The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

Aguzzi, Jacopo; Chatzievangelou, Damianos; Company, Joan B.; Thomsen, Laurenz; Marini, Simone; Bonofiglio, Federico; Juanes, Francis; Rountree, Rodney A.; Berry, Alan; Chumbinho, Rogerio; Lordan, Colm; Doyle, Jennifer; Río, Joaquín Del; Navarro, Joan; Leo, Fabio C. De; Bahamón, Nixon; García, José A.; Danovaro, P R; Francescangeli, Marco; López-Vázquez, Vanesa; Gaughan, Paul

doi:10.1093/icesjms/fsaa169

Cited by 34 publications

(31 citation statements)

References 116 publications

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“…This suggests that while a single image does not capture every fish active in the area during the 15-minute period it represents, the collection of images from any given site are representative of the community at that location and are likely to accurately reflect changes in species behavior, abundance, or diversity at the site over a range of time scales (e.g., daily vs. seasonal changes), perhaps more accurately than a limited series of diver observations would. Camera networks are an emerging method for monitoring fish activity and other behaviors on diel and seasonal timescales [ 63 , 64 ] and may reveal important information about temporal resource partitioning, predator avoidance, and other interspecific dynamics in the community [ 65 ]. Further study of camera captures vs. diver observations regarding species known to be wary of divers, either those using traditional open-circuit SCUBA or closed-circuit rebreathers [ 66 ], is an important line of future investigation to understand true reef fish occupancy and abundance.…”

Section: Discussionmentioning

confidence: 99%

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis

Bilodeau

Schwartz

et al. 2022

PLoS ONE

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Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques’ use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras’ potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns.

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Section: Discussionmentioning

confidence: 99%

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis

Bilodeau

Schwartz

et al. 2022

PLoS ONE

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“…The increase in the exploitation of marine resources enforces the necessity to develop new methods of environmental monitoring which, with the integration of new technologies, make the reaching of new frontiers possible in the field of biological features, namely for environmental, physical, and chemical parameters and sampling surveys [85]. In fact, in recent years, several projects had the goal of identifying new tools for the optimization of monitoring and sampling techniques for the improved assessment of an environmental status, which is the basis of several international management policies [86,87].…”

Section: Autonomous Monitoring Networkmentioning

confidence: 99%

“…Currently, the largest existing networks of underwater observing stations are represented by permanent infrastructures specifically intended for multidisciplinary monitoring and research in the fields of geology, oceanography, and ecology. The advantage of permanent infrastructural networks is that they can be connected directly to the coast or through a succession of nodes [85,86]. Connection by a cable transmission line directly provides power and real-time data transmission to and from the marine observatory.…”

Section: Marine Permanent Infrastructuresmentioning

confidence: 99%

Management and Sustainable Exploitation of Marine Environments through Smart Monitoring and Automation

Glaviano

Esposito

Cosmo

et al. 2022

JMSE

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Monitoring of aquatic ecosystems has been historically accomplished by intensive campaigns of direct measurements (by probes and other boat instruments) and indirect extensive methods such as aero-photogrammetry and satellite detection. These measurements characterized the research in the last century, with significant but limited improvements within those technological boundaries. The newest advances in the field of smart devices and increased networking capabilities provided by emerging tools, such as the Internet of Things (IoT), offer increasing opportunities to provide accurate and precise measurements over larger areas. These perspectives also correspond to an increasing need to promptly respond to frequent catastrophic impacts produced by drilling stations and intense transportation activities of dangerous materials over ocean routes. The shape of coastal ecosystems continuously varies due to increasing anthropic activities and climatic changes, aside from touristic activities, industrial impacts, and conservation practices. Smart buoy networks (SBNs), autonomous underwater vehicles (AUVs), and multi-sensor microsystems (MSMs) such as smart cable water (SCW) are able to learn specific patterns of ecological conditions, along with electronic “noses”, permitting them to set innovative low-cost monitoring stations reacting in real time to the signals of marine environments by autonomously adapting their monitoring programs and eventually sending alarm messages to prompt human intervention. These opportunities, according to multimodal scenarios, are dramatically changing both the coastal monitoring operations and the investigations over large oceanic areas by yielding huge amounts of information and partially computing them in order to provide intelligent responses. However, the major effects of these tools on the management of marine environments are still to be realized, and they are likely to become evident in the next decade. In this review, we examined from an ecological perspective the most striking innovations applied by various research groups around the world and analyzed their advantages and limits to depict scenarios of monitoring activities made possible for the next decade.

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“…Activities that have a major impact include oil drilling, fishing, and wind turbine deployment. An important factor in monitoring marine biodiversity and maintaining sustainable fish stocks is marine imaging [ 2 , 3 ]. Possible applications are, for example, creating a time series of different species by detecting and classifying species in the images.…”

Section: Introductionmentioning

confidence: 99%

ALMI—A Generic Active Learning System for Computational Object Classification in Marine Observation Images

Möller

Nattkemper

2021

Sensors

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In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from these data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. As a priori knowledge about the objects of interest in the images is almost never available, computational methods are required that are not dependent on the posterior availability of a large training data set of annotated images. In this paper, we propose a new strategy for collecting and using training data for machine learning-based observatory image interpretation much more efficiently. The method combines the training efficiency of a special active learning procedure with the advantages of deep learning feature representations. The method is tested on two highly disparate data sets. In our experiments, we can show that the proposed method ALMI achieves on one data set a classification accuracy A > 90% with less than N = 258 data samples and A > 80% after N = 150 iterations, i.e., training samples, on the other data set outperforming the reference method regarding accuracy and training data required.

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The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

Cited by 34 publications

References 116 publications

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis

Management and Sustainable Exploitation of Marine Environments through Smart Monitoring and Automation

ALMI—A Generic Active Learning System for Computational Object Classification in Marine Observation Images

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