  Savannahs are vital ecosystems whose sustainability is endangered by the
spread of woody plants. This research targets the accurate mapping of
fractional woody cover (FWC) at the species level in a South African savannah,
using EnMAP hyperspectral data. Field annotations were combined with very
high-resolution multispectral drone data to produce land cover maps that
included three woody species. The high-resolution labelled maps were then used
to generate FWC samples for each woody species class at the 30-m spatial
resolution of EnMAP. Four machine learning regression algorithms were tested
for FWC mapping on dry season EnMAP imagery. The contribution of multitemporal
information was also assessed by incorporating as additional regression
features, spectro-temporal metrics from Sentinel-2 data of both the dry and wet
seasons. The results demonstrated the suitability of our approach for
accurately mapping FWC at the species level. The highest accuracy rates
achieved from the combined EnMAP and Sentinel-2 experiments highlighted their
synergistic potential for species-level vegetation mapping.
