Fig 5 - uploaded by Alexander I. Filkov
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Multiplexed video file. Top window shows a yellow trapezium frame of georeferenced filmed area on the map. Bottom window shows an original video frame.
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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 f...
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... 4a) within the ArcGIS Pro software (Macdonald 2017). The video file was converted into an FMV-compliant format (georeferenced) before analysing the video footage. The metadata file containing sensor information is combined with the video file in a process called Multiplexing (Fig. 4b). The result is a video file with each frame georeferenced (Fig. 5, bottom window). Once it is multiplexed, the georeferenced frame of the filmed area appears on the map (Fig. 5, top window). The frame had a trapezium shape with dimensions of 100 m (top) Â 110 m (base) Â 60 m (sides). The multiplexed video file was then used to identify and spatially define fire fronts at set time intervals. The ...
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... (georeferenced) before analysing the video footage. The metadata file containing sensor information is combined with the video file in a process called Multiplexing (Fig. 4b). The result is a video file with each frame georeferenced (Fig. 5, bottom window). Once it is multiplexed, the georeferenced frame of the filmed area appears on the map (Fig. 5, top window). The frame had a trapezium shape with dimensions of 100 m (top) Â 110 m (base) Â 60 m (sides). The multiplexed video file was then used to identify and spatially define fire fronts at set time intervals. The process of multiplexing takes ,3 min for each minute of video (Intel i7 CPU, 32 Gb ...
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... the ignition line was started, the fire front produced fire tongues (Fig. 5, bottom window). Fire lines of two neighbouring tongues naturally merged together were identified as junction fire fronts and the angle between them as the initial angle. Parallel fire fronts were identified as two fire tongues burning parallel to each ...
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... measurements were done in the area surrounded by the trapezium frame with the longest base of 110 m (Fig. 5) and after 30 s from ignition to avoid the build-up phase influencing the results (Cheney and Gould 1997). A head fire takes time to reach equilibrium or steady-state ROS after ignition; this process has been referred as the fire growth or build-up (Cheney and Gould 1997). Cheney et al. (1993) found that line fires in grasslands ...
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... of video footage of merging fire fronts revealed that in all cases, junction fire fronts have a different shape to those in previous studies (Fig. 15). Different configurations of ignition lines can result in different ROS of the intersect point R P . It is hypothesised that the left and right shoulder (Fig. 15b) create complex convective structures and cause changes in the ROS. In our experiments, we observed increase of the ROS in almost 40% of junction fire fronts at the final ...
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... of video footage of merging fire fronts revealed that in all cases, junction fire fronts have a different shape to those in previous studies (Fig. 15). Different configurations of ignition lines can result in different ROS of the intersect point R P . It is hypothesised that the left and right shoulder (Fig. 15b) create complex convective structures and cause changes in the ROS. In our experiments, we observed increase of the ROS in almost 40% of junction fire fronts at the final stage of merging in Fig. 9. Variation of the rate of spread R P of the intersect point of two junction fire fronts. Angles in the legend indicate the value at the ...
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... slope of the regression line, Adj. R 2 is the adjusted R 2 , P is the significance value. R P at the 100% time step corresponds to the last time the angle between junction fire fronts became 1808. contrast to a decrease or no change in the ROS in previous research (Viegas et al. , 2013Raposo et al. 2018;Sullivan et al. 2019). This configuration (Fig. 15b) may also prevent an increase of an angle between two fire lines for initial angles smaller than 308 and requires further investigation. Although Sullivan et al. (2019) found that for the wind-driven experiments, there is an increase of the rate of propagation of the vertex above what would be expected from trigonometry alone (Eqn 2), ...
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Citations
... Thus, developing a better understanding of the dynamics of parallel behaviour and the impact of ambient conditions on fire spread is crucial. Recently, two experimental studies (Filkov et al. 2020;Ribeiro et al. 2022) have been carried out to understand the dynamics behind the parallel fire on a limited set of ambient conditions. Experimental studies are expensive which requires measuring devices, equipment, manpower, fire safety protocols and can be carried out only on a limited set of ambient conditions with very limited control on those conditions. ...
Background. Wildfire often shows complex dynamic behaviour due to the inherent nature of ambient conditions, vegetation and ignition patterns. Merging fire is one such dynamic behaviour that plays a critical role in the safety of structures and firefighters. Aim & method. The aim of this study was to develop better insight and understanding of the interaction of parallel merging firelines, using a numerical validation of a physics-based CFD wildfire model concerning merging fires. Conclusions. The validated model shows a relative error of 5-35% in estimating the rate of fire spread compared with the experimental observation in most of the cases. A physical interpretation is presented to show how parallel fire behaves and interacts with the ambient conditions, providing complementary information to the experimental study. Implications. The validated numerical model serves as a base case for further study in developing a better correlation for the rate of fire spread between parallel firelines with different ambient conditions, especially at the field scale.
... However, classical ORS, such as airborne and spaceborne sensors, have spatial and temporal resolution limitations and high operational costs, although images are usually free for users. These limitations can be solved with the use of Unmanned Aerial Vehicle & Remote Sensing (UAV-RS) technologies (Matese et al., 2015;Riveros-Burgos et al., 2021), which are widely used in forestry (Giannetti et al., 2020;Guerra-Hernández et al., 2019), agronomy (Jurado et al., 2020;Rallo et al., 2020;Viera-Torres et al., 2020), climate change research (van Tiggelen et al., 2021), natural risk management (Filkov et al., 2021;Weber et al., 2020), and soil science (Garg et al., 2020;Hout et al., 2020). UAV-RS has been used in soil pollution studies (Choe et al., 2008;Jia et al., 2021a) and, when compared with conventional methodologies, it is faster, less expensive, and non-invasive (Chabrillat et al., 2019). ...
The combination of a low-density geochemical survey, multispectral data obtained with Unmanned Aerial Vehicle-Remote Sensing (UAV-RS), and a machine learning technique was tested in the search for a statistically robust prediction of contaminant distribution in soil and vegetation for zones with a highly variable pollutant load. To this end, a novel methodology was devised by means of a limited geochemical study of topsoil and vegetation combined with multispectral data obtained by UAV-RS. The methodology was verified in an area affected by Hg and As contamination that typifies abandoned mining-metallurgy sites in recent decades. A broad selection of spectral indices were calculated to evaluate soil-plant system response, and four machine learning techniques (Multiple Linear Regression, Random Forest, Generalized Boosted Models, and Multivariate Adaptive Regression Spline) were tested to obtain robust statistical models. Random Forest (RF) provided the best non-biased models for As and Hg concentration in soil and vegetation, with R2 and rRMSE (%) ranging from 0.501 to 0.630 and from 180.72 to 46.31, respectively, and with acceptable values for RPD and RPIQ statistics. The prediction and mapping of contaminant content and distribution in the study area were well enough adjusted to the geochemical data and revealed superior accuracy for As than Hg, and for vegetation than topsoil. The results were more precise than those obtained in comparable studies that applied satellite or spectrometry data. In conclusion, the methodology presented emerges as a powerful tool for studies addressing soil and vegetation pollution and an alternative approach to classical geochemical studies, which are time-consuming and expensive.
... Previous attempts to examine the behaviour of junction fire were mostly experimental, at the laboratory scale (Viegas et al. 2012(Viegas et al. , 2013Raposo et al. 2018;Sullivan et al. 2019) with some work at the field scale (Raposo et al. 2018;Filkov et al. 2021). Viegas et al. (2012) conducted a set of non-slope junction fire experiments. ...
... However, there was no enhanced ROS over what was expected from geometric considerations. Filkov et al. (2021) conducted field-scale experiments and developed a method to track fire front propagation using emerging drone technologies for various fire scenarios including merging fire. They found almost constant propagation with an acceleration in the last phase for some cases, in contrast to Viegas et al. (2012Viegas et al. ( , 2013 and Raposo et al. (2018). ...
Background: Junction fires occur when two fire fronts merge. The rate of spread (ROS) and heat release rate (HRR) of the junction increase more quickly than that of each fire front, this effect exacerbated by slopes.
Aims: Numerical modelling of junction fires and an interpretation of their behaviour are given examining the key influencing factors.
Methods: Twenty physics-based simulations of laboratory-scale junction fires were performed for a shrub fuel bed using FIRESTAR3D, varying slope (0°–40°) and junction angles (15°–90°).
Key results: Accelerative and decelerative behaviours were observed for junction angles lower than 45°, but above this, deceleration was absent. The behaviour was firmly related to junction angle evolution, which controlled the flame and interactions between fire fronts. HRR followed similar trends; maximum HRR increased with increasing junction angle. Convection was the primary heat transfer mode in the initial propagation phase. In no-slope cases, radiation was the dominant method of heat transfer, but convection dominated fires on slopes.
Conclusions: The physics-based model provided great insight into junction fire behaviour. The junction angle was critical for determining ROS and fire behaviour.
Implications: The research helped to assess the effects of some topographical parameters in extreme fires. Situational awareness, operational predictions and firefighter safety will consequently improve.
... This problem was studied by Viegas et al. (2012), who initially described this phenomenon as a 'jump fire', and developed a conceptual analytical model for the rate of advance of the intersection point or vertex of two oblique and symmetric fire fronts. Several research works on experimental, analytical and numerical simulation of this problem followed (Sharples et al. 2013;Viegas et al. 2013;Raposo et al. 2015Raposo et al. , 2018Hilton et al. 2016; Thomas et al. 2017;Sullivan et al. 2019;Filkov et al. 2021). The fire fronts were always symmetrical in relation to the vertex V and there was no fuel bed to burn outside the linear straight fire lines. ...
Background. In Pedrógão Grande on 17 June 2017, two fire fronts merged and the propagation of the fire was influenced by the interaction of these non-symmetric fire fronts. Aims. This wildfire motivated us to study a junction fire with two non-symmetrical fire fronts. The analysis of the movement of the intersection point and the angle (γ) between the bisector of the fire lines and the maximum rate of spread (ROS) direction is of particular relevance. Methods. The study was carried out at Forest Fire Laboratory of the University of Coimbra in Lousã (Portugal) with laboratory experiments. Key results. We found that, for small rotation angles (δ), the nondimensional ROS of the intersection point depends on the slope angle (α) and the initial angle between fire fronts. Conclusions. For high α, the non-dimensional ROS was highly influenced by the convection process and γ where the maximum ROS occurred, increased when δ increased. However, the radiation process was more relevant for lower α and influenced the nondimensional ROS. For these cases, the maximum spread direction was close to that of the fire line bisector. Implications. The present work aimed to explain fire behaviour during the Pedrógão Grande wildfire.
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... The normalized difference vegetation index (NDVI) [19] has been used to identify scorch in other studies [20][21][22] and it is likely that the NDVI from SfM-derived data can be used similarly to classify and map scorched conifer foliage within individual tree crowns. In parallel, RPAS also allow for fine-scale fire behavior estimation [23], including with thermal imaging [24], and a logical step is to then relate radiant fire energy to post-fire scorch as observed from this close-range, overhead perspective. Collectively, these sensors and techniques offer powerful new methods to study the causes, patterns, and consequences of crown scorch phenomena. ...
Remotely piloted aircraft systems (RPAS) are providing fresh perspectives for the remote sensing of fire. One opportunity is mapping tree crown scorch following fires, which can support science and management. This proof-of-concept shows that crown scorch is distinguishable from uninjured canopy in point clouds derived from low-cost RGB and calibrated RGB-NIR cameras at fine resolutions (centimeter level). The Normalized Difference Vegetation Index (NDVI) provided the most discriminatory spectral data, but a low-cost RGB camera provided useful data as well. Scorch heights from the point cloud closely matched field measurements with a mean absolute error of 0.52 m (n = 29). Voxelization of the point cloud, using a simple threshold NDVI classification as an example, provides a suitable dataset worthy of application and further research. Field-measured scorch heights also showed a relationship to RPAS-thermal-camera-derived fire radiative energy density (FRED) estimates with a Spearman rank correlation of 0.43, but there are many issues still to resolve before robust inference is possible. Mapping fine-scale scorch in 3D with RPAS and SfM photogrammetry is a viable, low-cost option that can support related science and management.
... Most of experimental studies of merging fires have been conducted in the laboratory (Viegas et al. 2013;Oliveira et al. 2014;Sullivan et al. 2019), and only a few in the field (Raposo et al. 2018). Filkov et al. (2021) have demonstrated that the fire behaviour associated with merging fires in the field can be different and there remain 'scale-gaps' in the experimental data used to inform model development. Moreover, information about thermal behaviour of merging fires and burning depth is unknown. ...
... The use of a drone with a dual visual and thermal camera showed that the thermal camera was able to detect all active hot spots and fire fronts even through dense smoke, which was a significant constraint in our previous study (Filkov et al. 2021). ...
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... Despite these challenges, researchers have initiated data collection efforts on outdoor research fires in the United States (US), Australia and New Zealand (e.g. Finney et al. 2015;Ottmar et al. 2016;Mueller et al. 2017;McNamara and Mell 2018a;Clements et al. 2019;Pearce et al. 2019;Goodrick et al. 2020;Hiers et al. 2020;Johnson 2020;Parsons 2020;Cronan 2021;Filkov et al. 2021;Ottmar et al. 2021). Implementing these burns indicates an ability to overcome significant challenges such as generating funding, organising researchers and deploying sensors. ...
The advancement of three-dimensional, time-dependent fire behaviour models is best supported by publicly available, co-located, synchronised, quality-assured measures of pre-fire, active fire and post-fire conditions (i.e. integrated datasets). Currently, there is a lack of such datasets. Consequently, we discuss essential components to produce integrated datasets: metadata, implementation of geospatial and temporal standards, data management plans, quality assurance project plans and data quality objectives. We present example data quality objectives and a data model for grassland experiments developed based on our experience integrating data from the 2014 Camp Swift Fire and the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research experiments.
