A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach

Abstract

Abstract:The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. We present a novel spectral band ratio algorithm using 700 and 600 nm that is much less sensitive to the presence of chlorophyll.

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

“…Several BG algae have confirmed their apparent high carbon affinities since they could maintain slow net growth even at pH > 10 [7]. Light and nutrient availability are also characteristics which promote CHABs [5]. These facts enhance the importance of monitoring BG algae presence in water supply and recreational reservoirs.…”

“…One of the first cases of human fatalities associated with cyanobacteria and their toxins occurred in 1996 at Caruaru, PE, Brazil, where exposure through renal dialysis led to the death of approximately fifty patients [4]. Besides their toxic characteristic, Cyanobacterial Harmful Algal Blooms (CHABs) also degrade aquatic habitats due to their malodorousness [3,5] and the appearance of a scum layer or thick mat on the surface of water.…”

Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery

“…However, while the estimation of Chl-a is a relatively easy task for Case-1 water, monitoring algal blooms in lakes and coastal water classified as Case-2 waters is a challenging one. The remote sensing reflectance (R) spectra of these water bodies is generally complex because of the presence of many optically active components, such as chlorophylls and carotenoids from phytoplankton and aquatic plants, total suspended solids (TSS), colored dissolved organic matter (CDOM), and sometimes bottom reflectance [16]. Generally, the algorithms developed to detect blooms based on the absorption properties of a single variable absorbing near 700 nm (typical of Chl-a) are inadequate for this type of waters [15].…”

Interactive Correlation Environment (ICE) — A Statistical Web Tool for Data Collinearity Analysis

“…Ogashawara et al [13] analyzed the performance of several reflectance-based bio-optical models for the estimation of PC. Among the analyzed algorithms, three published band ratios were analyzed: Schalles and Yacobi [14] (R rs (650)/R rs (624)), Simis et al [15] (R rs (709)/R rs (620)) and Mishra et al [16] (R rs (700)/R rs (600)). Results from Ogashawara et al [13] showed that for high concentrations of PC Simis et al [15] and Mishra et al [16] band ratios showed better performance, however for low PC concentrations, locally tuned band ratios have the best performance.…”

“…Among the analyzed algorithms, three published band ratios were analyzed: Schalles and Yacobi [14] (R rs (650)/R rs (624)), Simis et al [15] (R rs (709)/R rs (620)) and Mishra et al [16] (R rs (700)/R rs (600)). Results from Ogashawara et al [13] showed that for high concentrations of PC Simis et al [15] and Mishra et al [16] band ratios showed better performance, however for low PC concentrations, locally tuned band ratios have the best performance. This finding enhances Oki's [10] idea of the importance of spectral bands selection for the bio-optical modeling of biogeochemical constituents.…”