Remote sensing of the 2019 CyanoHAB composition in Lake Erie: Spatial analysis as part of a Smart La

Session: Smart Lakes: Real-Time Monitoring, Networking, and Analytics Across the Great Lakes (1)

Joseph Ortiz, Kent State University, Dept. of Geology, [email protected]
Dulcinea Avouris, Kent State University, [email protected]

Abstract

Tracking the perennial summer Cyanobacterial and Harmful Algal Bloom (CyanoHAB) that develops on Lake Erie requires continuous monitoring. The KSU spectral decomposition method can be applied to multispectral and hyperspectral remote sensing images to identify signals related to cyanobacteria, algae, pigment degradation products and suspended sediment in each pixel. This information can be used as part of a developing “Smart Lake” system that would link in situsensors, which capture time series of information at discrete points with remote sensing images that provide the complete spatial pattern of various water quality constituents at discrete points in time. As proof of concept, we conducted field operations in the Western Basin of Lake Erie, using a bbe Fluoroprobe to collect vertical profiles and horizontal tows along a transect from the Toledo Lighthouse to the Detroit Lighthouse during coincident satellite overpasses. Extraction of pixel values from the MODIS Aqua/Terra and Sentinel-3A OLCI sensors yields agreement between in situ field and lab-basedmeasures of cyanobacterial, cryptophyte, diatoms and green algae, suspended sediment and pigment degradation products with r²>0.8. The spectral decomposition method, when compared against existing remote sensing monitoring methods exhibits both greater specificity and a lower detection limit.

Twitter handle of presenter
@EarthSci_Info