Mapping Carbon Dynamics in Coastal Wetlands: A High-Resolution PlanetScope Expedition
Bibliographic record
Abstract
Coastal wetlands play a vital role in climate change mitigation, and remote sensing tools offer a unique opportunity for monitoring carbon content. This study explores the use of high-resolution satellite data, specifically PlanetScope with 3m spatial resolution and eight spectral bands, for monitoring wetland carbon content. Vegetation-Sensitive spectral indices were calculated from the acquired surface reflectance product, and datasets for spring and fall seasons were created using field measurements, with 65% reserved for the training stage. Utilizing a Random Forest regression model, we mapped carbon content over coastal wetlands, producing seasonal maps for spring and fall 2021 with 3m spatial resolution. The analysis of the model revealed the importance of the employed spectral bands and indices. Evaluation metrics, including an RMSE of 90.35 and 325.88 mg g-1, along with R-squared values of 0.91 and 0.40 for the training and test stages, provide insights into the model's performance.
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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 | 0.000 |
| 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) | 0.001 | 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".