From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing

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 ofremote sensingapproaches. 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 similarspectral 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 toMODISimagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a singleplant speciesin the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to deriveα- (local relative abundance and richness of spectral species) andβ-diversity (turnover of spectral species) maps over wide geographical areas.