Precise spatial mapping of individual species using hyperspectral data is crucial for effective forest management and policy-making. This study focuses on Rhododendron arboreum, known for its medicinal properties attributed to the flavonoid Quercitrin. Sample data and spectroradiometer data were collected from the complex terrain of the Kumaon region in the Himalayas. Hyperspectral data, which includes signal variations based on biophysical and biochemical properties along with noise, were preprocessed using filtering techniques to enhance signal clarity by removing noise. Smoothing techniques were applied to remove noisy bands from the spectra, such as the Savitzky-Golay filter for reduced least square fit complexity and the Average Mean filter for taking mean spectral values. Subsequently, Spectral Analysis (SA) techniques, including first derivative, second derivative, and continuum removal, were employed. These mathematical transformations highlighted absorption troughs and determined the effect of Quercitrin on spectral wavelengths. Principal Component Analysis (PCA) was used to identify the most relevant bands related to Quercitrin. Additionally, regression analysis was applied on resampled spectral data, selected significant wavelengths based on variable importance values, pinpointing the most prominent wavelengths: 1196, 1229, 1328, 1383, 1425, 1636, 1661, 1699, 1785, and 1715 nm. Over 50 two-band combination indices were tested, and those with p-values less than 0.05 were deemed significant. For the development of prediction model, Machine Learning (ML) algorithms, including Support Vector Machine (SVM), Relevance Vector Machine (RVM), Random Forest (RF), and Artificial Neural Network (ANN), were applied. The Random Forest model, which splits input data into trees to simulate the best model based on observed values, demonstrated high effectiveness in predicting Quercitrin levels, achieving a training correlation of 0.864 and a testing correlation of 0.570. Hence RF proved to be a best technique of band selection as well as robust for Quercitrin prediction. This methodological approach highlights the importance of advanced data processing and analysis techniques in remote sensing applications for forest phytochemical prediction.

中文翻译：

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使用高光谱成像和机器学习对具有药用价值的槲皮素化合物进行定量和作图


使用高光谱数据对单个物种进行精确的空间制图对于有效的森林管理和政策制定至关重要。本研究的重点是杜鹃花，它以其归因于类黄酮槲皮素的药用特性而闻名。样本数据和光谱辐射仪数据是从喜马拉雅山 Kumaon 地区的复杂地形收集的。高光谱数据（包括基于生物物理和生化特性的信号变化以及噪声）使用过滤技术进行预处理，以通过去除噪声来提高信号清晰度。应用平滑技术从光谱中去除噪声条带，例如用于降低最小二乘拟合复杂度的 Savitzky-Golay 滤波器和用于获取平均光谱值的 Average Mean 滤波器。随后，采用了光谱分析 （SA） 技术，包括一阶导数、二阶导数和连续介质去除。这些数学变换突出了吸收槽，并确定了槲皮素对光谱波长的影响。主成分分析 （PCA） 用于鉴定与 Quercitrin 相关的最相关条带。此外，对重采样的光谱数据进行回归分析，根据可变重要性值选择有效波长，确定最突出的波长：1196、1229、1328、1383、1425、1636、1661、1699、1785 和 1715 nm。测试了 50 多个双波段组合指数，p 值小于 0.05 的指数被认为显著。为了开发预测模型，应用了机器学习 （ML） 算法，包括支持向量机 （SVM）、相关性向量机 （RVM）、随机森林 （RF） 和人工神经网络 （ANN）。 随机森林模型将输入数据拆分为树，以根据观测值模拟最佳模型，在预测槲皮素水平方面表现出很高的有效性，实现了 0.864 的训练相关性和 0.570 的测试相关性。因此，RF 被证明是条带选择的最佳技术，并且对槲皮素预测具有鲁棒性。这种方法强调了先进的数据处理和分析技术在森林植物化学预测遥感应用中的重要性。