Figure 1. Study area indicating investigated reaches A and B. Base map reprinted from ArcGIS Online maps under a CC BY license, with permission from Esri, original Copyright © 2018 Esri (Basemaps supported by Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus Ds, USDA, AEX, Getmapping, and the GIS User Community).
Data and Methods
Satellite Video
Satellite video was acquired over our study area on 5 February 2022 at 23:12 UTC by a Jilin-1 GF-03 sensor, part of the Jilin-1 constellation operated and developed by Chang Guang Satellite Technology Company. The Jilin-1 satellite video has a spatial resolution of 1.22 m and was acquired at native frame rate of 5 Hz for a duration of 28 seconds. To counter sensor platform movement as well as scene ‘morphing’ due to the changing view angle of the satellite overpass, we stabilized the video using FIJI’s TrakEM2 plugin (Cardona, 2006; Cardona et al., 2012). TrakEM2 relies on a Scale Invariant Feature Transform (SIFT) algorithm to align image stacks based on common features. To avoid geometric distortions and since frames from the video were acquired at a similar resolution, we utilized an affine transform to register our image stacks. Quantitative metrics detailing the minimum, maximum and mean displacement errors related with image stabilization are reported.
Large-scale Particle Image Velocimetry
LSPIV, based on Eulerian principles of motion (Euler, 2008), was originally introduced by Fujita et al ., (1998), enabling the estimation of instantaneous flow velocities from a series of consecutive images. LSPIV velocities were computed using PIVlab (Thielicke and Sonntag, 2021; Thielicke and Stamhuis, 2014) developed in MATLAB (R2022b, Mathworks, Natick, MA, USA). Computation of surface flow velocities in PIVlab is attained by cross-correlation algorithms applied to orthorectified images recorded at a known time interval, δt . Here, we evaluate the accuracy of both Fast Fourier Transform window deformation (direct FFT correlation with multiple passes and deforming windows) and Ensemble correlation (Figure 2). The multi-pass FFT window deformation approach allows for the spatial resolution of velocity measurements to be improved through multiple refinements of interrogation areas. Interrogation areas (IA), which are small windows of defined size (in pixels), are used to track the displacement of image patterns within a chosen larger search area (SA) in subsequent images. Ensemble correlation is better suited for sparsely seeded images as it relies on averaging correlation matrices followed by detecting a correlation peak with the resultant benefit of lower bias and displacement errors. Lewis et al . (2018) and Muste et al . (2008) provide comprehensive detail on the theory and application of LSPIV in riverine environments.
We cropped the video to reduce computational cost while focusing on two cloud-free and straight river reaches A and B (Figure 1). Individual frames were extracted from the cropped video at frame rates of 1, 0.5 and 0.25 Hz. Image pre-processing was performed to amplify the visibility of surface tracers with respect to the background (riverbanks/static ground), applying a Contrast-limited adaptive histogram equalization (CLAHE) filter to enhanced image contrast (Li and Yan, 2022; Masafu et al ., 2022). Distinct features on the water surface were difficult to discern in the raw images, which would be expected in natural rivers and given the height of the optical sensor. However, CLAHE contrast enhancement enabled the tracking of seeding surrogates in the image sequences, which occur when specular reflection formed by incident light interacts with free-surface deformations on the river. Image intensity variations associated with these surface deformations were visible in our post-processed images.
Sensitivity to Image Frame Rate and PIV algorithm
The primary free parameters in LSPIV are the sampling frequency (frame extraction rate), interrogation (IA) and search (SA) areas, and optimal configurations vary significantly (Kim et al., 2008; Legleiter and Kinzel, 2020; Sharif, 2022). Earlier studies (Tauro et al ., 2018; Zhu and Lipeme Kouyi, 2019) have demonstrated that the IA should be small enough to eliminate spurious velocities whilst being large enough to accommodate an adequate window for surface pattern tracking. Sampling frequency (frame extraction rate) and the IA are closely coupled and must be considered in tandem, with frame-to-frame displacement rates influencing the accuracy of pattern/particle detection on images.
FFT window deformation and Ensemble correlation algorithms were utilized with the maximum allowable number of PIV algorithm passes allowed within PIVlab (four) for our sensitivity analysis (see Zhu and Lipeme Kouyi, 2019). We processed images using an IA of 128×128 pixels with successive passes based on IA sizes of 64×64, 32×32 and 8×8 pixels, all with 50% overlaps, corresponding to a minimum spatial distance of 9.8 m. For the ~70 m wide River Darling at Tilpa, this was sufficient to allow the detection of displaced surface features. Whilst smaller IAs would allow for higher-resolution vector maps, this would also significantly increase noise and the amount of erroneous correlations.
We process two configurations based on FFT window deformation and Ensemble correlation algorithms at three sampling rates (1, 0.5 and 0.25 Hz) resulting in 6 different LSPIV runs for each scenario. This resulted in image sequences consisting of 28, 14 and 7 frames which enabled us to experiment with varied frame extraction rates for image-based velocity analysis. Following LSPIV cross-correlation, we post-processed the resultant velocity fields to filter out spurious velocities. Specifically, we utilized filters that removed velocity vectors that differed by 8 x (PIVLab’s default threshold) the standard deviation from the mean velocity and further applied a local median filter threshold of 3 x 3 pixels to remove outliers. Velocity vectors were georeferenced within PIVlab from an image coordinate system back into a projected coordinate reference system (GDA 1994 MGA Zone 55). We used the coordinates of the same distinct features as those used in PIVLab to assess the accuracy of our georeferencing against actual locations, based on 1 m Maxar satellite imagery in ArcGIS Pro.
Validation of PIV velocity vectors
We use a calibrated 2D hydraulic model to evaluate the accuracy of LSPIV velocities. 2D models offer particular value as they can map velocities in diverse hydraulic conditions rather than at a few idealized sections, including locations where the range and resolution of traditional equipment (such as aDcps and current meters) is limited or where the deployment of velocity sensors can be complex, time-consuming, and hazardous, particularly during flood events when flow depths and velocities prevent field deployment. We detail our model calibration process in the supplementary material (section 1).
Discharge estimation using LSPIV velocities
The velocity-area method was used to calculate discharge (Q ) (Turnipseed and Sauer, 2010). Channel depth and velocity are integrated from discrete locations along a channel’s width. Discharges estimated at each vertical sections spanning the channel width are summed to total discharge (Q ) (Cohn et al ., 2013).
\begin{equation} Q=\sum_{i=1}^{m}{A_{i}v_{i}}=\sum_{i=1}^{m}{b_{i}d_{i}v_{i}}\nonumber \\ \end{equation}
where m = number of verticals across channel;Ai = cross-sectional area of vertical i? ;bi = width of vertical i = (x i+1x i-1)/2 withx = horizontal distance of vertical from the edge of water;di = average depth of vertical i ; andvi = average downstream velocity in verticali . We define a minimum of 25 vertical subsections at each cross-section, with sub-sectional area extending half the distance to the preceding and following measurements.