Coastline and mudbank monitoring in French Guiana: contributions of radar and optical satellite imagery
Bibliographic record
Abstract
The constant migration of mudbanks resulting from the sedimentary discharge of the Amazon River has an enormous impact on the French Guianese economy. Consequently, there is great local interest in finding tools that can be used in mapping and monitoring the mudbank migration with an acceptable level of reliability and a high frequency of measurement. This involves the ability to detect submarine mudbanks that interfere with access to port channels and impede navigation. Radar and optical satellite images of French Guiana acquired between 1997 and 2001 have been analysed to assess their utility in detecting mudbanks and monitoring coastline change. In the present study, the information derived from radar and optical satellite imagery was analysed with respect to tidal height. The influence of the radar incidence angle was considered, as well as the advantages of having multidate sequences when mapping coastal zones undergoing significant change. A comparative study between radar (European Remote Sensing Satellite (ERS) and RADARSAT) and optical (advanced spaceborne thermal emission and reflection radiometer (ASTER)) images was also conducted. The results show that low-angle radar is more suitable for detecting mudbanks, whereas high-angle radar is more appropriate for monitoring coastline change. Emerged mudbanks and mudbanks under a shallow layer of water (a few tens of centimetres at the most) are easily detectable in radar images. For high tidal levels, the optical images provide more information on the mudbanks than the radar images. Conversely, for low tidal levels, the information on the mudbanks is more detailed with radar imagery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".