AUV Navigation Correction Based on Automated Multibeam Tile Matching.

Ocean science and hydroacoustic seafloor mapping rely on accurate navigation underwater. By exploiting terrain information provided by a multibeam echosounder system, it is possible to significantly improve map quality. This article presents an algorithm capable of improving map quality and accuracy by aligning consecutive pings to tiles that are matched pairwise. A globally consistent solution is calculated from these matches. The proposed method has the potential to be used online in addition to other navigation solutions, but is mainly targeted for post processing. The algorithm was tested using different parameter settings on an AUV and a ship-based dataset. The ship-based dataset is publicly available as a benchmark. The original accurate navigation serving as a ground truth, alongside trajectories that include an artificial drift, are available. This allows quantitative comparisons between algorithms and parameter settings.

Enhanced CO2 uptake at a shallow Arctic Ocean seep field overwhelms the positive warming potential of emitted methane.

Continued warming of the Arctic Ocean in coming decades is projected to trigger the release of teragrams (1 Tg = 106 tons) of methane from thawing subsea permafrost on shallow continental shelves and dissociation of methane hydrate on upper continental slopes. On the shallow shelves (<100 m water depth), methane released from the seafloor may reach the atmosphere and potentially amplify global warming. On the other hand, biological uptake of carbon dioxide (CO2) has the potential to offset the positive warming potential of emitted methane, a process that has not received detailed consideration for these settings. Continuous sea-air gas flux data collected over a shallow ebullitive methane seep field on the Svalbard margin reveal atmospheric CO2 uptake rates (-33,300 ± 7,900 µmol m-2⋅d-1) twice that of surrounding waters and ∼1,900 times greater than the diffusive sea-air methane efflux (17.3 ± 4.8 µmol m-2⋅d-1). The negative radiative forcing expected from this CO2 uptake is up to 231 times greater than the positive radiative forcing from the methane emissions. Surface water characteristics (e.g., high dissolved oxygen, high pH, and enrichment of 13C in CO2) indicate that upwelling of cold, nutrient-rich water from near the seafloor accompanies methane emissions and stimulates CO2 consumption by photosynthesizing phytoplankton. These findings challenge the widely held perception that areas characterized by shallow-water methane seeps and/or strongly elevated sea-air methane flux always increase the global atmospheric greenhouse gas burden.

TuLUMIS - a tunable LED-based underwater multispectral imaging system.

Multispectral imaging (MSI) is widely used in terrestrial applications to help increase the discriminability between objects of interest. While MSI has shown potential for underwater geological and biological surveys, it is thus far rarely applied underwater. This is primarily due to the fact light propagation in water is subject to wavelength dependent attenuation and tough working conditions in the deep ocean. In this paper, a novel underwater MSI system based on a tunable light source is presented which employs a monochrome still image camera with flashing, pressure neutral color LEDs. Laboratory experiments and field tests were performed. Results from the lab experiments show an improvement of 76.66% on discriminating colors on a checkerboard by using the proposed imaging system over the use of an RGB camera. The field tests provided in situ MSI observations of pelagic fauna, and showed the first evidence that the system is capable of acquiring useful imagery under real marine conditions.

DeepSurveyCam--A Deep Ocean Optical Mapping System.

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.

Analysis-ready optical underwater images of Manganese-nodule covered seafloor of the Clarion-Clipperton Zone.

We provide a sequence of analysis-ready optical underwater images from the Clarion-Clipperton Zone (CCZ) of the Pacific Ocean. The images were originally recorded using a towed camera sledge that photographed a seabed covered with polymetallic manganese-nodules, at an average water depth of 4,250 meters. The original degradation in visual quality and inconsistent scale among individual raw images due to different altitude implies that they are not scientifically comparable in their original form. Here, we present analysis-ready images that have already been pre-processed to account for this degradation. We also provide accompanying metadata for each image, which includes their geographic coordinates, depth of the seafloor, absolute scale (cm/pixel), and seafloor habitat class obtained from a previous study. The provided images are thus directly usable by the marine scientific community e.g., to train machine learning models for seafloor substrate classification and megafauna detection.

An automated image-based workflow for detecting megabenthic fauna in optical images with examples from the Clarion-Clipperton Zone.

Recent advances in optical underwater imaging technologies enable the acquisition of huge numbers of high-resolution seafloor images during scientific expeditions. While these images contain valuable information for non-invasive monitoring of megabenthic fauna, flora and the marine ecosystem, traditional labor-intensive manual approaches for analyzing them are neither feasible nor scalable. Therefore, machine learning has been proposed as a solution, but training the respective models still requires substantial manual annotation. Here, we present an automated image-based workflow for Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast). The workflow significantly reduces the required annotation effort by automating the detection of anomalous superpixels, which are regions in underwater images that have unusual properties relative to the background seafloor. The bounding box coordinates of the detected anomalous superpixels are proposed as a set of weak annotations, which are then assigned semantic morphotype labels and used to train a Faster R-CNN object detection model. We applied this workflow to example underwater images recorded during cruise SO268 to the German and Belgian contract areas for Manganese-nodule exploration, within the Clarion-Clipperton Zone (CCZ). A performance assessment of our FaunD-Fast model showed a mean average precision of 78.1% at an intersection-over-union threshold of 0.5, which is on a par with competing models that use costly-to-acquire annotations. In more detail, the analysis of the megafauna detection results revealed that ophiuroids and xenophyophores were among the most abundant morphotypes, accounting for 62% of all the detections within the surveyed area. Investigating the regional differences between the two contract areas further revealed that both megafaunal abundance and diversity was higher in the shallower German area, which might be explainable by the higher food availability in form of sinking organic material that decreases from east-to-west across the CCZ. Since these findings are consistent with studies based on conventional image-based methods, we conclude that our automated workflow significantly reduces the required human effort, while still providing accurate estimates of megafaunal abundance and their spatial distribution. The workflow is thus useful for a quick but objective generation of baseline information to enable monitoring of remote benthic ecosystems.

Delayed response of hermit crabs carrying anemones to a benthic impact experiment at the deep-sea nodule fields of the Peru Basin?

The deep Peru Basin is characterised by a unique abyssal scavenging community featuring large numbers of hermit crabs (Probeebei mirabilis, Decapoda, Crustacea). These are atypical hermit crabs, not carrying a shell, but on some occasions carrying an anemone (Actiniaria). The reason why some hermit crabs carry or not carry anemones is thought to be indicative of a changed environment, outweighing the cost/benefit of their relationship. Here we present the temporal variation of abundances of P. mirabilis with and without anemones, spanning more than two decades, following a benthic impact experiment. An overall decrease in hermit crab densities was observed, most noticeable and significant after 26 years and characterised by a loss of Actiniaria on the Probeebei mirabilis' pleon. Whether this is a delayed response to the benthic impact experiment carried out 26 years' prior or a natural variation in the population remains to be corroborated by an extension of the time-series. Attention is drawn to the limitations of our knowledge over time and space of the abyssal community dynamics and the urgent necessity to fill in these gaps prior to any type of deep-sea exploitation.

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean.

Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.

Explosives compounds from sea-dumped relic munitions accumulate in marine biota.

Relic munitions are a hazardous legacy of the two world wars present in coastal waters worldwide. The southwest Baltic Sea has an especially high prevalence of unexploded ordnance and dumped munition material, which represent a large potential source of toxic explosive chemicals (munition compounds, MC). In the current study, diverse biota (plankton, macroalgae, tunicate, sponge, mollusc, echinoderm, polychaete, anemone, crustacea, fish) were collected from the Kiel Bight and a munitions dumpsite at Kolberger Heide, Germany, to evaluate the potential bioaccumulation of explosives and their derivatives (2,4,6-trinitrotoluene, TNT; 2-amino-4,6-dinitrotoluene and 4-amino-2,6-dinitrotoluene, ADNT; 2,4-diamino-6-nitrotoluene and 2,6-diamino-4-nitrotoluene, DANT; 1,3-dinitrobenzene, DNB; and 1,3,5-trinitro-1,3,5-triazinane, RDX). One or more MCs were detected in >98% of organisms collected throughout the study region (n = 178), at a median level of 6 pmol/g (approximately 1 ng/g) and up to 2 × 107 pmol/g (TNT in Asterias rubens collected from Kolberger Heide). In most cases, TNT and its transformation product compounds ADNT and DANT were significantly higher in biota from the munitions dumpsite compared with other locations. Generally, DNB and RDX were detected less frequently and at lower concentrations than TNT, ADNT, and DANT. In commercially important fish species (plaice, flounder) from Kolberger Heide, TNT and ADNT were detected in 17 and 33% of samples, respectively. In contrast DANT was detected in every fish sample, including those outside the dumpsite. Dinitrobenzene was the second most prevalent MC in fish tissue. Fish viscera (stomach, kidney, liver) showed higher levels of DANT than edible muscle flesh, with highest DANT in liver, suggesting reduced risk to seafood consumers. This study provides some of the first environmental evidence for widespread bioaccumulation of MC in a coastal marine food web. Although tissue MC content was generally low, corrosion of munition housings may lead to greater MC release in the future, and the ecological risk of this exposure is unknown.

PlasPI marine cameras: Open-source, affordable camera systems for time series marine studies.

Imaging underwater can be particularly problematic and expensive given the harsh environmental conditions posed by salinity and for some deployments, pressure. To counter these difficulties, expensive waterproof pressure resistant housings are often used, commonly built from expensive materials such as titanium, if intended for long duration deployments. Further, environmental investigations often benefit from replicate data collection, which additionally increases study costs. In this paper we present a new camera system, developed with off the shelf and 3D printed cost effective components for use in shallow waters of <150 m depth. Integrating Raspberry Pi Zero W microcomputers with open source design files and software, it is hoped these camera systems will be of interest to the global marine and freshwater research communities.

Biological effects 26 years after simulated deep-sea mining.

The potential for imminent abyssal polymetallic nodule exploitation has raised considerable scientific attention. The interface between the targeted nodule resource and sediment in this unusual mosaic habitat promotes the development of some of the most biologically diverse communities in the abyss. However, the ecology of these remote ecosystems is still poorly understood, so it is unclear to what extent and timescale these ecosystems will be affected by, and could recover from, mining disturbance. Using data inferred from seafloor photo-mosaics, we show that the effects of simulated mining impacts, induced during the "DISturbance and reCOLonization experiment" (DISCOL) conducted in 1989, were still evident in the megabenthos of the Peru Basin after 26 years. Suspension-feeder presence remained significantly reduced in disturbed areas, while deposit-feeders showed no diminished presence in disturbed areas, for the first time since the experiment began. Nevertheless, we found significantly lower heterogeneity diversity in disturbed areas and markedly distinct faunal compositions along different disturbance levels. If the results of this experiment at DISCOL can be extrapolated to the Clarion-Clipperton Zone, the impacts of polymetallic nodule mining there may be greater than expected, and could potentially lead to an irreversible loss of some ecosystem functions, especially in directly disturbed areas.

An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis.

Optical imaging is a common technique in ocean research. Diving robots, towed cameras, drop-cameras and TV-guided sampling gear: all produce image data of the underwater environment. Technological advances like 4K cameras, autonomous robots, high-capacity batteries and LED lighting now allow systematic optical monitoring at large spatial scale and shorter time but with increased data volume and velocity. Volume and velocity are further increased by growing fleets and emerging swarms of autonomous vehicles creating big data sets in parallel. This generates a need for automated data processing to harvest maximum information. Systematic data analysis benefits from calibrated, geo-referenced data with clear metadata description, particularly for machine vision and machine learning. Hence, the expensive data acquisition must be documented, data should be curated as soon as possible, backed up and made publicly available. Here, we present a workflow towards sustainable marine image analysis. We describe guidelines for data acquisition, curation and management and apply it to the use case of a multi-terabyte deep-sea data set acquired by an autonomous underwater vehicle.

Compact-Morphology-based poly-metallic Nodule Delineation.

Poly-metallic nodules are a marine resource considered for deep sea mining. Assessing nodule abundance is of interest for mining companies and to monitor potential environmental impact. Optical seafloor imaging allows quantifying poly-metallic nodule abundance at spatial scales from centimetres to square kilometres. Towed cameras and diving robots acquire high-resolution imagery that allow detecting individual nodules and measure their sizes. Spatial abundance statistics can be computed from these size measurements, providing e.g. seafloor coverage in percent and the nodule size distribution. Detecting nodules requires segmentation of nodule pixels from pixels showing sediment background. Semi-supervised pattern recognition has been proposed to automate this task. Existing nodule segmentation algorithms employ machine learning that trains a classifier to segment the nodules in a high-dimensional feature space. Here, a rapid nodule segmentation algorithm is presented. It omits computation-intense feature-based classification and employs image processing only. It exploits a nodule compactness heuristic to delineate individual nodules. Complex machine learning methods are avoided to keep the algorithm simple and fast. The algorithm has successfully been applied to different image datasets. These data sets were acquired by different cameras, camera platforms and in varying illumination conditions. Their successful analysis shows the broad applicability of the proposed method.

Biological responses to disturbance from simulated deep-sea polymetallic nodule mining.

Commercial-scale mining for polymetallic nodules could have a major impact on the deep-sea environment, but the effects of these mining activities on deep-sea ecosystems are very poorly known. The first commercial test mining for polymetallic nodules was carried out in 1970. Since then a number of small-scale commercial test mining or scientific disturbance studies have been carried out. Here we evaluate changes in faunal densities and diversity of benthic communities measured in response to these 11 simulated or test nodule mining disturbances using meta-analysis techniques. We find that impacts are often severe immediately after mining, with major negative changes in density and diversity of most groups occurring. However, in some cases, the mobile fauna and small-sized fauna experienced less negative impacts over the longer term. At seven sites in the Pacific, multiple surveys assessed recovery in fauna over periods of up to 26 years. Almost all studies show some recovery in faunal density and diversity for meiofauna and mobile megafauna, often within one year. However, very few faunal groups return to baseline or control conditions after two decades. The effects of polymetallic nodule mining are likely to be long term. Our analyses show considerable negative biological effects of seafloor nodule mining, even at the small scale of test mining experiments, although there is variation in sensitivity amongst organisms of different sizes and functional groups, which have important implications for ecosystem responses. Unfortunately, many past studies have limitations that reduce their effectiveness in determining responses. We provide recommendations to improve future mining impact test studies. Further research to assess the effects of test-mining activities will inform ways to improve mining practices and guide effective environmental management of mining activities.

Estimating the spatial position of marine mammals based on digital camera recordings.

Estimating the spatial position of organisms is essential to quantify interactions between the organism and the characteristics of its surroundings, for example, predator-prey interactions, habitat selection, and social associations. Because marine mammals spend most of their time under water and may appear at the surface only briefly, determining their exact geographic location can be challenging. Here, we developed a photogrammetric method to accurately estimate the spatial position of marine mammals or birds at the sea surface. Digital recordings containing landscape features with known geographic coordinates can be used to estimate the distance and bearing of each sighting relative to the observation point. The method can correct for frame rotation, estimates pixel size based on the reference points, and can be applied to scenarios with and without a visible horizon. A set of R functions was written to process the images and obtain accurate geographic coordinates for each sighting. The method is applied to estimate the spatiotemporal fine-scale distribution of harbour porpoises in a tidal inlet. Video recordings of harbour porpoises were made from land, using a standard digital single-lens reflex (DSLR) camera, positioned at a height of 9.59 m above mean sea level. Porpoises were detected up to a distance of ∽3136 m (mean 596 m), with a mean location error of 12 m. The method presented here allows for multiple detections of different individuals within a single video frame and for tracking movements of individuals based on repeated sightings. In comparison with traditional methods, this method only requires a digital camera to provide accurate location estimates. It especially has great potential in regions with ample data on local (a)biotic conditions, to help resolve functional mechanisms underlying habitat selection and other behaviors in marine mammals in coastal areas.

Ocean currents shape the microbiome of Arctic marine sediments.

Prokaryote communities were investigated on the seasonally stratified Alaska Beaufort Shelf (ABS). Water and sediment directly underlying water with origin in the Arctic, Pacific or Atlantic oceans were analyzed by pyrosequencing and length heterogeneity-PCR in conjunction with physicochemical and geographic distance data to determine what features structure ABS microbiomes. Distinct bacterial communities were evident in all water masses. Alphaproteobacteria explained similarity in Arctic surface water and Pacific derived water. Deltaproteobacteria were abundant in Atlantic origin water and drove similarity among samples. Most archaeal sequences in water were related to unclassified marine Euryarchaeota. Sediment communities influenced by Pacific and Atlantic water were distinct from each other and pelagic communities. Firmicutes and Chloroflexi were abundant in sediment, although their distribution varied in Atlantic and Pacific influenced sites. Thermoprotei dominated archaea in Pacific influenced sediments and Methanomicrobia dominated in methane-containing Atlantic influenced sediments. Length heterogeneity-PCR data from this study were analyzed with data from methane-containing sediments in other regions. Pacific influenced ABS sediments clustered with Pacific sites from New Zealand and Chilean coastal margins. Atlantic influenced ABS sediments formed another distinct cluster. Density and salinity were significant structuring features on pelagic communities. Porosity co-varied with benthic community structure across sites and methane did not. This study indicates that the origin of water overlying sediments shapes benthic communities locally and globally and that hydrography exerts greater influence on microbial community structure than the availability of methane.

Diversity and biogeochemical structuring of bacterial communities across the Porangahau ridge accretionary prism, New Zealand.

Sediments from the Porangahau ridge, located off the northeastern coast of New Zealand, were studied to describe bacterial community structure in conjunction with differing biogeochemical regimes across the ridge. Low diversity was observed in sediments from an eroded basin seaward of the ridge and the community was dominated by uncultured members of the Burkholderiales. Chloroflexi/GNS and Deltaproteobacteria were abundant in sediments from a methane seep located landward of the ridge. Gas-charged and organic-rich sediments further landward had the highest overall diversity. Surface sediments, with the exception of those from the basin, were dominated by Rhodobacterales sequences associated with organic matter deposition. Taxa related to the Desulfosarcina/Desulfococcus and the JS1 candidates were highly abundant at the sulfate-methane transition zone (SMTZ) at three sites. To determine how community structure was influenced by terrestrial, pelagic and in situ substrates, sequence data were statistically analyzed against geochemical data (e.g. sulfate, chloride, nitrogen, phosphorous, methane, bulk inorganic and organic carbon pools) using the Biota-Environmental matching procedure. Landward of the ridge, sulfate was among the most significant structuring factors. Seaward of the ridge, silica and ammonium were important structuring factors. Regardless of the transect location, methane was the principal structuring factor on SMTZ communities.