Daniel Buscombe

Comprehensive Field Study of Swash-Zone Processes. I: Experimental Design with Examples of Hydrodynamic and Sediment Transport Measurements

AbstractA comprehensive study of swash-zone hydrodynamics and sediment transport was conducted on a macrotidal beach in Perranporth, United Kingdom. The unique study is the first to simultaneously measure suspended sediment and sheet flow sediment concentrations, water depth, near-bed velocity profiles, and high-resolution swash surface and bed-level changes on a natural beach. Data collected during the study are used to quantify the vertical profile of cross-shore and alongshore velocities and the importance of sheet flow sediment processes in the swash zone. The swash-zone boundary layer for cross-shore velocities is observed to generally occur over at least the lower 0.06 m of the water column. Alongshore velocities are often the same order of magnitude as the cross-shore velocities and are dominant near cross-shore flow reversal. Flows are often logarithmic in profile, but the instantaneous nature of the measurements renders application of the logarithmic model difficult. When valid, the logarithmic m...

Characterizing riverbed sediment using high‐frequency acoustics: 1. Spectral properties of scattering

Bed sediment classification using high-frequency hydroacoustic instruments is challenging when sediments are spatially heterogeneous, which is often the case in rivers. The use of acoustic backscatter to classify sediments is an attractive alternative to analysis of topography because it is potentially sensitive to grain scale roughness. Here a new method is presented which uses high-frequency acoustic backscatter from multibeam sonar to classify heterogeneous riverbed sediments by type (sand, gravel, and rock) continuously in space and at small spatial resolution. In this, the first of a pair of papers that examine the scattering signatures from a heterogeneous riverbed, methods are presented to construct spatially explicit maps of spectral properties from georeferenced point clouds of geometrically and radiometrically corrected echoes. Backscatter power spectra are computed to produce scale and amplitude metrics that collectively characterize the length scales of stochastic measures of riverbed scattering, termed “stochastic geometries.” Backscatter aggregated over small spatial scales have spectra that obey a power law. This apparently self-affine behavior could instead arise from morphological scale and grain scale roughnesses over multiple overlapping scales or riverbed scattering being transitional between Rayleigh and geometric regimes. Relationships exist between stochastic geometries of backscatter and areas of rough and smooth sediments. However, no one parameter can uniquely characterize a particular substrate nor definitively separate the relative contributions of roughness and acoustic impedance (hardness). Combinations of spectral quantities do, however, have the potential to delineate riverbed sediment patchiness, in a data-driven approach comparing backscatter with bed sediment observations (which is the subject of part two of this manuscript).

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

In this, the second of a pair of papers on the statistical signatures of riverbed sediment in high-frequency acoustic backscatter, spatially explicit maps of the stochastic geometries (length and amplitude scales) of backscatter are related to patches of riverbed surfaces composed of known sediment types, as determined by georeferenced underwater video observations. Statistics of backscatter magnitudes alone are found to be poor discriminators between sediment types. However, the variance of the power spectrum and the intercept and slope from a power law spectral form (termed the spectral strength and exponent, respectively) successfully discriminate between sediment types. A decision tree approach was able to classify spatially heterogeneous patches of homogeneous sands, gravels (and sand-gravel mixtures), and cobbles/boulders with 95, 88, and 91% accuracy, respectively. Application to sites outside the calibration and surveys made at calibration sites at different times were plausible based on observations from underwater video. Analysis of decision trees built with different training data sets suggested that the spectral exponent was consistently the most important variable in the classification. In the absence of theory concerning how spatially variable sediment surfaces scatter high-frequency sound, the primary advantage of this data-driven approach to classify bed sediment over alternatives is that spectral methods have well-understood properties and make no assumptions about the distributional form of the fluctuating component of backscatter over small spatial scales.

Automated Riverbed Sediment Classification Using Low-Cost Sidescan Sonar

AbstractThe use of low-cost, low-profile, and highly portable sidescan sonar is on the ascendancy for imaging shallow riverine benthic sediments. A new automated, spatially explicit, and physically-based method for calculating lengthscales of bed texture elements in sidescan echograms (a 2D plot of acoustic intensity as a function of slant range and distance) is suggested. It uses spectral analysis based on the wavelet transform of short sequences of echograms. The recursive application of the transform over small overlapping windows of the echogram provides a robust measure of lengthscales of alternating patterns of strong and weak echoes. This textural lengthscale is not a direct measure of grain size. Rather, it is a statistical representation that integrates over many attributes of bed texture, of which grain size is the most important. The technique is a physically-based means to identify regions of texture within a sidescan echogram, and could provide a basis for objective, automated riverbed sedime...

Spatially explicit spectral analysis of point clouds and geospatial data

The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is described, and its functionality illustrated with an example of a high-resolution bathymetric point cloud data collected with multibeam echosounder. HighlightsComputational/analytical methods for explicit spectral analysis of spatial data.Python toolbox (PySESA) for efficient analysis of geospatial point clouds.Spectral quantities are spatially referenced in a continuous sense as random fields.Allows appropriate scaling of amplitude statistics by internal spatial correlations.Generic texture/roughness characterization and feature detection/classification.

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

Abstract In recent years, lightweight, inexpensive, vessel-mounted ‘recreational grade’ sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing ‘democratization’ of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.

Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation

Received 24 January 2011; revised 26 January 2012; accepted 30 January 2012; published 3 April 2012. [1] In this, the first of a pair of papers which address the simulation and automated measurement of well-sorted natural granular material, a method is presented for simulation of two-phase (solid, void) assemblages of discrete non-cohesive particles. The purpose is to have a flexible, yet computationally and theoretically simple, suite of tools with well constrained and well known statistical properties, in order to simulate realistic granular material as a discrete element model with realistic size and shape distributions, for a variety of purposes. The stochastic modeling framework is based on three-dimensional tessellations with variable degrees of order in particle-packing arrangement. Examples of sediments with a variety of particle size distributions and spatial variability in grain size are presented. The relationship between particle shape and porosity conforms to published data. The immediate application is testing new algorithms for automated measurements of particle properties (mean and standard deviation of particle sizes, and apparent porosity) from images of natural sediment, as detailed in the second of this pair of papers. The model could also prove useful for simulating specific depositional structures found in natural sediments, the result of physical alterations to packing and grain fabric, using discrete particle flow models. While the principal focus here is on naturally occurring sediment and sedimentary rock, the methods presented might also be useful for simulations of similar granular or cellular material encountered in engineering, industrial and life sciences.