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Wind speed V during experiments. Data for Sonic 1 between 37 and 49 min are absent owing to relocation of the sensor between plots.
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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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... data were taken from the AWS owing to the direction component of the sonic sensors malfunctioning. Wind direction (direction from which wind originates) was northerly at the beginning of the experiment, switching to north-north-westerly at the end. Wind speed was measured every minute 1.5 m above ground and was in the range 1.8-7.0 m s À1 (Fig. 3) with an average speed of 3.6 and 4.7 m s À1 for Sonic 1 and 2 respectively. It was not possible to analyse the influence of wind on merging fires owing to short duration of the merging process (less than 1 min) and coarse temporal resolution (1 min) of the sonic sensors. The slope was less than 58 at all ...
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... was observed during the experiments. It can be assumed that because the developed parallel fire fronts were not perfectly aligned with the wind direction (not possible in the field experiments), the resulting ROS was different for the left and right fire lines. It is supposed that small fluctuations of the ROS related to change in the wind speed (Fig. 3) rather than fire itself. The ROS of the linear fire fronts R l did not change considerably during the lifetime of the merging fires and did not influence merging fire front development (Fig. 12). Standard deviation of R l varied in the range 0.04-0.55 m s À1 . Fig. 13 shows comparison of our results with studies of Sullivan et al. ...
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... that small fluctuations of the ROS related to change in the wind speed (Fig. 3) rather than fire itself. The ROS of the linear fire fronts R l did not change considerably during the lifetime of the merging fires and did not influence merging fire front development (Fig. 12). Standard deviation of R l varied in the range 0.04-0.55 m s À1 . Fig. 13 shows comparison of our results with studies of Sullivan et al. (2019), Viegas et al. (2012) and Thomas et al. (2017). Comparison with the null hypothesis (Eqn 2) and laboratory results of Sullivan et al. (2019) (Fig. 13a) showed that the non-dimensionless ROS R 0 in our experiments was greater and smaller than the null hypothesis (in ...
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... and did not influence merging fire front development (Fig. 12). Standard deviation of R l varied in the range 0.04-0.55 m s À1 . Fig. 13 shows comparison of our results with studies of Sullivan et al. (2019), Viegas et al. (2012) and Thomas et al. (2017). Comparison with the null hypothesis (Eqn 2) and laboratory results of Sullivan et al. (2019) (Fig. 13a) showed that the non-dimensionless ROS R 0 in our experiments was greater and smaller than the null hypothesis (in contrast to Sullivan et al. (2019)) ...
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... comparison with the simplified analytical model of Viegas et al. (2012), we modelled our data with the Belehradek model (Ross 1993) (Fig. 13b). Non-linear regression with the Levenberg-Marquardt algorithm (Ranganathan 2004) was used (adj. R 2 ¼ ...
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... to Sullivan et al. (2019), we did not get agreement with Viegas et al. (2012). Although Viegas et al. (2012) conducted experiments in no-wind conditions, the R 0 in their study was higher than that observed in our results ( Fig. 13b) and those of Sullivan et al. (2019) (Fig. ...
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... to Sullivan et al. (2019), we did not get agreement with Viegas et al. (2012). Although Viegas et al. (2012) conducted experiments in no-wind conditions, the R 0 in their study was higher than that observed in our results ( Fig. 13b) and those of Sullivan et al. (2019) (Fig. ...
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... of dimension ROS (R P ) with the numerical simulation of Thomas et al. (2017) shows good agreement (Fig. 13c) despite different fuel types and ...
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... fuel litter of 1.2 kg m À2 comprising fallen leaves, twigs and bark (10 times higher). Increasing fuel load may result in increase of the ROS owing to a significant effect on the efficiency of heat transfer to unburnt fuel ( Plucinski and Anderson 2008). A comparison with the numerical simulation of Thomas et al. (2017) shows good agreement (Fig. 13c) despite the fact that the fuel load in Thomas et al. (2017) was seven times higher and the experiments were conducted in no-wind conditions. A decrease of fuel moisture content and air relative humidity should increase the ROS (Rossa 2017); however, a limitation of the data is that we cannot estimate these values. Neither Viegas et al. ...
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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.
... GeoScene Pro is a GIS desktop software with powerful data management, mapping, spatial analysis and other capabilities by GeoScene Information Technology Co., Ltd. It integrates ArcMap, ArcSence, ArcGlobe three ArcGIS software functions, can realize the three-dimensional integration of synchronization [31]. ...
Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture.
... 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). ...
O período entre 2018 e 2022 mostrou-nos que o problema dos incêndios à escala global não está a diminuir, antes pelo contrário. Parece que as consequências das alterações climáticas já estão a afectar a ocorrência de incêndios florestais em várias partes do Mundo, de uma forma que só esperaríamos que acontecesse vários anos mais tarde. Em muitos países do Sul da Europa, bem como em algumas regiões dos EUA, Canadá e Austrália, onde estamos habituados a enfrentar a presença de incêndios muito grandes e devastadores, continuamos a ter eventos que quebram recordes. Alguns países, como os da Europa Central e do Norte, que não estavam habituados a ter grandes incêndios, experimentaram-nos durante estes anos. Os anos anteriores foram muito exigentes para todo o Mundo, também noutros aspectos que nos afectaram a todos. Referimo-nos às restrições impostas pela pandemia que limitaram as nossas reuniões e viagens, afectando em muitos casos a saúde dos membros da Comunidade Científica Wildfire. Felizmente, conseguimos encontrar novas formas de comunicação, ultrapassar essas limitações e manter-nos em contacto uns com os outros. Durante semanas e meses, para muitos de nós, as reuniões pessoais e o trabalho de grupo foram substituídos por ligações em linha. Apesar da economia de dinheiro e tempo, e da facilidade de reunir uma grande variedade de pessoas que estas reuniões desde que nos apercebêssemos de que não substituem as reuniões presenciais, que trazem consigo outras dimensões inestimáveis, que fazem parte da comunicação pessoal e ajudam a construir uma comunidade científica.
... 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.
