Toward a European Coastal Observing Network to Provide Better Answers to Science and to Societal Challenges; The JERICO Research Infrastructure

Farcy, Patrick; Durand, Dominique; Charria, Guillaume; Painting, S. J.; Tamminen, Timo; Collingridge, Kate; Grémare, Antoine; Delauney, Laurent; Puillat, Ingrid

doi:10.3389/fmars.2019.00529

Cited by 29 publications

(26 citation statements)

References 30 publications

(29 reference statements)

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“…Although a large amount of scientific literature has been produced in recent years about underwater content-based image analysis [15][16][17][18][19], the processing of video data within ecological application contexts is still mostly manual and cabled observatory platforms and their networks are not yet equipped with permanent software tools for the automated recognition and classification of biological relevant image content [3,20]. In this context, cabled observatory networks such as Ocean Network Canada (ONC, www.oceannetworks.ca), European Multidisciplinary Seafloor and water-column Observatories (EMSO, http://emso.eu/), and Lofoten-Vesterålen (LoVe, https://love.statoil.com/) among others, are missing the opportunity to increase their societal impact by enforcing service-oriented image acquisition and automatically processing target species of commercial relevance.…”

Section: The Human Bottleneck In Image Manual Processingmentioning

confidence: 99%

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

López-Vázquez¹,

López-Guede

Marini

et al. 2020

Sensors

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An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

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Section: The Human Bottleneck In Image Manual Processingmentioning

confidence: 99%

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

López-Vázquez¹,

López-Guede

Marini

et al. 2020

Sensors

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“…The particular challenge of simultaneously observing physical, chemical, and biological parameters for assessments of complex coastal processes remains an open issue in relation to the temporal scale of sampling. This will be addressed in the JERICO science strategy under development (Grémare et al, 2017;Farcy et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Threats to the marine environment were considered in terms of "pressures" and "impacts". Pressures were described as the human activities which have impacts on ecosystems or parts thereof (see Oesterwind et al, 2016 11 ), which is compatible with the driver-pressure-11 Pressures can be described as "a result of a driver-initiated mechanism (human activity/natural process) causing an effect state-impact-response (DPSIR) framework (Gabrielsen and Bosch, 2003;Elliott, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…This suggests that monitoring programmes should be underpinned by high-level scientific objectives and that research and monitoring should be well integrated. Data sharing, such as through the JERICO-NEXT research infrastructure, coastal observatories, and the EMODNet data infrastructure (Miguez et al, 2019), is vital to the current and future integration of research and monitoring (Farcy et al, 2019). Furthermore, the availability of data at local and regional scales is essential for the development of future monitoring and assessment approaches, particularly as new technologies and assessment tools are developed and become more readily available (e.g.…”

Section: Drivers Of Marine Monitoringmentioning

confidence: 99%

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Marine monitoring in Europe: is it adequate to address environmental threats and pressures?

Painting

Collingridge

Durand³

et al. 2020

Ocean Sci.

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Abstract. We provide a review of the environmental threats and gaps in monitoring programmes in European coastal waters based on previous studies, an online questionnaire, and an in-depth assessment of observation scales. Our findings underpin the JERICO-NEXT1 monitoring strategy for the development and integration of coastal observatories in Europe and support JERICO-RI2 in providing high-value physical, chemical, and biological datasets for addressing key challenges at a European level. This study highlights the need for improved monitoring of environmental threats in European coastal environments. Participants in the online questionnaire provided new insights into gaps between environmental threats and monitoring of impacts. In total, 36 national representatives, scientists, and monitoring authorities from 12 European countries (Finland, France, Germany, Greece, Ireland, Italy, Malta, Norway, Poland, Spain, Sweden, UK) completed the questionnaire, and 38 monitoring programmes were reported. The main policy drivers of monitoring were identified as the EU Water Framework Directive (WFD), the Marine Strategy Framework Directive (MSFD), Regional Seas Conventions (e.g. OSPAR), and local drivers. Although policy drivers change over time, their overall purposes remain similar. The most commonly identified threats to the marine environment were marine litter, shipping, contaminants, organic enrichment, and fishing. Regime change was identified as a pressure by 67 % of respondents. The main impacts of these pressures or threats were identified by the majority of respondents (> 70 %) to be habitat loss or destruction, underwater noise, and contamination, with 60 % identifying undesirable disturbance (e.g. oxygen depletion), changes in sediment and/or substrate composition, changes in community composition, harmful microorganisms, and invasive species as impacts. Most respondents considered current monitoring of threats to be partially adequate or not adequate. The majority of responses were related to the spatial and/or temporal scales at which monitoring takes place and inadequate monitoring of particular parameters. Suggestions for improved monitoring programmes included improved design, increased monitoring effort, and better linkages with research and new technologies. Improved monitoring programmes should be fit for purpose, underpin longer-term scientific objectives which cut across policy and other drivers, and consider cumulative effects of multiple pressures. JERICO-RI aims to fill some of the observation gaps in monitoring programmes through the development of new technologies. The science strategy for JERICO-RI will pave the way to a better integration of physical, chemical, and biological observations into an ecological process perspective.

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“…Note: All acronyms are defined in section "Glossary." Coastal and shelf-seas are of particular importance, as these are the most dynamic ocean regions with the highest ecosystem productivity and biodiversity and value to maritime industries and coastal recreation (Farcy et al, 2019;Jacox et al, 2020). Insufficient empirical data in coastal ocean models can hinder the blue economy and ultimately leads to inaccurate forecasts and un-informed policy decisions (Tanhua et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Fishing Gear as a Data Collection Platform: Opportunities to Fill Spatial and Temporal Gaps in Operational Sub-Surface Observation Networks

Vranken¹,

Vastenhoud²,

Manning

et al. 2020

Front. Mar. Sci.

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While the observation of the open ocean is well achieved by automated ocean measurement instruments, coastal and shelf seas suffer the lack of sub-surface collection platforms. Commercial fishing gear such as bottom trawls, pots, traps and long lines can act as platforms for sensors, which collect physical oceanographic data concurrently with normal fishing operations. The lack of observed in situ ocean data in coastal and shelf seas limits operational oceanography, weather forecasting, maritime industries, and climate change monitoring. In addition, using fishing gear as an ocean observation platform has auxiliary benefits for fisheries management including stakeholder involvement. This study quantifies and compares the existing sub-surface in situ data coverage with the spatial distribution of fishing activities. The results show that integration with fishing could contribute to filling some of the most pressing gaps in existing ocean observation systems in coastal and shelf seas. There are limitations related to opportunistic data collection, mainly related to spatial and temporal heterogeneity of fishing activities. However, we make the case that fisherybased observations have the potential to complement existing ocean observing systems in areas where oceanographic data are lacking and needed most in order to ensure long term sustainability of ocean monitoring.

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Toward a European Coastal Observing Network to Provide Better Answers to Science and to Societal Challenges; The JERICO Research Infrastructure

Cited by 29 publications

References 30 publications

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Marine monitoring in Europe: is it adequate to address environmental threats and pressures?

Fishing Gear as a Data Collection Platform: Opportunities to Fill Spatial and Temporal Gaps in Operational Sub-Surface Observation Networks

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