LAUSR

Multispectral remote sensing is very effective for the detection of different types of land cover. Satisfactory results have been obtained in the task of monitoring different types of vegetation covers using satellite images, including areas conquered by invasive aquatic plants such as water hyacinth. Several countries around the world are suffering from negative effects caused by the quick spread of these plants. For instance, several strategies have been carried out for their control and elimination in the Guadiana River, Spain. In this letter, we develop a new methodology capable of automatically finding the geolocation of the most frequent areas of accumulation of invasive aquatic plants in the Guadiana River. Our strategy exploits multispectral time series acquired by European Space Agency (ESA) Sentinel-2 satellite. Once the invasive plants have been detected using deep learning [a convolutional neural network (CNN)], a subsequent analysis is carried out using geographic information systems (GIS) to map the areas where water hyacinth patches are most frequently found. In this way, we demonstrate that the management of invasive aquatic plants in the Guadiana River can be successfully carried out.