Cartographie du substrat de l’habitat du saumon atlantique par analyse d’imagerie aéroportée \nhaute-résolution.
Bibliographic record
Abstract
La taille des grains du lit d’un cours d’eau joue un rôle prépondérant sur son utilisation possible pour l’habitat des salmonidés. Récemment, de nouvelles méthodes d’analyses ont été développées pour la cartographie de la taille du substrat à partir d’images aériennes haute résolution. Le présent projet a pour objectif d’utiliser la méthode développée par Carbonneau et al. (2004) afin de cartographier l’habitat du saumon atlantique sur un tronçon de la branche Nord-est de la rivière Sainte-Marguerite (Saguenay) sur laquelle se déroule un programme de translocation de saumons reproducteurs. \nÀ l’été 2014, des images aériennes de la rivière ayant une résolution au sol de 2.4 à 3.3 cm ont été acquises, en période d’étiage, à l’aide du système d’imagerie héliportée développé par Dugdale et al. (2013) et doté d’une caméra optique haute résolution. Tout juste avant le survol, des photographies au sol géoréférencées du substrat ont été obtenues sur les parties exondées et submergées du lit de 4 tronçons représentatifs de la rivière afin de servir de calibration à la méthode d’estimation du substrat à partir des images aériennes. Ces photographies ont été analysées à l’aide du logiciel libre BASEGRAIN afin de mesurer la distribution en taille des particules sur chacune des images et en calculer le D₁₆, le D₅₀ et le D₈₄. Les images aériennes ont ensuite été analysées afin de calculer l’entropie de la brillance des pixels à l’intérieur de fenêtres d’analyse de différentes tailles (63 x 63 cm à 123 x 123 cm) centrées sur chacun des points de calibration au sol de la granulométrie. L’analyse statistique de la relation entre les valeurs d’entropies et la granulométrie du substrat exondé démontre l’existence d’une relation significative négative qui pourra être utilisée afin d’estimer la taille du substrat sur la partie submergée du lit de l’ensemble des images aériennes. Substrate grain size distribution in a stream plays a major role in its potential habitat use by salmons. Recently, new methods of analysis have been developed for the mapping of the size of the substrate from high-resolution aerial images. The purpose of this project is to use the method developed by Carbonneau et al. (2004) to map the Atlantic salmon spawning habitat on a reach of the northeast branch of the Sainte-Marguerite River (Saguenay) where a conservation translocation program of adult salmon is ongoing. \nIn the summer of 2014, aerial images of the river with a ground resolution of 2.4 to 3.3 cm were acquired, during low-flow periods, using the helicopter imaging system developed by Dugdale et al. (2013) with a high-resolution optical camera. Just prior to the flight, georeferenced ground photos of the substrate were obtained from the exposed and submerged portions of the bed of 4 representative sections of the river to calibrate the grain-size estimation method from the aerial images. These photographs were analyzed using a free software called BASEGRAIN to measure the particle size distribution on each images and to calculate the D₁₆, D₅₀ and D₈₄. Aerial images were then analyzed to calculate the entropy of pixel brightness within windows of different sizes (63 x 63 cm to 123 x 123 cm) centered on each of the ground calibration points. The statistical analysis of the relationship between the entropy values and the particle size of the exposed substrate demonstrates the existence of a significant negative relationship that can be used to estimate substrate sizes on both the dry and wet portions of the bed of all aerial images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".