To clarify the response and interaction of soil element content and function at different depths under Pinus pumila forest to severe lightning fire interference, and to provide data reference and theoretical basis for the restoration of Pinus pumila ecosystem after severe lightning fire interference.

The study focused on the soil leaching and deposition layers in the severely lightning damaged area of Pinus pumila in the Greater Khingan Range. The carbon, nitrogen, phosphorus, and trace nutrient content of the soil were measured in the year and year after the fire, and the stoichiometric ratio was calculated. One-way ANOVA, Spearman correlation, and Mantel test were used to calculate differences and correlations; Random forest importance ranking is used to explain the contribution of soil elements to stoichiometry; The partial least squares structural equation model is used to demonstrate the comprehensive effect relationship between soil spatial pattern, recovery time, soil elements, and soil stoichiometry before and after fire disturbance.

The content and stoichiometry of carbon, nitrogen, phosphorus, copper, and mercury in the soil leaching layer and sediment upper layer showed significant responses to fire interference and fire exposure time. Fire interference increased the variability of trace nutrients in the leaching layer, and the changes in soil element content showed a lag with soil depth. The content of trace nutrients in soil is significantly correlated with soil carbon, nitrogen, phosphorus, and their stoichiometric ratios, and they can also significantly explain and predict soil stoichiometric ratios.

Fire interference inhibits the mineralization rate of organic matter in leached soil and increases the availability of phosphorus in soil sediments by high temperature burning and changing the source of substances. The soil element content undergoes significant changes in the short term after fire due to leaching, and lags as the soil layer deepens. Severe fire disturbance weakened the direct effect of soil spatial pattern on soil elements, enhanced the direct effect of recovery time on soil element content, and resulted in significant effects of soil spatial pattern and recovery time on soil stoichiometry.

The frequency and intensity of forest fires have increased considerably in recent years, driven largely by human activities and climate change. Vast quantities of carbon-containing substances released during forest fires exacerbate climate change through a positive feedback loop. Surface fires, the most common type of forest fire, account for more than half of the total emissions from forest fires. Accurate estimation of carbon emissions from surface fires is therefore essential for understanding their impact on carbon sinks and assessing the role of forest fires in driving climate change.

This study constructed indoor combustion beds to investigate the relationship between surface fire behavior and carbon consumption during the combustion of surface fuels in a planted Pinus koraiensis forest. Fuel beds were prepared with varying fuel loadings (0.4 kg·m^-2, 0.8 kg·m^-2, 1.2 kg·m^-2, and 1.6 kg·m^-2) and fuel moisture contents (5%, 10%, and 15%). Combustion experiments were conducted across 144 plots under varying slope conditions (0°, 10°, 20°, and 30°). The rate of spread, flame length, and fuel consumption during surface fire spread were measured, and fireline intensity was calculated using the Byram model. After the experiments, all combustion residues were collected, and their carbon content was determined using the dry burning method, enabling the calculation of carbon consumption from the fuel. A predictive model for fuel carbon consumption was developed by combining the fuel consumption parameters from the Byram fireline intensity equation with the observed fire behavior data. The model parameters were calibrated using the experimental results.

All 144 combustion experiments produced low-intensity surface fires. Most variable interactions significantly influenced the four forest fire behavior characteristics, except for the interaction between fuel load and moisture content, which mainly affected fuel consumption. The four measured fire behavior characteristics increased with higher fuel loads. As fuel moisture content increased, spread rate, flame length, and fireline intensity decreased, although moisture content did not have a significant effect on fuel consumption. Under low slope conditions (0°~20°), the spread rate and fireline intensity increased gradually with the slope. However, when the slope exceeded 20°, these characteristics increased substantially. Flame length also increased with slope, while fuel consumption decreased. Initially, the model predicted surface fire fuel carbon consumption with limited accuracy, yielding R² =0.59, MAE=0.22 kg·m^-2, and MAPE=73.00%. After refitting the model parameters using data from the combustion tests, predictive accuracy improved considerably, with R² =0.60, MAE=0.10 kg·m^-2, and MAPE=30.99%.

The factors examined in this study generally align with the principles of forest burning, though under specific conditions, fire behavior characteristics may deviate from expected patterns. Refitting the model parameters using laboratory combustion data significantly enhanced the model's applicability and accuracy in predicting carbon consumption from surface fuel burning in Pinus koraiensis plantation forests.

To explore the impact of forest fire on the emission mechanism of PM_2.5, provide basic data and theoretical support for the study of PM_2.5 emission from regional forest fire and the atmospheric environmental safety problems caused by PM_2.5 emission from forest fire, so as to improve the understanding of the ecological and environmental effects of PM_2.5 emission from forest fire.

In this study, the fuel on the surface of Pinus koraiensis plantation were used as experimental materials, and simulated combustion experiments were carried out with different fuel load, fuel moisture content and slope preset conditions. The effects of fire behaviors and preset conditions on water-soluble anions in PM_2.5 were analyzed by single factor analysis of variance, multivariate analysis of variance, correlation analysis and redundancy analysis (db-RDA).

Fuel load significantly affects the contents of F^-, Cl^-, NO₃^-, and SO₄^2-. The fuel moisture content significantly affects the content of NO₃^- and Cl^-, but has no significant effect on the content of F^- and SO₄^2-. Slope has a significant effect on F^- content and Cl^- content, but has no significant effect on NO₃^- and SO₄^2- content. Except humidity, other variables are positively correlated with the contents of F^-, Cl^-, NO₃^-, and SO₄^2-. Fuel bed depth, flame length and flame height are positively correlated with NO₃^- content and Cl^- content, while SO₄^2- content is positively correlated with flame length and flame height, while combustion efficiency has relatively little influence on F^-, Cl^-, NO₃^-, and SO₄^2- content. Further db-RDA analysis shows that the fire behavior characteristic that has the most significant influence on the contents of F^-, Cl^-, NO₃^-, and SO₄^2- is the flame height.

There is a significant correlation between the characteristics of fire behavior and the emission of water-soluble ions in forest fire PM_2.5, fuel load, fuel moisture content and slope can indirectly affect the content of water-soluble anions in PM_2.5 by influencing fire behavior.

To analyze the driving factors of forest fire in Tongren city and study the fire risk distribution, so as to provide reference for forest fire management in the study area.

Taking the fire point data of Tongren City from 2001 to 2020 as the research object, this study investigated 22 factors including weather, terrain, vegetation, and human activities related to forest fire occurrence in Tongren City, the time distribution of fire point was analyzed, and the main driving factors of forest fire occurrence were obtained based on Logistic regression model and random forest model. A probability model was established, and the probability and fire risk zoning map of forest fire occurrence in Tongren City was drawn.

In the past 20 years, the number of fire points data in Tongren City showed a downward trend and a peak occurred every 3-5 years. Besides, more than 70% of forest fires were concentrated from January to April. Population density, monthly average humidity, monthly average precipitation and distance from railway were selected as the main driving factors by the two models, all showed negative correlation. The AUC value and prediction accuracy of each sample in both models were greater than 0.750 and 70%. The occurrence probability of forest fire in Tongren City was highest in spring and lowest in winter. The high risk areas were mainly concentrated in Shiqian county, Yinjiang Tujia and Miao Autonomous County and Songtao Miao Autonomous County. The low risk areas were Yanhe Tujia Autonomous County, Dejiang County and Sinan County in northwest of Tongren City and Yuping Dong Autonomous County in south.

Climate factors are the main driving factors of forest fire occurrence in Tongren City. The prediction probability of random forest is higher than that of Logistic regression model, which is more suitable for predicting forest fire occurrence in Tongren City. The probability of forest fire occurrence and fire risk zoning map obtained from the study provide scientific support for the forest fire management department in Tongren city. In high-risk areas, patrol management should be strengthened, observation towers and monitoring equipment should be increased. Fire prevention education and publicity should be added in low-risk areas, and fire management should be strengthened during holidays to reduce the probability of fire occurrence.

The effects of fire disturbance on the mass concentration of PM_2.5 emitted from wildfires were studied, and the potential ability of PM_2.5 emitted from wildfires to participate in the atmospheric carbon cycle was understood. The effects of fire environmental conditions and fire behavior factors on the mass concentration of PM_2.5 emitted from wildfires and its water-soluble carbon were explored. It can provide a scientific basis for revealing the multi-process mutual feedback relationship between wildfire disturbance-wildfire emission particles-atmospheric environment, and is of great significance for further quantifying the impact of wildfires on the atmospheric environment.

In this study, the surface combustible materials of red pine plantation were used as experimental materials. Simulated combustion experiments with different load, moisture content and slope preconditions were carried out to quantify the effects of flame height, flame length, flame width, combustion efficiency and other fire behavior and fire environment factors on PM_2.5 and water-soluble carbon. The effects of fire environment on fire behavior, fire environment and fire behavior on PM_2.5 and water-soluble carbon mass concentration were analyzed by one-way ANOVA, multi-factor ANOVA, correlation analysis and random forest regression importance ranking methods.

The fuel load significantly affected the mass concentration of PM_2.5 and water-soluble carbon (P<0.01). The fuel moisture content significantly affected the mass concentration of water-soluble carbon (P<0.05), but had no significant effect on the mass concentration of PM_2.5 (P>0.05). The slope had no significant effect on the mass concentration of PM_2.5 and water-soluble carbon (P>0.05). There was a significant positive correlation between fuel loading and fire behavior (P<0.05). There was a significant negative correlation between fuel moisture content and fire behavior (P<0.05). Slope only significantly affected flame depth (P<0.05). Through random forest regression analysis, the flame height (39.12), bed thickness (36.27), flame depth (35.01) and flame length (30.76) had a higher impact on PM_2.5 mass concentration, and the bed thickness (40.05), flame height (31.98) and flame length (31.06) had a higher impact on water-soluble carbon mass concentration.

The mass concentration characteristics of PM_2.5 and water-soluble carbon emitted by surface combustible combustion in red pine plantation were significantly affected by fire behavior directly and fire environment indirectly. The fire environment can indirectly affect the mass concentration of PM_2.5 and water soluble carbon emitted by wildfire. Fire disturbance can have a lasting impact on the ecological environment of the wildfire area and its surrounding areas by affecting the mass concentration of PM_2.5 and water-soluble carbon emitted by wildfire.

The loading of forest surface litter affects the occurrence of forest fires and a series of fire behavior characteristics exhibited by forest fires. Accurately obtaining the loading of surface litter is crucial. The Euler number of image feature can characterize the number of objects in the image, analyze the relationship between Euler number and loading, and establish a load prediction model based on image Euler number, which is of great significance for load research.

The litter in typical forest stands of Cryptomeria fortunei and Phyllostachys heterocycla in Guizhou province was taken as the research object. Through forest stand and loading investigation, taking litter images and image feature processing, the relationship between Euler number and surface litter loading was analyzed. A load prediction model based on image Euler number was established, and the accuracy of the model was tested.

1) After selecting different thresholds for image binarization, not all extracted Euler numbers were correlated with the litter loading. A threshold of 0.1 showed a highly significant correlation between the Euler numbers of binarized images and the two types of litter loading; 2) As the Euler number of the image increased, the surface litter loading of forests of C. fortunei and P. heterocycla showed an overall downward trend; 3) Linear regression was chosen to establish a litter loading prediction model based on image feature Euler number. The absolute errors of the prediction models for the litter load in C. fortunei and P. heterocycla forests were 1.60 t·hm^-2 and 1.72 t·hm^-2, respectively, with mean relative errors of 20.03% and 20.71%. The predicted effect of surface litter loading in C. fortunei forest was better than that in P. heterocycla forest.

Through this study, the feasibility of predicting forest surface litter loading based on image features has been preliminarily verified, providing new ideas for accurately obtaining load research and of great significance for scientific forest fire management.