LAUSR

Monitoring the spatiotemporal distribution of invasive aquatic plants is a challenge in many regions worldwide. One of the most invasive species on Earth is the water hyacinth. These plants are harmful to biodiversity and create negative impacts on society and economy. The Guadiana river (one of the most important ones in Spain) has suffered from this problem since the early 2000s. Several efforts have been made to mitigate it. However, invasive plants, such as the water hyacinth, are still present in seed banks at the bottom of the river and can germinate even more than a decade after. In this article, we propose an automatic methodology, based on remote sensing and deep learning techniques, to monitor the water hyacinth in the Guadiana river. Specifically, a multitemporal analysis was carried out during two years using images collected by ESA's Sentinel-2 satellite, analyzed with a convolutional neural network. We demonstrate that, with our strategy, the river can be monitored every few days, and we are able to automatically detect the water hyacinth. Three experiments have been carried out to predict the presence of water hyacinth from a few scattered training samples, which represent invasive plants in different phenological stages and with different spectral responses.