Protecting forest resources and preventing forest fires are vital for social development and public well-being. However, current research studies on forest fire warning systems often focus on extensive geographic areas like states, counties, and provinces. This approach lacks the precision and detail needed for predicting fires in smaller regions. To address this gap, we propose a Transformer-based time series forecasting model aimed at improving the accuracy of forest fire predictions in smaller areas. Our study focuses on Quanzhou County, Guilin City, Guangxi Province, China. We utilized time series data from 2021 to 2022, along with remote sensing images and ArcGIS technology, to identify various factors influencing forest fires in this region. We established a time series dataset containing twelve influencing factors, each labeled with forest fire occurrences. By integrating these data with the Transformer model, we generated forest fire danger level prediction maps for Quanzhou County. Our model’s performance is compared with other deep learning methods using metrics such as RMSE, and the results reveal that the proposed Transformer model achieves higher accuracy (ACC = 0.903, MAPE = 0.259, MAE = 0.053, RMSE = 0.389). This study demonstrates that the Transformer model effectively takes advantage of spatial background information and the periodicity of forest fire factors, significantly enhancing predictive accuracy.

Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. To address these challenges, this paper proposes an enhanced forest fire detection model, YOLOv8n-SMMP (SlimNeck–MCA–MPDIoU–Pruned), based on the YOLO framework. Key innovations include the following: introducing the SlimNeck solution to streamline the neck network by replacing conventional convolutions with Group Shuffling Convolution (GSConv) and substituting the Cross-convolution with 2 filters (C2f) module with the lightweight VoV-based Group Shuffling Cross-Stage Partial Network (VoV-GSCSP) feature extraction module; integrating the Multi-dimensional Collaborative Attention (MCA) mechanism between the neck and head networks to enhance focus on fire-related regions; adopting the Minimum Point Distance Intersection over Union (MPDIoU) loss function to optimize bounding box regression during training; and implementing selective channel pruning tailored to the modified network architecture. The experimental results reveal that, relative to the baseline model, the optimized lightweight model achieves a 3.3% enhancement in detection accuracy (mAP@0.5), slashes the parameter count by 31%, and reduces computational overhead by 33%. These advancements underscore the model’s superior performance in real-time forest fire detection, outperforming other mainstream lightweight YOLO models in both accuracy and efficiency.

Rubber trees along the southeast coast of China always suffer severe damage from hurricanes. Quantitative assessments of the capacity for wind resistance of various rubber tree clones are currently lacking. We focus on a vulnerability assessment of rubber trees of different clones under wind disturbance impacts by employing multidisciplinary approaches incorporating scanned points, aerodynamics, machine learning and computer graphics. Point cloud data from two typical rubber trees belonging to different clones (PR107 and CATAS 7-20-59) were collected using terrestrial laser scanning, and a connection chain of tree skeletons was constructed using a clustering algorithm of machine learning. The concept of foliage clumps based on the trunk and first-order branches was first proposed to optimize rubber tree plot 3D modelling for simulating the wind field and assessing the wind-related parameters. The results from the obtained phenotypic traits show that the variable leaf area index and included angle between the branches and trunk result in variations in the topological structure and gap fraction of tree crowns, respectively, which are the major influencing factors relevant to the rubber tree’s capacity to resist hurricane strikes. The aerodynamics analysis showed that the maximum dynamic pressure, wind velocity and turbulent intensity of the wind-related parameters in rubber tree plots of clone PR107 (300 Pa, 30 m/s and 15%) are larger than that in rubber tree plots of clone CATAS-7-20-59 (120 Pa, 18 m/s and 5%), which results in a higher probability of local strong cyclone occurrence and a higher vulnerability to hurricane damage.

To address the problem that it is easy to form coverage blind areas when wireless sensor networks are randomly deployed, an improved coverage optimization algorithm based on improved Salpa swarm Intelligent algorithm (ATSSA) is proposed for wireless sensor networks. Firstly, the pop-ulation is initialized using tent chaotic sequence to enhance the optimization ability of the algo-rithm. Secondly, the T-distribution mutation is added to the update formula of the leaders for improving the ability to jump out of the local optimal value. Finally, an adaptive formula for updating the position of the follower is proposed, which not only guarantees the local searching ability of the algorithm in the late iteration period, but also improves the global searching ability of the algorithm in the early iteration period. The experimental results show that ATSSA algorithm can improve the coverage of the wireless sensor networks and reduce deployment costs com-pared with other algorithms, when it is used in the wireless sensor networks.

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework innovatively combines DEM data with Faster Regions with Convolutional Neural Networks (Faster R-CNN) and CNN-based methods, breaking through the limitations of traditional approaches that rely on manual feature extraction. It is capable of automatically identifying critical terrain features, such as mountain peaks and water systems, with higher accuracy and efficiency. DEMs provide high-resolution topographical information, which deep learning models leverage to accurately identify and delineate key geographical features. Our results show that the integration of DEMs and deep learning significantly improves the accuracy of fire risk assessment by offering detailed and precise terrain analysis, thereby providing more reliable inputs for fire behavior prediction. The extracted mountain peaks and water systems, as fundamental inputs for fire behavior prediction, enable more accurate predictions of fire spread and potential impact areas. This study not only highlights the great potential of combining geospatial data with advanced machine learning techniques but also offers a scalable and efficient solution for forest fire risk management in mountainous regions. Future work will focus on expanding the dataset to include more environmental variables and validating the model in different geographical areas to further enhance its robustness and applicability.