Mapping savannah woody vegetation at the species level with multispecral drone and hyperspectral EnMAP data

TL;DR

This study demonstrates a method for accurately mapping species-level woody vegetation in savannahs using combined hyperspectral EnMAP data, drone imagery, and machine learning, highlighting the benefits of multisensor data integration.

Contribution

It introduces a novel approach integrating hyperspectral, drone, and multispectral data for species-level woody cover mapping in savannahs, with assessment of multitemporal information.

Findings

High accuracy in species-level woody cover mapping achieved.

Synergistic use of EnMAP and Sentinel-2 data improves results.

Multitemporal data enhances fractional woody cover estimation.

Abstract

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.