# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 5 Conclusions  
The availability of satellite data and products allows for the estimation and update, both dynamically and periodically, of three out of four parameters for calculating the emissions from forest fires. However, there is still a lack of data with the same resolution and scale to contribute to the precision of the spatial analysis. This was illustrated by a comparison between the GFED data and our results, given that the values of the emissions were very similar, although local discrepancies were observed when data with a better spatial resolution were used.  
The present study contributes a product of burned areas that identifies areas affected by fires in vegetation, irrespective of the forest fires registered in the statistics. It is important to collaborate with CONAFOR in using remote sensing data to improve the statistics. Recently, CONAFOR has improved the method for estimating burned areas in the field, and has sometimes incorporated satellite data, in collaboration with CONABIO. Remote sensing data and field knowledge will increase the quality of information regarding emissions. We demonstrated the use of remote sensing data and machine learning to calculate parameters such as the burning efficiency, taking into account natural conditions on the date of the fire; this allows for a better understanding of emissions resulting from forest fires. The areas of interest with burned biomass emissions were located at a larger scale than that used in other works and included an adjustment to the environmental conditions on the date of the fire. This information can help to implement measures to prevent or to mitigate fires according to the environmental characteristics.  
The described methodology may be implemented on a countrywide scale because the variables and field data that it uses in the Random Forest Regression (RFR) model are to some degree flexible. As mentioned, it includes an evaluation of the accuracy of its estimates. This allows the methodology to be applied on other ecoregions. Finally, the satellite imagery proposed as input is generated periodically and is available on different distribution platforms.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the proposal; [PERSON] designed and applied the methods and analyzed the results, [PERSON] proposed the use of Random Forest and implemented the calculation of burning efficiency in the R programming language. [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] made corrections.  
This research received no external funding.  
**Acknowledgments:** We thank CONABIO for its data and equipment for processing, [PERSON] for his field data to establish the burning efficiency method. [PERSON] who provided the time series of vegetation index, and CONABIO for financial assistance for the publication. We thank [PERSON] for corrections to language.  
**Conflicts of Interest:** \"The authors declare no conflict of interest.\" \"The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results\".