15 Undergraduate Research Poster Session II (Posters)

Monday, 16 April 2018
8:00 AM - 5:30 PM
First Floor Lobby

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

Paper number: 15-19
Program: Theme Sessions
Time: 8:00 AM – 5:30 PM

Authors

Abstract

Recent developments in unmanned aerial vehicles (UAVs) and photogrammetry software enable the rapid collection of aerial photography and video over study areas of varying sizes, thereby providing ease of use and accessibility for studies of coastal geomorphology. 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.

A controlled field test (of 1.2 hectares) was performed to determine SfM’s sensitivity to the following flight parameters: 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 GCP spacing scheme (even, random). Through comparisons of the root mean squared error (RMSE) relative to the GCPs, 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, 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 most efficient survey parameters were six GCPs per hectare of land surveyed, 80 m flight altitudes, and 70% photo overlap. This study can be immediately referenced in future studies for its insight on conducting efficient and low-error UAV surveys.

Geological Society of America Abstracts with Programs. Vol. 50, No. 4

doi: 10.1130/abs/2018NC-313172

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