不同区域森林火灾对生态因子的响应及其概率模型

火灾是影响森林生态系统过程的重要干扰之一,其对森林生态系统内各生态因子的响应各不相同.由于植被状况及生态环境的不同,森林火灾的时空分布特征在中国不同植被气候类型内表现不同,根据植被气候类型分类系统,将中国主要森林火灾地区划分为4个区域:东北(冷温带松林)、华北(落叶阔叶林)、东南(常绿阔叶林)和西南(热带雨林),应用遥感监测数据和地面环境数据,以时空变量、生态因子(植被生长变化指数、湿度等)为可选自变量,应用半参数化Logistic回归模型,就森林火险对不同生态影响因子的响应规律进行了分析,建立了基于生态因子的着火概率模型和大火蔓延概率模型,通过模拟及实际数据散点图、火险概率图,评估了模型应用价值.结果表明,土壤湿度及植被含水量在落叶阔叶林、常绿阔叶林、热带雨林地区对着火概率影响显著.在4个植被气候区内,土壤及凋落物湿度对大火蔓延的作用较小.在冷温带松林、落叶阔叶林、常绿阔叶林地区,植被生长的年内变化对火灾发生的影响显著,在常绿阔叶林地区,年内植被生长变化对大火蔓延的作用较小.森林火险概率与各生态因子的相关关系主要呈现出非线性.不同植被气候区内,火险概率受不同生态因子组合的影响,这与不同区域的植被状况及生态环境不同有关.在不同植被气候类型,应用时空变量、生态因子建立半参数化logistic回归模型,进行着火概率和大火蔓延概率的模拟具有可行性和实际应用能力.为进一步分析森林生态系统与火灾之间的动态关系、展开生态系统火灾干扰研究提供了理论基础.     

Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China

Understanding the spatial patterns of fire occurrence and its response to climate change is vital to fire risk mitigation and vegetation management. Focusing on boreal forests in Northeast China, we used spatial point pattern analysis to model fire occurrence reported from 1965 to 2009. Our objectives were to quantitate the relative importance of biotic, abiotic, and human influences on patterns of fire occurrence and to map the spatial distribution of fire occurrence density (number of fires occurring over a given area and time period) under current and future climate conditions. Our results showed human‐caused fires were strongly related to human activities (e.g. landscape accessibility), including proximity to settlements and roads. In contrast, fuel moisture and vegetation type were the most important controlling factors on the spatial pattern of lightning fires. Both current and future projected spatial distributions of the overall (human‐ + lightning‐caused) fire occurrence density were strongly clustered along linear components of human infrastructure. Our results demonstrated that the predicted change in overall fire occurrence density is positively related to the degree of temperature and precipitation change, although the spatial pattern of change is expected to vary spatially according to proximity to human ignition sources, and in a manner inconsistent with predicted climate change. Compared to the current overall fire occurrence density (median value: 0.36 fires per 1000 km² per year), the overall fire occurrence density is projected to increase by 30% under the CGCM3 B1 scenario and by 230% under HadCM3 A2 scenario in 2081–2100, respectively. Our results suggest that climate change effects may not outweigh the effects of human influence on overall fire occurrence over the next century in this cultural landscape. Accurate forecasts of future fire‐climate relationships should account for anthropogenic influences on fire ignition density, such as roads and proximity to settlements.     

基于MAXENT模型的黑龙江大兴安岭森林雷击火火险预测

黑龙江大兴安岭是森林雷击火的高发地区,急需研发精确的火险预测模型对该区森林火灾进行预测.本文基于大兴安岭地区森林雷击火灾数据及环境变量数据,采用MAXENT模型进行森林雷击火的火险预测.首先对各环境变量进行共线性诊断,再利用累积正则化增益法和Jackknife方法评价了环境变量的重要性,最后采用最大Kappa值和AUC值检测了MAXENT模型的预测精度.结果表明: 闪电能量和中和电荷量的方差膨胀因子（VIF）值分别为5.012和6.230,与其他变量之间存在共线性,不能用于模型训练.日降雨量、云地闪电数量及云地闪回击电流强度是影响森林雷击火发生的3个最重要因素,日平均风速和坡向的影响较小.随着建模数据比例的增加,最大Kappa值和AUC值均有增大趋势.最大Kappa值都大于0.75,平均值为0.772; AUC值都大于0.5,平均值为0.859.MAXENT模型的预测精度达到中等精度,可应用于大兴安岭地区的森林雷击火火险预测.     

呼中林区火烧点格局分析及影响因素

林火是森林生态系统景观格局、动态和生态过程的重要自然驱动力,理解林火发生空间格局与影响因素对于林火安全管理具有重要的作用。采用点格局分析方法,以黑龙江大兴安岭呼中林区1990-2005年火烧数据为研究案例,分析了火烧点空间格局及其影响因素。结果表明,火烧点在空间上的分布是不均匀的,呈现聚集分布,存在一些火烧高发区和低发区。呼中林区火烧概率是0.004-0.012次/(km² · a),平均火烧概率为0.0077次/(km² · a)。人类活动因子、地形因子和植被因子对林火的发生均具有重要作用。应用空间点格局分析方法表明,距离居民点和道路的距离、高程、坡度和林型是影响林火发生的显著因子。因此在进行森林防火管理时,仅仅通过控制人类活动对于降低林火火险的效果是有限的,地形和林型也是林火防控时重点要考虑的因素。     

Long-term potential for fires in estimates of the occurrence of savannas in the tropics

Aim   This study aims to improve the formulation and results of the Brazilian Center for Weather Forecasting and Climate Studies Potential Vegetation Model (CPTEC-PVM) by developing a new parameterization for the long-term occurrence of fire in regions of potential savannas in the tropics. Compared with the relatively slow processes of carbon uptake and growth in vegetation, fast mortality and biomass consumption by fires may favour grasses and reduce tree coverage.
Location   The tropics.
Methods   For finding large-scale relationships between fires and other environmental factors, we made two main simplifying assumptions. First, lightning is the most important source of ignition for natural fires. Second, over continental areas in the tropics, lightning is mainly related to the zonal flux of moisture transport.
Results   The parameterization of fire occurrence was built based on a simple empirical relationship, combining information on mean and intra-annual variance of the zonal wind.
Main conclusions   The implementation of this new relationship improved the formulation and the results of the CPTEC-PVM. As a result of this new parameter, the accuracy of the model in allocating the correct vegetation (seasonal forests) instead of savannas for large regions in India and Southeast Asia is now substantially higher than in previous studies.     

我国北方针叶林人为火发生的预测模型

我国北方针叶林带是重要的森林资源储藏地,也是林火发生的重灾区,其自然火和人为火所占比例相当. 气象因子、地形特征、植被条件、人为基础设施等因素对人为火发生具有显著影响,国内目前应用空间分析技术对北方针叶林带人为火影响因子的研究还存在一定不确定性. 本文基于1974—2009年间人为火的空间地理坐标,结合研究地的气象因子、基础地理信息及矢量化林相图,应用ArcGIS 10.0中的空间分析工具和SPSS 19.0的逻辑斯蒂回归模型对影响人为火发生的主要驱动因子进行分析,并建立人为火发生的概率模型. 利用HADCM2模式下研究区域未来气象数据对塔河地区2015年人为火发生情况进行计算.结果表明： 距离铁路距离（x₁）和平均相对湿度（x₂）对研究区域人为火发生具有显著影响,并得到火险概率模型P=1/［1+e^{-(3.026-0.00011x1-0.047x₂)}］. 模型校验结果显示,模型的准确度可达到80%.林火发生预测结果表明,塔河地区2015年 4—6月、8月为人为火高发期,其中,4—5月的林火发生概率最高.从火险空间分布来看,高火险主要集中在塔河西部和西南部,铁路线路主要包含在此区域.     

我国林火发生预测模型研究进展

通过文献回顾,总结了国内林火发生预测模型的研究现状,并从林火发生驱动因子、林火发生概率预测模型、林火发生频次预测模型和模型检验方法等方面进行归纳分析。得出以下结论: 1)气象、地形、植被、可燃物、人类活动等因素是影响林火发生及模型预测精度的主要驱动因子;2)林火发生概率模型中,地理加权逻辑斯蒂回归模型考虑了变量之间的空间相关性,Gompit回归模型适宜非对称结构的林火数据,随机森林模型不需要多重共线性检验,在避免过度拟合的同时提高了预测精度,是林火发生概率预测模型的优选方法之一;3)林火发生频次模型中,负二项回归模型更适合对过度离散数据进行模拟,零膨胀模型和栅栏模型可以处理林火数据中包含大量零值的问题;4)ROC检验、AIC检验、似然比检验和Wald检验方法是林火概率和频次模型的常用检验方法。林火发生预测模型研究仍是我国当前林火管理工作的重点,预测模型的选择需要依据不同地区林火数据特点。此外,构建林火预测模型时需要考虑更多的影响因素,以提高模型预测精度;未来,需要进一步探索其他数学模型在林火发生预测中的应用,不断提高林火发生预测模型的准确度。     

Wildfire risk for main vegetation units in a biodiversity hotspot: modeling approach in New Caledonia,South Pacific

Wildfire has been recognized as one of the most ubiquitous disturbance agents to impact on natural environments. In this study, our main objective was to propose a modeling approach to investigate the potential impact of wildfire on biodiversity. The method is illustrated with an application example in New Caledonia where conservation and sustainable biodiversity management represent an important challenge. Firstly, a biodiversity loss index, including the diversity and the vulnerability indexes, was calculated for every vegetation unit in New Caledonia and mapped according to its distribution over the New Caledonian mainland. Then, based on spatially explicit fire behavior simulations (using the FLAMMAP software) and fire ignition probabilities, two original fire risk assessment approaches were proposed: a one‐off event model and a multi‐event burn probability model. The spatial distribution of fire risk across New Caledonia was similar for both indices with very small localized spots having high risk. The patterns relating to highest risk are all located around the remaining sclerophyll forest fragments and are representing 0.012% of the mainland surface. A small part of maquis and areas adjacent to dense humid forest on ultramafic substrates should also be monitored. Vegetation interfaces between secondary and primary units displayed high risk and should represent priority zones for fire effects mitigation. Low fire ignition probability in anthropogenic‐free areas decreases drastically the risk. A one‐off event associated risk allowed localizing of the most likely ignition areas with potential for extensive damage. Emergency actions could aim limiting specific fire spread known to have high impact or consist of on targeting high risk areas to limit one‐off fire ignitions. Spatially explicit information on burning probability is necessary for setting strategic fire and fuel management planning. Both risk indices provide clues to preserve New Caledonia hot spot of biodiversity facing wildfires.     

Altered ignition catchments threaten a hyperdiverse fire‐dependent ecosystem

Human activities affect fire in many ways, often unintentionally or with considerable time‐lags before they manifest themselves. Anticipating these changes is critical, so that insidious impacts on ecosystems, their biodiversity and associated goods and services can be avoided, mitigated or managed. Here we explore the impact of anthropogenic land cover change on fire and biodiversity in adjacent ecosystems on the hyperdiverse Cape Peninsula, South Africa. We develop a conceptual framework based on the notion of an ignition catchment, or the spatial extent and temporal range where an ignition is likely to result in a site burning. We apply this concept using fire models to estimate spatial changes in burn probability between historical and current land cover. This change layer was used to predict the observed record of fires and forest encroachment into fire‐dependent Fynbos ecosystems in Table Mountain National Park. Urban expansion has created anthropogenic fire shadows that are modifying fire return intervals, facilitating a state shift to low‐diversity, non‐flammable forest at the expense of hyperdiverse, flammable Fynbos ecosystems. Despite occurring in a conservation area, these ecosystems are undergoing a hidden collapse and desperately require management intervention. Anthropogenic fire shadows can be caused by many human activities and are likely to be a universal phenomenon, not only contributing to the observed global decline in fire activity but also causing extreme fires in ecosystems where there is no shift to a less flammable state and flammable fuels accumulate. The ignition catchment framework is highly flexible and allows detection or prediction of changes in the fire regime, the threat this poses for ecosystems or fire risk and areas where management interventions and/or monitoring are required. Identifying anthropogenic impacts on ignition catchments is key for both understanding global impacts of humans on fire and guiding management of human‐altered landscapes for desirable outcomes.     

不同植被类型森林火灾及雷击火自组织临界性

利用黑龙江省大兴安岭林区呼中区 196 5～ 2 0 0 2年的雷击火数据、黑龙江省 1981～ 2 0 0 0年森林火灾数据及森林资源数据 ,对雷击造成的森林火灾的自组织临界性及不同植被类型条件下的自组织临界性作了研究 ,比较了在不同尺度和植被类型条件下火干扰的自组织临界性、自相似性 ,并与传统的森林火灾元胞自动机模型模拟的结果进行比较。结果表明 :中国黑龙江省不同森林类型的火干扰具有自组织临界行为 ,森林可燃物已经达到临界状态 ,其临界值在 1.8～ 2 .86之间 ,具有自相似性 ;当森林的面积过小时 ,森林火灾的“面积 -频率”分布曲线上会出现频率峰 ,表现出“有限面积效应”现象。     

Drivers of fire occurrence in a mountainous Brazilian cerrado savanna: Tracking long-term fire regimes using remote sensing

Fire is a natural disturbance in savannas, and defines vegetation physiognomy and structure, often influencing species diversity. Fire activity is determined by a wide range of factors, including long and short term climatic conditions, climate seasonality, wind speed and direction, topography, and fuel biomass. In Brazil, fire shapes the structure and composition of cerrado savannas, and the impact of fire on vegetation dynamics is well explored, but the drivers of variation in fire disturbance across landscapes and over time are still poorly understood. We reconstructed 31 years of fire occurrence history in the Serra do Cipó region, a highly-diverse cerrado landscape, located in the southern portion of the Espinhaço mountain range, state of Minas Gerais, Southeastern Brazil. We mapped burn scars using a time series of Landsat satellite images from 1984 to 2014. Our questions were 1) How does fire occurrence vary in time and space across the Serra do Cipó cerrado landscape? 2) Which climatic drivers may explain the spatial and inter-annual variation in fire occurrence on this landscape? 3) Is fire occurrence in this cerrado landscape moisture-limited or fuel-limited? We evaluated the inter-annual variation and distribution of burned areas, and used linear models to explain this variation in terms of rainfall amount (determinant of fuel load production), seasonal rainfall distribution (determinant of dry fuel availability), abnormality of precipitation (Standardized Precipitation Index – SPI), and vegetation type (Enhanced Vegetation Index – EVI). Contrary to our expectations, annual rainfall volume was weakly and negatively correlated with burned area, and the strongest predictor of burned area was drought during the ignition season. The length of the dry season and the distribution of rain along the season determined ignition probability, increasing fire occurrence during the driest periods. We conclude that the mountain cerrado vegetation at Serra do Cipó has a moisture-dependent fire regime, in contrast to the fuel-dependent fire regimes described for African savannas. These findings imply that savannas at different continents may have different recovery and resilience capabilities when subjected to changes in the fire regime, caused by direct anthropogenic activities or indirectly through climatic changes. The possible effects of these changes on cerrado landscapes are still unknown, and future studies should investigate if currently observed fire regimes have positive or negative impacts on vegetation diversity, recovery, resilience and phenology, thus helping managers to include fire management as conservation measure.     

全球变化背景下野火研究进展

野火是森林和多种植被生态系统面临的最重要自然干扰,也是一种重要的自然灾害;而人类活动已在全球范围内显著影响了野火的发生与分布,因此野火成为全球变化及其环境影响研究的关键议题之一。本文基于国际野火研究的文献搜索和统计分析,从野火的观测-评估-预警技术、野火时空格局研究、气候变化和人类活动对野火的影响、野火的环境-生态-进化效应等方面入手,综述了自21世纪以来的国际野火研究进展。概括起来,遥感技术的快速发展,推动了野火观测的时空分辨率不断提高,对野火时空格局的刻画从单一因子向多重指标的火烧体系评估转变。气候变化在某些区域已经显著影响了野火的发生频率,预计随着全球变暖野火风险将进一步加大,并且极端大火的发生机制和生态影响越来越受到关注。人类活动一方面通过增加火源提高了野火频率,另一方面又通过提高生态系统管理的强度、扑救火灾以及降低可燃物的连通性抑制了野火的发生。植被在长期演化过程中形成了一系列适应火的功能机制,这些功能属性影响着生态系统对野火的响应,并对火后生态恢复和重建具有科学指导价值。未来野火研究将向跨时空尺度、观测和模拟深度融合、典型机制和大尺度效应相结合的方向发展。     

Recent fire regime characteristics and potential natural vegetation relationships in Spain

Abstract. In heavily altered landscapes, where vegetation is not natural and where people are the main source of ignitions, relationships between fire occurrence and climate conditions may be unclear. The objective of this study was to evaluate to what extent territories with similar Potential Natural Vegetation (PNV) in peninsular Spain differ in their forest fire characteristics. From 1974 to 1994, more than 174 000 fires occurred. We used (1) the Spanish data base of forest fires, (2) a PNV map and (3) a land use map. Separate fire characteristics, based either on the number of fires occurred or the area burned, were obtained for each of the ca. 5000 grid‐cells (10 km × 10 km) into which peninsular Spain is divided in the UTM projection. Also, meteorological conditions at the time of fire ignition, cause of ignition and present forest cover were referred to the same grid‐cells as external factors potentially determinant of fire occurrence. The relationships between fire regime characteristics and PNV units were explored with Principal Components Analysis (PCA). The role of the three sets of external factors in the fire characteristics was evaluated with Redundancy Analysis (RDA). Groups of similar PNV types were clearly segregated, suggesting a gradient of fire characteristics. Higher fire incidence (higher frequencies and spatial incidence of fires, but lower proportions of grid‐cells affected by large fires) was associated with Atlantic, warm territories with deciduous forests as PNV. Intermediate fire frequency and rotation period, but with a higher relative incidence of medium and large fires occurred in Mediterranean PNV units, dominated by sclerophyllous oak forests. Low fire frequency and long rotation periods, with strong seasonal and yearly variability occurred for PNV units in the cold uplands (Fagus, Pinus, Abies, Juniperus) or in the semi‐arid, shrubby PNV units. The cause of ignition best explained the patterns of forest fire characteristics, followed by weather conditions. Our results indicate that, even in human influenced regions, climate and soil conditions exert control on the resulting forest fire characteristics, as indicated by the high segregation of the PNV types. However, the role of man was crucial in shifting the patterns of fire incidence. This was so that highest fire incidence occurred in regions that, otherwise, would be expected to have a much lower one, thus posing a serious threat for such areas. PNV maps, by providing a phytogeographical framework for characterizing forest fires, could be valuable tools for applying research results to forest fire management policies, taking properly into account the underlying determinant factors.     

Factors affecting wild boar abundance across an environmental gradient in Spain

The recent Europe-wide increase in wild boarSus scrofa, Linnaeus, 1758 abundance is undoubtedly due to many different factors, the relative importance of which differs from region to region. In Aragón (northeastern Spain), wild boar enlarged its distribution area eight times in the 1990s as compared with the century before, occupying the whole region. We studied wild boar abundance along an environmental gradient in Aragón to determine which factors are most responsible for its variation. Relative abundance of wild boars was estimated by catch-effort methods in 134 hunting estates (sampling units) for 5 consecutive hunting seasons. To characterise the environmental conditions, we quantified landscape composition and structure, topographical factors and climatic factors. Hunting pressure indices were also calculated for our sampling units. The average wild boar catch per hunting activity and per 100 km² ranged from 0.72 to 16.31. Our results suggest thatwild boar abundances are affected by landscape structure, mainly by landscape diversity. In addition, lowland arid agrosystems (characterized by high temperatures and open juniper woodlands with little food availability) constrain the abundance of wild boar populations in spite of their wide plasticity to colonize new habitats.     

A Bootstrap-Based Covariate Selection Method for Modeling the Risk of Lightning-Induced Fires at a Local Scale: A Case Study in Northwest Spain

In lightning-induced fire risk prediction models, the number of potential predictors is usually high, with some redundancy among them. It is therefore important to select the best subset of predictors that obtain models with the greatest discrimination capacity. With this aim in mind, the logistic generalized linear model was used to estimate lightning-induced fire occurrence using a case study of the province of León (northwest Spain). A bootstrap-based test was used to obtain the optimal number of predictors and to model this optimal number of predictors displaying the largest area under the receiver operating characteristics curve. The results show that of the 16 variables initially considered, only three were necessary to obtain the model with the best discriminatory capacity for estimating lightning-induced fire occurrence. Moreover, this model can be considered equivalent to another nine alternative models with three covariates. Both the optimal and the equivalent models are useful in the spatially explicit assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and the design of long-term wildfire management strategies. The methodology used for this case study can be applied to other wildfire risk assessment situations where multiple and interconnected covariates are available.     

A review of the relationships between drought and forest fire in the United States

The historical and presettlement relationships between drought and wildfire are well documented in North America, with forest fire occurrence and area clearly increasing in response to drought. There is also evidence that drought interacts with other controls (forest productivity, topography, fire weather, management activities) to affect fire intensity, severity, extent, and frequency. Fire regime characteristics arise across many individual fires at a variety of spatial and temporal scales, so both weather and climate – including short‐ and long‐term droughts – are important and influence several, but not all, aspects of fire regimes. We review relationships between drought and fire regimes in United States forests, fire‐related drought metrics and expected changes in fire risk, and implications for fire management under climate change. Collectively, this points to a conceptual model of fire on real landscapes: fire regimes, and how they change through time, are products of fuels and how other factors affect their availability (abundance, arrangement, continuity) and flammability (moisture, chemical composition). Climate, management, and land use all affect availability, flammability, and probability of ignition differently in different parts of North America. From a fire ecology perspective, the concept of drought varies with scale, application, scientific or management objective, and ecosystem.     

气候变化背景下江西省林火空间预测

林火是森林生态系统中重要的干扰因子之一,深刻地影响森林景观结构和功能。在全球气候化背景下,揭示气候变化对林火空间分布格局的影响,可为林火管理和防火资源分配提供科学指导。因此,基于江西省2001—2015年MODIS火影像数据(MCD14ML)和年均气温、年均降水量、植被、地形、人口密度、距道路距离、距居民点距离7个因子数据,利用增强回归树模型:(1)分析林火发生影响因子的相对重要性及其边际效应;(2)将GFDL-CM3和GISS-E2-R气候变化模式中的年均气温和年均降水量作为未来的气象数据,在3个温室气体排放量情景(RCP2.6、RCP4.5、RCP8.5)下,对2050年(2041—2060的平均值)和2070年(2061—2080的平均值)江西省林火分布进行预测,生成林火发生概率图。并采用受试者工作特征(ROC曲线)和混淆矩阵评估模型预测的精度。研究结果表明:(1)年均气温和海拔与江西省林火发生的相关性较强,年均降水量、居民点距离、人口密度、道路距离与林火发生的相关性较弱,但是与林火发生密切相关的如降水、风速等也应重点关注;(2)训练数据(70%)和验证数据(30%)的AUC值(ROC曲线下面积值)均为0.736,混淆矩阵对火点预测的正确率为67.8%,表明模型能够较好地预测研究区林火的发生;(3)在RCP8.5排放情景中林火发生的增幅最明显,其增幅较大的区域由赣南向赣北移动;(4)未来2050年和2070年林火发生与当前气候(2001—2015年)下相比,赣州市、鹰潭市的增幅较为明显,其他区域不明显。江西省各林业管理部门要加强林火高发区及潜在发生区的森林监测和管理,加大防火宣传力度,提升民众的森林防火意识。     

大兴安岭地区森林雷击火与闪电的关系

林火是森林生态系统的重要影响因子.随着全球气候变暖,林火,特别是雷击火有增多的趋势.本文对我国东北大兴安岭地区1966-2007年的林火数据及相应的闪电和气象资料进行分析.结果表明:1966-2007年,该地区雷击火的次数及其过火面积都呈显著增加趋势.影响雷击火的气象条件与研究时间尺度有关,年尺度的雷击火与降水显著相关,相关系数达-0.489;月尺度的雷击火则与气温显著相关,相关系数为0.18.雷击火与闪电的关系也与时间尺度有关,年尺度的雷击火与闪电关系不明显,受降水影响较大;月尺度的雷击火与闪电具有较好的相关性,且受降水的影响;日尺度的雷击火在降水量＞5 mm时与闪电关系不明显,但当降水量＜5 mm时,雷击火与闪电次数呈正相关.据此,发展了基于闪电定位仪数据的火险指数算法以及大兴安岭地区的森林雷击火预报模型.经2005-2007年雷击火验证,该模型的预报准确率＞80％.     

Factors Causing Victims of Wildland Fires in Spain (1980–2010)

The worst consequence of wildland fires is the loss of human lives, a regular phenomenon over the last few decades worldwide. This work analyzes all recorded wildland fires in Spain with victims between 1980 and 2010. We classified causality causes during wildland fires to study the most frequent causes of fatalities and how they were related to regions, fire size, and extreme weather conditions (i.e., high temperature days). Trends in number of both injured and killed individuals were analyzed. We observed that the annual number of victims did not decrease in the study period. Entrapment is the most frequent cause of death within the fire suppression employees. Fire size is a key factor in the occurrence of victims because 95% of fatalities in wildland fires (not counting aerial casualties) happened in fires larger than 100 ha. High temperature days also were important because 60% of entrapments were produced in this kind of days.     

A Bayesian modeling of wildfire probability in the Zagros Mountains,Iran

The preparation of probability distribution maps is the first important step in risk assessment and wildfire management. Here we employed Weights-of-Evidence (WOE) Bayesian modeling to investigate the spatial relationship between historical fire events in the Chaharmahal-Bakhtiari Province of Iran, using a wide range of binary predictor variables (i.e., presence or absence of a variable characteristic or condition) that represent topography, climate, and human activities. Model results were used to produce distribution maps of wildfire probability. Our modeling approach is based on the assumption that the probabilities reflect the observed proportions of the total landscape area occupied by the corresponding events (i.e., fire incident or no fire) and conditions (i.e., classes) of predictor variables. To assess the effect of each predictor variable on model outputs, we excluded each variable in turn during calculations. The results were validated and compared by the receiver operating characteristic (ROC) using both success rate and prediction rate curves. Seventy percent of fire events were used for the former, while the remainder was used for the latter. The validation results showed that the area under the curves (AUC) for success and prediction rates of the model that included all thirteen predictor variables that represent topography, climate, and human influences were 84.6 and 80.4%, respectively. The highest AUC for success and prediction rates (86.8 and 84.6%) were achieved when the altitude variable was excluded from the analysis. We found slightly decreased AUC values when the slope-aspect and proximity to settlements variables were excluded. These findings clearly demonstrate that the probability of a fire is strongly dependent upon the topographic characteristics of landscapes and, perhaps more importantly, human infrastructure and associated human activities. The results from this study may be useful for land use planning, decision-making for wildfire management, and the allocation of fire resources prior to the start of the main fire season.