Monitoring Saltmarsh Restoration in the Upper Bay of Fundy Using Multi-Temporal Sentinel-2 Imagery and Random Forests Classifier
Bibliographic record
Abstract
Saltmarshes provide important ecosystem services, including coastline protection, but face decline due to human activities and climate change. There are increasing efforts to conserve and restore saltmarshes worldwide. Our study evaluated the effectiveness of Sentinel-2 satellite imagery to monitor landcover changes using a saltmarsh restoration project undergoing its 9th to 12th year of recovery in the megatidal Bay of Fundy in Maritime Canada. Specifically, in 2019–2022, five satellite images per growing season were acquired. Random Forests classification for 13 landcover classes (ranging from bare mud to various plant communities) achieved a high overall classification accuracy, peaking at 96.43% in 2021. Field validation points confirmed this, with high validation accuracies reaching 93.02%. The classification results successfully distinguished ecologically significant classes, such as Spartina alterniflora–S. patens mix. Our results reveal the appearance of high marsh species in restoration sites and elevational-based zonation patterns, indicating progression. They demonstrate the potential of Sentinel-2 imagery for monitoring saltmarsh restoration projects in north temperate latitudes, aiding 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".