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

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

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.

Drought and Spatiotemporal Variability of Forest Fires Across Mexico

Understanding the spatiotemporal links between drought and forest fire occurrence is crucial for improving decision-making in fire management under current and future climatic conditions. We quantified forest fire activity in Mexico using georeferenced fire records for the period of 2005–2015 and examined its spatial and temporal relationships with a multiscalar drought index, the Standardized Precipitation-Evapotranspiration Index(SPEI). A total of 47975 fire counts were recorded in the 11-year long study period, with the peak in fire frequency occurring in 2011. We identified four fire clusters, i.e., regions where there is a high density of fire records in Mexico using the Getis-Ord G spatial statistic. Then, we examined fire frequency data in the clustered regions and assessed how fire activity related to the SPEI for the entire study period and also for the year 2011. Associations between the SPEI and fire frequency varied across Mexico and fire-SPEI relationships also varied across the months of major fire occurrence and related SPEI temporal scales. In particular, in the two fire clusters located in northern Mexico(Chihuahua, northern Baja California), drier conditions over the previous 5 months triggered fire occurrence. In contrast, we did not observe a significant relationship between drought severity and fire frequency in the central Mexico cluster, which exhibited the highest fire frequency. We also found moderate fire-drought associations in the cluster situated in the tropical southern Chiapas where agriculture activities are the main causes of forest fire occurrence. These results are useful for improving our understanding of the spatiotemporal patterns of fire occurrence as related to drought severity in megadiverse countries hosting many forest types as Mexico.

Mid and late Holocene forest fires and deforestation in the subalpine belt of the Iberian range,northern Spain

The conversion of subalpine forests into grasslands for pastoral use is a well-knownphenomenon, although for most mountain areas the timing of deforestation has not been determined. The presence of charcoal fragments in soil profiles affected by shallow landsliding enabled us to date the occurrence of fires and the periods of conversion ofsubalpine forest into grasslands in the Urbión Mountains, Iberian Range, Spain. We found that the treeline in the highest parts of the northwestern massifs of the Iberian Range(the Urbión, Demanda, Neila, and Cebollera massifs) is currently between 1500 and 1600 m a.s.l., probably because of pastoral use of the subalpine belt, whereas in the past it would have reached almost the highest divides(at approximately 2100–2200 m a.s.l.). The radiocarbon dates obtained indicate that the transformation of the subalpine belt occurred during the Late Neolithic, Chalcolithic, Bronze Age, Iron Age, and Middle Ages. Forest clearing was probably moderate during fires prior to the Middle Ages, as the small size of the sheep herds and the local character of the markets only required small clearings, and therefore more limited fires. Thus, it is likely that the forest recovered burnt areas in a few decades; this suggests the management of the forest and grasslands following a slash-andburn system. During the Middle and Modern Ages deforestation and grassland expansion affected most of the subalpine belt and coincided with the increasing prevalence of transhumance, as occurred in other mountains in the Iberian Peninsula(particularly the Pyrenees). Although the occurrence of shallow landslides following deforestation between the Neolithic and the Roman Period cannot be ruled out, the most extensive shallow landsliding processes would have occurred from the Middle Ages until recent times.

Economic Appraisal of Damages Caused by Forest Fires Adopting a New Prevention and Suppression System： The Case of Rhodes Island, Greece

Rhodes is one of the most forested islands of Greece, in the Prefecture of Dodecanese, in southeast of Aegean Sea. The island in recent times has been struck by big and devastating fires. After 1993, the local Forest Service and the local political authority have adopted a new prevention and suppression system relied on the fast fire detection and suppression at its initial stages. By the present research, comparing the results of 1993-2006 （a time span when the above method was applied） with the results of the immediately precedent equal time of 1978-1992, was made certain that the firefighting system applied after 1993 had very good results irrespective from the primary agency in charge of extinguishing the forest fires. Among others, it was made clear that, during the period that this method was applied, a much less area was burnt per year than the period before the application in spite of the fact that in the same period （1993-2006） there has been a significant increase of forest fires. It is also estimated that the economic damage occurred in the first period （1978-1992） on average was 12.4 times per year higher compared to the second period （1993-2006）.

Estimation of Direct Carbon Emissions from Chinese Forest Fires

Many studies indicated that the products of biosphere burning have short and long-term effectson the atmosphere. Vegetation burning can produce some gases which have significant influence onenvironment, including some greenhouse gases as CO2 and CH4, etc. Smoke aerosols produced fromburning also influence global climate and atmospheric chemistry. The paper calculates the consumedbiomass due to forest fires according to the statistics of forest fires from 1991 to 2000 and research resultsof biomass of Chinese forests. During the study period, forest fires burned average 5 Tg ~7 Tg biomasseach year and directly emitted 20.24 Tg^28.56 Tg carbon. In 1991~2000, average emission of carbondioxide and CH4 account for 2.7%~3.9% and 3.3%~4.7% of the total emission of China (calculating withthe data of 2000), respectively.

Forest Fires and Prevention Strategies in Northwestern Region of China

The paper described the natural conditions and forest types in Northwestern Region of China. Most forests in the region are distributed in subalpine areas. It is important to protect the existent forests in the region for maintaining ecological balance. According to the statistics results of 1991~2000, the paper analyzes the forest fires distribution and fire severity. Annually the numbers of forest fires range from 52 to 240. The incidence rate of forest fires in Northwestern Region is under 0.33 per t...

Prediction of forest fire occurrence in China under climate change scenarios

Climate change has an impact on forest fire patterns.In the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest ecosystems.This study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were integrated.Temperature and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate data.Under climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC curves.The results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the country.Compared with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).

Monitor Forest Fires with FY Serial Satellites

Remote sensing as the measure to monitor disasters has the advantage of temporal resolution and large scale. Since "5.6 catastrophe" in 1987, China began to monitor forest fires broadly. In the summer of 2002, many forest/grass fires occurred in the Daxing'anling Mountains, and the damage was very heavy. In the forest fires fighting, the meteorological satellites play an important role in monitoring the fires. Especially the FY serial meteorological satellites have the advantage of large scale monitorin...

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.

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.

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.

Estimating costs of salvage logging for large-scale burned forest lands: A case study on Turkey's Mediterranean coast

Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change.Useful as well as harmful aspects of forest fires are a multi-disciplinary research topic.Geographical information systems(GIS)and remote sensing(RS)methods offer a number of benefits for researchers and operators in the field of forest fire research.The present study analyses timber pricing based on forest contractor demands of post-salvage logging processes.The effect of timber obtained from compartment units on producers’pricing policy was modelled.Sapadere forest fire area(2500 ha)located in Antalya in Turkey was selected as the main study area.Topography parameters(aspect,slope and slope position),stand types(diameter class and crown closure),and burn severity were analyzed together using GIS and R software packages.A multi-linear regression model(R^(2)=0.752)demonstrated that factors that had the most impact on pricing were slope position,aspect,stand age,crown closure and burn severity.This model can be used to estimate salvage logging prices in Calabrian pine(Pinus brutia Ten.)stands with similar parameters.Forest administrators and contractors may readily address the unit price of timber by estimating approximate costs in a given forest area for which they are going to bid.This will help reduce operational planning times of harvesting procedures in burned stands.

A Novel Approach to Ethanol Fuel Production using Rotary Collection of Forest Debris

In this article, the authors propose the production of ethanol from cellulose as an alternative to oil. Cellulosic-ethanol will reduce greenhouse gas emissions, and provide a means to prevent forest fires. This liquid dense fuel was selected because it： （1） easily transported and dispensed as a fuel; （2） can be handled by the existing fuel distribution infrastructure; and （3） unlike its commercial competitor, Me-OH （Methanol）, Et-OH （Ethanol）, is edible, thus being biodegradable and nontoxic. Forest residue ethanol is cheaper to produce and more environmentally friendly than other forms of ethanol fuel. Furthermore, forests would have less available ground fuel for fires. The potential decline of forest fires would then reduce the carbon footprint attributed directly to forest fires. In combination with ethanol fuel combustion, carbon emissions can be reduced by more than 70% compared to gasoline combustion. We used GREET （Greenhouse gases, Regulated Emissions, and Energy use in Transportation） software to assess the life cycles of different fuel pathways. In conclusion, cellulosic ethanol fuel is clearly an answer to decrease dependency on current oil imports and prevent forest fires.

Study of Biodiversity Indices in Forest Formations of the Province of Saida （Algeria）

Biodiversity is a multidimensional concept involving several scientific disciplines. The study of biodiversity allows to initiate a multidisciplinary analysis and ecological reading of an environmental problem. The objective of this study is the knowledge of plant biodiversity and risk factors affecting the plant diversity in forest formations of Saida province （Algeria）. Plant biodiversity is estimated at 77 species with 37 best represented families （Asteraceae, Lamiaceae and Fabaceae）. The geophytes dominate with a rate of 44.15%. Among the degradation factors that threaten the biodiversity in the study area, the forest fires, overgrazing and trampling by domestic animals was mentioned.

Identifying Possible Climate Change Signals Using Meteorological Parameters in Short-Term Fire Weather Variability for Russian Boreal Forest in the Republic of Sakha (Yakutia)

The Boreal forest is a terrestrial ecosystem highly vulnerable to the impacts of short-term climate and weather variabilities. Detecting abrupt, rapid climate-induced changes in fire weather and related changes in fire seasonality can provide important insights to assessing impacts of climate change on forestry. This paper, taking the Sakha Republic of Russia as study area, aims to suggest an approach for detecting signals indicating climate-induced changes in fire weather to express recent fire weather variability by using short-term ranks of major meteorological parameters such as air temperature and atmospheric precipitation. Climate data from the “Global Summary of the Day Product” of NOAA (the United States National Oceanic and Atmospheric Administration) for 1996 to 2018 were used to investigate meteorological parameters that drive fire activity. The detection of the climate change signals is made through a 4-step analysis. First, we used descriptive statistics to grasp monthly, annual, seasonal and peak fire period characteristics of fire weather. Then we computed historical normals for WMO reference period, 1961-1990, and the most recent 30-year period for comparison with the current means. The variability of fire weather is analyzed using standard deviation, coefficient of variation, percentage departures from historical normals, percentage departures from the mean, and precipitation concentration index. Inconsistency and abrupt changes in the evolution of fire weather are assessed using homogeneity analysis whilst a Mann-Kendall test is used to detect significant trends in the time series. The results indicate a significant increase of temperature during spring and fall months, which extends the fire season and potentially contributes to increase of burned areas. We again detected a significant rainfall shortage in September which extended the fire season. Furthermore, this study suggests a new approach in statistical methods appropriate for the detection of climate change signals on fire weather variability using short-term climate ranks and evaluation of its impact on fire seasonality and activity.

Influence of high-temperature convective flow on viability of Scots pine needles(Pinus sylvestris L.)

During a forest fire,plants are affected by high temperatures causing stress.At the time of burning,it is difficult to record temperature changes in tree crowns and the associated effects on photosynthesis.This paper presents the results of modelling a high-temperature effect simulating a convective flow from a ground fire.Evaluation of the response was carried out by the parameters of rapid fluorescence(Fv/Fm,ETR),the state of the pigment complex,and the relative water content in the needles.To characterize the degree of heat endurance and short-term effects concerning thermal damage,saplings of Scots pine(Pinus sylvestris L.)were used at different times during the growing season(June,July,August,September).Experimental heating at 55℃ lasted for 5 and 10 min.There were different levels of heat resistance by the needles.Data in June show that heating of the saplings significantly suppressed photosynthesis.In July,August,and September,the photochemical quantum yield(Fv/Fm)was restored to 75%and 60%from the initial level after 5-and 10-min heating,respectively.The electron transport rate(ETR)for saplings in September was restored to their initial level within 3 days after a short heat exposure.Restoration of the photosynthetic activity in needles was observed after a 5-min impact,but by the end of the study period,restoration had not reached control values.A longer heating of 10 min resulted in an irreversible suppression of photosynthesis and destruction of the photosynthetic apparatus,as evidenced by the decrease in the number of photosynthetic pigments.

Daytime-nighttime variations in the concentration of PM_(0.1) carbonaceous particles during a biomass fire episode in Chiang Mai,Thailand

Carbonaceous aerosols affect air quality adversely,affect global warming,and human health.However,our understanding of the impact of ultrafine(PM_(0.1))carbonaceous particulate matter is incomplete,particularly the effects during haze episodes.This study monitored diurnal variations in PM_(0.1) in Chiang Mai,Thailand,from March to April 2020.We investigated carbonaceous PM_(0.1) collected by an ambient nano-sampler and evaluated their effect by using a carbon analyzer(IMPROVE_TOR).The results showed that burning large open areas in the dry season was crucial for increasing the particle mass concentration because of the large open burnings that occurred in this area.The majority of biomass fires near the sampling site occurred during the night,which would allow more particles to be released thus resulting in higher concentrations of PM_(0.1).Hence,the release of PM_(0.1) during the night would obviously result in higher concentrations than that during the day.In the eight carbon profiles,organic carbon 3(OC3)was predicted to be a marker of biomass fires.The carbon indices displayed that PM_(0.1) was influenced by biomass burning both daytime and nighttime.The findings reported herein should be of great impor-tance in terms of establishing biomass burning control policies for mitigating heavy haze pollution in Thailand and elsewhere.

Carbon Emissions from Forest Vegetation Caused by Three Major Disturbances in China

To investigate forest carbon sequestration and its role in addressing global climatic change, it is important to assess carbon emissions caused by major disturbances from forest ecosystems to the atmosphere. Based on forestry statistics on the occurrence of each disturbance and acceptable assumptions on the process and proportion of biomass carbon transferred to other pools due to each disturbance, this paper estimates the direct carbon emission from Chinese forest vegetation caused by three major disturbances, that is, wood harvesting, fire, and DPR, from 1990 to 2009. Results showed that over the past two decades, Chinese forests have been disturbed rather intensively by wood harvesting, fires, and DPR, with clear upward occurrence trends of the three disturbances in the early 21 st century. As a result, the average annual carbon emissions caused by wood harvesting, fires, and DPR were 34.25 Tg, 1.61 Tg, and 4.29 Tg, respectively, during 1990–2009. The aggregate annual carbon emission due to these three major disturbances was 40.15 Tg during 1990–2009, which was 30.79 Tg during 1990–1999 and 49.51 Tg during 2000–2009. According to the analysis of carbon emissions from different forest regions, there were obvious regional characteristics of the average annual carbon emission caused by each disturbance. However, it was difficult to identify clear cause and effect relationships among disturbances to explain the spatial variation of carbon emissions from forest vegetation in China. Disturbances have significant influences on carbon balance of forest ecosystems in China. This finding suggests the opportunities for increasing forest carbon sequestration by disturbance-aimed sustainable long-term management of forest resources, as well as the necessity of considering the role of major disturbances in carbon budget models for forest ecosystems or terrestrial ecosystems.

Extreme fire weather is the major driver of severe bushfires in southeast Australia

In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian temperate forests.Temperate forest fires have extensive socio-economic,human health,greenhouse gas emissions,and biodiversity impacts due to high fire intensities.A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia.Here,we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25°grid based on several biophysical parameters,notably fire weather and vegetation productivity.Our model explained over 80%of the variation in the burnt area.We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather,which mainly linked to fluctuations in the Southern Annular Mode(SAM)and Indian Ocean Dipole(IOD),with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season,and model developers working on improved early warning systems for forest fires.