With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm². Meanwhile, the real fire area in FIRER is 5800.9 hm²; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

Burned area is a critical input to biomass burning carbon emissions algorithms and for understanding variability in fire activity due to climate change. This study presents the spatial and temporal patterns of wildland fires in the Mongolian Plateau (MP) using Collection 6 NASA MCD64A1 500 m global Burned Area product from 2001 to 2021. Both inter- and intra-annual fire trends and variations in two subregions, Mongolia and China’s Inner Mongolia, were analyzed. The results indicated that an average area of 1.3 × 10⁴ km² was consumed by fire per year in the MP. The fire season has two peaks: spring (March, April, and May) and autumn (September, October, and December). The profiles of the burnt area for each subregion exhibit distinct seasonality. The majority of wildfires occurred in the northeastern and southwestern regions of the MP, on the border between Mongolia and China. There were 2.7 × 10⁴ km² of land burned by wildfires in the MP from 2001 to 2021, 57% of which occurred in spring. Dornod aimag (province) of Mongolia is the most fire-prone region, accounting for 51% of the total burned area in the MP, followed by Hulunbuir, at 17%, Sukhbaatar, at 9%, and Khentii at 8%. The changing patterns of spatiotemporal patterns of fire in the MP were analyzed by using a spatiotemporal cube analysis tool, ArcGIS Pro 3.0.2. The results suggested that fires showed a decreasing trend overall in the MP from 2001 to 2021. Fires in the southern region of Dornod aimag and eastern parts of Great Xing’an Mountain showed a sporadic increasing trend. Therefore, these areas should be priorities for future fire protection for both Mongolia and China.

Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing’an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China’s boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO₂ emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China’s boreal forests, providing a scientific basis for future boreal forest management and fire management.

Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for ecological balance, social well-being and economic stability. In light of the problems of noise misclassification and manual design of the components in the current forest fire detection model, particularly the limited capability to identify subtle and unnoticeable smoke within intricate forest environments, this paper proposes an improved smoke detection model for forest fires utilizing YOLOv8 as its foundation. We expand the channel depth for fine-grain feature learning and retain more feature information. At the same time, lightweight convolution reduces the parameters of the model. This model enhances detection accuracy for smoke targets of varying scales and surpasses the accuracy of mainstream models. The outcomes of experiments demonstrate that the improved model exhibits superior performance, and the mean average precision is improved by 3.3%. This model significantly enhances the detection ability while also optimizing the neural network to make it more lightweight. These advancements position the model as a promising solution for early-stage forest fire smoke detection.

Wildfire hazard is a prominent issue in subtropical forests as climate change and extreme drought events increase in frequency. Stand-level fuel load and forest structure are determinants of forest fire occurrence and spread. However, current fuel management often lacks detailed vertical fuel distribution, limiting accurate fire risk assessment and effective fuel policy implementation. In this study, backpack laser scanning (BLS) is used to estimate several 3D structural parameters, including canopy height, crown base height, canopy volume, stand density, vegetation area index (VAI), and vegetation coverage, to characterize the fuel structure characteristics and vertical density distribution variation in different stands of subtropical forests in China. Through standard measurement using BLS point cloud data, we found that canopy height, crown base height, stand density, and VAI in the lower and middle-height strata differed significantly among stand types. Compared to vegetation coverage, the LiDAR-derived VAI can better show significant stratified changes in fuel density in the vertical direction among stand types. Among stand types, conifer-broadleaf mixed forest and C. lanceolata had a higher VAI in surface strata than other stand types, while P. massoniana and conifer-broadleaf mixed forests were particularly unique in having a higher VAI in the lower and middle-height strata, corresponding to the higher surface fuel and ladder fuel in the stand, respectively. To provide more informative support for forest fuel management, BLS LiDAR data combined with other remote sensing data were advocated to facilitate the visualization of fuel density distribution and the development of fire risk assessment.

Presently, as human activity and climate warming gradually increase, straw burning leads to more accidental burning in neighbouring wetlands, which threatens wetland carbon stores. Plants are important carbon fixers in wetlands, converting carbon dioxide to biomass through photosynthesis and releasing carbon into the soil as plants die off. Nitrogen and phosphorus limitation in wetlands is a key factor affecting plant growth, and different burning seasons have different effects on mitigating this limitation. To further elucidate the effects of nitrogen and phosphorus distribution on wetland inter-month nutrient dynamics after different burning seasons, we selected a Calamagrostis angustifolia wetland in the Sanjiang Plain that was burned in spring and autumn, respectively, and conducted a monthly survey from May to September. We found that the leaf nitrogen content in September at spring burning sites was 3.59 ± 2.69 g/kg, which was significantly lower than that in July, while the difference at the unburned sites was only 0.60 ± 3.72 g/kg, and after the autumn burning, soil nitrogen and phosphorus contents remained higher than at the unburned sites in August, being 0.55 ± 1.74 g/kg and 0.06 ± 0.12 g/kg, respectively. Our results indicate that spring burning immediately increased the nitrogen and phosphorus contents in soil and plants but that these effects only lasted for a short time, until June. In comparison, autumn burning had a long-term effect on soil nitrogen and phosphorus levels and significantly increased the aboveground biomass. Thus, we recommend that conducting autumn burning before the commencement of agricultural burning not only reduces combustible accumulation to prevent fires but also promotes nitrogen and phosphorus cycling in wetlands, and the increase in plant biomass after autumn burning also enhances the carbon fixation capacity of the wetland.