Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges

Abstract

Cyanobacteria are major contributors to algal blooms in inland waters, threatening ecosystem function and water uses, especially when toxin-producing strains dominate. Here, we examine 140 hyperspectral (HS) images of five representatives of the widespread, potentially toxin-producing and bloom-forming genera Microcystis, Planktothrix, Aphanizomenon, Chrysosporum and Dolichospermum, to determine the potential of utilizing visible and near-infrared (VIS/NIR) reflectance for their discrimination. Cultures were grown under various light and nutrient conditions to induce a wide range of pigment and spectral variability, mimicking variations potentially found in natural environments. Importantly, we assumed a simplified scenario where all spectral variability was derived from cyanobacteria. Throughout the cyanobacterial life cycle, multiple HS images were acquired along with extractions of chlorophyll a and phycocyanin. Images were calibrated and average spectra from the region of interest were extracted using k-means algorithm. The spectral data were pre-processed with seven methods for subsequent integration into Random Forest models, whose performances were evaluated with different metrics on the training, validation and testing sets. Successful classification rates close to 90 % were achieved using either the first or second derivative along with spectral smoothing, identifying important wavelengths in both the VIS and NIR. Microcystis and Chrysosporum were the genera achieving the highest accuracy (>95 %), followed by Planktothrix (79 %), and finally Dolichospermum and Aphanizomenon (>50 %). The potential of HS imagery to discriminate among toxic cyanobacteria is discussed in the context of advanced monitoring, aiming to enhance remote sensing capabilities and risk predictions for water bodies affected by cyanobacterial harmful algal blooms.