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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».