Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment

Resource quantification is vital in developing a tidal stream energy site but challenging high areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide novel, low cost, risk approach that improves spatial coverage compared to ADCP methods. For the first time, this study quantifies performance of technique for resource assessment, using three sites. Videos sea surface were captured while concurrent validation data obtained (ADCP and drifters). Currents estimated from videos LSPIV software. Variation accuracy was attributed wind, geometry current velocity. Root mean square errors (RMSEs) against drifters 0.44 m s?1 winds (31 km/h) 0.22 (10 km/h). Better correlation found more constrained (r2 increased by 4%); differences between flood ebb indicate importance upstream bathymetry generating trackable features. Accuracy better higher velocities. A power law profile approximation enables translation currents at depth with satisfactory (RMSE = 0.32 under winds). Overall, drone video derived velocities are suitably accurate “first-order” assessments favourable environmental conditions.