Remote sensing-based operational modeling of fuel ignitability in Hyrcanian mixed forest, Iran

To date, the efficiency and effectiveness of early warning systems of satellite imagery for preventing and mitigating wildfire remain a challenging issue. The heat of pre-ignition ( $$Q_{{{\text{ig}}}}$$ Q ig ) can be an index of fire likelihood, which is further enhanced with remotely sensed data, active fire data, and fuels information for operational application of satellite imagery in fire early warning systems. $$Q_{{{\text{ig}}}}$$ Q ig is a prerequisite for forest fires by the side of ignition sources and weather. This study analyzed the effect of $$Q_{{{\text{ig}}}}$$ Q ig variation on fire occurrences to develop a remote sensing-based initial fire likelihood index for identifying areas that have a high probability of fire. In this study, $$Q_{{{\text{ig}}}}$$ Q ig of Rothermel’s fire spread model daily data is retrieved at 1 km pixels from MODIS data. MODIS active fire products were used to interpret the $$Q_{{{\text{ig}}}}$$ Q ig of fuels for 10 days before the days of fire occurrences in November 2010 to determine the pre-fire conditions. A formula for converting $$Q_{{{\text{ig}}}}$$ Q ig into an initial fire likelihood index (IFLI) was then used by binary logistic regression method. Analyses show that there was a positive association between suggested IFLI and fire occurrences during the study period with a fair diagnostic accuracy of 92%, and 80% for dead and live fuels, respectively. Mann–kendall test suggested that there are significant trends in the fuel moisture content time-series for both live and dead fuels. Further analysis using the Hosmer–Lemeshow test represents that the models showed an acceptable fit. The suggested IFLI is an effective tool for fire management decision-making whenever a near real-time fire likelihood is required.