Bathymetric measurement of rivers by remote sensing techniques : a review
Bibliographic record
Abstract
Current river hydraulics or river hydrobiology models integrate 3-D measurements of the riverbed that are mainly obtained from ground topographic survey when sonar is not available. Some specific topographic methods, adapted to river constraints and to models requirements, have even been developed. Such methods allow to obtain accurate measurements of the river immersed topography. However, these methods are time-consuming and necessitate a lot of people in the field. As a consequence, the ratio cost/area covered is very high, and investigation is reduced to small parts of the river. Finally, models cannot be validated for a sufficently representative section of the river. Remote sensing has hence been tested with a view to solve this scale problem. Remote sensing is also a non-contact measurement method which allows to collect data about the river when it cannot be accessed. In this study we propose a bibliographic review of the remote sensing techniques used for river bathymetry. Frequently, these techniques had been developed for marine environment first and then transposed to riverine environments. These techniques can be divided into two types : active remote sensing, such as Ground Penetrating Radar (GPR) and bathymetric LIDAR or passive remote sensing, such as through-water photogrammetry and radiometric models. A brief analysis of the scientific literature shows that the latter technique is the most used. It consists in finding a logarithmic relationship between the river depth and the values of the image spectral bands. Few references exist that deal with the other techniques, typically one or two for each. Moreover, most studies about rivers with remote sensing techniques focus on the landscape, or habitat characterization, or even deal with the dry part of the river. Our work is focused on the immersed topography. This paper proposes hence, for each depth measurement method, an explanation of the physical basis and then a review of the results obtained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".