Scattered and Received Wave Polarization Optimization for Enhanced Peatland Classification and Fire Damage Assessment Using Polarimetric PALSAR
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 R0with 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 R0) 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".