Constructing intertidal topography for sandy beaches by combining Sentinel-2 imagery and water level data
Bibliographic record
Abstract
Sandy beaches are the most wide distributed coastal type worldwide, serving as a crucial transitional zone between land and sea. However, accurately mapping the intertidal zone of sandy beaches poses challenges due to water-level fluctuations and limited in-situ measurements in sparsely populated areas. Leveraging free-access Sentinel-2 optical imagery and station-based water-level data in coastal zones, we explored the integration of Sentinel-2 satellite imagery and water-level data to derive the intertidal topography of sandy beaches. Our study conducted in Texas, USA, demonstrates the generation of a detailed Digital Elevation Model (DEM) with an accuracy of 0.42 m. This satellite-derived intertidal topography offers valuable insights for mapping coastal lowlands and estimating coastal slopes of sandy beaches. In the future, our method holds significant potential for global-scale applications in generating intertidal topography, coastal slopes, and lowland areas for sandy beaches. Furthermore, our method can enhance our understanding of these important coastal environments and support decision-making for conservation and management efforts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".