Comparison of burned area mapping products and combustion efficiency approaches for estimating GHG and particulate emissions from Italian fires
Notice bibliographique
Résumé
Vegetation fires contribute to 38% to the emission of CO2 into the atmosphere, against 62% caused by the combustion of fossil fuels. Further, it could approach levels of anthropogenic carbon emissions, especially in years of extreme fire activity (e.g. 2003, 2017). According to the equation first proposed by Seiler and Crutzen (1980), fire emission estimation use information on the amount of burned biomass, the emission factors associated with each specific chemical species, the burned area, and the combustion efficiency. Still, simulating emission from forest fires is affected by several errors and uncertainties, due to the different assessment approach to characterize the various parameters involved in the equation. For example, regional assessment relied on fire-activity reports from forest services, with assumptions regarding the type of vegetation burned, the characteristics of burning, and the burned area. Improvements and new advances in remote sensing, experimental measurements of emission factors, fuel consumption models, fuel load evaluation, and spatial and temporal distribution of burning are a valuable help for predicting and quantifying accurately the source and the composition of fire emissions. With the aim to contribute to a better estimation of biomass burning emission, in this work we compared fire emission estimations using two different types of burned area products and combustion efficiency approaches in the framework of the recently developed system for modeling fire emission in Italy (Bacciu et al., 2012). This methodology combines a fire emission model (FOFEM - First Order Fire Effect Model, Reinhardt et al., 1997) with spatial and non-spatial inputs related to fire, vegetation, and weather conditions. The perimeters and burned area of selected large fires that occurred in 2017 in Italy were obtained by the former Corpo Forestale dello Stato (actually Carabinieri C.U.F.A.A.) and by the Copernicus Emergency Management Service (EMS). The vegetation types were derived from CORINE LAND COVER (2012). For each vegetation type, fuel loading was assigned using a combination of field observations and literature data (e.g., Mitsopoulos and Dimitrakopoulos 2007; Ascoli et al., 2019). Fuel moisture conditions, influencing the combustion efficiency, were derived from the daily Canadian Fine Fuels Moisture Code (FFMC), calculated from MARS interpolated weather data (25km x 25km). The daily FFMC was then associated with the two types of fire data with the aim of group fires in function of their relative ease of ignition and flammability of fine fuel (burning conditions, from low to extreme). For the EMS fire, it was also possible to further define fire severity and thus the percentage of combusted crown through the assessed fire damage grade. The results showed differences in the total emissions according to the fire product and the approach to estimate the combustion efficiency. Furthermore, it seems that the difference in the evaluation of severity - and therefore in the degree of combustion of the canopy- affects more than the differences in terms of area burned. Overall, the results pointed out the crucial role of appropriate fuel, fire, and weather data and maps to attain reasonable simulations of fuel consumption and smoke emissions.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».