Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.
Depths Inferred from Velocities Estimated by Remote Sensing: A Flow Resistance Equation-Based Approach to Mapping Multiple River Attributes at the Reach Scale
