Optimum RADARSAT-1 configurations for wetlands discrimination: a case study of the Mer Bleue peat bog
Bibliographic record
Abstract
This study investigated the effect of the RADARSAT-1 incidence angle on the discrimination of the wetlands, including open bog, treed bog, and marsh, in the Mer Bleue bog conservation area in Ontario, Canada. The results demonstrated that RADARSAT-1 can penetrate tall herbaceous vegetation in marsh at all incidence angles and short shrub vegetation in open bog with standing water at low incidence angles only, which reveals that RADARSAT-1 backscatter double bounce from open wetlands (marsh and bog) with standing water is related to the vegetation types. The analysis of the RADARSAT-1 synthetic aperture radar (SAR) backscatter of wetland and non-wetland land cover types confirmed that the backscatter return of each land cover type decreases as the incidence angle increases. It was also found that the separations of marsh, treed bog, and open bog without standing water do not vary significantly with increasing incidence angles. However, the incidence angle does affect the discrimination of open bog with standing water. The results from separability measures of land cover classes demonstrate that the combination of a low incidence angle (<31°) RADARSAT-1 standard beam mode image with Landsat-7 data can provide good separation for all class pairs in the study area. The combination of RADARSAT-1 standard beam mode 1 (S1) and Landsat-7 images gave better classification results than the combination of RADARSAT-1 standard beam mode 5 (S5) and Landsat-7 images: the classification accuracies (kappa indices) of various wetlands increased in the range 0.02-0.13. For optimal spatial detail, fine beam mode data are desirable because of the higher spatial resolution (~9 m). However, in terms of the discrimination and classification accuracy of wetlands, fine beam mode data are not suggested because of the high incidence angle. Therefore, it is concluded that a lower incidence angle RADARSAT-1 image (e.g., S1) is the optimal beam mode when combining with Landsat images for the discrimination and classification of wetlands.
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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 | 0.001 | 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".