Quantification of braided river channel change using archival digital image analysis
Bibliographic record
Abstract
Historical archives of grey-scale river channel imagery are extensive. Here, we present and test a methodology to extract detailed quantitative topographic date from such imagery of sand-bed rivers. Extracting elevation information from rivers is difficult as they are characterized by a low relative relief (less than 4 m); the area of interest may be spatially extensive (e.g. active channel widths > 500 m in large braided rivers); the rate of change of surface elevation is generally low except in the vicinity of individual channel banks where the rate of change is very high: there is the complication that comes from innundation: and there may be an added complication caused by blockage of the field of view by vegetation. Here, we couple archival photogrammetric techniques with image processing methods and test these for quantification of sand-bed braided river dynamics, illustrated for a 500 m wide, 3 km long reach of the Spouth Sasketchewan River, Canada. Digitial photogrammetry was used to quantify dry areas and water edge elevations. A methodology was then used to calibrate the special signature of inundated areas by combining established two media digital photogrammetric methods and image matching. This allowed determination of detailed depth maps for inundated area and, when combined with dry area data, creation of depths detectable from sequential digital elevation models. The result was a series of elevation models that demonstrate the potential for acquiring detailed and precise elevation data from any historical aerial imagery of rivers without needing associated calibration data, provided that imagery is of the necessary scale to capture the features of interest. We use these data to highlight several aspects of channel change on the South Saskatchewan River, including bar movement, bank erosion and channel infilling.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".