基于遥感技术的林业有害生物检测研究进展

As a crucial pillar of China's national economy,forestry faces challenges from environmental changes and global trade,exacerbating vulnerabilities to natural disturbances like diseases and insects.Given the current scattered nature of research on forestry pest detection,this article,after outlining forestry pests,systematically reviews advancements in remote sensing technology in pest detection from 2015 to 2024.Traditional monitoring heavily relies on labor-intensive field inspections,prone to gaps and errors in large forest areas.However,with advances and widespread adoption of remote sensing,especially through drones and satellite technologies,forestry management is undergoing revolutionary change.Remote sensing enables managers and researchers to comprehensively and timely assess forest health using multispectral,hyperspectral,and thermal infrared data.Integrating drones and satellite remote sensing not only enhances accuracy and efficiency in pest monitoring but also expands spatial and temporal monitoring scopes significantly.These technologies provide decision-makers with comprehensive and scientific data support,aiding in effective forest health management strategies for sustainable utilization and protection of valuable forest resources.The article details existing remote sensing technologies and their applications in forestry pest detection,including traditional machine learning methods like Support Vector Machines,Random Forests,and clustering algorithms.Additionally,with the rapid advancement of artificial intelligence,deep learning models such as Convolutional Neural Networks(CNNs)and YOLO algorithms have brought breakthrough progress in pest detection.Looking ahead,the article proposes research directions:1)emphasizing integration and innovation of multiple technologies,combining high-resolution remote sensing with big data analytics to build intelligent monitoring systems for enhanced accuracy and real-time capability in pest detection;2)continuing to optimize deep learning models to improve generalization and resistance to interference,developing specific models for pest identification to strengthen remote sensing's role in early warning and precise control of pests;3)promoting the integration and industrialization of remote sensing detection systems to achieve integrated data collection,processing,analysis,early warning,and decision support,providing efficient and convenient technical solutions for forestry pest management and supporting China's sustainable forestry development.