Water Hyacinth (Eichhornia crassipes) Detection Using Coarse and High Resolution Multispectral Data

Abstract

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.

Automatic Detection of Aquatic Weeds: A Case Study in the Guadiana River, Spain

“…The main goal of this work is to develop a ML/DL methodology to automatically detect the spatial-temporal distribution of aquatic invasive plants in the Guadiana river. To the best of our knowledge, there are no previous studies about the detection of aquatic weeds in this river combining GIS, RS and AI techniques [34]. Specifically, we could not find any works using DL to address this problem.…”

Estimation of Water Hyacinth Using Computer Vision

“…Current new approaches are used for vegetation spreading and infestation monitoring in large scale areas. Those modern approaches use a variety of tools, ranging from remote sensing technologies (Simpson et al, 2020), high resolution satellite imagery to artificial intelligence techniques (Datta et al, 2021;Feng et al, 2020;Pádua et al, 2022;Xie et al, 2008). These works, specifically, address the issue of detection and monitoring for analytical and preventive purposes.…”

Multispectral remote sensing approach of predicting the potential distribution and evaluating the current spread of water hyacinth (Eichhornia crassipes)

“…The wetland zone residents are mostly shermen and smallholder farmers whose livelihoods are highly dependent on these natural systems (Shiferaw et al, 2018). Its invasion has caused the destruction of croplands, loss of pasture and indigenous grasses including some tree species (Ant et al, 2022;Peter, 2009). Remote sensing has been frequently employed in assessing the spread of the water hyacinth.…”