters)

Wednesday, 25 October 2017: 9:00 AM-6:30 PM

Halls 4EF (Washington State Convention Center)

Geological Society of America Abstracts with Programs. Vol. 49, No. 6
doi: 10.1130/abs/2017AM-303074

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