[1] Measurements of suspended sediment volume concentrations, particle size and number density are routinely collected in marine and fresh-water environments with LISST-100X instruments to understand sediment transport, biological processes and fundamental opto-acoustic problems. A LISST-100X was simultaneously deployed with a novel holographic camera (holocam) in UK coastal waters to assess the performance of the laser diffraction technique when measuring natural suspensions. Volume distributions from the LISST-100X, truncated to exclude non-overlapping size bins with the holocam, exhibit an increase in small particles and median particle size is elevated in comparison to the holocam by 20-40%. We observe positive offsets between LISST-100X and holocam number distributions of up to 2 orders of magnitude for particle sizes between 58-218 mm, with discrepancies rising to 4 orders of magnitude for finer and coarser sizes. To explain these differences, a novel multiscale representation of particle size is used. The method quantifies individual dimensions that make up any two-dimensional geometrical structure, it can be used as a metric for particle complexity, and offers a plausible explanation for an apparent increase in small particles (<58 mm) reported by the LISST-100X. The results suggest that for non-spherical natural suspensions the LISST-100X may be sensitive to optical scattering from sub-scales within larger particles, reporting them as individual particles regardless of the way in which they may be packaged into particles of larger overall size. We urge caution in over interpretation of LISST size distributions obtained in natural suspensions without verification with independent particle imaging.

Currents, wind, bathymetry, and freshwater runoff are some of the factors that make coastal waters heterogeneous, patchy, and scientifically interesting—where it is challenging to resolve the spatiotemporal variation within the water column. We present methods and results from field experiments using an autonomous underwater vehicle (AUV) with embedded algorithms that focus sampling on features in three dimensions. This was achieved by combining Gaussian process (GP) modeling with onboard robotic autonomy, allowing volumetric measurements to be made at fine scales. Special focus was given to the patchiness of phytoplankton biomass, measured as chlorophyll a (Chla), an important factor for understanding biogeochemical processes, such as primary productivity, in the coastal ocean. During multiple field tests in Runde, Norway, the method was successfully used to identify, map, and track the subsurface chlorophyll a maxima (SCM). Results show that the algorithm was able to estimate the SCM volumetrically, enabling the AUV to track the maximum concentration depth within the volume. These data were subsequently verified and supplemented with remote sensing, time series from a buoy and ship-based measurements from a fast repetition rate fluorometer (FRRf), particle imaging systems, as well as discrete water samples, covering both the large and small scales of the microbial community shaped by coastal dynamics. By bringing together diverse methods from statistics, autonomous control, imaging, and oceanography, the work offers an interdisciplinary perspective in robotic observation of our changing oceans.

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Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from oversegmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets.

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