Quantification of oil lost from tanker vessel using space borne radar datasets - Case study of Haldia port oil spill, July 2018.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".