The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects of European spruce forests. A crucial measure in pest control is the removal of infested trees before the beetles leave the bark, which generally happens before the end of June. However, stressed tree crowns do not show any significant color changes in the visible spectrum at this early-stage of infestation, making early detection difficult. In order to detect the related forest stress at an early stage, we investigated the differences in radar and spectral signals of healthy and stressed trees. How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season, which covered the period before attacks (April), early-stage infestation (‘green-attacks’, May to July), and middle to late-stage infestation (August to October). The results show that spectral differences already existed at the beginning of the vegetation season, before the attacks. The spectral separability between the healthy and infested samples did not change significantly during the ‘green-attack’ stage. The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones. These stress-induced spectral changes could be more efficient indicators of early infestations than the ‘green-attack’ symptoms.

In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019. The red and SWIR bands from Sentinel-2 showed the highest separability of healthy and stressed samples. The backscatter from Sentinel-1 and additional bands from Sentinel-2 contributed only slightly in the Random Forest classification models. We therefore propose the Normalized Distance Red & SWIR (NDRS) index as a new index based on our observations and the linear relationship between the red and SWIR bands. This index identified stressed forest with accuracies from 0.80 to 0.88 before the attacks, from 0.80 to 0.82 in the early-stage infestation, and from 0.81 to 0.91 in middle- and late-stage infestations. These accuracies are higher than those attained by established vegetation indices aimed at ‘green-attack’ detection, such as the Normalized Difference Water Index, Ratio Drought Index, and Disease Stress Water Index. By using the proposed method, we highlight the potential of using NDRS with Sentinel-2 images to estimate forest vulnerability to European spruce bark beetle attacks early in the vegetation season.

欧洲云杉的树皮甲虫（Ips typographus[L.]）是欧洲云杉林最具破坏性的害虫之一。害虫防治的一项关键措施是在甲虫离开树皮之前清除受侵染的树木，这种情况通常发生在6月底之前。然而，在此侵染的早期阶段，受胁迫的树冠在可见光谱中没有显示任何明显的颜色变化，这使得早期检测变得困难。为了及早发现相关的森林压力，我们调查了健康树木和受压树木的雷达和光谱信号的差异。分析了整个植被季节（包括4月），袭击初期（5月至7月的“绿色袭击”）和中后期的整个侵扰期的应力树特征随时间的变化。 （八月至十月）。结果表明，在袭击之前的植被季节开始时就已经存在光谱差异。健康和受感染样品之间的光谱可分离性在“绿色攻击”阶段没有显着变化。结果表明，树木在遭受攻击之前已经受到压力，并且具有与健康树木不同的光谱特征。这些压力引起的光谱变化可能比“绿色袭击”症状更有效地指示了早期侵染。

在这项研究中，我们使用了Sentinel-1和2张瑞典南部测试站点在2018年和2019年4月至10月的图像。Sentinel-2的红色和SWIR波段显示出健康样品和压力样品的最高可分离性。在“随机森林”分类模型中，Sentinel-1的反向散射和Sentinel-2的其他波段贡献很小。因此，基于我们的观察以及红色和SWIR波段之间的线性关系，我们提出了标准化距离红色和SWIR（NDRS）指数作为新的指数。该指数确定了受灾森林的准确度在袭击前为0.80至0.88，在早期侵染时从0.80至0.82，在中期和晚期侵扰时从0.81至0.91。这些精度高于旨在“绿色攻击”检测的既定植被指数所获得的精度，例如标准化差异水指数，比率干旱指数和疾病胁迫水指数。通过使用所提出的方法，我们强调了将NDRS与Sentinel-2图像一起使用来评估森林在植被季节早期对欧洲云杉皮甲虫袭击的脆弱性的潜力。