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.     

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.     

Spatial pattern modelling of wildfires in Catalonia,Spain 2004–2008

The paper has three objectives: firstly, to evaluate how the extent of clustering in wildfires differs across the years they occurred; secondly, to analyse the influence of covariates on trends in the intensity of wildfire locations; and thirdly, to build maps of wildfire risks, by year and cause of ignition, in order to provide a tool for preventing and managing vulnerability levels. For these objectives we analysed the spatio-temporal patterns produced by wildfire incidences in Catalonia, located in the north-east of the Iberian Peninsula. The methodology used has allowed us to quantify and assess possible spatial relationships between the distribution of risk of ignition and causes. These results may be useful in fire management decision-making and planning. The methods shown in this paper may contribute to the prevention and management of wildfires, which are not random in space or time, as we have shown here.     

Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery

Relatively little is known about the disturbance ecology of large wildfires in the southern Appalachians. The occurrence of a 4000-ha wildfire in the Linville Gorge Wilderness area in western North Carolina has provided a rare opportunity to study a large fire with a range of severities. The objectives of this study were to 1) assess the potential for using multi-temporal Landsat imagery to map fire severity in the southern Appalachians, 2) examine the influences of topography and forest community type on the spatial pattern of fire severity; and 3) examine the relationship between predicted fire severity and changes in species richness. A non-linear regression equation predicted a field-based composite burn index (CBI) as a function of change in the Normalized Burn Ratio (dNBR) with an R² of 0.71. Fire severity was highest on drier landforms located on upper hillslopes, ridges, and on southwest aspects, and was higher in pine communities than in other forest types. Predicted CBI was positively correlated with changes in species richness and with the post-fire cover of pine seedlings (Pinus virginiana, P. rigida, and P. pungens), suggesting that burn severity maps can be used to predict community-level fire effects across large landscapes. Despite the relatively large size of this fire for the southern Appalachians, severity was strongly linked to topographic variability and pre-fire vegetation, and spatial variation in fire severity was correlated with changes in species richness. Thus, the Linville Gorge fire appears to have generally reinforced the ecological constraints imposed by underlying environmental gradients.     

A comparative analysis of the use of NOAA‐AVHRR NDVI and FWI data for forest fire risk assessment

Fires are a major hazard to forests in the Mediterranean region, where, on average, half a million hectares of forested areas are burned every year. The assessment of fire risk is therefore at the heart of fire prevention policies in the region. The estimation of forest fire risk often involves the integration of meteorological and other fuel‐related variables, leading to an index that assesses the different levels of risk. Two indices frequently used to estimate the level of fire risk are the Fire Weather Index (FWI) and the Normalized Difference Vegetation Index (NDVI). Although a correlation between the number of fires and the level of risk determined by these indices has been demonstrated in previous studies, the analyses focused on the changes in fire risk levels in areas where fires took place. The present study analyses the behaviour of the fire risk indices not only in areas where fires occurred but also in areas where fires did not take place. Specifically, the objective of this work was to compare the potential of the two indices to discriminate different levels of fire risk over large areas. Qualitative and quantitative methods were used to compare the statistical distributions of fire event frequencies with those of fire risk levels. The qualitative method highlights graphically the statistical difference between the values of the indices computed over burnt areas and the overall distribution of the values of the indices. The quantitative method, based on the use of the so‐called performance index, was used to evaluate and compare numerically the potential of the indices. The analyses were performed considering very extensive datasets of fire events, satellite data and meteorological data for Spain during a 10‐year period. Although the NDVI is assumed to describe the vegetation status as related to fire ignition, the results show conclusively an enhanced performance of the FWI over the NDVI in identifying areas at risk of fires.     

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) - direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km², and 93% of all reference fires > 10 km². The burned area was highly correlated (R² = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km² in size, improving to ± 31% at a fire size of 100 km². Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km², and 66% results from fires > 100 km²). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km² spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.     

Risk of fire emergency evacuation in complex construction sites: Integration of 4D-BIM,social force modeling,and fire quantitative risk assessment

Construction industry is claimed to be the fourth most dangerous sector by number of fatalities. In complex construction sites, emergency evacuation risk assessment is a challenging task due to their ever-changing nature. This study developed a model to analyze the risk of fire emergency occurrence, and risks which are associated with evacuation performance (in response to that emergency) through an integrated approach in complex construction sites. To analyze the evacuation scenarios more realistically, we utilized Social Force Model (SFM) simulation engine. Using SFM for simulating the evacuation of complex construction sites has not been adequately addressed in the literature. Microscopically simulating the evacuation scenarios for all workdays of the studied complex project required high computation efforts. To tackle this computation challenge, a parallel computing technique was coupled with SFM simulation engine. More importantly, in this paper site’s evacuation performance was evaluated multi-objectively considering evacuation time and evacuation safety. The construction site’s emergency scenarios were modeled by 4D-BIM, potential for trigger fire emergency was determined by a fire ignition Quantitative Risk Assessment (QRA) module, and site evacuation was simulated by SFM simulation engine. The proposed framework handled the collaboration of 4D-BIM, fire QRA module, and SFM engine. This research study benefited from data driven from a real mega project. The findings demonstrated that analyzing the risk of evacuation through an integrated approach by the proposed model could render more realistic results. The results also provided the project managers with a reliable safety decision-making support.     

Fighting fire and fatigue: sleep quantity and quality during multi-day wildfire suppression

This study examined firefighters’ sleep quantity and quality throughout multi-day wildfire suppression, and assessed the impact of sleep location, shift length, shift start time and incident severity on these variables. For 4 weeks, 40 volunteer firefighters’ sleep was assessed using wrist actigraphy. Analyses revealed that the quantity of sleep obtained on fire days was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. On fire days, total sleep time was less when: (i) sleep location was in a tent or vehicle, (ii) shifts were greater than 14 h and (iii) shifts started between 05:00 and 06:00 h. This is the first empirical investigation providing objective evidence that firefighters’ sleep is restricted during wildfire suppression. Furthermore, sleep location, shift length and shift start time should be targeted when designing appropriate controls to manage fatigue-related risk and preserve firefighters’ health and safety during wildfire events.

Practitioner Summary: During multi-day wildfire suppression, firefighters’ sleep quantity was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. Furthermore, total sleep time was less when: (i) sleep occurred in a tent/vehicle, (ii) shifts were >14 h and (iii) shifts started between 05:00 and 06:00 h.     

基于北斗和GPS的森林防火人员调度指挥系统

森林防火人员监控调度指挥系统是根据森林防火人员调度指挥工作的实际需要,设计并实现的基于北斗定位导航技术、GPS、GIS的软硬化一体平台。系统实现的功能主要有:人员实时定位、重点区域或人员监控、巡护人员与系统互通信、人员历史路径查询、人员巡山防火执勤工作量统计等。该系统构筑了森林巡护工作的信息服务网络,加强了森林巡护工作的落实,并为科学地应急指挥调度提供决策支持。     

融合注意力机制的无锚点森林火灾检测算法

森林火灾、野火是一个重大的自然灾害问题,每年全球各地植被都会受到严重的破坏。为了提高森林火灾的防控精度,针对传统方法具有火灾背景复杂、准确率低、效率低等问题,本文提出一种基于CenterNet的森林火灾检测算法。CenterNet作为一种无锚的方法,将目标定义为一个点,通过关键点估计定位目标的中心点,可以有效避免小目标的漏检。同时基于高效深层特征提取网络ResNet50,融合ECA模块以抑制无用信息,增加模型的特征提取能力。在公开森林火灾数据集上进行实验表明,与其他算法相比,本文提出的森林火灾检测算法误检率低,识别精度达到92.39%,F1值为0.86,Recall值为79.75%,FPS为43.31。本文提出的方法检测精度高,可满足实时检测森林火灾和实施精准施救的要求。     

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。     

Short contributions

Studies of ERS-1 synthetic aperture radar (SAR) imagery have shown that fire scars in Alaskan forests are significantly brighter (3–6 dB) than surrounding unburned forest. The signature varies seasonally and changes as vegetation re-establishes on the site over longer time periods (>5years). Additionally, it is known that soil water content typically increases following forest fires due to changes in evapotranspiration rates and melting of the permafrost.

The objective of this study was to understand the relation between soil water content and the ERS-1 SAR signature at fire-disturbed sites. To accomplish this objective, we compared soil water in six burned black spruce (Picea mariana (Mill.) B.S.P.) forest sites in interior Alaska to ERS-1 SAR backscalter measurements. The six sites are of various age since burn. Soil water was periodically measured at each site during the summer of 1992 and at one site in 1993 and 1994 when the ERS-1 imaging radar was scheduled to pass overhead. Results indicate that a positive linear relation exists between soil water content and the SAR backscatter coefficient in young burns ( < ～4years). Older burns do not show this relation, a result of vegetation establishment following the burn. This interaction between soil moisture condition and ERS-1 SAR backscatter shows great potential for measuring soil water content and monitoring seasonal variations in soil water content in black spruce sites recently disturbed by wildfire.     

Development and application of a geospatial wildfire exposure and risk calculation tool

Applying wildfire risk assessment models can inform investments in loss mitigation and landscape restoration, and can be used to monitor spatiotemporal trends in risk. Assessing wildfire risk entails the integration of fire modeling outputs, maps of highly valued resources and assets (HVRAs), characterization of fire effects, and articulation of relative importance across HVRAs. Quantifying and geo-processing wildfire risk can be a complex and time-intensive task, often requiring expertise in geospatial analysis. Researchers and land managers alike would benefit from a standardized and streamlined ability to estimate wildfire risk. In this paper we present the development and application of a geospatial wildfire risk calculation tool, FireNVC. We describe the major components of the tool and how they align with a geospatial wildfire risk assessment framework, detail a recent application of the tool to inform federal wildfire management and planning, and offer suggestions for future improvements and uses of the tool.     

基于MODIS数据的亚马逊热带雨林火灾时空变化规律研究

频繁发生的森林大火对亚马逊热带雨林造成了大面积破坏,获取不同年份的火灾影响范围以及植被破坏情况,有助于了解该地区火灾时空演变规律以及火灾与植被的相互作用关系,进而探究火灾发展机理,为防灾减灾提供科学依据。为此,利用2015~2019年MODIS植被指数产品与地表温度产品,构建MODIS全球扰动指数模型（MGDI）,结合火点数据 （以下统称MOD14A1）、植被连续场数据（Vegetation Continuous Field,VCF）提取1 000 m分辨率下的燃烧范围和燃烧强度,并分析研究区域5年内的火灾分布时空规律。实验结果表明：①火灾主要分布在巴西中部以及巴西与玻利维亚的交界处,占燃烧区总面积的67%左右;②燃烧范围以及燃烧强度的综合信息显示火灾整体呈现出“升—降—升”的趋势;③火灾多发生于灌木草地（50%以上）以及阔叶林（30%）,且火灾多发在旱季;在全球变暖大背景下,火灾发生频率呈上升趋势;（4）人类活动范围扩张、不合理农业开垦、森林砍伐导致研究区内草地退化严重,农业用地以及建筑用地逐年上升,在一定程度上为火灾的发生、传导提供了良好的条件。     

An intelligent system for forest fire risk prediction and fire fighting management in Galicia

Over the last two decades in southern Europe, more than 10 million hectares of forest have been damaged by fire. Due to the costs and complications of fire-fighting a number of technical developments in the field have been appeared in recent years. This paper describes a system developed for the region of Galicia in NW Spain, one of the regions of Europe most affected by fires. This system fulfills three main aims: it acts as a preventive tool by predicting forest fire risks, it backs up the forest fire monitoring and extinction phase, and it assists in planning the recuperation of the burned areas. The forest fire prediction model is based on a neural network whose output is classified into four symbolic risk categories, obtaining an accuracy of 0.789. The other two main tasks are carried out by a knowledge-based system developed following the CommonKADS methodology. Currently we are working on the trail of the system in a controlled real environment. This will provide results on real behaviour that can be used to fine-tune the system to the point where it is considered suitable for installation in a real application environment.     

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。     

Adaptive Neuro Fuzzy Inference System (ANFIS) based wildfire risk assessment

Wildfires are extremely destructive disasters that cause significant loss of lives, forest cover and wildlife. This is due to their uncontrolled, erratic, rapid spread and behaviour. The incidence of wildfires is expected to increase worldwide because of Global Warming. Henceforth, it becomes increasingly important to detect and tackle such fires in their infancy to minimise their adverse effects. IoT technology has shown an exponential growth in recent years. Moreover, deployment of IoT devices to monitor and collect time-critical data is pressing need of hour. This research proposes an effective Fog-IoT centric framework for timely detection of wildfires. The proposed methodology provides an efficient real-time solution to dilute the destruction caused by wildfires. Initially, K-means Clustering is used to detect the wildfire outbreak at fog layer followed by real-time alert generation to the administration and community. Furthermore, cloud layer based Adaptive Neuro Fuzzy Inference System is used for assessing the vulnerability of a forest block to forest fires as well as classifying it into one of the five risk zones based on Forest Fire Vulnerability Index. Implementation results of the proposed framework prove its efficiency in detecting and predicting wildfires. In addition, real-time alert generation further enhances the efficacy of the proposed system.     

Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data

Recent advances in instrument design have led to considerable improvements in wildfire mapping at regional and global scales. Global and regional active fire and burned area products are currently available from various satellite sensors. While only global products can provide consistent assessments of fire activity at the global, hemispherical or continental scales, the efficiency of their performance differs in various ecosystems. The available regional products are hard-coded to the specifics of a given ecosystem (e.g. boreal forest) and their mapping accuracy drops dramatically outside the intended area. We present a regionally adaptable semi-automated approach to mapping burned area using Moderate Resolution Imaging Spectroradiometer (MODIS) data. This is a flexible remote sensing/GIS-based algorithm which allows for easy modification of algorithm parameterization to adapt it to the regional specifics of fire occurrence in the biome or region of interest. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and therefore retains the variability of spectral response of the area affected by fire and has the potential to be used beyond binary burned/unburned mapping for the first-order characterization of fire impacts from remotely sensed data. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1) and the MODIS Active Fire product (MOD14) and outputs yearly maps of burned area with dNBR values and beginning and ending dates of mapping as the attributive information. Comparison of this product with high resolution burn scar information from Landsat ETM+ imagery and fire perimeter data shows high levels of accuracy in reporting burned area across different ecosystems. We evaluated algorithm performance in boreal forests of Central Siberia, Mediterranean-type ecosystems of California, and sagebrush steppe of the Great Basin region of the US. In each ecosystem the MODIS burned area estimates were within 15% of the estimates produced by the high resolution base with the R² between 0.87 and 0.99. In addition, the spatial accuracy of large burn scars in the boreal forests of Central Siberia was also high with Kappa values ranging between 0.76 and 0.79.     

基于元胞自动机的林火蔓延三维模拟仿真研究

为了在虚拟三维环境中模拟出森林火灾的发展蔓延状态,利用三维引擎进行仿真研究。说明了三维元胞的定义、状态、邻域和转换规则函数;通过求得林火蔓延公式的参数在在8个邻域方向上的分量,得到林火蔓延公式在相应方向上的分量公式。利用Unity3D三维引擎建立野外三维地形,通过不断调整其自带粒子系统的参数,建立适用于不同条件的火焰库和烟雾库,输入气象因素后即可实现林火在三维环境下的模拟蔓延。将野外实地点烧数据与模拟效果进行对比,可以看出模拟的效果与实地点烧相近,模拟效果符合要求。