Many lakes in New Zealand and other parts of the world are frequently affected by harmful algal blooms, where a rapid buildup of algae mass becomes toxic to local ecosystems, aquaculture and even human health. Freshwater scientists monitor these algal blooms by collecting water samples and analysing them in a lab to estimate algal concentration, to flag when water becomes unsafe. Our project aims to use machine learning techniques on satellite image data to detect harmful algal blooms. This remote sensing approach has the potential to improve the cost, frequency and spatial resolution of algal bloom monitoring over traditional methods. We aim to improve this technology to enable a more global coverage of algal bloom detection, and rapidity of harm detection.
This project is part of the MBIE Taiao Programme.
Keywords: Water Quality, Semi-supervised learning, Transfer Learning