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
As 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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".