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Pinus koraiensis is one of the important tree species in Northeast China. Due to its high pine-needle-oil content and the density of human activities in its habitat, the forest-fire prevention situation is severe in the context of climate change. The rate of surface-fire spread is one of the key indicators for scientifically advancing early fire prevention and is crucial for guiding forest firefighting operations. In this study, we investigated how moisture content, load, and slope impact the surface-fire spread rate using indoor-simulated fire-spread experiments. Furthermore, we analyzed the limitations in the Rothermel model for predicting the surface-fire spread rate in P. koraiensis plantations and proposed modifications to the model by the modification method of priority to no-wind or slope conditions and slope conditions. Additionally, we evaluated the prediction accuracy of the original Rothermel model and two other modified models on the surface-fire spread rate. A high moisture content and low slope demonstrated an absolute inhibitory effect on the rate of surface-fire spread, whereas the promotional effect of a low moisture content and high slope was easily disturbed by the other factors. Under high-slope conditions, an overestimation situation was observed in the Rothermel model. Both of the modification methods involving priority to no-wind or slope conditions and slope conditions could improve this situation. Furthermore, the modification method demonstrated a better improvement effect on the prediction accuracy. Our findings provide valuable insights for refining the Rothermel model and offer guidance for improving the accuracy of predicting fire spread rates and behavior for Pinus koraiensis. This bears immense significance for advancing the understanding and calculation of the ROS of forest fires in the region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As an active ecological agent in the boreal forest, wildfires have many important impacts on the underlying permafrost. However, investigations of post‐fire changes in the features of ecosystem‐protected permafrost are extremely limited in the patchy permafrost regions, Northeast China. Additionally, the impacts of wildfires on patchy permafrost are complicated in this region. In this paper, based on field surveys and observations, permafrost features and their influencing factors were analyzed in the area burned in 2006. The results showed notably lower soil temperatures in shrub‐wetland (NWH1, unburned and NWH2, light burn) in comparison with those in the birch‐larch forest (NWH3, light burn and NWH4, moderate burn). Changes in shallow ground temperatures (0–4 m in depth) were significant. Mean annual soil temperatures (MAST) and annual maximum soil temperature at the light‐burn NWH2 site were 0.3–5.2°C higher than those of unburned NWH1, but annual minimum soil temperatures, 0.3–2.1°C lower. MASTs at the moderately burned NWH4 were 0.6–1.6°C higher than those at the light‐burn NWH3. The active layer thickness (ALT) was greater at burned sites, and; at the moderately burned site, only seasonal frost was found when 13 years after the burn. The contents of soil organic carbon and total nitrogen were the highest at the unburned NWH1 from 0.0 to 0.4 m in depth, followed by those at the lightly‐burned NWH2 and NWH3, and the lowest at the moderately‐burned NWH4. Climate warming, forest fires, and changing local factors (e.g., soil moisture contents, snow cover, soil texture and drainage conditions) worked together to increase ground temperature and ALT. These research results could provide important scientific basis for the protection of boreal forest and wetlands and the ecosystem‐protected permafrost under a warming climate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• WUI areas in China have exhibited a rapid increasing trend from 2008 to 2020. • Lower rainfall and higher temperatures are linked to occurrence of WUI fires. • Human activity explains as much about the WUI fires occurrence as climate. Urbanization and climate change have led to an increasing number of wildfires in China in recent years, posing a direct threat to life, property, and social development. These areas facing severe wildfire risk are mainly concentrated in the Wildland-Urban Interface (WUI). Although much research has been conducted on wildfire management in China, one of the critical constraints is that there is not yet a WUI distribution map that can meet the needs of forest fire management in China. The present study proposes an approach to identify WUI areas in mainland China between 2008 and 2020 by analyzing the impact of wildfire events on communities. Using historical fire occurrence data, we explored the spatial and temporal distribution patterns of WUI fires in various geographic regions. Using the random forest model, we predicted the spatial distribution of WUI fires and identified the key factors affecting their occurrence. The results show that the WUI area in China has been rapidly expanding since 2008, with a growth rate of 15.2 × 103 km2/a, especially in the densely populated and economically developed eastern regions. The seasons of WUI fires varied across regions. Both spring and autumn are major seasons for WUI fire occurrence in northern China. In contrast, more than 80 % of WUI fires in southern China occurred in winter and spring. According to the findings generated by the random forest analysis, weather patterns marked by low levels of rainfall and elevated temperatures during both spring and fall seasons are the primary atmospheric contributors to the incidence of WUI fires. Our study indicated that repeated human activities within southern China also increased the probability of fire occurrence. We suggest that better prediction of fire-prone weather and improved regional forest fire management can help decrease the incidence of WUI fires. In addition, future efforts should focus on enhancing the long-term monitoring and predictive capabilities of fuel moisture content. [ABSTRACT FROM AUTHOR]

China's forest cover has increased by approximately 10% as a result of sustainable forest management since the late 1970s. The forest ecosystem area affected by fire is increasing at an alarming rate of approximately 600,000 ha per year. The northeastern part of China, with a forest cover of 41.6%, has the greatest percentage of acres affected by forest fires. This study combines field and satellite weather data to determine factors that influence dead fuel moisture content (FMC). It assesses the use of the Canadian forest fire weather index to determine the daily forest fire danger in a typical temperate forest in Northeastern China during autumn. Based on the Wilcoxon test for paired samples, the observed and predicted values of FMC showed similar variation in eight of eleven sampling sites (72.7%), with a p value > 0.05. Three sampling plots presented lower predicted values of FMC than observed values (27.3%), with a p value < 0.05. The calculation of fire risk using the Canadian Forest Fire Weather Rating System (CFFDRS) in Maoer Mountain forest ecosystems presented low, medium or high risk; thus, the CFFDRS is suitable for determining fire danger in our study region. Along with these results, this study served to compare the use of FMC-metre field data and China Weather Station data to evaluate fire danger. The results of this study led us to suggest the multiplication of meteorological stations in fire-prone regions. [ABSTRACT FROM AUTHOR]

• dNBR is optimal in evaluating the burn severity in subtropical forests of China. • Subtropical forests in study area is dominated by low and moderate burn severity. • Burn areas are mainly distributed at lower altitudes due to human-caused fires. • The removal of fuels should focus on areas prone to developing high burn severity. Burn severity assessment is vital for the development of effective fire management strategies. Subtropical forests in Southern China support rich forest resources but are also disturbed by frequent forest fires, resulting in varying burn severity areas. However, the lack of accuracy assessment of burn severity based on spectral indices has limited our understanding of spatial patterns and drivers of burn severity in this region and the implementation of effective forest fire management. In this study, we compared the accuracy of different spectral indices from Landsat 8 Operational Land Imager (OLI) in assessing burn severity. Moreover, the optimal spectral index was selected to map burn severity and analyze its spatial patterns and drivers by using the random forest model. The results showed that (1) dNBR, RdNBR, and RBR outperformed the others in accuracy of fitting burn severity, and dNBR was slightly higher in accuracy classification; (2) vegetation (pre-fire NDVI) and human activity (distance to the nearest road or settlement) were the most important drivers affecting burn severity patterns; (3) the proportion of moderate and low burn severity was higher, and most burn areas were concentrated near settlements and roads, but areas with higher burn severity tended to be distributed in uphill, sunny slopes, and high altitudes. In view of this, we recommend targeted forest fire management according to the distribution pattern and drivers of burn severity in this region, including setting up fire belts near residential areas and roads, and prioritizing fuel removal plans in areas prone to develop higher burn severity. [ABSTRACT FROM AUTHOR]

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires. [ABSTRACT FROM AUTHOR]

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires. [ABSTRACT FROM AUTHOR]