Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

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.

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.

Research Progress of Polymer Fire Extinguishing Gel and Its Application in Forest Fire Prevention

The paper summarizes the structure and water-absorbing mechanism,classification,and preparation method of polymer fire extinguishing gel,and prospects for its application in aerial firefighting,forest ground fire extinguishing,opening of firebreaks,and mitigating human casualties in forest fire extinguishing.

Study on the Relationship among Forest Fire,Temperature and Precipitation and Its Spatial-temporal Variability in China

[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.

Forest fire prevention management legal regime between China and the United States

Precautions against forest fires,a significant element in the prevention and reduction of natural disasters in China,are very important to the development of public emergency systems,as well as to the safety of forest resources,ecology,people’s lives and properties.The USA has extensive experience in forest fire management,which has been widely accepted and used by other countries.The precautions taken by China and the USA to prevent forest fires have been compared in a great number of previous studies.However,most of the studies have focused merely on fire extinguishing technologies and management methods;they have lacked a comparative study on the legal aspects of management.This paper will consider five distinct aspects related to forest fire management between China and the USA and will analyze the similarities and differences as well as study other features to facilitate work related to precautions against forest fires in China.

Numerical Studies of Wind Speed Fields in Front of Forest Fire Fronts

In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) The wind speed fields in front of fire front generally can be divided into 3 zones and there is always an eddy immediately at the corner between just in front of the fire and the ground. (2) The shape and dimension of the division of the 3 zones is mainly decided by slope angle and ambient wind speed given fire line intensity. (3) There exits an upwind zone in front of fire front. Ambient wind speeds have little effect on the magnitude of the upwind speed when slope angle is 0. But when the slope angle is negative, the upwind is apparently stronger.

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.

Future impacts of climate change on forest fire danger in northeastern China

Climate warming has a rapid and far-reaching impact on forest fire management in the boreal forests of China. Regional climate model outputs and the Canadian Forest Fire Weather Index （FWI） Sys- tem were used to analyze changes to fire danger and the fire season for future periods under IPCC Special Report on Emission Scenarios （SRES） A2 and B2, and the data will guide future fire management planning. We used regional climate in China （1961 1990） as our validation data, and the period （1991–2100） was modeled under SRES A2 and B2 through the weather simulated by the regional climate model system （PRECIS）. Meteorological data and fire danger were interpolated to 1 km 2 by using ANUSPLIN software. The average FWI value for future spring fire sea- sons under Scenarios A2 and B2 shows an increase over most of the region. Compared with the baseline, FWI averages of spring fire season will increase by 0.40, 0.26 and 1.32 under Scenario A2, and increase by 0.60, 1.54 and 2.56 under Scenario B2 in 2020s, 2050s and 2080s, respectively. FWI averages of autumn fire season also show an increase over most of the region. FWI values increase more for Scenario B2 than for Scenario A2 in the same periods, particularly during the 2050s and 2080s. Average future FWI values will increase under both scenarios for autumn fire season. The potential burned areas are expected to increase by 10% and 18% in spring for 2080s under Scenario A2 and B2, respectively. Fire season will be prolonged by 21 and 26 days under ScenariosA2 and B2 in 2080s respectively.

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.

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.

Spatial and temporal distribution of forest fire frequency and forest area burnt in Jilin Province, Northeast China

Forest fires caused by natural forces or human activities are one of the major natural risks in Northeast China.The incidence and spatial distribution of these fires vary over time and across the forested areas in Jilin Province,Northeast China.In this study,the incidence and distribution of 6519 forest fires from 1969 to 2013 in the province were investigated.The results indicated that the spatiotemporal distribution of the burnt forest area and the fire frequency varied significantly by month,year,and region.Fire occurrence displayed notable temporal patterns in the years after forest fire prevention measures were strictly implemented by the provincial government.Generally,forest fires in Jilin occurred in months when stubble and straw were burned and human activities were intense during traditional Chinese festivals.Baishan city,Jilin city,and Yanbian were defined as fire-prone regions for their high fire frequency.Yanbian had the highest frequency,and the fires tended to be large with the highest burned area per fire.Yanbian should thus be listed as the key target area by the fire management agency in Jilin Province for better fire prevention.

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 considers both environmental(altitude,precipitation,forest type,terrain and humidity index)and socioeconomic(population 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 probability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999 s,2009 s,and 2019 s 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 accessibility 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 considering socio-economic and environmental conditions.

A review of the effects of forest fire on soil properties

Forest fires are key ecosystem modifiers affecting the biological,chemical,and physical attributes of forest soils.The extent of soil disturbance by fire is largely dependent on fire intensity,duration and recurrence,fuel load,and soil characteristics.The impact on soil properties is intricate,yielding different results based on these factors.This paper reviews research investigating the effects of wildfire and prescribed fire on the biological and physico-chemical attributes of forest soils and provides a summary of current knowledge associated with the benefits and disadvantages of such fires.Low-intensity fires with ash deposition on soil surfaces cause changes in soil chemistry,including increase in available nutrients and pH.High intensity fires are noted for the complete combustion of organic matter and result in severe negative impacts on forest soils.High intensity fires result in nutrient volatilization,the break down in soil aggregate stability,an increase soil bulk density,an increase in the hydrophobicity of soil particles leading to decreased water infiltration with increased erosion and destroy soil biota.High soil heating(> 120℃) from high-intensity forest fires is detrimental to the soil ecosystem,especially its physical and biological properties.In this regard,the use of prescribed burning as a management tool to reduce the fuel load is highly recommended due to its low intensity and limited soil heating.Furthermore,the use of prescribed fires to manage fuel loads is critically needed in the light of current global warming as it will help prevent increased wildfire incidences.This review provides information on the impact of forest fires on soil properties,a key feature in the maintenance of healthy ecosystems.In addition,the review should prompt comprehensive soil and forest management regimes to limit soil disturbance and restore fire-disturbed soil ecosystems.

Driving Factors for Forest Fire Occurrence in Durango State of Mexico:A Geospatial Perspective

Forest fire is one of the major causes of forest loss and therefore one of the main constraints for sustainable forest management worldwide.Identifying the driving factors and understanding the contribution of each factor are essential for the management of forest fire occurrence.The objective of this study is to identify variables that are spatially related to the occurrence and incidence of the forest fire in the State of Durango,Mexico.For this purpose,data from forest fire records for a five-year period were analyzed.The spatial correlations between forest fire occurrence and intensity of land use,susceptibility of vegetation,temperature,precipitation and slope were tested by Geographically Weighted Regression(GWR) method,under an Ordinary Least Square estimator.Results show that the spatial pattern of the forest fire in the study area is closely correlated with the intensity of land use,and land use change is one of the main explanatory variables.In addition,vegetation type and precipitation are also the main driving factors.The fitting model indicates obvious link between the variables.Forest fire was found to be the consequence of a particular combination of the environmental factors,and when these factors coexist with human activities,there is high probability of forest fire occurrence.Mandatory regulation of human activities is a key strategy for forest fire prevention.

Estimation of the carbon storage of forest vegetation and carbon emission from forest fires in Heilongjiang Province,China

The forest resource of Heilongjiang province has important position in china. On the basis of the six times of national forest inventory data （1973-1976, 1977-1981, 1985-1988, 1989-1993, 1994-1998, 1999-2003） surveyed by the Forestry Ministry of P. R. China from 1973 to 2003, the carbon storage of forests in Heilongjiang Province are estimated by using the method of linear relationship of each tree species between biomass and volume. The results show that the carbon storage of Heilongjiang forests in the six periods （1973-1976, 1977-1981, 1985-1988, 1989-1993, 1994-1998, 1999-2003） are 7.164×10^8 t, 4.871×10^8 t, 5.094×10^8 t, 5.292×10^8 t, 5.594×10^8 t and 5.410×10^8 t, respectively., which showed a trend of decreasing in early time and then increasing. It indicated that Heilongjiang forests play an important role as a sink of atmospheric carbon dioxide during past 30 years. Based on the data of forest fires from 1980 to 1999 and ground biomass estimation for some forest types in Heilongjiang Province, it is estimated that the amount of mean annual consumed biomass of forests is 391758.65t-522344.95t, accounting for 6.4%-8.4% of total national consummation from forest fires, and the amount of carbon emission is 176 291.39t-235 055.23t, about 8% of total national emission from forest fires. The emission of CO2, CO, CH4 and NMHC from forest fires in Heilongjiang Province are estimated at 581761.6-775682.25 t, 34892.275-46523.04 t, 14091.11-18788.15 t and 6500-9000 t, respectively, every year.

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.

Spatiotemporal variation in forest fire danger from 1996 to 2010 in Jilin Province,China

We evaluated the spatial and temporal patterns of forest fires in two fire seasons (March to June and September to November) from 1996 to 2010 in Jilin Province, China, using the Canadian Forest Fire Weather Index System. Fire data were obtained from the Provincial Fire Agency, and historical climate records of daily weather observations were collected from 36 weather stations in Jilin and its neighboring provinces. A linear regression model was used to analyze linear trends between climate and fire weather indices with time treated as an independent variable. Correlation analysis was used to detect correlations between fire frequency, areas burned, and fire weather indices. A thin-plate smooth spline model was used to interpolate the point data of 36 weather stations to generate a surface covering the whole province. Our analyses indicated fire frequency and areas burned were significantly correlated with fire weather indices. Overall, the Canadian Forest Fire Weather Index System appeared to be work well for determining the fire danger rating in Jilin Province. Also, our analyses indicated that in the forthcoming decades, the overall fire danger in March and April should decrease across the province, but the chance of a large fire in these months would increase. The fire danger in the fall fire season would increase in the future, and the chance of large fire would also increase. Historically, because most fires have occurred in the spring in Jilin Province, such a shift in the future fire danger between the two fire seasons would be beneficial for the province's fire management.

Modeling forest fires in Mazandaran Province, Iran

We used a spafio-temporal shot-noise Cox process to study the distribution of forest fires reported between 2006 and 2010 in the Mazandaran Province＇s forests. The fitted model shows that daily temperature, altitude, and slope-exposure impacted fire occurrence. Forest fire occurred in the region had an aggregated behavior, which increased in radius below 1-km away from fired areas; a periodic pattern of fire occurrence in the region was verified. The risk of forest fire is significantly higher for areas with southern exposure and slope between 30° and 50°, northern exposure and slope between 0° and 50°, and eastern exposure and slope between 0° and 30°. The risk of fire was also significantly higher at altitudes between 1350 and 3000 m asl. Human causes were the main ignition source for forest fires in the region. The fire occurrence rate stayed above average during the drought period from September 2008 to September 2009. Our findings could lead to the development of fire-response and fire-suppression strategies appropriate to specific regions.

Forest fire smoke recognition based on convolutional neural network

Traditional fire smoke detection methods mostly rely on manual algorithm extraction and sensor detection;however,these methods are slow and expensive to achieve discrimination.We proposed an improved convolutional neural network(CNN)to achieve fast analysis.The improved CNN can be used to liberate manpower.The network does not require complicated manual feature extraction to identify forest fire smoke.First,to alleviate the computational pressure and speed up the discrimination efficiency,kernel principal component analysis was performed on the experimental data set.To improve the robustness of the CNN and to avoid overfitting,optimization strategies were applied in multi-convolution kernels and batch normalization to improve loss functions.The experimental analysis shows that the CNN proposed in this study can learn the feature information automatically for smoke images in the early stages of fire automatically with a high recognition rate.As a result,the improved CNN enriches the theory of smoke discrimination in the early stages of a forest fire.