Coastal Reef and Seagrass Monitoring for Coastal Ecosystem Management
Bibliographic record
Abstract
The growth of human occupations in coastal areas and climate change impact have changed the dynamics of seagrass cover and accelerated the damage to coral reefs globally. For these reasons, coastal management measures need to be developed and renewed to preserve the state of seagrass beds and coral reefs. An example includes the improvement of spatial and multitemporal analyses. This study sought to analyze changes in seagrass cover and damages to coral reefs in Gili Sumber Kima, Buleleng Regency, Bali based on multitemporal Sentinel 2A-MSI imagery. The algorithms of a machine learning, Random Forest (RF), and a Support Vector Machine (SVM) were used to classify the benthic habitats (seagrass beds and coral reefs). Also, a change detection analysis was performed to identify the pattern and the extent to which seagrass beds had changed. The multispectral classification of, particularly, coral reefs was used to explain the condition of this benthic habitat. The results showed +-70% to +-83% accuracies of estimated seagrass cover, and the change detection analysis revealed three directions of change, namely an increase of 27.9 ha, a decrease by 86 ha, and a preserved state in 157 ha of seagrass cover. The product of coral reefs mapping had an accuracy of 42%, and the coral reefs in Gili Sumber Kima were split almost equally between the good (1505 ha) and damaged ones (1397 ha). With the spatial information on seagrass beds and coral reefs in every region, the ecological functions of the coast can be assessed more straightforwardly and appropriately incorporated as the basis for monitoring the dynamics of resources and coastal area management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".