A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Trends in Global and Mexico Research in Wildfires: A Bibliometric Perspective

In the last two decades, unprecedented changes have taken place in the frequency and severity of wildfires;in different regions of the world, some fires were even classified as megafires. Although there are studies about the diverse effects of fire, which have made significant theoretical contributions, a comprehensive review of the changes in fire research is required to understand worldwide patterns, particularly in those countries where fire activity is on the rise, such is the case of Mexico. The objective of this study was to analyze the trends in the research on wildfires published in Mexico and worldwide over a 40-year timescale. For this purpose, the Web of Science database, bibliometric tools, and the keywords TI = Forest fire* OR TI = Wildfire* were used to extract as many articles as possible related to fires from 1980 to 2020, without being restricted to those studies whose title included any of the variants of the keywords. There were 8458 publications about fires in the vegetation cover, with a notable increase in the frequency of studies in the previous decade;52% of the studies were concentrated in five countries and 20% of the articles focused on the study of different aspects of the soil. Mexico ranks thirteenth in volume of scientific production and studies in the country have focused mainly on the description of the quantitative relationship between the size of the affected area and the number of occurrences in the landscape, meanwhile, studies on fires and the consequences on the biotic interactions have been little explored.

基于BIMFire-YOLO的消防监控图像识别方法

在传统条件下,通常存在着火灾现场情况多变,建筑物内构造复杂等问题,使得消防监控系统难以通过现有方法精准检测识别火灾第一现场和所处室内环境。为了优化检测算法在复杂场景下的应用效果,文中提出BIMFire-YOLO检测方法,在以往YOLOv3主干网络中增加了新的卷积方式,在扩大了卷积感受野范围的同时,也保证了输出特征的不变性,对小火焰和烟雾等目标的检测能力有所提升。通过引入新的损失函数,降低了火灾发生的目标漏检率,融合BIM技术不仅提升了定位精度也实现了自动识别视频和图像中失火的场景。该算法不仅提高了检测效率,也实现了火灾的信息共享与管理的统一,在实际医院场景下的综合识别精度达到了45.6%,检测速度和精度均优于其他同类方法。

基于FireNet的古建筑火灾检测方法研究及改进

针对古建筑火灾检测需要快速、准确及实时的需求,建立了一个专门用于古建筑火灾检测的数据集,用于古建筑火灾检测的深度学习研究。利用CBAM注意力机制模块,结合多尺度特征融合,对FireNet网络进行改进,提出适用于古建筑火灾检测的轻量级FireNet-AMF网络,在FireNet数据集和本文构建的古建筑火灾检测数据集上验证了FireNet-AMF网络的火灾检测能力。与改进前的网络相比,FireNet-AMF网络在FireNet数据集上对火灾识别的准确率达到了95.08%,与原网络相比提高了1.17%,在本文构建的古建筑火灾检测数据集上的准确率达到了95.62%,比原网络提高了1.62%。该网络在保证轻量级的同时也保证了在古建筑火灾检测中较高的检测精度。

Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

Lightning in a Forest (Wild) Fire: Mechanism at the Molecular Level

The mechanism of lightning that ignites a forest fire and the lightning that occurs above a forest fire are explained at the molecular level. It is based on two phenomena, namely, internal charge separation inside the atmospheric cloud particles and the existence of a layer of positively charged hydrogen atoms sticking out of the surface of the liquid layer of water on the surface of rimers. Strong turbulence-driven collisions of the ice particles and water droplets with the rimers give rise to breakups of the ice particles and water droplets into positively and negatively charged fragments leading to charge separation. Hot weather in a forest contributes to the updraft of hot and humid air, which follows the same physical/chemical processes of normal lightning proposed and explained recently[1]. Lightning would have a high probability of lighting up and burning the dry biological materials in the ground of the forest, leading to a forest (wild) fire. The burning of trees and other plants would release a lot of heat and moisture together with a lot of smoke particles (aerosols) becoming a strong updraft. The condition for creating lightning is again satisfied which would result in further lightning high above the forest wild fire.

基于改进FireNet的轻型火灾实时检测方法

针对火灾检测精确不高,时间长等问题。设计了基于改进FireNet的轻型火灾实时检测方法,通过获取视频图像数据,网络模型进行火灾分析和识别;首先,在FireNet特征提取阶段使用多尺度卷积网络并引入通道注意力机制,以提高回归精度。其次,对全连接层的神经元个数进行压缩优化,减少计算耗时。实验表明,改进的FireNet算法模型检测精度达到96.43%,模型存储空间0.96MB,检测帧率37。相比标准算法精度提高2.5%,存储空间压缩85%,检测帧率提升35%。

Impacts of the Kuwait Oil Fires on the Mount Qomolangma Region

Mt. Qomolangma (also known as Mt. Everest), the world's highest mountain, is situated over the world＇s highest plateau, the Tibetan Plateau. Because of its height and because of its distance from industrialized areas, the environmental state of the Mt. Qonlolangma region can normally be considered 'undisturbed'. It is interesting to investigate how this “undisturbed” state has been changing with time and whether it has been influenced by large environmentally disruptive events such as the Kuwait oil fires of 1990 and 1991 (Small, 1991). In order to do this, river water samples were collected from the Rongpu River at Rongpu Temple Station in the summers of 1992 and 1993,as was done in 1975, and aerosol samples were collected in the summer of 1992 at the same station as was done in 1980. River water samples were analyzed using atomic absorption spectroscopy (AAS) at the Chinese Academy of Sciences. Aerosol samples were analyzed using proton-induced x-ray emission (PIXE) at the University of Fudan in Shanghai. The results show that the concentrations of chemical species in the river water at Rongpu Temple Station were much higher in the summer of 1992 than they were in 1975 and 1993, and the concentrations of atmospheric chemical species were much higher in 1992 than they were in 1980. The environment of the north slope of Mt.Qomolangma was therefore heavily polluted before and / or during the summer of 1992, possibly due to the Kuwait oil fires in 1990 and 1991.

Fire behaviour of multiple fires in a mine drift with longitudinal ventilation

The fire behaviour involving multiple fires in a mine drift with longitudinal ventilation was analysed. The conditions and fire phenomena occurring were described. The analysis was based upon experimental data from model-scale fire experiments. A fire involving several fuel items may lead to flames tilted horizontally and filling up the entire cross section, leading to earlier ignition, higher fire growth rates, higher fire spread rate and severe fire behaviour. Longer flame lengths will also result due to decreased air entrainment. A correlation for the continuous flame length was proposed. The results of the analysis will help identifying and preventing potentially dangerous fire situations with several large combustible items distributed along a mine drift.

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.

Spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field(North-east of Viet Nam) using Landsat time-series data

Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.

Analysis of underground fires in Polish hard coal mines

In the period of the first twenty years after World War II the number of fires in Polish hard coal mines reached annually the value of several thousands of cases. About 80% of fires constituted spontaneous fires. Investigations into the development of new methods of fire hazard prediction and implementation of new methods and means of fire prevention as well as the introduction of prohibition concerning the use of products manufactured of combustible organic materials in underground mine workings re-duced considerably the hazard of underground fire rise. The worked out at the Central Mining Institute (GIG) new method of un-derground fire prediction allows the correct selection of fire prevention means. The introduction into common use of fire-resistant conveyor belts, the main factor giving rise to spontaneous fires, and methods of assessment of their fire resistance eliminated prac-tically the fire hazard. These activities contributed in an efficient way to the reduction of the number of underground fires to a sat-isfactory level.

Determination of fire-access zones along road networks in firesensitive 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 fire- sensitivity 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.

Environmental and Economic Impact of Forest Fires in Puerto Rico 2013-2014

This study presents an analysis of the impact of forest fires in Puerto Rico for the period from 2013-2014. The climatological factors analyzed included precipitation, temperature, relative humidity, and wind. Several factors have combined to the increase of these forest fires, among others, a decrease in precipitation during this period, as well as an increase in the human involvement in these fires from approximately 40% occurs in the night period (5:00 pm to 8:00 am), where the weather conditions do not favor the appearance of these phenomena. An increase in fires of 44% occurred in 2013 compared to 2014, causing an economic loss of $13.8 million. Fire also adversely affected the flora and fauna of the island, but this was not evaluated in this paper.

Temporal and spatial analysis of vegetation fire activity in the circum-Arctic during 2001–2020

Vegetation fires become the concern worldwide due to their substantial impacts on climate and environment,and in particular in the circum-Arctic.Assessing vegetation fires and associated emissions and causes can improve understanding of fire regime and provide helpful information for vegetation fires solution.In this study,satellitebased vegetation fires and emissions during 2001–2020 were investigated and contributions of different types of fires were analyzed.Furthermore,climate anomalies related to extreme vegetation fires were explored.The main results showed that the region south of the Arctic circle(50°N-67°N)experienced a greater number of vegetation fires compared to the Arctic(north of 67°N).During 2001–2020,interannual variability of vegetation fires between 50°N and 67°N appeared to be decreasing while emissions(including carbon,dry matter,PM_(2.5),and BC)appeared to be increasing overall,which were contributed by the increasing summer boreal forest fires in this region largely.In the Arctic,vegetation fires and emissions increased in recent years distinctly,and those were dominated by the summer forest fires.Spatially,large increases of vegetation fires were located in the eastern Siberia and northern North America while large decreases were located in the northwestern Eurasia mainly.Additionally,in the Arctic,the unprecedented vegetation fires were observed in the eastern Siberia and Alaska in 2019 and in the eastern Siberia in 2020,which could be attributed to high pressure,high near-surface temperature,and low air moisture anomalies.Meanwhile,obvious anticyclonic anomalies in Alaska in 2019 and in the eastern Siberia in 2020 and cyclonic anomalies in the western Siberia in 2019,also played an important role on fire occurrences making drier conditions.

Geospatial Model as Strategy to Prevent Forest Fires: A Case Study

The forests of the State of Durango have been severely affected by fires in recent years. Early detection of fires through watchtowers is essential. In this work a geospatial model was generated to optimize strategic visualization points, using a GIS environment. Analysis of the area of visibility was developed by integrating a digital model of elevation and a plant cover map. The resulting distribution generates more than 50% coverage of the studied area, in points that were not always the highest. It was concluded that this strategy would permit to increase the efficiency, mainly favoring the communities of pine, whose economic importance would justify the required investment.

Forest Fires and Climate Correlation in Mexico State: A Report Based on MODIS

Forest fires are one of the most important threats for forests in the State of Mexico. Therefore, understanding their geographical patterns is a priority for the design of forest management strategies. We processed the records obtained with the MOD14A2 product (for thermal anomalies and fire) of MODIS sensor. Such scenes correspond to dry seasons (from March 15 to June 30) from 2000 to 2012 in the State of Mexico. We analyzed such records in a GIS environment to learn their spatial patterns and establish their geographical correlations as a first step to understand the causal agents of forest fires. As a result, forest fires in the State of Mexico showed a clustered spatial trend with a southwest tendency and a slight spatial relation with total winter precipitation and maximal temperature in summer.

Serpentinite Slurries against Forest Fires

Forest fires are one of the commonest natural hazards. Forest fires make the largest contribution to CO2 emissions after the burning of fossil fuels. Here a new technology is proposed to extinguish forest fires not with water, but with a slurry of serpentine. Serpentinites are abundantly available in many countries on every continent. If serpentine is calcined, it weathers very fast and captures CO2. Calcination, however, requires a lot of heat, which makes it counterproductive to produce calcined serpentine for CO2 capture. In cases, however, where heat is the problem, like in forest fires, one can extinguish them to greater advantage by using serpentinite slurries instead of plain water. The calcined residue that is left as a thin cake on the burning material prevents oxygen to reach the burning material. It also prevents the escape of inflammable gases, and the calcination itself withdraws large quantities of heat from the fire. After the fire is extinguished, the calcined material in contact with the atmosphere will rapidly weather and capture CO2. This compensates part of the CO2 that is produced by the fire. In tests, where the efficacy of quenching fires with serpentine slurries was compared to the effect of water, it turned out that serpentinite slurries performed far better.

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 controlled fire, fire lines and mapping of fire prone areas are fundamental principles of fire hazard reduction in these areas.

Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia,China

Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and many fires occur in the area every year.However,there are few models for estimation of carbon emissions from grassland fires.Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon.In this study,the regression equations for aboveground biomass(AGB)of grassland in growing season and MODIS NDVI(Normalized Difference Vegetation Index)were established through field experiments,then AGB during Nov.–Apr.were retrieved based on that in Oct.and decline rate,finally surface fuel load was obtained for whole year.Based on controlled combustion experiments of different grassland types in Inner Mongolia,the carbon emission rate of grassland fires for each grassland type were determined,then carbon emission was estimated using proposed method and carbon emission rate.Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978×1012 kg.The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2,with the annual average area of 311.69 km2.The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia.The total carbon emissions from grassland fires amounted to 2.24×107 kg with an annual average of 1.32×106 for the study area.The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner.The spatial characteristics of carbon emission depend on the location of grassland fire,mainly in the northeast of Inner Mongolia include Hulunbuir City,Hinggan League,Xilin Gol League and Ulanqab City.The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions.This study provides a reference for estimating carbon emissions from steppe fires.The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.