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Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

By Peter D. Hunter, Andrew N. Tyler, Laurence Carvalho, Geoffrey A. Codd and Stephen C. Maberly

Abstract

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological\ud status of inland waterbodies and for human and animal health. In this study we examined the performance\ud of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data\ud acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer\ud for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were\ud compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing\ud algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the\ud ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a\ud marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the\ud retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The\ud concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell\ud numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins\ud (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were\ud able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning\ud thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute\ud to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional\ud and global scales

Topics: Ecology and Environment, Health
Publisher: Elsevier
Year: 2010
DOI identifier: 10.1016/j.rse.2010.06.006
OAI identifier: oai:nora.nerc.ac.uk:10886

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