Reconstructing wildland fire burned area for Asian Russia (1979–2000) using AVHRR GAC satellite data to provide an improved baseline for assessing long-term change
Bibliographic record
Abstract
Wildland fire is a vital ecological disturbance at northern latitudes that has strong interactions with weather and climate systems. Multi-decadal fire records are critical for assessing changing fire regimes and vegetation mosaic patterns. While such records are available for Alaska, Canada, and Fennoscandia, accurate pre-2000 data for Russia are notably lacking. Continuous moderate-to-high-resolution data for Asian Russia are not available before the 2000s. In this study we defined fire scars using Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data to develop a continuous, long-term, burned-area database for Asian Russia that spans from 1979 through 2000. We generated monthly composites of fire scars from daily GAC data and used a combination of aerosol and visible smoke data to confirm that observed spectral changes were due to fire and to determine dates of active burning. Accuracy of burned areas was evaluated using available Landsat Thematic Mapper (TM) data and correlations with previously published burn scar data. The coefficient of determination (R 2 ) for linear regressions between Landsat validation burned areas and GAC data was 0.84 for all fires sampled and 0.97 for large fires greater than 10,000 ha (ha), north of 53 degrees latitude. Omission-Commission analysis also show higher accuracy with larger fires. The overall comparison with previously published large burn-scar data had an R 2 of 0.88. The largest errors were with fires less than 10,000 ha, which make up less than 7 % of the burned area. We present seasonal fire patterns and spatial and ecozone distribution of burned areas. In a typical season, fires started in the south and spread to the north over spring and early summer. We observed high interannual variation in the spatial patterns of burned area. Total annual burned areas ranged from 0.4 to 11.9 million hectares (Mha), with an average burned area of 4.8 Mha per year. Our estimates for most years are several times higher than official Russian burned-area reports and are typically larger than burned-area data reported in previous publications. Our data represent the first validated long-term historical burned-area data for Asian Russia, which provides an essential basis for analyses of the interactions between these diverse and unique ecoregions, fire regimes, and weather and climate feedbacks. When combined with existing data from other northern regions, our data will enable accurate assessments of long-term fire patterns and fire-climate interactions across the critical boreal-Arctic region for almost 50 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".