Remote sensing of fluvial environments: Riverscape characterization of in-stream hydraulic habitat heterogeneity
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".