Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Light...Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.展开更多
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen...The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.展开更多
To verify the detection capability of X-band dual-polarization phased-array radar for forest fires,this paper utilizes X-band dual-polarization phased-array radar data,Himawari-8 satellite data,combined with ground me...To verify the detection capability of X-band dual-polarization phased-array radar for forest fires,this paper utilizes X-band dual-polarization phased-array radar data,Himawari-8 satellite data,combined with ground meteorological automatic station data.A case study of a forest fire in Ao Feng Mountain on February 19,2021,was conducted to comparatively analyze the monitoring results from these two remote sensing methods.The results show that both methods exhibit significant features associated with the forest fire process observed and are effective modern methods of forest fire monitoring.The Himawari-8 satellite identified the fire point at 07:10(LST;LST=UTC+8)with subsequent observations every 10 minutes until 10:00,nearly two hours before the fire was fully extinguished.Compared with the satellite,the Xband dual polarization phased array radar detectedthe fire 14 minutes earlier,with an improved temporal resolution of one minute,and was not affected by cloud cover.In the triggering stage,vigorous stage,sustained burning stage,and extinguishing stage of the forest fire,radar characteristic factors including reflectivity(Z),differential reflectivity(ZDR),and correlation coefficient(CC)showed strong correlations with the fire progression.The radar monitoring results were continuous,complete,and precise.In summary,the X-band dual-polarization phased-array radar offers more detailed detection information,shorter detection time interval,and higher detection spatial accuracy.It presents a promising new method for forest fire detection,providing crucial guidance for on-site rescue operations,particularly for small-scale fire events.展开更多
The increasing frequency and intensity of forest fires,driven by climate change and human activities,pose a significant threat to vital forest ecosystems,particularly where fire is not a natural element in the regener...The increasing frequency and intensity of forest fires,driven by climate change and human activities,pose a significant threat to vital forest ecosystems,particularly where fire is not a natural element in the regeneration cycle.This study aims to identify the indicators influencing forest fire vulnerability and compare maps of forest fire susceptibility that are based on the Intergovernmental Panel on Climate Change tripartite model,with a focus on the vulnerable Hyrcanian forest region in Golestan Province,northern Iran,where forest fires have caused considerable economic losses.On the basis of expert opinions and a literature review,we used geographic information systems,remote sensing and machine learning techniques to select and weigh 30 biophysical,environmental and socioeconomic indicators that affect forest fire vulnerability in the study area.These indicators were rigorously normalized,weighted and amalgamated into a comprehensive forest fire vulnerability index to analyze forest exposure,sensitivity and adaptive capacity.We thus identified and mapped areas with very high forest fire exposure,high sensitivity and low adaptive capacity for urgent targeted intervention and strategic planning to mitigate the impacts of forest fires.The results also revealed a set of critical indicators that contribute more significantly to forest fire vulnerability(e.g.,precipitation,elevation and factors related to biodiversity,human activity and economic reliance on forest resources).Our results provide insights that can inform policy-making,community engagement and environmental management strategies to mitigate the vulnerabilities associated with forest fires in the Hyrcanian forest.展开更多
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 dev...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.展开更多
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 potenti...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.展开更多
Forest fire accidents caused by distribution line faults occur frequently,resulting in heavy impacts on people’s safety and social and economic development.Currently,there are few risk assessments for forest fires in...Forest fire accidents caused by distribution line faults occur frequently,resulting in heavy impacts on people’s safety and social and economic development.Currently,there are few risk assessments for forest fires induced by over-head distribution lines,and existing assessment methods may have difficulties in data acquisition.On this basis,a novel as-sessment framework based on an analytic hierarchy process,a Bayesian network and a Fussel-Vesely importance metric is proposed in this paper.The framework combines field research and historical operation and maintenance data to assess the regional-scale risk of forest fires induced by overhead distribution lines to derive the probability of forest fires and to identify high-risk lines and key hazard events in the assessment region.Finally,taking the southern Anhui region as an ex-ample,the annual fire probability of forest fires induced by overhead distribution lines in the southern Anhui region is 5.88%,and rectification measures are proposed.This study provides management with a complete assessment framework that optimizes the difficulty of data collection and allows for additional targeted corrective measures to be proposed for the entire region and route on the basis of the assessment results.展开更多
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...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.展开更多
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 exti...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.展开更多
[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...[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.展开更多
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...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.展开更多
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) Th...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.展开更多
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 d...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.展开更多
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 ana...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.展开更多
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, ...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.展开更多
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 a...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.展开更多
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 Jil...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.展开更多
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,...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.展开更多
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,f...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.展开更多
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） survey...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.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.
基金supported by the Fujian Provincial Science and Technology Program“University-Industry Cooperation Project”(2024Y4015)National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project(2018YFE0207800).
文摘The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
基金National Key R&D Program of China(2022YFC3004101)Guangdong Basic and Applied Basic Research Foundation(2023A1515011971)+3 种基金Science and Tech-nology Projects in Guangzhou(2023B04J0232)Science and Technology Development Fund Project of Guangdong Meteor-ological Bureau(GRMC2022Q23,GRMC2022Q01)Jiangmen Basic and Applied Basic Research Key Programs(202312)Science and Technology Development Fund Project of Jiangmen Meteorological Bureau(202008,202004,201907,202007,201704)。
文摘To verify the detection capability of X-band dual-polarization phased-array radar for forest fires,this paper utilizes X-band dual-polarization phased-array radar data,Himawari-8 satellite data,combined with ground meteorological automatic station data.A case study of a forest fire in Ao Feng Mountain on February 19,2021,was conducted to comparatively analyze the monitoring results from these two remote sensing methods.The results show that both methods exhibit significant features associated with the forest fire process observed and are effective modern methods of forest fire monitoring.The Himawari-8 satellite identified the fire point at 07:10(LST;LST=UTC+8)with subsequent observations every 10 minutes until 10:00,nearly two hours before the fire was fully extinguished.Compared with the satellite,the Xband dual polarization phased array radar detectedthe fire 14 minutes earlier,with an improved temporal resolution of one minute,and was not affected by cloud cover.In the triggering stage,vigorous stage,sustained burning stage,and extinguishing stage of the forest fire,radar characteristic factors including reflectivity(Z),differential reflectivity(ZDR),and correlation coefficient(CC)showed strong correlations with the fire progression.The radar monitoring results were continuous,complete,and precise.In summary,the X-band dual-polarization phased-array radar offers more detailed detection information,shorter detection time interval,and higher detection spatial accuracy.It presents a promising new method for forest fire detection,providing crucial guidance for on-site rescue operations,particularly for small-scale fire events.
基金funding provided by University of Natural Resources and Life Sciences Vienna(BOKU).
文摘The increasing frequency and intensity of forest fires,driven by climate change and human activities,pose a significant threat to vital forest ecosystems,particularly where fire is not a natural element in the regeneration cycle.This study aims to identify the indicators influencing forest fire vulnerability and compare maps of forest fire susceptibility that are based on the Intergovernmental Panel on Climate Change tripartite model,with a focus on the vulnerable Hyrcanian forest region in Golestan Province,northern Iran,where forest fires have caused considerable economic losses.On the basis of expert opinions and a literature review,we used geographic information systems,remote sensing and machine learning techniques to select and weigh 30 biophysical,environmental and socioeconomic indicators that affect forest fire vulnerability in the study area.These indicators were rigorously normalized,weighted and amalgamated into a comprehensive forest fire vulnerability index to analyze forest exposure,sensitivity and adaptive capacity.We thus identified and mapped areas with very high forest fire exposure,high sensitivity and low adaptive capacity for urgent targeted intervention and strategic planning to mitigate the impacts of forest fires.The results also revealed a set of critical indicators that contribute more significantly to forest fire vulnerability(e.g.,precipitation,elevation and factors related to biodiversity,human activity and economic reliance on forest resources).Our results provide insights that can inform policy-making,community engagement and environmental management strategies to mitigate the vulnerabilities associated with forest fires in the Hyrcanian forest.
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘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.
基金the following grants:The National Key R&D Program of China(2019YFA0606600)the Natural Science Foundation of China(31971577)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘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.
基金This work was supported by the National Key Research and Development Program of China(2022YFC3003101)the Fundamental Research Funds for the Central Universities(WK2320000050)the Science and Technology Program of State Grid Anhui Electric Power Co.,Ltd.(521205220001).
文摘Forest fire accidents caused by distribution line faults occur frequently,resulting in heavy impacts on people’s safety and social and economic development.Currently,there are few risk assessments for forest fires induced by over-head distribution lines,and existing assessment methods may have difficulties in data acquisition.On this basis,a novel as-sessment framework based on an analytic hierarchy process,a Bayesian network and a Fussel-Vesely importance metric is proposed in this paper.The framework combines field research and historical operation and maintenance data to assess the regional-scale risk of forest fires induced by overhead distribution lines to derive the probability of forest fires and to identify high-risk lines and key hazard events in the assessment region.Finally,taking the southern Anhui region as an ex-ample,the annual fire probability of forest fires induced by overhead distribution lines in the southern Anhui region is 5.88%,and rectification measures are proposed.This study provides management with a complete assessment framework that optimizes the difficulty of data collection and allows for additional targeted corrective measures to be proposed for the entire region and route on the basis of the assessment results.
文摘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.
基金Central Finance Forestry Science and Technology Promotion Demonstration Project(H[2023]TG31).
文摘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.
基金Supported by National Natural Science Foundation of China(40801216/D011002)~~
文摘[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.
基金supported by the State Bureau of Forestry 948 project(2015-4-35)the Fundamental Research Funds for the Central Universities(2572015CA10)National Natural Science Foundation of China(31400551)
文摘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.
基金TheresearchissupportedbyFoundationforDoctoralStudiesofMinistryofEducation (No .19980 0 2 2 0 6 )
文摘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.
文摘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.
基金support by National Science and Technology Support Plan(2007BAC03A02)National Natural Science Foundation of China(30671695)
文摘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.
基金The sludy was supported by a grant of the National Natural Science Foundation of China （No. 70373044 and 30470302） and National Key TechnolooiesR＆D Program （No. 2001BA510B07）
文摘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.
文摘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.
基金financially supported by the National Key Research and Development Plan(2017YFD0600106)the National Natural Science Foundation of China under Grant31470497+1 种基金Project 2013-007,Jilin Provincial Forestry Departmentsupported by the Program for New Century Excellent Talents in University(NCET-12-0726)
文摘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.
文摘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.
基金supported by the National Key Research and Development Program of China(Grant No.2019YFE0127700)。
文摘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.
基金This study was supported by National Natural Sci-ence Foundation of China (No.30471404)National Doctoral Subject Fund of China (No.20040225003)+1 种基金Natural Science Fund of Heilongjiang Province (ZJD04-0102)Research Program of Science and Tech-nology of Heilongjiang Province (GB05B602)
文摘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.
