Performance of Through-Water Structure-from-Motion Photogrammetry in Gravel-Bed Rivers: An Experimental Investigation
Bibliographic record
Abstract
Structure-from-Motion (SfM) photogrammetry has become an efficient approach in acquiring high-resolution three-dimensional topographic data in geosciences and can be used for measuring submerged riverbed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for gravel-bed surfaces, which limits its application to the morphodynamics of gravel-bed rivers in both field investigations and flume experiments. The measurement quality includes: (i) accuracy and precision of the measured underwater bed surface elevations; and (ii) statistical properties (first four moments and structural functions) of the bed surface elevation distributions.In order to evaluate the influence of bed texture, flow rate, ground control point (GCP) layout, and refraction correction (RC) on the measurement quality of through-water SfM photogrammetry, we conducted a series of experiments in a 70 m-long and 7 m-wide flume with a straight artificial channel under strictly controlled conditions. The channel size was comparable to a small natural stream so that the results could provide insights for not only flume experiments but also for UAV-based field investigations. Bed surfaces with strongly contrasting textures (fine sand cover vs. gravel cover) in two 4 m-long reaches were measured under five constant flows with three GCP layouts, including both dry and underwater GCPs. All the submerged surface models were compared with the corresponding dry bed surfaces to quantify their errors in elevations, moments, and outcomes of structural functions.The results illustrated that the poorly sorted gravel-bed led to smaller elevation errors than the bed covered by fine sand. The use of underwater GCPs made significant improvements to the elevation accuracy of direct through-water SfM photogrammetry, but counteracted with RC. The elevation errors of the submerged models linearly increased with water depth for all the tested conditions of bed textures, GCP layouts, and discharges in the uncorrected models, but the increasing slopes varied with bed texture. Fine sediment transport caused significant elevation errors, while the static sand dunes and grain clusters did not lead to noticeable errors in the corrected models with dry GCPs. The movement of fine sediment at high flows also led to significant errors in the second to fourth moments, horizontal correlation scales, Hurst exponents, and the errors in statistical properties for both uncorrected and corrected submerged models. The results show that through-water SfM photogrammetry is promising in capturing the topographic and statistical properties of underwater gravel-bed surfaces if fine sediment transport is carefully addressed.Keywords: Topographic measurement; Structure-from-Motion (SfM); through-water photogrammetry; gravel-bed river; refraction correction
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".