In the light of unprecedented change in global biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use of remote sensing approaches. Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called “spectral species”. Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method to MODIS imagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a single plant species in the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to derive α- (local relative abundance and richness of spectral species) and β-diversity (turnover of spectral species) maps over wide geographical areas.

鉴于全球生物多样性的空前变化，实时，准确的生态系统和生物多样性评估变得越来越重要。然而，由于以下几个原因，利用生态田间数据估算生物多样性可能很困难。例如，对于非常大的区域，收集提供可靠信息的数据具有挑战性。通过使用遥感方法可以避免地球观测中的某些限制。各种研究都根据光谱变化假说（SVH）估算了生物多样性。根据该假设，空间网格的不同像素单元上的光谱异质性反映出较高的生态位异质性，从而允许更多生物共存。最近，光谱物种的概念已经产生，在考虑到景观尺度上的光谱异质性对应于共享相似光谱特征的子空间的组合之后。通过在局部尺度上使用高分辨率遥感数据，可以将这些子空间识别为单独的光谱实体，即所谓的“光谱种类”。我们的方法将这一概念扩展到更大的空间范围，并扩展到更高层次的生物组织。我们将此方法应用于整个欧洲的MODIS影像数据。显然，在这种情况下，MODIS识别的光谱物种与田间的单一植物物种无关，而是与物种集合，栖息地或生态系统相关。基于这种光谱信息，我们提出了一种简单的方法来推导 这些子空间可以识别为单独的光谱实体，即所谓的“光谱种类”。我们的方法将这一概念扩展到更大的空间范围，并扩展到更高层次的生物组织。我们将此方法应用于整个欧洲的MODIS影像数据。显然，在这种情况下，MODIS识别的光谱物种与田间的单一植物物种无关，而与物种集合，栖息地或生态系统相关。基于这种光谱信息，我们提出了一种简单的方法来推导 这些子空间可以识别为单独的光谱实体，即所谓的“光谱种类”。我们的方法将这一概念扩展到更大的空间范围，并扩展到更高层次的生物组织。我们将此方法应用于整个欧洲的MODIS影像数据。显然，在这种情况下，MODIS识别的光谱物种与田间的单一植物物种无关，而是与物种集合，栖息地或生态系统相关。基于这种光谱信息，我们提出了一种简单的方法来推导 MODIS识别的光谱物种与田间的单一植物物种无关，而是与物种集合，栖息地或生态系统相关。基于这种光谱信息，我们提出了一种简单的方法来推导 MODIS识别的光谱物种与田间的单一植物物种无关，而是与物种集合，栖息地或生态系统相关。基于这种光谱信息，我们提出了一种简单的方法来推导大范围地理区域的α-（光谱物种的局部相对丰度和丰富度）和β-多样性（光谱物种的周转）图。