Fire-prone Areas Research Articles

Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

Modeling fire susceptibility in fire-prone areas of forest ecosystems was essential for providing guidance to implement prevention and control measures of forest fires. Traditional models were developed on the basis of random selection of absence data (i.e., nonfire data from unburned areas), which could bring uncertainties to modeling results. Here, a new model with the genetic algorithm for Rule-set Production (GARP) algorithm and 10 environmental layers was proposed to process presence-only data in the susceptibility modeling of forest fires in Chongqing city. To do this, 70% of 684 fire occurrence data (479) during the period of 2000–2018 were applied to train the proposed model. And, 30% of these fire occurrence data (205) and the same amount of no-fire data (205) were emerged as validation dataset. The results showed that, for some environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), their P values were less than 0.05, indicating that these 9 environmental layers have significant influence on the spatial distribution of fire susceptibility in Chongqing city. On the contrary, with a higher P value (i.e., 0.126), the slope layer has an insignificant effect on fire susceptibility in the study area. Furthermore, the results of receiver operating characteristic analysis (ROC) showed that the proposed model has a good performance with an AUC value of 0.869, an accuracy value of 0.732, a sensitivity value of 0.59, a specificity value of 0.873, a positive predictive value of 0.823, and a negative predictive value of 0.681. This study revealed the validity of the proposed model in modeling the susceptibility of forest fires.

Pengaruh Metode Pengukuran Jarak pada Algoritma k-NN untuk Klasifikasi Kebakaran Hutan dan Lahan

Forest and land fires are a serious and recurring problem in Indonesia. The high intensity of forest fires is caused by the distribution of hotspots in fire-prone areas. One of the efforts to prevent and minimize the risk of forest fires is to identify the types of hotspots using a classification approach. One of the most popular classification algorithms is k Nearest Neighbor (k-NN). The algorithm uses a distance calculation approach in classifying objects. The purpose of this study is to classify the types of hotspots scattered in Indonesia using the k-NN algorithm and to analyze the effect of the distance calculation method on the k-NN algorithm. The types of distance measurement methods analyzed include Euclidean, Canberra, Chebyshev, and Manhattan. The dataset used is the distribution of hotspots in Indonesia obtained from Global Forest Watch (GFW). The study designed a dataset with two conditions, through the pre-processing stage and not. In general, the model accuracy of the k-NN combination with various distance measurement methods is above 90%. The pre-processing stage can increase the model's performance 1-8 times. The combination of k-NN with Manhattan is the best choice to identify the types of hotspots with an accuracy of 92.6%.

Managing Wildfire Risk in Mosaic Landscapes: A Case Study of the Upper Gata River Catchment in Sierra de Gata, Spain

Fire prevention and suppression approaches that exclusively rely on silvicultural measures and containment infrastructure have become increasingly ineffective in stopping the spread of wildfires. As agroforestry landscape mosaics consisting of a mix of different land cover and use types are considered less prone to fire than forests, approaches that support the involvement of rural people in agriculture and forestry activities have been proposed. However, it is unknown whether, in the current socio-economic context, these land-use interventions will nudge fire-prone landscapes towards more fire-resistant ones. We report on a case study of the Gata river catchment in Sierra de Gata, Spain, which is a fire-prone area that has been a pilot site for Mosaico-Extremadura, an innovative participatory fire-risk-mitigation strategy. Our purpose is to assess the efficacy of project interventions as “productive fuel breaks” and their potential for protecting high-risk areas. Interventions were effective in reducing the flame length and the rate of spread, and almost 40% of the intervention area was in sub-catchments with high risk. Therefore, they can function as productive fuel breaks and, if located strategically, contribute to mitigating wildfire risk. For these reasons, and in view of other economic and social benefits, collaborative approaches for land management are highly recommended.

Improved Prediction of Forest Fire Risk in Central and Northern China by a Time-Decaying Precipitation Model

With the increase in extreme climate events, forest fires burn in much larger areas. Therefore, it is important to accurately predict forest fire frequencies. Precipitation is an important factor that affects the probability of future forest fires. Previous models used average precipitation values, but the attenuation of precipitation was not considered. In this study, a time-decaying precipitation algorithm was used to calculate the comprehensive precipitation index. This method can better represent the effect of precipitation in predicting the occurrence of forest fires. Moreover, observed fire spots were converted into a continuous density of fire spots. The structure of the prediction model is more realistic, which is conducive to obtaining higher-precision prediction results. Additionally, the support vector machine (SVM) regression model was used to construct a forest fire warning model. When the comprehensive precipitation index was compared with the average precipitation value, the accuracy of the four forest areas in central and northern China in the test set was improved by approximately 10%. The findings are relevant to forest ecologists and managers for future mitigation of forest fires, and also for successful prediction of other fire-prone areas.

Ecophysiological and Growth-Related Traits of Two Geophytes Three Years after the Fire Event in Grassland Steppe.

Deliblato Sands is the single largest expanse of sand in Europe. It is the most fire-prone area of Serbia due to the absence of surface water, sandy soils, specific microclimate conditions, and vegetation composition. Post-fire regeneration is a long-term process that includes many aspects of vegetation regrowth and habitat recovery. In the third year following one of the disastrous fires, the growth dynamics of two geophyte species in unburned and burned sites were studied. During the growing season, burned and unburned populations of Crocus reticulatus Steven ex Adam and Iris pumila L. were assessed for growth parameters (biomass production, specific leaf area, leaf area index) and leaf-level ecophysiological traits (photosystem II efficiency, chlorophyll amount, relative water content). Species acclimated differently to changed abiotic and biotic site conditions after the fire event. C. reticulatus burned and unburned populations differed significantly in terms of flowering phenology and ecophysiological traits, whereas I. pumila burned and unburned populations differed significantly in terms of growth parameters. The findings support the assertion that geophytes are generally well adapted to environmental disturbances. Species, however, responded differently to fire-induced changes in the physicochemical and biotic environment, depending on their ecological requirements and adaptive capacity.

Spatial and temporal expansion of global wildland fire activity in response to climate change

Global warming is expected to alter wildfire potential and fire season severity, but the magnitude and location of change is still unclear. Here, we show that climate largely determines present fire-prone regions and their fire season. We categorize these regions according to the climatic characteristics of their fire season into four classes, within general Boreal, Temperate, Tropical and Arid climate zones. Based on climate model projections, we assess the modification of the fire-prone regions in extent and fire season length at the end of the 21st century. We find that due to global warming, the global area with frequent fire-prone conditions would increase by 29%, mostly in Boreal (+111%) and Temperate (+25%) zones, where there may also be a significant lengthening of the potential fire season. Our estimates of the global expansion of fire-prone areas highlight the large but uneven impact of a warming climate on Earth’s environment.

Contrasting the efficiency of landscape versus community protection fuel treatment strategies to reduce wildfire exposure and risk

We examined the financial efficiency and effectiveness of landscape versus community protection fuel treatments to reduce structure exposure and loss to wildfire on a large fire-prone area of central Idaho (USA). The study area contained 63,707 structures distributed in 20 rural communities and resorts, encompassing 13,804 km2. We used simulation modeling to estimate expected structure loss based on burn probability and characteristics of the home ignition zone. We then designed three fuel management strategies that targeted treatments to: 1) the surrounding areas predicted to be the source of exposure to communities from large fires, 2) the home ignition zone, and 3) a combination of the landscape and home ignition zone. We evaluated each treatment scenario in terms of exposure and expected structure loss compared to a no-treatment scenario. The potential revenue from wood products was estimated for each scenario to assess the cost-efficiency. We found that the combined landscape and home ignition zone treatment scenario which treated 5.7% of the study area resulted in the highest overall reduction in predicted exposure (47.5%, 100 structures yr−1) and predicted loss (69.1%, 57 structures yr−1). Home ignition zone treatments provided the best predicted economic and per area treated performance where exposure and loss were reduced by one structure by treating 89 and 111 ha per year, respectively, with an annual cost of $33,645 and $73,672. Revenue from thinning was the highest for landscape fuel treatments and covered 16% of the required investment. This work highlighted economic and risk tradeoffs associated with alternative fuel treatment strategies to protect developed areas from large wildland fires.

Short-Term Influence of Burning on Species Abundance, Biomass Production, Wood Plant Density and Browsing Unit in an Albany Thicket of the Eastern Cape, South Africa

Fire is regarded as management practice for maintaining grasslands and savannas. The vegetation occurring in fire prone areas becomes highly adapted to fire occurrences in savannas and grasslands. However, documentation on the influence of burning on vegetation is still limited in an albany thicket biome. The aim of the study was to evaluate the short-term influence of burning on species abundance, biomass production, wood plant density and browsing unit. Six plots (2500 m2) were demarcated; in each plot two parallel transects of 120 m2 with 5 m distance apart were measured. Three plots were burned in spring season, while no burning was done on the other remaining plots. Forty points per plot were obtained using step-point method to determine the relative abundance at burned and unburned plots. Woody plants occurred within a transect were identified, counted and recorded to determine density and browsing unit. Three quadrats (i.e. 0.25 m2) per plot were randomly laid within the transect; aboveground plant material within a quadrat were harvested. A total of 18 samples were harvested and oven dried at 60˚C for 48 hours to determine biomass production. The results showed that Themeda triandra (14%) and Panicum maximum (10%) were most abundant grass species at the burned plots. Burned plots had significantly higher biomass production (4804 kg/ha) compared to unburned plots (3641 kg/ha). Vachellia karoo (burned: 65.85% & unburn: 13.70%) and Searsia pallens (burned: 26.83% & unburned: 6.85%) were most dominant wood plant species at both burned and unburned plots. Burned plots had the highest browsing unit (3221 BU/ha) compared to unburned plots (2058 BU/ha). The decrease of woody plants at the burned plots proved that fire has a tremendous potential for managing woody plants. This study provided information on short-term influence of burning, however further long-term trials are required to determine the effects of burning.

Professional wildfire mitigation competency: a potential policy gap

Studies show that effective strategies to mitigate the risk of structural damage in wildfires include defensible spaces and home hardening. Structures in the western United States are especially at risk. Several jurisdictions have adopted codes that require implementation of these strategies. However, construction and landscaping professionals are generally not required to obtain credentials indicating their competency in mitigating the risk of structural damage in a wildfire. We discuss the implications of this policy gap and propose a solution to bolster competency of professionals in wildfire protection as communities further expand in fire-prone areas.

Short-term hydrological response of soil after wildfire in a semi-arid landscape covered by Macrochloa tenacissima (L.) Kunth

A proper monitoring and management of semi-arid landscapes affected by wildfire is needed to reduce its effects on the soil hydrological response in the wet season. Despite ample literature on the post-fire hydrology in forest soils, it is not well documented how the hydrologic processes respond to changes in vegetation cover and soil properties of semi-arid lands (such as the forest and areas with sparse forests) after wildfire. To fill this gap, this study evaluates soil hydrology in a semi-arid soil of Central Eastern Spain dominated by Macrochloa tenacissima (a widely-spread species in Northern Africa and Iberian Peninsula) after a wildfire. Rainfall simulations were carried out under three soil conditions (bare soil, burned and soils with unburned vegetation) and low-to-high slopes, and infiltration, surface runoff and erosion were measured. Infiltration rates did not noticeably vary among the three soil conditions (maximum variability equal to 20%). Compared to the bare soil, the burned area (previously vegetated with M. tenacissima) produced a runoff volume lowered by 27%. In contrast, in the area covered by the same species but unburned, runoff was lowered by 58%. The burned areas with M. tenacissima produced soil losses that were similar as those measured in bare soils, and, in steeper slopes, even higher. Erosion was instead much lower (−83%) in the sites with unburned vegetation. Overall, the control of erosion in these semi-arid lands is beneficial to reduce the possible hydrological effects downstream of these fire-prone areas. In this direction, the establishment of vegetation strips of M. tenacissima in large and steep drylands of bare soil left by fire may be suggested to land managers.

Peramalan Jumlah Titik Api Pada Lahan Gambut Kalimantan Menggunakan Model Zero-Inflated Poisson Regression

Forest fires are a global problem that often occurs in Indonesia, especially during the dry season. One indication of forest fires is the occurrence of hotspots. Monitoring of hotspots is part of the effort to make early warnings of the dangers of forest fires. The island of Kalimantan is an island where most of the land consists of peatlands and is prone to hotspots. The hot, dry season due to sunlight and the lack of rainfall can make peatlands a fire-prone area. This study uses the Zero-Inflated Poisson Regression model to predict the number of hotspots on peatlands by considering climatic factors, namely sun exposure and rainfall. This research was conducted in four square areas (Area-1, Area-2, Area-3, Area-4) with the Tjilik Riwut Meteorological Station as the center point. The study uses hotspot monitoring data from the Terra Satellite owned by NASA (the National Aeronautics and Space Administration) and climate data in the form of data on sun exposure and rainfall from the BMKG (Meteorology, Climatology, and Geophysics Agency). The results show that the Zero-Inflated Poisson Regression model can model the four observed areas quite well with an RMSE of 12.69 in Area-1, Area-2 with an RMSE value of 10.05, RMSE of 11.53 in Area-3, and finally Area-4 with an RMSE value of 16.40.

Promoting Landscape-Level Forest Management in Fire-Prone Areas: Delegate Management to a Multi-Owner Collaborative, Rent the Land, or Just Sell It?

Forest management at the landscape level is a requirement for reducing wildfire hazard. In contexts where non-industrial private forest ownership prevails, the collaboration among multiple owners has been proposed as the way forward to reach consistent fuel management at that level. The current literature has been focused on identifying the factors that lead to collaboration among owners. In this study we explored other ways to reach landscape-level management in addition to the collaborative way, such as those that may be promoted through land renting or selling. Different contexts and owner types may require different solutions. Thus, we explicitly asked which alternative would be chosen by a given forest owner, from the following set: keeping individual management, entering a multi-owner collaborative arrangement where they delegate management, renting to a pulp company; or selling the land. In a context of small-scale ownership and high recurrence of wildfires in Portugal, a face-to-face survey was carried out to a sample of landowners. Our results suggest that there is not an a priori generalized unwillingness of owners to delegate management, rent or sell the land and thus they seem prone to align themselves with policy strategies to promote management at the landscape level. Multinomial logit regression modelling allowed us to explain and predict owners’ choices among the aforementioned set of alternative management options. We found that choosing multi-ownership collaboration, as opposed to keeping current individual management, is associated with passive management under harsher conditions, by non-residents without bonding capital. The identified factors of owners’ choices show the limited scope of tenancy and land-market mechanisms to promote landscape-level management. The best policy option was found to depend on the owner profiles prevailing in the target area. This suggests that studying the existing context and owner types is required to design effective policies.

Spatiotemporal Patterns of Burned Areas Based on the Geographic Information System for Fire Risk Monitoring

Forest and land fires occur every year in Indonesia. Efforts to handle forest and land fires have not been optimal because fires occur in too many places with unclear patterns and densities. The study analyzed the spatiotemporal patterns of burned areas and fire density in fire-prone areas in Indonesia. Data of burned areas were taken from http://sipongi.menlhk.go.id/. The website collected its data from NOAA (National Oceanic and Atmospheric Administration) images. Data were analyzed using the hot spot analysis to determine the spatiotemporal patterns of the burned areas and the kernel density analysis to examine the density of land fires. Findings showed that the spatiotemporal pattern from 2016 to 2019 formed a hot spot value in the peatland area with a confidence level of 90–99%, meaning that land fires were clustered in that area. In addition, the highest density of land fires also occurred in the peatland areas. Clustered burned areas with high fire density were found in areas with low–medium vegetation density—they were the peatland areas. The peatland areas must become the priority to prevent and handle forest and land fires to reduce fire risks.

Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands

Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.

Quantifying the joint distribution of drought indicators in Borneo fire-prone area

Borneo island is prone to fire due to its large peat soil area. Fire activity in Borneo is associated with regional climate conditions, such as total precipitation, precipitation anomaly, and dry spells. Thus, knowing the relationship between drought indicators can provide preliminary knowledge in developing a fire risk model. Therefore, this study aims to quantify the copula-based joint distribution and to analyze the coincidence probability between drought indicators in Borneo fire-prone areas. From dependence analysis, we found that the average of 2 months of total precipitation (TP), monthly precipitation anomalies (PA), and the total of 3 months of dry spells (DS) provides a moderate correlation to hotspots in Borneo. The results show the probability of the dry-dry period is 26.63, 17.66, and 18.54 % for TP-DS, PA-DS, and TP-PA, respectively. All of these are higher than the probability of the wet-wet period, which is 25.01, 16.12, and 17.98 % for TP-DS, PA-DS, and TP-PA, respectively. Through the probability, the return period of TP-DS in the dry-dry situation 3.2 months/year, meaning the dry situation in total precipitation and dry spells that occur simultaneously could appear about 3 months in a year on average. Furthermore, the return period of PA-DS and TP-PA in the dry-dry situation is 2.12 and 2.22 months/year, respectively. Moreover, the probability of dry spells in dry conditions when given total precipitation in dry conditions is higher than given precipitation anomalies in dry conditions.

Peatland ecological restoration based on independent community groups through revegetation in Tanjung Leban Village, Bengkalis Regency, Riau

Ecological restoration of peatlands leads to the destruction of peatland ecosystems caused by human activities, whether intentional or not. As a result of the damage caused various kinds of problems such as land fires. Tanjung Leban Village is one of the villages with a fairly high rate of fire, especially in 2012, 2013, and 2015. Many burnt lands are left unused, which in turn becomes a fire-prone area due to the lack of control over the land. In response to this, it is necessary to carry out peatland ecological restoration activities in Tanjung Leban Village through revegetation activities. Implementation of activities with the mentoring method through village facilitators stationed in Tanjung Leban Village. The implementation process begins with socialization, which continues with group formation, procurement of seeds, land clearing, and planting. The implementation of this revegetation focuses on the participation of the community who are members of the Peat Care Community Group (MPG) in Tanjung Leban Village. This activity was carried out in the community area (group members) with an area of 14.7 ha and 14,381 plants. The number of plants is divided into 2 categories, namely natural wood species and fruit plants.

Copula-based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas

Copula-based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas

Indirect contributions of global fires to surface ozone through ozone–vegetation feedback

Abstract. Fire is an important source of ozone (O3) precursors. The formation of surface O3 can cause damage to vegetation and reduce stomatal conductance. Such processes can feed back to inhibit dry deposition and indirectly enhance surface O3. Here, we apply a fully coupled chemistry–vegetation model to estimate the indirect contributions of global fires to surface O3 through O3–vegetation feedback during 2005–2012. Fire emissions directly increase the global annual mean O3 by 1.2 ppbv (5.0 %) with a maximum of 5.9 ppbv (24.4 %) averaged over central Africa by emitting a substantial number of precursors. Considering O3–vegetation feedback, fires additionally increase surface O3 by 0.5 ppbv averaged over the Amazon in October, 0.3 ppbv averaged over southern Asia in April, and 0.2 ppbv averaged over central Africa in April. During extreme O3–vegetation interactions, such a feedback can rise to >0.6 ppbv in these fire-prone areas. Moreover, large ratios of indirect-to-direct fire O3 are found in eastern China (3.7 %) and the eastern US (2.0 %), where the high ambient O3 causes strong O3–vegetation interactions. With the likelihood of increasing fire risks in a warming climate, fires may promote surface O3 through both direct emissions and indirect chemistry–vegetation feedbacks. Such indirect enhancement will cause additional threats to public health and ecosystem productivity.

A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego

Early detection of forest fire is helpful for monitoring the spread of fire promptly, minimizing the loss of forests, wild animals, human life, and economy. The performance of brightness temperature (BT) prediction determines the accuracy of fire detection. Great efforts have been made on BT prediction model building, but there still remains some uncertainty. Based on the widely used contextual BT prediction model (CM) and temporal-contextual BT prediction model (TCM), we proposed a spatio-temporal contextual BT prediction model (STCM), which involves historical images to contrast the BT correlation matrix between the pixel to be predicted and its background pixels within a dynamic window, and the spatial distance factor was introduced to modify the BT correlation matrix. We applied the STCM to a fire-prone area in San Diego, California, US, and compared it with CM and TCM. We found that the average RMSE of STCM was 12.54% and 9.12% lower than that of CM and TCM, and the standard deviation of RMSE calculated by STCM was reduced by 12.04% and 15.57% compared with CM and TCM, respectively. In addition, the bias of STCM was concentrated around zero and the range of bias of STCM was 88.7% and 15.3% lower than that of CM and TCM, respectively. The results demonstrated that the STCM can be used to obtain the highest BT prediction accuracy and most robust performance, followed by TCM, and CM performed worst. Our research on the BT prediction of potential fire pixels is helpful for improving the fire detection accuracy and is potentially useful for the prediction of other environmental variables with high spatial and temporal autocorrelation. However, the requirement of high-quality continuous data will limit the application of STCM in cloudy and rainy areas.

Characteristics of the spatial and temporal distribution of fire regime in ONE OF the most fire prone Region Of The Russian Far East

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.