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

Abstract

The spread of aquatic invasive plants is a major concern in several zones of the world's geography. These plants, which are not part of the natural ecosystem, cause a negative impact to the environment, as well as to economy and society. In Spain, large areas of Guadiana (the second longest river in Spain) have been invaded by such plants. Among the strategies to address this problem, monitoring and detection play an important role to control the spatio-temporal distribution of the invasive plants. The main objective of this work is to develop a methodology able to automatically detect the geo-location of aquatic invasive plants using remote sensing and machine learning techniques. To this end, several classification algorithms have been applied to freely available multispectral satellite imagery, collected by ESA's Sentinel-2 satellite. A quantitative and comparative assessment is conducted using different machine and deep learning algorithms, from classical methods such as unsupervised K-means to supervised random forests (RFs) and convolutional neural networks (CNNs). This study also proposes a methodology for validating the obtained classification results, generating synthetic ground truth images based on available high spatial resolution imagery. The obtained results demonstrate the suitability of some of the considered algorithms for automatic detection of aquatic weeds in satellite images with medium spatial resolution.