The early detection of forest pests and diseases is a primary focus of remote sensing applications for forest health monitoring. Pine Wilt Disease (PWD), which causes significant damage to pine resources in many countries and regions, has been a key area where the close-range hyperspectral remote sensing has demonstrated its advantages for early diagnosis. However, it remains unclear whether PWD can be detected during the pre-visual stage and, if so, how to achieve hyperspectral detection. This study aimed to investigate the impacts of pine species, infection responses, and data types on hyperspectral detection of PWD, particularly in the pre-visual stage. Artificial inoculation experiments were conducted across three locations with 76 sample trees of two pine species, and hyperspectral data were collected regularly using ground-based non-imaging and UAV imaging spectrometers Five infection responses were identified: keep healthy (KH), quick infection (QI), slow recovery (SR), quick recovery (QR), and slow infection (SI). Spectral analysis revealed dynamic changes in the indices RVI (680–550,750) and NDVI (560,680), corresponding well with the spectral characteristics of the five infection responses. The infected trees with QI response could be spectrally detected starting from day 14, with over 50 % accuracy. Importance analysis using RF identified RVI (554,677) and NDVI (531,570) as consistent in detecting pre-visual stages. In contrast, the six VIs determined by PCA-S (RARSb, RVI (900, 680), RVI (800, 680), RVI (760, 500), RVI (800, 635), and REP) exhibited high consistency and played a crucial role in identifying pre-visual stage infected trees. These VIs combined with specific color bands, enabled the creation of false-color images highlighting infected trees starting from day 14 post-inoculation. The study highlighted the importance of recognizing infection response patterns for accurate PWD detection, with only QI response trees showed a stable infection cycle, making day 14 post-infection a meaningful starting point for spectral detection. Additionally, imaging and non-imaging data types did not significantly affect the detection process, and the impact of spectral resolution variations between 1 nm and 3.5 nm was negligible. Further research is required to determine the threshold for larger differences of spectral resolution and to explore detection across various pine species and growth environments.

中文翻译：

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松树种类、侵染响应和数据类型对近距离高光谱遥感检测 Bursaphelenchus xylophilus 的影响


森林病虫害的早期检测是森林健康监测遥感应用的主要关注点。松枯病 （PWD） 对许多国家和地区的松树资源造成重大破坏，一直是近距离高光谱遥感在早期诊断方面发挥优势的关键领域。然而，目前尚不清楚是否可以在视觉前阶段检测到 PWD，如果可以，如何实现高光谱检测。本研究旨在调查松树种类、感染反应和数据类型对 PWD 高光谱检测的影响，尤其是在视觉前阶段。在三个地点对两种松树的 76 棵样本树进行了人工接种实验，并使用地面非成像和无人机成像光谱仪定期收集高光谱数据。确定了五种感染反应：保持健康 （KH）、快速感染 （QI）、缓慢恢复 （SR）、快速恢复 （QR） 和缓慢感染 （SI）。光谱分析揭示了 RVI （680–550,750） 和 NDVI （560,680） 指数的动态变化，与五种感染反应的光谱特征非常吻合。从第 14 天开始，就可以对具有 QI 反应的感染树木进行光谱检测，准确率超过 50%。使用 RF 的重要性分析确定 RVI （554,677） 和 NDVI （531,570） 在检测视觉前阶段方面是一致的。相比之下，PCA-S 确定的 6 个 VIs （RARSb、RVI （900， 680）、RVI （800， 680）、RVI （760， 500）、RVI （800， 635） 和 REP） 表现出高度一致性，在识别视觉前阶段感染的树木方面发挥了关键作用。 这些 VI 与特定的色带相结合，能够从接种后第 14 天开始创建突出显示受感染树木的伪彩色图像。该研究强调了识别感染反应模式对于准确 PWD 检测的重要性，只有 QI 反应树显示出稳定的感染周期，这使得感染后第 14 天成为光谱检测的有意义的起点。此外，成像和非成像数据类型对检测过程没有显著影响，1 nm 和 3.5 nm 之间的光谱分辨率变化的影响可以忽略不计。需要进一步的研究来确定光谱分辨率差异较大的阈值，并探索在各种松树物种和生长环境中的检测。