Search

Understanding bushfire–atmosphere interactions is essential for accurate prediction of fire behaviour, and for the safe and effective strategic management of fires to mitigate risk to people and property. Bushfires with feedbacks to thunderstorms represent the most extreme form of fire–atmosphere interaction, with potential to initiate tornadoes, lightning and hazardous winds causing dangerous fire behaviour and new ignitions many kilometres from the fire front. However, there is very little evidence that links quantitative fire behaviour with observed thunderstorm dynamics. Here we combine stochastic modelling of fire behaviour with satellite and mobile weather radar data of a bushfire thunderstorm in Queensland, Australia. The results show the coupling between fire behaviour and thunderstorm development in a conditionally unstable atmosphere. The process by which the coupling occurs raises questions as to the cause and effect relationship of the bushfire-initiated thunderstorms and associated fire behaviour. Recommendations for future research are made, highlighting the need for understanding links between modelled and observed fire behaviour dynamics and atmospheric thermodynamics. Bushfires can support the evolution of thunderstorms. Here, we combine a variety of observations of a bushfire thunderstorm with ensemble fire spread modelling and fire severity mapping. Results show the coupling between the intensity of the fire and its burning through forest canopy with thunderstorm updrafts. [ABSTRACT FROM AUTHOR]

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection. [ABSTRACT FROM AUTHOR]