Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China

A forest fire can be a real ecological disaster regardless of whether it is caused by natural forces or human activities, it is possible to map forest fire risk zones to minimize the frequency of fires, avert damage, etc. A method integrating remote sensing and GIS was developed and applied to forest fire risk zone mapping for Baihe forestry bureau in this paper. Satellite images were interpreted and classified to generate vegetation type layer and land use layers （roads, settlements and farmlands）. Topographic layers （slope, aspect and altitude） were derived from DEM. The thematic and topographic information was analyzed by using ARC/INFO GIS software. Forest fire risk zones were delineated by assigning subjective weights to the classes of all the layers （vegetation type, slope, aspect, altitude and distance from r3ads, farmlands and settlements） according to their sensitivity to fire or their fire-inducing capability. Five categories of forest fire risk ranging from very high to very low were derived automatically. The mapping result of the study area was found to be in strong agreement with actual fire-affected sites.     

Analysis of the seasonal characteristics of forest fires in South Korea using the multivariate analysis approach

For efficient forest fire management, special precautions are required in dry and strong-wind seasons vulnerable to severe forest fires. To extract the seasonal characteristics of forest fires in South Korea, the statistics over the past 16 years, 1991 through 2005, were investigated. The daily records of the number of fire occurrences, the total area burned and the average burned area per occurrence were examined to identify the seasonal patterns of forest fires using cluster analysis and principal component analysis; the risk of daily fires was also assessed using the ordered logit model. As a result, the fire patterns were classified into five clusters and a general danger index for forest fires was derived from the first principal component, showing relatively large-scaled fire regimes in spring, and frequent small-scaled fire regimes in autumn and winter. In connection with the ordered logit model, the probability for the five ranks of forest fire risk was calculated and the threshold for high-risk fires was detected. As an implementation of the results above, the proper forest fire precautionary period in South Korea was estimated, and consequently October 21 through May 17 was recognized as a dry season at a high risk of forest fires. This period began 10 days earlier in autumn and extended into midwinter (late December and January) as opposed to the existing precautionary period, indicating the need of more cautious forest fire management earlier in autumn and continuing through midwinter.     

Wildland fires and moist deciduous forests of Chhattisgarh,India: divergent component assessment

We studied moist deciduous forests of Chhattisgarh, India （1） to assess the effect of four levels of historic wildland fire frequency （high, medium, low, and no-fire） on regeneration of seedlings in fire affected areas during pre and post-fire seasons, （2） to evaluate vegetation struc- ture and diversity by layer in the four fire frequency zones, （3） to evalu- ate the impact of fire frequency on the structure of economically impor- tant tree species of the region, and （4） to quantify fuel loads by fire fre- quency level. We classified fire-affected areas into high, medium, low, and no-fire frequency classes based on government records. Tree species were unevenly distributed across fire frequency categories. Shrub density was maximum in zones of high fire frequency and minimum in low- frequency and no-fire zones. Lower tree density after fires indicated that regeneration of seedlings was reduced by fire. The population structure in the high-frequency zone was comprised of seedlings of size class （A） and saplings of size class （B）, represented by Diospyros melanoxylon, Dalbergia sissoo, Shorea robusta and Tectona grandis. Younger and older trees were more abundant for Tectona grandis and Dalbargia sis- soo after fire, whereas intermediate-aged trees were more abundant pre- fire, indicating that the latter age-class was thinned by the catastrophic effect of fire. The major contributing components of fuel load included duff litter and small woody branches and twigs on the forest floor. Total fuel load on the forest floor ranged from 2.2 to 3.38 Mg/ha. The netchange in fuel load was positive in high- and medium-frequency fire zones and negative under low- and no-fire zones. Repeated fires, how- ever, slowly reduced stand stability. An ecological approach is needed for fire management to restore the no-fire spatial and temporal structure of moist deciduous forests, their species composition and fuel loads. The management approach should incorporate participatory forest manage- ment. Use of c     

Forest Fire Risk Zone Modeling Using Logistic Regression and GIS: An Iranian Case Study

Forest fires are an important environmental concern worldwide, affecting the soil, forests and human lives. During the process of burning, soil nutrients are depleted and the soil is subsequently more vulnerable to erosion. Nowadays it is necessary to identify the factors influencing the occurrence of fire and fire hazard areas, in order to minimize the frequency of fire and avert damage. Logistic regression was used to study the forest fire risk and identify the most influential factors in the occurrence of forest fires. Climatic variables (temperature and annual precipitation), human factors (distance from streams and farmland) and physiography (land slope and elevation) were considered and their correlation with the occurrence of fires investigated. Results of model validation and sensitivity of various areas to fire were examined with the ROC coefficient and Hosmer–Lemeshow test. The estimated coefficients for the independent variables indicated that the probability of occurrence of fire is negatively related to land slope, site elevation and distance from farmlands, but is positively related to amount of annual precipitation.     

A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets

All of the Mediterranean countries face a serious forest fire problem. The main factors that affect the problem of forest fires in Greece are vegetation, climate conditions and most of all, arson (Proceedings of Forest Fires in Greece, Thessaloniki, 1990, p. 97). In Greece, after 1974, the number of forest fires and the total burned areas have risen dramatically. The design of an effective fight and prevention policy is a very important matter, as it can minimize the destruction. This paper describes an expert system that classifies the prefectures of Greece into forest fire risk zones, using a completely new methodology. The concept of fuzzy expected intervals (F.E.I.) was defined by Kandel and Byatt (Proc. IEEE, 66, 1978, 1619) and offered a very good approach towards forest fire risk classification. Fuzzy expected intervals are narrow intervals of values that best describe the forest fire problem in the country or a part of the country for a certain time period. Fuzzy logic was applied to produce a F.E.I. for each prefecture of the country. A successful classification of the prefectures of Greece (in forest fire risk zones) was performed by the expert system by comparing the produced fuzzy expected intervals to each other and by using a supervised machine learning algorithm that assigns a certain weight of forest fire risk to each prefecture (Machine Learning, John Wiley and Sons, 1995).     

Evaluating the efficiency of the Dong model in determining fire vulnerability in Iran's Zagros forests

Recent fires in Iran's Zagros forests have inflicted heavy,extensive losses to the environment,forests,villages,and forest inhabitants,resulting in a huge financial loss to the country.With the increasing risk of fire and the resulting losses,it has become ever more necessary to design and develop efficient fire control and prediction procedures.The present study utilizes the Dong model to develop a map of areas vulnerable to fire in the Zarivar lake forests as a representative sample of Zagros forests.The model uses as its inputs some of the most significant factors(such as vegetation,physiographic features,and the human component) that affect the fire occurrence and spread.Having assigned weights to each factor based on the model,all maps were overlapped in the ArcMap and then the region was divided into five zones.The results showed that 74% of the region was located in three classes:highly vulnerable,vulnerable,and medially vulnerable.To validate the proposed zoning map it was compared with a map based on real data obtained from previous fires.The results showed that 81% of fire incidents were located in highly vulnerable,vulnerable and medially vulnerable zones.Furthermore,the findings indicated a medium to a high degree of fire vulnerability in Zarivar Lake forests.     

2003—2018年中国森林火灾时空分布格局研究

基于2003—2018年的中国森林火灾统计数据,全面分析了森林火灾发生的时空分布格局,定量分析了其统计学规律,旨在为森林火灾预测、管理和风险决策提供基础支撑.分析结果表明,森林火灾的发生具有极强的随机性和离散性,通过森林火灾总次数、火场面积和受害森林面积的平均数来反映森林火灾发生风险将会导致评估结果偏高.森林火灾发生次...     

Emulating natural disturbances as a forest management goal: Lessons from fire regime simulations

Emulating natural forest disturbance is an increasingly popular forest management paradigm that is considered a means of achieving forest sustainability. Adopting this goal requires a sound understanding of natural disturbances at scales that correspond to management policies and strategies. In boreal forest landscapes driven by periodic stand-replacing fires this requires knowledge of fire regime characteristics, especially their spatial and temporal variability as well as stochasticity. The major goal of this study was to demonstrate the utility of fire regime simulation modeling to explore the variability of fire regime characteristics, with respect to formulating and assessing forest management strategies. We conducted a modeling experiment in a boreal forest landscape of northwestern Ontario, Canada, to examine its long-term fire regime in relation to forest policies on harvest size distribution. We used BFOLDS, a spatially explicit fire regime model that simulates individual fire events mechanistically in response to fire weather, fuel patterns, and terrain. The fire regimes in four large eco-regions were modeled for a 200-year period under three fire-weather (cold, normal, and warm) scenarios, with replications. We found that fire size distribution in all eco-regions followed power law under all weather scenarios, but their slopes and intercepts varied among eco-regions and fire weather scenarios. Warming fire weather increased burn rates and fire numbers in all eco-regions, albeit to different degrees. Overall, the variability among eco-regions was higher than the variability among fire weather scenarios, and among replicates. Comparisons of simulated fire size classes with those from an 86-year long fire history showed that empirical data cannot capture the variability that could be revealed by simulation modeling. We also show that fire size distribution is spatially heterogeneous within eco-regions, and provide several suggestions for forest policy directions with respect to forest harvest size distributions and harvest rates, based on the variability of fire regime characteristics. An assessment of present forest policies of emulating natural disturbances that guide forest harvest sizes showed that these are incongruent with simulated fire size distributions under all scenarios with one exception. Overall, this study illustrates the value of scenario simulation modeling to explore and quantify the variability of forest fire regime, for use in forest policies and strategies that attempt to emulate natural disturbance.     

Spatio-temporal analysis of forest fire events in the Margalla Hills,Islamabad,Pakistan using socio-economic and environmental variable data with machine learning methods

Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study consid-ers both environmental (altitude,precipitation,forest type,terrain and humidity index) and socioeconomic (popula-tion density,distance from roads and urban areas) factors to analyze how human behavior affects the risk of forest fires.Maximum entropy (Maxent) modelling and random forest (RF) machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC) curve and the area under the ROC curve (AUC) were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the prob-ability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999s,2009s,and 2019s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to acces-sibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by consid-ering socio-economic and environmental conditions.     

Modeling Mediterranean forest fuels by integrating field data and mapping tools

Fire behavior modeling systems are important in predicting wildfire risk, fire growth, and fire effects. However, simulation software requires a new fuel modeling to include fuel treatments, prescribed fire and the transition to crown fire. The thirteen Rothermel models are insufficient in completely representing Mediterranean ecosystems. In this sense, the new American modeling includes five fuel types, requiring the acquisition of hybrid models made up of the mixture of grass and shrub and the grass or shrub mixed with litter from forest canopy. Respecting meteorological conditions and shrub characteristics, field studies have shown significant differences between American and Mediterranean models. As a consequence, the definition of new Mediterranean models requires the adjustment of specific parameters such as fuel load by category (live and dead) and particle size class (1-, 10- and 100-h time-lag), fuelbed depth and surface area-to-volume ratio. These new parameters were obtained in situ of sample itineraries, prescribed fires, and forest fires. The availability of this new modeling, validated on a field of regional scale, will facilitate preventive planning and management as well as an efficient application of suppression techniques, both ground and aerial operations, required in defending a territory against forest fires.     

基于INFORM模型与GEE的根河火烧迹地冠层含水量反演研究

[目的]提出一种结合辐射传输模型与遥感云平台反演火烧迹地冠层含水量(Canopy water content,CWC)的新方法,弥补目前对火烧迹地恢复阶段植被含水量的监测,为定量监测植被水分与火灾预警提供理论参考.[方法]以内蒙古自治区根河市火烧迹地为研究对象,基于INFORM辐射传输模型,使用查找表的方法反演森林冠层...     

龙胜县森林火险等级区

以广西龙胜县为研究区,调查分析该县森林火险状况及影响要素,结合森林资源载量及森林防火实际,选取树种（组）燃烧类型、人口密度、气象因子、火灾次数等11项主要林火影响因子,综合运用层次分析法(AHP)、聚类分析法及ArcGIS空间分析法,区划森林火险等级。结果表明,各村（分场）可区划为Ⅰ（高火险区）、Ⅱ（中火险区）、Ⅲ（低火险区）3个火险等级;Ⅰ、Ⅱ、Ⅲ级火险区面积分别占研究区总面积的35.47%、38.33%、26.20%。高火险区主要分布在东部和南部,中火险区集中于西部和北部,低火险区分散在高、中火险区之间。区划结果有利于总结、突出研究区森林火情特点和重点。     

An easy,accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler

To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk(FFR) maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model(susceptibility map, hazard map, vulnerability map, economic value map,and potential loss map) and was produced according to Portuguese Forest Authority's(AFN, Autoridade Florestal Nacional) rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category ‘‘very high' or ‘‘high' fire risk. The ‘‘very high'fire risk area is mainly classified as broad-leaved forest and has the steepest slopes(15 %). The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads.In addition, 92.6 % of the ‘‘high' and ‘‘very high' risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS proprietary software. Although the results were obviously the same, the model developed here presents several advantages over the other two approaches. Besides being faster,it is easy to change the model parameters according to user needs(i.e., to the rules of different countries), and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena(e.g.,floods, earthquakes, landslides). The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.     

福建林火的发生特点与防治对策探讨

对福建省近年发生的１９７起森林火灾归纳、分析表明，森林火灾发生频率最高的是生产用火，林火发生以２、３月份最多；上午１０时至下午１８时为林火发生的高峰；预防林火发生，应确定森林防火戒严期，开展火险预测预报，完善防火设施，以利于迅速扑火。     

基于随机森林模型的交界域火灾风险分析

【目的】城镇森林交界域火灾频繁发生,使民生经济遭受严重危害。使用随机森林模型对省域内的城镇森林交界域火灾风险与影响因子的关系进行空间建模,探究随机森林模型在拟合、解释交界域火灾风险方面的优势,并与森林火灾风险的影响因子对比,为进一步评估城镇森林交界域火险提供依据。【方法】研究基于安徽省2002—2011年火灾历史数据,采用气候、地理环境、人类活动、社会经济等方面的9个因子作为自变量,月均火灾密度作为因变量。使用特征选择方法得到模型内不同自变量的贡献度、统计特征以及内部模型的平均表现,选择出进入最后模型中的自变量;使用随机森林模型对城镇森林交界域火灾风险进行解释,分析影响城镇森林交界域火灾风险和森林火灾风险的重要因子。【结果】关键自变量对城镇森林交界域火灾风险的影响程度大小排序依次为:道路线密度、铁路线密度、月均最高温度、归一化植被指数、人口密度以及海拔;对森林火灾风险则为:月均最高温度、归一化植被指数、道路线密度、铁路线密度、人口密度以及海拔;经过训练与计算发现随机森林模型在5个子模型的训练集与测试集的表现基本一致,拟合值与实际值的简单相关系数均达0.90以上,可见随机森林模型对交界域火灾风险和森林火灾风险表现出显著的解释能力;此外,随机森林模型在总体数据集上进行了拟合,得到城镇森林交界域火灾风险的拟合值与实际值的相关性为0.997,森林火灾风险的拟合值与实际值的相关性为0.996,表明了随机森林模型具备极强的火灾风险拟合性能。【结论】影响城镇森林交界域火灾发生的最重要自变量是道路和铁路线密度,而对森林火灾则是月均最高温度与归一化植被指数,可见城镇森林交界域火灾发生人类活动因素密切相关。随机森林算法对城镇森林交界域火灾风险和森林火灾风险都能表现出稳健的和非常准确的拟合能力,是一个非常有用的工具。     

Fuel types and crown fire potential in <Emphasis Type="Italic">Pinus halepensis</Emphasis> forests

There is a lack of knowledge to identify and classify forest structures according to the risk of crown fires, especially in Mediterranean regions. In this study, for the first time, we use real information, obtained after a wildfire that burnt under extreme meteorological conditions, to classify forest structures of Pinus halepensis into fuel types as a function of crown fire potential. We identified fourteen forest structures which characterize many forest types in Western Mediterranean areas depending on canopy closure, number of tree layers, percent of each tree layer and overall tree density. By using the pattern of fire types that burnt the most numerous forest structures, we have identified four fire hazard groups of forest structures which are considered different fuel types. The first two had the lowest risk of active crown fires and they differed in the proportion of surface fires and passive crown fires. The third fuel type was the threshold between structures with low and high extreme fire behavior; while the fourth had a high risk of active crown fires. Firefighters and forest managers who are demanding this kind of schema, will test and upgrade this classification of fuel types in function of crown fire potential during future wildfires.     

基于气象因子深度学习的森林火灾预测方法

森林火灾一旦发生将对生态系统造成严重的破坏,间接导致气候的变化和极端天气频发。对森林火灾的发生进行准确预测可提前采取有效的防控措施,具有重要意义。传统林火预测模型多为数学方法和浅层神经网络,当数据量增大时易出现建模困难以及预测精度降低等问题。深度学习模型在处理大量非线性数据上具有一定的优势,其模型具有多层网络结构,通过训练大量数据可提取出具有代表性的特征值,发现数据间的隐含关系,达到准确分类预测的目的。因此,本研究提出一种基于深度学习的林火预测方法,将深度信念网络(deep belief network,DBN)作为预测模型,气象因子作为输入数据,以解决传统林火预测模型在面对大量数据时预测效果不佳的问题;同时结合过采样SMOTE(synthetic minority oversampling technique)算法,平衡林火数据集和增加训练数据量,提升了森林火灾的预测准确度。结果表明,在面对更大的数据量时,该模型预测精度明显优于其他传统林火预测模型,证明了将深度学习应用在林火预测的优越性。该研究可为深度学习在林业领域的应用提供参考。     

Determination of fire-access zones along road networks in fire-sensitive forests

Wild forest fires are one of the greatest environmental disasters affecting forest resources. Along the coastal zone of the Mediterranean region in Turkey,forested areas are classified as first-degree, fire-sensitive areas. Every year, thousands of hectares of forests have been destroyed in Turkey. In this study, fire-access zones were determined in the Mediterranean forests of Turkey, by utilizing geographic information systems(GIS) technology. The effective reach distance of fire hoses from both sides of roads was considered in order to delineate fireaccess zones. The effective reach distance can vary based on the technical capabilities and hydraulic capacity of fire trucks(minimum and maximum pressures on water pump);terrain structures(uphill, downhill and flat); and ground slope. These factors and their influences were studied in fire sensitive forest areas located in the eastern Mediterranean city of Kahramanmaras? in Turkey. First, terrain structures on both sides of the road network and groundslope classes were determined based on GIS data layers.Then, fire access zones were delineated according to water pressure data, terrain structures, and ground-slope classes.The results indicated that 69.30 % of the forested areas were within the fire-access zones, while the rest of the forest was out of reach the fire hoses. The accessible areas were also calculated for forested areas with different firesensitivity degrees. The accessible areas were 69.59, 69.96,and 67.16 % for the forested areas that are sensitive to fires at the first, second, and third degrees, respectively. This finding has implications for the monitoring and management of fire threats in areas outside of the reach distance.The outside areas should receive extra attention and monitoring during the fire season so that fires are detected ahead of time and management has sufficient time to react.Besides, new roads should be considered for these areas in order to access more lands in a shorter amount of time.     

内蒙古大兴安岭森林火险等级预报模型研究

为提高森林火险等级预报的准确率和及时率,减少森林资源损失。以内蒙古大兴安岭地区为研究区域,利用遥感MODIS-NDVI数据反演可燃物的湿度指数,以气象指数、植被指数、闪电指数、湿度指数共同计算得出森林火险指数,以火险指数作为内蒙古大兴安岭地区森林火险等级预报模型的量化指标,并对火险等级进行分级,获得内蒙古大兴安岭森林火险等级预报方法。该方法结合当地的实际情况,将闪电指数引入到预报模型中,并实现了定量化估测。同时,以近几年发生在该地区重特大森林火灾为例,对该预报模型进行了验证。该方法可较好地对内蒙古大兴安岭地区森林火险等级进行定量化预报。     

南方人工林森林火灾发生和危害之评估

根据全国25省区和南方4省区1999～2001年的森林火灾数据,分析评估了南方人工林发生森林火灾的原因、过程和危害的程度;探讨山火防范和控制,减少危害和投资风险的途径,并以肇庆市国有林场过去10年森林火灾发生情况作验证。人工林森林火灾受气候、地区森林覆盖率和特征、人工林特征、经营管理系统等的综合影响,每年10月至次年2月份为干旱季节,森林火灾发生最为频繁。肇庆国有林场过去10年森林火灾面积比率为0．023％0～4、840％0。通过设立健全的森林防火和控制系统,有效的营林管理,南方人工林发生火灾的面积机率可以最大限度地降低,应可以控制在1％以下。