2018
Journal article
Open Access
A marine information system for environmental monitoring: ARGO-MIS
Pieri G., Cocco M., Salvetti O.
Oil spill monitoring Environmental decision support systems Ocean Engineering risk maps Risk maps proactive environmental monitoring oil spill monitoring Marine information system Civil and Structural Engineering environmental decision support systems Proactive environmental monitoring Water Science and Technology marine information system
Sea shipping routes have become very crowded and this, coupled with an always increasing demand of oil based products, contributes to the increase in maritime traffic density, as a consequence pollution risks have increased. Therefore, it is important to have information systems capable of detecting and monitoring environmental endangering situations like oil spills at sea. In this paper, a Marine Information System, acting as an integrated and inter-operable monitoring tool is proposed and discussed. The discussion focuses on a system that is able to integrate different data acquired from various electronic sensors, and that is inter-operable among marine operators and ship traffic authorities. The available data on the system are all geo-referenced, and flows seamlessly through the system, where they are integrated in a consistent and usable manner. An important result of this integration is the capability to produce a collection of proactive services such as Decision Support ones, which can be used to improve the functionalities and facilities concerned in an intervention operation. Through the implementation of these services, we aim to demonstrate how an efficient environmental management system could benefit from being supported by a Marine Information System that can provide the dynamic links between different data, models and actors.
Source: Journal of marine science and engineering 6 (2018). doi:10.3390/jmse6010015
Publisher: Molecular Diversity Preservation International, Basel
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42. Schwartz, G.; Kourti, N. Algorithm Development Searching for Unidentified Maritime Objects (SUMO). Jt. Res. Centre Tech. Note I 2001, 1, 1-11.
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Nevalainen, M.; Helle, I.; Vanhatalo, J. Preparing for the unprecedented-Towards quantitative oil risk assessment in the Arctic marine areas. Mar. Pollut. Bull. 2017, 114, 90-101.
3. Abascal, A.J.; Castanedo, S.; Medina, R.; Liste, M. Analysis of the reliability of a statistical oil spill response model. Mar. Pollut. Bull. 2010, 60, 2099-2110.
4. Kinerson, R.S.; Kittle, J.L.; Duda, P.B. BASINS: Better assessment science integrating point and nonpoint sources. In Decision Support Systems for Risk-Based Management of Contaminated Sites; Springer: New York, NY, USA, 2009; pp. 1-24.
5. Paine, R.T.; Ruesink, J.L.; Sun, A.; Soulanille, E.L.; Wonham, M.J.; Harley, C.D.; Brumbaugh, D.R.; Secord, D.L. Trouble on oiled waters: Lessons from the Exxon Valdez oil spill. Annu. Rev. Ecol. Syst. 1996, 27, 197-235.
6. Balseiro, C.; Carracedo, P.; Gómez, B.; Leitao, P.; Montero, P.; Naranjo, L.; Penabad, E.; Pérez-Muñuzuri, V. Tracking the Prestige oil spill: An operational experience in simulation at MeteoGalicia. Weather 2003, 58, 452-458.
7. Fingas, M. Oil Spill Science and Technology; Gulf Professional Publishing: Cambridge, MA, USA, 2016.
8. Ferraro, G.; Meyer-Roux, S.; Muellenhoff, O.; Pavliha, M.; Svetak, J.; Tarchi, D.; Topouzelis, K. Long term monitoring of oil spills in European seas. Int. J. Remote Sens. 2009, 30, 627-645.
9. Fingas, M.; Brown, C. Review of oil spill remote sensing. Mar. Pollut. Bull. 2014, 83, 9-23.
10. Guo, W.; Wang, Y. A numerical oil spill model based on a hybrid method. Mar. Pollut. Bull. 2009, 58, 726-734.
11. Jordi, A.; Ferrer, M.; Vizoso, G.; Orfila, A.; Basterretxea, G.; Casas, B.; Alvarez, A.; Roig, D.; Garau, B.; Martínez, M. Scientific management of Mediterranean coastal zone: A hybrid ocean forecasting system for oil spill and search and rescue operations. Mar. Pollut. Bull. 2006, 53, 361-368.
12. Cortés, U.; Sànchez-Marrè, M.; Ceccaroni, L.; R-Roda, I.; Poch, M. Artificial intelligence and environmental decision support systems. Appl. Intell. 2000, 13, 77-91.
13. Fedra, K.; Winkelbauer, L. A hybrid expert system, GIS, and simulation modeling for environmental and technological risk management. Comput.-Aided Civ. Infrastruct. Eng. 2002, 17, 131-146.
14. Janeiro, J.; Zacharioudaki, A.; Sarhadi, E.; Neves, A.; Martins, F. Enhancing the management response to oil spills in the Tuscany Archipelago through operational modelling. Mar. Pollut. Bull. 2014, 85, 574-589.
15. Kimball, R. The Data Warehouse Lifecycle Toolkit; John Wiley & Sons: Hoboken, NJ, USA, 2008.
16. U.S. Environment Protection Agency. BASINS 4.1 (Better Assessment Science Integrating Point and Non-Point Sources) Modeling Framework; National Exposure Research Laboratory, RTP: Durham, NC, USA, 2015. Available online: https://www.epa.gov/exposure-assessment-models/basins (accessed on 22 November 2017).
17. Bourgeois, W.; Hogben, P.; Pike, A.; Stuetz, R. Development of a sensor array based measurement system for continuous monitoring of water and wastewater. Sens. Actuators B Chem. 2003, 88, 312-319.
18. Persaud, K.C.; Wareham, P.; Pisanelli, A.M.; Scorsone, E. “Electronic Nose”-A New Monitoring Device for Environmental Applications (S & M 0613). Sens. Mater. 2005, 17, 355.
19. Lukowiak, A.; Maciejewska, M.; Szczurek, A.; Maruszewski, K. Application of a titania thin film for the discrimination between diesel fuel and heating oil. Thin Solid Films 2007, 515, 7005-7010.
20. Alpers, W.; Hühnerfuss, H. The damping of ocean waves by surface films: A new look at an old problem. J. Geophys. Res. Oceans 1989, 94, 6251-6265.
21. Hovland, H.A.; Johannessen, J.A.; Digranes, G. Slick detection in SAR images. In Proceedings of the IGARSS'94 Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 8-12 August 1994; Volume 4, pp. 2038-2040.
22. Clark, R.B.; Frid, C.; Attrill, M. Marine Pollution; Clarendon Press: Oxford, UK, 1989; Volume 4.
23. International Maritime Organisation (IMO). Guidelines for the Onboard Operational Use of Shipborne Automatic Identification Systems (AIS), as Amended by ITU-1371. Resolution A.917(22). London, 2001. Available online: https://www.navcen.uscg.gov/pdf/AIS/IMO_A_917(22)_AIS_OPS_Guidelines.pdf (accessed on 20 January 2017).
24. Brekne, T.M.; Holmemo, S.; Engen, F.; Skeie, G.M. Norwegian Clean Seas Association for Operating Companies (NOFO)-Research and Development Program for Next Generation Arctic Recovery Equipment. In Proceedings of the Interspill Conference 2004, Trondheim, Norway, 14-17 June 2004.
25. Nieto-Borge, J.C.; Mata-Moya, D.; Jarabo-Amores, P.; Reichert, K.; Hessner, K. Application of conventional marine radars for measuring ocean wave fields in shallow water conditions. In Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA, 25-30 July 2010; pp. 4338-4341.
26. Van der Meer, F.D.; De Jong, S.M. Imaging Spectrometry: Basic Principles and Prospective Applications; Springer Science & Business Media: Amsterdam, The Netherlands, 2011; Volume 4, pp. xxi-xxxiii.
27. Lammoglia, T.; de Souza Filho, C.R. Spectroscopic characterization of oils yielded from Brazilian offshore basins: Potential applications of remote sensing. Remote Sens. Environ. 2011, 115, 2525-2535.
28. Andreou, C.; Karathanassi, V.; Kolokoussis, P. Spectral library for oil types. In Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, Australia, 10-15 April 2011; pp. 10-15.
29. Andreou, C.; Karathanassi, V. Estimation of the number of endmembers using robust outlier detection method. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 247-256.
30. Andreou, C.; Karathanassi, V. Endmember detection in marine environment with oil spill event. In Proceedings of the SPIE 8180, Image and Signal Processing for Remote Sensing XVII, Prague, Czech Republic, 27 October 2011; Volume 8180, p. 81800P.
31. Andreou, C.; Karathanassi, V. New automated method for estimating the number of endmembers in hyperspectral images. In Proceedings of the 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), Shanghai, China, 4-7 June 2012; pp. 1-4.
32. SOLMAR Project. Available online: http://www.solmar.nurc.nato.int (accessed on 1 January 2017).
33. Stommel, H. The slocum mission. Oceanography 1989, 2, 22-25.
34. Alvarez, A.; Caiti, A.; Onken, R. Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J. Ocean. Eng. 2004, 29, 418-429.
35. Alvarez, A.; Garau, B.; Caiti, A. Combining networks of drifting profiling floats and gliders for adaptive sampling of the Ocean. In Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma, Italy, 10-14 April 2007; pp. 157-162.
36. Gasparotti, C.; Rusu, E. Methods for the risk assessment in maritime transportation in the Black Sea basin. J. Environ. Prot. Ecol. 2012, 13, 1751-1759.
37. Vergara, L.; Soriano, A.; Safont, G.; Salazar, A. On the fusion of non-independent detectors. Digit. Signal Process. 2016, 50, 24-33.
38. Martinelli, M.; Moroni, D.; Pieri, G.; Tampucci, M. A proactive gis infrastructure for environmental monitoring. In Proceedings of the Seventeenth International Water Technology Conference (IWTC17), Istanbul, Turkey, 5-7 November 2013.
39. Tampucci, M.; Martinelli, M.; Moroni, D.; Pieri, G.; Salvetti, O.; Villa, P. A Proactive System for Oil Spills and Marine Environment Monitoring. Bollettino di Geofisica Teorica e Applicata 2013, 54, 222-223.
40. Laxhammar, R.; Falkman, G.; Sviestins, E. Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator. In Proceedings of the 12th International Conference on Information Fusion (FUSION'09), Seattle, WA, USA, 6-9 July 2009 ; pp. 756-763.
41. Moroni, D.; Pieri, G.; Salvetti, O.; Tampucci, M.; Domenici, C.; Tonacci, A. Sensorized buoy for oil spill early detection. Methods Oceanogr. 2016, 17, 221-231.
42. Schwartz, G.; Kourti, N. Algorithm Development Searching for Unidentified Maritime Objects (SUMO). Jt. Res. Centre Tech. Note I 2001, 1, 1-11.
43. Moroni, D.; Pieri, G.; Tampucci, M.; Salvetti, O. A proactive system for maritime environment monitoring. Mar. Pollut. Bull. 2016, 102, 316-322.
44. ARGOMARINE Project. FP7-Transport-234096. Available online: http://www.argomarine.eu/ (accessed on 31 December 2017).
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BibTeX entry
@article{oai:it.cnr:prodotti:383494,
title = {A marine information system for environmental monitoring: ARGO-MIS},
author = {Pieri G. and Cocco M. and Salvetti O.},
publisher = {Molecular Diversity Preservation International, Basel },
doi = {10.3390/jmse6010015},
journal = {Journal of marine science and engineering},
volume = {6},
year = {2018}
}
0000-0001-5068-2861CNR ExploRA
ISTI Repository
DOIAlso available from
ISTI Repository
DOIAlso available from
Journal of Marine Science and Engineering
Journal of Marine Science and Engineering
ARGOMARINE
Automatic Oil-Spill Recognition and Geopositioning integrated in a Marine Monitoring Network
