Comparison of burned area mapping products and combustion efficiency approaches for estimating GHG and particulate emissions from Italian fires
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".