Discrete-event modelling of fire spreading

We deal here with the application of discrete-event System Specification (DEVS) formalism to implement a semi-physical fire spread model. Currently, models from physics finely representing forest fires are not efficient and still under development. If current softwares are devoted to the simulation of simple models of fire spread, nowadays there is no environment allowing us to model and simulate complex physical models of fire spread. Simulation models of such a type of models require being easily designed, modified and efficient in terms of execution time. DEVS formalism can be used to deal with these problems. This formalism enables the association of object-oriented hierarchical modelling with discrete-event techniques. Object-oriented hierarchical programming facilitates construction, maintenance and reusability of the simulation model. Discrete-events reduce the calculation domain to the active cells of the propagation domain (the heated ones).     

Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data

Fires in boreal and temperate forests play a significant role in the global carbon cycle. While forest fires in North America (NA) have been surveyed extensively by U.S. and Canadian forest services, most fire records are limited to seasonal statistics without information on temporal evolution and spatial expansion. Such dynamic information is crucial for modeling fire emissions. Using the daily Advanced Very High Resolution Radiometer (AVHRR) data archived from 1989 to 2000, an extensive and consistent fire product was developed across the entire NA forest regions on a daily basis at 1-km resolution. The product was generated following data calibration, geo-referencing, and the application of an active fire detection algorithm and a burned area mapping algorithm. The spatial-temporal variation of forest fire in NA is analyzed in terms of (1) annual and monthly patterns of fire occurrences in different eco-domains, (2) the influence of topographic factors (elevation zones, aspect classes, and slope classes), and (3) major forest types and eco-regions in NA. It was found that 1) among the 12 years analyzed, 1989 and 1995 were the most severe fire years in NA; 2) the majority of burning occurred during June-July and in low elevation zones (< 500 m) with gentle slopes (< 10°), except in the dry eco-domain where more fires occurred in higher elevation zones (> 2000 m); 3) most fires occurred in the polar eco-domain, sub-arctic eco-division, and in the taiga ( boreal forests), forest-tundras and open woodlands eco-provinces in the boreal forests of Canada. The tendency for multiple burns to occur increases with elevation and slope until about 2500 m elevation and 24° slope, and decreases therefore. In comparison with ground observations, the omission and commission errors are on the order of 20%.     

A downslope fire spread correction factor based on landscape-scale fire behaviour

There is currently no fundamental understanding of the effects of topography on the behaviour of fires burning over a landscape. While a number of empirical models are employed operationally around the world, the effects of negative slopes on fire spread are ignored in all but one prediction system which may result in incorrect predictions. The general observation that large fires burning for some time over undulating topography can be approximated by assuming fire spread over flat ground is used to construct a quasi-empirical model framework for downslope rate of spread correction called kataburn. Kataburn is formulated for two alternative interpretations of slope spread–planar and linear–and can be applied to any empirical upslope spread correction model. Versions of kataburn derived using such models from Australia, the US and Canada are compared against experimental downslope data from the literature and found to better represent downslope spread than the existing operational downslope function.     

Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments

Fire is a prominent disturbance factor and a major force of environmental change especially in the African savannas. The development of an accurate system to map and monitor fires on the African continent is a priority of numerous international research centers and programs. This effort has produced a flurry of research projects in recent years to detect and map areas affected by fires at the continental scale using coarse-resolution satellite imagery. The end product of these projects consists of weekly or monthly maps of burned area, several of which are available to the user community on the internet. It is argued here that the algorithms used to generate these products are designed to capture relatively large and contiguously burned areas and that the heterogeneous patterns of burn scars created by mosaic burning regimes pose a problem for current detection methodologies. Fine-scale burned area maps are generated using a series of Landsat ETM+imagery covering the 2002-2003 fire season for the study area in the savanna of southern Mali. These maps document a seasonal-mosaic pattern of burning in which burning begins early in the dry season and continues for several months ultimately affecting over 50% of the landscape. The majority of these fires burn relatively small areas producing a highly fragmented landscape pattern. A comparison of the fine scale maps with those from two widely available coarse-resolution products finds that the latter fail to detect approximately 90% of the burned area. A general argument is developed which suggests that the documented bias in the coarse resolution products is a function of low-resolution bias which derives from the fine-scale spatiotemporal pattern of burning not uncommon to savanna and other frequently burned environments. The study demonstrates how low-resolution bias can result in a significant underestimation of burned areas and/or a shift in the seasonal burned area profile in areas where mosaic burning occurs. The findings have implications for the development of broad-scale burned area detection algorithms as well as their applications to natural resource management and global environmental change research.     

An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United States, where most fires are small and relatively cool, the MODIS version 4 contextual algorithm can be adjusted and improved for more accurate regional fire detection. Based on the MODIS version 4 contextual algorithm and a smoke detection algorithm, an improved algorithm using four TIR channels and seven solar reflectance channels is described. This approach is presented with fire events in the southeastern United States. The study reveals that the T₂₂ of most small, cool fires undetected by the MODIS version 4 contextual algorithm is lower than 310 K. The improved algorithm is more sensitive to small, cool fires in the southeast especially for fires detected at large scan angles.     

Evaluation of global fire detection algorithms using simulated AVHRR infrared data

This paper provides a comparison of selected algorithms that have been proposed for global active fire monitoring using data from the NOAA Advanced Very High Resolution Radiometer (AVHRR). A simple theoretical model was used to generate scenes of AVHRR infrared channel 3 and channel 4 data containing fires of various sizes and temperatures in a wide range of terrestrial biomes and climates. Three active fire detection algorithms were applied to the simulated AVHRR images and their performance was characterized in terms of probability of fire detection and false alarm as functions of fire temperature and area, solar and viewing geometry, visibility, season and biome. Additional comparisons were made using AVHRR imagery. Results indicate that while each algorithm has a comparable probability of detecting large (1000m2) fires in most biomes, substantial differences exist in their ability to detect small fires, their tolerance of smoke and neighbouring fires, the number of false alarms, and their overall suitability for global application. An improved automatic algorithm is finally presented. It offers enhanced active fire detection with comparable or reduced false alarm rates in most biomes.     

森林场景可视化和林火模拟仿真技术研究综述

森林是生态环境系统的重要组成部分。随着气候变暖,恶劣气候气象条件造成全球森林火灾频繁发生,给国民经济和消防救援带来巨大挑战,森林火灾已成为全球主要的自然灾害。因此,森林场景可视化建模、3维场景仿真、林火模拟仿真、火场复现、预测和灾害评估成为林业虚拟仿真研究热点。本文对树木形态结构建模技术、森林场景大规模重建和实时渲染、森林场景可视化、林火模型和林火模拟仿真等前沿技术和算法进行综述。对相关的林木、植被的形态结构表达和真实感可视化建模方法进行归纳分类,并对不同可视化方法的算法优劣、复杂度、实时渲染效率和适用场景进行讨论。基于规则的林木建模方法和基于林分特征的真实场景重建方法对大规模森林场景重建技术进行分类,基于物理模型、经验模型和半经验模型对森林火灾的林火模型、单木林火、多木林火模拟和蔓延进行总结,对影响林火蔓延的不同环境气象因子（如地形地貌、湿度、可燃物等）和森林分布对林火发生、扩散和蔓延的影响进行分析,对不同算法的优劣进行对比、分析和讨论,对森林场景可视化和林火模拟仿真技术未来的发展方向、存在问题和挑战进行展望。本文为基于森林真实场景的森林火灾模拟仿真和数字孪生沉浸式互动模拟系统的构建提供了理论方法基础,该平台可以实现森林场景快速构建、不同火源林火模拟、火场蔓延模拟仿真以及不同气象影响条件的火场预测,可对森林火场救援指挥、火场灾害评估和火场复原提供可视化决策支持。     

Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS

The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites image most of the Earth multiple times each day, providing useful data on fires that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures, which strongly influence how fires spread, the amount and chemistry of their gas and aerosol emissions, and their impacts on ecosystems. In this study, we use multiple endmember spectral mixture analysis (MESMA) to retrieve subpixel fire sizes and temperatures from MODIS, possibly overcoming some limitations of existing methods for characterizing fire intensities such as estimating the fire radiative power (FRP). MESMA is evaluated using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to assess the performance of FRP and MESMA retrievals of fire properties from a simultaneously acquired MODIS image, for a complex of fires in Ukraine from August 21, 2002. The MESMA retrievals of fire size described in this paper show a slightly stronger correlation than FRP does to fire pixel counts from the coincident ASTER image. Prior to this work, few studies, if any, had used MESMA for retrieving fire properties from a broad-band sensor like MODIS, or compared MESMA to higher-resolution fire data or other measures of fire properties like FRP. In the future, MESMA retrievals could be useful for fire spread modeling and forecasting, reducing hazards that fires pose to property and health, and enhancing scientific understanding of fires and their effects on ecosystems and atmospheric composition.     

A BIM-based simulation framework for fire safety management and investigation of the critical factors affecting human evacuation performance

Fire hazards are a big threat to human life and property safety. The U.S. fire statistics reveal that, in 2017 alone, 1,319,500 fires caused 3400 deaths and 14,670 injuries, which resulted in a loss of $23 billion [1]. Effective evacuation planning in densely occupied buildings should be primarily put in place if both the number of injuries/fatalities and the level of property loss are to be minimized. However, it is not realistic, and is unethical to study human evacuation performance under a burning building. For this reason, computational tools tend to be the best approach for simulating fire growth as well as human response to fire hazards. This study aims to develop a BIM-based simulation framework that implements the Fire Dynamic Simulator (FDS) and agent-based modeling (ABM) for simulating fire growth and evacuation performance for different building layout scenarios. An experimental implementation is conducted to validate the proposed framework, which verified the benefits of (1) using BIM to offer a platform for conducting simulation design and visualizing the simulation results of (a) hazardous fire zones and (b) effective escape routes; (2) simulating fire growth using the FDS tool; (3) developing an agent-based model that accounts for the critical factors affecting evacuation performance; and (4) applying a statistical analysis for investigating the effects of influential parameters from the proposed model. As a result, the simulation outputs can be used to optimize the building design and to investigate the influential factors on human evacuation efficiency. The proposed framework contributes to building fire safety management by enabling to minimize both injuries/fatalities and property loss.     

Detection rates of the MODIS active fire product in the United States

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1 km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (≥ 18 ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1 km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105 ha when combining Aqua and Terra (195 ha for Aqua and 334 ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included.     

Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon

Vegetation fires remain as one of the most important processes governing land use and land cover change in tropical areas. The large area extent of fire prone areas associated with human activities makes satellite remote sensing of active fires a valuable tool to help monitor biomass burning in those regions. However, identification of active fire fronts under optically thick clouds is not possible through passive remote sensing, often resulting in omission errors. Previous analyses of fire activity either ignored the cloud obscuration problem or applied corrections based on the assumption that fire occurrence is not impacted by the presence of clouds. In this study we addressed the cloud obscuration problem in the Brazilian Amazon region using a pixel based probabilistic approach, using information on previous fire occurrence, precipitation and land use. We implemented the methodology using data from the geostationary GOES imager, covering the entire diurnal cycle of fire activity and cloud occurrence. Our assessment of the method indicated that the cloud adjustment reproduced the number of potential fires missed within 1.5% and 5% of the true fire counts on annual and monthly bases respectively. Spatially explicit comparison with high resolution burn scar maps in Acre state showed a reduction of omission error (from 58.3% to 43.7%) and only slight increase of commission error (from 6.4% to 8.8%) compared to uncorrected fire counts. A basin-wide analysis of corrected GOES fire counts during 2005 showed a mean cloud adjustment factor of approximately 11%, ranging from negligible adjustment in the central and western part of the Brazilian Amazon to as high as 50% in parts of Roraima, Para and Mato Grosso.     

Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy

Remote sensing is the most practical means of measuring energy release from large open-air biomass burning. Satellite measurement of fire radiative energy (FRE) release rate or power (FRP) enables distinction between fires of different strengths. Based on a 1-km resolution fire data acquired globally by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor aboard the Terra and Aqua satellites from 2000 to 2006, instantaneous FRP values ranged between 0.02 MW and 1866 MW, with global daily means ranging between 20 and 40 MW. Regionally, at the Aqua-MODIS afternoon overpass, the mean FRP values for Alaska, Western US, Western Australia, Quebec and the rest of Canada are significantly higher than these global means, with Quebec having the overall highest value of 85 MW. Analysis of regional mean FRP per unit area of land (FRP flux) shows that at peak fire season in certain regions, fires can be responsible for up to 0.2 W/m² at peak time of day. Zambia has the highest regional monthly mean FRP flux of ~ 0.045 W/m² at peak time of day and season, while the Middle East has the lowest value of ~ 0.0005 W/m². A simple scheme based on FRP has been devised to classify fires into five categories, to facilitate fire rating by strength, similar to earthquakes and hurricanes. The scheme uses MODIS measurements of FRP at 1-km resolution as follows: category 1 (< 100 MW), category 2 (100 to < 500 MW), category 3 (500 to < 1000 MW), category 4 (1000 to < 1500 MW), category 5 (≥ 1500 MW). In most regions of the world, over 90% of fires fall into category 1, while only less than 1% fall into each of categories 3 to 5, although these proportions may differ significantly from day to day and by season. The frequency of occurrence of the larger fires is region specific, and could not be explained by ecosystem type alone. Time-series analysis of the proportions of higher category fires based on MODIS-measured FRP from 2002 to 2006 does not show any noticeable trend because of the short time period.     

An effective graph embedded YOLOv5 model for forest fire detection

The existing YOLOv5-based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high-quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small-scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph-embedded YOLOv5 forest fire detection framework, which can improve the performance of small-scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi-head self-attention (MSA) module before each YOLO head. The experimental results on FLAME and our self-built fire dataset show that our proposed model improves the accuracy of small-scale forest fire detection. The proposed model achieves high performance in real-time performance by fully utilizing the advantages of the YOLOv5 framework.     

Calibrating a forest landscape model to simulate frequent fire in Mediterranean-type shrublands

In Mediterranean-type ecosystems (MTEs), fire disturbance influences the distribution of most plant communities, and altered fire regimes may be more important than climate factors in shaping future MTE vegetation dynamics. Models that simulate the high-frequency fire and post-fire response strategies characteristic of these regions will be important tools for evaluating potential landscape change scenarios. However, few existing models have been designed to simulate these properties over long time frames and broad spatial scales. We refined a landscape disturbance and succession (LANDIS) model to operate on an annual time step and to simulate altered fire regimes in a southern California Mediterranean landscape. After developing a comprehensive set of spatial and non-spatial variables and parameters, we calibrated the model to simulate very high fire frequencies and evaluated the simulations under several parameter scenarios representing hypotheses about system dynamics. The goal was to ensure that observed model behavior would simulate the specified fire regime parameters, and that the predictions were reasonable based on current understanding of community dynamics in the region. After calibration, the two dominant plant functional types responded realistically to different fire regime scenarios. Therefore, this model offers a new alternative for simulating altered fire regimes in MTE landscapes.     

Visualization of wildfire simulations

The paper considers how Los Alamos researchers are partnering with Los Angeles County Fire Department, United States Forest Service, and Kennedy Space Center personnel for wildfire simulation studies. Fire behavior is highly dependent upon winds, temperatures and moisture. It is crucial to predict these weather parameters over the small regions where they directly affect a fire. Weather conditions in these small regions are driven by dynamic weather patterns such as cold fronts and high-pressure systems that develop over much larger geographic areas. The Regional Atmospheric Modeling System (RAMS), originally developed at Colorado State University, predicts these variable weather patterns. The RAMS model uses measurements from weather stations all over the United States to predict winds, temperatures, and moisture into the near future. To model the interactions between winds and fire, Higrad has been combined with a simple fire behavior model (Behave) from the US Forest Service. This combined modeling system (Higrad/Behave) is the first step in predicting the actual progress and heat release of a wildfire. Two fires have been simulated using this combined model     

Neural network and GA approaches for dwelling fire occurrence prediction

This paper describes three approaches for the prediction of dwelling fire occurrences in Derbyshire, a region in the United Kingdom. The system has been designed to calculate the number of fire occurrences for each of the 189 wards in the Derbyshire. In terms of the results from statistical analysis, eight factors are initially selected as the inputs of the neural network. Principal Component Analysis (PCA) is employed for pre-processing the input data set to reduce the number of the inputs. The first three principal components of the available data set are chosen as the inputs, the number of the fires as the output. The first approach is a logistic regression model, which has been widely used in the forest fire prediction. The prediction results of the logistic regression model are not acceptable. The second approach uses a feed-forward neural network to model the relationship between the number of fires and the factors that influence fire occurrence. The model of the neural network gives a prediction with an acceptable accuracy for the fires in dwelling areas. Genetic algorithms (GAs) are the third approach discussed in this study. The first three principle components of the available data set are classified into the different groups according to their number of fires. An iterative GA is proposed and applied to extract features for each data group. Once the features for all the groups have been identified the test data set can be easily clustered into one of the groups based on the group features. The number of fires for the group, which the test data belongs to, is the prediction of the fire occurrence for the test data. The three approaches have been compared. Our results indicate that the neural network based and the GA based approaches perform satisfactorily, with MSEs of 2.375 and 2.875, respectively, but the GA approach is much better understood and more transparent.     

新一代气象雷达回波探测在输电线路防山火中的应用

为有效弥补传统山火监测方法的不足,准确研判火场发生发展态势,将新一代气象雷达应用于山火监测之中,开辟了火灾监测领域的一个新天地,该气象雷达能对周边120公里范围内的地形环境进行扫描,基于多普勒原理技术对着火点进行判别与判定,能在火灾发生后的6分钟内迅速识别着火点经纬度坐标,同时开发出手机客户端APP软件,当接收到雷达系统传送来的着火点经纬度时,软件进行检索查询出着火点附近一定范围内的杆塔和护线员线路巡视人员能及时根据预警信息进行应对处理。同时,该雷达探测对闷烧型火灾的监测效果比其它方法要好,具有全天候监测、监测连续性好、发现早、预警早、定位更精准等特点,更能检测到其它监测系统不能预警的火灾。通过应用案例表明,在清明节、冬季等山火易发时节,极大的促进山火防治工作,有助于减少因山火造成的跳闸事故。     

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%-60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66-80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean-climate California.     

A fire risk simulation system for multi-purpose building based fire statistics

Statistical data over the past 24 years detailing the number of fires and building floor areas published by the Taiwan government was employed to determine the fire probability, frequency, and cycle for each building category. By applying a matrix calculation, the fire probability, frequency, cycle, and risk ratio for each functional area within a multi-purpose building were obtained. With assistance from the Taiwan government, the fire case investigation and statistical data for building fires were established. By adopting the risk ratio concept, the weight values for 20 fire safety assessment criteria and 4 fire safety strategies for a multi-purpose building were acquired to indicate the possible causes of fires and the quantitative extent of fire influence.     

Two-dimensional visual simulation of flames,smoke and the spread of fire

Since CG simulation of natural phenomena on the basis of their forms and motions has many applications, such as various landscape designs and special effects in films, it is very important and interesting to develop efficient techniques for their visual simulation. It is especially interesting to produce realistic images and animations of flames and smoke, on account of their complicated patterns of behaviour. Effective simulation methods for flames and smoke are expected to satisfy the following requirements:

• 1 The motion of flames or smoke produced in an interaction with obstacles can be simulated.
• 2 The motion of flames and smoke can be easily controlled according to scenarios.
• 3 The spread of fire can be simulated.

Although several useful representation methods have been proposed so far, simulating flames and smoke still remains a challenging problem. In this paper, we first describe our basic two-dimensional visual simulation method based on particle-based simulation, but not based on exact physical simulation. Roughly speaking, our method assumes that the images of flames and smoke are basically obtained by visualizing turbulence, that is, the particles of flames and smoke play the role of tracers in the field of turbulence. Next, we present an improved method for simulating the spread of fire and the appearance of smoke produced in an incomplete combustion. Simultaneously, we show several examples of the simulation. Finally, we will touch slightly on further problems for extending the model to one which works in three-dimensional space.