E. Planas

Mathematical models and calculation systems for the study of wildland fire behaviour

This is a review of the most important work in wildland fire mathematical modelling which has been carried out at different research centres around the world from the beginning of the 1940s to the present. A generic classification is proposed which allows wildland fire models to be sorted. Surface fire spread models, crown fire initiation and spread models, spotting and ground fire models are reviewed historically and the most significant ones are analysed in depth. The last two sections are dedicated to wildland fire behaviour calculation systems based on the reviewed models. The evolution and complexity of these systems is analysed in parallel with the development of new technologies. Special attention is given to the tools most commonly in current use by forestry agencies.

Long-term forest fire retardants: a review of quality, effectiveness, application and environmental considerations

Since the beginning of the 1930s research has been directed towards improving the effectiveness of water as a forest fire extinguishing agent. Throughout this time various chemical substances have been added to the water, and this is still the case today. Among these substances are the various types of long-term forest fire retardant, which maintain their ability to alter combustion when the water has been removed by evaporation. In order to provide an account of the current state of development of studies on long-term forest fire retardants, we carried out a bibliographic analysis with special attention to work done after 1976 on the different aspects that influence the final effectiveness of forest fire retardant: quality (programs and evaluation), effectiveness, application and environmental impact on streams and aquatic organisms, vegetation and humans. The scope of this work covers the wide subject of fire retardants and it introduces the significant works related to all the aspects of fire retardant use.

Short-term fire front spread prediction using inverse modelling and airborne infrared images

A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.

EFFECTS OF THIN-LAYER BOILOVER ON HYDROCARBON POOL FIRES

ABSTRACT Large scale hydrocarbon pool fire experiments with three different diameters were conducted to obtain data on the features of burning diesel oil and gasoline on water. After flame spreading from the ignition point and fire development, a steady-state period was observed for a certain time until the heal transfer from the fire plume through the fuel surface was sufficient to cause boiling of the water sublayer. At this point, a so-called thin-layer boilover phenomenon occurred, resulting in a distinct change in the fire behavior of the entire pool. Basic features of the thin-layer boilover phenomenon such as fuel burning rate, boilover intensity, burnt mass ratio at the onset of boilover, time required for the water sublayer to start to boil, and flame behavior have been analyzed. Finally, a set of conclusions were drawn from experimental results.

Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors

A variety of remote sensing techniques have been applied to forest fires. However, there is at present no system capable of monitoring an active fire precisely in a totally automated manner. Spaceborne sensors show too coarse spatio-temporal resolutions and all previous studies that extracted fire properties from infrared aerial imagery incorporated manual tasks within the image processing workflow. As a contribution to this topic, this paper presents an algorithm to automatically locate the fuel burning interface of an active wildfire in georeferenced aerial thermal infrared (TIR) imagery. An unsupervised edge detector, built upon the Canny method, was accompanied by the necessary modules for the extraction of line coordinates and the location of the total burned perimeter. The system was validated in different scenarios ranging from laboratory tests to large-scale experimental burns performed under extreme weather conditions. Output accuracy was computed through three common similarity indices and proved acceptable. Computing times were below 1 s per image on average. The produced information was used to measure the temporal evolution of the fire perimeter and automatically generate rate of spread (ROS) fields. Information products were easily exported to standard Geographic Information Systems (GIS), such as GoogleEarth and QGIS. Therefore, this work contributes towards the development of an affordable and totally automated system for operational wildfire surveillance.

Summary of workshop large outdoor fires and the built environment

Large outdoor fires present a risk to the built environment. Wildfires that spread into communities, referred to as Wildland-Urban Interface (WUI) fires, have destroyed communities throughout the world, and are an emerging problem in fire safety science. Other examples are large urban fires including those that have occurred after earthquakes. Research into large outdoor fires, and how to potentially mitigate the loss of structures in such fires, lags other areas of fire safety science research. At the same time, common characteristics between fire spread in WUI fires and urban fires have not been fully exploited. In this paper, an overview of the large outdoor fire risk to the built environment from each region is presented. Critical research needs for this problem in the context offire safety science are provided. The present paper seeks to develop the foundation for an international research needs roadmap to reduce the risk of large outdoor fires to the built environment.

Characterization of Laboratory-Scale Fires Propagating Under the Effect of a Long-Term Retardant

Laboratory experiments were conducted in straw fuel beds in order to characterize the effect of a widely used long-term retardant on fire behavior under different conditions: no slope–no wind, upslope–no wind, and no slope–upwind. The results are reported in terms of reduction factors for a set of variables characterizing fire behavior. In the experimental conditions the values of upslope–no-wind and no-slope–upwind fires showed no statistically significant differences from those of no-slope–no-wind fires. For all types of fire, values obtained for the reduction factors on rate of spread, fuel consumption ratio, fire intensity, and flame length were 63%, 36%, 77%, and 54%, respectively.

Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications

Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data-driven applications. This article presents the specific problem of data-driven wildfire spread simulation (with a strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimising the loss of accuracy. The proposed methodology was compared with the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference – pre-run– wind map sets. Results showed a major reduction in computation time (~100 times once the reference fields are available) with average deviations of 3% in wind speed and 3° in direction. This indicates that high-resolution wind fields can be interpolated from a finite set of base maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important effect on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.

GIS-based integration of spatial and remote sensing data for wildfire monitoring

The impact of wildfires on society is increasing. Annually burned area, fire season length and fire severity have increased during the last decade in several regions of the world, and a number of tragic events have occurred recently in fire-prone areas. Whereas forest fires are natural phenomena, emergency management must be improved in order to minimise human and economic losses as well as undesired effects to ecosystems not resilient enough to fire. However, wildland fire behaviour is not completely understood. Fire rate of spread depends on terrain, weather and vegetation, but the exact relationship between all involved variables is unknown. Remote sensing can help to solve this issue because it has a great potential to measure real-scale fire behaviour with a high spatio-temporal resolution. In this paper, we propose the use of a Geographic Information System (GIS) to combine wildfire remote sensing data with spatial information about vegetation, weather and terrain. This integrated framework facilitates the systematic analysis of multisource data and the study of observed relationships between variables. We describe which operations may be applied to remote sensing and geospatial data in order to display the observed fire evolution and extract relevant statistical relationships. Among others, fire spread is displayed over a topographic map; burned area and fire perimeter evolution are measured; spatially-explicit rates of spread (ROS) are computed; surface ROS is derived from horizontal ROS using a Digital Elevation Model (DEM); and relationships between ROS, intensity, slope and vegetation type are studied.

Flame filtering and perimeter localization of wildfires using aerial thermal imagery

Airborne thermal infrared (TIR) imaging systems are being increasingly used for wild fire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.