School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemQuantifying merging fire behaviour phenomena using unmanned aerial vehicle technology(CSIRO PUBLISHING, 2020-12-10)Catastrophic wildfires are often a result of dynamic fire behaviours. They can cause rapid escalation of fire behaviour, increasing the danger to ground-based emergency personnel. To date, few studies have characterised merging fire behaviours outside the laboratory. The aim of this study was to develop a simple, fast and accurate method to track fire front propagation using emerging technologies to quantify merging fire behaviour at the field scale. Medium-scale field experiments were conducted during April 2019 on harvested wheat fields in western Victoria, Australia. An unmanned aerial vehicle was used to capture high-definition video imagery of fire propagation. Twenty-one junction and five inward parallel fire fronts were identified during the experiments. The rate of spread (ROS) of junction fire fronts was found to be at least 60% higher than head fire fronts. Thirty-eight per cent of junction fire fronts had increased ROS at the final stage of the merging process. Furthermore, the angle between two junction fire fronts did not change significantly in time for initial angles of 4–14°. All these results contrast with previous published work. Further investigation is required to explain the results as the relationship between fuel load, wind speed and scale is not known.
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ItemDetermination of firebrand characteristics using thermal videos(MDPI AG, 2020-12-01)Burning firebrands generated by wildland or prescribed fires may lead to the initiation of spot fires and fire escapes. At the present time, there are no methods that provide information on the thermal characteristics and number of such firebrands with high spatial and temporal resolution. A number of algorithms have been developed to detect and track firebrands in field conditions in our previous study; however, each holds particular disadvantages. This work is devoted to the development of new algorithms and their testing and, as such, several laboratory experiments were conducted. Wood pellets, bark, and twigs of pine were used to generate firebrands. An infrared camera (JADE J530SB) was used to obtain the necessary thermal video files. The thermograms were then processed to create an annotated IR video database that was used to test both the detector and the tracker. Following these studies, the analysis showed that the Difference of Gaussians detection algorithm and the Hungarian tracking algorithm upheld the highest level of accuracy and were the easiest to implement. The study also indicated that further development of detection and tracking algorithms using the current approach will not significantly improve their accuracy. As such, convolutional neural networks hold high potential to be used as an alternative approach.
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ItemFrequency of Dynamic Fire Behaviours in Australian Forest Environments(MDPI, 2020-03)Wildfires can result in significant social, environmental and economic losses. Fires in which dynamic fire behaviours (DFBs) occur contribute disproportionately to damage statistics. Little quantitative data on the frequency at which DFBs occur exists. To address this problem, we conducted a structured survey using staff from fire and land management agencies in Australia regarding their experiences with DFBs. Staff were asked which, if any, DFBs were observed within fires greater than 1000 ha from the period 2006–2016 that they had experience with. They were also asked about the nature of evidence to support these observations. One hundred thirteen fires were identified. Eighty of them had between one and seven DFBs with 73% (58 fires) having multiple types of DFBs. Most DFBs could commonly be identified through direct data, suggesting an empirical analysis of these phenomena should be possible. Spotting, crown fires and pyro-convective events were the most common DFBs (66%); when combined with eruptive fires and conflagrations, these DFBs comprise 89% of all cases with DFBs. Further research should be focused on these DFBs due to their high frequencies and the fact that quantitative data are likely to be available.
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ItemImproving Fire Behaviour Data Obtained from Wildfires(MDPI, 2018-02)Organisations that manage wildfires are expected to deliver scientifically defensible decisions. However, the limited availability of high quality data restricts the rate at which research can advance. The nature of wildfires contributes to this: they are infrequent, complex events, occur with limited notice and are of relatively short duration. Some information is typically collected during wildfires, however, it is often of limited quantity and may not be of an appropriate standard for research. Here we argue for a minimum standard of data collection from every wildfire event to enhance the advancement of fire behaviour research and make research findings more internationally relevant. First, we analyse the information routinely collected during fire events across Australia. Secondly, we review research methodologies that may be able to supplement existing data collection. Based on the results of these surveys, we develop a recommended list of variables for routine collection during wildfires. In a research field typified by scarce data, improved data collection standards and methodologies will enhance information quality and allow the advancement in the development of quality science.