Zhang Xiao, Gao Yingqun, Fu Lianjin, Zhang Yiran, Li Zeyu, Shu Qingtai
College of Soil and Water Conservation, Southwest Forestry University, Kunming, 650224, China.
College of Forestry, Southwest Forestry University, Kunming, 650224, China.
Sci Rep. 2025 Jul 21;15(1):26490. doi: 10.1038/s41598-025-10696-6.
Pine Wilt Disease (PWD), caused by the pine wood nematode, is a major global forest pathology characterized by the rapid death of pine trees within a span of three months. Forestry management policy requires the eradication of all infected trees during the initial outbreak phase of a disease to contain its spread. This measure substantially relies on the timely identification of diseased trees. Accurate early diagnosis is a critical core component for effective disease control, preventing the spread of the epidemic, and maintaining the integrity of forest ecosystems. Therefore, this study proposes a new approach for early detection of PWD using hyperspectral data combined with measured physiological parameters to obtain diagnostic spectra and optimal biochemical parameters for early detection. This study investigated early-stage PWD by integrating 350-2500 nm hyperspectral data acquired with an ASD FieldSpec 4 and biochemical analysis. Results revealed significant declines in total sugar, reducing sugar, and moisture content during early infection. Study identified the spectral ranges 455-677 nm and 1974-2340 nm as optimal diagnostic windows. Using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA), we identified diagnostic spectral bands: CARS selected 20 moisture-sensitive bands in red-edge (680-760 nm) and SWIR regions, while SPA pinpointed 4 critical bands (758, 1074, 1124, 1663 nm) across red-edge, NIR, and SWIR. This leaf-scale methodology establishes a technical foundation for regional-scale airborne and satellite hyperspectral monitoring of PWD. The XGBoost classifier achieved 91% accuracy (CARS) and 83% accuracy (SPA) in distinguishing healthy from early-stage infected trees, with AUC > 0.8 for both feature sets, demonstrating reliable spectral discrimination of infection status. This study proposes a novel method for the early detection of PWD based on spectral characteristics, offering valuable insights for the application of hyperspectral remote sensing at a regional scale.
松材线虫病（PWD）由松材线虫引起，是一种主要的全球性森林病害，其特征是松树在三个月内迅速死亡。林业管理政策要求在疾病初期爆发阶段根除所有受感染树木，以控制其传播。这一措施很大程度上依赖于对患病树木的及时识别。准确的早期诊断是有效控制疾病、防止疫情蔓延和维护森林生态系统完整性的关键核心要素。因此，本研究提出了一种利用高光谱数据结合测量的生理参数来获取诊断光谱和早期检测最佳生化参数的新方法，用于松材线虫病的早期检测。本研究通过整合使用ASD FieldSpec 4采集的350 - 2500 nm高光谱数据和生化分析来研究松材线虫病早期阶段。结果显示，在早期感染期间，总糖、还原糖和水分含量显著下降。研究确定455 - 677 nm和1974 - 2340 nm光谱范围为最佳诊断窗口。使用竞争性自适应重加权采样（CARS）和连续投影算法（SPA），我们确定了诊断光谱带：CARS在红边（680 - 760 nm）和短波红外（SWIR）区域选择了20个对水分敏感的波段，而SPA在红边、近红外（NIR）和SWIR区域确定了4个关键波段（758 nm, 1074 nm, 1124 nm, 1663 nm）。这种叶尺度方法为松材线虫病的区域尺度机载和卫星高光谱监测奠定了技术基础。XGBoost分类器在区分健康树木和早期感染树木方面，基于CARS特征集的准确率达到91%，基于SPA特征集的准确率达到83%，两个特征集的曲线下面积（AUC）均大于0.8，表明对感染状态具有可靠的光谱鉴别能力。本研究提出了一种基于光谱特征的松材线虫病早期检测新方法，为区域尺度高光谱遥感的应用提供了有价值的见解。
