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Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Ecoregions

National Commission for the Knowledge and Use of Biodiversity, Liga Periférico-Insurgentes Sur 4903, Col. Parques del Pedregal, Alcaldía de Tlalpan C.P. 14010, Cd. Mexico
Institute of Geography, UNAM, Investigación Científica, Ciudad Universitaria, Alcaldía de Coyocán, Cd. México C.P. 04510, Mexico
University of Alcala de Henares, Colegios 2, Alcalá de Henares, C.P. 28801 Madrid, Spain
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1185;
Received: 2 April 2019 / Revised: 8 May 2019 / Accepted: 15 May 2019 / Published: 18 May 2019
PDF [5216 KB, uploaded 18 May 2019]


This study shows a simplified approach for calculating emissions associated with forest fires in Mexico, based on different satellite observation products: the biomass, burnt area, emission factors, and burning efficiency. Biomass loads were based on a Mexican biomass map, updated with the net primary productivity products. The burning efficiency was estimated from a Random Forest Regression (RFR) model, which considered the fuel, weather and topographical conditions. The burned areas were the downloaded Maryland University MCD64c6 product. The emission factors were obtained from well-known estimations, corrected by a dedicated US Forest Service and Mexican campaign. The uncertainty was estimated from an integrative method. Our method was applied to a four-year period, 2011–2014, in three Mexican ecoregions. The total burned in the study region was 12,898 km2 (about 4% of the area), producing 67.5 (±20) Tg of CO2. Discrepancies of the land cover maps were found to be the main cause of a low correlation between our estimations and the Global Emission Database (GFED). The emissions were clearly associated to precipitation patterns. They mainly affected dry and tropical forests (almost 50% of all emissions). Six priority areas were identified, where prevention or mitigation measures must be implemented. View Full-Text
Keywords: gas emissions; remote sensing; machine learning gas emissions; remote sensing; machine learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Cruz-López, M.I.; Manzo-Delgado, L.L.; Aguirre-Gómez, R.; Chuvieco, E.; Equihua-Benítez, J.A. Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Ecoregions. Remote Sens. 2019, 11, 1185.

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