New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems

Aguzzi, Jacopo; Chatzievangelou, Damianos; Marini, Simone; Fanelli, Emanuela; Danovaro, Roberto; Flögel, Sascha; Lebris, Nadine; Juanes, Francis; Leo, Fabio C. De; Río, Joaquín Del; Thomsen, Laurenz; Costa, Corrado; Riccobene, G.; Tamburini, Christian; Lefèvre, D.; Gojak, C.; Poulain, Pierre-Marie; Favali, P.; Griffa, Annalisa; Purser, Autun; Cline, Danelle E.; Edgington, D.R.; Navarro, Joan; Stefanni, Sérgio; Dhondt, S.; Priede, Imants George; Rountree, Rodney A.; Company, Joan B.

doi:10.1021/acs.est.9b00409

Cited by 105 publications

(176 citation statements)

References 133 publications

(176 reference statements)

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“…In full operation, the system not only collects data but also extracts biological information on-board and presents results for the user. A major part of our development includes processing images in order to track and classify motile species of fishes, cephalopods, echinoderms, and crustaceans [20]; and in the case of long-term monitoring, establish information on activity patterns at diel and seasonal scales, as well as growth patterns of CWCs [4,7,18,21].…”

Section: Ai Developments For Image Processingmentioning

confidence: 99%

“…Now, mechatronics and the Internet of Things paradigm are setting the basis for the development of improved platform mobility and autonomy in navigation and sampling [2,3]. In this scenario, cabled observatories and their infrastructures are integrated with spatial mobile networks where the fixed nodes coordinate the functioning of movable platforms (e.g., docked Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs)) in-between [4]. Such development will promote the spatially extensive and temporally intensive monitoring of ecosystem biodiversity and functioning in relation to, for example, the rate and quantity of energy exchanged within the food web and resulting biomass production with special reference to management services for exploited fish stocks.…”

Section: Introductionmentioning

confidence: 99%

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A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring

Aguzzi

Albiez²,

Flögel

et al. 2020

Sensors

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This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.

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Section: Ai Developments For Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring

Aguzzi

Albiez²,

Flögel

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The main stimulation of organisms to emit light around those static structures comes on impact when swimming or as induced by turbulence behind them. The KM3NeT-Italia and ANTARES neutrino telescopes, off Capo Passero (Sicily, Western Ionian Sea) located at a depth of more than 2 km are an example of the threedimensionality of those infrastructures (reviewed by Aguzzi et al, 2019). Telescope moorings cover the benthopelagic dimension in the form of a cubic kilometer scale matrix of vertically extended, flexible strings which rise for hundreds of meters above the seabed (Sapienza and Riccobene, 2009).…”

Section: Low-light Imaging Technologiesmentioning

confidence: 99%

“…The development of fixed and autonomous mobile platforms is revolutionizing our ability to explore the deep-sea benthic and pelagic environments, acquiring information at a high resolution not achievable with vessels (Wynn et al, 2014;Aguzzi et al, 2019). A wide spectrum of oceanographic, geochemical, optic, and acoustic sensors can be installed on those platforms to explore the seafloor, the subseafloor, and the water column variability, including the potential presence of extant life (see TABLE 1).…”

Section: Marine Platforms and Their Network For Exo-ocean Explorationmentioning

confidence: 99%

“…Due to strict data communication constraints, it is mandatory to equip the observation platforms with software solutions able to identify and transmit only the relevant information collected. This problem happens also in deep-sea research, where solutions are provided by data science, pattern analysis, and artificial intelligence methodologies (Skiena, 2017;Aguzzi et al, 2019). Simple computer vision algorithms can be executed on board platforms' imaging asset, to identify any subject different from the water or seabed itself (Corgnati et al, 2016;Marini et al, 2018a).…”

Section: Landing Platform Delivery On Surface and Data Communicatiomentioning

confidence: 99%

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Exo-Ocean Exploration with Deep-Sea Sensor and Platform Technologies

et al. 2020

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The slow rise of technology: Computer vision techniques in fish population connectivity

Lopez‐Marcano

Brown

Sievers

et al. 2020

Aquatic Conservation

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Technological advancements in data collection and analysis are producing a new generation of ecological data. Among these, computer vision (CV) has received increased attention for its robust capabilities for rapidly processing large volumes of digital imagery. In marine ecosystems, the study of fish connectivity provides fundamental information for assessing fisheries stocks, designing and implementing protected areas and understanding the impact of habitat loss. While the field of fish connectivity has benefited from technological advancements, the extent to which novel techniques, such as CV, have been utilized has not been assessed. To inform future directions and developments, this study reviewed the current use of CV in fish connectivity research, quantified how the implementation of such technology in fish connectivity research compared with other areas of marine research and described how this field could benefit from CV. The review found that the use of remote camera systems in fish connectivity research is increasing, but the implementation of automated analysis of digital imagery has been slow. Successful implementation and expansion of CV frameworks in aquaculture and coral reef ecology suggest that CV techniques could greatly benefit fish connectivity research. A case study of potential use of CV in fish connectivity research, scaling up optimal foraging models to predict marine population connectivity, highlights how beneficial it could be. The capacity for CV techniques to be adopted alongside traditional approaches, the unparalleled speed, accuracy and reliability of these approaches and the benefits of being able to study ecosystems along multiple spatial–temporal scales, all make CV a valuable tool for assessing connectivity. Ultimately, these technologies can assist data‐driven decisions that directly influence the health and productivity of marine ecosystems.

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New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems

Abstract: Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA-IT),

Cited by 105 publications

References 133 publications

A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring

A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring

Exo-Ocean Exploration with Deep-Sea Sensor and Platform Technologies

The slow rise of technology: Computer vision techniques in fish population connectivity

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