TY - JOUR T1 - The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms JF - Sensors Y1 - 2020 A1 - Aguzzi, Jacopo A1 - Chatzievangelou, Damianos A1 - Francescangeli, Marco A1 - Marini, Simone A1 - Bonofiglio, Federico A1 - del Rio, Joaquin A1 - Danovaro, Roberto KW - artificial intelligence KW - cabled observatories KW - crawler KW - cyber-infrastructures KW - data banking KW - ecological indicators KW - ecological information treatment KW - imaging AB - Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals. VL - 20 U1 - All arrays U2 - ER - TY - JOUR T1 - Obsea: A Decadal Balance for a Cabled Observatory Deployment JF - IEEE Access Y1 - 2020 A1 - Del-Rio, Joaquin A1 - Nogueras, Marc A1 - Toma, Daniel Mihai A1 - Martínez, Enoc A1 - Artero-Delgado, Carola A1 - Bghiel, Ikram A1 - Martinez, Marc A1 - Cadena, Javier A1 - Garcia-Benadi, Albert A1 - Sarria, David A1 - Aguzzi, Jacopo A1 - Masmitja, Ivan A1 - Carandell, Matias A1 - Olive, Joaquim A1 - Gomariz, Spartacus A1 - Santamaria, Pep A1 - Làzaro, Antoni Mànuel KW - Europe KW - Instruments KW - monitoring KW - Observatories KW - Oceans KW - Sea measurements KW - Underwater cables AB - The study of the effects of climate change on the marine environment requires the existence of sufficiently long time series of key parameters. The study of these series allows both to characterize the range of variability in each particular region and to detect trends or changes that could be attributed to anthropogenic causes. For this reason, networks of permanent cabled observation systems are being deployed in the ocean. This paper presents a balance of a decade of activity at the OBSEA cabled observatory, as an example of ocean monitoring success and drawbacks. It is not the objective of this article to analyze the scientific and technical aspects already presented by the authors in different publications (Table 4). We will evaluate the overall experience by retracing the different steps of infrastructure deployment and maintenance, focusing on routines for in situ control, damages experienced, breakdowns and administrative constraints by local administrations. We will conclude by providing a set of guidelines to improve cabled observatories scientific outreach, societal projection, and economic efficiency. As a result of this work, a 10-years dataset has been published in Pangaea that is available for the community. VL - 8 UR - https://app.dimensions.ai/details/publication/pub.1125026314 https://ieeexplore.ieee.org/ielx7/6287639/8948470/08998177.pdf U1 - All arrays U2 - ER -