Oil spills in the marine environment increasingly represent a significant problem for the ecosystem. The intensification of the operations of extraction, transport and storage of hydrocarbons has in fact caused a greater frequency of the occurrence of accidents, with consequent dispersion of the material in an uncontrolled manner. Remote sensing plays a fundamental role in identifying and monitoring these phenomena. In particular, using satellite images obtained with Synthetic Aperture Radar (SAR) technology, spill recognition methodologies have been developed through automatic and semi-automatic approaches. This paper analyses the accident that occurred in March 2017 in the Persian Gulf, through the Sentinel-1 Single Look Complex (SLC) and Ground Range Detected (GRD) SAR image Change Detection technique, to evaluate the dispersion of hydrocarbons. The use of the SentiNel Application Platform (SNAP) software of the European Space Agency (ESA) made it possible to determine the amount of oil involved in the spill. Furthermore, through the Web-based application General NOAA Oil Modeling Environment (WebGNOME) the dynamic propagation model of the spilled hydrocarbons was produced based on the morphology and meteorological conditions of the site examined which further confirmed the validity of the Change Detection operations Sentinel-1 images. Finally, the results were compared with those obtained using automatic methodologies developed by ESA, confirming the greater accuracy of the procedures used in this study.
Use of the Sentinel-1 Satellite Data in the SNAP Platform and the WebGNOME Simulation Model for Change Detection Analyses on the Persian Gulf Oil Spill / Caporusso, G.; Dell'Olio, M.; Tarantino, E. (LECTURE NOTES IN ARTIFICIAL INTELLIGENCE). - In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[s.l] : Springer Science and Business Media Deutschland GmbH, 2022. - ISBN 978-3-031-10544-9. - pp. 369-386 [10.1007/978-3-031-10545-6_26]
Use of the Sentinel-1 Satellite Data in the SNAP Platform and the WebGNOME Simulation Model for Change Detection Analyses on the Persian Gulf Oil Spill
Caporusso G.
;Tarantino E.
2022-01-01
Abstract
Oil spills in the marine environment increasingly represent a significant problem for the ecosystem. The intensification of the operations of extraction, transport and storage of hydrocarbons has in fact caused a greater frequency of the occurrence of accidents, with consequent dispersion of the material in an uncontrolled manner. Remote sensing plays a fundamental role in identifying and monitoring these phenomena. In particular, using satellite images obtained with Synthetic Aperture Radar (SAR) technology, spill recognition methodologies have been developed through automatic and semi-automatic approaches. This paper analyses the accident that occurred in March 2017 in the Persian Gulf, through the Sentinel-1 Single Look Complex (SLC) and Ground Range Detected (GRD) SAR image Change Detection technique, to evaluate the dispersion of hydrocarbons. The use of the SentiNel Application Platform (SNAP) software of the European Space Agency (ESA) made it possible to determine the amount of oil involved in the spill. Furthermore, through the Web-based application General NOAA Oil Modeling Environment (WebGNOME) the dynamic propagation model of the spilled hydrocarbons was produced based on the morphology and meteorological conditions of the site examined which further confirmed the validity of the Change Detection operations Sentinel-1 images. Finally, the results were compared with those obtained using automatic methodologies developed by ESA, confirming the greater accuracy of the procedures used in this study.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.