Efficient detection and monitoring procedures of invasive plant species are required. It is of
crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity
and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth
(Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose,
high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution
data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth
located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used
to perform a pixel-based detection of this invasive species in both datasets. From the different
classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy
(OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by
Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks
(OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of
water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser
resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with
a UAV-based survey. Combining both datasets and even considering the different resolutions, it was
possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to
be an effective way to assess the effects of the mitigation/control measures taken in the study areas.
Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor
their changes over time.
This research activity was funded by POCI-FEDER as part of the project “BioComp_2.0—
Produção de compostos orgânicos biológicos para o controlo do jacinto de água e para a valorização
de subprodutos agropecuários, florestais e agroindustriais” (POCI-01-0247-FEDER-070123) and by
national funds through FCT (Portuguese Foundation for Science and Technology) under the projects
UIDB/04033/2020 and UIDB/00690/2020.
