Exploring open-source multispectral satellite remote sensing as a tool to map long-term evolution of salt marsh shorelines
Bibliographic record
Abstract
From an ecological and socio-economic perspective, salt marshes are one of the most valuable natural assets on Earth. As external pressures are causing their extensive degradation and loss globally, the ability to monitor salt marshes on a long-term scale and identify drivers of change is essential for their conservation. Remote sensing has been demonstrated to be one of the most adept methods for this purpose and open-source multispectral satellite remote sensing missions have the potential to provide worldwide long-term time-series coverage that is non-cost-prohibitive. This study derives the long-term lateral evolution of four salt marsh patches in the Ria Formosa coastal lagoon (Portugal) using data from the Sentinel-2 and Landsat missions as well as from aerial photography surveys to quantitatively examine the accuracy and associated uncertainty in using open-source multispectral satellite remote sensing for this purpose. The results show that these open-source satellite archives can be a useful tool for tracking long-term salt marsh extent dynamics. During 1976–2020, there was a net loss of salt marsh in the study area, with erosion rates reaching an average of −3.3 m/yr opposite a tidal inlet. The main source of error in the satellite results was the dataset spatial resolution limits, but the specific salt marsh shoreline environment contributed to the relative magnitude of that error. The study notes the influence of eco-geomorphological dynamics on the mapping of sedimentary environments, so far not extensively discussed in scientific literature, highlighting the difference between mapping a morphological process and a sedimentary environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.004 |
| 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".