Surface Flow Velocities from Space

Submitted by atripp on

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges and make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, researchers 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, a refined end-to-end workflow involved stabilization and georeferencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and georeference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed production of high-resolution surface velocity fields.  Field measurements of depth-averaged flow velocity were used to assess accuracy. 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. Further research and technological development may improve the ability to measure surface flow velocities from satellite video and provide a viable tool for streamflow monitoring in certain fluvial environments.

Maps of PIV-derived surface flow velocities for different frame rates: (A) 1 Hz, (B) 0.5 Hz, and (C) 0.25 Hz. The locations of the ADCP velocity measurements used for accuracy assessment are shown in red.


Author Name
Carl J. Legleiter
Author Email