A simple index for assessing fire danger rating

Fire danger rating systems are used to assess the potential for bushfire occurrence, fire spread and difficulty of fire suppression. Typically, fire danger rating systems combine meteorological information with estimates of the moisture content of the fuel to produce a fire danger index. Fire danger indices are used to declare fire bans and to schedule prescribed burns, among other applications. In this paper a simple fire danger index F that is intuitive and easy to calculate is introduced and compared to a number of fire danger indices pertaining to different fuel types that are used in an operational setting in Australia and the United States. The comparisons suggest that F provides a plausible measure of fire danger rating and that it may be a useful pedagogical tool in the context of fire danger and fire weather.     

Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study

Fire danger indices are used by fire management agencies to assess fire weather conditions and issue public warnings. The most widely used fire danger indices in Australia are the McArthur Fire Forest Danger Index and the Grassland Fire Danger Index. These indices are calculated at weather stations using measurements of weather variables and fuel information. For a vast country like Australia when assessing the risk of severe fire weather events, it is also important to calculate the spatial distribution of these indices considering the extreme tail of the distribution. The spatial distribution of one of the fire weather danger indices regularly used in Australia is presented in this paper. In particular, we present the spatial distribution of the long-term tendency of extreme values of the McArthur Forest Fire Danger Index (FFDI). This indicator of fire weather conditions was assessed by calculating the return period of its extreme values by fitting extreme value distributions to data sets of FFDI at 78 recording stations around Australia. The spatial distribution of these return periods was obtained by using spatial interpolation algorithms with the recording stations measurements. Two conventional and two new algorithms based on machine-learning techniques were tested. This study shows that the best interpolation results for the FFDI can be obtained by using a combination of random forest and inverse distance weighting interpolation algorithms. The spatial distribution of the seasonal FFDI return period shows that the highest FFDI over large parts of southern Australia occurs during the summer months whilst in northern Australia it occurs in spring. The results also show that the FFDI in eastern Australia, the most populated region of the country, is higher inland than in the coastal areas particularly during spring and summer.     

Chaparral recovery following a major fire with variable burn conditions

Wildfires are a common occurrence in California shrublands, maintaining ecosystem functions with the regeneration of key shrub species. The Cedar Fire of 2003 in southern California was unique in that a portion of it burned with wildfire accelerated by dry, strong northeasterly Santa Ana winds that later subsided, while the remaining area burned under an onshore, westerly wind of lower velocity and higher humidity. These nearby areas, having similar terrain, fuel type, and environments, burned under these different conditions. Our goal is to understand the connection between vegetation response to extreme fire events by analysing life-form regrowth in chaparral from the Santa Ana wind driven, Santa Ana backing, and non-Santa Ana fire types. Study sites representing these three fire conditions were based on fire progression maps generated from Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data. Shrub cover before and six years after the fire were mapped based on a spatial contextual classifier applied to colour infrared orthoimagery, and analysed per slope aspect and angle, elevation, and fire characteristic categories to isolate shrub regrowth patterns. Six years after the fire, shrub cover in the Santa Ana wind driven site was substantially lower than in the other two sites. Such differences in shrub cover at the landscape scale may have resulted from different wind speed, direction, and humidity during the fire, coupled with terrain differences on wildfire behaviour and different rates of recovery associated primarily with moisture availability to plants. The information gathered from this study can help land managers assess shrub regrowth and possibility of vegetation type change after extreme fire events in southern California shrubland ecosystems.     

Retrieval of forest fuel moisture content using a coupled radiative transfer model

Forest fuel moisture content (FMC) dynamics are paramount to assessing the forest wildfire risk and its behavior. This variable can be retrieved from remotely sensed data using a radiative transfer model (RTM). However, previous studies generally treated the background of forest canopy as soil surface while ignored the fact that the soil may be covered by grass canopy. In this study, we focused on retrieving FMC of such forestry structure by coupling two RTMs: PROSAIL and PRO-GeoSail. The spectra of lower grass canopy were firstly simulated by the PROSAIL model, which was then coupled into the PRO-GeoSail model. The results showed that the accuracy level of retrieved FMC using this coupled model was better than that when the PRO-GeoSail model used alone. Further analysis revealed that low FMC condition fostered by fire weather condition had an important influence on the breakout of a fire during the study period.     

Deriving forest fire ignition risk with biogeochemical process modelling

Climate impacts the growth of trees and also affects disturbance regimes such as wildfire frequency. The European Alps have warmed considerably over the past half-century, but incomplete records make it difficult to definitively link alpine wildfire to climate change. Complicating this is the influence of forest composition and fuel loading on fire ignition risk, which is not considered by purely meteorological risk indices. Biogeochemical forest growth models track several variables that may be used as proxies for fire ignition risk. This study assesses the usefulness of the ecophysiological model BIOME-BGC's ‘soil water’ and ‘labile litter carbon’ variables in predicting fire ignition. A brief application case examines historic fire occurrence trends over pre-defined regions of Austria from 1960 to 2008. Results show that summer fire ignition risk is largely a function of low soil moisture, while winter fire ignitions are linked to the mass of volatile litter and atmospheric dryness.     

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication Model (RSCM) to improve a tactical wildfire remote-sensing system to better meet the time-sensitive information requirements of emergency response managers in San Diego County, USA. A thermal infrared airborne remote-sensing system designed and operated by the United States Forest Service Pacific Southwest Research Station for active wildfire monitoring is documented and updated based on the RSCM. Analysis of the thermal infrared remote-sensing system in the context of the RSCM identified three configuration changes that can improve the effectiveness of the information produced when employed by wildfire incident commanders for suppression prioritization: (1) limit spectral sampling collection to a single waveband; (2) complete image processing steps on-board the aircraft; and (3) provide information on wildfire locations to incident commanders in the form of a static map.     

The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system

The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.     

Integrating fire spread patterns in fire modelling at landscape scale

Fire spread modelling in landscape fire succession models needs to improve to handle uncertainty under global change processes and the resulting impact on forest systems. Linking fire spread patterns to synoptic-scale weather situations are a promising approach to simulating fire spread without fine-grained weather data. Here we present MedSpread—a model that evaluates the weights of five landscape factors in fire spread performance. We readjusted the factor weights for convective, topography-driven and wind-driven fires (n = 123) and re-assessed each fire spread group's performance against seven other control simulations. Results show that for each of the three fire spread patterns, some landscape factors exert a higher influence on fire spread simulation than others. We also found strong evidence that separating fires by fire spread pattern improves model performances. This study shows a promising link between relevant fire weather information, fire spread and fire regime simulation under global change processes.     

基于MODIS数据的森林可燃物分类方法——以黑龙江省为实验区

森林可燃物是森林燃烧环理论中的重要三要素之一,它是森林火灾发生的内因,对于火险等级预报、火行为预报、火灾扑救等森林防火工作具有重要意义。介绍了基于MODIS数据与GIS技术相结合的森林可燃物分类方法。在研究中,以黑龙江省为实验区,利用多时相的MODIS数据生成实验区16天的最大归一化植被指数后,通过主成分分析,采用非监督分类与监督分类相结合的方法,并在GIS技术的支持下,完成了实验区内的森林可燃物的分类,为进一步获得适合全国的利用现代高新技术的森林可燃物分类方法打下了坚实基础。     

基于MODIS数据的森林可燃物分类方法——以黑龙江省为实验区

森林可燃物是森林燃烧环理论中的重要三要素之一,它是森林火灾发生的内因,对于火险等级预报、火行为预报、火灾扑救等森林防火工作具有重要意义。介绍了基于MODIS数据与GIS技术相结合的森林可燃物分类方法。在研究中,以黑龙江省为实验区,利用多时相的MODIS数据生成实验区16天的最大归一化植被指数后,通过主成分分析,采用非监督分类与监督分类相结合的方法,并在GIS技术的支持下,完成了实验区内的森林可燃物的分类,为进一步获得适合全国的利用现代高新技术的森林可燃物分类方法打下了坚实基础。     

Fire-induced changes in green-up and leaf maturity of the Canadian boreal forest

Recent studies of vegetation phenology of northern forests using satellite data suggest that the observed earlier spring increase and peak amplitude of the normalized difference vegetation index (NDVI) are a result of climate warming. In addition to undergoing an increase in temperature, the northern forests of Canada have also seen a dramatic increase in area burned by wildfire over the same time period. Using the Canadian Large Fire Database, we analyzed the impact fire had on the phenological dates derived from fitting a logistical model to yearly data from 2004 for several different subsets of both AVHRR-NDVI and MODIS LAI in wildfire dominated terrestrial ecozones. Fire had a significant but complex effect on estimated phenology dates. The most recently burned areas (1994–2003) had later green-up dates in two ecozones for AVHRR data and all ecozones for MODIS. However, older forested (not burned during 1980–2003) had estimated green-up dates 1 to 9 days earlier than the entire forested area in the MODIS LAI data. These data corroborate studies in Canada and demonstrate that fire history is influencing boreal forest phenology and growing season LAI.     

国家级森林火险等级预报方法研究

对国家级森林火险等级的定量化预报方法进行了探讨,即:利用MODIS数据反演获得可燃物状态指数;将通过网络获得的全国气象数据和建立的可燃物类型分布、森林火险区划等基础数据库,在ArcGIS平台上数量化后计算背景综合指数,由这两者计算获得火险指数,以火险指数为国家级森林火险等级预报的量化指标,并利用它进行全国森林火险等级的分级,从而实现了全国森林火险等级从定性描述到定量估测。同时,以近几年我国发生的重特大森林火灾为例,对该方法进行了验证。实验表明:该方法可较好地对国家级森林火险等级进行定量化预报。     

Wildfire temperature and land cover modeling using hyperspectral data

Wildfire temperature retrieval commonly uses measured radiance from a middle infrared channel and a thermal infrared channel to separate fire emitted radiance from the background emitted radiance. Emitted radiance at shorter wavelengths, including the shortwave infrared, is measurable for objects above a temperature of 500 K. The spectral shape and radiance of thermal emission within the shortwave infrared can be used to retrieve fire temperature. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data were used to estimate fire properties and background properties for the 2003 Simi Fire in Southern California, USA. A spectral library of emitted radiance endmembers corresponding to a temperature range of 500-1500 K was created using the MODTRAN radiative transfer model. A second spectral library of reflected solar radiance endmembers, corresponding to four vegetation types and two non-vegetated surfaces, was created using image spectra selected by minimum endmember average root mean square error (RMSE). The best fit combination of an emitted radiance endmember and a reflected solar radiance endmember was found for each spectrum in the AVIRIS scene. Spectra were subset to reduce the effects of variable column water vapor and smoke contamination over the fire. The best fit models were used to produce maps of fire temperature, fire fractional area, background land cover, land cover fraction, and RMSE. The highest fire temperatures were found along the fire front, and lower fire temperatures were found behind the fire front. Saturation of shortwave infrared channels limited modeling of the highest fire temperatures. Spectral similarity of land cover endmembers and smoke impacted the accuracy of modeled land cover. Sensitivity analysis of modeled fire temperatures revealed that the range of temperatures modeled within 5% of minimum RMSE was smallest between 750 and 950 K. Hyperspectral modeling of wildfire temperature and fuels has potential application for fire monitoring and modeling.     

基于贝叶斯网络的林火概率预测系统设计与实现

针对林火预测具有影响因素多、机制复杂、难以结构化等特点,设计并实现了一个基于贝叶斯网络的实用林火概率预测系统。该系统以气象、植被、地理、人类活动等数据作为输入,综合林火历史数据建立贝叶斯网络模型,并应用联合树算法进行概率推理,进而预测出林火发生概率。在某省实际林火历史数据上对系统进行了测试,比较了所设计系统与加拿大火险天气指标系统（FWI）的预测性能,验证了系统的可行性和实用性。     

Predicting slow‐drying fire weather index fuel moisture codes with NOAA‐AVHRR images in Canada's northern boreal forests

Fire danger predicted by the Canadian Fire Weather Index, a system based on point‐source weather records, is limited spatially. NOAA‐AVHRR images were used to model two slow‐drying fuel moisture codes, the duff moisture code and the drought code of the fire weather index, in boreal forests of a 250,000 km² portion of northern Alberta and the southern Northwest Territories, Canada. Temporal and spatial factors affecting both codes and spectral variables (normalized difference vegetation index, surface temperature, relative greenness, and the ratio between normalized difference vegetation index and surface temperature) were identified. Models were developed on a yearly and seasonal basis. They were strongest in spring, but had a tendency to saturate. Drought code was best modelled (R ² = 0.34–0.75) in the spring of 1995 when data were categorized spatially by broad forest cover types. These models showed improved spatial resolution by mapping drought code at the pixel level compared to broadly interpolated weather station‐based estimates. Limitations and possible improvements of the study are also discussed.     

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.     

Comparison of fire temperature and fractional area modeled from SWIR, MIR, and TIR multispectral and SWIR hyperspectral airborne data

Spectral mixture modeling has previously been used to retrieve fire temperature and fractional area from multiband radiance data containing emitted radiance from fires. While this type of temperature modeling has potential for improving understanding of fire behavior and emissions, modeled temperature and fractional area may depend on the wavelength region used for modeling. Using airborne hyperspectral (Airborne Visible Infrared Imaging Spectrometer; AVIRIS) and multispectral (MODIS/ASTER Airborne Simulator; MASTER) data acquired simultaneously over the 2008 Indians Fire in California, we examined changes in modeled fire temperature and fractional area that occurred when input wavelength regions were varied. Temperature and fractional area modeled from multiple MASTER runs were directly compared. Incompatible spatial resolutions prevented direct comparison of the AVIRIS and MASTER model runs, so total area modeled at each temperature was used to indirectly compare temperature and fractional area retrieved from these two sensors. AVIRIS and MASTER model runs using shortwave infrared (SWIR) bands produced consistent fire temperatures and fractional areas when modeled temperatures exceeded 800 K. Temperatures and fire fractional areas were poorly correlated for temperatures below 800 K and when the SWIR bands were excluded as model inputs. The single temperature blackbody assumption commonly used in mixing model retrieval of fire temperature is potentially useful for modeling higher temperature fires, but is likely not valid for lower temperature smoldering combustion due to mixed radiance from multiple fuel elements combusting at different temperatures. SWIR data contain limited emitted radiance from combustion at lower temperatures, and are thus essential for consistent modeling of fire temperature and fractional area at higher fire temperatures.     

Forecasting and visualization of wildfires in a 3D geographical information system

This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open-source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connections of several users for monitoring a real wildfire event. The system is able to make a realistic composition of what is really happening in the area of the wildfire with dynamic 3D objects and location of human and material resources in real time, providing a new perspective to analyze the wildfire information. The user is enabled to simulate and visualize the propagation of a fire on the terrain integrating at the same time spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-to-date meteorological data. The client application implements a realistic 3D visualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system.     

Simulating wildfires backwards in time from the final fire perimeter in point-functional fire models

Fire propagation models simulate wildfires forward in time from a set of ignition locations, but are usually unable to be used backwards if only a final fire perimeter is available. This approach is useful to search fire ignition points, reconstruct past fire events, adjust fire simulators and other purposes. This study proposes three different algorithms: a short time range backwards in time simulation from the perimeter, a statistical analysis related to the likelihood of a fire ignition location over the domain, and an analysis aiming to multiple ignition locations. The methods presented are fast to be solved and may be used with any empirical fire propagation model as a core engine as long as the ROS is locally defined and the model is not coupled to the weather.     

Modelling Mediterranean landscape succession-disturbance dynamics: A landscape fire-succession model

We present a spatially explicit Landscape Fire-Succession Model (LFSM) developed to represent Mediterranean Basin landscapes and capable of integrating modules and functions that explicitly represent human activity. Plant-functional types are used to represent spatial and temporal competition for resources (water and light) in a rule-based modelling framework. Vegetation dynamics are represented using a rule-based community-level modelling approach that considers multiple succession pathways and vegetation climax states. Wildfire behaviour is represented using a cellular-automata model of fire spread that accounts for land-cover flammability, slope, wind and vegetation moisture. Results show that wildfire spread parameters have the greatest influence on two aspects of the model: land-cover change and the wildfire regime. This sensitivity highlights the importance of accurately parameterising this type of grid-based model for representing landscape-level processes. We use a pattern-oriented modelling approach in conjunction with wildfire power-law frequency-area scaling exponent β to calibrate the model. Parameters describing the role of soil moisture on vegetation dynamics are also found to significantly influence land-cover change. Recent improvements in understanding the role of soil moisture and wildfire fuel loads at the landscape-level will drive advances in Mediterranean LFSMs.