Analysis of two decades of Landsat satellite images reveals long-term changes in aquatic and terrestrial vegetation in Bimini, The Bahamas with coastal development
Bibliographic record
Abstract
Tropical coastal ecosystems are often characterized by vegetated marine habitats, including mangroves and seagrass beds. Over past decades, these habitats have been impacted by coastal development resulting in vegetation losses with consequences for habitat-dependent species. In Bimini, The Bahamas, development has occurred in proximity to important lemon shark nursery habitats. The NASA/U.S. Geological Survey (USGS) Landsat satellite suite have captured imagery since 1984, allowing for long-term mapping to quantify habitat change. However, mapping is often limited to years with in situ habitat data for training of map classifications. Therefore, we employed the automatic adaptive signature generalisation (AASG) algorithm to map aquatic and terrestrial habitats across a 21-year time-series, requiring only 1 year of in situ habitat data. This resulted in the successful creation of 16 maps from 1999 to 2020 with high overall accuracy for identifying seagrass habitat (82%). In years of terrestrial deforestation, seagrass extent decreased, especially when deforestation was coupled with sediment deposition. However, seagrass extent rebounded within 2–5 years depending on location. Our study highlights the effectiveness of the AASG algorithm in mapping across time as well as the importance of mitigation efforts in reducing impacts on seagrass beds. This is especially important for identified consistent seagrass areas, which are in proximity to proposed future developments.
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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".