UAS‐based remote sensing of fluvial change following an extreme flood event
Bibliographic record
Abstract
Abstract The effects of large floods on river morphology are variable and poorly understood. In this study, we apply multi‐temporal datasets collected with small unmanned aircraft systems (UASs) to analyze three‐dimensional morphodynamic changes associated with an extreme flood event that occurred from 19 to 23 June 2013 on the Elbow River, Alberta. We documented reach‐scale spatial patterns of erosion and deposition using high‐resolution (4–5 cm/pixel) orthoimagery and digital elevation models (DEMs) produced from photogrammetry. Significant bank erosion and channel widening occurred, with an average elevation change of −0.24 m. The channel pattern was reorganized and overall elevation variation increased as the channel adjusted to full mobilization of most of the bed surface sediments. To test the extent to which geomorphic changes can be predicted from initial conditions, we compared shear stresses from a two‐dimensional hydrodynamic model of peak discharge to critical shear stresses for bed surface sediment sizes. We found no relation between modeled normalized shear stresses and patterns of scour and fill, confirming the complex nature of sediment mobilization and flux in high‐magnitude events. However, comparing modeled peak flows through the pre‐ and post‐flood topography showed that the flood resulted in an adjustment that contributes to overall stability, with lower percentages of bed area below thresholds for full mobility in the post‐flood geomorphic configuration. Overall, this work highlights the potential of UAS‐based remote sensing for measuring three‐dimensional changes in fluvial settings and provides a detailed analysis of potential relationships between flood forces and geomorphic change. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".