Advances in Global Remote Sensing Monitoring of Discolored Pine Trees Caused by Pine Wilt Disease: Platforms, Methods, and Future Directions

文献类型： 外文期刊

第一作者： Shi, Hao

作者： Shi, Hao;Chen, Meixiang;Zhang, Danzhu;Wu, Qiangjia;Zhang, Ruirui;Shi, Hao;Chen, Liping;Zhang, Danzhu

作者机构：

关键词： pine wilt disease; discolored trees; identification; remote sensing; deep learning

期刊名称：FORESTS （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 15 卷 12 期

页码：

收录情况： SCI

摘要： Pine wilt disease (PWD), caused by pine wood nematodes, is a major forest disease that poses a serious threat to global pine forest resources. Therefore, the prompt identification of PWD-discolored trees is crucial for controlling its spread. Currently, remote sensing is the primary approach for monitoring PWD. This study comprehensively reviews advances in the global remote sensing monitoring of PWD. It explores the remote sensing platforms and identification methods used in the detection of PWD-discolored trees, evaluates their precision, and provides prospects for existing problems. Three observations were made from existing studies: First, unmanned aerial vehicles (UAVs) are the dominant remote sensing platforms, and RGB data sources are the most commonly used for identifying PWD-discolored trees. Second, deep-learning methods are increasingly applied to identify PWD-discolored trees. Third, the early monitoring of PWD-discolored trees has gained increasing attention. This study reveals the problems associated with the acquisition of remote sensing images and identification algorithms. Future research directions include the fusion of multiple sensors to enhance the identification precision and early monitoring of PWD-discolored trees to obtain an optimal detection window period. This study aimed to provide technical references and scientific foundations for the comprehensive monitoring and control of PWD.

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