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HSI-HABS

GRC Hyperspectral Imaging of Harmful Algal Blooms in Lake Erie

What was the purpose?

Harmful Algal Blooms (HABs) in Lake Erie can grow rapidly in the warm summer months and can produce toxins that are harmful to humans and animals. Remote sensing is a useful process for detecting and monitoring HAB areas of concern so that people, especially water treatment operators can take corrective action to mitigate the effects of HABs on those that use the water. Hyperspectral imaging, using many wavelengths of light, is useful for distinguishing HABs from other algal bloom types.

What is collected?

Visible to near infrared hyperspectral data collected with a custom-made hyperspectral imager onboard GRC aircraft. Data was processed to a georeferenced spectral irradiance in units $$W/(m^2∙sr∙nm)$$. The spectral resolution has been resampled to 400-900 nm at 10 nm steps. The ground resolution while aircraft altitude dependent was typically 1 m per pixel.

Figure 1: HSI3 and Inertial Navigation Unit installed on NASA Twin Otter aircraft.

Ground in-situ sampling data was collected from external research partners from Kent State University, University of Toledo, University of Cincinnati, Michigan Tech Research Institute, Bowling Green State University, and others. Radiometer data includes solar irradiance, ground/water radiance, and calibrated target radiance. Additionally, water samples were taken at specific waypoints and analyzed in the laboratory.

When and where was the data collected?

GRC Aircraft remote sensing data is readily available for western Lake Erie (WLE) from 2015 to 2019.

Figure 2: Composite HSI flight runs overlayed on mapping software. Red in the water signifies high bloom intensity.

Data Access

HSI-HABS data can be downloaded from the Ocean Data web portal.

References

Lekki, J., Ruberg, S., Binding, C., Anderson, R., Vander Woude, A. (2019). Airborne hyperspectral and satellite imaging of harmful algal blooms in the Great Lakes Region: Successes in sensing algal blooms. Journal of Great Lakes Research, Volume 45, Issue 3, 405-412, 10.1016/j.jglr.2019.03.016