Quantification of oil lost from tanker vessel using space borne radar datasets - Case study of Haldia port oil spill, July 2018.
Notice bibliographique
Résumé
Abstract 686884 Determining the spilled volume of the marine oil pollutant is an essential requisite for the oil spill modellers and the responders. Generally, the mass of the spilled pollutant is computed from the total quantity and the remaining quantity of the storage tank of the distressed vessel. A method to estimate the quantity of the spilled oil pollutant using the space -borne synthetic aperture radar dataset is elaborated here. The synthetic aperture radar data, its ability to penetrate cloud cover, irrespective of weather conditions, has been widely used to detect the signature of spilt oil. SAR data available from European Space Agency and Canadian Space Agency were used to detect the oil spills as they are proved to be appropriate for oil spill detection. Minor oil spill occured off Haldia Port, off Kolkata from SSL tanker vessel on 14 July 2018. The geographical location of the distressed vessel is 88.775 ′E, 21.441 ′N. The zone of the vessel distress was monitored for oil slicks. The acquisition plan of the Radar satellite Sentinel -1A was obtained from European Space Agency. As per that, the pass of the Sentinel -1A was available on 15 July 2018 and 17 July 2018 for the region of study. The Synthetic Aperture Radar (SAR) datasets were obtained from Sentinel -1A as per their availability. Those datasets were processed using Sentinel Application Platform (SNAP) tool box. The SAR data is subjected to terrain correction, which automatically reprojects the radar scene. The next stage is performing radiometric calibration, which converts the amplitude into intensity values. The radar reflectance values are converted to Sigma0 intensity values in Sentinel tool box. This Sigma0 values were wrote in netcdf format for identifying the oil slicks. The pixels of lesser intensity values are identified and are interpreted for oil slicks. The zone of the oil slicks in the radar scene are considered as irregular polygons. The area of those polygons were computed. Later the volume of the spilled oil is computed using the thickness of the spilled oil pollutant. Finally the mass of the pollutant is computed. It was collectively estimated from the SAR datasets, that, 33 Tons of Fuel oil was lost from SSL vessel that sank off Haldia Port. This paper elaborates in detail about the method of processing SAR dataset and estimating the quantity of oil lost from the vessel using SAR datasets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».