Study on Spatial-Distribution Characteristics Based on Fire-Spot Data in Northern China

Abstract

Forest fires are an important disturbance in forest ecosystems and can affect the structure and function of forests. These must be mitigated, to eliminate the associated harmful impacts on forests and the environment as well as to have a healthy and sustainable environment for wildlife. The northern region of China (Heilongjiang, Jilin, Liaoning, and Hebei provinces) is one of the important deciduous broadleaf forests and boreal-forest ecosystems in China. Based on the monitoring of historical remote-sensing products, this study analyzes and explores the spatial- and temporal-distribution patterns of forest fires in Northern China in 2020 and 2021, providing a strong scientific basis for forest-fire prevention and management. The number of monthly forest fires in the northern region in 2020 and 2021 was counted, to obtain seasonal and interannual forest-fire variation. The results show that the number of forest fires occurring in Heilongjiang, Jilin, and Liaoning provinces in 2021 is smaller than that in 2020. The occurrence of forest fires is, mainly, concentrated in spring and autumn, especially in April and October. The number of forest fires that occurred in Hebei Province in 2020 and 2021 was almost the same, showing a slight increasing trend, especially with more growth in February. It is worth noting that Heilongjiang Province is the region with the highest number of forest fires, regardless of the comparison of the total number of forest fires in two years or the number of forest fires in a single year. Spatial-clustering analysis (Ripley’s K) was used to analyze the spatial-distribution pattern of forest fires, in each province of northern China, and the results showed that forest fires were significantly aggregated in all four provinces. The experimental analysis conducted in this paper can provide local forest managers and firefighting agencies with the opportunity to better plan for fighting fires and improve forest-management effectiveness. Based on mastering the characteristics of the spatial and temporal dynamics of forest fires, fire-prevention publicity and education should be strengthened, and scientific forest-fire-prevention measures should be applied to plan reasonable forest-protection policies. This will contribute towards a healthy and sustainable environment.

Accelerating decline of wildfires in China in the 21st century

“…Kernel density analysis is often used to estimate the unit density of element measurements within a certain neighborhood Land cover types and regional divisions distribution in China. to visualize their spatial clustering in a continuous area (Tian et al, 2022). In this study, the frequency and FRP of wildfires were analyzed using the kernel density method to reflect the spatial aggregation characteristics and intensity differences.…”

“…China has the largest subtropical forest in the world (Wang et al, 2013), which combined with the prevalence of Asian summer winds, makes China a typical region for subtropical fire activity in a clarified monsoon climate. Since the 1987 extreme wildfires, China has implemented strict fire prevention and suppression policies, but major wildfire incidents are still frequently reported (Chen et al, 2017;Yi et al, 2017;Fang et al, 2021;Jin et al, 2022;Tian et al, 2022). For example, in 2003, the Heilongjiang forest fire was the second-largest forest fire since the founding of the country after 1987 (Zhang et al, 2012).…”

Spatiotemporal Analysis of Forest Fires in China from 2012 to 2021 Based on Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires

“…The primary sources of active fires in China are cropland fires and forest fires [30]. Cropland fires have predominantly occurred in the agricultural regions of Northeastern, Northern, and Northwestern China [31,32], and forest fires have frequently been observed in the forested regions of Northeastern, Southwestern, and Southeastern China [33][34][35]. However, most of these studies have solely focused on the frequency of fire spots, rather than on fire events.…”