利用多角度高光谱数据中的植被指数和植物性状对玉米北方叶枯病进行早期监测

Anting Guo, Wenjiang Huang, Kun Wang, Binxiang Qian, Xiangzhe Cheng
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摘要

玉米北方叶枯病(MNLB)的特点是自下而上的发展过程,是一种影响玉米生长的普遍性、破坏性病害。早期监测对于及时干预从而减少产量损失至关重要。高光谱遥感技术是早期作物病害监测的有效手段。然而,传统的单角度垂直高光谱遥感方法由于受到玉米冠层上部叶片的阻碍,在监测玉米冠层下部的早期 MNLB 方面面临挑战。因此,我们提出了一种多角度高光谱遥感方法来监测早期 MNLB。从多角度高光谱数据(-60° 至 60°)中,我们提取并筛选出了植被指数(VIs)和植物性状(PTs),这些植被指数和植物性状在健康玉米样本和病害玉米样本之间存在显著差异。我们的研究结果表明,除了结构性 PTs(LAI 和 FIDF)外,其他 PTs(如 Cab、Car、Anth、Cw、Cp 和 CBC)也具有很强的病害鉴别能力。利用这些选定的特征,我们用随机森林(RF)算法开发了一个疾病监测模型,并将 VI 和 PT 整合在一起(PTVI-RF)。结果表明,PTVI-RF 优于仅基于 VI 或 PT 的模型。例如,PTVI-RF 模型在 0° 时的总体准确率(OA)为 80%,分别比仅依赖 VI 和 PT 的模型高出 4% 和 6%。此外,我们还探讨了观察角度对模型准确性的影响。结果表明,与天顶角度(0°)的准确性相比,较小的偏离天顶角度(±10°至±30°)的准确性更高,而较大角度(±40°至±60°)的准确性较低。具体来说,PTVI-RF 模型在 ±10° 至 ±30° 范围内的 OA 为 80% 至 88%,Kappa 为 0.6 至 0.76,在 -10° 时准确度最高(OA = 88%,Kappa = 0.76)。相比之下,在 ±40° 至 ±60° 时,OA 为 72% 至 80%,Kappa 为 0.44 至 0.6。总之,这项研究表明,通过融合从多角度高光谱数据中提取的 VI 和 PT 而构建的 PTVI-RF 可以有效监测早期 MNLB。这为早期预防和控制 MNLB 提供了依据,并为早期监测具有类似自下而上发展过程的作物病害提供了有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Monitoring of Maize Northern Leaf Blight Using Vegetation Indices and Plant Traits from Multiangle Hyperspectral Data
Maize northern leaf blight (MNLB), characterized by a bottom-up progression, is a prevalent and damaging disease affecting maize growth. Early monitoring is crucial for timely interventions, thus mitigating yield losses. Hyperspectral remote sensing technology is an effective means of early crop disease monitoring. However, traditional single-angle vertical hyperspectral remote sensing methods face challenges in monitoring early MNLB in the lower part of maize canopy due to obstruction by upper canopy leaves. Therefore, we propose a multiangle hyperspectral remote sensing method for early MNLB monitoring. From multiangle hyperspectral data (−60° to 60°), we extracted and selected vegetation indices (VIs) and plant traits (PTs) that show significant differences between healthy and diseased maize samples. Our findings indicate that besides structural PTs (LAI and FIDF), other PTs like Cab, Car, Anth, Cw, Cp, and CBC show strong disease discrimination capabilities. Using these selected features, we developed a disease monitoring model with the random forest (RF) algorithm, integrating VIs and PTs (PTVI-RF). The results showed that PTVI-RF outperformed models based solely on VIs or PTs. For instance, the overall accuracy (OA) of the PTVI-RF model at 0° was 80%, which was 4% and 6% higher than models relying solely on VIs and PTs, respectively. Additionally, we explored the impact of viewing angles on model accuracy. The results show that compared to the accuracy at the nadir angle (0°), higher accuracy is obtained at smaller off-nadir angles (±10° to ±30°), while lower accuracy is obtained at larger angles (±40° to ±60°). Specifically, the OA of the PTVI-RF model ranges from 80% to 88% and the Kappa ranges from 0.6 to 0.76 at ±10° to ±30°, with the highest accuracy at −10° (OA = 88%, Kappa = 0.76). In contrast, the OA ranges from 72% to 80% and the Kappa ranges from 0.44 to 0.6 at ±40° to ±60°. In conclusion, this research demonstrates that PTVI-RF, constructed by fusing VIs and PTs extracted from multiangle hyperspectral data, can effectively monitor early MNLB. This provides a basis for the early prevention and control of MNLB and offers a valuable reference for early monitoring crop diseases with similar bottom-up progression.
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