Cyanobacterial Harmful Algal Blooms (CyanoHABs) are expanding geographically in both fresh and marine water bodies due to coastal eutrophication and global climate change and are restructuring the microbial ecology of these systems. Cyanobacterial autofluorescence can pose a significant impediment to accurately identifying prokaryotic taxonomic groups in environmental samples using fluorescence in situ hybridization (FISH). This can hinder our ability to accurately quantify, and therefore fully understand ecological changes. As abundances of FISH target cells and autofluorescent cells can often be of the same the order of magnitude, simply subtracting average autofluorescent cell concentrations-determined from enumerating unhybridized samplesyields apparent concentrations of target cells with unacceptably large analytical uncertainty. Here we present a CuSO 4 /EtOH chemical pretreatment protocol that significantly reduces undesirable autofluorescence in hybridized environmental samples. We apply a novel data filtration routine to FISH images that efficiently removes residual autofluorescent cells from final cell counts. We then subject images to an automated image analysis routine that accurately enumerates probe-positive cells. This method is inexpensive and easy to implement as part of a routine FISH workflow. By applying this method to cyanobacteria rich samples, we can better understand how microbial community changes are contributing to globally changing biogeochemical cycles.