@conference {bnh-8286,
	title = {Predicting merging fire behaviour in Planned Burning},
	booktitle = {AFAC21},
	year = {2021},
	month = {10/2021},
	publisher = {AFAC},
	organization = {AFAC},
	address = {Online},
	abstract = {<p>It was found that laboratory scale fire experiments do not replicate large scale fires, and therefore, extensive high temporal and spatial resolution experimental data at larger scales are required to fully understand how a given fire will merge and spread. Unmanned aerial vehicles (UAVs or drones) may be a useful tool in obtaining these measurements. To provide operational and management personnel with a better understanding of the dynamic nature of fire line merging, UAVs were used in this study to measure fire rate of spread throughout the burns recording linear fire rates of spread and merging fire rate of spread in a range of fuel types in western Victoria. Using special software, we were able to record the rates of spread over time and determine the effect of ignition patterns on fire behaviour. These utilisation outcomes can be used to better support decision making around ignition methods during planned burning operations, as well as maximising the information available to support firefighter training and further research and development.</p>},
	keywords = {Fire behaviour, fire predictions, UAVs},
	url = {https://www.afac.com.au/events/proceedings/05-10-21/article/predicting-merging-fire-behaviour-in-planned-burning},
	author = {Alex Filkov}
}
