Spotting ignition of larch (Larix gmelinii) fuel bed by different firebrands

Spot fire increase the difficulty of fire-fighting and threaten public safety, and therefore it is important to study ignition probabilities of fuel bed by different firebrands, in order to understand ignition mechanisms and analyze the formation of spot fires. This will provide an important basis for further study to improve the fire-fighting efficiency and reduce casualties. In this study, the ignition probabilities of larch (Larix gmelinii) fuel beds with different moisture levels and packing ratios by diffreent firebrands, including cones and twigs of different sizes, was investigated. Ignition experiments were conducted at different wind speeds generated by fans. The results show that, regardless of moisture content and packing ratio, ignition probability is zero when there is no wind. Both moisture content and wind speed significantly influence ignition probability, while packing ratio has almost no effect. The maximum moisture content at which firebrand ignition occurred was 50%, and ignition probability increased with wind speed and decreased with moisture content. Cones have the highest ignition probability, followed by large twigs and by small twigs. Ignition probability is also affected by firebrand shapes and sizes that determine their potential heat and contact area to the fuel bed. Two empirical models were established to link ignition probability with fuel properties and wind speed. This study will help clarify the mechanism of spot ignition and reduce corresponding losses.

Experiments on the influence of spot fire and topography interaction on fire rate of spread

Spotting is thought to increase wildfire rate of spread (ROS) and in some cases become the main mechanism for spread. The role of spotting in wildfire spread is controlled by many factors including fire intensity, number of and distance between spot fires, weather, fuel characteristics and topography. Through a set of 30 laboratory fire experiments on a 3 m x 4 m fuel bed, subject to air flow, we explored the influence of manually ignited spot fires (0, 1 or 2), the presence or absence of a model hill and their interaction on combined fire ROS (i.e. ROS incorporating main fire and merged spot fires). During experiments conducted on a flat fuel bed, spot fires (whether 1 or 2) had only a small influence on combined ROS. Slowest combined ROS was recorded when a hill was present and no spot fires were ignited, because the fires crept very slowly downslope and downwind of the hill. This was up to, depending on measurement interval, 5 times slower than ROS in the flat fuel bed experiments. However, ignition of 1 or 2 spot fires (with hill present) greatly increased combined ROS to similar levels as those recorded in the flat fuel bed experiments (depending on spread interval). The effect was strongest on the head fire, where spot fires merged directly with the main fire, but significant increases in off-centre ROS were also detected. Our findings suggest that under certain topographic conditions, spot fires can allow a fire to overcome the low spread potential of downslopes. Current models may underestimate wildfire ROS and fire arrival time in hilly terrain if the influence of spot fires on ROS is not incorporated into predictions.

Analysis of Variation in Distance, Number, and Distribution of Spotting in Southeast Australian Wildfires

Spotting during wildfires can significantly influence the way wildfires spread and reduce the chances of successful containment by fire crews. However, there is little published empirical evidence of the phenomenon. In this study, we have analysed spotting patterns observed from 251 wildfires from a database of over 8000 aerial line scan images capturing active wildfire across mainland southeast Australia between 2002 and 2018. The images were used to measure spot fire numbers, number of “long-distance” spot fires (> 500 m), and maximum spotting distance. We describe three types of spotting distance distributions, compare patterns among different regions of southeast Australia, and associate these with broad measures of rainfall, elevation, and fuel type. We found a relatively high correlation between spotting distance and numbers; however, there were also several cases of wildfires with low spot fire numbers producing very long-distance spot fires. Most long-distance spotting was associated with a “multi-modal” distribution type, where high numbers of spot fires ignite close to the source fire and isolated or small clumps of spot fires ignite at longer distances. The multi-modal distribution suggests that current models of spotting distance, which typically follow an exponential-shaped distribution, could underestimate long-distance spotting. We also found considerable regional variation in spotting phenomena that may be associated with significant variation in rainfall, topographic ruggedness, and fuel descriptors. East Victoria was the most spot-fire-prone of the regions, particularly in terms of long-distance spotting.

Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

Effects of fuel morphology on ember generation characteristics at the tree scale

This work reports characteristics of embers generated by torching trees and seeks to identify the important physical and biological factors involved. The size of embers, number flux and propensity to ignite spot fires (i.e. number flux of ‘hot’ embers) are reported for several tree species under different combinations of number (one, three or five) and moisture content (11–193%). Douglas-fir (Pseudotsuga menziesii), grand fir (Abies grandis), western juniper (Juniperus occidentalis) and ponderosa pine (Pinus ponderosa) trees were evaluated. Embers were collected on an array of fire-resistant fabric panels and trays filled with water. Douglas-fir trees generated the highest average ember flux per kilogram of mass loss during torching, whereas grand fir trees generated the highest ‘hot’ ember flux per kilogram of mass loss. Western juniper produced the largest fraction of ‘hot’ embers, with ~30% of the embers generated being hot enough to leave char marks. In contrast, only 6% of the embers generated by ponderosa pine were hot enough to leave char marks. Results from this study can be used to help understand the propensity of different species of tree to produce embers and the portion of embers that may be hot enough to start a spot fire.

Drivers of long-distance spotting during wildfires in south-eastern Australia

We analysed the influence of wildfire area, topography, fuel, surface weather and upper-level weather conditions on long-distance spotting during wildfires. The analysis was based on a large dataset of 338 observations, from aircraft-acquired optical line scans, of spotting wildfires in south-east Australia between 2002 and 2018. Source fire area (a measure of fire activity) was the most important predictor of maximum spotting distance and the number of long-distance spot fires produced (i.e. >500m from a source fire). Weather (surface and upper-level), vegetation and topographic variables had important secondary effects. Spotting distance and number of long-distance spot fires increased strongly with increasing source fire area, particularly under strong winds and in areas containing dense forest and steep slopes. General vegetation descriptors better predicted spotting compared with bark hazard and presence variables, suggesting systems that measure and map bark spotting potential need improvement. The results from this study have important implications for the development of predictive spotting and wildfire behaviour models.

Investigation of the effects of interactions of intersecting oblique fire lines with and without wind in a combustion wind tunnel

Quantification of the interaction of intersecting and non-intersecting fire perimeters over a range of shapes, sizes and orientations is essential to understanding the behaviour of high-intensity wildfires that have become discontiguous as a result of spot fires or effects of broken topography or fuels. One key configuration is that of the V-shaped fire where two individual lines of fire intersect at oblique angles. Previous work under calm conditions in pine needle litter and straw found the speed of propagation of the vertex of the intersection to increase non-linearly as the angle of intersection decreased. The present paper investigates this relation in dry eucalypt forest litter in both the absence and presence of wind (~1.0ms−1) and found that the increase in vertex speed under calm conditions was no greater than would be expected due to the geometry of the configuration. Conversely, in the presence of wind, the increase in the vertex propagation speed was substantially greater than explained by the geometry alone. Although these results suggest that fire line interactions can influence the behaviour and spread of coalescing fire fronts, further research is required to both identify the precise mechanisms driving this behaviour and quantify the resultant effects.