Detection of changes in coral reef communities using Landsat-5 TM and Landsat-7 ETM+ data
Bibliographic record
Abstract
Satellite remote sensing is increasingly used to map and monitor coral reefs. From 1984 to the present, Landsat-5 thematic mapper (TM) and Landsat-7 enhanced thematic mapper plus (ETM+) images provide the longest time series available for change detection analysis over coral reefs. A time series of four Landsat-5 images and one Landsat-7 image spanning 1984‐2000 was analyzed to detect changes in "coral-dominated", "sand", "algae", and "substrate" benthic classes for Carysfort Reef in the Florida Keys. To properly analyze this time series, a set of corrections was undertaken, which included noise-reduction correction, atmospheric correction, and TM‐ETM+ data normalization. All images were classified with a Mahalanobis distance classifier using statistics from the 1984 image to identify the four benthic classes. The results were compared with historical ground-truthing data, a combination of high-resolution aerial photography and Ikonos satellite data, and results from a temporal texture change detection analysis. All data sets provided consistent results, with an extreme loss in coral cover between 1982 and 2000. The Landsat time series provided across-time progression and locations of the coral-dominated zones for all of Carysfort Reef. This study demonstrates the feasibility and utility of combining Landsat-5 TM and Landsat-7 ETM+ images for coral reef community scale change detection studies at a decadal scale. It opens the possibility of a cost-effective larger scale study, which could include an entire reef tract.
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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".