Remote Sensing Of Spectral Diversity: A New Methodological Approach To Account For Spatio-Temporal Dissimilarities Between Plant Communities

The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land man-agement. This results in strong temporal variation of spectral diversity, which cannot be accurately represented by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao's quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral diversity of a region (gamma SD) into additive alpha (alpha SD, within communities) and spatio-temporal beta (beta SD; between communities) components, allowing the calculation of beta SD from community mean spectral features, in-dependent from alpha SD. Second, we illustrated our methodological approach with a case study in which beta SD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in beta SD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to beta SD was assessed, and the results were validated against a dataset of in-situ measured beta diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimi-larities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.