Study of a Simple Volume Scattering Model on Burned Forest Using Polarimetric PALSAR-2 Data
Bibliographic record
Abstract
Several studies have taken advantage of polarimetric synthetic aperture radar (PolSAR) to monitor forest disturbance caused by wildfire given its higher sensitivity to forest structure compared to single polarization SAR. This letter explores the capability of a simple volume scattering model (SVSM) to characterize burned forested area caused by wildfire. The SVSM considers a shape factor and geometric randomness to model a nth cosine probability density function (PDF) assumption of the rotation angle with respect to the line of sight. The shape factor describes the shape of elements that constitute the forest canopy, while the geometric randomness represents the variance of the PDF. Two quad polarization L-band PALSAR-2 data acquired over Fort McMurray, AB, Canada, in 2015 and 2016 before and after a severe wildfire are used for this exploration. The ability of the shape factor is evaluated first for the coniferous and broadleaf tree classification, which achieves an overall accuracy as high as 77.41% and kappa of 0.55. The simple linear regression between the burn classes and geometric randomness change shows that the geometric randomness change has a high potential for the light, modest, and severe burn classes estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".