The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.

PM_2.5 is one of major pollutants emitted from forest fires. High PM_2.5 concentration not only affects short-term human respiration health, but also poses a long-term threat to human cardiopulmonary functionality. Therefore, it is of great importance to quantitatively assess the PM_2.5 released by forest combustion in forest fire studies. In this study we examine relationships between the PM_2.5 concentration and environment and fuel characteristics laboratory experiments. In the experiments, fuel beds with controlled moisture contents and loads were first built; then 144 ignition experiments were conducted for various combinations of wind speeds using a wind tunnel device. Fire behavior characteristics and PM_2.5 concentrations released from fuel combustion were measured and analyzed. The experimental results show that the relationship between fire characteristics, fire intensity and the influencing factors of wind speed, fuel moisture content, and fuel load can be explained by the fundamental theory of forest combustion. Although PM_2.5 concentration rises with the increase of wind speed, the decrease of fuel moisture content, and the increase of fuel load, there appears to be a fuel load threshold for a given combination of wind speed and fuel moisture content that the increase of PM_2.5 concentration decelerates quickly after the load passes the threshold value. After screening fire behavior characteristics that affect PM_2.5 concentration, we found that fire line intensity and flame width are the ones with the strongest association with the concentration. With flame width as independent variable, we have built two regression models to predict PM_2.5 and fire line intensity which are treated as dependent variable; the models have high accuracy with R² = 0.92 for predicting PM_2.5 and R² = 0.97 for predicting fire line intensity. Study results can be used as reference to protect the health of forest fire fighters, and can be helpful for forest fire smoke management.

Introduction: Quantifying fire severity is an important aspect of studying the response mechanism of terrestrial ecosystems to wildfire, and it is of great significance to fire ecology. In this paper we comprehensively introduce and compare the classification and quantification methods for fire severity; we discuss the development and application status of various methods, and we elucidate their existing problems. Results: 1) According to features of the burned area, fire severity can be classified as light, moderate, and heavy. 2) Using composite burn index (CBI) to quantify and record the fire severity. 3) In quantifying fire severity with vegetation change, there are certain limitations and theoretical problems to be solved. 4) Remote sensing could very well be an important means of measuring fire severity in the future, but there are still many problems that need to be solved before the remote sensing index can become a global fire severity indicator. Discussion and Conclusion: Only by clarifying the relationship between fire behavior, fire severity, time related variables and the pre- and post-fire ecosystem can the existing models be perfected or new, better fire severity measurement models be proposed for broad applications.

Fungi were isolated using a rose bengal chloramphenicol agar as the culture medium. Congo red staining was used on sodium carboxymethyl cellulose medium to screen fungal strains that have potential to produce cellulolytic enzymes according to the cellulolytic index (CI). The ability of these isolates to break down holocellulose in three forest litter substrates (broad-leafed: Juglans mandshurica; coniferous: Larix gmelinii; broadleaf-conifer mixed: J. mandshurica and L. gmelinii) was tested over 80 days of incubation. The holocellulose content and the decomposition rule were studied. The strain with the most efficient degradation effect on natural cellulose in forest litter was selected. The growth of fungi was observed by scanning electron microscopy (SEM). The hydrolytic circles indicated the activity of cellulase produced by the fungi, and it implied that the fungi could degrade cellulose. The results showed that eight strains were able to degrade cellulose. The strain A2 (Peniophora incarnate) showed the highest CI, while A4 (Sarocladium strictum) was most capable of degrading holocellulose in various litter substrates. The SEM micrographs revealed that A4 had the ability to invade leaf tissue and degrade holocellulose in leaves. This study could be helpful for forest litter management, which provides a new way to cleanup forest litter using cellulose-degrading fungi.