Coral Reef Mapping Using Remote Sensing Techniques and a Supervised Classification Algorithm
Bibliographic record
Abstract
The vitality of the Great Barrier Reef (GBR) is threatened by many human-made impacts. Monitoring this ecosystem makes it possible to study the general condition and the health of the GBR. However, due to the large extent of the GBR and limited accessibility in the ocean environment, mapping and monitoring this ecosystem has been always challenging task and connived. In this regard, Remote Sensing (RS) is an effective technique that provides valuable information for mapping and monitoring this ecosystem. In an attempt to monitor the GBR, this article applied a supervised machine learning algorithm to classify the Landsat 8 imagery collected over the GBR. To this end, the spectral responses of coral reefs, shallow water, deep ocean, rocks and sands, and green alga were initially determined from the satellite images. This information was then ingested to the Maximum Likelihood supervised classifier to map coral reefs in the GBR. Additionally, this study discusses how the GBR has been affected by anthropogenic disturbance. The results provide confirmatory evidence that RS techniques present great promise as a means of mapping coral reefs and monitoring their general conditions. We used the ambiguity matrix and validation data to estimate the accuracy of the proposed method. Overall, the proposed method was able to identify 5 different classes considered in this article with an average accuracy of 90%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".