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Distribution characteristics and the influence factors of forest fires in China
... Research on fire origin, the characteristics of fire ignition sources and their temporal and spatial distributions has always caught the attention of forestry scientists and scholars in wildfire-swept countries, including China, France, Spain, and the USA. An ignition source has been recognized to be highly reliant on the areas with frequent human activities, such as the farmland and settlements nearby a forest [4,6,8,9]. Given that farming, agricultural, industrial, and domestic fires often occur in adjacent to a forest, fire spread may result in disaster in the nearby forests. ...
... Fires usually occur in two seasons, spring and autumn, and are usually sorted as three categories, namely general, major, and extremely large, depending on the burnt area and casualties [2]. In comparison with the northern areas, more fire incidents happen in southern China forest areas every year, reflecting dense population and living style of the residents in the fire-prone areas [2,8,13]. In light of the ascertainment status, fire incidents are split into identified and unidentified cases, whereas the fire causes are classified into four categories: fire use in agricultural activities (FAA), fire use in nonagricultural activities (FUA), other human-related ignition sources (OHR), and natural ignition sources (NIS). ...
... Over the years between 2003 and 2017, the proportion of fire incidents caused by natural factors, such as lightning (LTN) and spontaneous combustion (SPC) fluctuated from 0.6% to 9.2% at an average of 1.2% (see Table 1 and Figure 1). Owing to China's vast territory and variations in climate conditions, a big difference was observed in the proportion of natural fires in the southern and northern forest areas: the number of lightning fires in northern forest areas, especially northeastern forest areas, was relatively high, whereas the probability of lightning fire in southern provinces was low [8,21]. This disparity is essentially governed by local weather and humidity as well as the alive status of surface plants in lightning seasons. ...
Control of forest fire ignition sources is the top priority in fire management practices. China has gained great success in reducing forest fires in recent years, and the relevant safety measures taken during this process are worthy of investigation and publicity. Based on fire statistical data through the years between 2003 and 2017, we analyzed the detailed classification of fire ignition sources and their contribution to the annual forest fire occurrence. The role of different ignition sources in altering fire occurrence was quantified and ranked by defining a contribution extent parameter. A statistical tool was also applied to conduct correlation analysis to identify variation patterns of time series data from individual fire causes. The annual fire numbers declined after 2008 and stabilized at a level < 2000 in recent years, pointing to the containment of several major ignition sources. Starting from the legislative development, an accountability system was established at all levels from administrative heads to local residents, paving the way for the multifaceted and full-coverage fire prevention publicity and education as well as the fire use restriction in particular seasons. The effectiveness of management measures in lessening forest fire occurrence was interpreted using the results of correlation analysis among the fire numbers initiated by individual ignition sources.
... Although many studies have tried to disentangle the drivers of fire occurrence and interannual burned area across China (Chang et al., 2015;Fang et al., 2021;Tian et al., 2013;Yao et al., 2017;Ying et al., 2018), there has been less work on examining the drivers of fire-induced forest mortality. One problem is that neither fire-driven mortality nor fire regimes can be easily measured in the field at long temporal and large spatial scales. ...
... However, our results show that from 2003 to 2016, forest burned area across China decreased significantly (Fig. 1). Previous studies have found that most forest fires in China are caused by anthropogenic ignition (Tian et al., 2013;Ying et al., 2018). In recent years, especially since the catastrophic forest fire of 1987 in the Greater Xing'an Mountains of Northeast China, the Chinese government, at all administrative levels, has made forest fire management a high priority and placed an emphasis on the management of human ignition. ...
... Therefore, we believe that the decline in human-caused fires is the main reason for the decrease in forest fires in China. In accordance with previous studies (Tian et al., 2013), we also found that most of the forest fires across China (84.9 %) occurred in the spring and winter, However, fire-driven forest mortality was greatest from late spring to mid-summer. ...
Wildfire is one of the most prevalent natural or human-induced disturbances across the world, which can kill trees directly during the active combustion phase or cause delayed forest mortality through interactions with other disturbance agents. With its diverse range of climate and forest ecosystems, China also has diverse forest fire regimes. However, the spatial and temporal patterns of fire-driven forest mortality on the national scale remain largely unexplored. In this study, we employed satellite observations and statistical models to investigate the spatiotemporal patterns and underlying drivers of fire-induced forest mortality across China. Our results demonstrate pronounced temporal patterns of forest burned area and fire-driven forest mortality. On the seasonal scale, forest fires mostly occurred in the winter and spring, whereas the forest mortality is highest from May to July. Both forest burned area and fire-driven mortality exhibited a significant decreasing trend during 2003–2016. Forest burned areas are mainly distributed in Northeast China, Southwest China and South China. There exists a tight spatial consistency between areas with high mortality and burned area with East China as an exception. Correlation analysis and multiple linear regression analysis show that fire size, fire spread rate, fire duration and drought intensity had significant impacts on postfire forest mortality, with fire size and fire spread rate accounting for 38.3% and 37.1%, respectively, of the model’s explanatory power. Our results highlight the importance of fire regime impacts on postfire forest mortality. Future climate change that drives more intense fire regimes might thus lead to enhanced forest mortality and increase the loss of forest ecosystem services.
... The effect of temperature on active fire is significant. The high temperature will accelerate the evaporation of water and the drying of combustibles, which will easily cause fire and make the fire more violent [44]. Active fires in the Chinese mainland principally occur in the area of 14~19 °C (Figure 9a), which shows that the active ...
... The effect of temperature on active fire is significant. The high temperature will accelerate the evaporation of water and the drying of combustibles, which will easily cause fire and make the fire more violent [44]. Active fires in the Chinese mainland principally occur in the area of 14~19 • C (Figure 9a), which shows that the active fires are mainly concentrated in spring and autumn, which is similar to the monthly statistics of active fires (mainly distributed in February-April and October). ...
... This may be the main reason for the difference between the results of this study and those of this paper. Wang et al. [44] reported that the frequency of active fires in Gansu province has an obvious negative correlation with the distance to the nearest path. The fire spots are concentrated in areas 0-20 km away from the road. ...
Based on the FIRMS MODIS active fire location data in the Chinese mainland from 2001 to 2018, the GIS fishing net (1 km × 1 km) was used to analyze the spatiotemporal distribution characteristics of active fire occurrence probability and intensity, and a GWLR fire risk assessment model was established to explore its influencing factors. The results show that active fires in the Chinese mainland are mainly low intensity. They are mainly distributed in the area where the annual average temperature is 14–19 °C, the precipitation is 400–800 mm, the surface temperature is 15–20 °C, the altitude is 1000–3000 m, the slope is <15°, and the NDVI value is >0.6. The GWLR fire risk assessment model was constructed to divide mainland China into five fire risk zones. NDVI, temperature, elevation, and slope have significant spatial effects on the occurrence of active fires in the Chinese mainland. Eight fire risk influencing factor areas were divided by calculation, and differentiated fire prevention suggestions are put forward.
... Remote-sensing multi-source data [21], combined remote sensing and ecosystem modeling [22], provincial statistical data [23], and county-level statistical data [24] show that Southwest China is a region with a high prevalence of forest fires. Yunnan is a province in Southwest China with frequent forest fires, where the main flammable species include According to the records of the Yunnan Provincial Fire Brigade, Yunnan had the most forest fire events in the country [35]. ...
... Compared with RHmin and Tmax, the altitude threshold for 50% forest fire probability is relatively large, and the degree of variation of the threshold and the related partial derivative reveals no obvious regularity. Overall, the variation of threshold and the related partial derivatives differ between counties (Figure 7e,f) Relative importance(%) [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] No concern with the factor No suitable model At 50% forest fire probability, the threshold of RH min trends downward from Northwest Yunnan, through Southwest Yunnan and Southeast Yunnan, and finally to Central Yunnan (Figure 7a), where the partial derivative RH min < −8 mainly occurs in Northwest Yunnan, Southeast Yunnan, Southwest Yunnan, and in the areas around the Lancang River and the Nujiang River, which indicates that for every 1% decrease in RH min , the marginal forest fire probability of 50% in this region increases by at least 8% (Figure 7b). The high T max threshold mainly occurs in Central Yunnan, Northwest Yunnan, and Southwest Yunnan, decreasing in these three regions upon approaching the surrounding area. ...
... Controlling the sources of fire in the forest zone is also a vital step to curb fire outbreaks. According to recent statistical data, human-caused forest fires have become more frequent [21], even exceeding the number of forest fires caused by climate change [67], and the use of fires for farming and culture in spring and summer, especially in the agricultural and forest lands, should be a major concern for the authorities. To better understand the combustibility of combustible substances in different regions, combustibility experiments should be implemented on different plant species to understand how they adapt to different degrees of forest fire, and also to raise public awareness of forest fire prevention through related scientific activities. ...
Forest fire is an ecosystem regulating factor and affects the stability, renewal, and succession of forest ecosystems. However, uncontrolled forest fires can be harmful to the forest ecosystem and to the public at large. Although Yunnan, China is regarded as a global hotspot for forest fires, a general lack of understanding prevails there regarding the mechanisms and interactions that cause forest fires. A logistic regression model based on fire points in Yunnan detected by satellite in 2005–2019 was used to estimate how environmental factors in local areas affect forest fire events. The results show that meteorology is the dominant cause of the frequent forest fires in the area. Other factors of secondary importance are the daily minimum relative humidity and the daily maximum temperature. When using the logistic regression model based on the data of fire points in Yunnan over the period 2005–2019, the key threshold for the daily minimum relative humidity is 28.07% ± 11.85% and the daily maximum temperature is 21.23 ± 11.15 °C for a forest fire probability of 50%. In annual and monthly dynamic trends, the daily minimum relative humidity also plays a dominant role in which combustible substance load remains relatively stable from January to March, and the impact on forest fire becomes greater in April, May, and June, which plays a secondary role compared with the interannual climate. The maximum daily temperature ranks third in importance for forest fires. At the county level, minimum relative humidity and maximum temperature are the top two factors influencing forest fires, respectively. Meanwhile, the differences in forest fire points between counties correspond to the pathways of the two monsoons. This study applies quantitative expressions to reveal the important environmental factors and mechanisms that cause forest fires. The results provide a reference for monitoring and predicting forest fires.
... The data required for fire analysis can be obtained by analyzing and extracting the images. Topography, meteorological factor, vegetation, and human activities are recognized as four types of fire ignition factors [12][13][14][15][16][17][18][19]. ...
... Meteorological factor, such as temperature, is a significant inducement of fire ignition [17]. A high temperature will result in a low soil moisture or even drought, which makes forests prone to fire [18][19][20]. ...
... Dry areas are prone to fire and spread rapidly, while wet areas are hard to cause fires [16,20]. Figure 6 shows the base map of TWI and the weight of factors is listed in Table 5. Meteorological factor [17] is a significant inducement of fire ignition. The temperature has a great impact on regional hydrology [18]. ...
Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.
... As the importance of wood as an accessible building material has increased, largescale studies on the Russian Far East were carried out in the 1980s. A modern detailed analysis of fire characteristics has been made for Da Hinggan Ling prefecture, located in Heilongjjiang Province, China (Tian et al. 2013b). Fig. 1 shows that the Jewish Autonomous Region (JAR) holds one of the leading positions in the Far Eastern Federal District in terms of the relative number of wildfires and burned area per 1 million hectares (Sheshukov et al. 2009). ...
... For example, Kurbatsky and Tsvetkov (1986) estimated 5 fires for Koryaksky and 33 fires for Komi-Permyatsky Autonomous Districts, 13 -for Aginsky-Buryatsky Autonomous District, 2 -for the Republic of Adygea, and others (Kurbatsky and Tsvetkov 1986). However, this is less than half of the number of fires in the neighboring province of Heilongjiang in China (Tian et al. 2013a(Tian et al. , 2013b. The average area of a single fire in the Jewish Autonomous Region is 134 hectares. ...
... The average area of a single fire in the Jewish Autonomous Region is 134 hectares. Near the border with China, province of Heilongjiang, this area is 1167 hectares (Tian et al. 2013a(Tian et al. , 2013b. ...
Wildfires affect the structure and distribution of vegetation all over the globe and have their own specifics in different regions. In this study, we considered the spatial and temporal distribution of fires in the Jewish Autonomous Region (JAR), which is the most fire-prone area of the Russian Far East. Using data from the Department of Natural Resources of the Jewish Autonomous Region, fires and burned areas for more than 40 years were analyzed. The average annual number of fires is near 100, and the average area of one fire is 134 hectares, which is significantly higher compared to other regions of Russia. The largest number of fires and fires with the greatest extent took place in 1975. The intra-annual distribution of fires is bimodal and depends on the climate characteristics of the region. Mapping of burning areas showed that most of the fires occurred near settlements and along roads. The main centers of fire ignition were areas with a large number of small fires (no more than 5 hectares), located within several types of locations: (1) asphalt and dirt roads, railroads and river valleys near settlements; (2) areas of former logging that have several large burned spots of more than 300 hectares; (3) plains with a high concentration of fires over a large region; and (4) small burned spots on the mountain slopes, along the field roads and small rivers. Regions with different degree of fire exposure were identified. Sedge-reed mixed grassy meadows and Agricultural land with shaded meadows are the plant formations most prone to wildfires. At the same time, more fires were detected in Cedar-deciduous forests as well as Oak and black birch forests. The findings are useful for environmental protection agencies in planning fire management strategies, optimizing the fire services and firefighting actions.
... The hybrid (mix of several approaches) classification was utilized in 11 of the reviewed studies [95,288,339,349]. The "Manual" group of studies were those that manually produced BA classes and BS mapping through digitizing polygons, using vegetation and spectral indices, extracting features, hotspot detection, and change analysis in forest patterns [350][351][352][353][354][355][356][357][358][359][360][361][362][363][364][365][366]. The burned area extraction and dating (BAED) algorithm [367], multiscale curvature classification [233], spectral angle mapper classification [155,200,317], discriminant analysis [282], and rare class prediction in the absence of true labels [368] classification methods were used less than three times, and, therefore, they were included in the "Other" group of methods. ...
... The "Manual" group of studies were those that manually produced BA classes and BS mapping through digitizing polygons, using vegetation and spectral indices, extracting features, hotspot detection, and change analysis in forest patterns [350][351][352][353][354][355][356][357][358][359][360][361][362][363][364][365][366]. The burned area extraction and dating (BAED) algorithm [367], multiscale curvature classification [233], spectral angle mapper classification [155,200,317], discriminant analysis [282], and rare class prediction in the absence of true labels [368] classification methods were used less than three times, and, therefore, they were included in the "Other" group of methods. ...
Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.
... These factors could be divided into six categories: geographical location, meteorology, climate, topography, society, and vegetation. Researchers have studied the influencing factors of these forest fires [72,73]. The drivers of forest fires were obtained by feature selection, such as longitude, latitude, mean surface temperature, daily maximum surface temperature, cumulative precipitation, mean relative humidity, sunshine duration, mean temperature, daily maximum temperature, elevation, population, GDP, and NDVI. ...
... In addition, altitude and vegetation can affect the occurrence of forest fires. Tian et al. (2013) believed that forest fires mainly occurred in low-altitude areas, fires are more influenced by human activities at low altitudes [79]. Chuvieco et al. (2004) found that the higher the NDVI value, the higher the vegetation cover and the more flammable trees there are, and the more likely they are to cause problems related to forest fires [80]. ...
Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.
... Rich forest areas located in all geographical regions of Turkey are under the risk of fire. Thus, fire prevention strategies are necessary to preserve these forests and minimize the damages by determining spatial distribution of fires [15,16]. Risk assessment is the basic element for planning and formulating strategies. ...
... By this means, the most effective prevention and response planning can be made against possible fires. Spatial distribution of fires and fire risk assessment are crucial to improve fire prevention and response strategies [15] ...
Fires all around the world have caused irreparable environmental and economic losses due to the increase in global warming. Risk assessments are curial for taking measures to keep negative effects of natural hazards to a minimum and to ensure sustainability of the nature. In this regard, the aim of this study is to evaluate rural area fire risk in the seven geographical regions of Turkey. Since fire sensitive areas in rural regions are generally composed of forest areas, data on the size of forest areas, number of forest fires and temperatures are the main starting points of this study. Analysis was carried out using the Analytical Hierarchy Process and Fuzzy Logic approaches. In this study the Analytical Hierarchy Process and Fuzzy Logic approaches were integrated with the Geographical Information Systems. The results obtained from the AHP and Fuzzy Logic approaches were compared and evaluated. The results of the study show that the Aegean, Mediterranean and Marmara regions have the highest rural area fire risk levels. The fire risk assessments results obtained by the AHP and Fuzzy Logic approaches overlap on the basis of geographical regions. Integrating the AHP and Fuzzy Logic with GIS for rural area fire risk assessment provide valid, reliable and very important results in making the necessary planning and ensuring sustainability of the environment.
... Comparing this study with the study of forest fire in other countries in South Asia it was found that the fire occurrence month in other South-Asian countries were almost similar to Nepal. The study by Tian (2013) [22] shows that among the forest fires in China, March was the month with the most fires (60.0%), followed by April ( ...
... Comparing this study with the study of forest fire in other countries in South Asia it was found that the fire occurrence month in other South-Asian countries were almost similar to Nepal. The study by Tian (2013) [22] shows that among the forest fires in China, March was the month with the most fires (60.0%), followed by April ( ...
This study was objectively conducted to assess trends of forest fire occurrence, burnt area and its causes and management measures in Province-2, Nepal. Altogether 48 Questionnaire Survey and 32 Key Informant Interviews were organized to collect primary data while secondary data were collected from Fire Information for Resource Management System from year, 2002 to 2019. Total 36 maps were produced showing fire occurrences (18) and burnt area pattern (18) of targeted area. Result showed that total 5289 forest fire incidents and total 499,538.9 ha were burnt from 2002 to 2019. The highest number of forest fire incidents was observed in March with 2975 incidents covering 56.24%. The highest incidence was recorded in Lower Tropical Sal and Mixed Broadleaf Forest with 3237 observations. One-Way ANOVA showed that fire occurrence and burnt area among Lower Tropical Sal and Mixed Broadleaf Forest (LTSMF), Hill Sal Forest (HSF) and Outside Forest Region (OFR) were significantly different at 95% confidence level. Mann-Kendal correlation showed that there was positive correlation (R=0.393) between year and forest fire occurrence in LTSMF as well as between year and burnt area of HSF (R=0.09). Principal Component Analysis in Parsa district showed, unextinguished cigarette butts and litter fall was positively correlated.
... Southwest China is an area of relatively high frequency of wildfire events. The consensus has been reached with the national-scale patterns of wildfires, according to multi-source data from remote sensing (Tian et al., 2013), remote sensing combining with ecosystem models , provincial statistics (Yi et al., 2017), and county-level surveys (Ying et al., 2018). As a province in Southwest China, Yunnan is typical for the prevalent occurrence of wildfires, and the common ecosystems in Yunnan have been widely reported as fire-prone ecosystems (Su et al., 2015;Han et al., 2018), especially for the most widely distributed Yunnan Pine (Pinus yunanensis) forests and its mixed forests with evergreen broadleaved trees (Tang et al., 2013;Han et al., 2015), and shrubs and grass communities dominating in the dry valleys of the large rivers Li et al., 2020). ...
... Second, restricted ignition control is required. Human-related wildfires are increasingly frequent (Tian et al., 2013), and even outweigh the effects of climate change (Liu et al., 2012a). Agricultural and cultural fire usages in spring, especially within the agroforestry landscape, should draw the most attention of managers and the public. ...
Wildfires are land-surface processes and ecological disturbances occurring around the world. The wildfire regime in Yunnan Province of Southwest China is recognized as similar to that of the adjoining Indo-China Peninsula, a global hotspot for wildfires. However, the ignition mechanisms in this region remain unclear, with interactions among ecosystem features, local agricultural activities, and fire weather controlled by the West Pacific and Indian Ocean monsoons. Based on the ground records of 5145 confirmed wildfire events in Yunnan during the period of 2003–2015, we used a logistic regression model to estimate the local environmental controls on wildfire ignitions. Results highlighted the primary role of meteorology-driven characters, especially the prevalent importance of daily minimum relative humidity across the region. The threshold of the relative humidity was 37.48% ± 15.60% for the 50% ignition probability. Relative humidity also dominated ignition over years, with fuel conditions relatively stable and playing a minor role in contrast to the inter-annual climate changes. Moreover, agriculture-related ignition comprised most wildfire records, and human activities deeply shaped the spatiotemporal patterns of ignition. The distance to the nearest village was the primary factor during the beginning of the agricultural season, with a farming radius of 1.2 km as a key threshold for ignition. The complementary roles among influential factors were prominent at county scale. Among counties, the variation of ignition mechanisms corresponded to the influence paths of the two monsoons. This study highlights the importance of ground wildfire records in deriving critical wildfire ignition information such as environmental thresholds and change rates, which can provide important insights for sustainable forest management in this region, including wildfire monitoring, ignition control, fuel structure adjusting, and implementing differentiated strategies for fire prevention with regard to the environmental contexts.
... Thus, the search for methodologies to assist in the prediction, prevention, and combat of forest fires in natural areas has become increasingly frequent. Çolak and Sunar (2020), Eugenio et al. (2016), Juvanhol et al. (2015), Lewis et al. (2015), Mota et al. (2019), Naderpour et al. (2019), Ribeiro et al. (2012), Soares Neto et al. (2016), Soto (2012), Tian et al. (2013), Torres et al. (2017), Venkatesh et al. (2020) and White et al. (2016) have elucidated the potential of the application of geotechnologies in fostering information to help minimize the damage caused by forest fires. ...
... To obtain the meteorological variables of temperature and precipitation, historical series were used, 35 (1985-2020) and 30 (1990-2020) years of 109 weather stations located in Espírito Santo and adjacent states (Minas Gerais and Bahia). To process the historical series data, the agroclimatological water balance proposed by Thornthwaite and Mather (1955), using Microsoft Office Excel® software, version 2019, was applied. After generating the water balance, the electronic spreadsheets were imported into the ArcGIS® software program, version 10.3, more specifically from the latitude and longitude fields, to enable spatial vectoring of the meteorological stations and their respective attribute tables with the fields referring to the water balances. ...
Although forest fires are indispensable for some ecosystems, they can have profound economic, environmental, and social implications, especially when they reach high intensities. There are two crucial factors in fighting forest fires: the availability of water resources and the service network. The objective of this study was to propose an alternative methodology for allocating water reservoirs to fight forest fires. The research was divided into three stages: zoning of fire risk, delimitation of viable areas for the implementation of water reservoirs, and determining strategic locations for reservoir allocation. The variables analyzed were land use and occupation, provision of watercourses, relief orientation, slope, proximity to roads, temperature, and precipitation. Fuzzy logic, Euclidean distance, and network analysis were used as the modeling techniques. Scenarios with all risk classes and only the high- and very high-risk classes were analyzed. A total of 66% of the area was represented by the low- and moderate-risk fire classes and 53.16% had a low potential for reservoir allocation, influenced by the low availability of water resources in the area. The proposed model efficiently allocated the water collection points in the different scenarios, and allowed the determination of the areas most susceptible to the occurrence of forest fires and the optimal locations for the installation of reservoirs, with the allocation of 21 water reservoirs to attend the areas of high- and very high-risk of occurrence of fires at a safe speed (40 km h⁻¹) and 47 reservoirs to meet all risk classes at the same speed. The proposed methodology is feasible, applicable, and adjustable and can be implemented in other conservation units and areas of economic interest.
... Geology and geomorphology data do not directly affect the risks of forest fires but wildfires can cause problems such as landslides and erosions [23,24]. The roads were downloaded from Geological Survey of India and the road and built-up area proximity of the 200-m buffer zone can significantly increase forest fire risk [25,26]. Therefore, a 200 m buffer zone is included around roads and built-up areas. ...
... and geomorphology data were downloaded from Geological survey of India [2 ogy and geomorphology data do not directly affect the risks of forest fires but can cause problems such as landslides and erosions [23,24]. The roads were dow from Geological Survey of India and the road and built-up area proximity of th buffer zone can significantly increase forest fire risk [25,26]. Therefore, a 200 m bu is included around roads and built-up areas. ...
The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.
... Besides abiotic factors, the main cause of Indonesia's forest fires is the close link between poverty and fire at the village next to conservation area (Edwards et al., 2020). Many of the fire in China's forest was triggered by anthropogenic combustion, which was closely linked to residential distribution and development modes (Tian et al., 2013). The density of fires near human settlements is high although overall contribution of fires near settlements is low in Indonesia (Cattau et al., 2016). ...
... Most of the people who live around conservation areas want to be involved in management to sustain their economy. Utilizing spatial trend analysis, a research in China found that forest fires mostly occurred in 2 sparsely populated areas of over 100 people km and less than 1 km away from resettlement to conservation areas (Tian et al., 2013). On the other hand, Cattau et al (2016) stated that relatively few hotspots are located in 5 km of settlement in Indonesia, but it is possible that other types of land use contribute substantially to the fire landscape. ...
Forest fire was a persistent concern management in conservation areas of Mount Ciremai National Park (MCNP) and Kuningan Botanical Garden (KBG). Many of the forest fire was sparked by anthropogenic ignitions like careless fire use for extracting forest honey. This study aims to map multi stakeholder roles on fire management in conservation areas. Twenty-seven actors were interviewed to learn who are the fire actors and network. These multi stakeholders included government officials, local businessmen, non-governmental organizations and community members. Study site and data collection were carried out in seven villages around conservation areas from July to September 2019. The relationships between the actors were analyzed with the software Node XL Basic and Gephi 9.0.2 using the Social Network Analysis. Our results identify close relationships and strong connections to all actors of more than half (63.2%) but social or personal approach between all actors were still required. Head of MCNP, Head of KBG and Head of AKAR (Aktivitas Anak Rimba) acted as the important actors. To prevent the area from further fire occurrences, management authorities should establish mutual confidence and make other actors believe that heads of conservation areas are a solid team to prevent conservation areas from burning.
... According to other authors (Martínez et al., 2008;Tian et al., 2013;Torres et al., 2014), the road network strongly relates with fire beginning because surrounding combustible material has greater likelihood of ignition ( Fig. 4e and Fig. 6e) due to anthropogenic causes. In this sense, this behavior is confirmed by obtained results once the most vulnerable areas to forest fires occur close to the road network. ...
Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.
... According to other authors (Martínez et al., 2008;Tian et al., 2013;Torres et al., 2014), the road network strongly relates with fire beginning because surrounding combustible material has greater likelihood of ignition ( Fig. 4e and Fig. 6e) due to anthropogenic causes. In this sense, this behavior is confirmed by obtained results once the most vulnerable areas to forest fires occur close to the road network. ...
Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.
... Because cities, villages and towns have higher population densities, fires in these places were not considered in this study, which seems to emphasize the role of human activities in extinguishing and suppressing wildfires (Archibald et al., 2012). We also noted that wildfires were more likely to occur relatively close to farmland, which reflects the fact that local wildfires are mainly triggered by human agricultural activities (Benali et al., 2017;Tian et al., 2013). Unlike the seasonal factors, the above anthropogenic variables maintained relative consistency across seasons regarding changes in response curves. ...
Wildfires directly affect global ecosystem stability and severely threaten human life. The mountainous areas of Southwest China experience frequent wildfires. Mapping the susceptibility patterns and analyzing the drivers of wildfires are crucial for effective wildfire management, especially considering that the inclusion of seasonal dimensions will produce more dynamic results. Using Yunnan Province of China as a case study area, a method was attempted to distinguish dependable wildfires by season, while possible wildfire drivers were obtained and refined within seasons. The patterns of wildfire susceptibility in different seasons were modeled based on the Maxent and random forest models. Then, the spatial relationships between wildfire and potential drivers were analyzed integrating with GeoDetector to evaluate the variable importance of drivers and the marginal effect of drivers. The results showed that the two models effectively depicted each season's wildfire susceptibility. The susceptible wildfire areas in spring and winter are located throughout Yunnan Province, with anthropogenic factors being the most significant drivers. During the summer and autumn, wildfire risk areas are relatively concentrated, showing a trend of dominant drought-driven and humid conditions. The differences in wildfire drivers across seasons reflect the lagged effect of climate factors on wildfires, leading to significant discrepancies in the marginal effects of how seasonal drivers affect wildfires. The findings improve our understanding of the effects of the interseasonal variability of environmental variables on wildfires and promote the development of specific seasonal wildfire management strategies.
... From 2001 to 2020, the cumulative frequency of active fires in Heilongjiang Province reached 30.91 × 10 4 , accounting for 67% of Northeast China, mainly distributed in the Sanjiang Plain, Songnen Plain, and Daxinganling area (Figure 5e). Heilongjiang has vast farmland and virgin forests (Figure 5f), which provide abundant combustible materials for (Figure 5f), which provide abundant combustible materials for open fires and are easily affected by human activities and an arid climate, especially in spring and autumn [33]. In the past 20 years, the annual average frequency of active fires in Heilongjiang Province was about 1.55 × 10 4 , which was consistent with the trend in Northeast China and generally showed a trend of first increasing and then decreasing, but the highest peak was 3.48 × 10 4 in Remote Sens. 2023, 15, 54 9 of 15 2017 and the second peak was 3.38 × 10 4 in 2015 (Figure 5a). ...
Currently, fires (e.g., biomass burning and/or straw burning) are still prevailing and serious globally. However, the issue of the characteristics, types, and drives of fire occurrence is always a challenge and varies distinctively worldwide. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) active fire products during 2001–2020, here, we analyzed the occurrence frequencies and spatiotemporal characteristics of active fires at the provincial and regional to national scales and at the monthly and annual scales in China. The accumulated occurrence frequencies of MODIS C6 active fires in China were up to 184.91 × 104 in the past two decades, and the average annual level was 9.25 × 104, especially in 2014 (15.20 × 104). The overall trend of active fires was rising and then falling, but with significant spatial and temporal differences in the last 20-years. Temporally, nearly 61% of active fires occurred in spring (36%) and autumn (25%), particularly in August (16%), April (14%), and October (13%). Spatially, about 90% of active fires occurred in the east of the Hu Huanyong Line, particularly in Northeast China (25%), South China (23%), and East China (20%). In China, the most active fires were concentrated in the Northeast Plain, the North China Plain, the southeast hills, and the Yunnan–Kweichow Plateau. In terms of temporal differences across regions, active fires in Northeast China, North China, and Northwest China were concentrated in spring and autumn, especially in March, April, and October; in East China, they were concentrated in summer, especially in June; and in South China and Southwest China, they were concentrated in winter and spring, especially from December to April of the following year. Our study provides a full analysis of spatio–temporal characteristics and changes of active fires in China, and it can also assist in supplying a beneficial reference for higher monitoring and controlling of fires such as straw burning.
... Figures A1-A4 show that the majority of forest fires occur in low-altitude regions due primarily to the concentration of intense human activity there, which will undoubtedly raise the likelihood of human-caused fires. Additionally, the weather will alter the vegetation cover and soil moisture as the altitude increases, making it less likely that fires will start [5,54,55]. After the formulation of regulations, human-caused fires decreased significantly, while the number of lightning fires increased and mainly occurred in high-altitude areas. ...
Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.
... Effective monitoring and forecasting of their development is the basis for competent resource planning and informed decision making [15,16], including through the use of remote sensing methods [14,17]. This task is especially relevant for Irkutsk Oblast, the territory with the highest forest cover (78%) among the subjects of the Russian Federation, where fire-hazardous coniferous plantations predominate (more than 90% of the entire area covered by forest) [13]. ...
Forest fire is one of the serious threats to the population and infrastructure of Irkutsk Oblast because its territory is heavily forested. This paper discusses the main stages of solving the problem of forecasting the risk of forest fires via a case-based approach, including data preprocessing, formation of a case model, and creation of a prototype of a case-based expert system. The main contributions of the paper are the following: a case model that provides a compact representation of information about weather conditions, vegetation type, and infrastructure of the region in relation to the possible risk of a wildfire; a case-base containing information about wildfires in Irkutsk Oblast for the period from 2017 to 2020; and a methodology for creating prototypes of case bases providing the transformation of decision tables of a special type. The approbation of the approach was carried out for separate forest districts, namely Bodaibinsk and Kazachinsk-Lena. The accuracy score was used for the evaluation of the results of forecasting the risk of wildfires. The average score value reached 0.51. The evaluation results revealed that application of the case-based approach can be considered as the initial stage for deeper investigations with the use of different methods (data mining, neural networks) for more accurate forecasting.
... Spatio-temporal information on the distribution and characteristics of forest fires can aid in risk reduction efforts and guide the formulation of integrated fire management policies [120][121][122]. Understanding the dynamics of the ecosystem after a fire further helps predict the regeneration capacity of the burned area, so that decision-makers know whether to invest in restoration practices and how to allocate their resources. ...
Incidences of forest fires have increased in recent decades largely as a result of climate change and human factors, resulting in great environmental and socioeconomic losses. Post-fire forest restoration is therefore indispensable for maintaining forest ecological integrity and for the sustainability of the affected forest landscapes. In this study, we conduct a systematic review of the available literature on forest restoration in the past two decades (2002–2022) and propose a comprehensive framework for consideration in forest restoration after the occurrence of forest fires. The Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) model was adopted for this study, where three academic literature databases (Scopus, CAB Direct, Web of Science), the Google Scholar search engine, and specialized websites were used for literature searches. A final list of 36 records from the initial 732 was considered for this study after the screening stage and subsequent inclusion/exclusion of articles as per the stipulated eligibility criteria. The study findings reveal a dearth of information in the field of post-fire forest restoration in an integrated, balanced, and comprehensive manner, as there was no single methodology or unified protocol that guides post-fire forest restoration. There was also a notable bias in the geographical distribution of the relevant studies in restoration as influenced by economic prosperity, political stability, and scientific and technical advancement. This study recommends a 6-criteria comprehensive framework with 29 indicators for post-fire forest restoration based on the reviewed studies. The criteria integrate environmental, economic, social, cultural and aesthetic, management, infrastructure, and education objectives in their design and implementation for better outcomes in achieving the restoration goals.
... Although wildfires based on natural factors such as lightning, extreme temperatures, and spontaneous igniting of fuels cannot be predicted, human causes such as discarded cigarette butts, electric arcs on power lines, campfires, deliberate sabotage and stubble burning may be anticipated. The negative, irreversible effects of wildfires require identification of their spatial relationships for the generation of wildfire risk maps and prevention procedures (Tian et al. 2013), and this has become critical in the last decade (Miller and Ager 2013). ...
Turkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate. An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to natural and human-related causes. The study area is sensitive to fires caused by lightning, stubble burning, discarded cigarette butts, electric arcing from power lines, deliberate fire setting, and traffic accidents. However, 52% of causes could not be identified due to intense wildfires occurring at the same time and insufficient equipment and personnel. Since wildfires destroy forest cover, ecosystems, biodiversity, and habitats, they should be spatially evaluated by separating them according to their causes, considering environmental, climatic, topographic and forest structure variables that trigger wildfires. In this study, wildfires caused by lightning, the burning of agriculture stubble, discarded cigarette butts and power lines were investigated in the provinces of Aydın, Muğla and Antalya, where 22% of Turkey’s wildfires occurred. The MaxEnt method was used to determine the spatial distribution of wildfires to identify risk zones for each cause. Wildfires were used as the species distribution and the probability of their occurrence estimated. Additionally, since the causes of many wildfires are unknown, determining the causes is important for fire prediction and prevention. The highest wildfire occurrence risks were 9.7% for stubble burning, 30.2% for lightning, 4.5% for power lines and 16.9% by discarded cigarette butts. In total, 1,266 of the 1,714 unknown wildfire causes were identified by the analysis of the cause-based risk zones and these were updated by including cause-assigned unknown wildfire locations for verification. As a result, the Area under the ROC Curve (AUC) values were increased for susceptibility maps.
... The presence of SWIR bands, which at least penetrate thin clouds, could explain the higher performance of NBR and NDMI (Lozano et al., 2007). NDMI is also more useful in detecting water stress areas that are susceptible to forest fire since it is more susceptible to moisture levels in crops and trees (Tian et al., 2013). ...
Forest fires would be a global disaster if they were not addressed seriously? From 2015 to 2021, the number of forest fires in India nearly tripled. According to FSI, the North Eastern Himalayas, one of UNESCO's 36 Biodiversity Hotspots, account for 36% of forest fires in India. This state is dominated by tribal population which practices shifting agriculture. It’s 76.01% of total forest cover is highly prone to Forest fires. Despite this, there hasn't been any time-series research on forest fires in this region. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burnt Ratio (NBR), Aerosol Free Vegetation Index (AFRI 1600), and Land Surface Temperature may all be linked to forest fires (LST). For Mizoram, India, random samples were taken every 16 days using Landsat 8 satellite data across different land cover types, including dense forest, sparse forest, farmland, and bare land etc. The study was conducted on a bimonthly basis from January 2016 to June 2021. The findings of this work show that an automated temporal analysis utilizing GEE may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats.
... The frequency, burn area, and intensity of forest fires are expected to increase with global warming (Fang et al., 2021). Several smoke pollutants released by combustion have serious impacts on the atmosphere and forest ecosystems in the vicinity of the burned area (Adame et al., 2018;Stefania et al., 2002;Tian et al., 2013). Since the 1960s and early 1970s, scientists led by Nobel laureate Paul Crutzen have explored the pollutant emissions from biomass combustion (Paul and Susan, 1980;Paul and Uta, 1983) and the relevant research continues to today (Zhang et al., 2000;Zhang et al., 2011), gaining more and more attention as climate change leads to more frequent and severe forest fires (Weise et al., 2022;Guo et al., 2020). ...
The moisture content of forest floor fuel influences fire initiation and spread, but little is known about its impact on the emissions of pollutants. This study aimed to estimate quantitatively the characteristic change of pollutants emitted by forest fires at various fuel moisture content. Thus, litter fuel (both branches and leaves) of two dominant coniferous species (Cunninghamia lanceolata and Pinus massoniana) and two dominant broad-leaved species (Eucalyptus robusta and Cinnamomum camphora) in Southeast China were experimentally burned in an indoor biomass combustion facility, and smoke and non-methane hydrocarbons (NMHCs) were analyzed by gas chromatography-mass spectrometry. The results showed that CO, CO2, NOx, and SO2 reached peak concentrations faster at low moisture content. However, emission factors of CO increased, whereas emission factors of CO2, NOx, and SO2 decreased as the moisture content of fuels increased. Emission factors of CO, CO2, NOx and SO2 released from combustion of conifer fuels significantly increased with fuel moisture content compared to broad-leaved fuels. Total NMHCs emission was positively affected by the fuel moisture content. As the moisture content of the fuels increased, multi-branched and long-chain alkane emissions increased, whereas olefin and aromatic hydrocarbon emissions decreased but emissions of olefins and aromatic hydrocarbons with more branched chains increased. The findings provide valuable insight about the impact of forest fire on atmospheric environment and fire prevention measures.
... From the last centuries, results of forest fires have been documented, such as destruction of environment, natural resources, property and agricultural land particularly in several parts of Australia, Africa, America and South Asia . Forest fire is a recurrent natural and manmade disaster all over the world, so the spatial analysis and accurate demarcation of forest fire susceptibility are an essential step to improve the strategies against forest fire (Tian et al. 2013). Prediction of forest fire zone provides us an optimal use of this natural resources and also offers us the large benefit of natural sustainability (Amiri and Shariff 2012;Zolekar and Bhagat 2015). ...
Periodic forest fires destruct to biodiversity, ecosystem productivity and multiple ecosystem services. Forest fires are currently turning a leading cause of forest degradation. The principal objective of this research is to predict forest fire vulnerable zones over Similipal biosphere reserve (SBR; Odisha) using different machine learning (ML) models, such as support vector machine (SVM), random forest (RF) and multivariate adaptive regression splines (MARS). Different resampling methods (CV and bootstrap) have also been applied for optimizing the result and better accuracy. Results show that 10-fold cross validation (CV) technique performed best on SVM model (AUC = 0.83) whereas bootstrap performed best on RF (AUC = 0.80) and MARS model (AUC= 0.84). The main advantage of MARS model is that it only uses input variable and significantly increases the performance of the model. The novelty of this research is application of various ML algorithms through resampling techniques to reduce the biasness and improves the reliability of the models.
... The vital information of wildfires can be extracted through satellite remote sensing data. It has the characteristics of real-time and large area and has been widely used in fire modeling at different scales around the world (Tian et al. 2013;Abatzoglou et al. 2018;Chen et al. 2020). Therefore, understanding the changes in land cover, environmental climate conditions and past historical fire data will help wildfire risk modeling and wildfire management and prevention. ...
Wildfire is a common disaster in the world, and it has a considerable impact on the safety of residents and ecological disturbance. Periodic wildfires are an urgent problem to be solved. This research uses big data from relevant departments to extract environmental indicators that affect wildfires, including satellite images, meteorological observations, and field surveys and establishes a risk model for the Spatio-temporal distribution of wildfires based on risk analysis. Previous studies using Differenced Normalized Burn Ratio (dNBR) to assess fire severity and distinguish wildfire ruins did not deal with the impact of atmospheric humidity on dNBR values. In this study, an adjustable fire threshold was developed to enable dNBR to improve the accuracy of identifying wildfire locations. Regarding the temporal distribution of wildfire risks, environmental vulnerability cannot specifically reflect the frequency of actual wildfires. If the hazard degree is introduced to calculate the wildfire risk, the coefficient of determination can be increased from 0.49 to 0.79. The verification of the village boundary zone depicts that the risk analysis can effectively show the temporal and spatial distribution of wildfire hotspots. On this basis, a village-level wildfire disaster prevention strategy can be formulated.
... The climate is diverse in this region, including a tropical monsoon climate, subtropical monsoon climate, and plateau alpine climate [23,24]. Due to the warm climate and abundant vegetation resources, this area experiences some of the highest frequencies of forest fires in China [25]. In addition, the specific geographical location, topography, climate, and distribution of forest resources lead to the uniqueness of forest and grassland fires in Southwest China, which are characterized by a low fire intensity but high frequency. ...
Smoke injection height (SIH) determines the distance and direction of smoke transport, thus impacting the atmospheric environment. In this study, we used Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations data coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Hybrid Single-Particle Lagrangian Integrated Trajectory model to derive the SIH values during the peak forest and grassland fire seasons from 2012 to 2017 in Southwest China. The results suggest that the SIH values ranged from 2500 m to 2890 m. An analysis of the dependence of SIH on fire characteristics revealed no obvious correlation between SIH and fire radiative power (FRP) because other factors in addition to FRP have an important impact on SIH. Moreover, MODIS FRP data has a drawback in representing the energy released by real fires, also leading to this result. The topographic variables of forest and grassland fires in Southwest China are very different. Complex topography affects SIH by affecting fire intensity and interactions with wind. A comparison of the SIHs with boundary layer height reveals that 75% of the SIHs are above the boundary layer. Compared with other areas, a higher percentage of free troposphere injection occurs in Southwest China, indicating that smoke can cause air pollution over large ranges. Our work provides a better understanding of the transport and vertical distribution of smoke in Southwest China.
... (©2021 IJAB. All rights reserved) procedures (Tian et al., 2013). Effective planning is essential to the success of fire management programs in order to achieve the goals of fuel hazard reduction and fire regime restoration and maintenance (Keifer et al., 1999). ...
The coastal region of Chlef (northwester of Algeria) suffers from both forest fires and the lack of scientific research in the subject, so in an effort to remedy that, the choice came to Dahra's municipality for a GIS and Remote sensing-based study for mapping forest fire risk. The model used combines six wildfire-causing factors for demarcating the forest fire risk zone map. Use a multitude of software and rely on multiple sources for data collection, the following variables were derived for the study area: vegetation moisture, slope, aspect, elevation, distance from roads, and the vicinity of settlements in the form of weighted layers. The result of the established modelling is the map of the fire risk index, where 50.5 % of the study area represents a high to very high risk.
... Regions of China that are used largely for agricultural purposes include plains situated in Northeast and North China, and the Sichuan Basin in Southwest China [16]. Slash-and-burn cultivation and crop residue burning are traditional agricultural practices [17], and a large amount of crop residue is burned in China [14] during the summer/autumn harvest season in order to eliminate agricultural straw [16]. Northeast and North China each have two main fire seasons: spring (February to May, with a peak in March) and autumn (late October to early November) in the former, and summer (late May to early June) and autumn (September to October) in the latter. ...
Burning crop residues is a common way to remove them during the final stages of crop ripening in China. To conduct research effectively, it is critical to reliably and quantitatively estimate the extent and location of a burned area. Here, we investigated three publicly available burned area products—MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area—and evaluated their relative performance at estimating total burned areas for cropland regions in China between 2015 and 2019. We compared these burned area products at a fine spatial and temporal scale using a grid system comprised of three-dimensional cells spanning both space and time. In general, the Copernicus Burnt Area product detected the largest annual average burned area (37,095.1 km2), followed by MCD64A1 (21,631.4 km2) and FireCCI 5.1 (12,547.99 km2). The Copernicus Burnt Area product showed a consistent pattern of monthly burned areas during the study period, whereas MCD64A1 and FireCCI 5.1 showed frequent changes in monthly burned area peaks. The greatest spatial differences between all three products occurred in Northeast and North China, where cultivated land is concentrated. The burned area detected by Copernicus in Xinjiang Province was larger than that detected by the other two products. In conclusion, we found that all three products underestimated the amount of crop residues present in a burned area. This limits the ability of end users to understand fire-related impacts and burned area characteristics, and hinders them in making an informed choice of which product is most appropriate for their application.
... Hence, there is a great temporal-spatial heterogeneity in forest fires. As the temporal-spatial information of forest fire occurrences plays a significant role in understanding fire dynamics and fire prevention and reduction efforts, many studies are devoted to the temporal-spatial distribution analysis of forest fires [18,39,40]. A consensus has been reached that forest fires vary in time and space due to the complex interactions between human intervention and biophysical factors. ...
As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.
... Despite the overall increase in ESV in the Sichuan-Yunnan ecological barrier, the area is free from ecological threats. It is prone to forest fires (Fornacca et al., 2017;Tian et al., 2013), which can have negative impacts on ecosystem services (Martinez-Harms et al., 2017;Vukomanovic and Steelman, 2019). Increasing frequency of forest fires in the study area has accelerated the transfer frequency between Forest and Grassland. ...
Key ecological function areas are responsible for protecting and restoring ecosystems and alleviating regional ecological deterioration. Revealing the inherent relationship between land use/cover (LULC) change and ecosystem service value (ESV) in such areas is of great significance for sustainable development. We used LULC and other data from 2000, 2010, and 2018 to analyze the spatiotemporal evolution of ESV in China’s Sichuan-Yunnan Ecological Barrier based on six LULC types: Farmland, Forest, Grassland, Water, Built-up land, and Other. With the goal of maximizing both ESV and economic benefits, we used coupled gray multi-objective optimization (GMOP) and patch-generating land-use simulation (PLUS) models to assess three scenarios (business-as-usual, BAU; ecological development priority, EDP; and ecological and economic balance, EEB) in terms of the spatial distribution and optimization of LULC structure in 2026. The study area was dominated by Forest and Grassland, with major LULC changes from 2000 to 2018 mainly deriving from transfers between Farmland, Forest, and Grassland along with Farmland conversion to Built-up land. ESV trended upward during the study period, mainly due to contributions from Forest and Water. Under EDP scenario in 2026, the expansion Built-up land was eased, which expansion area is the smallest among the 3 scenarios at 643.03 km², the Forest area increased by 673.80 km², the overall LULC structure was improved, and the total ESV increased by 2.502 billion yuan. Under EEB scenario, Forest area decreased by 405.95 km², but the economic benefits increased remarkably, showing the effect of supporting larger-scale economic growth with less land resource consumption. Under EDP scenario, ESV changes were most dramatic at local scales. The use of coupled GMOP-PLUS models for LULC optimization allowed improved assessment of social, economic, and environmental factors and provided a new way to address key technical problem in land-use planning in large-scale ecological function areas.
... Most forests burn as surface fires (Zong and Tian, 2019); crown fires mainly occur in coniferous and mixed forest areas , and ground fires only occur in small areas in the northeastern and northwestern forest regions (Zhang and Di, 2018). Previous studies have shown that the number of small fires in evergreen broad-leaved forests is higher in southern China, and large fires often occur in deciduous coniferous forests and mixed forests in the northeast (Tian et al., 2013(Tian et al., , 2015Wu et al., 2020). Fires in southern areas are mainly caused by human activities, and are easily discovered at an early stage due to the high population density (Tian et al., 2015). ...
Understanding fire regimes is central to fire management. In our study, we analyzed the fire weather and forest fire regime of China using fire data from satellite remote sensing and statistics from 2000 to 2020. The fire weather index system was calculated from observed weather data for 2007 to 2017. Using qualitative and quantitative methods, we created a zoning system for China based on the spatial distribution characteristics of fire regimes and vegetation. The fire seasons varied between regions because of differences in vegetation, climate and ignition sources. The fire seasons in the north were spring and autumn. In the south and southwest forest regions, the fire seasons were winter and spring. Most forest fires occurred in southern China, but the average burned area per fire was lower compared with fires in the northeast. The zoning system includes 13 forest fire regime zones with specific fire characteristics according to quantitative variables. These zones are further divided into 17 fire regime units based on qualitative variables. Each fire regime unit has unique characteristics for regime, climate and vegetation type. Human activity was the main cause of fires, especially in south China, where the population density is high. Fire management should be tailored to each fire regime type based on fire characteristics and management targets.
... Time, place of occurrence, and quantitative distribution of fires throughout the year are variables that assist prevention planning, since they allow us to identify periods with higher risk of forest fires (RODRÍGUEZ et al., 2013). Assessment of which vegetation types are most susceptible to fire is also relevant, as ground for adopting specific management actions that may reduce the occurrence and effects of fires (SAMARA et al. 2018;TIAN et al. 2013). Particularly in the case of fires in urban areas and their interfaces, these data may lead to the FLORESTA formulation of guidelines toward more appropriate decision-making and planning. ...
Forest fires represent significant environmental, economic, and social damage in many countries. Historical knowledge of their characteristics aids in making preventive decisions, as well as fighting forest fires. However, the general data of fires in Paraná are outdated. The objective of this study was to evaluate the forest fires in the state of Paraná in 2018 and 2019, surveying the following information: municipality and region affected; month and day of occurrence; and vegetation type. To this end, data obtained from the Paraná Fire Department through the SysBMNew-CCB platform were analyzed. The fire density by region was verified and compared through cluster analysis. Compared to the previous year, 2019 showed a 42.25% increase in the number of fires. In both years, most forest fires occurred in the North-central region, followed by the Metropolitan Region of Curitiba. The municipality of Curitiba recorded the highest number of fires in both years. According to the Fire Department classification, the vegetation type most affected by the fires was vacant lots. From the data obtained, we verified the need for environmental education measures aimed at the prevention of fires in vacant lots. Further research is recommended so that a profile of forest fires can be traced in the state and thus base prevention and control measures.
... Human actions are also a contributing factor to forest fires. It remains the major cause of forest fires, especially during the dry weather when there is high level of water stress (Tian et al., 2013). Jaiswal et al. (2002) documented that forested areas with close proximities to settlements and road networks are more vulnerable to engulf fire. ...
Forest fires are a serious environmental hazard within the forest ecosystem, which can be studied with Remote Sensing and GIS. The aim of the study is to identify and model forest fires severity and risk zones within the Cross – Niger transition forest. To achieve this aim, remotely sensed data, such as Landsat – 8 OLI (2020) and ASTER DEM were used to produce land cover maps and topography parameters such as aspect, elevation and slope. The topographic maps and the Google Earth imagery were used to extract human settlements and road networks. The final forest fire risk zone (FFRZ) map was prepared by integrating the different parameters such as Land cover, aspect, elevation, slope, proximities to roads and settlements in the ArcGIS environment. The FFRZ was categorized into three categories as low, moderate and high risk zones, based on their fire susceptibility. The category of low, moderate and high FFRZ were represented as 2731.7 km² (12.5%), 17997.69 km² (82.59 %) and 1061.63 km² (4.87%) respectively. The study shows that Remote Sensing and GIS are excellent tools for modelling forest fire risk zones, hence proving that fires are anthropogenic in origin.
... In addition, forest fire might also change the balance of energy, ecosystem homeostasis and ecological integrity of forest ecosystem. The spatial patterns of forest fires at a landscape scale are determined by a combination of various biophysical conditions, such as topography, forest edge, and anthropogenic factors (Tian et al. 2013;Chen et al. 2014;Numata et al. 2017;Santika et al. 2020). ...
Ekasari I, Sadono R, Marsono D, Witono JR. 2021. Species composition and richness of viable seed bank after fire events in Mount Ciremai National Park and Kuningan Botanic Gardens, West Java, Indonesia. Biodiversitas 22: 3437-3447. Forest fire is an environmental disaster that can decline ecosystem function and restoration efforts must be considered to restore forest ecosystems after fire events. Natural regeneration using existing soil seed banks is a promising approach in restoration due to its advantage in terms of minimizing cost. This study aimed to examine the species composition and richness of germinable seed banks in several post-fire sites in Mount Ciremai National Park (MNCP) and Kuningan Botanic Gardens (KGB), West Java, Indonesia. One hundred fifty-eight soil samples were collected from the study sites representing fire events (i.e., four post-fire sites and one non-fire site), and soil depths (i.e., upper, middle, and lower). The collection of soil samples and identification of seedlings emergence were conducted from September 2019 to February 2020. Data were analyzed using ANOVA and correspondence analysis using SPSS Version 22. In total, 4626 emergence seedlings were recorded, belonging to 158 species and 58 families in which 41 families in the upper soil layer, 35 families in the middle soil layer, and 33 families in the lower soil layer. The results showed that Poaceae, Asteraceae, and Euphorbiaceae as the most dominant families. The upper soil layer of post-fire site 2018 had the highest species richness (R=11.98), while the lower soil layer of post-fire site 2012 had the lowest species richness (R=2.64). Our findings suggest that when carrying out restoration activities in post-fire areas, it is preferable to use native species that do not compete with species persisted in soil seed banks.
... In China, large fires occur in large forests in the northeastern and southwestern regions of the country (Tian et al., 2013), but northern regions have received greater attention in literature due to higher frequency of fire events. On the other hand, the literature also suggests that southern China has high fire frequency of smaller fires compared to the north. ...
Wildfire is one of the most common natural hazards in the world. Fire risk estimation for the purposes of risk reduction is an important aspect in disaster studies around the world. The aim of this research was to develop a machine learning workflow process for South East China to monitor fire risks over a large region by learning from a grid file database containing a time series of several of the important environmental parameters largely extracted from remote sensing data products, and highlight areas as fire risk or non-fire risk over a couple of weeks in the future. The study employed fire threshold and the transductive PU learning method to identify reliable non-fire/negative training samples from the grid file database using fire/positive training samples, labeled using the MODIS MCD14ML fire location product. Different models were trained for the three natural vegetation land covers, namely evergreen broadleaf forest, mixed forest, and woody savannas in the study area. On the test dataset, the three models exhibited high sensitivity (>80%) by identifying the majority of fires in the test dataset for all land covers. The use of the reliable negatives identified though the fire threshold and PU learning process resulted in low precision and accuracy. During the model verification process, the model for the mixed forest land cover performed the best with 70% of verification fires falling within the classified fire zone. It was found that the better representation of mixed forest in the training samples made this model perform more reliably as compared to others. Improving the individual models constructed for different land covers and combining them can provide fire classification for a larger region. There is room to improve the spatial precision of fire cell classification. Introducing finer scale features that have higher correlation with fire activity and exhibit high spatial variability seems a viable way forward.
... Fujian is a province in China with the highest forest coverage rate in China, and had experienced 6164 forest fires from 2000 to 2016, with an average annual burnt area of 7328.97 hm 2 . Studies have shown that more than 95% of forest fires in the province are caused by human activities, and a large number of branch and leaf litter are accumulated on the forest surface, which induces the occurrence of forest fires (Guo et al., 2004;Tian et al., 2013), The forest floor fuel is mainly composed of leaves, branches and other components (a very little amount of bark), among which the proportion of fallen leaves is relatively large, accounting for 54.5%, followed by the proportion of fallen branches, accounting for 41.6%, and other components, accounting for 3.9%. (Ju et al., 2019). ...
The moisture content of forest floor fuels changes continuously with the influence of environmental factors; thus it has an important impact on the concentration and chemical composition of particulate matter emitted during forest fire. However, most previous studies quantify emissions of particulate matter and constituents using dry samples. In this study, we use a self-designed semi closed combustion simulator to quantify emission of total carbon (TC), organic carbon (OC), elemental carbon (EC) and water-soluble ions in fine particulate matter (PM2.5) using fuels of four tree species that differ in moisture content (0, 10, 20 and 30%). The results showed that the emissions of TC, OC and EC and total water-soluble inorganic ions increased significantly (<0.05) with increasing moisture content of fuels, and fuels of coniferous species emitted significantly more pollutants than fuels of broadleaved species. Similarly combustion of leaf samples emitted more carbonaceous components and water-soluble ions than combustion of branches. K⁺, NH4⁺ and Cl⁻ were the main components of water-soluble ionic species, and emissions of K⁺, Ca²⁺, Na⁺, Mg²⁺, NH4⁺, Cl⁻, Br⁻, NO3⁻, NO2⁻, SO4²⁻ increased with increasing moisture content of fuels. Fuel moisture content had a great impact on the inorganic salt composition in the particulate matter emitted during combustion. The findings have an important implication on the use of prescribed early fire as management tools as the moisture content of the fuels early during the dry season is still high.
... From 1950 to 1990, there were 6.26 × 10 5 fires and 8.2 × 10 5 hectares (ha) of burned area (BA) in China [13]. According to research statistics, most of the hotspots in China from 2008 to 2012 were caused by prescribed, agricultural burning and forest fires [15]. ...
Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence.
... Studies by Tian et al. (2013) and Torres et al. (2017b) corroborate the choice for the relevance function (Fuzzy Small) used to determine the influence of the proximity of roads, as they showed that the occurrence of fire outbreaks decreases as the area moves away from the road network. Thus, in Figure 4 it is possible to see that the road distribution in the studied perimeter had a direct influence on the 79.77% in the risk classes for the occurrence of "high" and "very high" fires. ...
The maintenance of biodiversity is a global concern in economic, social and environmental terms. Thus, conservation units for the protection of natural environments were created. Despite the importance of these areas, forest fires have caused immeasurable and constant damage to Conservation Units. In view of this, the objective of this study was to determine the risk areas of forest fire occurrence through Fuzzy logic modeling in the Córrego Grande Biological Reserve, located in the Mata Atlântica Brazilian biome. In order to prospect for areas at risk of forest fires, the following variables were inserted in the model: land use, road network, slope and relief orientation. Finally, the model was validated by comparing the location of fire occurrences between the years 2008 and 2018, and a layout of the risk classes in the study area. In doing so, it was found that 65.87% of the area is between the 'moderate' and 'very high' range of fire risk classes, and that 70.22% of the fires which occurred in the studied period also occurred in that class range. The study concluded that the most influential variable on the risk level of fire occurrence is the forest road network. In this way, the proposed methodology can be applied to any other areas and types of land cover.
... Xiaorui Tian et. all [8] in their work combined satellite data on the occurrence of hotspots and statistical data for analyzing the distribution characteristics of wildfires for 2008-2012. Ana Teodoro and Ana Amaral in their work [9] analyzed data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). ...
Using statistical data, the dynamics of forest fires in the Volga federal district of the Russian Federation from 2000 to 2020 years is analyzed. The number and area of forest fires were considered as the initial data. At the same time, the total area of forest fire and of burned forests were taken into account separately. It was found that during the period under review, the minimum number of fires was recorded in 2000, and the maximum in 2018. Out of 14 subjects included in the Volga federal district, forest fires in the Republic of Bashkortostan were studied in detail. The dependence of the number of fires by season is established. Using correlation analysis of the statistical data for 2000-2020, the fact of strong dependence between the number of fires in the Volga federal district and forest area covered by fire was established.
... The ecosystem changes of surface fires have been described in great numbers by researchers from around the world [37][38][39], while small-area underground fires have been presented to very little time. Heretofore, the issue of vegetation on burning coal dumps has been discussed only in part, including analysis of the development of vegetation in these areas (e.g. ...
The paper presents the impact of thermal processes on the dynamics of changes in vegetation and soil properties in the area of coal-waste dumps where self-heating and self-ignition processes occur. Vegetation analysis involved the determination of species composition, life forms, and synecological affiliation. The mosaic diversity of the granulometric composition of the stored material and dynamically changing soil temperature had an impact on the character of vegetation. A specific type of flora, with various ecological requirements, was formed. Hemicryptophytes and apophytes predominated, especially in thermally active zones. The distribution of the range of vegetation due to changes in soil thermics was examined during three periods within a selected transect, in which three types of surfaces with varying soil thermics and smoldering fire directions were distinguished. Temperatures ranged from 9.9 to 139ºC at a depth of 20 cm and, simultaneously, from 3.1 to 69.0ºC on the surface. Total organic carbon content in all samples ranged from 1.7 to 7.6 and, simultaneously, from 3.1 to 4.5% in the active fire spots. The concentration of total nitrogen ranged from 0.023 to 0.29%. Soil reaction (pH) fluctuated between 5.8 and 8.0 (in H2O).
The variability of the range of vegetation in time and space indicated the directions of movement of fire spots. The analysis showed that underground temperature has a significant impact on the distribution and species composition of plants growing on coal-waste dumps.
Every year, an average of 250 forest fires occur in Turkey and 10,000 hectares of forest area are destroyed by natural and human-caused forest fires. Moreover, 90% of the world's pine honey production is produced in red pine (Pinus brutia) forests infested with Marchalina Hellenica. However, the limited production sites for pine honey are destroyed by forest fires and most of the sites overlap with the regions where susceptibility to forest fires is highest. In particular, in 2021 and 2022, pine honey production in Muğla province decreased by half due to the large forest fires.
In this study, susceptibility to forest fires caused by lightning, cigarette butts, stubble burning and power lines was modeled separately for all pine honey production sites via MaxEnt. Each risk map overlapped with the Marchalina Hellenica distribution map to determine which fire causes put each region at risk. When the results were examined, 1357.6 km² (56.6%) of the 2396 km² pine honey production site was found to be at risk from lightning-caused forest fires. For human-caused forest fires, 184.7 km² (7.7%) were at risk from power lines and 136 km² (5.7%) from stubble fires. 116.8 km² of pine honey production areas are threatened by forest fires caused by cigarette butts, which is the least threatening cause in the study area. The findings obtained in this study provide important information on the measures that can be taken against forest fires and on the planning of early intervention procedures to protect pine honey production areas.
Geçmişten günümüze toplumların çeşitli afetlere maruz kaldıkları, değişen koşullarla birlikte afet çeşitliliğinin ve yaşanma sıklığının değiştiği söylenebilir. Afetleri genel olarak doğal ve teknolojik kökenli afetler olarak sınıflandırmak mümkündür. Çalışmanın konusunu oluşturan orman yangınlarının da afet sınıflaması içerisinde yer aldığı görülmektedir. Bu bağlamda yapılan çalışmada; Türkiye’de, 2005-2020 yılları arasında meydana gelen orman yangınlarının; zamansal, bölgesel, nedensel ve yanan alan miktarı açısından değerlendirilmesi amaçlanmıştır. Ayrıca 2005-2020 yılları arasında internet kullanıcılarının “Google Trends” özelinde orman yangınları konusundaki eğilimlerinin değerlendirilmesi de hedeflenmiştir. Çalışmada; orman yangınlarının çıkış nedenleri arasında ihmal-kaza olaylarının ilk sırada yer aldığı görülmüştür. 2005, 2009, 2011, 2013, 2015 ve 2019 yıllarında çıkan yangın sayıları açısından il statüleri arasında istatistiksel olarak anlamlı bir farklılık bulunmaktadır (p
We analyzed the dynamics of pollutant emissions from wildfires in mainland China from 2001 to 2019 using MODIS fire products combined with the measurements of emission factors of different vegetation types. The biomass distribution in Mainland China has heterogeneous temporal and spatial pattern, with inter-year variations and a decreasing trend from east to west. Overall, from 2001 to 2019, biomass combustion in Mainland China reached 479.59 Tg (25.24 Tg·a-1), in which northeast, north, east, south, central, northwest, and southwest regions accounted for 20.95%, 31.14%, 8.89%, 9.06%, 3.98%, 0.33% and 25.64% of total biomass combustion, respectively. The emissions of CO, CO2, CxHy, NOx, PM2.5, TC, OC and EC were 47.30, 288.05, 12.90, 0.40, 1.43, 0.83, 0.70, and 0.12 Tg (1 Tg = 1012g), respectively. PM2.5, TC and OC emissions increased in the southwest, while all pollutant emissions declined significantly in the southern region. For particulate matter from wildfires, both the ratio of its emissions to total dust and the ratio of its concentration to atmospheric PM2.5 showed an increasing trend, implying that the relative environmental impacts of particulate emissions from wildfires may be rising. In addition, our results show that the current Chinese wildfire management has successfully reduced on average more than 80% of pollutant emissions from wildfire from 2001 to 2019 compared to the natural wildfire regime (no strict wildfire management). This research on the temporal-spatial changes of pollutant emissions from wildfires in Mainland China provides support for further exploration of wildfire impacts on regional environments, and indicates the effectiveness of Chinese current wildfire policy on the pollutant emission mitigation.
Warm and dry climate conditions favor the occurrence of forest fires. Forest burning leads to the discharge of large amounts of particles and trace gases that play an important role in air quality degradation and have impact on human health. To date, most studies on China's forest fire emissions have concentrated in certain regions, also with a relatively coarse temporal resolution. In this study, we used the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO), as well as high-resolution land cover data to compile a forest fire emission inventory for China in 2020. The variations of forest combustion emissions were then analyzed at the provincial and seasonal level. The results show that forest fires were concentrated in southern China and northeastern China, which are in agreement with MODIS observations. Total CO2, CO, CH4, NOx, SO2, PM2.5, OC, and BC emissions were estimated to be 3.06 × 10⁴, 1.87 × 10³, 96.92, 34.95, 13.84, 208, 116.03, and 9.95 Gg, respectively. Provinces with higher emissions were found in Yunnan, Guangdong, Hunan, and Sichuan. Peak emission from forest fire occurred in spring and winter, mainly from January to April, during which contributed 70% of the total forest fire contaminants emissions. The algorithm used in this study can be easily coupled in the meteorological model and air quality model to estimate the occurrence of fire and calculate pollutant emission online. This study updated emissions information that may support future research and policy development on greenhouse gas reduction and air pollution control.
Biomass burning (i.e., forest fires or open-field burning) is strongly correlated with regional air pollution. However, the correlation varied in different regions because of the complex interaction with other driving factors, leaving a substantial gap in understanding the mechanism of air pollution formation. This study aimed to clarify the spatial heterogeneity and corresponding factors of the biomass burning-air pollution correlation in China in recent decades (2001–2019). Multisource datasets, including satellite observations, in situ measurements, and annual statistics, were adopted for the evaluation through mathematical statistics and spatial-temporal analysis. The results showed that biomass burning mainly occurred in Northeast, Central and South China, which decreased slightly by −153.08 km²·yr⁻¹ during the study period. Under this background, air quality improved significantly, particularly after 2013. The strongest correlations occurred in Northeast China at the spatial scale or in spring at the temporal scale. Notably, the correlation was affected by several factors and their complex interactions. Specifically, the dominant factors of the correlations were gross domestic product (GDP) in Northeast China, atmospheric humidity in Central China, and population in South China. In addition, these factors exerted significant interactions, which were characterized by the two-factor enhancement of temperature-GDP in Northeast China, the nonlinear enhancement of temperature-DEM in Central China, and the nonlinear enhancement of temperature-population in South China. The results of this study were helpful for capturing the physical correlation between biomass burning and air pollution, which would further support relevant air pollution management and control.
Wildfire is a common disaster in the world, and it has a considerable impact on the safety of residents and ecological disturbance. Periodic wildfires are an urgent problem to be solved. This research uses big data from relevant departments to extract environmental indicators that affect wildfires, including satellite images, meteorological observations, and field surveys and establishes a risk model for the Spatio-temporal distribution of wildfires based on risk analysis. Previous studies using Differenced Normalized Burn Ratio (dNBR) to assess fire severity and distinguish wildfire ruins did not deal with the impact of atmospheric humidity on dNBR values. In this study, an adjustable fire threshold was developed to enable dNBR to improve the accuracy of identifying wildfire locations. Regarding the temporal distribution of wildfire risks, environmental vulnerability cannot specifically reflect the frequency of actual wildfires. If the hazard degree is introduced to calculate the wildfire risk, the coefficient of determination can be increased from 0.49 to 0.79. The verification of the village boundary zone depicts that the risk analysis can effectively show the temporal and spatial distribution of wildfire hotspots. On this basis, a village-level wildfire disaster prevention strategy can be formulated.
Wildfire predictions provide useful information for fire management planning and implementation. Temperature and precipitation have been used as the primary meteorological predictors for wildfires in China. This study is to improve the prediction skills of long-range (monthly, seasonal, and annual) wildfires in China by identifying other important meteorological predictors. Provincial data during 1999–2020 were used to calculate the correlations between fire properties (fire count and burned area) and meteorological variables of maximum temperature, precipitation, relative humidity, wind speed, and vapor pressure deficit (VPD) and drought indices of Keetch-Bryam Drought Index (KBDI), Palmer Drought Severity Index (PDSI), and Standardized Precipitation Index (SPI). The fitting rates of the linear regression fire prediction models were compared among these meteorological variables and drought indices. The results indicate that the number of provinces with significant correlations and / or high fitting rates is the largest with VPD for monthly fires, KBDI for seasonal fires, and KBDI, PDSI, and SPI for annual fires. The number is larger in Northeast, Central, and South China than in other China regions. The number is comparable between spring and other seasons for KBDI but often smaller in spring for meteorological variables. The number is generally smaller for burned areas than fire count. It is concluded that the skills of long-range fire predictions are expected to be improved in many provinces of China by using VPD and KBDI as well as some other drought indices.
Forest fires can cause serious harm. Scientifically predicting forest fires is an important basis for preventing them. Currently, there is little research on the prediction of long time-series forest fires in China. Choosing a suitable forest fire prediction model and predicting the probability of Chinese forest fire occurrence are of great importance to China’s forest fire prevention and control work. Based on fire hotspot, meteorological, terrain, vegetation, infrastructure, and socioeconomic data collected from 2003 to 2016, we used a random forest model as a feature-selection method to identify 13 major drivers of forest fires in China. The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and created a spatial distribution map of the areas with high incidences of forest fires. The results showed that the prediction accuracy of the four forest fire prediction models was between 75.8% and 89.2%, and the area under the curve value was between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and area under the curve value (0.96); thus, it was used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments can improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helps in understanding the main drivers of forest fires in China, provides a reference for the selection of high-precision forest fire prediction models, and provides a scientific basis for China’s forest fire prevention and control work.
Forest fires are a recurrent management problem in the Western Ghats of India. Although most fires occur during the dry season, information on the spatial distribution of fires is needed to improve fire prevention. We used the MODIS Hotspots database and Maxent algorithm to provide a quantitative understanding of the environmental controls regulating the spatial distribution of forest fires over the period 2003–07 in the entire Western Ghats and in two nested subregions with contrasting characteristics. We used hierarchical partitioning to assess the independent contributions of climate, topography and vegetation to the goodness-of-fit of models and to build the most parsimonious fire susceptibility model in each study area. Results show that although areas predicted as highly prone to forest fires were mainly localised on the eastern slopes of the Ghats, spatial predictions and model accuracies differed significantly between study areas. We suggest accordingly a two-step approach to identify: first, large fire-prone areas by paying special attention to the climatic conditions of the monsoon season before the fire season, which determine the fuels moisture content during the fire season; second, the most vulnerable sites within the fire-prone areas using local models mainly based on the type of vegetation.
Large wildland fires are major disturbances that strongly influence the carbon cycling and vegetation dynamics of Canadian boreal ecosystems. Although large wildland fires have recently received much scrutiny in scientific study, it is still a challenge for researchers to predict large fire frequency and burned area. Here, we use monthly climate and elevation data to quantify the frequency of large fires using a Poisson model, and we calculate the probability of burned area exceeding a certain size using a compound Poisson process. We find that the Poisson model simulates large fire occurrence well during the fire season (May through August) using monthly climate, and the threshold probability calculated by the compound Poisson model agrees well with historical records. Threshold probabilities are significantly different among different Canadian ecozones, with the Boreal Shield ecozone always showing the highest probability. The fire prediction model described in this study and the derived information will facilitate future quantification of fire risks and help improve fire management in the region.
Forest fire regimes are sensitive to alterations of climate, fuel load, and ignition sources. We investigated the impact of human activities and climate on fire occur-rence in a dry continental valley of the Swiss Alps (Valais) by relating fire occurrence to population and road density, biomass removal by livestock grazing and wood harvest, temperature and precipitation in two distinct periods (1904–1955 and 1956–2006) using generalized additive modeling. This study provides evidence for the role played by humans and temperature in shaping fire occurrence. The existence of ignition sources promotes fire occurrence to a certain extent only; for example, high road density tends to be related to fewer fires. Changes in forest uses within the study region seem to be particularly important. Fire occurrence appears to have been negatively associated with livestock pasturing in the forest and wood harvesting, in particular during the period 1904–1955. This study illus-trates consistently how fire occurrence has been influenced by land use and socioeconomic conditions. It also suggests that there is no straightforward linear relationship between human factors and fire occurrence.
The spatial pattern of forest fire locations is important in the study of the dynamics of fire disturbance. In this article we used a spatial point process modeling approach to quantitatively study the effects of land cover, topography, roads, municipalities, ownership, and population density on fire occurrence reported between 1970 and 2002 in the Missouri Ozark Highland forests, where more than 90% of fires are human-caused. We used the AIC (Akaike information criterion) method to select an appropriate inhomogeneous Poisson process model to best fit to the data. The fitted model was diagnosed using residual analysis as well. Our results showed that fire locations were spatially clustered, and high fire occurrence probability was found in areas that (1) were public land, (2) within 6 km to 17 km of municipalities, and (3) <500 m from roads where forests are accessible to humans. In addition, fire occurrence probability was higher in pine-oak forests on moderate (<25 degree) slopes and xeric aspects and at higher (>270 m) elevations, reflecting the effects of natural factors on fire occurrence. The results serve as a provisional hypothesis for expanding fire risk estimation to surrounding areas. The spatial scale of analysis (approximately 1 ha) provides new information to guide planning and risk reduction efforts.
Understanding the spatial patterns of fire occurrence and its response to climate change is vital to fire risk mitigation and vegetation management. Focusing on boreal forests in Northeast China, we used spatial point pattern analysis to model fire occurrence reported from 1965 to 2009. Our objectives were to quantitate the relative importance of biotic, abiotic, and human influences on patterns of fire occurrence and to map the spatial distribution of fire occurrence density (number of fires occurring over a given area and time period) under current and future climate conditions. Our results showed human-caused fires were strongly related to human activities (e.g. landscape accessibility), including proximity to settlements and roads. In contrast, fuel moisture and vegetation type were the most important controlling factors on the spatial pattern of lightning fires. Both current and future projected spatial distributions of the overall (human- + lightning-caused) fire occurrence density were strongly clustered along linear components of human infrastructure. Our results demonstrated that the predicted change in overall fire occurrence density is positively related to the degree of temperature and precipitation change, although the spatial pattern of change is expected to vary spatially according to proximity to human ignition sources, and in a manner inconsistent with predicted climate change. Compared to the current overall fire occurrence density (median value: 0.36 fires per 1000 km2 per year), the overall fire occurrence density is projected to increase by 30% under the CGCM3 B1 scenario and by 230% under HadCM3 A2 scenario in 2081–2100, respectively. Our results suggest that climate change effects may not outweigh the effects of human influence on overall fire occurrence over the next century in this cultural landscape. Accurate forecasts of future fire-climate relationships should account for anthropogenic influences on fire ignition density, such as roads and proximity to settlements.
Climate change that results from increasing levels of greenhouse gases in the atmosphere has the potential to increase temperature and alter rainfall patterns across the boreal forest region of Canada. Daily output from the Canadian Climate Centre coupled general circulation model (GCM) and the Hadley Centre's HadCM3 GCM provided simulated historic climate data and future climate scenarios for the forested area of the province of Ontario, Canada. These models project that in climates of increased greenhouse gases and aerosols, surface air temperatures will increase while seasonal precipitation amounts will remain relatively constant or increase slightly during the forest fire season. These projected changes in weather conditions are used to predict changes in the moisture content of forest fuel, which influences the incidence of people-caused forest fires. Poisson regression analysis methods are used to develop predictive models for the daily number of fires occurring in each of the ecoregions across the forest fire management region of Ontario. This people-caused fire prediction model, combined with GCM data, predicts the total number of people-caused fires in Ontario could increase by approximately 18% by 2020–2040 and50% by the end of the 21st century.
In this paper, we analyse spatial patterns of fire occurrence in Catalonia (NE Spain) during 1975–98. Fire scar maps, discriminated by means of 30–60 m resolution remote sensing imagery, have been used as a source of fire occurrence. We employ several visual or analytical approaches to interpret fire occurrence in this region, such as those of Minnich and Chou (1997), Ricotta et al. (2001) or Krummel et al. (1987). Crucial spatial patterns such as fire size distribution, fire frequency distribution, spots and residual vegetation islands are documented. In addition, several geographical layers were overlaid with burned area maps in order to determine interactions between fire occurrence and environmental parameters such as altitude, slope, solar radiation, and burned land cover. Assuming that fire occurrence is well determined by such a posteriori empirical factors we detect areas most prone to fire in this region and aim to enhance the local forest management and conservation plans.
In this study, we investigated the impact of future climate change on fire activity in 12 districts across Portugal. Using
historical relationships and the HIRHAM (High Resolution Hamburg Model) 12 and 25km climate simulations, we assessed the
fire weather and subsequent fire activity under a 2 × CO2 scenario. We found that the fire activity prediction was not affected by the spatial resolution of the climate model used
(12 vs. 25km). Future area burned is predicted to increase 478% for Portugal as a whole, which equates to an increase from
1.4% to 7.8% of the available burnable area burning annually. Fire occurrence will also see a dramatic increase (279%) for
all of Portugal. There is significant spatial variation within these results; the north and central districts of the country
generally will see larger increases in fire activity.
Topography, vegetation, and climate act together to determine the spatial patterns of fires at landscape scales. Knowledge of landscape-fire-climate relations at these broad scales (1,000s ha to 100,000s ha) is limited and is largely based on inferences and extrapolations from fire histories reconstructed from finer scales. In this study, we used long time series of fire perimeter data (fire atlases) and data for topography, vegetation, and climate to evaluate relationships between large 20 th century fires and landscape characteristics in two contrasting areas: the 486,673-ha Gila/Aldo Leopold Wilderness Complex (GALWC) in New Mexico, USA, and the 785,090-ha Sel-way-Bitterroot Wilderness Complex (SBWC) in Idaho and Montana, USA. There were important similarities and differences in gradients of topography, vegetation, and climate for areas with different fire frequencies, both within and between study areas. These unique and general relationships, when compared between study areas, highlight important characteristics of fire regimes in the Northern and Southern Rocky Mountains of the Western United States. Results suggest that amount and horizontal continuity of herbaceous fuels limit the frequency and spread of surface fires in the GALWC, while the moisture status of large fuels and crown fuels limits the fre-quency of moderate-to-high severity fires in the SBWC. These empirically described spatial and temporal rela-tionships between fire, landscape attributes, and climate increase understanding of interactions among broad-scale ecosystem processes. Results also provide a historical baseline for fire management planning over broad spatial and temporal scales in each wilderness complex.
Forest-fire waiting times, defined as the time between successive fires above a certain size in a given region, are obtained for Italy. The probability densities of the waiting times are found to verify a scaling law, despite that fact that the distribution of fire sizes is not a power law. The meaning of such behavior in terms of the possible self-similarity of the process in a nonstationary system is discussed. We find that the scaling law arises as a consequence of the stationarity of fire sizes and the existence of a nontrivial "instantaneous" scaling law, sustained by the correlations of the process; as a consequence, the nonstationary Poisson process model does not account for all the complexity of the structure of fire occurrence.
Using actual observation data in forty years, the paper established the spatiotemporal relation of El nino and precipitation anomaly in Heilongjiang Province. The result shows as follows:in El nino year and the last year, the climatic characteristics in Heilongjiang Province is drought with few of rainfall from April through June in spring and flood with abundant rainfall in July and August when summer is changing into antumn;and in the next year,the main trend of the climate is drought with few of rainfall in April and June in spring and in August and September in Autumn.
The data of forest fires from 1980 to 1999 in Heilongjiang Province, NIN03.4 index and Southern Oscillation Index were analyzed by spectral analysis and correlation method, and the spectral periodicities were got individually. The periodicities of fire number and fire area were 10.00 years and 6.67 years respectively. The periodicities of both El Niño and La Niña were 5.00 years in these 20 years. The correlations between fire number or area and La Niña or El Niño were very significant. The fire area and fire number were negatively correlated with El Niño, and the correlation coefficients were -0.523 1 and -0.659 4 respectively. The fire number and fire area were positively correlated with La Niña, and the correlation coefficients were 0.525 4 and 0.536 3 respectively. The influence of La Niña was less than that of El Niño. The fire area and fire number increased abnormally after warm ENSO events year.
Here we report how fire recurrence increases the distribution of a scarce forest type in NE Spain that is
dominated by the resprouter tree species Arbutus unedo. We used a combination of GIS and field surveys
to determine the effect of fire and pre-fire vegetation on the appearance of A. unedo forests. In the field,
we also analyzed the factors that promote fire and lead to the appearance of A. unedo forests. Our results
reveal an increased occurrence of A. unedo forests in NE Spain in recent years; this phenomenon was
strongly related to fire recurrence and the vegetation type present prior to fire. Most Pinus halepensis
forests that burned more than once gave rise to A. unedo forests. Our results indicate that these
conversions were related to a reduction in pine density coupled with increases in the density and size of
A. unedo trees due to recurrent fires. Given that fires are increasing in number and magnitude in the
Mediterranean, we predict a major change in landscape structure and composition at the regional scale.
Lightning strike, fire weather, and fire occurrence data were used to model (i) the probability that a lightning strike causes a sustainable ignition on the forest floor and (ii) the probability of an ignition being detected and reported to the fire management agency for each ecoregion in the province of Ontario. An index that tracks duff moisture content in very sheltered areas of a forest stand (near the tree boles) was the most significant predictor in each ignition model. The presence of positive cloud-to-ground lightning strikes was also found to have a significant and positive influence on the probability of ignition in most areas of the province with the exception of the far northwest. Weather conditions following a lightning storm influence the probability that a lightning strike causes a sustainable ignition. Models of the probability of detecting a fire ignited by lightning were also created for each of the ecoregions across Ontario. The form of these models varied somewhat among ecoregions, but contained an indicator of receptive surface fire spread conditions and an indicator of the dryness of the heavier fuels (the organic layer) in the forest floor.
Fire occurrence, which results from the presence of an ignition source and the conditions for a fire to spread, is an essential component of fire risk assessment. In this paper, we present and compare the results of the application of two different methods to identify the main structural factors that explain the likelihood of fire occurrence at European scale.Data on the number of fires for the countries of the European Mediterranean region during the main fire season (June–September) were obtained from the European Fire Database of the European Forest Fire Information System. Fire density (number of fires/km2) was estimated based on interpolation techniques and was used as the dependent variable in the model. As predictors, different physical, socio-economic and demographic variables were selected based on their potential influence in fire occurrence and on their availability at the European level. Two different methods were applied for the analysis: traditional Multiple Linear Regression and Random Forest, the latter being a non-parametric alternative based on an ensemble of classification and regression trees. The predictive ability of the two models, the variables selected by each method and their level of importance were compared and the potential implications to forest management and fire prevention were discussed.The Random Forest model showed a higher predictive ability than Multiple Linear Regression. Furthermore, the analysis of the residuals also indicated a better performance of the Random Forest model, showing that this method has potentiality to be applied in the assessment of fire-related phenomena at a broad scale. Some of the variables selected are common to both models; precipitation and soil moisture seem to influence fire occurrence to a large extent. Unemployment rate, livestock density and density of local roads were also found significant by both methods. Maps of the likelihood of fire occurrence were obtained from each method at 10 km resolution, based on the selected variables. Both models show that the spatial distribution of fire occurrence likelihood is highly variable in this region: highest fire likelihood is prevalent in the northwest region of the Iberian Peninsula and southern Italy, whereas it is low in northern France, northeast Italy and north of Greece. In the most fire-prone areas, preventive measures could be implemented, associated to the factors identified by both models.
Understanding the effect of variation in climate on large-fire occurrence across broad geographic areas is central to effective fire hazard assessment. The El Nino- Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) affect winter tem- perature and precipitation regimes in western North America through mid-latitude tele- connections. This study examines relationships of ENSO and the PDO to drought-induced fire occurrence in subalpine forests of three study areas across the Rocky Mountains: Jasper National Park (JNP, northern Rockies), Yellowstone National Park (YNP, central Rockies) and Rocky Mountain National Park (RMNP, southern Rockies) over the 1700-1975 period. Large-scale climatic anomalies captured by ENSO (NINO3) and PDO indices had differ- ential effects on large-fire occurrence across the study areas. Superposed epoch analysis (SEA) showed that large fires in RMNP occurred during extreme La Nina years, while the PDO, although predominantly negative during fire years, did not depart significantly from the mean. In YNP and JNP, neither ENSO nor PDO indices were significantly different from the mean during large-fire years, although fires tended to occur during El Nino and positive PDO years. Constructive phases (years of combined warm (positive) or cool (neg- ative) phases) of ENSO and the PDO were significantly associated with large-fire occurrence across the Rockies, even though these large-scale climatic anomalies were not significant when considered singly in SEAs. Combined warm phases (positive PDO during El Nino) co-occurred with large fires in the central and northern Rockies, while the combined cool phases (negative PDO during La Nina) appeared to promote large fires in the southern Rockies. Almost 70% of large fires in RMNP burned during La Nina events that coincided with a negative PDO, although these phases co-occurred during only 29% of the 1700- 1975 period. Spatial teleconnection patterns between drought, PDO and ENSO across west- ern North America independently support the sign and strength of relationships between these climatic anomalies and subalpine fire occurrence along a broad north-south gradient of the Rockies. Forecasts of ENSO that are dependent on the expected PDO phase suggest promise for fire hazard prediction across the West.
Historical variability of fire regimes must be understood within the context of climatic and human drivers of disturbance occurring at multiple temporal scales. We describe the relationship between fire occurrence and interannual to decadal climatic var-iability (Palmer Drought Severity Index [PDSI], El Niñ o/Southern Oscillation [ENSO], and the Pacific Decadal Oscillation [PDO]) and explain how land use changes in the 20th century affected these relationships. We used 1701 fire-scarred trees collected in five study sites in central and eastern Washington State (USA) to investigate current year, lagged, and low frequency relationships between composite fire histories and PDSI, PDO, and ENSO (using the Southern Oscillation Index [SOI] as a measure of ENSO variability) using superposed epoch analysis and cross-spectral analysis. Fires tended to occur during dry summers and during the positive phase of the PDO. Cross-spectral analysis indicates that percentage of trees scarred by fire and the PDO are spectrally coherent at 47 years, the approximate cycle of the PDO. Similarly, percentage scarred and ENSO are spectrally coherent at six years, the approximate cycle of ENSO. However, other results suggest that ENSO was only a weak driver of fire occurrence in the past three centuries. While drought and fire appear to be tightly linked between 1700 and 1900, the relationship between drought and fire occurrence was disrupted during the 20th century as a result of land use changes. We suggest that long-term fire planning using the PDO may be possible in the Pacific Northwest, potentially allowing decadal-scale management of fire regimes, prescribed fire, and vege-tation dynamics.
In the southwestern U.S., wildland fire frequency and area burned have steadily increased in recent decades, a pattern attributable
to multiple ignition sources. To examine contributing landscape factors and patterns related to the occurrence of large (⩾20ha
in extent) fires in the forested region of northern Arizona, we assembled a database of lightning- and human-caused fires
for the period 1 April to 30 September, 1986–2000. At the landscape scale, we used a weights-of-evidence approach to model
and map the probability of occurrence based on all fire types (n=203), and lightning-caused fires alone (n=136). In total, large fires burned 101,571ha on our study area. Fires due to lightning were more frequent and extensive
than those caused by humans, although human-caused fires burned large areas during the period of our analysis. For all fires,
probability of occurrence was greatest in areas of high topographic roughness and lower road density. Ponderosa pine (Pinus ponderosa)-dominated forest vegetation and mean annual precipitation were less important predictors. Our modeling results indicate
that seasonal large fire events are a consequence of non-random patterns of occurrence, and that patterns generated by these
events may affect the regional fire regime more extensively than previously thought. Identifying the factors that influence
large fires will improve our ability to target resource protection efforts and manage fire risk at the landscape scale.
An ecological data base for the San Jacinto Mountains, California, USA, was used to construct a probability model of wildland
fire occurrence. The model incorporates both environmental and human factors, including vegetation, temperature, precipitation,
human structures, and transportation. Spatial autocorrelation was examined for both fire activity and vegetation to determine
the specification of neighborhood effects in the model. Parameters were estimated using stepwise logistic regressions. Among
the explanatory variables, the variable that represents the neighborhood effects of spatial processes is shown to be of great
importance in the distribution of wildland fires. An important implication of this result is that the management of wildland
fires must take into consideration neighborhood effects in addition to environmental and human factors. The distribution of
fire occurrence probability is more accurately mapped when the model incorporates the spatial term of neighborhood effects.
The map of fire occurrence probability is useful for designing large-scale management strategies of wildfire prevention.
Mapping probability of fire occurrence in
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