Conclusions
This review has examined the remote sensing of grassland biodiversity
and six functional traits with a focus on recent technological and
methodological developments. Advances in UAV technology have accelerated
the increase in grasslands surveys featuring very high spatial
resolutions, with 3D components and increasingly employing multispectral
and hyperspectral sensors. At the same time, machine learning methods
are becoming prevalent within the research community, requiring a strong
understanding of the many parameters needed to construct a useful
statistical or predictive model. While these developments open the door
to new approaches and new discoveries, they also present new sources of
error and uncertainty. This requires a more structured and systematic
approach to investigating, documenting and addressing these issues.
Utilising UAV surveys as a bridge between point-based groundwork and
satellite remote sensing, helping to integrate measurements across
spatial and temporal scales, is one step in this this process that can
be implemented in many locations across the planet. This could be
further enhanced by making better use of currently existing and future
hyperspectral satellite platforms, such as EnMAP, PRISMA and the Firefly
constellation. Finally, we stress the importance of a global database,
similar to TRY (Kattge et al., 2011) of traits, species and related
spectra from multi- and hyper-spectral devices, that could enhance the
development of more robust, scalable and generalisable remote sensing
models. This could then contribute to more accurate monitoring of
grassland species diversity as well as functional traits.