The journal Science recently declared that new technologies are making remote sensing of the ocean a “new wave” of oceanography. This growing array of lower-cost, high-tech instruments—satellites, robotic gliders, moored sensors, underwater observatories—is transforming the discipline of oceanography, possibly reducing the need for expensive research vessels. A new class of automated biological sensors are nearing readiness, and NOAA’s National Centers for Coastal Ocean Science (NCCOS) are advancing their development for use in coastal monitoring programs, ocean observing systems, and planned ecological forecasting capabilities.
Leading the way are two commercially available sensors. The Environmental Sample Processor (ESP) is a robotic sensor that collects discrete water samples, concentrates microorganisms or particles, and uses molecular probes to identify microorganisms and their genetic signatures. The sensor then transmits its results to scientists and managers ashore. The Imaging Flow Cytobot (IFC) uses a laser-based system to detect algae and takes microscope photographs for species identification in real time. This automated underwater microscope enables continuous monitoring of algal communities and alerts managers to rising levels of toxic algae.
NOAA and our partners continue to demonstrate these and other sensors to detect harmful algae and their toxins and pathogens in the Gulf of Mexico, Gulf of Maine, California, and Puget Sound. NCCOS-funded research projects in these regions are developing deployment and recovery strategies; designing optimal sensor networks; expanding the number of in-water tests available for algae, toxins, and pathogens; and piloting private sector pathways to expand adoption of ESPs, IFCs, and other marine sensors soon to be commercially available.
NCCOS investments in these new sensors demonstrate are harnessing the power of these new sensing technologies to enhance advance NOAA priorities in regional research, observing, and ecological forecasting.
Learn more about individual sensor projects we are working on or funding: