Mapping savannah woody vegetation at the species level with multispecral drone and hyperspectral EnMAP data
This work addresses the need for accurate vegetation monitoring in savannah ecosystems, which is crucial for conservation efforts, but it is incremental as it applies existing machine learning methods to new data.
The study tackled the problem of mapping fractional woody cover at the species level in a South African savannah using hyperspectral EnMAP data, achieving high accuracy rates by combining it with Sentinel-2 data.
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.