The detection of susceptible and attacked trees is a key factor in the management of bark beetle infestations. The challenge of early detection of infestations, due to invisible changes in the canopy color, consequently hinders the control of outbreaks in a timely manner. While many studies have examined the spectral characteristics during the green-attack stage, with no discernible needle discoloration, few have focused on the temporal-scale spectral characteristics from pre-infestation to the infestation stages, accurately detected in the field. We investigated the spectral differences among three classes of forest areas: healthy, susceptible to bark beetle attacks, and green-attacked trees using individual wavebands and spectral vegetation indices (SVIs). We assumed that the spectral characteristics of these forests vary significantly due to different temporal-scale susceptibility to the bark beetle attacks. We used 16 imageries, between 2 April and 5 September 2020, acquired from PlanetScope satellite. The spatial position of attacked trees during the growing season was recorded in the field using the ArcGIS Collector App. Ancillary datasets of forest management units were used to randomly select non-attacked trees from areas with properties similar to the attacked areas. The statistical analyses were performed on the final cleaned datasets to assess the separability among the tree classes. Our findings indicate that the Green and Red wavelengths are promising bands to distinguish healthy, susceptible, and green-attacked trees. We also found the existing spectral differences between healthy and susceptible trees before bark beetle attacks, suggesting the presence of abiotic stress to facilitate the infestation. Among different SVIs, the Enhanced Vegetation Index (EVI) and Visible Atmospherically Resistant Index (VARI) were found to be capable of specifically detecting susceptible trees to the infestation. However, it is important to note that the scope of our study is limited to a one-year period and does not focus on individual trees but rather on groups of trees, indicating that future research could benefit from a multi-year analysis at the tree level to enhance the understanding and management of bark beetle dynamics.

中文翻译：

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挪威云杉的绿色攻击和树皮甲虫易感性检测：利用 PlanetScope 多光谱图像进行三阶段光谱可分离性分析

检测易受影响和受攻击的树木是管理树皮甲虫侵扰的关键因素。由于树冠颜色的看不见的变化，早期发现虫害的挑战阻碍了及时控制疫情。虽然许多研究已经检查了绿色攻击阶段的光谱特征，没有明显的针变色，但很少有研究关注在现场准确检测到的从侵染前到侵染阶段的时间尺度光谱特征。我们使用单独的波段和光谱植被指数 (SVI) 研究了三类森林区域之间的光谱差异：健康的、易受树皮甲虫攻击的树木和受绿色攻击的树木。我们假设，由于对树皮甲虫攻击的时间尺度敏感性不同，这些森林的光谱特征存在显着差异。我们使用了 2020 年 4 月 2 日至 9 月 5 日期间从 PlanetScope 卫星获取的 16 张图像。使用 ArcGIS Collector 应用程序在现场记录生长季节期间受攻击树木的空间位置。利用森林经营单位的辅助数据集，从与受灾地区性质相似的地区随机选择未受灾树木。对最终清理的数据集进行统计分析，以评估树类之间的可分离性。我们的研究结果表明，绿色和红色波长是区分健康、易受影响和受绿色侵袭的树木的有希望的波段。我们还发现在树皮甲虫攻击之前健康和易受影响的树木之间存在光谱差异，这表明非生物胁迫的存在促进了侵扰。在不同的 SVI 中，增强植被指数 (EVI) 和可见光抗性指数 (VARI) 被发现能够特异性检测对侵染敏感的树木。然而，值得注意的是，我们的研究范围仅限于一年，并不关注单棵树，而是关注树群，这表明未来的研究可能会受益于对树的多年分析水平，以加强对树皮甲虫动态的了解和管理。