In the past decade, massive outbreaks of bark beetles (Ips spp.) have caused large-scale decline of coniferous-dominated, prevailingly managed forests of Central Europe. Timely detection of newly infested trees is important for minimizing economic losses and effectively planning forest management activities to stop or at least slow outbreaks. With the advancement of Copernicus services, a pair of Sentinel-2 satellites provides a unique remote sensing data source of multi-temporal observations in high spatial resolution on the scale of individual forest stands (although not allowing for individual tree detection). This study investigates the potential for using seasonal trajectories of Sentinel-2 bands and selected vegetation indices in early detection of bark beetle infestation (so–called green-attack stage detection) in Norway spruce monoculture forests in the Czech Republic. Spectral trajectories of nine bands and six vegetation indices were constructed for the 2018 vegetation season using 14 satellite observations from April to November to distinguish four infestation classes. We used a random forest algorithm to classify healthy (i.e., stands not infested) and infested trees with various trajectories of decay. The seasonal trajectories of vegetation indices separated the infestation classes better than did the individual bands. Among the most promising vegetation indices we identified the tasselled cap wetness (TCW) component and normalized difference index constructed from near and shortwave infrared bands. Analysing the inter-annual change of the indices was more promising for early detection than is single-date classification. It achieved 96% classification accuracy on day of year 291 for the tested data set. The algorithm for early detection of tree infestation based on the assessment of seasonal changes in TCW was applied on the time series of Sentinel-2 observations from 2019 and its outputs were verified using field observations of forest conditions conducted on 80 spruce forest plots (located in spruce monoculture stands). The overall accuracy of 78% was achieved for the separation of healthy and green-attack classes. Our study highlights the great potential of multi-temporal remote sensing and the use of shortwave infrared wavelengths for early detection of spruce forest decline caused by bark beetle infestation.

在过去的十年中，大规模爆发了树皮甲虫（Ipsspp。）导致了中欧针叶林为主的，普遍经营的森林的大规模衰退。及时发现新受侵害的树木对于最大程度地减少经济损失并有效地计划森林管理活动以阻止或至少减缓疾病爆发非常重要。随着哥白尼服务的发展，一对Sentinel-2卫星提供了独特的遥感数据源，可在单个林分的规模上以高空间分辨率提供多时相观测（尽管不允许单独检测树木）。这项研究调查了在捷克共和国的挪威云杉单一栽培林中，利用Sentinel-2带的季节性轨迹和选定的植被指数来早期发现树皮甲虫侵扰（所谓的绿色袭击阶段）的潜力。利用4月至11月的14颗卫星观测数据，构建了2018年植被季节9个波段和6个植被指数的光谱轨迹，以区分4种侵染等级。我们使用随机森林算法对具有各种衰减轨迹的健康（即未受侵害的林地）和受侵害的树进行分类。植被指数的季节性轨迹比单个波段更好地划分了侵扰等级。在最有希望的植被指数中，我们确定了流穗盖湿度（TCW）分量和由近波和短波红外波段构成的归一化差异指数。与单日分类相比，分析指数的年际变化对早期发现更有希望。对于测试数据集，它在291年的一天达到了96％的分类精度。 将基于TCW季节性变化评估的树木侵染早期检测算法应用于2019年以来的Sentinel-2观测值的时间序列，并使用对80个云杉林地（位于云杉单一栽培林）。区分健康和绿色攻击类别的总体准确度达到了78％。我们的研究强调了多时相遥感的巨大潜力，以及短波红外波长在树皮甲虫侵扰引起的云杉林衰退的早期检测中的应用。