1. Introduction
Since the 21st century, global active fire has become increasingly frequent [
1,
2,
3]. Active fire is a process that affects the carbon cycle of the global terrestrial ecosystem [
4,
5]. Active fire can drastically change environmental conditions in a short period through vegetation burning and temperature increase, affecting terrestrial ecosystems and the natural carbon cycle [
6]. It can quickly change the physical and chemical properties of plant soil [
7,
8,
9,
10], affect the distribution structure of vegetation, reduce the fertility of surface soil and the ability of water conservation, and has a substantial impact on the climate system and biogeochemical cycle [
11]. It has a huge impact on animals, plant microorganisms, and even the whole ecosystem, seriously affecting species diversity [
10,
11]. At the same time, active fire also threatens the safety of human life and property and has a significant impact on regional societies and economies [
3].
At present, information about active fire is mainly obtained by remote sensing technology [
12,
13]. Through satellite monitoring, the brightness temperature of the potential fire is compared with that of the surrounding land cover. If the brightness temperature difference exceeds the given threshold, the potential fire is recognized as an active fire or “hot spot” [
6]. The surface temperature anomalies monitored by remote sensing mainly include volcanic eruption, forest, grassland, burning of artificial targets, open burning of straw, and industrial heat emissions [
14,
15]. From the perspective of monitoring and spatiotemporal distribution of active fires, there are few studies on active fires in the Chinese mainland, mainly focusing on fire research, and most of them are areas with high fire incidence on a small regional scale, such as in the Beijing–Tianjin–Hebei region [
16], Jilin province [
17], Yunnan province [
18], and Fujian province [
19]; there are few studies on a large regional scale. The development of remote sensing technology makes it possible to obtain data in a large range, and large-scale active fire monitoring can be carried out [
15]. Studying the probability, intensity, and spatiotemporal distribution characteristics of active fires in the Chinese mainland can not only provide a physical basis for remote sensing of vegetation fires on a regional scale, but also be of great significance for their economic losses, air pollution, and public safety assessment [
20].
From the perspective of the influencing factors of active fire, combustibles, combustion-supporting substances, and ignition sources are the three basic elements for fire occurrence. The occurrence of active fire is closely related to local meteorology, terrain, vegetation, human activities, and other factors. Depending on the geographic location, the influencing factors also change, and the likelihood of active fires is also different. Currently, the research methods used are mainly to determine the influencing factors of fire risk and establish prediction models, such as principal component analysis, Fuzzy clustering analysis, the Keetch –Byram Drought Index method, Fuzzy comprehensive evaluation method, and system dynamics [
21,
22,
23,
24,
25]. These methods mainly use experience and mathematical statistics, with low accuracy and poor realism. However, the occurrence mechanism of active fire is very complex, and the influencing factors vary greatly in different regions. The fire risk assessment model can pass through the complicated appearance of the fire, after a large number of quantitative and qualitative analyses, simplify the complex problem, and master its essence, but it can also verify the model and evaluate the error [
26,
27]; thus, this paper adopts the fire risk assessment model to analyze the influence factors of active fires in the Chinese mainland. The main fire models include the BLR model, GWR model, GWLR model, and so on [
28,
29,
30,
31]. The GWLR model introduces geographic location as a variable into the regression parameters, which helps to improve the reliability of the model when studying the fire risk in a large area, conducting more scientific fire risk zoning, and accurately exploring the influencing factors of active fire occurrence.
In this paper, the occurrence probability and intensity of active fires in the Chinese mainland from 2001 to 2018 were analyzed to reveal the temporal and spatial distribution characteristics of active fires with different probability and intensity levels, providing a research basis for future fire prevention work. The fire risk influencing factors are selected from the four aspects of meteorology, topography, vegetation, and human activities, and the relationship between active fire and each influencing factor is analyzed. Then, multiple linear tests are carried out, and the appropriate factors are selected to participate in the establishment of the GWLR fire risk assessment model. Finally, the modeling results are analyzed to obtain the fire risk probability and fire risk impact factor zoning in the Chinese mainland to explore the influence factors of active fires and put forward corresponding fire prevention suggestions.
2. Data Sources
The data used herein mainly include five types: active fire, meteorology, DEM, NDVI, and socioeconomic data (
Table 1). Active fire data products were mainly obtained from NASA’s fire information resource management system (FIRMS). It has made certain progress in fire condition assessment [
32]. This paper selected MODIS C6 active fire position vector data for subsequent analysis. The MODIS C6 data from 2001 to 2018 are mainly obtained by real-time observation of Terra and Aqua satellite sensors four times a day. Terra passes over the equator daily at approximately 10:30 AM and 10:30 PM (MLT) local time to obtain active fire information, with data acquisition starting in November 2000. Aqua (EOS PM) passes by the equator at about 1:30 PM and 1:30 AM (MLT) to obtain active fire information, with data acquisition starting in July 2002. The coverage is global, the spatial resolution is 1 km, and the coordinate system is WGS 84. Although there are VIIRS V1 data, which are mainly passed by the VIIRS sensor mounted on the S-NPP satellite near the equator with a higher spatial resolution (375 m), they are a shorter time series (2012 to date) that cannot meet the research needs [
33,
34]. Therefore, they were not considered.
In the research on the influencing factors of active fires in the Chinese mainland, many factors affect the occurrence and spread of active fire, and the relationship between each factor is complex. Referring to relevant studies [
35,
36,
37,
38,
39], nine indicators including temperature (QW), precipitation (JS), surface temperature (LST), elevation (GC), slope (PD), normalized vegetation index (NDVI), distance to the nearest path (DL), population density (POP), and GDP were selected as fire risk impact factors from four aspects: meteorology, topography, vegetation, and human activities. The sources and main purposes of the above data, which all have a spatial resolution of 1 km, can be found in
Table 1.
The data processing are as follows: (1) The annual average temperature spatial interpolation dataset, China annual vegetation index (NDVI) spatial distribution dataset, China GDP spatial distribution kilometer grid dataset, and China population spatial distribution kilometer grid dataset are used to calculate the average value of 2000–2018 through the grid calculator in ArcGIS and to obtain the QW, GDP, and POP factors. (2) The DL factor is generated by calculating the Euclidean distance from the road spatial distribution data. (3) The GC factor is obtained from the national DEM data, and then the PD factor is generated by the ArcGIS slope calculation. JS and LST factors are calculated by ArcGIS from the monthly precipitation dataset in China and the surface temperature dataset in China. Finally, the raster data of all factors were projected and reclassified. The projection is “China Lambert Conformal Conic”.
5. Discussion
First of all, this study presents the spatial distribution characteristics of active fires in the Chinese mainland from 2001 to 2018 at different time scales (such as annual scale and monthly scale). The time distribution characteristics of active fires indicate that the number of active fires varies greatly in different years. The years of active fires are mainly concentrated in 2013–2015, which are the super El Niño years. El Niño is an important influencing factor in the global atmospheric terrestrial ecosystem. It will cause temperature rise and continuous drought, lead to regional precipitation reduction and the increase in land water storage capacity, and further aggravate the occurrence of active fires in forests and farmland [
40]. The number of active fires varies in different months, mainly from February to April and October. Due to the changeable and windy weather, there are many combustibles in this period, and combustibles and waste plastic from branches are naturally piled up, making it very easy to cause fires due to the careless use of open fires. In addition, this period coincides with the Chinese New Year and National Day holidays, with frequent human activities and more man-made fires.
Additionally, taking the Chinese mainland as the whole research area, the temporal and spatial variation characteristics of various fire types, the temporal and spatial distribution of occurrence probability and intensity, and the influence factors are analyzed and discussed from shallow to deep. As a developing country with the largest population in the world, it has a vast territory with complex and diverse landforms, climate types, and vegetation types. For instance, the climate is relatively dry in the north and wet in the south. The climate is dominated by the East Asian monsoon in East China, where the summer is wet and hot while winter is dry and cold [
50]. It is known that about 75% of the land areas of China are covered by different vegetation types, mainly including forests, savannas, croplands, and grasslands [
51]. Therefore, it is reasonable and appropriate to take the Chinese mainland as the research area. On the one hand, it can provide a reference for relevant international research. On the other hand, it is helpful for the planning and implementation of fire risk-related policies. In addition, most scholars devote themselves to the study of a certain type of fire. For instance, Ma et al. [
52] used a random forest algorithm to analyze and study forest fires in China; Mohammadi et al. [
53] took Iran as an example and studied the modeling of forest fire hazard areas based on the methods of logistic regression and GIS. Moreover, more attention is paid to some areas with high fire incidences, such as Li et al.’s [
54] analysis and research on the 16-year forest fire risk of typical subtropical regions in Zhejiang province. Therefore, there may be some problems, such as insufficient attention to the overall situation of active fires and insufficient overall grasp. This study also shows and analyzes the typical provinces of active fire in the Chinese mainland (Heilongjiang, Guangxi, Henan, Shandong, Fujian, and Qinghai). The results showed that the dynamic changes in the frequency and intensity of active fires in the above provinces were similar to those in the Chinese mainland as a whole. At the same time, Zhang et al. [
55] used MODISC6 data from 2001 to 2019 and VIIRS V1 data from 2012 to 2019 to analyze the spatiotemporal characteristics of active fires in the Arctic region and establish a fire risk assessment model based on logistic regression. The results obtained from the two sets of active fire data are consistent, which further proves the scientificity of the research results in this paper. Chang et al. [
56] used the data collected by from the Chinese Forestry Science Data Center to study the forest fires in Heilongjiang province from 1980 to 2009. Their research results are similar to the results of this study, which further explains the reliability of the results of this study.
The spatiotemporal distribution results of active fires further show that there are significant differences in the occurrence probability and intensity of active fires in various provinces of the Chinese mainland. This is because there is spatial heterogeneity in climatic conditions, topography, vegetation status, and population density in various regions. The slope of the plain is gentle, the terrain is flat, the precipitation is abundant, and among the forest types, the proportion of artificial young and middle-aged forests and high-fat tree species is large. It is easy to catch fire in arid and high-temperature environments, and the spread speed is fast, which increases the possibility of forest fires [
11]. In addition, because of the large population density and frequent human activities in the plain area, fires occur very easily when smoking and burning wasteland, charcoal, graves, and paper in places with combustibles, and the fire is more difficult to extinguish due to the long rescue time.
In some northern provinces, straw burning in the open air is the leading cause of fire, especially in the three northeastern provinces. A large amount of CO
2 generated by straw burning accelerates the rise of ambient temperature, leading to active fires [
57]. The comprehensive utilization of straw resources in Northeast China is still in the primary extensive recycling stage. As the cost of returning straw to fields and leaving fields in Northeast China is higher compared to other regions, the rough treatment of open burning leads to an increase in the number of active fires. In addition, affected by snow cover, ground freezing, and other factors, the peak of active fire mainly occurs in March, April, and October. Heilongjiang province is located in the northeast of China, and there are many forest fires caused by lightning, agricultural fires, and outdoor smoking. Lightning fire is the most frequent and harmful fire source [
56]. The southern region is rich in forest resources, high in vegetation coverage, and prone to forest fires. The provinces with the largest number of active fires in the south are Guangdong, Guangxi Zhuang Autonomous Region, and Jiangxi. Influenced by the tradition of “smelting mountains”, active fires are high in spring. The “Mountain Refining” activity is mainly carried out in February and March of each year (Lantern Festival), mainly to thoroughly clean up the cutting residues, attachments on the ground, and weeds, etc., by burning for afforestation, to achieve the goal of regulating the forest land at a low cost. Although many provinces in Southern China have successively promulgated the policy of forbidding mountain burning for afforestation, especially during the special forest fire prevention period, due to the deep-rooted traditional concept, the land preparation activities mainly based on “mountain burning” still occur frequently in forestry production [
48]. In addition, Guangxi province has a subtropical humid monsoon climate with obvious dry and wet seasons. In the winter half year, it is mainly affected by the denatured polar continental air mass. The weather is cold, the humidity is low, and the temperature changes greatly, so forest fires occur easily [
58]. Guangdong province is located in the coastal area of China, with a developed economy, high industrial level, and large population density. Due to many industrial heat sources, people use fire and electricity carelessly, which increases the possibility of fire.
Then, this study comprehensively explored the influencing factors of fire risk from four aspects: meteorology, topography, vegetation, and human activities. Based on the GWLR fire risk assessment model, this paper analyzes the fire risk influencing factors and their relationship in the Chinese mainland and draws the zoning map of fire risk influencing factors. When considering the influence factors of fire risk, the influence factors of human activities, such as distance to the nearest path, population density, and GDP, are considered and taken as important influence factors, which is also different from other literature. For example, Wang et al. [
42] did not consider the population density and GDP factor data in the analysis due to their limited access. 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. However, we found that the number of active fires in the Chinese mainland is mostly concentrated in the area <60 km away from the road. Of course, differences in research scales may also lead to different research results. Tian et al. [
44] studied the spatiotemporal distribution characteristics of forest fires in China from 2008 to 2012 and found that most forest fires occurred in spring (83%), with the highest incidence in March (60.0%), which is consistent with the results of this study. In short, compared with previous relevant studies, we have selected more comprehensive risk fire influencing factors. The multilinearity test not only improves the reliability of the GWLR fire risk assessment model but also makes the research results more reasonable.
Furthermore, through the analysis of eight zones of fire risk influencing factors, it is found that different types of zones have different fire influencing factors. The vegetation cover is better in areas mainly affected by temperature and NDVI. In these areas, especially in the case of high-temperature weather and abnormal surface temperature, it is necessary to improve the protection awareness of areas with high vegetation coverage and increase the removal of forest combustibles. The region mainly affected by temperature has a dry climate, less precipitation, and low air humidity. When the temperature rises, the combustibles reach the ignition point, making it easy to cause fires. At present, the main countermeasures for desertification control in China are grass squares and planting sand-proof plants. If there is a fire in these places, it will have a serious impact on desertification control in China. These areas should strengthen the monitoring of extremely high-temperature weather to avoid fire. The areas mainly affected by NDVI are covered with vegetation, but the temperature is not high. Vegetation is the only factor that affects the occurrence of active fires. Therefore, attention should be paid to forest fire prevention to avoid man-made fires. The area mainly affected by elevation has a high elevation, which makes disaster relief and fire prevention more difficult. Areas mainly affected by NDVI, temperature, elevation, and slope, and areas with high elevation and gradients, have vegetation coverage. More attention should be paid to mountain fires in these areas, as well as to the vegetation growth status and temperature variation.
To sum up, this study takes the Chinese mainland as the research area and reveals and explores in detail the spatiotemporal distribution characteristics of the occurrence probability and intensity of active fires and their influencing factors from 2001 to 2018. In the process of exploring the influencing factors, the factors of meteorology, terrain, vegetation, and human activities were considered, and the GWLR model was reasonably constructed through multiple linear tests. When considering the influencing factors of human activities, we took population density and GDP factors into account, which makes our research results more scientific. Secondly, based on clarifying the relationship between active fire and impact factors, the fire risk impact factor zoning map was drawn. We put forward differentiated fire prevention suggestions for eight different types of fire risk impact factor zoning. Our work has enriched the research cases of active fires on different space–time scales. The research ideas and methods are suitable for expansion in different spatial scales (global or local). Taking the Chinese mainland as the research area not only helps to understand and grasp the temporal and spatial characteristics and development trends of active fires in the Chinese mainland, but also provides a scientific reference for the prevention of fire risks and the formulation and realization of the “double carbon” target plan with the country or province as a whole. In addition, the zoning map of fire risk impact factors can also provide a theoretical basis for the Chinese government to formulate differentiated fire prevention policies, which is of great significance for the protection of regional ecosystems.
Although the spatiotemporal distribution characteristics of active fires in China were revealed and its influencing factors were explored in this study, there are unavoidably several uncertainties. On the one hand, it is impossible to effectively monitor all regions with the MODIS instruments aboard the Terra and Aqua EOS satellites, owing to the influence of cloud shadow, thick smoke, forest shrubs, and rainy weather, which may result in the deviation of the spatial distribution pattern of active fires in some regions. In addition, the active fire data have a lower spatial resolution (1 km). The size of the fishing net grid (1 km × 1 km) and the small land area in a fishing net at the boundary of the study area may affect the accuracy of statistical results. However, for the entire research area, the Chinese mainland, its influence is almost negligible. Moreover, this study’s algorithm of the probability and intensity of active fires is more accurate. On the other hand, there are a few limitations in the construction of the fire risk assessment model. For instance, the quantity of randomly selected fire points for modeling and testing is less, which may affect the accuracy of the model. Moreover, the influencing factors of active fires and their relationships with each other are very complex. Although the influencing factors from four aspects—meteorology, topography, vegetation, and human activities—were considered, they are still not comprehensive enough. Additionally, fire risk influencing factor data with lower accuracy and spatial resolution may inevitably result in the deviation of results. Correspondingly, finding and exploring the spatiotemporal patterns, process evolution, and influence mechanism of various fire risks at different spatial and temporal scales by obtaining higher spatial resolution remotely sensed images of active fire and impact factors should be a focus of future work.