Detection of Azolla fillaculoides in River Systems using Sentinel 2 Imagery and Random Forest Classification in the Google Earth Engine

The proliferation of Azolla filiculoides, a fast-growing invasive aquatic fern, threatens river ecosystems worldwide by altering water quality and outcompeting native species. This study presents a novel approach for the global detection of A. filiculoides using Sentinel-2 satellite imagery and Random Forest classification within the Google Earth Engine (GEE) platform.

We utilized the high-resolution spectral data from Sentinel-2 to capture the unique reflectance characteristics of A. filiculoides. A Random Forest classifier, trained with ground-truth data from multiple riverine environments, was applied to distinguish A. filiculoides from other aquatic vegetation and surface water features. The robustness of the model was tested across time to ensure broad applicability. The method was tested and validated on the Tagus River (Spain) with manually labeled speies observations over several years.

The primary objective is to develop a scalable and user-friendly GEE application that enables near real-time monitoring and detection of A. filiculoides in river systems globally. This app is designed to support environmental managers and policymakers by providing accessible tools for early detection and effective management of this invasive species as well as to provide large scale species distribution data to leverage biogeogeographic studies of A. filiculoides.

Preliminary results demonstrate high classification accuracy (R² = 0.94) and the potential for the GEE app to facilitate large-scale monitoring. By integrating machine learning with cloud computing, our approach offers a cost-effective and efficient solution for the global challenge of invasive aquatic plant detection.