Abstract
BackgroundUnderstanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming. At present, no system for prediction of fire occurrence risk based on fuel dryness conditions is available in Mexico. As part of an ongoing national-scale project, we developed an operational fire risk mapping tool based on satellite and weather information.ResultsWe demonstrated how differing monthly temporal trends in a fuel greenness index, dead ratio (DR), and fire density (FDI) can be clearly differentiated by vegetation type and region for the whole country, using MODIS satellite observations for the period 2003 to 2014. We tested linear and non-linear models, including temporal autocorrelation terms, for prediction of FDI from DR for a total of 28 combinations of vegetation types and regions. In addition, we developed seasonal autoregressive integrated moving average (ARIMA) models for forecasting DR values based on the last observed values. Most ARIMA models showed values of the adjusted coefficient of determination (R2 adj) above 0.7 to 0.8, suggesting potential to forecast fuel dryness and fire occurrence risk conditions. The best fitted models explained more than 70% of the observed FDI variation in the relation between monthly DR and fire density.ConclusionThese results suggest that there is potential for the DR index to be incorporated in future fire risk operational tools. However, some vegetation types and regions show lower correlations between DR and observed fire density, suggesting that other variables, such as distance and timing of agricultural burn, deserve attention in future studies.
Highlights
Understanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming
Some systems such as the Fire Potential Index (FPI; Burgan et al 1998) have integrated satellite information by means of fuel greenness indices based on relative values of the Normalized Difference Vegetation Index (NDVI) for each vegetation type (Burgan and Hartford 1993, 1997; Burgan et al 1996; Burgan et al 1998), combined with daily 10 h fuel moisture content calculated from observations of weather stations (Fosberg and Deeming 1971) to map fuel greenness and associated fire risk
The present study focused on understanding temporal patterns of active fire density by vegetation type and region in Mexico for the period 2003–2014, explored its relationships with a MODIS-based fuel greenness index, and developed forecast models for the prediction of the fuel greenness index for the following month based on previously observed values
Summary
Understanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming. Satellite sensors have been utilized in recent years to monitor fuel greenness and associated fire occurrence risk (Chuvieco et al 2004; Lozano et al 2007, 2008; Chuvieco et al 2010; López et al 2002; Yebra et al 2008; Yebra et al 2013) Some systems such as the Fire Potential Index (FPI; Burgan et al 1998) have integrated satellite information by means of fuel greenness indices based on relative values of the Normalized Difference Vegetation Index (NDVI) for each vegetation type (Burgan and Hartford 1993, 1997; Burgan et al 1996; Burgan et al 1998), combined with daily 10 h fuel moisture content calculated from observations of weather stations (Fosberg and Deeming 1971) to map fuel greenness and associated fire risk. These operational fire risk systems have largely been utilized in the United States of America (Burgan et al 1998; Preisler and Westerling 2007; Preisler et al 2009; Preisler et al 2015) or on the European continent (Sebastian-Lopez et al 2002), including Spain (Huesca et al 2007; Huesca et al 2009; Huesca et al 2014).
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