# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 4 Discussion  
The use of satellite Earth observation data allowed for the spatial and temporal estimation of emissions, providing an annual updating of the biomass and burned areas, as well as the evaluation of environmental conditions for estimating the burning efficiency. The parameter with the highest uncertainty was the biomass load (27%). Even though the biomass map had an RMSE of 54% in its validation [26], it was still considered acceptable because similar results have been reported by other projects; e.g., the GlobBiomasss project, University of Leicester (2017), reported RMSE values of 51%, 53% and 55% for the years 2005, 2010, and 2015, respectively, in the Central Mexico and Yucatan Peninsula regions. As a complement to the present work, the emissions were estimated by using only the biomass map [26] for each of the four years; this led to a value that was lower by 19 Tg, thereby reinforcing the importance of annual updating. The burning efficiency results obtained for March 2006 were within those values reported elsewhere: (a) in the literature [50]; (b) in the documentation of prescribed fires [54], and (c) in a study to estimate the spatial variability in burning efficiency in Spain [55]. Hence, they were considered appropriate, although they were lower than those reported in the literature. The results also indicated that the behavior of the burning efficiency can be explained by the vegetation type; temperate forests have a relatively low species diversity, and the fire behavior is more uniform than in the species-diverse tropical forests. Here, field data are a fundamental aspect of the method. We considered that the Random Forest approach [36] offers an approximation for estimating the burning efficiency over a large and biodiverse territory such as Mexico; it considers spatial and temporal changes in environmental conditions when a fire occurs, without depending on a unique and static value. Despite the relatively low accuracy revealed by the omission error for the burned area product, MCD64 has been the best operational product with the historical and recent data, because the data are processed with the same algorithm. This is supported by a study [56] that concluded that the Collection 5.2 MCD64 was the best of three evaluated burned area products, and Collection 6 identifies more burned areas than Collection 5.2. The emission factors obtained from the MILAGRO project are higher than those reported in the global study of [41] for CO\({}_{2}\) and CH\({}_{4}\) but lower for CO. These differences have implications in the estimation of emissions because CO\({}_{2}\) is the gas with the highest emission factor. However, the MILAGRO data are specific to Mexican vegetation. Southern Sierra Madre (ecoregion 13.5) had the highest percentage of emissions, but the vegetation type most affected was the tropical forest distributed in ecoregions 15.2 (Plain and Hills of the Yucatan Peninsula) and 14.5 (Southern Pacific Coastal Plain and Hills). If the emissions originating in temperate forests are added, the result is similar in tropical forests. Therefore, it is important to highlight two elements in the calculation of emissions: the biomass quantity and burning efficiency. Although the amount of emissions per ecoregion reflects the area of that region, it is also influenced by the type of vegetation, the date, and the causes of the fire. For example, ecoregion 13.5 represented 34.8% of the study area and 54.8% of the emissions over the period, whereas ecoregion 15.2 represented 44.3% of the study area but only 28.7% of the emissions. The differences found between the GFED data and our results are due to the scale of the analysis; GFED is at a global scale without regional detail, giving a general view of the problem, whereas our results have a local precision. We also identified an important difference between our results and a