The validity and utility of MODIS data for simple estimation of area burned and aerosols emitted by wildfire events
Bibliographic record
Abstract
Wildfire emissions are challenging to measure and model, but simple and realistic estimates can benefit multiple disciplines. We evaluate the potential of MODIS (Moderate Resolution Imaging Spectroradiometer) data to address this objective. A total of 11 004 fire pixels detected over 92 days were clustered into 242 discrete fire events in a mountainous region of North America. Burned areas were estimated with spatial buffers around the MODIS detections, and all events were matched and compared with administrative fire records based on their location and duration. Linear regression between recorded and estimated burned areas showed excellent agreement (slope = 0.93 and R2 = 0.96). Aerosol emission rates were estimated for each MODIS detection using its fire radiative power measurement. Results were compared with estimates from the Canadian Fire Behaviour (CANFB) prediction system in Canada and the US Emissions Production Model (USEPM) for detections in the US. Median emission rates were similar for the MODIS and CANFB methods (600 and 579 g s–1 respectively) but not for the MODIS and USEPM methods (575 and 382 g s–1 respectively). The MODIS rates were much more variable in both comparisons. Linear regression on emission rates summed daily across the study area shows that the MODIS method is more consistent with CANFB (slope = 0.71, R2 = 0.71) than with USEPM (slope = 0.24, R2 = 0.68). We conclude that simple calculations based on remote sensing data can yield results that are comparable with those obtained with more complex methods.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".