Monitoring Temporal Sandbar and Shoreline Changes at Saint Louis, Senegal: Using Sentinel-2 Imagery (2015–2022)
Bibliographic record
Abstract
Understanding beach dynamics, both in time and in space, is paramount to better understand how and when to intervene to improve coastal management strategies. Beach morphodynamics is expressed in a variety of ways. As indicators of beach change, we can measure the shoreline, the beach topography, and the bathymetry; e.g., in situ measurements rarely cover large extents, are often collected on a local scale (beach), and rarely cover a sufficient time span with a sufficient surveying frequency or a simultaneous measurement of the beach and bar system. Regular-revisit satellites, such as the ESA’s Sentinel-2 mission, provide the opportunity to regularly monitor both shoreline and sandbar dynamics, and the time span is increasing and likely to continue for the decades to come. Using the satellite-derived shoreline and bar position, here, we show that the shoreline and bar are intrinsically coupled. Using Sentinel-2 satellite imagery, we show that the actual erosion/accretion status of the beach at Saint Louis (Senegal) is strongly influenced by the sandbar dynamics. There is a coupled behavior in their seasonal evolution and trend. Our results show that a very large accretive wave of about 50 m observed on the beach is driven by a local welding of the inner sandbar to the beach. Finally, we conclude that this type of event could be anticipated by an analysis of the sandbar.
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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".