Semi-Automated Extraction of Active Fire Edges from Tactical Infrared Observations of Wildfires

The aim of this paper is to propose a more practical mountain fire spread model for fire behavior prediction and management in Southwest forest area of China. These areas are covered mainly with spatial heterogeneous flammable forest and are characterized by undulating terrain and steep slopes. This model can produce more accurate fire propagation maps by combining CA (Cellular Automaton) framework with Wang Zhengfei fire physical velocity model in fine scale. Considering the inherent uncertainties of fire propagation, the model has been built on multi-dimension geophysical and environmental components and also sound knowledge of fire spread physical mechanism. Regarding small fuel patches as spatial homogenous cells, this approach makes it easier to generate higher level complex fire behavior maps from CA simple local rules and local behavior integrated with high resolution vegetation images, fine scale terrain maps and surface wind field. Because the model focuses primarily on the study of surface fire front propagation behavior, it attempts to simplify complex fuel modeling. Additionally, this Wang-Geophysical-CA model is able to analyze the time series spatial pattern of fire-front spread and model local behavior instead of the final fire spread pattern of the conventional approach. In this work, not only single influence verification tests have been made, but also simulation tests with multiple influences are carried out to demonstrate the capability of the model with fine scale vegetation maps, surface wind field, terrain, moisture content and man-made structures. Consequently, it is believable that the model predictions are in good agreement with experimental data for steady-state fire simulation. The proposed model helps to gain a greater understanding of the fire front spread local behavior and can quickly generate a sequence of complex fire front contours. It enables local managers to plan practical fire prevention activities in Southwest forest area of China as well as improve fire management skills, and will enhance the effectiveness of fire fighting strategies.

Wildfires create burnt areas which may be vulnerable to increased erosion when affected by high intensity or long duration storm rainfall. This vulnerability is most apparent in the first year after fire and reduces as soil and vegetation recover. Post-fire erosion events may deliver large quantities of sediment and associated contaminants to streams and reservoirs, potentially resulting in water that is undeliverable to cities and towns. A suitable model to determine the magnitude and likelihood of large sediment loads initiated by the random combination of fire and storm rainfall in south-eastern Australian forests does not currently exist. A new model is being developed that determines the risk of these high-magnitude sediment loads based on the combined probability of fire and storm rainfall events. The model combines deterministic models for fire spread, erosion and sediment delivery processes within an annual Monte Carlo simulation of fire ignitions and storm rainfall. By simulating fire behaviour and analysing historical fire and weather records, it is possible to estimate the probability, extent and severity of a large range of possible wildfires. This range will be combined with a range of possible storm rainfalls to give the risk of large sediment producing conditions. The Monte Carlo model is initially to be implemented in Melbourne's two largest water supply catchments, the Upper Yarra and the Thompson. These forested catchments provide Melbourne with approximately 80% of its water supply. Current work on this model is focused on the fire modelling and this paper will deal exclusively with this part of the system. While the fire modelling method is complete, the fire model is currently in the process of calibration and validation. Fire behaviour is simulated using the PHOENIX fire behaviour model developed at the University of Melbourne. PHOENIX is presently used by Victoria's Department of Sustainability and Environment to simulate fire behaviour and model fire management scenarios. Simulated fire spread is determined by a weather time series which may also be used to calculate an instantaneous Forest Fire Danger Index (FFDI). A revised FFDI was used by PHOENIX to incorporate changes in fuel moisture in response to the variation of solar radiation over the course of the day. Wildfires were simulated using historical fire weather for the 344 worst fire days on record. These 344 days compose a partial duration series of severe fire weather days containing all daily peak FFDI days greater than the lowest annual maximum FFDI on record. These fires were individually ignited on a 6km grid covering a large area of central Victoria. These simulations provided a dataset of over 80,000 possible fires to inform the Monte Carlo model. The described approach to modelling fire behaviour will deliver the dataset required by the overall Monte Carlo model while working within the limitations of PHOENIX and the available historical weather and fire records.

Wildland fire radiant energy emission is one of the only measurements of combustion that can be made at wide spatial extents and high temporal and spatial resolutions. Furthermore, spatially and temporally explicit measurements are critical for making inferences about fire effects and useful for examining patterns of fire spread. In this study we describe our methods for capturing and analysing spatially and temporally explicit long-wave infrared (LWIR) imagery from the RxCADRE (Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment) project and examine the usefulness of these data in investigating fire behaviour and effects. We compare LWIR imagery captured at fine and moderate spatial and temporal resolutions (from 1 cm2 to 1 m2; and from 0.12 to 1 Hz) using both nadir and oblique measurements. We analyse fine-scale spatial heterogeneity of fire radiant power and energy released in several experimental burns. There was concurrence between the measurements, although the oblique view estimates of fire radiative power were consistently higher than the nadir view estimates. The nadir measurements illustrate the significance of fuel characteristics, particularly type and connectivity, in driving spatial variability at fine scales. The nadir and oblique measurements illustrate the usefulness of the data for describing the location and movement of the fire front at discrete moments in time at these fine and moderate resolutions. Spatially and temporally resolved data from these techniques show promise to effectively link the combustion environment with post-fire processes, remote sensing at larger scales and wildland fire modelling efforts.

Wildland fire fuels database for Corsican - Mediterranean Forest stand types

With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

BackgroundFire managers tasked with assessing the hazard and risk of wildfire in Alaska, USA, tend to have more confidence in fire behavior prediction modeling systems developed in Canada than similar systems developed in the US. In 1992, Canadian fire behavior systems were adopted for modeling fire hazard and risk in Alaska and are used by fire suppression specialists and fire planners working within the state. However, as new US-based fire behavior modeling tools are developed, Alaskan fire managers are encouraged to adopt the use of US-based systems. Few studies exist in the scientific literature that inform fire managers as to the efficacy of fire behavior modeling tools in Alaska. In this study, I provide information to aid fire managers when tasked with deciding which system for modeling fire behavior is most appropriate for their use. On the Magitchlie Creek Fire in Alaska, I systematically collected fire behavior characteristics within a black spruce (Picea mariana [Mill.] Britton, Sterns & Poggenb.) ecosystem under head fire conditions. I compared my fire behavior observations including flame length, rate of spread, and head fire intensity with fire behavior predictions from the US fire modeling system BehavePlus, and three Canadian systems: RedAPP, CanFIRE, and the Crown Fire Initiation and Spread system (CFIS).ResultsAll four modeling systems produced reasonable rate of spread predictions although the Canadian systems provided predictions slightly closer to the observed fire behavior. The Canadian fire behavior prediction modeling systems RedAPP and CanFIRE provided more accurate predictions of head fire intensity and fire type than BehavePlus or CFIS.ConclusionsThe most appropriate fire behavior modeling system for use in Alaskan black spruce ecosystems depends on what type of questions are being asked. For determining the rate of fire movement across a landscape, REDapp, CanFIRE, CFIS, or BehavePlus can all be expected to provide reasonably accurate estimates of rate of spread. If fire managers are interested in using predicted flame length or energy produced for informing decisions such as which firefighting tactics will be successful, or for evaluating the ecological impacts due to burning, then the Canadian fire modeling systems outperformed BehavePlus in this case study.

(1) South African fynbos (sclerophyllous shrubland) vegetation is fire-prone, and fire is important in fynbos management. No data on fire behaviour in fynbos are presently available. (2) The behaviour of fourteen experimental fires in fynbos tall open shrublands is described. Rates of spread ranged from 0.04 to 0.89 m s-1, flame lengths from 2 8 to 7.0 m and fire intensities from 515 to 20 709 kW m-1. (3) The fire behaviour is compared to predictions from Rothermel's fire spread model, which uses fuel characteristics and environmental conditions to predict fire spread and intensity. Predictions of rate of spread and flame length were good but fire intensity was underestimated where biomass and fire hazard were high. (4) The results are compared to fire behaviour in other shrubland ecosystems. Rates of fire spread and fire intensity are greater in fynbos than in Scottish heathland, and are equivalent to those reported for Californian chaparral. (5) The inclusion of fire danger indices and predictions based on Rothermel's model in fynbos fire records will enhance their value. The model can also be useful in fire research, particularly in homogenous vegetation, and represents an improvement on techniques such as the measurement of fire temperature.

In order to solve the problem of unsatisfactory monitoring information transmission and large time overhead during conventional building fire monitoring, an optimisation method of building fire information monitoring based on adaptive clustering scheduling is proposed. In this method, a channel model for building fire information monitoring is constructed through the bi-directional link transmission control method, and then node deployment for building fire information monitoring is optimised through the shortest path optimisation method. The deployment of the largest coverage of fire information monitoring sensor nodes is designed through the self-adaptive rotation scheduling, and balance control of the output link layer of internet of things is performed through the adaptive clustering scheduling method to improve the accurate forwarding and real-time transmission capabilities of internet of things for fire detection information, and then a building fire information monitoring model is constructed. Experimental results show that the proposed method can effectively improve the success rate of fire information monitoring packet forwarding with an average increase of 24.7%, which greatly improves the monitoring information transmission efficiency, and it reduces the time overhead of fire information monitoring by 160s. The proposed method meets the actual needs and ensures the effectiveness of fire monitoring.

All-optical modulator for gated range finding and active imaging in LWIR

Accurate prediction of bushfire behaviour is essential for effective fire management. Such knowledge allows for the timely determination of the potential threat and impacts of a fire and provides the basis for sound fire-management decision-making. Fire behaviour prediction combines quantitative and qualitative information sources that are based on scientific principles and personal experience describing the combustion and behaviour of fire in a range of weather, fuel and topographic conditions. 'Amicus', the National Fire Behaviour Knowledge Base, is a new software-based tool under development that endeavours to provide a unique framework in which each of these information sources is accessible and utilisable in a consistent and comprehensive manner for the sole purpose of operational prediction of the behaviour of bushfires by trained fire behaviour analysts. The Amicus system comprises four primary components: fuel description, fuel moisture models, wind models and fire behaviour models and uses these to predict fire characteristics (e.g. rate of spread, flame height, fireline intensity, onset of crowning, spotting potential) for a broad range of burning conditions. This paper details the current development of the fire behaviour component of Amicus and its proposed integration with the Australian Bushfire Fuel Classification System being developed for use across all Australian jurisdictions. The fire behaviour component will integrate a suite of fire behaviour models covering the main Australian fuel types: eucalyptus forests, exotic pine plantations, grasslands and shrublands. Further development of Amicus will integrate the fire weather, fuel dynamics, and suppression capability knowledge and science to help fire managers better predict bushfire behaviour and better plan prescribed burns.

Vegetation and peat fires contribute substantially to global emissions of greenhouse gases (GHG). According to latest estimates, net fire emissions amount to about 6% of global fossil fuel GHG emission. Improving the management of fires in frequently burning ecosystems can help reduce GHG emissions and thus contribute to mitigation of climate change. Monitoring and analysis of fires over large and often remote areas is only feasible with the help of Earth Observation (EO) satellites. Over the last decades, availability of free EO data has increased enormously, as has the availability of computing power, network speed and web based geospatial visualization and analysis technologies. Thermal sensors on geostationary or polar orbiting platforms make it possible to observe active fires with high frequency, while sensors in visible to short wave infrared wavelength on the Sentinel and Landsat satellite series enable the production of burned area maps with high spatial resolution every week. We introduce here an approach to integrate monitoring of fire activity and carbon fluxes, weekly updated burned areas, daily analysis and forecast of relevant weather parameters, long time series of fire emissions to calculate baselines, fire risk and vulnerability maps and tools to monitor success of fire management planning and implementation in a web based solution. Coupling of remote sensing data with weather information and fire spread models enables forecasting and detailed hindsight analysis of the behavior of wildfires. To develop a new information product to analyze fire intensity, we assessed fire spread and fire radiative energy release rate (fire radiative power) over savanna fires using infrared sensors with different spatial, spectral and temporal resolutions. From these results we derive metrics on fire behavior in our study areas. We relate our results to outputs of fire behavior models and to results to published values. Finally, we discuss how organizations can make use of the provided information products to implement, monitor and document success in fire management.

It is important to investigate the urban and wildland fire behavior to mitigate the fire hazards. There have been many studies on such fires, but the need of real time fire simulations has recent increased and a demand to predict fire spread patterns in urban and wildland regions for decision-making strategies against fires has emerged. However, the knowledge of fire spread behavior is still insufficient, particularly for the condition of discrete fuel distributions. Under this condition the fire spread behavior shows high complexity due to the significant interactions between the radiation, conduction and convection heat transfer, especially under significant ambient wind effects. This paper investigates urban and wildland fire spread behavior by utilizing CFD simulations for two types of fuels under the effect of wind. A 15×15 square array, consisting of 225 fuel sources, is used to simulate the discrete fuel distribution, with varying fuel spacing and wind speed. The simulation method is similar to that used in our previous study, but with different ignition heaters. The comparison of the simulated results for the reduced and real scale models is reasonable, as verified by the similarity law. The critical fire spread distance, the wind effect upon fire spread, and the variation of fire spread rate for the two types of fuels are extensively investigated.

The project OFIDIA2 (Operational FIre Danger preventIon plAtform 2), funded by the Interreg Greece-Italy 2014-2020 Programme, proposed a pragmatic approach to improve the operational capacity of the stakeholders to detect and fight forest wildfires. A data analytics system was designed and implemented within the project to manage, transform, and extract knowledge from heterogenous data sources, through forecasting models such as weather, fire danger, and fire behaviour models. The high-resolution weather forecasting network previously developed in OFIDIA1 was enhanced by using a mesoscale configuration of the WRF-ARW model over the Central Mediterranean Sea. A nested domain over the Southern Italy at ~2km horizontal resolution allows getting high-resolution weather forecasts (2x2km) and processing data into fire danger models. Fires, fuel, topography and weather data were collected from several sources and used to run and calibrate fire models (FlamMap and Wildfire Analyst) in Apulia region (Italy). Based on the analyses of recurrent weather conditions leading to large fires, fire metrics’ maps for prevention and fire-fighting activities were produced. Finally, a Decision Support System (DSS) was also developed to provide support for 1) the selection of fire behaviour scenarios by means of mathematical models; and 2) the prevention of emergencies thanks to weather forecast information with fire danger indices at high resolutions.

Simard et al. (2011) have produced a comprehensive data set and analysis concerning mountain pine beetle (MPB; Dendroctonus ponderosae)-caused mortality and associated crown fire feedbacks in lodgepole pine (Pinus contorta)-dominated forests. Misapplication of the NEXUS fire modeling system (Scott and Reinhardt 2001) results in the suspect conclusion that active crown fire (perpetuation of flames through the canopy) probability is reduced in all post-mortality stages. Simard et al. (2011) assert that the loss of canopy fuel following tree mortality overwhelms the concomitant loss of foliar moisture content (FMC) but do not fully account for this drop in moisture or the resulting increase in surface fuels. Here, we show how to account for decreases in FMC and increases in surface fuels within NEXUS and report findings contrary to Simard et al.’s (2011) conclusions for the red stage (dead needles still within canopy). Overall, NEXUS is a questionable choice for this research due to its inherent lack of crown fire predictive capability (Cruz and Alexander 2010) and empirically derived crown fire models developed using living canopies (Van Wagner 1977, Rothermel 1991, Scott and Reinhardt 2001). NEXUS and related fire modeling systems (e.g., FARSITE, FlamMap, BehavePlus, and FFE-FVS) rely on the integration of Rothermel’s (1972) surface and crown (1991) fire spreadmodels with VanWagner’s (1977, 1993) crown fire transition and propagation models (Scott 2006, Cruz and Alexander 2010). The fire modeling community recognizes the need for calibration of model input variables and parameters, including custom surface fuel models, which Simard et al. (2011) employ, to achieve accurate representations of observed, and thus predicted, fire behavior. Without calibration, quantitative fire behavior output values can be wildly unrealistic, as large under-prediction biases are prevalent (Cruz and Alexander 2010). For example, necessitating 1000 km/h wind speeds to initiate crown fire in undisturbed lodgepole pine forests (Simard et al. 2011). However, relative comparisons of disturbed and undisturbed forests’ fire behavior are possible when full consideration is given to the primary drivers of fire behavior.

 Modelling and computer simulations have been used to manage and fight wildfires for many years. The primary goal of these numerical models is to predict fire spread and behaviour under various scenarios - i.e. weather, vegetation and topography. The advantage of cellular automata resides primarily in proposing a fire propagation model which attempts to simplify tremendously the physical model of known fire dynamics. Therefore, the model should be defined by a relatively small and simple set of rules that apply locally as to reduce the problem complexity while not sacrificing the model explanatory power at larger scale.The objective of our work is to test the suitability of the continuous cellular automata approach to model grass fire behaviour in highly heterogeneous landscapes. After reviewing and testing previous works (Karafyllidis and Thanailakis 1997, Berjak and Hearne 2002, Hernández Encinas et al. 2007), we derive a generalized CA ruleset that eschews most physical considerations in favour of the conservation of large scale fire properties in simplified landscapes, such as fire front shapes, area burned and behaviour in the presence of rate of spread heterogeneities.The ruleset is then incorporated into a GIS based model where each cell holds properties derived from discretised landscape features. With this approach, the landscape becomes a lattice of cells in the cellular automaton domain where the state of a cell is represented by its percentage of ‘area burned' and where the local fire front behaviour is influenced by terrain, wind and vegetation cover at a given cell. The fire spread model is then tested for north Australia savannas where the vegetation cover is dominated by open forests and woodlands with a grassy understorey. The results of the simulations are used to create fire extent ‘risk maps' centred on known points/zones of ignition, typically defence bombing range and defence training areas. A better validation of the model would be to compare the simulation results to historical fires. However, some challenges remain in integrating detailed vegetation cover, fine grained grass curing maps and weather data.