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.

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.

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.

Chemical Characteristics of Snow-Firn Pack in Altai Mountains and Its Environmental Significance

In order to study the chemical characteristics of snow-firn pack in Altai Mountains and its environmental significance, a 1.5-m deep snow-firn pack was dug in the accumulation zone （3 300 m a.s.I.） of the Kanas Glacier in August 2009. A total of 15 samples were analyzed for major ions. Results show that the concentrations of major ions in the snow-firn pack are NH4＋〉SO4 2-〉Ca2＋〉NO3＋〉 Na＋〉CI〉K＋〉Mg2＋. NH4＋ is the dominant cation with a medium value of 4.7 ueq-L-1, accounting for 39.8% of the total cations, and SO42- is the dominant anion, with a medium value of 4.0 ueq.L-I, which accounts for 33.9% of the total cations. Compared with Belukha Glacier （Altai）, Urumqi Glacier No. 1, and the Inilchek Glacier （Tienshan）, there is a lower ion concentrations in the Kanas snow-firn pack. Potential sources of these chemical species are explored using correlation and empirical orthogonal function （EOF） analyses. The analyses indicate that major ion concentrations （except NH4＋） originate from crustal dust. Backward trajectory analysis was applied to get the origin of the air mass to Kanas Glacier. The results suggest that air pollutants emitted by forest fires in Siberia could be transported and influence the NH4＋, NO3, and SO42 concentrations on the Kanas Glacier.

Advances in Climate Change Research 12(2021)48-65 Review

In boreal and arctic regions,forest fires exert great influences on biogeochemical processes,hydrothermal dynamics of the active layer and near-surface permafrost,and subsequent nutrient cycles.In this article,the studies on impacts of forest fires on the permafrost environment are reviewed.These studies indicate that forest fires could result in an irreversible degradation of permafrost,successions of boreal forests,rapid losses of soil carbon stock,and increased hazardous periglacial landforms.After forest fires,soil temperatures rise;active layer thickens;the release of soil carbon and nitrogen enhances,and;vegetation changes from coniferous forests to broad-leaved forests,shrublands or grasslands.It may take decades or even centuries for the fire-disturbed ecosystems and permafrost environment to return to pre-fire conditions,if ever possible.In boreal forest,the thickness of organic layer has a key influence on changes in permafrost and vegetation.In addition,climate warming,change of vegetation,shortening of fire return intervals,and extent of fire range and increasing of fire severity may all modify the change trajectory of the fire-impacted permafrost environment.However,the observations and research on the relationships and interactive mechanisms among the forest fires,vegetation,carbon cycle and permafrost under a changing climate are still inadequate for a systematic impact evaluation.Using the chronosequence approach of evaluating the temporal changes by measuring changes in the permafrost environment at different stages at various sites(possibly representing varied stages of permafrost degradation and modes),multi-source data assimilation and model predictions and simulations should be integrated with the results from long-and short-term field investigations,geophysical investigations and airborne surveys,laboratory testing and remote sensing.Future studies may enable quantitatively assess and predict the feed-back relationship and influence mechanism among organic layer,permafrost and active layer processes,vegetation and soil carbon under a warming climate at desired spatial and temporal scales.The irreversible changes in the boreal and artic forest ecosystem and their ecological and hydrothermal thresholds,such as those induced by forest fires,should be better and systematically studied.

Development and implementation of a validation protocol for crisis maps: reliability and consistency assessment of burnt area maps

Validation is an emerging topic in the field of geospatial information for emergency response.In the framework of the Global Monitoring for Environment and Security initiative,the Services and Applications for Emergency Response project seeks to implement and validate the pre-operational version of Emergency Response Core Services.In that context,a dedicated effort has been made in the development and implementation of a comprehensive,productoriented validation protocol tailored to crisis maps and data sets.This paper presents the approach for validation of crisis maps and its application in the context of rapid mapping of forest fires in Corsica during the summer of 2009.It also discusses the main challenges and the critical aspects of validation of geospatial information in emergency response services.