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Record W7054764900

Analytical methods for mapping river bathymetry from multi-spectral satellite images

2021· dissertation· en· W7054764900 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2021
Typedissertation
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBathymetryChannel (broadcasting)SatelliteField (mathematics)Main riverDrainage basinAtmospheric correctionStreamflowSatellite imageryTidal river
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.332
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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