Knowledge of an oil spill’s extent and its quantification are fundamental to limit damage and assess impacts. Remote sensing permits the observation of large areas in a short time, to locate and quantify the phenomenon. We present the case study of the River Lambro, where an oil spill occurred on February 23, 2010 and then flowed into the River Po. The Agenzia di Protezione Civile della Regione Emilia-Romagna quickly commissioned two aerial surveys over the polluted area, performed by Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS) with a hyperspectral sensor, AISA Eagle 1K, in order to obtain qualitative and quantitative assessment of the spilled substances and to support rapid decision-making with real-time monitoring. The method used a Spectral Angle Mapper (SAM) classification to locate the pollution. Results showed a successful applicability in the production of the pollution map used for the containment phase.

Hyperspectral images to monitor oil spills in the River Po

Pietrapertosa C.;Pavan A.;Sterzai P.;Paganini P.;Vellico M.;Coren F.
2016-01-01

Abstract

Knowledge of an oil spill’s extent and its quantification are fundamental to limit damage and assess impacts. Remote sensing permits the observation of large areas in a short time, to locate and quantify the phenomenon. We present the case study of the River Lambro, where an oil spill occurred on February 23, 2010 and then flowed into the River Po. The Agenzia di Protezione Civile della Regione Emilia-Romagna quickly commissioned two aerial surveys over the polluted area, performed by Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS) with a hyperspectral sensor, AISA Eagle 1K, in order to obtain qualitative and quantitative assessment of the spilled substances and to support rapid decision-making with real-time monitoring. The method used a Spectral Angle Mapper (SAM) classification to locate the pollution. Results showed a successful applicability in the production of the pollution map used for the containment phase.
2016
oil spills; remote sensing; hyperspectral imagery
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14083/2529
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