The loss of biodiversity and the associated decline of ecosystem
services vital for sustaining human life demand a comprehensive
monitoring of plant biodiversity. Measuring biodiversity in the field on
large areas generates issues like the need of a robust sampling design,
the high demand on human and monetary resources and different biases
introduced by humans and environmental conditions. These circumstances
have recently triggered an extended use of remote sensing data to
quantify biodiversity in a cost- and time-efficient way. Remotely sensed
datasets represent the Earth surface at a certain point in time. Yet, it
is not well studied what the use of a single dataset in time implies for
biodiversity estimates. The functional dimension of biodiversity,
expressed through functional traits within or between species, varies
according to the phenological cycle. Further in grasslands, mowing and
grazing events lead to temporal variations in the remotely sensed
diversity. We provide an approach in which we integrate the temporal
dimension in the quantification of biodiversity from space. Functional
diversity is partitioned into a spatial and a temporal component. In
particular, Sentinel-2 satellite datasets are well suited for this
purpose, providing a complete landscape picture with high revisit time.
In our study case, the incorporation of the temporal dimension and the
interaction between spatial and temporal diversity by employing multiple
datasets improves the retrieval of functional diversity in differently
managed alpine grasslands. In comparison to the use of a single dataset,
our approach provides more reliable recommendations for conservation and
restoration decision-making on a regional scale.