Large-scale dieback of ash trees (Fraxinus spp.) caused by the fungus Hymenoscyphus fraxineus is posing an immense threat to forest health in Europe, requiring effective monitoring at large scales. In this study, a pipeline was created to find ash trees and classify dieback severity using high-resolution hyperspectral imagery of individual tree crowns (ITCs). Hyperspectral data were collected in four forest sites near Cambridge, UK, where 422 ITCs were manually delineated and labelled using field-measurements of species and dieback severity (for ash trees). Four algorithms, namely linear discriminant analysis (LDA), principal components analysis coupled with LDA (PCA-LDA), partial least squares discriminant analysis (PLS-DA) and random forest (RF), were used to build classification models for species and dieback severity classification. The effect of dark-pixel filtering on classification accuracy was evaluated. The best performing models were then coupled with automatic ITC segmentation to map species and ash dieback distribution over 16.8 hectares of woodland. We calculated and partitioned the coefficient of variation (CV) of the reflected ash spectra to find variable wavebands associated with dieback. PLS-DA and LDA were most accurate for classifying ITC species identifies (overall accuracy >90%), whereas RF was most accurate for classifying ash dieback severity (overall accuracy 77%). Dark pixel filtering further increased the accuracy of species classification (+6%), but not disease classification. The reflectances of narrow blue (415 nm), red-edge (680 nm) and NIR (760 nm) bands had high CV across disease classes and should be included if multispectral imagery were to be used to monitor ash dieback. The study demonstrates the possibility of using remote sensing to forward epidemiological research by monitoring forest pathogens in landscape scales, which would allow temperate forest managers to control pathogen outbreaks, assess associated impacts and restore affected forests much more effectively.

中文翻译：

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使用高光谱遥感监测英国森林中的灰枯萎病（Hymenoscyphus fraxineus）

由真菌Hymenoscyphus fraxineus引起的白蜡树（水曲柳属）大规模枯死正在对欧洲的森林健康构成巨大威胁，需要进行大规模的有效监测。在这项研究中，创建了一个管道来寻找白蜡树并使用单个树冠 (ITC) 的高分辨率高光谱图像对枯萎严重程度进行分类。在英国剑桥附近的四个森林地点收集了高光谱数据，其中 422 个 ITC 使用物种和枯死严重程度的现场测量（对于白蜡树）手动描绘和标记。四种算法，即线性判别分析（LDA）、主成分分析结合LDA（PCA-LDA）、偏最小二乘判别分析（PLS-DA）和随机森林（RF），用于建立物种和枯死的分类模型严重性分类。评估了暗像素过滤对分类精度的影响。然后将性能最佳的模型与自动 ITC 分割相结合，以绘制超过 16.8 公顷林地的物种和灰烬枯萎分布图。我们计算并划分了反射灰分光谱的变异系数 (CV)，以找到与枯死相关的可变波段。PLS-DA 和 LDA 在分类 ITC 物种识别方面最准确（总体准确率 >90%），而 RF 在分类灰枯病严重程度方面最准确（总体准确率 77%）。暗像素过滤进一步提高了物种分类的准确性（+6%），但没有提高疾病分类的准确性。窄蓝色 (415 nm)、红边 (680 nm) 和 NIR (760 nm) 波段的反射率在不同疾病类别中具有高 CV，如果要使用多光谱图像来监测灰烬枯死，则应包括在内。