Description: Coastal marine environments are contaminated globally with a vast quantity of unexploded ordnance and munitions from intentional disposal. These munitions contain organic explosive compounds as well as a variety of metals, and represent point sources of chemical pollution to marine waters. Most underwater munitions originate from World Wars at the beginning of the twentieth century, and metal munitions housings have been impacted by extensive corrosion over the course of the following decades. As a result, the risk of munitions-related contaminant release to the water column is increasing. The behavior of munitions compounds is well-characterized in terrestrial systems and groundwater, but is only poorly understood in marine systems. Organic explosive compounds, primarily nitroaromatics and nitramines, can be degraded or transformed by a variety of biotic and abiotic mechanisms. These reaction products exhibit a range in biogeochemical characteristics such as sorption by particles and sediments, and variable environmental behavior as a result. The reaction products often exhibit increased toxicity to biological receptors and geochemical controls like sorption can limit this exposure. Environmental samples typically show low concentrations of munitions compounds in water and sediments (on the order of ng/L and μg/kg, respectively), and ecological risk appears generally low. Nonetheless, recent work demonstrates the possibility of sub-lethal genetic and metabolic effects. This review evaluates the state of knowledge on the occurrence, fate, and effect of munition-related chemical contaminants in the marine environment. There remain a number of knowledge gaps that limit our understanding of munitions-related contaminant spread and effect, and the need for additional work is made all the more urgent by increasing risk of release to the environment.

Description: This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement (〉0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms.

Description: Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor.