Evaluation of C-Band SAR for Identification of Flooded Vegetation in Emergency Response Products
Bibliographic record
Abstract
A synthetic aperture radar (SAR) data set of the Peace Athabasca Delta, Alberta, was used to evaluate approaches to flooded vegetation mapping. A primary objective was to identify how to add a flooded vegetation layer to the Emergency Geomatics Service (EGS) SAR-derived flood products. Field data were used to identify non-flooded and flooded vegetation. A combination of statistical analyses and box plot visual inspection was used to evaluate the magnitude-only images, the polarimetric and compact polarimetric parameters/decompositions, and the coherence products for mapping flooded vegetation. This paper provides some background on the use of SAR for flood mapping, describes the data and processing methods, and presents the results of this comparison. To some degree all polarizations and techniques were effective for mapping flooded vegetation due to the increased backscatter intensity and the phase shift from the double bounce scattering. In particular, all polarization combinations, the HH/HV ratio, Shannon entropy, and the m-chi double bounce produce good separation. The water/vegetation interface remains coherent when flooded, also allowing flooded vegetation to be identified with seasonal coherence. These results demonstrate that the use of multi-mode RADARSAT Constellation Mission data for providing a flooded vegetation layer to EGS flood maps is possible.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".