资源环境科技发展态势分析平台

Image cross‐correlation techniques, such as Particle Image Velocimetry (PIV), can estimate water surface velocity (v_surf) of streams. However, discharge estimation requires water depth and the depth‐averaged vertical velocity (U_m). The variability of the ratio U_m/v_surf introduces large errors in discharge estimates. We demonstrate a method to estimate v_surf from Unmanned Aerial Systems (UASs) with PIV technique. This method does not require any Ground Control Point (GCP): the conversion of velocities from pixels per frame into meters per time is performed by informing a camera pinhole model; the range from the pinhole to the water surface is measured by the drone‐board radar. For approximately uniform flow, U_m is a function of the Gauckler‐Manning‐Strickler coefficient (K_s) and v_surf. We implement an approach that can be used to jointly estimate Ks and discharge by informing a system of 2 unknowns (Ks and discharge) and 2 non‐linear equations: i) Manning's equation ii) mean‐section method for computing discharge from U_m. This approach relies on bathymetry, acquired in‐situ a‐priori, and on UAS‐borne v_surf and water surface slope measurements. Our joint (discharge and Ks) estimation approach is an alternative to the widely used approach than relies on estimating U_m as 0.85·v_surf. It was extensively investigated in 27 case studies, in different streams with different hydraulic conditions. Discharge estimated with the joint estimation approach showed a mean absolute error in discharge of 19.1% compared to in‐situ discharge measurements. Ks estimates showed a mean absolute error of 3.2 m^1/3/s compared to in‐situ measurements.

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