# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 1 Introduction  
The greenhouse gas (GHG) effect is a natural process that helps to maintain the thermal balance for life on Earth. However, the atmospheric composition is changing as a result of human activities, such as the burning of fossil fuels (coal, oil, natural gas), deforestation, and forest degradation. The consequent increase in GHGs has been associated with a rise in global temperatures [1; 2; 3], including in the period 2011-2015, which has been the warmest on record [4]. Wildfires contribute between 24% and 35% of carbon dioxide (CO\({}_{2}\)), carbon monoxide (CO), Nitrogen Oxide (NOx) and methane (CH\({}_{4}\)) emissions to the atmosphere, as well as a significant quantity of aerosols [5]. For this reason, and because of its role in vegetation changes, fire disturbance has been designated an Essential Climate Variable (ECV) [5] by the Global Climate Observing System (GCOS) to better understand Climate Change trends [6]. The GHGs associated with forest fires are CO\({}_{2}\), CH\({}_{4}\), and nitrogen oxides (NOx). CO is not a GHG, but it generates ozone (O\({}_{3}\)) in the troposphere and has a deleterious effect on living organisms [7]. These gases are the result of chemical reactions resulting from the combination of three components: vegetation, heat, and oxygen [8]. Their emissions vary spatially and temporally, depending on the fuel moisture conditions: the more moisture, the lower the gas emissions (owing to incomplete combustion) [9; 10]. Earth observation data are essential for monitoring natural and anthropogenic processes. Their application can contribute to estimations of emissions, owing to the timely and reliable flow of information; the results can be spatially comparable, and this is an ideal method for estimating emissions from fires. Of the various methods previously proposed, the bottom-up approach [11] has been the most commonly used. It considers four main variables: biomass loss, burned area, burning efficiency, and emission factors. These variables can be ascertained from statistical, cartographic, or satellite data. A variation of this model uses the Fire Radiative Power (FRP), estimated from the radiance detected by satellite middle infrared sensors, as a surrogate of the biomass consumed by the fire [12]. However, this method has some limitations, as polar orbiting satellites have only a few observations on a single-fire activity, while geostationary satellites may miss small fires because of their coarse spatial resolution. Remote sensing products have been used to calculate GHGs for various initiatives. The most relevant is the Global Fire Emissions Database (GFED) [13]. The GFED generates and publishes information, at the global and regional levels, about gases derived from biomass burning, following the proposal by [PERSON] and [PERSON] [11]. A similar system is the Global Fire Assimilation System (GFAS), which uses the FRP method [14], within the framework of the Copernicus Atmosphere Monitoring System (CAMS). Other authors have worked at a regional level, such as North America [15] and southern Africa [16], or at a national level, such as Colombia [17], using Moderate Resolution Imaging Spectrometer (MODIS) products. The GHG emissions associated with forest fires in Mexico are an annual phenomenon and are of great importance in view of their impact on the natural, social, and economic sectors at the local and regional levels, e.g., the damage to biodiversity and closure of schools. The direct effects on local areas can expand into other areas and territories by the drift of emissions. The interannual variation in emissions is associated with meteorological conditions that favor fire ignition, propagation, and extinction. The main factors in Mexico are the seasonal distribution of precipitation and the phenology of vegetation. Human activities have also been decisive in the generation of forest fires and, therefore, in the emissions, as most Mexican forest fires are anthropogenic [18].