Considerations on Acoustic Mapping Velocimetry (AMV) Application for in-situ Measurement of Bedform Dynamics

Abstract

Non-intrusive technologies for the in-situ measurement of river morphological features are increasingly popular in the scientific and practice communities due to their efficient and productive data acquisition. While the measurement of suspended load with optical and acoustic technologies is currently an active area of research, the measurement of bedform dynamics has not experienced similar progress. We have successfully demonstrated through laboratory experiments that, by combining acoustic mapping with image velocimetry concepts, we can characterize the planar dynamics of the bedform migration. The technique, labeled Acoustic Mapping Velocimetry (AMV), is currently transferred to field conditions using multiple-beam echo-sounders (MBES) for producing acoustic maps. During this transfer, new questions emerged because, in field conditions, many of the morphologic features targeted by AMV measurements are not a priori known. Moreover, the image velocimetry processing can be approached with several alternatives, each of them characterized by strength and limitations. This paper assembles guidelines for establishing optimal parameters for the acquisition of the acoustic maps based on analytical considerations, and for selecting essential features of the processing for image velocimetry. We test these guidelines using MBES data acquired in the Mississippi River.

On the Capabilities of Emerging Nonintrusive Methods to Estimate Bedform Characteristics and Bedload Rates

“…(2022) and You, Muste, et al. (2021) demonstrate the impact of data acquisition protocols on the AMV image processing component. We note here that the judicious selection of the size of the Interrogation Areas and the time step between image pairs is critical for the accuracy of the bedform dynamics estimates.…”

“…The accuracy of the estimates of the dune characteristics is more sensitive to the density of the measured points along a profile than on the method used to extract the dune characteristics from a profile (Wilbers, 2004). The optimal outcome of Step 1f is obtained if the bedform characteristic extraction is made along the same directions as those used in Step 1a for surveying the targeted area as this approach ensures a more accurate timing for the IV processing (You, Muste, et al., 2021). There are multiple approaches to quantify the average characteristics of the dune geometry (e.g., Bradley & Venditti, 2017; Cisneros et al., 2020; Gutierrez et al., 2018; Le Guern et al., 2021; Zomer et al., 2022) These procedures are based on various principles (from statistical to wavelet analysis) with the goal to automatically extract representative bedform heights and wavelengths.…”

“…There are multiple instruments for non‐intrusively obtaining bathymetric maps (see Figure 1, Step 1a). The accuracy of the captured bedforms is controlled by the MBES sounding density which in turn depends on the instrument footprint size, pinging rate, as well as on the vessel speed and the local water depth (You, Muste, et al., 2021). Given the efficiency of the MBES measurements, the river bottom can be surveyed relatively quickly over large bed area.…”

“…Soon thereafter, a preliminary study was carried out to test the AMV capabilities for estimating bedload transport rates in‐situ (Baranya et al., 2016). More recently, two other studies have investigated optimizations of various AMV aspects relevant to bedload transport: evaluation of the IV approaches for AMV (You, Kim, et al., 2021) and formulation of practical guidance for data acquisition to adequately capture the bedform dynamics in natural rivers (You, Muste, et al., 2021). This paper is the first attempt to evaluate the AMV performance for in‐situ estimation of the bedform transport discharge as originally proposed in Muste et al.…”

Mapping 2‐D Bedload Rates Throughout a Sand‐Bed River Reach From High‐Resolution Acoustical Surveys of Migrating Bedforms

“…Very few studies have assessed the bedload spatio-temporal variability at the bedform scale (Muste et al, 2016;Strick et al, 2019). Muste et al (2016) applied Large-Scale Particle Image Velocimetry (LSPIV) to map bedform velocities from repeat bathymetric surveys, a method they called Acoustic Mapping Velocimetry, also introduced by Kim et al (2016) and by You et al (2021) who recently proposed general guidelines for its application to field surveys. However, to compute bedload rates, they used the mean bedform heights computed over along-channel profiles and did not determine the cross-stream component of bedload.…”