Mapping invasive herbaceous plant species with sentinel-2 satellite imagery: echium plantagineum in a mediterranean shrubland as a case study

Abstract

Invasive alien plants (IAPs) pose a serious threat to biodiversity, agriculture, health, and economies globally. Accurate mapping of IAPs is crucial for their management, to mitigate their impacts and prevent further spread where possible. Remote sensing has become a valuable tool in detecting IAPs, especially with freely available data such as Sentinel-2 satellite imagery. Yet, remote sensing methods to map herbaceous IAPs, which tend to be more difficult to detect, particularly in shrubland Mediterranean-type ecosystems, are still limited. There is a growing need to detect herbaceous IAPs at a large scale for monitoring and management; however, for countries or organizations with limited budgets, this is often not feasible. To address this, we aimed to develop a classification methodology based on optical satellite data to map herbaceous IAP's using Echium plantagineum as a case study in the Fynbos Biome of South Africa. We investigate the use of freely available Sentinel-2 data, use the robust non-parametric classifier Random Forest, and identify the most important variables in the classification, all within the cloud-based platform, Google Earth Engine. Findings reveal the importance of the shortwave infrared and red-edge parts of the spectrum and the importance of including vegetation indices in the classification for discriminating E. plantagineum. Here, we demonstrate the potential of Sentinel-2 data, the Random Forest classifier, and Google Earth Engine for mapping herbaceous IAPs in Mediterranean ecosystems.