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亚高山典型泥炭湿地植被光谱特征分析

Spectral characteristic analysis of vegetation in typical subalpine peatlands

  • 摘要: 本研究以大九湖湿地23种常见植物的叶片高光谱数据为研究对象,基于多物种原始光谱的比较分析,借助Savitzky-Golay滤波、一阶导数变换(FD)、多元散射校正(MSC)等数据处理方法以及光谱特征参数分析,探究物种独有的光谱特征及其种间差异;在此基础上运用偏最小二乘判别分析(PLS-DA)对不同物种的光谱进行聚类判别,以揭示物种光谱的种间识别能力。结果显示:(1)原始光谱反射特征符合典型湿地植被光谱规律,大部分物种在近红外波段存在明显差异,光谱处理变换通过提取特征波段位置信息,有利于突出种间差异;(2)光谱特征参数与植被生化特性密切相关,“三边”参数的提取可以有效地识别植物的光谱差异,从而更好地区分物种;(3)经FD和MSC预处理后的光谱PLS-DA模型的平均判定系数达0.548,显示出较强的判别解释力,但不同物种的分类精度差异显著。综上,本研究深化了对湿地植被光谱参数-理化特性关系的理解,验证了利用高光谱遥感进行物种分类的可行性和有效性,为后续湿地植被的动态监测和合理管护提供了科学依据和技术支撑。

     

    Abstract: Dajiuhu Wetland, a rare mid-latitude peatland ecosystem, harbors a diverse assemblage of rare and unique plant species. Given its vulnerability to intensifying climate extremes and anthropogenic disturbances, high-resolution monitoring of vegetation composition and spatial dynamics is essential to support effective conservation and functional integrity. Hyperspectral remote sensing, grounded in spectral feature analysis, is crucial for species identification and community dynamics monitoring. This study focused on the leaf-level hyperspectral data of 23 representative plant species in Dajiuhu Wetland. Comparative analysis of raw spectral profiles across multiple species, followed by application of Savitzky-Golay smoothing, first derivative transformation (FD), multiplicative scatter correction (MSC), and spectral feature parameter analysis, enabled the extraction of distinct spectral signatures and interspecies variation. Characteristic spectral parameters were computed and used as input for partial least squares discriminant analysis (PLS-DA) to evaluate species separability. Results showed that (1) raw spectra exhibited canonical features of wetland flora, with pronounced differences in the near-infrared region among most species, which was further enhanced by spectral transformations; (2) derived spectral parameters, particularly those associated with the “three-edge” regions, were closely related to the biochemical properties of vegetation and facilitated species-level discrimination; (3) PLS-DA models incorporating FD and MSC preprocessing achieved a mean coefficient of determination of 0.548, confirming the robustness of spectral-based classification despite variable predictive accuracy across taxa. In summary, this study elucidated critical linkages between wetland vegetation spectral parameters and physicochemical characteristics, validated the feasibility and effectiveness of species classification using hyperspectral remote sensing, and provided scientific evidence and support for dynamic monitoring and rational management of wetland vegetation.

     

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