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 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 | 0.000 | 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) | 0.000 | 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".