Remote Sensing Seagrasses in Cambodia: Comparing Sentinel-2 and Planetscope for detecting and monitoring seagrasses in Cambodia’s Koh Rong and Koh Sdach Archipelagos
Bibliographic record
Abstract
Tropical seagrass is an integral part of the world’s fisheries as it acts as a nursery for benthic reproduction, leading to it being the center of many conservation efforts. Monitoring seagrass by high resolution satellite is commonplace, but such data quality is often unavailable for many conservation groups, leading to a question of whether open-source satellite data is sufficient to meet the rigorous needs of seagrass patch detection. 10m images from the European Space Agency’s (ESA) Sentinel-2 and 3m images from Planet Labs’ Planetscope covering the Koh Sdach and Koh Rong Archipelagos in Cambodia were collected, processed, and classified, with outputs being compared for seagrass detection ability and suitability. Image scenes were merged and processed to produce depth invariant indexes using the Lyzenga algorithm. Random forest classification with ground truth data yielded accuracies of 79.5% for Sentinel and 79.7% for Planetscope using unprocessed rasters, while depth invariant rasters resulted in accuracies of 80.8% and 70.0% respectively. Sentinel-2 and Planetscope are qualitatively compared based on technical specifications and factors such as band wavelength, radiometric quality, and revisit time. Sentinel-2 shows that it is able to match the seagrass detection ability of the higher resolution Planetscope due to its additional blue band and deeper radiometric depth. Results of this analysis are placed in the context of wider research on detecting seagrass patches through satellite image classification.
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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 | 0.001 |
| 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".