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.

A Survey of the Machine Learning Models for Forest Fire Prediction and Detection

Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.

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.

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.

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.

Post-fire soil nutrient dynamics in a tropical dry deciduous forest of Western Ghats,India

Background: The effect of forest fire on soil is complex and relatively less understood than its above ground effect.Understanding the effect of fire on forest soils can allow improving management of valuable forest ecosystems as adequate and proper information is very important for efficient management. We have studied the recovery of soil properties after fire, using a chronosequence approach(two, five and fifteen years after fire and control). Soil samples were collected from each plot of four fire patches(B0, B2, B5 & B15) from three different depths viz. 0–10(Top), 10–20(Middle), and 20–30 cm(Bottom).Results: Soil organic carbon was lower than unburned plots after the fire and could not recover to the level of unburned plot(B0) even in 15 years. Total N, available P, and extractable K were lower 2-years and 5-years after the fire but are higher than unburned plot after 15-years. Available nitrogen(NO_3^- and NH_4^+) remain unchanged or higher than B0 in burned patches. Soil pH, Bulk Density, Water Holding Capacity, and Electrical Conductivity was lower initially after the fire. Forest fires have affected soil properties considerably. The response of soil properties varied with years after fire and soil depth.Conclusion: Forest fires occur very frequently in the study area. Significant quantities of carbon and total nitrogen are lost to the atmosphere by burning of litter, duff, and soil OM. Because nitrogen is one of the most important soil nutrients, the recapture of N lost by volatilization during a fire must receive special attention. Long-term studies are required to better understand the recovery of soil nitrogen.

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.

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.

The impact of climate change on forest fire danger rating in China’s boreal forest

The Great Xing＇an Mountains boreal forests were focused on in the northeastern China.The simulated future climate scenarios of IPCC SRES A2a and B2a for both the baseline period of 1961-1990 and the future scenario periods were downscaled by the Delta Method and the Weather Generator to produce daily weather data.After the verification with local weather and fire data,the Canadian Forest Fire Weather Index System was used to assess the forest fire weather situation under climate change in the study region.An increasing trend of fire weather severity was found over the 21st century in the study region under the both future climate change scenarios,compared to the 1961-1990 baseline period.The annual mean/maximum fire weather index was predicted to rise continuously during 2010-2099,and by the end of the 21st century it is predicted to rise by 22%-52% across much of China＇s boreal forest.The significant increases were predicted in the spring from of April to June and in the summer from July to August.In the summer,the fire weather index was predicted to be higher than the current index by as much as 148% by the end of the 21st century.Under the scenarios of SRES A2a and B2a,both the chance of extremely high fire danger occurrence and the number of days of extremely high fire danger occurrence was predicted to increase in the study region.It is anticipated that the number of extremely high fire danger days would increase from 44 days in 1980s to 53-75 days by the end of the 21st century.

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.

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.

Vegetation regrowth trends in post forest fire ecosystems across North America from 2000 to 2010

The goal of this study was to determine whether climate has affected vegetation regrowth over the past decade (2000 to 2010) in post-fire forest ecosystems of the United States and Canada. Our methodology detected trends in the monthly MODerate resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) timeseries within forest areas that burned between 1984 and 1999. The trends in summed growing season EVI (composited to 8 km spatial resolution) within all burned area perimeters showed that nearly 1.6% post-fire forest area declined in vegetation greenness cover significantly (p < 0.05) over the past decade. Nearly 62% of all post-fire forest area showed a non significant EVI regrowth trend from 2000 to 2010. Regression results detected numerous significantly negative trend pixels in post-fire areas from 1994-1999 to indicate that forest regrowth has not yet occurred to any measurable level in many recent wildfire areas across the continent. We found several noteworthy relationships between annual temperature and precipitation patterns and negative post-fire forest EVI trends across North America. Change patterns in the climate moisture index (CMI), growing degree days (GDD), and the standardized precipitation index (SPI) were associated with post-fire forest EVI trends. We conclude that temperature warming-induced change and variability of precipitation at local and regional scales may have altered the trends of large post-fire forest regrowth and could be impacting the resilience of post-fire forest ecosystems in North America.

Mapping forest fires by nonparametric clustering analysis

Fires have a noteworthy role to play with regards to ecological and environmental losses in Mediterranean forests. In addition to ecological impacts, fire may create economic, social as well as cultural changes. The detection of fire-scars has critical importance to help decrease losses.In the present study, forest fires recorded in Antalya, one of the most important ecological and tourist regions within the Western Mediterranean, were clustered and mapped. Since the dominant factors and devastation records derived from the cases had nominal-scaled properties, a categorical databased nonparametric clustering algorithm was performed in this evaluation. The proposed tool, k-modes algorithm,uses modes instead of means for clustering. The algorithm may be implemented quickly and does not make distributional assumptions concerning the available data. It uses a frequency-based method to update the modes of the fires.The derived modes from the maps may be useful information for local authorities to manage. In conclusion, the proposed nonparametric clustering procedure may be employed to build a decision-support system to monitor and identify fire activities and to enhance fire management efficiency.

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.

Spatial heterogeneity of soil respiration in a Larix gmelinii forest and the response to prescribed fire in the Greater Xing0an Mountains,China

This study was conducted in a fire-prone region in the Greater Xing＇an Mountains, the primary forested area of northeastern China. We measured soil respiration and the affecting soil factors, i.e., soil microbial biomass and soil moisture, within an experimental plot of Larix gmelinii Rupr. A low-intensity, prescribed fire was applied as the treatment. Traditional descriptive statistics and geostatistics were used to analyze the spatial heterogeneity of soil respiration and the response of respiration to fire disturbance. Coefficients of variation （CVs） for pre-fire and post-fire soil respiration were 23.4 and 32.0 %, respec- tively. CVs for post-fire soil respiration increased signifi- cantly, with a moderate variation of all CVs. Soil respiration pre-fire was significantly correlated with soil microbial biomass carbon, biomass nitrogen, and soil moisture （W）; post-fire soil respiration was not correlated with these factors. From the geostatistical analyses, the Co ＋ C （sill） for post-fire soil respiration increased sig- nificantly, indicating that the post-fire spatial heterogeneity of soil respiration increased significantly. The nugget effect （nc） of soil respiration and the affecting factors pre-fire and post-fire disturbance were in the range of 12.5-50 %, with strong spatial autocorrelation. Fire disturbance changed the components of spatial heterogeneity, and the proportion of functional heterogeneity increased significantly post-fire. The ranges （a） for pre-fire and post-fire soil respiration were 81.0 and 68.2 m, respectively. The homogeneity of the distribution of post-fire soil respiration decreased and the spatial heterogeneity increased, thus the range for post- fire soil respiration decreased significantly. The fractal dimension （D） for soil respiration increased post-fire, the spatial heterogeneity of soil respiration affected by random components increased, indicating that the change in spatial heterogeneity of post-fire soil respiration should be con- sidered within the scale of the forest stand. Following Kriging interpolation, the increase in the patchiness of post-fire soil respiration was illustrated using a contour map. Based on these preliminary results, the change in the spatial heterogeneity of post-fire soil respiration was likely caused by changes in the distribution of soil moisture and microbial activity within the experimental plot at the scale of the forest stand.

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

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

Effect of fire intensity on active organic and total soil carbon in a Larix gmelinii forest in the Daxing'anling Mountains,Northeastern China

Studying contents and seasonal dynamics of active organic carbon in the soil is an important method for revealing the turnover and regulation mechanism of soil carbon pool. Through 3 years of field sampling and lab analysis, we studied the seasonal variations, content differences, and interrelationships of total organic carbon （TOC）, light fraction organic carbon （LFOC）, and particulate organic carbon （POC） of the soil in the forest areas burned with different fire intensities in the Daxing＇anling Mountains. The mean TOC content in the low-intensity burned area was greater than that in the unburned area, moderate-intensity, and high-intensity burned areas in June and November （P 〈 0.05）. LFOC and POC in the low-intensity burned area were greater than that in either moderate-intensity or high-intensity burned areas, with significant differences in LFOC in September and November （P 〈 0.05）. A significant difference in LFOC between the unburned and burned areas was only found in July （P 〈 0.05）. However, the differences in POC between the unburned and burned areas were not significant in all the whole seasons （P 〉 0.05）. Soil LFOC and POC varied significantly with the seasons （P 〈 0.05） in the Daxing＇anling Mountains. Significant linear relationships were observed between soil TOC, LFOC, and POC, which were positively correlated with soil nitrogen and negatively correlated with soil temperature in the Daxing＇anling Mountains.