Monitoring Coastal Estuarine Habitats for Biodiversity Along the Temperate Bioregion of South Africa
Bibliographic record
Abstract
Abstract Coastal wetlands provide critical ecosystem services, including the enhancement of biodiversity, carbon sequestration, and flood protection. Although these ecosystems have been mapped for country‐level biodiversity typing, improved extent mapping is necessary to account for estuarine dynamics and improved reporting to the Kunming‐Montreal Global Biodiversity Framework (GBF) by 2030. We achieved an overall coastal wetland accuracy of 90.7% (95% confidence interval: 90.2%–91.4%) utilizing a dense time series of very high spatial resolution (3 m) PlanetScope satellite imagery to map coastal wetlands with a combination of Random Forest to develop training data, U‐Net convolutional neural networks, and a final decision tree to determine discrete ecosystem extents. Across the 84 mapped estuaries totaling 67,452 ha and 2,135 images, we mapped 9,131.1 ± 1,596.9 ha (13.5% of total estuarine functional zone extent) of salt marsh & reed beds and 1,718.6 ± 234.3 ha (2.5%) of Submerged Aquatic Vegetation (SAV). In addition to our earth observation analysis, we calculated tidal amplitudes and water level trends for 20 water level gauges across the region. We found tidal amplitude was a significant driver of salt marsh extent, explaining 33.6% of the variation (F (1,19) = 9.62, p = 0.005). We demonstrate a repeatable methodology for improved mapping of ecosystem zonation and utilize water level data to explore potential drivers of ecosystem distribution. Our method could be incorporated into a robust earth observation approach for reporting progress toward the goals of the/reporting to the GBF and Sustainable Development Goals (SDGs).
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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 | 0.002 |
| 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".