Michigan Tech Research Institute
The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015 to 2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 h) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand.
Journal of Great Lakes Research
Sawtell, R. W.,
Lekki, J. D.,
Real time HABs mapping using NASA Glenn hyperspectral imager.
Journal of Great Lakes Research,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/629
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This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
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© 2019 The Authors. Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. Publisher’s version of record: https://doi.org/10.1016/j.jglr.2019.02.007