Examining an Oil Spill Plume Mapping Method based on Satellite NIR Data
Bibliographic record
Abstract
Reliable information on the spreading of oil plume on water caused by massive oil spills is essential for making proper clean-up measures. Satellite remote sensing technology has advantages over other methods in terms of larger coverage and without ex- pensive operating costs to detect oil spills. In this study, an oil plume delineation method based on the Near-Infrared (NIR) satellite data is used to examine oil spill plume area and size for the BP Deepwater Horizon Oil Spill in the offshore water of Gulf of Mexico and for the recent Norilsk oil spill in a Northern inland water region. To get accurate results noise signals such as land from the data are masked out using SNAP based DEM data and Normalized Difference Water Index method, whereas cloud signals are removed using MODIS cloud masking. Cox-Munk model is used to compute the sun glint radiance. Results of DP oil spill case depicts a 4838.84 km2 thicker oil plume along with the 20635.53 km2 thinner portion of the oil slicks using MODIS NIR data at a 500-meter resolution. It is subsequently applied to the recent Norilsk Oil Spill using higher resolution Sentinel-2 NIR data to test the method for detecting spill plume in an inland river water system. Reasonable high-resolution results at 10 meter have been obtained for the smaller scale oil spill onto river water compared to larger offshore area, considering that the river site has complex conditions including shallow water and river reddish soil close to oil color. The developed method is suitable for detecting thick oil plume in ocean or deep inland water bodies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".