Evaluation of RADARSAT-1 images acquired in fine mode for the study of boreal peatlands: a case study in James Bay, Canada
Bibliographic record
Abstract
Dans le cadre d'une étude sur le cycle du carbone dans les tourbières boréales, la télédétection radar a été utilisée avec l'objectif d'obtenir différentes informations sur ces milieux encore mal connus. Cet article présente une étude pilote réalisée sur trois tourbières du bassin versant de la rivière La Grande, Québec, Canada. Sept images multi-temporelles acquises en mode fin ont été analysées et comparées avec des données de terrain recueillies simultanément, dans le but de vérifier si les conditions hydrologiques des tourbières influencent le signal radar. Les changements hydrologiques observés sur le terrain se reflètent sur les images radar, là où les arbres sont petits et se trouvent en faible densité. Une classification du maximum de vraisemblance basée sur les patrons morphologiques de surface des tourbières a également été réalisée à partir des images en mode fin (filtrées et textures), puis comparée à une classification similaire effectuée à partir d'images de textures multi-temporelles acquises en mode standard. Les résultats de classification obtenus à partir des images en mode fin sont moins bons que ceux d'une classification utilisant les images en mode standard, malgré leur meilleure résolution (35 % vs. 59 % de précision totale utilisant 10 classes et 37 % vs. 74 % de précision totale ayant 4 classes). \n \n<h2>Abstract</h2> \nAs part of a wider study of carbon cycling in boreal peatlands, radar remote sensing was used with the objective of obtaining diverse environmental information related to these peatland areas. This paper presents a case study of three peatlands located in the La Grande River watershed, Quebec, Canada. An analysis of multitemporal fine-mode RADARSAT-1 images was carried out, with the support of collected field data, to verify if hydrological conditions influence radar backscatter coefficients. Changes in hydrological conditions are reflected in the radar backscatter coefficient for areas having low and sparse trees. A maximum likelihood classification (MLC) on speckle-filtered images and textures was also carried out, evaluated, and compared with a similar classification procedure on standard-mode images. MLC using textures generated from multitemporal fine-mode images gave poorer results than a similar classification using multitemporal standard-mode textural images (35% versus 59% of overall accuracy using 10 classes and 37% versus 74% of overall accuracy using four classes).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".