CYANOBACTERIA BLOOM CLASSIFICATION IN INLAND WATERS USING HYPERSPECTRAL DATA

Cyanobacteria Harmful Algal Blooms (CHABs) and their associated toxicity are a significant issue in inland waters. Remote sensing is a useful tool for monitoring CHABs in large lakes due to the extensive spatial coverage and frequent availability of satellite images. Advances in hyperspectral remote sensing have opened up a new frontier in CHAB monitoring. This study evaluates the feasibility of detecting cyanobacteria genera using hyperspectral data in hypereutrophic Clear Lake, CA, USA. In situ data were collected during 12 field events in 2021-2022 across all seasons to characterize the in situ cyanobacteria community composition, including coincident measurements of phytoplankton speciation and enumeration and spectral reflectance. Additionally, three field events were conducted concurrent with whole-lake hyperspectral image acquisitions by the DESIS sensor on the International Space Station. We apply the Spectral Mixture Analysis for Surveillance of HABs (SMASH) framework for Multiple Endmember Spectral Mixture Analysis (MESMA) to our DESIS scenes to demonstrate the potential to identify cyanobacteria genera from hyperspectral images. Both the original SMASH cyanobacteria spectral library and a Clear Lake-specific spectral library built from our field spectra measurements are employed. The results of this study will support the continued development of tools utilizing satellite-based hyperspectral images to identify the cyanobacteria genera present in a bloom, and thus assess the potential for cyanotoxin production.

Presentation Preference: Oral

Primary Presenter: Samantha Sharp, UC Davis (samanthalsharp@gmail.com)

Authors:

Alicia Cortés, UC Davis  (alicortes@ucdavis.edu)

Alexander Forrest, UC Davis  (alforrest@ucdavis.edu)

Liane Guild, NASA  (liane.s.guild@nasa.gov)

Yufang Jin, UC Davis  (yujin@ucdavis.edu)

Carl Legleiter, USGS  (cjl@usgs.gov)

S. Geoffrey Schladow, UC Davis  (gschladow@ucdavis.edu)