Hyperspatial Remote Sensing of Channel Reach Morphology and Hydraulic Fish Habitat Using an Unmanned Aerial Vehicle (UAV): A First Assessment in the Context of River Research and Management
Bibliographic record
Abstract
Abstract In this paper, we assess the capabilities of an unmanned/uninhabited aerial vehicle (UAV) to characterize the channel morphology and hydraulic habitat of a 1‐km reach of the Elbow River, Alberta, Canada, with the goal of identifying the advantages and challenges of this technology for river research and management. Using a small quadcopter UAV to acquire overlapping images and softcopy photogrammetry, we constructed a 5‐cm resolution orthomosaic image and digital elevation model (DEM). The orthomosaic was used to map the distribution of geomorphic and aquatic habitat features, including bathymetry, grain sizes, undercut banks, forested channel margins, and large wood. The DEM was used to initialize and run River2D, a two‐dimensional hydrodynamic model, and resulting depth and velocity distributions were combined with the mapped physical habitat features to produce refined estimates of available habitat in terms of weighted usable area. Based on 297 checkpoints, the vertical root‐mean‐squared error of the DEM was 8.8 cm in exposed areas and 11.9 cm in submerged areas following correction of the DEM for overprediction of elevations as a result of the refractive effects of water. Overall, we find several advantages of UAV‐based imagery including low cost, high efficiency, operational flexibility, high vertical accuracy, and centimetre‐scale resolution. We also identify some challenges, including vegetation obstructions of the ground surface, turbidity, which can limit bathymetry extraction, and an immature regulatory landscape, which may slow the adoption of this technology for operational measurements. However, by enabling dynamic linkages between geomorphic processes and aquatic habitat to be established, we believe that the advantages of UAVs make them ideally suited to river research and management. Copyright © 2014 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 | 0.001 |
| 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 | 0.001 | 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 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".