Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted in severe fire incidents. The present study sought to investigate the correlation between the incidence of forest fires and critical meteorological elements, including temperature, humidity, precipitation, and wind speed, over a period of 25 years, from 1998 to 2023. We analyzed 169 recorded fire events, collectively burning approximately 109,400 hectares of forest land. Employing sophisticated machine learning algorithms, Random Forest (RF), and Gradient Boosting Machine (GBM) revealed that temperature and relative humidity during the critical fire season, which spans May through July, are key factors influencing fire activity. Conversely, wind speed was found to have a negligible impact. The RF model demonstrated superior predictive accuracy compared to the GBM model, achieving an RMSE of 5803.69 and accounting for 49.47% of the variance in the burned area. This study presents a novel methodology for predictive fire risk modeling under climate change scenarios in the region, offering significant insights into fire management strategies. Our results underscore the necessity for real-time early warning systems and adaptive management strategies to mitigate the frequency and intensity of escalating forest fires driven by climate change.

Introduction: Mapping forest fire risk is essential for effective prevention and efficient resource allocation, as it accurately assesses fire hazards across regions. This study conducts a comparative evaluation of the Analytical Hierarchy Process (AHP), the Fuzzy Analytical Hierarchy Process (F-AHP), and the Dong model for forest fire risk mapping in Liangshui National Nature Reserve by analyzing the weights of the factors contributing to fire risk.Methodology: The forest fire risk maps were developed based on several contributing factors: aspect, elevation, slope, topographic wetness index, proximity to roads, distance to settlements, population density, Land Use Land Cover, temperature, precipitation, wind speed, normalized difference vegetation index (NDVI), and normalized difference moisture index (NDMI).Results: Receiver Operating Characteristic (ROC) curve analysis was employed to validate and assess the predictive performance of the models. The evaluation of the Area Under the Curve (AUC) values revealed that the Analytical Hierarchy Process (AHP) model achieved high prediction accuracy with an AUC of 0.92, while the Dong model slightly lowered with an AUC of 0.91. In contrast, the Fuzzy Analytical Hierarchy Process (F-AHP) yielded an AUC of 0.90. These results indicate that the fire risk map generated by the AHP provides the most accurate and reliable prediction.Conclusion: Overall, the findings underscore the effectiveness of the proposed modeling approaches and demonstrate their potential to enhance decision-making processes in forest fire risk management and the strategic allocation of prevention resources.

This study investigated post-fire vegetation recovery in Algeria’s Tenira forest using statistical traits (PCA), RFM, and LANDIS-II spatial analysis. The dataset included satellite imagery and environmental variables such as precipitation, temperature, slope, and elevation, spanning over a decade (2010–2020). Tenira forest is composed of Mediterranean species (36.5%); the biological types encountered are dominated by therophytes (39.19%). Ninety fire outbreaks were recorded, resulting in a loss of 1400.56 ha of surface area. Following the PCA results, precipitation, temperature, slope, and elevation were the main drivers of recovery (PC1 explained 43% alone, with the first five principal components accounting for 90% of observed variance, reflecting significant environmental gradients). Based on these components, an RFM predicted the post-fire recovery with an overall accuracy of 70.5% (Cost-Sensitive Accuracy), Quantity Disagreement of 3.1%, and Allocation Disagreement of 76%, highlighting spatial misallocation as the primary source of errors. The evaluation also identified PC4 (species richness) and PC3 (elevation) as significant predictors, collectively accounting for >50% of the variation in post-fire recovery. In the spatial analysis using LANDIS-II, the growth of vegetation, mainly in mid-altitude areas, was shown to be stronger, with the species consisting of those areas being more diverse. As a result, it demonstrated the connection between species richness and recovery capability. These findings can be useful in developing a management and development strategy, as well as proposing actions for species recovery after fire, such as the construction of firebreaks or the introduction of fireproof species, to make the forest more resistant to weather changes in Mediterranean ecosystems.

(1) Background: In recent years, forest fires have become increasingly frequent both domestically and internationally. The pollutants emitted from the burning of fuel have exerted considerable environmental stress. To investigate the influence of forest fires on the atmospheric environment, it is crucial to analyze the variations in PM_2.5 emissions from various forest fuels under differing fire conditions. This assessment is essential for evaluating the effects on both the atmospheric environment and human health. (2) Methods: Indoor simulated combustion experiments were conducted on the branches, leaves, and bark of typical tree species in the Liangshui National Natural Reserve, including Pinus koraiensis (PK), Larix gmelinii (LG), Picea koraiensis (PAK), Betula platyphylla (BP), Fraxinus mandshurica (FM), and Populus davidiana (PD). The PM_2.5 concentrations emitted by six tree species under various combustion states were measured and analyzed, reflecting the impact of moisture content on the emission of pollutants from fuel combustion, as indicated by the emission factors for pollutants. (3) Results: Under different fuel loading and moisture content conditions, the mass concentration values of PM_2.5 emitted from the combustion of different organs of various tree species exhibit variability. (4) Conclusions: Among the various tree species, broad-leaved varieties release a greater quantity of PM_2.5 compared to coniferous ones. A positive correlation exists between the moisture content of the fuel and the concentration of PM_2.5; changes in moisture content notably influence PM_2.5 levels. The emission of PM_2.5 from fuel with varying loads increases exponentially. Utilizing the Response Surface Methodology (RSM) model for simulation, it was determined that both moisture content and fuel load exert a significant combined effect on the release of PM_2.5 during combustion.

Wildfires and pests are natural disturbance agents in many forest ecosystems that often contribute to ecological succession, nutrient cycling, and forest species composition. Mongolian pine (Pinus sylvestris var. mongolica) is a coniferous species that plays an important role as an ecological barrier, and is widely spread in northern China. Its wood is loose; its branches, leaves, and cones contain a high level of resin and volatile oils that make the species highly flammable and the stands dominated by the species are very vulnerable to fire. Recently, resinosis on boles of Pinus sylvestris became an epidemic in China. To explore the potential effects of pests on fire, we compared the flammability of Mongolian pine barks with or without resinosis on boles using a cone calorimeter and several combustion analyses. We found that the barks from boles with resinosis had a greater oil content than the healthy trees. The study also indicated that the ignition times of the barks from boles with and without resinosis were 6.00 s (±1.73) and 22.67 s (±1.15), respectively, and that the heat release rate curves were parabolic, with peaks 225.19 and 75.27 kW/m², respectively, for the two bark types. Additionally, because resinosis was on the low- to mid-bole of infested trees, the barks from boles with resinosis tended to be ignited much easier than those without resinosis. This clearly evidenced that pests could affect fire severity and behavior by increasing forest flammability. More information about the role that pests play in the different forest cover types is needed to increase our understanding of fire danger and to develop sound forest management policies.

PM_2.5 emissions significantly impact atmospheric environments and human health in the context of forest fires. However, research on PM_2.5 emissions from forest fires remains insufficient. This study systematically investigated PM_2.5 emission characteristics from broadleaf tree combustion through controlled experiments examining three key factors: species variation (Acer tegmentosum [AT], Acer ukurunduense [AU], Acer pictum [AP], Tilia amurensis [TA], Phellodendron amurense [PA], Ulmus davidiana [UD], Ulmus laciniata [UL], Prunus padus [PP], Prunus maackii [PM]), moisture content (0%–20%), and phenological stages (budding [A], growing [B], defoliation [C]). The results demonstrated: (1) Significant interspecies differences, with UL showing the lowest, and PM the highest emissions; (2) A unimodal moisture—emission relationship peaking at 15% moisture content across most species, while AT, UL and PM exhibited unique linear responses; (3) Distinct phenological patterns, including triphasic fluctuations during the growing and defoliation phases. The LightGBM model effectively predicted emissions (R² = 0.97), identifying species (36.2% importance) and moisture content (21.6%) as dominant factors. These findings provide critical data for wildfire emission modeling and highlight the need for species-specific parameters in air quality forecasts.