Wildfires are a type of natural hazard that is affected by many environmental factors and the interactions between those factors [1
]. Wildfires play a macro-control role in the self-renewal and succession of ecosystems over large scale spatial and temporal ranges. The fire occurrence cycle affects changes in the geochemical cycle and the speed and stage of community succession [2
]. Under global warming, the global climate has become abnormal in recent decades, and aridification has increased in the middle and high latitudes [3
]. Specifically, high-temperatures, low-temperature freezing damage, uneven precipitation, continuous droughts in the spring and summer and other abnormal climatic phenomena are increasing [4
]. As a result, the fire frequency and the duration of disturbances are increasing [5
]. These changes affect the material cycles, energy flows, and information transmission of ecosystems. The spatial distribution pattern of fire occurrences has an important effect on the spatial distribution and balance of the fuel productivity, landscape characteristics, and land use patterns [6
]. Moreover, fires can burn large areas within a short time, destroying the ecosystem structure and function, reducing the grade of vegetation products, dramatically changing the environment, and even threatening people’s lives and property and national ecological resources [7
]. Therefore, the study of the spatial distribution patterns and influencing factors of wildfires is helpful for revealing the natural causes of fire occurrences and their impacts on various ecological processes.
A common approach when analysing the influencing factors of wildfires is the use of a traditional method that can be used to predict wildfire occurrences with respect to different variables. The traditional method is powerful in terms of its prediction power, but it is limited by the assumptions of normality and linear relationships [8
]. In recent years, the Random Forest (RF) model has been widely applied in the field of ecology and exhibits a high prediction accuracy [9
]. A few scholars have applied RF models to forest fire prediction, and those models have shown good predictive power [10
]. Compared with traditional methods, machine learning algorithms can overcome subjective factors and are widely used in various research fields [11
]. The commonly used machine learning algorithms include artificial neural networks, support vector machines and classification and regression trees. However, the above machine learning methods also have some shortcomings. Artificial neural networks have the disadvantages of difficulty in determining the initial weights and a slow convergence speed. Classification and regression trees are sensitive to data noise and training sample errors. How the selection of the kernel functions of support vector machines affects the classification accuracy remains uncertain [12
]. These shortcomings affect the accuracies of the researches results. By contrast, RF is a non-parametric machine learning ensemble algorithm that has a strong anti-noise ability and low sensitivity to outliers and can effectively overcome the over fitting phenomenon [13
]. Based on these advantages, RF has achieved high classification accuracy in research, and it is gradually becoming widely used in the remote sensing image classification field [14
The Mongolian Plateau is the main region of temperate grassland in Eurasia and is an area where fires are extremely active [15
]. Some research results in this area show that the fire occurrence in parts of the Mongolian Plateau is dominated by climatic factors such as precipitation, humidity, temperature and wind speed [16
]. In the context of global warming, wildfire prevention is facing a severe challenge [17
]. The occurrence of a single wildfire may be regarded as a random event, but the occurrence and distribution of wildfires on the landscape and even at the regional scale are not completely random; rather, they present certain spatial and temporal distribution characteristics [19
]. Traditional wildfire occurrence information is mainly derived from statistical data; it is difficult to cover large areas because data collection is difficult, and quantifying the data space is challenging. Around the year 2000, the rapid development of remote sensing technology began to greatly improve the accuracy of large-scale and sudden wildfire monitoring. Burned area data are widely available in near-real time by remote sensing. We select the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MCD64A1 data to study wildfires on the Mongolian Plateau and accomplish the following objectives: (1) identify the spatial distribution of wildfires on the Mongolian Plateau, (2) understand the comprehensive influencing factors affecting fire occurrence, and (3) produce spatially explicit statistical models and a map predicting patterns of wildfires on the Mongolian Plateau based on various driving factors.
When Ripley’s K function is used to calculate the fire risk period distribution pattern on the Mongolian Plateau and the scale is less than 1150 km, the wildfires are clustered. This distance scale is less than 1/2 the length of the Mongolian Plateau; that is, it does not exceed the boundary effects in the study area and meets the requirements of Ripley’s K function. Therefore, the result of the wildfire aggregation distribution pattern in the study area is credible. In northeast Inner Mongolia, the fire risk periods of wildfires are distributed in a cluster pattern; among them, the aggregated distribution patterns in January, December, and July are not significant, and those for the rest of the months are significant [35
]. The spatial distribution is similar to the results of our study.
The RF model was used to analyse the driving factors of fire occurrence, and the results showed that the contribution rates of the FVC, LA, elevation, pre, wet, and tmx were the largest, while the influence of the aspect was the smallest. The results of this study showed that the FVC is the material basis for the occurrence of wildfires. The occurrence and development of wildfires is closely related to the characteristics, quantity, and spatiotemporal distribution of the FVC. The FVC changes with different seasons and within different time periods of the same season [26
]. Meteorological factors mainly affect the occurrence of wildfire on a large scale [5
], e.g., high temperatures and sunshine duration are variables that either alone or together can contribute to increased potential evaporation from fuels and decreased moisture of wildfire fuel, leading to an increased possibility of wildfire occurrence [34
]. High precipitation and relative humidity contribute to fuel moisture, which in turn decreases the probability of wildfires [36
]. Topographic factors have an indirect impact on the occurrence and spread of wildfires by affecting microclimates in local areas. Studies have shown that the topography has a certain impact on the occurrence of wildfires, and wildfires occur more frequently especially in arid sunny slopes, on ridges and in low altitude areas [37
]. In this paper, the elevation has a greater impact than do the slope or aspect.
In addition to the above natural factors, human factors and differences between the two administrative regions also play important roles. In recent decades, the lifestyle of the herdsmen in Inner Mongolia has gradually changed from nomadism to settlement. The number of live stock has been recklessly increased to pursue economic benefits, which has caused the vegetation to exceed its threshold carrying capacity. Thus, the land degradation is very serious [38
]. In Mongolia, herdsman still maintain a nomadic life. Continuous migration and recycling of the ecosystem allow for a sufficient recovery period, which mitigates degradation, and the inhibitory effect on fire is weaker [39
]. Moreover, human activity and landscape destruction in areas with higher populations also reduce the area subjected to fire. In Inner Mongolia, strict control measures and efforts to fight fires in the region have been strongly suppressed. In addition, human and financial constraints have a negative effect on fire mitigation and fire-fighting, and management measures in Mongolia are relatively underdeveloped [40
]. Herdsmen take a natural approach towards wildfire. Therefore, the occurrence of wildfire is close to the natural level observed in the ecosystem. The differences in wildfire characteristics between Inner Mongolia and Mongolia indicate that in Inner Mongolia human activities have become an important factor affecting wildfire behaviours.
The likelihood distribution of wildfires on the Mongolian Plateau shows that wildfires are mainly concentrated in the border areas of Inner Mongolia and Mongolia, the northern part of Mongolia, and the northeastern and central parts of Inner Mongolia. In the northern and eastern areas of the Mongolian Plateau, due to the good meteorological and vegetation conditions, the species regeneration and biomass increase rapidly after burning [41
]. The fuels reaccumulate in a short time and cause wildfire. Therefore, the northern and eastern areas have the highest probability of wildfires. Because of the serious desertification and less litter fuel, it is difficult for wildfires to occur and spread in desert areas, and the probability of fire occurrence is the lowest in these areas [42
]. On the other hand, the contribution rate of human factors in some areas with a high probability of wildfire occurrence is also higher. For example, in the eastern part of Inner Mongolia, there is more cultivated land; in the spring and autumn, farmers burn straw infrequently to increase the probability of wildfires [29
This study continuation the theory and techniques of wildfire event research and advance the research of risk and risk chains by enabling decision-makers to determine the probability of wildfire in the next year according to the driving factors or fire risk zone and to plan for disaster prevention. However, the Mongolian Plateau is composed a variety of land use types, which are influenced by various factors (in addition to the driving factors considered in this paper), and lead to uncertain wildfire driving factors. Moreover, because part of the data in the study area cannot be obtained, the driving factors cannot be comprehensively analysed and the satellite data cannot be verified. Therefore, to further explore the spatial distribution mechanism of wildfire occurrence, we need to further consider the impacts of specific factors on wildfire occurrence. For example, the moisture content of combustibles is an important factor to determine whether combustibles can ignite and burn. The flammability of combustibles increases with the decrease in the moisture content. The moisture content of grassland combustibles is constantly changing under the influence of rainfall, snow and air humidity. Among the human activity factors, the population size, age structure, industrial structure, and farming methods all influence fire occurrence. There is a certain relationship between these driving factors and fire occurrence, which makes fire occurrence more complex. Selecting appropriate research scales in later studies and studying the specific driving factors affecting fire occurrence is one of the most important methods by which to reveal the law of fire occurrence. The relationship between the parameters of wildfires and their influencing factors varies with the regional scale chosen. Therefore, future research should focus on how to establish a clear relationship between wildfires and their influencing factors. This approach would lay the foundation for further predicting the occurrence of wildfires under future climate change.
The distribution of wildfires in the fire risk period is an aggregated distribution within 1150 km. The aggregation intensity in May is the largest, followed by those in June, September and October, and the smallest is in April. The contribution rates of various influencing factors and the probability of fire occurrence on the Mongolian Plateau were analysed using an RF model, and the results indicated that the FVC, LA, elevation, pre, wet and tmx had the largest contribution rates, while aspect had the lowest contribution rate. The areas with the highest wildfire probabilities were mainly concentrated in the northern, eastern and southern parts of the Mongolian Plateau and in the border area between Inner Mongolia and Mongolia, but wildfires do not occur or occur less frequently in the hinterland area. The wildfire risk zones on the Mongolian mostly occur in the east and north and seldom occur in the south and west. In Inner Mongolia, the wildfire risk zone shows a striped pattern from the northeast to southwest. The RF model fits all the samples very well. The positive and negative residuals of the RF model were smaller across the Mongolian Plateau. Maps depicting the probability of wildfire occurrence identify fire prone zones on the Mongolian Plateau, where more fire prevention resources such as fire towers and inspection stations should be allocated.