Evaluating satellite-based depth mapping for large river monitoring: A case study of Peace River, British Columbia, Canada
Bibliographic record
Abstract
Accurate and reliable methods to measure water depth are essential for informed river research and management. Remote sensing presents a powerful tool for efficient characterization of fluvial systems, but more research is needed into the applicability of bathymetric mapping in a range of conditions and environments. This study assessed the utility of mapping water depth from satellite imagery in a large, seasonally turbid river. Steps included analysis of relationships between image properties and water depth, assessment of the effects of image pre-processing, and comparison of two methods for calibrating image values to depths. It was determined that spectral depth mapping is applicable in the system, with a depth signal described by the ratio of green to red wavelengths. Pre-processing steps including the automated removal of shadows and water surface reflections and the application of a median filter improved the quality of depth calibrations, and calibration methods gave similar results with errors of 25% of reach average depth. The resulting depth maps reliably captured the overall hydromorphic forms and distribution of habitat features, although results were less reliable in deep areas (> 2 m) due to the saturation of the depth signal. Overall, satellite depth mapping can be an appropriate tool for repeat, broad-scale, long-term large river monitoring in general, and could potentially improve flood inundation models based primarily on subareal LiDAR surveys.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".