Geological Survey (USGS) streamgage network consists of only 111 continuous monitoring locations, a density of coverage far less than in the contiguous U.S. For example, although the state of Alaska features over 1,200,000 km of rivers and streams, the U.S. However, obtaining basic information on river discharge can be difficult and costly, particularly in remote, inaccessible locations. Regular, reliable monitoring of streamflow is crucial for a number of management applications including water supply forecasting, flood hazard assessment, habitat conservation, and provision of recreational opportunities. 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. 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. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger ( R 2 = 0.76), suggesting that satellite video could play a role in measuring river discharge.

Although agreement between PIV-derived velocity estimates and field measurements was weak ( R 2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. 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. 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.

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. 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 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).

Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data.

United States Geological Survey, Water Mission Area, Integrated Modeling and Prediction Division, Geomorphology and Sediment Transport Laboratory, Golden, CO, United StatesĬonventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges.

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