Abstract Hyperspectral data, with their detailed spectral information at different wavelengths, offer multiple ways to assess biodiversity. One approach, known as the “spectral variation hypothesis” (SVH), proposes that biodiversity is linked to spectral diversity. However, SVH-based approaches, which we refer to as “spectral diversity metrics”, can be confounded by soil exposure and are sensitive to the spatial resolution of the data. To address these issues, we 1) investigated the impact of soil exposure on spectral diversity, 2) identified optimal bands for mapping biodiversity using a spectral diversity metric based on dimension reduction, and 3) assessed the impact of spatial resolution on spectral diversity metrics. In this study, α-diversity (species richness) was used as a measure of plant biodiversity. The study was based on two imaging spectrometry data sets from the Cedar Creek Ecosystem Science Reserve in Central Minnesota, USA, at two levels: proximal and airborne. The data sets included varying degrees of soil background sampled at two different spatial resolutions (1 mm and 0.75 m). We explored five spectral diversity metrics, including the coefficient of variation, convex hull volume, spectral angle mapper, spectral information divergence, and a newly proposed dimension reduction-based metric called “convex hull area.” For the proximal data set (pixel size of 1 mm), filtering soil pixels by applying a normalized difference vegetation index (NDVI) threshold improved the performance of all spectral diversity metrics significantly, with the coefficient of variation showing the highest correlation with species richness. In the airborne data set (pixel size of 0.75 m), the convex hull area outperformed other metrics. These findings demonstrate promising approaches for remote sensing of biodiversity, illustrate a confounding effect of soil background on remote diversity measurement, and indicate that the most informative regions of the electromagnetic spectrum for estimating species richness can vary with spatial scale.

中文翻译：

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生物多样性遥感：土壤校正和数据降维方法改进了草原生态系统中α-多样性（物种丰富度）的评估

摘要 高光谱数据及其在不同波长的详细光谱信息，提供了多种评估生物多样性的方法。一种称为“光谱变异假设”（SVH）的方法提出生物多样性与光谱多样性有关。然而，基于 SVH 的方法，我们称之为“光谱多样性度量”，可能会被土壤暴露混淆，并且对数据的空间分辨率很敏感。为了解决这些问题，我们 1) 研究了土壤暴露对光谱多样性的影响，2) 确定了使用基于降维的光谱多样性度量来绘制生物多样性的最佳波段，以及 3) 评估了空间分辨率对光谱多样性度量的影响。在这项研究中，α-多样性（物种丰富度）被用作衡量植物生物多样性的指标。该研究基于美国明尼苏达州中部雪松溪生态系统科学保护区的两个成像光谱数据集，分为两个级别：近端和空中。数据集包括以两种不同空间分辨率（1 mm 和 0.75 m）采样的不同程度的土壤背景。我们探索了五个光谱多样性度量，包括变异系数、凸包体积、光谱角度映射器、光谱信息发散和新提出的基于降维的度量，称为“凸包面积”。对于近端数据集（像素大小为 1 mm），通过应用归一化差异植被指数 (NDVI) 阈值过滤土壤像素显着提高了所有光谱多样性指标的性能，变异系数与物种丰富度的相关性最高。在机载数据集（像素大小为 0.75 m）中，凸包面积优于其他指标。这些发现展示了生物多样性遥感的有前景的方法，说明了土壤背景对远程多样性测量的混杂影响，并表明用于估计物种丰富度的电磁频谱中信息量最大的区域可能随空间尺度而变化。