Modeling and Mapping Forest Fire Occurrence from Aboveground Carbon Density in Mexico

• Published in: Forests Vol. 10; no. 5; p. 402
• Main Authors: Carlos Ivan Briones-Herrera, Daniel José Vega-Nieva, Norma Angélica Monjarás-Vega, Favian Flores-Medina, Pablito Marcelo Lopez-Serrano, José Javier Corral-Rivas, Artemio Carrillo-Parra, Miguel Ángel Pulgarin-Gámiz, Ernesto Alvarado-Celestino, Armando González-Cabán, Stéfano Arellano-Pérez, Juan Gabriel Álvarez-González, Ana Daría Ruiz-González, William Mathew Jolly
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-05-2019
• Subjects: aboveground biomass; ecoregions; fire occurrence
• DOI: 10.3390/f10050402
• EISSN: 1999-4907

Understanding the spatial patterns of fire occurrence is key for improved forest fires management, particularly under global change scenarios. Very few studies have attempted to relate satellite-based aboveground biomass maps of moderate spatial resolution to spatial fire occurrence under a variety of climatic and vegetation conditions. This study focuses on modeling and mapping fire occurrence based on fire suppression data from 2005−2015 from aboveground biomass-expressed as aboveground carbon density (AGCD)-for the main ecoregions in Mexico. Our results showed that at each ecoregion, unimodal or humped relationships were found between AGCD and fire occurrence, which might be explained by varying constraints of fuel and climate limitation to fire activity. Weibull equations successfully fitted the fire occurrence distributions from AGCD, with the lowest fit for the desert shrub-dominated north region that had the lowest number of observed fires. The models for predicting fire occurrence from AGCD were significantly different by region, with the exception of the temperate forest in the northwest and northeast regions that could be modeled with a single Weibull model. Our results suggest that AGCD could be used to estimate spatial fire occurrence maps; those estimates could be integrated into operational GIS tools for assistance in fire danger mapping and fire and fuel management decision-making. Further investigation of anthropogenic drivers of fire occurrence and fuel characteristics should be considered for improving the operational spatial planning of fire management. The modeling strategy presented here could be replicated in other countries or regions, based on remote-sensed measurements of aboveground biomass and fire activity or fire suppression records.