Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations

The Future Is Big—and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

“…In contrast, satellites, aerial flights, and on-demand UAVs can collect spectral imagery (chlorophyll- a and phycocyanin pigments) ( Fig. 2 ) to quantify spatial and temporal bloom dynamics rapidly and across large areas ( 19 , 26 ). These methods measure cyanobacteria or algal biomass and can evaluate potential environmental drivers (auxiliary remotely sensed data products) ( Fig.…”

“…Interactions within plankton communities and among heterotrophic bacteria and bloom-forming species ( 22 , 27 ) indicate that omics approaches (e.g., 16S metabarcoding, metagenomics, and metabolomics) ( Fig. 2 ) coupled with remotely sensed algal or cyanobacterial biomass ( 26 ) would be a powerful approach to predict bloom dynamics. For example, comparisons between microbial community composition, concentrations of the cyanobacterial toxin microcystin, and microcystin biosynthesis genes suggest that algicidal and microcystin-degrading bacteria may control toxicity of cyanobacterial blooms ( 28 ).…”

“…Remotely sensed data ( Fig. 2 ) can be used to test for interactive effects of stressors, such as light-limiting algal blooms from chlorophyll- a and phycocyanin, warming from thermal sensors, and freshwater discharge events ( 13 , 19 , 26 , 38 ) on disease outbreaks. Indeed, remotely sensed thermal anomalies were recently linked to wasting disease severity (L. R. Aoki, B. Rappazzo, D. S. Beatty, L. K. Domke, G. L. Eckert, M. E. Eisenlord, O. J. Graham, L. Harper, T. L. Hawthorne, M. Hessing-Lewis, K. Hovel, Z. L. Monteith, R. Mueller, A. M. Olson, C. Prentice, C. Ritter, J. J. Stachowicz, F. Tomas, B. Yang, J. E. Duffy, C. Gomes, and C. D. Harvell, submitted for publication).…”

“…We can model, monitor, and predict ecological change by pairing UAV, aerial, and satellite remote sensing with in situ surveys. From sensors that detect visible (red, blue, and green), near-infrared, microwave, and thermal bands, we can derive variables such as chlorophyll- a levels, spectral signatures for green leaf index (GLI), normalized difference vegetation index (NDVI) for above-ground plant biomass, phycocyanin for cyanobacterial biomass, and temperature ( 13 , 16 , 18 , 26 ). Discussions on sensors and derived products can be found in detail elsewhere ( 13 , 16 , 18 , 41 ).…”

waterquality : an open-source R package for the detection and quantification of cyanobacterial harmful algal blooms and water quality

“…(Figure 1). Harsha Lake was chosen for this case study because it is a location of recent and frequently occurring algal blooms, including CHABs, and is a site of previous and ongoing research (Beck et al 2016(Beck et al , 2018Johansen et al 2018a;Xu et al 2018). Harsha Lake is also a source of drinking water coupled with heavy recreational use, making re-occurring blooms and their associated toxins potentially dangerous for humans and wildlife, as well as posing potentially significant impacts to the local economy (USEPA 2012a).…”