Analytical methods for mapping river bathymetry from multi-spectral satellite images
Bibliographic record
Abstract
Rivers are an important part of the aquatic environment, which supply freshwater essential to human life, support economic activities, and provide natural habitats for aquatic species. The river environment needs to be managed properly for the protection of river floods, channel erosion and water pollution as well as for the safety of in-stream hydraulic and other river engineering structures. River management needs data of river channel bathymetry as fundamental input. The purpose of this research is to explore new, efficient methods for mapping channel bathymetry. Traditionally, field methods are used for point-by-point measurements of flow depth, which need an operator to use instrument at a river site. The field methods are costly and inconvenient, true particularly for remote river sites. Recently, advancing remote sensing technology has offered promising opportunities for mapping river bathymetry, leading to the development of some empirical methods for converting light intensity in satellite images to river flow depth. A major shortcoming of the methods is that the conversion involves a light attenuation coefficient; its value needs to be determined using adequate field measurements from a river site of application, which are often not available. This thesis reports new analytical methods for retrieving river bathymetry from multi-spectral high-resolution satellite images. No field measurements are needed for the determination of regression relationships. The analytical methods are applied to a 25-km reach of the Nicolet River in Quebec, Canada. The application uses multi-spectral high-resolution images from WorldView-2 and WorldView-3 satellites. The methods involve radiometric corrections to images in order to remove the atmospheric effect on wavelengths and calculations of effective attenuation coefficient that allows for the effects of water column on the wavelengths. After removing the ambient effects, the ratio of a pair of selected wavelengths is used in algorithms for determining the flow depth. The bathymetry results show an 85% accuracy for WorldView-3 satellite image. The accuracy is lower for WorldView-2 satellite image due to a lack of two atmospheric factors in radiometric correction. The results offer a spatial resolution as high as 1.2-m (for WorldView-3 image). Analytical methods have been used in coastal water and marine applications. This study is perhaps the first application to the river environment, where the spatial gradient of depth is typically much larger than those of the coastal and marine environment. There is no doubt that future satellite operations will provide increasing spatial resolution and coverage. There is a great potential to revolutionise the approach to mapping river bathymetry and to substantially reduce the need of costly and time-consuming field efforts.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".