Scattered and Received Wave Polarization Optimization for Enhanced Peatland Classification and Fire Damage Assessment Using Polarimetric PALSAR
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Bibliographic record
Abstract
The complementarity of the “scattered” and “received” wave polarization signatures is demonstrated for enhanced characterization of peatlands and surrounding forests. Polarimetric L-band ALOS-PALSAR data collected for the Athabasca oil sand exploration region, with peatlands and forests partially affected by multiple wildfires, are used. It is shown that the scattered wave polarization signature, which represents the explicit variations of the degree of polarization (DoP) and the total scattered intensity R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> with transmitted polarization, permits enhanced discrimination of treed bogs from upland forests and improved identification of wildfire damage in peatlands and surrounding forests. Scattered wave optimization is used as a convenient method for efficient exploitation of the scattered wave polarization signature. The Touzi decomposition is adopted for the optimization of the received wave polarization signature. The unique potential of the scattering-type phase generated with the Touzi decomposition is confirmed for enhanced discrimination of poor fens from bogs. These two important peatland classes cannot be separated with the scattered wave optimization, the conventional multipolarization (HH-HV-VV) channels, and the Freeman model based decomposition. Finally, the Touzi decomposition is combined with the extrema of the scattered wave main parameters (DoP and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) for optimum extraction of polarimetric PALSAR information. This information is, then, fused with Landsat-5-TM for enhanced peatland classification. The comparison with optical Landsat5-TMbased classification confirms the valuable added information that a long penetrating polarimetric L-band PALSAR can provide for enhanced peatland classification and efficient assessment of peat health in burnt peatlands.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it