Estimation of Northern Burnt Forest Mortality in the 21st Century Based on MODIS Data on Fire Intensity
Bibliographic record
Abstract
Abstract This paper presents estimates of forest mortality from fires that occurred in the northern territories in the 21st century, based on the analysis of fire burning intensity obtained from MODIS instruments installed on satellites Terra and Aqua. A brief analysis of fire distribution and forest mortality resulting therefrom is also presented in the paper both by year and by different territories (countries). The analysis shows that from 2002 to 2021, 70 659 fires were registered in Zone 60 (the area north of 60° N) and 5997 fires were registered in Zone AC (the area north of the Arctic Circle). Moreover, 33 892 fires in Zone 60 were registered from 2002 to 2011 and 36 767 fires from 2012 to 2021; 2395 fires were registered in Zone AC from 2001 to 2011 and 3602 fires from 2012 to 2021. Between 2002 and 2021, 102 million ha of forest land were covered by fires in Zone 60 and 8 million ha in Zone AC. At the same time, more than 22 million ha of forests died in Zone 60 (they received the fifth grade of the average weighted category state in the final fire year), and over 2 million ha died in Zone AC. Over 2002–2011, 7 015 000 ha of forests died in Zone 60 (1.2 % of all forest vegetation in the zone; an average of 19.6% of the area affected by fire) and 15 372 000 ha of forests over 2012–2021 (2.6 and 23.3%, respectively); over 2002–2011, 641 thousand ha of forests died in Zone AC (2.8 and 23.7%, respectively) and 1 379 000 ha of forests over 2012–2021 (1.9 and 26.5%, respectively). The paper also presents information on forest death by territories (countries) in the analyzed zones. The presented data made it possible to draw the following preliminary conclusions: over recent decades, there have been no significant changes in the number of fires in Zone 60. Nevertheless, it is worth noting that, in 2019–2020, the number of fires in Zone AC increased drastically in Russia; in the second decade of the analyzed period, an increase in forest death from fires in the analyzed zones in Russia was observed; over the study period, no trends were observed in the ratio of the area of dead forests and total forest area in the analyzed countries; the average percentage of dead forests for the entire study period is comparable in Russia, the United States, and Canada, though it is significantly lower in Northern European countries; the same picture is observed in the ratio of dead forest area and fire affected area.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".