Using Drone Imagery to Map Intertidal Oyster Reefs along Florida’s Gulf of Mexico Coast
Bibliographic record
Abstract
Eastern oyster (Crassostrea virginica) reefs offer vital ecosystem services and support economically and culturally important fisheries. However, environmental and anthropogenic stressors have led to significant decline in oyster reef coverage globally and locally in places like the Suwannee Sound in Florida, USA. Current monitoring methods are insufficient for timely and accurate assessment of oyster resources in the region. Here we demonstrate how drone imagery can be used to delineate intertidal oyster reef coverage rapidly and reliably. The high spatial resolution offered by drone imagery enables accurate delineations. We use a segmentation algorithm to delineate reefs, which produces consistently detailed outlines that are more representative of reef morphology than manual delineations. In total, 1,394 reefs were delineated, which corresponds with 497, 670 m2 of reef area. Of the delineated reefs, 236 (17%) were newly mapped, aligning with 19,848 m2 of newly mapped intertidal oyster reef habitat. The overlapping drone imagery also enabled the production of digital surface models, which were used to calculate volume to area ratio as an indicator of reef condition. These delineations and features serve as accurate baseline data that can be compared to future surveys to monitor how reefs are changing over time in the Suwannee Sound. These methods can also be expanded to other geographical areas and can aid in identifying early signs of decline in oyster reefs.
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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 | 0.001 | 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".