Validation and calibration of Canada-wide coarse-resolution satellite burned area maps
Bibliographic record
Abstract
Satellite-based mapping can provide a timely and efficient means of identifying burned vegetation at continental scales for estimating greenhouse gas emissions and its effects on the terrestrial carbon budget. In this study, we used a sample of 55 Landsat Thematic Mapper (TM) scenes distributed across Canada to validate and calibrate 1998 and 1999 national-level burned areas maps produced using coarse resolution (~ 1-km) SPOT VEGETATION and NOAA AVHRR imagery. Commission and omissions errors, based on fire events > 200 ha, were found to be small in the coarse resolution maps (4% and 1%, respectively). However, the coarse resolution burned area estimates were 72 percent larger than the crown fire burned area mapped at 30 m using Landsat TM (11,039 vs. 6,403 ha average area). This bias was attributed to spatial aggregation effects in which the coarse resolution product included the tree crown fire, partial burn, and unburned fractions of a pixel. A regression calibration model (R2=0.95, p< 0.0005, RMS=3,015 ha, n=155) based on a VGT/TM double sampling approach was derived to correct for the aggregation bias and to provide Canada-wide estimates of crown fire burned area.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".