Monitoring Water Turbidity in a Temperate Floodplain Using UAV: Potential and Challenges
Bibliographic record
Abstract
The Lake Saint-Pierre (LSP) is a wide (≈300 km2) and shallow (≈3 m) lake created through a widening of the St. Lawrence River. Each spring, freshet makes it the largest floodplain in the province of Quebec. Agricultural practices in the littoral increase the water turbidity, which deteriorate the habitat’s quality of many fish species. However, measuring spatio-temporal turbidity patterns in the LSP floodplain remain difficult because turbidity is highly variable in space and time. This study aims to evaluate the potential to use an Unmanned Aerial Vehicle (UAV) to measure the water turbidity in the LSP’s floodplain. The results show that the UAV can efficiently measure the variation of turbidity in the LSP with a RMSE of 28.22 FNU. We also compared the turbidity retrieved from UAV with those retrieved from Sentinel-2 observations. The results show that the two models are comparable, even if Sentinel-2 yields better results. However, challenges remain when using UAV for turbidity monitoring, such as software limitations for mosaics creation over large water bodies. Nevertheless, the high spatial and temporal information can provide insights into the complex water turbidity patterns which characterize floodplains. The method could help land use management to improve the water quality of these ecosystems.
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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 | 0.000 |
| 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".