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Record W7161951795 · doi:10.82308/22622

Remote sensing of fluvial environments: Riverscape characterization of in-stream hydraulic habitat heterogeneity

2017· dissertation· en· W7161951795 on OpenAlexaboutno aff
Fabien Hugue

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatRiffleHydrology (agriculture)FluvialRiver morphologySpatial heterogeneityEcosystemWetlandDigital elevation model

Abstract

fetched live from OpenAlex

Anthropogenic or natural disturbances to river ecosystems (driven by river regulation or land use and climate changes) often lead to long term changes in morpho-sedimentological dynamics and, in turn, to gradual modifications to riverine habitats over extended river lengths. Moreover, since many high value river organisms such as 'potamodromous' fish species migrate across complementary habitats distributed along stream networks, it is important for river ecosystem conservation to characterize efficiently variations in habitat types over extensive river segments. To this end, the 'riverscape' approach (Fausch et al., 2002) provides a framework which considers degrees of connectivity across various types of river habitats, as well as degrees of habitat heterogeneity (HH) over reaches of various lengths. However, such riverscape characterization requires high resolution (< 5 m), continuous habitat data collected over long river segments, which is difficult and expensive to acquire, especially through field data collection. Bridging this data gap is the objective of this thesis. The research presented here provides low-cost methods, based on satellite and airborne imagery, capable of extracting various metrics of in-stream hydraulic habitat, calculated over scale flexible moving windows, for long river segments (10 – 100 km). The approach is based on combining remote sensing with simplified, "pseudo-2D" hydraulic modeling for the flow conditions at the time of image capture. It can generate 1 m resolution depth and velocity maps from which hydraulic habitat variables can be quantified over various selectable windows (such as pool depth and area statistics, riffle lengths, mean velocity and Froude number profiles, as well as various indices of reach scale hydraulic habitat heterogeneity HH, etc.). We also investigate the use of airborne hyperspectral images to classify and to quantify in-stream habitat features such as the bed substrate composition and submerged aquatic vegetation density, based on the analysis of hyper-spectral signatures.We also apply these methods to extract hydraulic habitat data over ten (10) rivers across five (5) different physiographic regions of Canada (for a total of 163 km of river length) and demonstrate that reach scale hydraulic HH was correlated with local valley scale features (such as tributary junctions and lateral channel constraints) reflecting the regional physiographic context. Further investigation on the HH distributions along the ten (10) studied Canadian river segments revealed three (3) major scales of along river HH variability, governed by dominant geomorphic processes specific to each scale.The methods and concepts presented in this thesis contribute to advancing the field of riverscape science by providing analytical tools available to river scientists and managers, given the decreasing costs and increasing resolution of satellite imagery. The applications of this framework present opportunities to bridge the gap between river ecologists and geomorphologists, and to remedy the lack of uniform methods for characterizing and analyzing lotic ecosystems at the riverscape extent, using scale-flexible metrics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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