Optical characterization of two cyanobacteria genera, Aphanizomenon and Microcystis, with hyperspectral microscopy

Abstract

Cyanobacterial blooms are a nuisance and a potential hazard in freshwater systems worldwide. Remote sensing has been used to detect cyanobacterial blooms, but few studies have distinguished among genera of cyanobacteria. Because some genera are more likely to be toxic than others, this is a useful distinction. Hyperspectral imaging reflectance microscopy was used to examine cyanobacteria from Upper Klamath Lake, Oregon, at high spatial and spectral resolution to determine if two species found commonly in the lake, Aphanizomenon flos-aquae and Microcystis aeruginosa, can be separated spectrally. Of the analytical methods applied, a spectral shape algorithm applied to the derivative was found to be most successful in classifying these species in microscope scenes. Further work is required to determine if the spectral characterization of cyanobacterial genera can be scaled up to remote sensing applications. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Using Advanced Spectroscopy and Organic Matter Characterization to Evaluate the Impact of Oxidation on Cyanobacteria

“…The ~700 nm peak is not used in remote sensing applications because of the interference from water; this interference comes from the application of HSI in reflectance mode as opposed to transmittance mode, which is typically applied at the bench-scale [35]. The differences in spectra shape have been taken advantage of to differentiate between Aphanizomenon flos-aqua and Microcystis at the bench-scale [33], but this was accomplished with a spectral shape algorithm applied to the derivative of the spectrum to obtain meaningful results, which is beyond the scope of this work.…”

“…The black background in EDM allows the instrument to collect scattered light from the desired (i.e., cells) pixels of a sample. HSI has been used successfully to identify cyanobacteria blooms in laboratory and remote sensing applications [33,34,35,36]; however, it has not yet been applied to evaluate the impact of oxidation on cyanobacteria cells.…”

Mapping Benthic Algae and Cyanobacteria in River Channels from Aerial Photographs and Satellite Images: A Proof-of-Concept Investigation on the Buffalo National River, AR, USA

“…Not all algae are harmful, but a capacity to identify those that are potentially toxic could more effectively inform management actions. A plausible way forward was pioneered by Paine et al [56] and Slonecker et al [57], who showed that reflectance spectra measured in a lab while viewing algal samples under a microscope could be used to distinguish between taxa. Slonecker et al [58] have already published a spectral library consisting of 13 genera that is freely available to support this type of work; these authors plan to expand the library to include additional taxa [57].…”

Hyperspectral Reflectance Characteristics of Cyanobacteria

“…CyanoHABs are one of the emerging climate change issues that are currently drawing interest from both the civilian and intelligence remote sensing communities. Obviously related to ecosystem health and suitability of water quality for a range of uses, CyanoHABs also carry relevant attention to the defense and intelligence communities [9] [10] in that large coastal populations may suddenly be denied a major food source as a result of CyanoHAB contamination, potentially destabilizing large populations.…”

“…The Aphanizomenon-Microcystis Index uses a ratio of the width of the major chlorophyll a reflectance feature to the width of the phycocyanin absorption feature and was developed using aerial hyperspectral imagery. The algorithm provides an estimate of the relative abundance of Microcystis and Aphanizomenon in a pixel and was calibrated with measured cell counts [10].…”