Estimation of Northern Burnt Forest Mortality in the 21st Century Based on MODIS Data on Fire Intensity
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».