Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province,southwestern China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.

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.

RS AND GIS-BASED FOREST FIRE RISK ZONE MAPPING IN DA HINGGAN MOUNTAINS

The Da Hinggan Mountains is one of the most important forest areas in China, but forest fire there is also of high frequency. So it is completely necessary to map forest fire risk zones in order to effectively manage and protect the forest resources. Two forest farms of Tuqiang Forest Bureau (53 degrees 34'-52 degrees 15'N,124 degrees 05'-122 degrees 18'E) were chosen as typical areas in this study. Remote sensing (RS) and Geographic Information System (GIS) play a vital role and can be used effectively to obtain and combine different forest-fire-causing factors for demarcating the forest fire risk zone map. Forest fire risk zones were described by assigning subjective weights to the classes of all the coverage layers according to their sensitivity to fire, using the ARC/INFO GIS software. Four classes of forest fire risk ranging from low to extremely high were generated automatically in ARC/INFO. The results showed that about 60.33% of the study area were predicted to be upper moderate risk zones, indicating that the forest fire management task in this area is super onerous. The RS and GIS-based forest fire risk model of the study area was found to be highly compatible with the actual fire-affected sites in 1987. Therefore the forest fire risk zone map can be used for guidance of forest fire management, and as basis for fire prevention strategies.

Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk(FFR)mapping in Melghat Tiger Reserve(MTR)forest

A comparative study of Frequency Ratio(FR)and Analytic Hierarchy Process(AHP)models are performed for forest fire risk(FFR)mapping in Melghat Tiger Reserve forest,central India.Identification of FFR depends on various hydrometeorological parameters altitude,slope,aspect,topographic position index,normalized differential vegetation index,rainfall,air temperature,land surface temperature,wind speed,distance to settlements,and distance by road are integrated using a GIS platform.The results from FR and AHP show similar trends.The FR model was significantly higher accurate(overall accuracy of 81.3%,kappa statistic 0.78)than the AHP model(overall accuracy 79.3%,kappa statistic 0.75).The FR model total forest fire risk areas were classified into five classes:very low(7.1%),low(22.2%),moderate(32.3%),high(26.9%),and very high(11.5%).The AHP fire risk classes were very low(6.7%),low(21.7%),moderate(34.0%),high(26.7%),and very high(10.9%).Sensitivity analyses were performed for AHP and FR models.The results of the two different models are compared and justified concerning the forest fire sample points(Forest Survey of India)and burn images(2010-2016).These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.

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 pro- prietary software. Although the results were obviously the same, the model developed here presents several advan- tages 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.

Forest fire risk indices and zoning of hazardous areas in Sorocaba,Sao Paulo state,Brazil

This study compares the performance of three fire risk indices for accuracy in predicting fires in semideciduous forest fragments,creates a fire risk map by integrating historical fire occurrences in a probabilistic density surface using the Kernel density estimator(KDE)in the municipality of Sorocaba,Sao Paulo state,Brazil.The logarithmic Telicyn index,Monte Alegre formula(MAF)and enhanced Monte Alegre formula(MAF+)were employed using data for the period 1 January 2005 to 31 December 2016.Meteorological data and numbers of fire occurrences were obtained from the National Institute of Meteorology(INMET)and the Institute for Space Research(INPE),respectively.Two performance measures were calculated:Heidke skill score(SS)and success rate(SR).The MAF+index was the most accurate,with values of SS and SR of 0.611%and 62.8%,respectively.The fire risk map revealed two most susceptible areas with high(63 km^2)and very high(47 km^2)risk of fires in the municipality.Identification of the best risk index and the generation of fire risk maps can contribute to better planning and cost reduction in preventing and fighting forest fires.

Forest fire risk assessment in parts of Northeast India using geospatial tools

Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sensing and Geographic Information System （GIS） in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover （LULC）, vegetation type, Digital elevation model （DEM）, slope, aspect and proximity to roads and settlements, factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. TO validate the results, Along Track Scanning Radiometer （ATSR）, the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55-63% of agreement with ATSR dataset.

Study on Forecasting Method of Forest Fire Risk Grade in Putian City, China

From January 1, 2014, the basic stations of meteorological observation countries have changed from small evaporation observations to large-scale evaporation observations. National general weather stations have canceled observations on evaporation, but small evaporation is very important for forest fire risk prediction. In order to make the prediction of forest fire risk level objectively, weather data in Putian City, China and the multi-linear regression analysis method is used to calculate the daily evaporation amount in the more advanced SPSS16.0 software (English version), and the data of the last 5 years of each site are selected and fitted. Results showed that we accurately calculated the evaporation of the next day to make up for the lack of data due to the adjustment of the evaporation observation project. According to the forest fire risk weather index corresponding to many meteorological factors such as evaporation, temperature, humidity, sunshine and wind speed, the forest fire risk meteorological grade standard was designed to make a more accurate forest fire risk grade forecast.

GIS, Forest Fire Prevention and Risk Matrix in the National Forest of Khoudida, Sidi Bel Abbes, Algeria

Forest fires in Algeria are ravaging an average of more than 32,000 hectares annually despite the prevention and control plan put in place. They are the most damaging factor of degradation of the forest and weigh heavily on the environment and the local economy. Conventional methods for fire prevention and control are time consuming and are not always reliable in view of the complexity and diversity of forest ecosystems. The main idea behind this study is to use the GIS and remote sensing for the development of a fire risk map of the Khoudida State Forest (Algeria). The approach adopted involves three parameters that control the fire behavior, which are: the top-morphology of the field, the combustibility of the plant cover and hazards. For each factor its correlation with risk was evaluated;the combination of the slope, altitude and exposure parameters in the topo-morphological index and the hazard map made it possible to evaluate the average risk for an area of more than 2132 hectares, 1521 hectares high and only 493 hectares, respectively 51.4%, 36.7% and 11.9%.

Determination of fire risk to assist fire management for insular areas: the case of a small Greek island

Forest fire risk estimation constitutes an essential process to prevent high-intensity fires which are associated with severe implications to the natural and cultural environment. The primary aim of this research was to determine fire risk levels based on the local features of an island,namely, the impact of fuel structures, slope, aspects, as well as the impact of the road network and inhabited regions. The contribution of all the involved factors to forest fires ignition and behavior highlight certain regions which are highly vulnerable. In addition, the influence of both natural and anthropogenic factors to forest fire phenomena is explored. In this study, natural factors play a dominant role compared to anthropogenic factors. Hence essential preventative measures must focus on specific areas and established immediately. Indicative measures may include: the optimal allocation of watchtowers as well as the spatial optimization of mobile firefighting vehicles;and, forest fuel treatments in areas characterized by extremely high fire risk. The added value of this fire prediction tool is that it is highly flexible and could be adopted elsewhere with the necessary adjustments to local characteristics.

Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems(DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.

内蒙古森林火灾发生风险及其驱动因素

森林火灾是危害森林健康的主要灾害之一,科学预测森林火灾是预防森林火灾的重要依据。以中国新旧森林火灾政策作为分界线,将内蒙古森林历史火灾数据分为四个阶段,基于增强回归树模型建立内蒙古森林火灾发生模型,预测森林火灾发生情况,解释不同时期森林火灾和火灾风险变化的差异。预测结果表明:(1)4个时期建模精度AUC均大于0.94,表明BRT模型能够较好地预测研究区森林火灾的发生;(2)气温日较差、日最小相对湿度、上一年春防累计降水量、上一年秋防累计降水量、上一年春防最高地表气温海拔、距火点最近公路距离被确定为影响内蒙古森林火灾发生的重要驱动因素。(3)新旧《森林防火条例》实施前后森林火灾风险等级变化:1981—1988年3月14日,中、高和极高森林火灾风险区分布在呼伦贝尔的东部,而2009—2020年中、高和极高森林火灾风险区分布在呼伦贝尔南部和中部、赤峰市的西南部、锡林郭勒盟和呼和浩特市的中部、乌兰察布市和包头市的南部以及鄂尔多斯市的东部。有助于了解不同时期《森林防火条例》影响下的内蒙古森林火灾的驱动因素和火险等级的变化,为优化森林火灾管理政策及预测预报工作提供科学依据。

基于隐马尔可夫模型的火灾风险评估研究

近年来全球气候变化越来越复杂,自然灾害频发,森林防火形式日益严峻,森林火灾风险评估工作越来越重要。为此,基于隐马尔可夫模型(Hidden Markov Model, HMM)提出一种森林火险评估模型。首先构建森林火灾风险指标体系,将其分为气象条件、森林特征以及防火意识和能力3类12个指标,并根据层次分析法计算指标权重。同时,针对HMM模型求解过程中Baum-Welch算法对初始值有较强的依赖性且容易陷入局部最优的问题,提出通过粒子群优化算法搜索全局最优解作为HMM初始值,再使用Baum-Welch算法进行局部校正,使其快速收敛到全局最优解。利用优化前后的2个模型分别对重庆市森林火灾风险数据开展评估,验证优化后的模型能够有效地评估森林火灾风险,并且与改进前相比更精确,可为区域森林防火工作提供有效指导。

云南顶瓶梗霉对森林地表凋落物的降解效果

【目的】探究云南顶瓶梗霉GZUIFR 22.409对不同类型森林地表凋落物的降解效果,以期利用该真菌促进森林地表凋落物的降解,减少森林地表凋落物的载量,从而降低森林火险等级,并为利用微生物降解法降低贵州林区森林可燃物载量提供理论依据和基础数据。【方法】采集林地的针叶和阔叶地表凋落物,利用云南顶瓶梗霉活菌和粗酶分别处理凋落物,并按照野外调查地表凋落物床层参数设置床层后,于25 ℃温度和80%相对湿度条件下测定两类地表凋落物的失重动态,分析云南顶瓶梗霉活菌和粗酶对两类地表凋落物的降解效果。【结果】云南顶瓶梗霉活菌和粗酶对两类地表凋落物均具有降解作用且都在第16天降解率最大。对于针叶凋落物,粗酶的降解率为9.74%,菌的降解率为13.14%;经降解模型拟合,粗酶的降解系数k为0.004,降解t_(0.5)为165 d、t_(0.95)为740 d,菌的降解系数k为0.005,降解t_(0.5)为131 d、t_(0.95)为588 d;对于阔叶凋落物,粗酶的降解率为15.76%,菌的降解率为15.37%;经降解模型拟合,粗酶的降解系数k为0.007,降解t_(0.5)为93 d、t_(0.95)为422 d,菌的降解系数k为0.006,降解t_(0.5)为103 d、t_(0.95)为487 d。【结论】云南顶瓶梗霉活菌及其粗酶对针阔叶地表凋落物都有降解效果,对阔叶凋落物的降解效果优于针叶凋落物,应用该菌可在一定程度上能够达到减少森林地表可燃物载量的效果。因此云南顶瓶梗霉在调控森林地表凋落物载量方面具有较好的应用前景。

野外-城市界域森林火险时空演变趋势及火险等级划分

野外-城市界域(WUI)人类活动密集,火灾风险极大,在该区域进行森林火险的合理性评价和时空演变趋势分析,对保护区域生态环境和社会经济可持续发展具有重要意义。以北京近郊九龙山林场为研究对象,基于相关火险驱动因子指标数据,运用层次分析法评估研究区2004、2009、2014、2019年森林火险指数,通过Theil-Sen Median趋势分析、全局莫兰指数和局部莫兰指数分析探究森林火险指数的变化趋势及空间聚集性,并基于历史火情数据和地理探测器模型验证火险区划的合理性和准确性。结果表明:(1)九龙山林场内火险指数整体的集聚特征变化较大,61.58%的区域火险指数不显著增加,5.62%的区域微显著增加,32.80%区域为不显著减少和微显著减少。(2)森林火险指数空间上呈正相关,森林火险高值聚集区域分布于东部低海拔地区,在研究时间段内面积减少4.89%,低值聚集区域分布于西部高海拔地区,面积减少2.19%。(3)森林火险区划结果显示,九龙山林场森林高火险区域面积占比最小,主要分布于研究区中部,由于人类活动的影响,高火险区覆盖范围扩大。(4)区划合理性验证结果显示,研究区森林火险等级空间分异性较好,与实际火点分布具有高度一致性。研究结果较好的反应了研究区森林火险时空格局变化,为九龙山林场进一步合理规划防火设施布局,加强火险管控力度,完善防火应急能力奠定数据基础,提示管理部门应加强防火宣传,以最大限度发挥九龙山的生态环境效益,保障林区周边人民生命财产安全。

气象要素与潜在林火风险关联性研究

为探究川西南地区复杂气候与森林火灾之间的关联性,以四川省凉山彝族自治州木里藏族自治县(以下简称“四川省凉山州木里县”)为例,通过局部规模的气象异常值变化,分析了各气象要素与森林火灾的潜在联系性。再通过FWI与FFDI,分析木里县历史火险变化,验证了其与木里县森林火灾的关联性。结果表明:森林火灾发生前后气压、气温、湿度、风速、蒸发量出现不同周期的连续增加(或衰减)。同时,木里县森林火险指数FWI与FFDI呈现出年际震荡且逐渐增大的趋势,且木里县林火频数与FWI的年均相关系数(46.9%)、月均相关系数(80.7%)均大于FFDI(年相关系数38.7%、月相关系数53.1%)。此外,林火风险值出现极端异常还与同期(或同年)发生的气候事件密切相关。因此,气候异常事件所导致的持续高温、低湿、多风的气象条件是森林火险等级快速升高或重大森林火灾事故的本质原因之一。

基于多指标综合评价的配电网接地故障山火风险等级划分

近年来由电力故障引发的森林火灾时有发生,配电网接地故障若未被及时切除,故障电弧可能引燃周边植被,进而引发森林火灾。为开展差异化的防山火措施,提出一种基于多指标综合评价的配电网接地故障山火风险等级划分方法。结合无人机巡线数据、配电线路历史故障数据、气象数据及自主设计的植被电弧燃烧性实验,分析相关山火风险因子。利用德尔菲-网络分析法确定各山火风险因子权重及风险得分,加权求和得到山火风险综合得分,从而确定配电线路山火风险等级。对湖南电网某10 kV配电线路进行实际案例分析,验证了该方法能较为准确地划分配电线路山火风险等级,明确线路山火防治重要区段,为面向山火防治的配电线路接地故障处置装置运行机理提供有力辅助参考。

浙江省毛竹总汞分布特征及其生态风险评估

毛竹作为浙江地区重要优良竹种,占浙江省总竹林面积的87.75%。研究竹林总汞分布特征对进一步了解其对汞生物地球化学循环过程的影响具有重要意义。本文对浙江省内9片受人为汞排放源影响较小的(对照林)和12片靠近燃煤火电厂的(火电林)毛竹林鲜叶、落叶、土壤总汞浓度进行取样分析,探讨了人为汞排放源和海拔对毛竹林总汞分布的影响;同时,运用潜在生态风险因子评价了毛竹林土壤汞风险。结果表明,毛竹林可以经叶片吸收大气汞,再通过落叶分解最终将汞积累在土壤中,毛竹林鲜叶总汞浓度均值(88.6±49.0 ng/g)<落叶总汞浓度均值(139.9±71.6 ng/g)<土壤总汞浓度均值(216.4±130.3 ng/g),且鲜叶与落叶(R^(2)=0.57)、落叶与土壤(R^(2)=0.81)的总汞浓度之间均成正相关关系;燃煤电厂排放的大气汞,不仅会直接给周边毛竹林带来较大的汞沉积,而且会促进鲜叶对大气汞的吸收,火电林所有样品总汞浓度均相应高于对照林并且火电林的落叶、土壤样品显著高于对照林(P<0.05);毛竹鲜叶总汞浓度随海拔升高而增加,落叶、土壤总汞浓度与海拔均无显著相关性;火电厂周边毛竹林土壤汞有较强的潜在生态风险。

中国森林防灭火的风险挑战与思考展望

在全球气候变化的背景下,中国森林火灾的风险显著增加,对国家生态安全和社会经济发展构成了严峻挑战。本研究通过对中国不同省份的气象因子与森林火灾之间的相关性进行细致分析,旨在揭示这些变量在空间分布上的差异性及其对火灾风险的潜在影响。研究结果显示,风速、最高温度、降水量、相对湿度以及平均温度等因子在各省的相关性系数分布表现出显著的区域特征,这些特征反映了各地区森林火灾发生与气候条件存在密切关联,可为制定针对性的防控措施提供重要依据。通过对这些气象因子相关性的深入分析,本研究进一步探讨了在中国不同地区实施有效的森林防火策略的可能性,以及如何在变化的气候条件下优化防灭火资源配置,增强森林火灾的应急响应能力。

新形势下森林防灭火工作面临的新挑战与对策探析——以浙江省青田县为例

青田县目前共有森林消防队伍44支,配备了15辆防火专用大型车和27辆防火专用小型车,还配置有短波、超短波、微波通信设备139个,卫星通信系统设备312个,大型机动灭火装备37台,已建立了监督问责机制,布设了61套高位预警监控视频系统,形成了全社会共同参与的森林防火宣教与普及格局。但森林防灭火工作仍面临新的挑战,诸如防火环境复杂性增加,林火阻隔体系和以水灭火设施尚不完善等,藉此,提出建立健全适应气候变化的森林防火预警体系,强化林区管理与人为活动监管,降低火灾风险,优化森林植被结构,完善森林防火基础信息系统化、数字化融合等策略,以提高青田县森林防灭火能力。