Bark beetle disturbances increasingly threaten structure and functionality of temperate and boreal forests globally. The early detection of bark beetle-infested trees, i.e. before beetles’ emergence from the breeding tree, is essential for an effective outbreak mitigation. Terrestrial control surveys as traditionally employed for infestation detection, however, are resource-intensive and approach their limits in difficult terrain and during mass outbreaks. Developments in remote sensing and detection algorithms are increasingly giving hope that early-infested trees will be detectable remotely, thereby improving control success and management efficacy. Yet, a comprehensive quantitative evaluation of the approaches currently being developed is lacking to date. This review synthesises the state-of-the-art of recent research on early infestation (or green-attack) detection by remote sensing, and places it in the context with underlying biological constraints, technical opportunities and potential management applications. Since each bark beetle-host tree system has specific characteristics and detectability, we focus on the system with the greatest impact on European forests, the European spruce bark beetle (), which attacks Norway spruce (). By screening research published within the period 2000–2022, we included 26 early detection studies in our analyses. All studies reviewed were purely exploratory, testing a variety of data and/or classification algorithms with a relatively limited spatial and temporal coverage. Among tested platforms and sensor types, satellite and multispectral imagery were most frequently investigated. Promising spectral wavelength range or index highly varied among studies and regions. Timeliness and accuracy of detection were found to be insufficient for efficient management, regardless of the tested platform, sensor type, and spatial resolution applied. The main reasons preventing better performance include the rapid development of in combination with the delayed and highly variable vitality response of the spruce crown, and frequent cloud cover in spruce-dominated regions across Europe. In conclusion, current remote sensing survey methods cannot yet replace terrestrial control surveys for timely bark beetle management. Nevertheless, they might be supportive either as a back-up to regular and frequent terrestrial surveys, or in specific situations, e.g. to detect hibernation trees, in terrain with difficult accessibility, or in extensively managed forests without sufficient survey capacity. We suggest that the term ‘early detection’ be used consistently as a synonym for ‘pre-emergence detection’ to avoid ambiguity. Finally, we provide recommendations for future research based on the lessons learned from the studies analysed, namely to use a more rigorous and targeted study design, to ensure interdisciplinarity, and to communicate research results explicitly.

中文翻译：

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通过遥感早期检测树皮甲虫（Ipstyrographus）侵扰——对近期研究的批判性回顾

树皮甲虫的干扰日益威胁着全球温带和北方森林的结构和功能。及早发现受树皮甲虫侵扰的树木，即在甲虫从繁殖树中出现之前，对于有效缓解疫情爆发至关重要。然而，传统上用于检测侵染的陆地控制调查是资源密集型的，并且在困难地形和大规模爆发期间接近其极限。遥感和检测算法的发展越来越让人希望能够远程检测早期受感染的树木，从而提高控制成功率和管理效率。然而，迄今为止还缺乏对目前正在开发的方法的全面定量评估。这篇综述综合了通过遥感检测早期侵染（或绿色攻击）的最新研究成果，并将其置于潜在的生物限制、技术机会和潜在管理应用的背景下。由于每种树皮甲虫-寄主树系统都有特定的特征和可检测性，因此我们重点关注对欧洲森林影响最大的系统，即攻击挪威云杉的欧洲云杉树皮甲虫 ()。通过筛选 2000 年至 2022 年期间发表的研究，我们在分析中纳入了 26 项早期检测研究。所有审查的研究都是纯粹的探索性研究，在相对有限的空间和时间覆盖范围内测试各种数据和/或分类算法。在测试的平台和传感器类型中，最常研究的是卫星和多光谱图像。有前景的光谱波长范围或指数在不同研究和地区之间差异很大。无论使用何种测试平台、传感器类型和空间分辨率，检测的及时性和准确性都不足以实现有效管理。阻碍更好表现的主要原因包括云杉树冠的延迟和高度可变的活力响应的快速发展，以及欧洲云杉为主地区频繁的云层覆盖。总之，目前的遥感调查方法尚不能取代陆地控制调查来及时管理树皮甲虫。然而，它们可能作为定期和频繁的陆地调查的后备，或者在特定情况下提供支持，例如在交通不便的地形中或在没有足够调查能力的粗放管理的森林中检测冬眠树木。我们建议将“早期检测”一词始终用作“出现前检测”的同义词，以避免歧义。最后，我们根据分析研究的经验教训为未来的研究提供建议，即使用更严格和更有针对性的研究设计，确保跨学科性，并明确传达研究结果。