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 -