A subregional shallow water bathymetry derivation method for coral reef using ICESat-2 and Sentinel-2 combined with sediment information
Bibliographic record
Abstract
The bathymetric data is of great significance to coral reef protection. Traditional measurement methods have low efficiency, high cost, and are easily affected by natural conditions. Satellite-derived bathymetry (SDB) in the shallow water area has high efficiency. However, the accuracy of conventional single-band, logarithmic ratio, and multi-band model is easily affected by the difference of sediment types, that is, the portability of a single model is poor. It is necessary to take into account the coral reef sediment types and improve the accuracy of SDB. Fine sediment classification relies on field acoustic and spectral measurements, which is costly and subject to many restrictions. In this study, a subregional shallow water satellite-derived bathymetry method in combination with sediment information (SDB_SS) is proposed by integrating various algorithms. Firstly, based on the Sentinel-2 multi-spectral imagery, the study area is roughly divided into six types of sediment samples: Sand, Rubble, Seagrass, Rock, Microalgae mat, and Coral / Algae. Then, the sediment pixel information is counted, and the coral reef area is divided into three sub-regions of high, medium and low reflection by threshold segmentation method. Finally, combined with ICESat-2 and Sentinel-2 data, the improved subregional satellite-derived bathymetry model is constructed to obtain the shallow bathymetry results. Taking the Yongle Islands in the South China Sea as an example, root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) of the retrieval results are 0.97 m, 0.90 m and 21.23 %, respectively, compared with the measured depth. Compared with conventional single-band, logarithmic ratio, multi-band and other models, they are reduced by 0.27 m ∼ 1.65 m, 0.11 m ∼ 1.76 m and 2.5 % ∼ 65.27 %. It is proven that the proposed method can provide a reference for improving the SDB accuracy of coral reefs without measured spectral data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".