Abstract
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.
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Introduction
Forests are the main source of the biodiversity and habitat richness of a region, closely related to species distribution and ecosystem services (Demeke and Afework 2014; Suryabhagavan et al. 2016). Biodiversity and habitat are threatened by wildfires and these result in environmental transformation, land use change, deforestation, and climate change (FAO 2007). Declining precipitation, increasing seasonal temperatures and the effects of climate change make wildfires a recurrent problem world-wide and the risk of wildfires increases yearly. The causes of wildfires may be divided into natural and man-made sources. 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).
Several methods have been used to generate spatially wildfire risk maps to predict wildfires and damages to the environment to maintain ecosystem services and optimum use of resources (Ahamed et al. 2000; Collins et al. 2001; Malczewski 2004; Amiri and Shariff 2012; Zolekar and Bhagat 2015). The prominent methods in the generation of wildfire risk maps are geographical information systems (GIS) tools (Jaiswal et al. 2002; Setiawan et al. 2004; Xu et al. 2005; Hernandez-Leal et al. 2006; Chuvieco et al. 2010; Sowmya and Somashekar 2010; Puri et al. 2011; Adab et al. 2013; Ajin et al. 2016; Pourghasemi, 2016; Baqer Rasooli and Bonyad 2019; Austin et al. 2020; Kayet et al. 2020; Ziccardi et al. 2020), multi-criteria decision analysis (MCDA) (Iwan et al. 2004; Vadrevu et al. 2010; Eugenio et al. 2016; Suryabhagavan et al. 2016; Güngöroğlu 2017; You et al. 2017; Sakellariou et al. 2019), and generalized linear models (Syphard et al. 2008; Martinez et al. 2009; Kwak et al. 2012; Vilar et al. 2016; Costafreda-Aumedes et al. 2017, 2018; Li et al. 2019).
With the ability to combine quantitative and qualitative factors (Pomerol and Romero 2000), MCDA methods have been integrated into wildfire susceptibility generation mapping. Evaluating all the methods, machine learning-based maximum entropy (MaxEnt) and random forest algorithms are non-parametric methods widely used to estimate the spatial distribution of wildfires using spatial layers and presence locations (Hastie et al. 2009). The MaxEnt model is often referred to as a nonlinear regression model, and in wildfire occurrence mapping, historical wildfires are considered as species distribution to reveal the probability of occurring outside of historical wildfire locations (Parisien and Moritz 2009; Renard et al. 2012; Bar-Massada et al. 2012, 2013; De Angelis et al. 2015; Vacchiano et al. 2018; Yago et al. 2019; Bekar et al. 2020; Banerjee 2021; Tariq et al. 2022). Random forest is also a non-parametric model that uses clustering techniques to predict the probability of wildfire occurrence (Archibald et al. 2009; Aldersley et al. 2011; Olivera et al. 2012; Arpaci et al. 2014; Rahmati et al. 2016).
When evaluating recent studies on wildfire susceptibility mapping, many criteria are used in the field of environmental, social, topographic, climatic, and meteorological parameters. Although studies have focused on specific relationships between topography and forests, climate and forests and artificial features (buildings, roads) and forests, most have examined relationships between variables. Considering the ones used in wildfire risk mapping, elevation, slope, aspect, vegetation type, temperature and distance to roads are the most frequently used (Table 1). Variables such as tree cover density distance to rivers and water sources, probability of soil wetness, and solar radiation are not widely used. Consideration of a limited number of factors leads to the generation of superficial risk maps in all methods used in wildfire susceptibility mapping, as wildfire probabilities require the assessment of comprehensive relationships of variables and their interaction with each other. Therefore, after an in-depth evaluation of the elements used in recent studies and the nature of wildfire occurrence, 25 were specified and used in this study to generate reliable and accurate wildfire susceptibility maps. In addition, the separate evaluation of stubble burning, lightning strikes, fires from discarded cigarette butts and power line-induced wildfire susceptibility maps make the results invaluable for wildfire occurrence rate assessment.
The aim of this study is to specify unknown wildfire causes by generating wildfire susceptibility maps based on fires originating from stubble burning, lightning, discarded cigarette butts and power lines using the MaxEnt model. The study should provide valuable information about the source of unknown wildfire causes. There were 3347 wildfire locations detected, and the cause of 1714 were unidentified. Considering the main causes of these 3,347 wildfires, 87.5% were due to stubble burning, lightning strikes, discarded cigarette butts and power line-induced ignitions. Other 12.5% of the causes are sabotage, camp fires and traffic accidents. Because 52.0% of the causes are unknown in the study area, specifying their origin will be useful for prevention procedures and further understanding the main causes of wildfires.
Material method
Study area
The study area is in Muğla, Aydin and Antalya provinces, an area of 40,947 km2 located between 26°59′59" and 32°39′ 29" W and 36°5′31" and 38°6′53" N. It is at the intersection of the Aegean and Mediterranean seas and is important for tourism, pine honey production (90% of the world total) and the forest industry (Fig. 1). The population triples in the tourist season (June–September) compared to the other months. Both the increasing population and the fire-prone forests make the area very vulnerable to wildfires. Approximately 20% of wildfires occurred in Aydın (2.0%), Muğla (10.5%) and Antalya (9.5%) provinces. (URL 1Footnote 1). The climate of the study area is hot and temperate. Precipitation is mostly in the winter months, with relatively less in the summer. The area is mostly covered by coniferous forests between 100 and 2100 m.
Methodology
MaxEnt is a machine learning, system-based algorithm used to determine the spatial distribution of species by evaluating sample point locations and dependent variables. It uses presence data to predict the possible distribution of species according to the maximum entropy theory (Phillips et al. 2006; Elith et al. 2011). The MaxEnt model is based on an iterative comparison of predictive (independent) variables at the presence locations (Phillips et al. 2006; Elith et al. 2011), with a large subsample extracted from the study area and used as non-realization values.
MaxEnt estimates the probability of an event’s distribution by adjusting the possible distribution of the maximum entropy. In addition, the model identifies the most overlapping value of each presence point and spatial variables to determine the possible distribution of species in each variable, i.e., it adjusts the probability to the most uniform or diffuse distribution according to the independent variables at each presence point (Phillips et al. 2006). Therefore, the model requires presence data (existing wildfires) and independent variables (environmental, topographic, forest and meteorological criteria) to determine the distribution. Existing wildfires are considered as species to estimate their distribution in this study. MaxEnt software allows the user to generate the possible distribution through various parameters to be used for learning and graphical result outputs, and produces validation and accurate outputs based on AUC (Area Under the Curve) measurements and the jack-knife method which is a resampling technique useful for bias and variance estimation.
Variable selection
There are several spatial variables used to generate spatial distribution maps of wildfires caused by stubble burning, lightning, power lines and discarded cigarette butts. The variables were specified by considering the parameters that trigger and cause wildfires or indirectly affect wildfires in the subjects of topographic, meteorological, environmental, climatic and forest dynamics. A total of 25 variables were identified and mapped with ArcGIS 10.5 software. An example of elevation and forest type variable maps are provided (Fig. 2).
All variables were retrieved from global and regional data sources at the specific resolutions in Table 2 and mapped in the WGS84 projection, with 18,850 columns and 6740 rows (0.0031-pixel sizes in the WGS84 projection). All maps have been converted from GeoTiff to ASCII raster file for use in MaxEnt software.
Historical wildfire data
Data from 3347 wildfires that occurred in the provinces of Aydın, Muğla and Antalya between 2013 and 2020 were used (Fig. 3). Wildfire locations, extinguishing time, causes, duration, area burnt and other information such as intervention staff, vehicles and used equipment, were retrieved from the Republic of Turkey General Directorate of Forestry Ministry wildfires database. The main causes were burning of stubble, lightning strikes, power lines, and discarded cigarette butts. While there are other causes such as vehicles (traffic accidents), campfires, heat objects, terrorism, and traffic accidents, these are insufficient to reveal accurate and spatially dependent results, as they occur randomly and were excluded from the sample point dataset. All wildfires with the same causes were extracted as a separate file for use in MaxEnt software using ArcGIS 10.5 software.
Considering the distribution of wildfires each year, 29% are caused by lightning, 5% by discarded cigarette butts, 4% by power lines, 3% by stubble burning, and 7% by other causes (sabotage, traffic accidents, campfires, heat objects, explosions) However, when evaluating the wildfire statistics, the cause of 52% of the total wildfires could not be determined. Unfortunately, the fact that the cause of more than half of all forest fires is unknown creates a major deficiency in forest fire prevention and prediction. On the other hand, when the yearly distribution of wildfire causes is analyzed, there is a significant increase due to other causes. The cause of the decrease in unknown wildfires in 2019 and 2020 is likely because of the new observation systems of drones and watchtowers and the increased number of forest guards. Excluding unknown wildfires, most wildfires are caused by lightning. The causes of wildfires and the distribution of their total numbers are shown in Fig. 4.
Results and discussion
Stubble burning
Although stubble burning is effectively banned by local governments, it remains the most common way to dispose of grain residues after harvest. Setting fire to straw stubble can easily ignite dry material and quickly destroy lands near forests. Since stubble burning takes place only in agricultural lands, the highest gain and relationship should be determined for the land use variable.
The stubble burning-induced occurrence probability map was generated by using 49 presences and 44 test points. The AUC values were 0.870 for sample points, 0.690 for test points and 0.794 if only test points were used. The presence and test points AUC values (0.870 and 0.794) revealed that selection of sample and test points was homogeneously and that the resulting distribution map was accurate (Fig. 5).
When the rates of contribution of the variables were evaluated, the highest was determined by the land use and road variables with 55.7% and 9.7%, as stubble burning occurs only in agricultural lands. Since stubble burning usually starts from the roadsides of agricultural lands for easier control, a higher contribution rate was determined for the road variable. When the agricultural lands are examined, the majority are located between 500 − 1500 m elevation where the average forest density is below 50%. Therefore, stubble burning does not pose a threat to the dense forest cover in the study area. Contribution rates and AUC graphs are shown in Fig. 6.
Considering the overlapping results of wildfire locations with the land use variable, 38.3% overlapped with agricultural lands and 13.8% with transitional woodlands that form the border between agricultural lands and forests. Therefore, 52.1% of wildfires occurred in agricultural lands. On the other hand, 45.7% overlapped with coniferous forests, 26.6% with broad-leaved forests, and 27.6% with deforested areas. Statistics for stubble burning wildfire locations and overlapping results are summarized in Table 3 (for the first six variables only). Since the land use and forest type variables were categorical, statistical values were not determined.
Lightning
Lightning-induced wildfires occur more frequently than other wildfire causes in the study area. Between 2013 and 2020, 950 wildfires occurred due to lightning strikes. Higher elevations and dense forest covers appear to be highly prone to lightning strikes. As lightning is a natural event, it is difficult to predict and prevent compared to other causes, but it is one of the main triggering causes of wildfires.
The spatial distribution of possible lightning-induced wildfire occurrence probability map was generated using 950 wildfire locations, of which 564 were assigned as presence points and 386 as test points. The AUC values were determined as 0.836 for sample points, 0.783 for test points and 0.815 if only test points were used. The AUC values showed that presence and test point selections were performed homogeneously and that the resulting probability map was accurate (Fig. 7).
When the contribution values of the variables were examined, BIO7 (22.3%), land use (16.7%), BIO3 (15.3%) and leaf type (13.8%) provided the most useful information about lightning-induced wildfire occurrence. Contribution rates and AUC graphs are given in Fig. 8.
The land use variable was highly correlated with lightning as expected, and 76.5% of the wildfire locations overlapped with the forest class. There were 54.7% of lightning-induced wildfires in coniferous forests and 23.2% in transitional forests. Wildfires occurred at varying elevations from 2 to 2030 m, with most above 800 m. Since BIO7 is an indicator of the annual temperature range and lightning occurs mostly in forest areas, leaf type has a decisive role on the ignition of wildfires when lightning occurs (Table 4).
Power lines
Contact by trees with high-voltage power lines can cause arcing (Mitchell 2013), and winds can move branches to power lines and create sparks. The electric arc can fall to the ground or directly ignite trees. In this case, trees near power lines are more at risk than others.
The spatial distribution of the power line-based wildfire susceptibility map (Fig. 9) was created using 132 wildfire locations, 90 of which were designated as presence points and 42 as test points. The AUC values were 0.904 for sample points, 0.765 for test points and 0.860 if only test points were used. In this study, the highest gain was in power lines with a value of 0.904.
While developing the power line-induced wildfire susceptibility map, the variables of roads (33.8%), power lines (12.3%), precipitation (8.5%) and land use (7.8%) play a decisive role in ignition by electric arcs. The road variable was highly correlated with arc-based ignitions, as power lines are often established along roads due to highly sloping topography. The AUC values graph and jackknife test results are shown in Fig. 10.
Most of the wildfires occurred at a distance of less than 500 m from roads; the average distance was calculated as 418.3 m. Considering the elevations, it has been determined that power lines under 400 m are suceptible to wildfires. High-voltage power lines above 1000 m are not suceptible due to established measures such as linear clear cut areas and preferred plant-free areas. Thus, the average value of the distance to the power lines was calculated as 1676.5 m (Table 5).
Cigarette butts
Discarded cigarette butts are one of the most common causes of wildfires and are difficult to detect because they have no relationship to any environmental variable as cigarettes may be thrown from cars, roads, campsites and residential areas. For this reason, it is difficult to identify areas that are prone to wildfires caused by cigarette butts and that need to be examined or spatially determined, considering the land use variable. Although the reason is simple, prevention is difficult.
The spatial distribution of cigarette butt-induced wildfire susceptibility map was created using 166 wildfire locations, of which 100 were presence points and 66 test points. The AUC values were 0.868 for the sample points, 0.808 for the test points and 0.860 if only test points were used. The results reveal that most of the wildfire locations were clustered in dense forest cover near settlements in Aydın and Muğla provinces (Fig. 11).
BIO15 (23.1%), land use (9.7%) and wind (8.5%) play a decisive role in cigarette butt-induced wildfires. BIO15 (seasonal precipitation) has the highest contribution to the dampness of forest areas (Fig. 12). As this variable increases, the probability of cigarette butts igniting dry materials on the forest floor decreases. In addition, the land use variable made a high contribution as people generally throw cigarette butts on roads, camping areas and in settlements. Wind may be a trigger for the increase of wildfire occurrence.
The average wind speed was determined as 2.5 km/h, which is a higher wind speed than those for other causes. Therefore, it is possible that wind has an initial role in cigarette butt-induced wildfires. When assessing land use, 30.7% of wildfires overlapped with coniferous forests and 19.3% with agricultural lands and transitional forest cover. The road variable is 6.0% and represents the most prominent locations for spatially risky areas where forest roads are prone to ignition (Table 6).
Identifying unknown causes of wildfires
For determining the causes of unknown wildfires, spatial intersection analysis was used to evaluate the relationships between stubble burning, discarded cigarette butts, power lines and lightning-induced susceptibility maps and unknown wildfire locations. All locations were individually overlapped with the susceptibility maps and the risk value with the highest correlation was assigned as the related wildfire cause (Table 7). The highest correlation values were typed bold in Table 7.
In Table 7, there is a clear difference between the intersection ratios of the susceptibility maps. The difference values allowed the causes to be identified more clearly and accurately. Lightning strikes, stubble burning, and power line-induced wildfire detections were more accurately determined than cigarette butts as these causes are highly correlated with the land use. For example, while stubble burning is carried out only on agricultural lands, power lines are established along roadsides and in forested areas. Since the discarding of cigarette butts is related to human activities, they can be thrown into any land use class such as roads, camping areas, residential areas, forested areas, and agricultural lands. Therefore, the assignment of cigarette butt-induced wildfire causes to unknown wildfire origin is less accurate. Considering the total intersection points of wildfire locations (the sum of the intersection rates of each risk map), rates of 73.1% for power lines, 65.3% for lightning, 63.4% for stubble burning and 58.6% for cigarette butts were determined.
After cause assignment to unknown wildfires, 281 new forest fire points were added to stubble burning, 290 to lightning, 334 to power lines and 361 to cigarette butts. A total of 1266 unknown forest fire causes were identified, and for 448 forest fire locations, no relationship could be detected as was possibly due to sabotage, traffic accidents, heat objects and other causes such as terrorism. Forest fires caused by terrorism usually aim to destroy very large areas and are sometimes started at multiple points at the same time by using very flammable fuels to provide quick spread of fires..The MaxEnt model was applied to all causes by involving new wildfire points to verify the similarity of the distribution. Spatial distribution maps of wildfires are provided for both before and after cause assignment to unknown wildfire in Fig. 13. The spatial distribution maps produced for all causes overlap and the distribution characteristics of the clusters and risk zones were similar.
Besides the similarity of both types of maps, the statistical values and variable contributions should be evaluated to determine whether cause assignment was accurate or not (Table 8). For all wildfire causes, training AUC values were increased by including unknown wildfire causes and test AUC values were decreased only for power line and cigarette butt causes. This increase is a valuable indicator for the accuracy of cause assignment and should be evaluated together with contribution rates of variables. When evaluating the contribution rates of variables, the order of variables was not changed. On the other hand, there was a substantial change in [contribution rates for 1st order variables where these changes are rather smaller than 1st orders.] Differences between 1st order variable contribution rates, land-use 11.9% for stubble burning, BIO7 14.7% for lightning, roads 10.1% for power lines and BIO15 1.9% for cigarette butts were determined. Because the order of variables was not changed and the differences between variable contribution rates are not large, spatial distribution maps of wildfires were similar.
In addition to the similarity of both types of maps, statistical values and variable contributions should also be evaluated to decide whether the reason assignment is done correctly. Comparisons of statistical values are summarized in Table 8. For all wildfire causes, training AUC values increased when unknown wildfire locations were included, and test AUC values decreased for power line and cigarette butt causes only. This increase is a valuable indicator for the accuracy of the cause assignment and should be evaluated together with the contribution rates of the variables. When the contribution rates are evaluated, it is seen that the order of the variables was not changed. On the other hand, it is seen that these changes mostly occurred in the 1st order variables compared to the other order variables. The highest contribution rate change was determined for the land use variable with 14.7% in stubble burning spatial distribution assessment.
The relationship between fire ignition and environmental causes has been analyzed using different models. Most studies used logistic regression-based models (Chuvieco et al. 2009; Martinez et al. 2009) and generalized linear models (Syphard et al. 2008; Martinez et al. 2009; Kwak et al. 2012; Vilar et al. 2016; Costafreda-Aumedes et al. 2017, 2018; Li et al. 2019) and the random forest algorithm (Archibald et al. 2009; Aldersley et al. 2011; Olivera et al. 2012; Arpaci et al. 2014; Rahmati et al. 2016) due to the presence of nonlinear and nonparametric variables. Another approach is the machine learning algorithms of the MaxEnt method to discover the relationship between variables and ignition possibilities (Parisien and Moritz 2009; Bar-Massada et al. 2012, 2013; Renard et al. 2012; De Angelis et al. 2015; Vacchiano et al. 2018; Yago et al. 2019; Bekar et al. 2020; Banerjee, 2021; Tariq et al. 2022).
While most studies considered wildfires only as a site of occurrence, this study considered historical wildfires according to their anthropogenic and natural causes to provide a cause-level vulnerability analysis. The results show that the variables responsible for each are different and need to be evaluated separately to produce reliable, accurate wildfire susceptibility maps. The advantage of this study is that both anthropogenic and natural causes of forest fires are evaluated separately. This approach is used by Vacchiano et al. (2018) and the fires were studied by clustering them into summer and winter wildfires. However, in this study, mainly meteorological and bioclimatic variables were considered for mapping wildfire susceptibility using the MaxEnt method and maps of anthropogenically induced forest fire susceptibility were generated using only the variables of distance to roads and settlements. Settlements, roads, power lines, imperviousness, and land use variables were used to determine the relationship between wildfires and anthropogenic causes. One of the main differences, and the originality of our study, was to determine these causes in detail, i.e., discarded cigarette butts, power lines and stubble fires. Therefore, rather than creating general susceptibility maps, this study created susceptibility maps for the individual causes, resulting in more accurate and reliable maps for preparedness and prediction.
Both Vacchiano et al. (2018) and this study found that lightning is the most common natural cause of wildfires. In terms of lightning strike probability, land use and topography are the strongest predictors of ignition possibility (Krawchuk et al. 2006). In addition to land use, lightning-induced wildfire occurs when it reaches the forest floor where plants and dry soil initiate a surface fire (Wotton et al. 2003; Conedera et al. 2006). However, information about the forest floor and its degree of dryness are unknown and cannot be presented as a variable for mapping susceptibility. Instead, consideration of multiple variables also relates to this variable, as the degree of soil dryness depends on weather conditions, leaf type, and stand density (Schoennagel et al. 2004).
Conclusion
This study presents 3 main results in terms of including the highest number of variables in the literature for susceptibility mapping, producing susceptibility maps separately according to the causes of wildfires, and assigning the cause of forest fires whose cause cannot be determined. Susceptibility maps created can be used to determine prevention procedures, considering the main causes of forest fires. For example, lightning rods can be installed by considering the regions that are highly affected by lightning strikes, since more lightning-induced forest fires occur compared to other causes. Similarly, warning signs can be placed in areas where forest fires from discarded cigarette butts are most common to raise awareness. Because stubble burning takes place after the harvest season, control and early intervention methods such as directing fire trucks to highly sensitive areas and real-time monitoring systems using drones can be used. Since 25 variables were included in this study and numerous causes of forest fires were considered, the risk zones and areas susceptible to fires were not determined superficially. When the accuracy results of the test points are evaluated, it is seen that the generated susceptibility maps can be used for authorized personnel and institutes. In addition to these contributions, since 73.8% of unknown causes of forest fires were identified in this study, these determined the highest contribution to forest fire assessment studies.
There are also some limitations when generating MaxEnt-based sabotage, terrorism, and traffic accident susceptibility maps. Numerous sabotage and terrorism-induced forest fires occurred in Turkey, especially in 2021. While some of these were caused by terrorism, many wildfires occurred as people deliberately started forest fires, called sabotage, to gain new farmland or construction sites. Since the study area has a high tourism value, new residential areas are needed. For this reason, forest fires caused by sabotage mostly occur on the coasts. Although generating susceptibility maps for these causes is important for the protection of forest cover, these causes have a heterogeneous distribution and are not related to any variable, since ignition can start from roads, forests, and urban areas in all seasons and in all climatic conditions. Therefore, sabotage and terrorism-induced forest fire susceptibility maps will not reflect the actual situation of sensitive areas, as they are not spatially related to any variable class. Similarly, since traffic accidents can occur on any roads in the study area, fires caused by these accidents may occur in any season and under any conditions. Therefore, because of this study, 26.2% of the total unknown forest fire causes are still undetermined.
Change history
03 July 2022
The original version is updated due to missing of the corresponding editor details
Notes
URL1, Turkey Governmental Forest Statistics https://www.ogm.gov.tr/lang/en/Pages /Forests/StatisticalInfo.aspx (accessed, 12.08.2021).
References
Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65(3):1723–1743
Ahamed N, Rao GK, Murthy R (2000) GIS-based fuzzy membership model for crop-land suitability analysis. Agric Syst 63:75–95
Ajin RS, Loghin AM, Jacob MK, Vinod PG, Krishnamurthy RR (2016) The risk assessment of potential forest fire in Idukki Wildlife Sanctuary using RS and GIS techniques. Int J Adv Earth Sci Eng 5:308–318
Aldersley A, Murray SJ, Cornell SE (2011) Global and regional analysis of climate and human drivers of wildfire. Sci Total Environ 409:3472–3481
Amiri F, Shariff MA (2012) Application of geographic information systems in land use suitability evaluation for beekeeping: A case study of Vahregan watershed (Iran). African J Agr Res 7(1):89–97
Archibald S, Roy DP, Van Wilgen BW, Scholes RJ (2009) What limits fire? An examination of drivers of burnt area in Southern Africa. Glob Chang Biol 15:612–630
Arpaci A, Malowerschnig B, Sass O, Vacik H (2014) Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests. Appl Geogr 53:258–270
Austin LV, Silvis A, Mark Ford W, Powers KE (2020) Effects of historic wildfire and prescribed fire on site occupancy of bats in Shenandoah National Park, Virginia, USA. J for Res 31:1255–1270. https://doi.org/10.1007/s11676-019-00923-y
Banerjee P (2021) Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya. Scand J for Res 36(4):275–288. https://doi.org/10.1080/02827581.2021.1918239
Bar Massada A, Syphard AD, Stewart SI, Radeloff VC (2012) Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA. Int J Wildland Fire 22(2):174–183. https://doi.org/10.1071/WF11178
Bar Massada A, Syphard AD, Stewart SI, Radeloff VC (2013) Wildfire ignition-distribution modelling: A comparative study in the Huron-Manistee National Forest, Michigan, USA. Int J Wildland Fire 22:174–183
Bekar İ, Tavşanoğlu Ç, Pezzatti GB, Vacik H, Pausas JG, Bugmann H, Petter G (2020) Cross-regional modelling of fire occurrence in the Alps and the Mediterranean Basin. Int J Wildland Fire 29:712–722
Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Zamora R (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model 221(1):46–58
Collins MG, Steiner FR, Rushman MJ (2001) Land-use suitability analysis in the United States: historical development and promising technological achievements. Environ Manage 28(5):611–621
Conedera M, Cesti G, Pezzatti GB, Zumbrunnen T, Spinedi F (2006) Lightning-induced fires in the Alpine region: An increasing problem. In: Viegas DX (ed) International Conference on Forest Fire Research, Coimbra, 27–30 November 2006. Coimbra, ADAI/CEIF, University of Coimbra (CD-ROM), Portugal, pp 1–9
Costafreda-Aumedes S, Cosmas C, Vega-Garcia C (2017) Human-caused fire occurrence modelling in perspective: A review. Int J Wildland Fire 26:983–998
Costafreda-Aumedes S, Vega-Garcia C, Cosmas C (2018) Improving fire season definition by optimized temporal modelling of daily human-caused ignitions. J Environ Manage 217:90–99
De Angelis A, Ricotta C, Conedera M, Pezzatti GB (2015) Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions. PLoS ONE 10(2):e0116875. https://doi.org/10.1371/journal.pone.0116875
Demeke D, Afework B (2014) Habitat association and distribution of rodents and insectivores in Chebera Churchura National Park, Ethiopia. Trop Ecol 55:221–229
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Eugenio FC, Dos Santos AR, Fiedler NC, Ribeiro GA, Da Silva AG, Dos Santos ÁB, Paneto GG, Schettino VR (2016) Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil. J Environ Manage 173:65–71
FAO (2007) Fire management: global assessment 2006. A thematic study prepared in the framework of the Global Forest Resources Assessment 2005. Issue 151. ISBN: 978-92-5-105666-0
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315
Güngöroğlu C (2017) Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: The case of Turkey/Çakırlar. Hum Ecol Risk Assess Int J 23(2):388–406
Hastie T, Tibshirani R, Friedman J (2009) Overview of Supervised Learning. In: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-84858-7_2
Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A (2006) Fire risk assessment using satellite data. Adv Space Res 37(4):741–746
Iwan S, Mahmud AR, Mansor S, Mohamed Shariff AR, Nuruddin AA (2004) GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prevention and Management: an International Journal 13(5):379–386
Jaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs 4(1):1–10
Kayet N, Chakrabarty A, Pathak K, Sahoo S, Dutta T, Hatai BK (2020) Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk (FFR) mapping in Melghat Tiger Reserve (MTR) Forest. J for Res 31(2):565–579. https://doi.org/10.1007/s11676-018-0826-z
Krawchuk MA, Cumming SG, Flannigan MD, Wein RW (2006) Biotic and abiotic regulation of lightning fire initiation in the mixedwood boreal forest. Ecology 87:458–468. https://doi.org/10.1890/05-1021
Kwak H, Lee WK, Saborowski J, Lee SY, Won MS, Koo KS, Lee MB, Kim SN (2012) Estimating the spatial pattern of human-caused forest fires using a generalized linear mixed model with spatial autocorrelation in South Korea. Int J Geogr Inf Sci 26:1589–1602
Li J, Shan YL, Yin SN, Wang MX, Su L, Wang DN (2019) Nonparametric multivariate analysis of variance for affecting factors on the extent of forest fire damage in Jilin Province. China J for Res 30(6):2185–2197. https://doi.org/10.1007/s11676-019-00958-1
Malczewski J (2004) GIS-based land-use suitability analysis: A critical overview. Prog Plann 62:3–65
Martínez J, Vega-Garcia C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manage 90:1241–1252
Miller C, Ager AA (2013) A review of recent advances in risk analysis for wildfire management. Int J Wildland Fire 22:1–14
Mitchell JW (2013) Power line failures and catastrophic wildfires under extreme weather conditions. Eng Fail Anal 35:726–735
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JM (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. For Ecol Manage 275:117–129
Parisien MA, Moritz MA (2009) Environmental controls on the distribution of wildfire at multiple spatial scales. Ecol Monogr 79:127–154
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3–4):231–259
Pomerol JC, Barba-Romero S (2000) Outranking Methods. In: Multicriterion Decision in Management. International Series in Operations Research & Management Science, vol 25. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4459-3_7
Pourghasemi HR (2016) GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models. Scand J Forest Res 31(1):80–98
Puri K, Areendran G, Raj K, Mazumdar S, Joshi PK (2011) Forest fire risk assessment in parts of Northeast India using geospatial tools. J for Res 22(4):641–647
Rahmati O, Pourghasemi HR, Melesse AM (2016) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region. Iran Catena 137:360–372
Rasooli SB, Bonyad AE (2019) Evaluating the efficiency of the Dong model in determining fire vulnerability in Iran’s Zagros forests. J for Res 30(4):1447–1458. https://doi.org/10.1007/s11676-018-0765-8
Renard Q, Pélissier R, Ramesh BR, Kodandapani N (2012) Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wildland Fire 21:368–379
Sakellariou S, Tampekis S, Samara F, Flannigan M, Jaeger D, Christopoulou O, Sfougaris A (2019) Determination of fire risk to assist fire management for insular areas: the case of a small Greek island. J for Res 30(2):589–601. https://doi.org/10.1007/s11676-018-0666-x
Sarı F (2021) Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. For Ecol Manage 480:118644. https://doi.org/10.1016/j.foreco.2020.118644
Schoennagel T, Veblen TT, Romme WH (2004) The interaction of fire, fuels, and climate across rocky mountain forests. Bioscience 54:661–676. https://doi.org/10.1641/0006-3568054[0661:TIOFFA]2.0.CO;2
Setiawan I, Mahmud AR, Mansor S, Mohamed Shariff AR, Nuruddin AA (2004) GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang. Malaysia Disaster Prevent Manag 13(5):379–386
Sowmya SV, Somashekar RK (2010) Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary. India J Environ Biol 31(6):969–974
Suryabhagavan KV, Alemu M, Balakrishnan M (2016) GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia. Trop Ecol 57(1):33–43
Syphard AD, Radeloff VC, Keuler NS, Taylor RS, Hawbaker TJ, Stewart SI, Clayton MK (2008) Predicting spatial patterns of fire on a southern California landscape. Int J Wildland Fire 17:602–613
Tariq A, Shu H, Siddiqui S, Munir I, Sharifi A, Li Q, Lu L (2022) Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods. J for Res 33:183–194. https://doi.org/10.1007/s11676-021-01354-4
Tian X, Zhao F, Shu L, Wang M (2013) Distribution characteristics and the influence factors of forest fires in China. For Ecol Manage 310:460–467
Vacchiano G, Foderi C, Berretti R, Marchi E, Motta R (2018) Modeling anthropogenic and natural fire ignitions in an inner-alpine valley. Nat Hazards Earth Syst Sci 18:935–948. https://doi.org/10.5194/nhess-18-935-2018
Vadrevu KP, Eaturu A, Badarinath KV (2010) Fire risk evaluation using multicriteria analysis-a case study. Environ Monit Assess 166(1–4):223–239
Vilar L, Gómez I, Martínez-Vega J, Echavarría P, Riaño D, Martín MP (2016) Multitemporal modelling of socio-economic wildfire drivers in Central Spain between the 1980s and the 2000s: Comparing generalized linear models to machine learning algorithms. PLoS ONE 11:e0161344
Wotton BM, Martell DL, Logan KA (2003) Climate change and people-caused forest fire occurrence in Ontario. Clim Change 60:275–295. https://doi.org/10.1023/A:1026075919710
Xu D, Dai LM, Shao GF, Tang L, Wang H (2005) Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin. China J for Res 16(3):169–174. https://doi.org/10.1007/BF0285680
Yago M, María Z, Marcos RM (2019) Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain. Geomat Nat Haz Risk 10(1):385–411. https://doi.org/10.1080/19475705.2018.1526219
You W, Lin L, Wu L, Ji Z, You J, Zhu J, Fan Y, He D (2017) Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecol Indic 77:176–184
Ziccardi LG, Thiersch CR, Yanai AM, Fearnside PM, Ferreira-Filho PJ (2020) Forest fire risk indices and zoning of hazardous areas in Sorocaba, São Paulo state. Brazil J for Res 31:581–590. https://doi.org/10.1007/s11676-019-00889-x
Zolekar RB, Bhagat VS (2015) Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Comput Electron Agr 118:300–321
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Sari, F. Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models. J. For. Res. 34, 355–371 (2023). https://doi.org/10.1007/s11676-022-01502-4
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DOI: https://doi.org/10.1007/s11676-022-01502-4