Combining In-Situ Testing with Remote Sensing to Create a Historical Record of Cyanobacterial Blooms in Lake Lowell
Additional Funding Sources
This project was made possible by the NSF Idaho EPSCoR MURI Program and by the National Science Foundation under award number IIA-1301792.
Presentation Date
7-2018
Abstract
Harmful Algal Blooms (HAB) occur when cyanobacteria grow out of control, producing toxins that can have negative effects on people, pets, wildlife, fishing, and recreation. Lake Lowell is a popular location for swimming, boating, fishing, and other water recreation; therefore, water quality monitoring to determine historic and present blooms and knowledge of the conditions that contribute to HABs is important in protecting the health of the reservoir ecosystem. Remote sensing offers the opportunity to study historical HABs using satellite images such as those from Landsat or Sentinel and performing algorithms to calculate the cyanobacterial index (CI) with Medium Resolution Imaging Spectrometer (MERIS) imagery. The CI algorithm will be performed using Google Earth Engine, a cloud-based platform that allows users to perform analyses on satellite data. Remote data is compared with in-situ testing, including microscopic identification and quantification of algae and cyanobacteria, and UV-VIS analysis of chlorophyll and phycocyanin at 680 nm and 620 nm respectively.
Combining In-Situ Testing with Remote Sensing to Create a Historical Record of Cyanobacterial Blooms in Lake Lowell
Harmful Algal Blooms (HAB) occur when cyanobacteria grow out of control, producing toxins that can have negative effects on people, pets, wildlife, fishing, and recreation. Lake Lowell is a popular location for swimming, boating, fishing, and other water recreation; therefore, water quality monitoring to determine historic and present blooms and knowledge of the conditions that contribute to HABs is important in protecting the health of the reservoir ecosystem. Remote sensing offers the opportunity to study historical HABs using satellite images such as those from Landsat or Sentinel and performing algorithms to calculate the cyanobacterial index (CI) with Medium Resolution Imaging Spectrometer (MERIS) imagery. The CI algorithm will be performed using Google Earth Engine, a cloud-based platform that allows users to perform analyses on satellite data. Remote data is compared with in-situ testing, including microscopic identification and quantification of algae and cyanobacteria, and UV-VIS analysis of chlorophyll and phycocyanin at 680 nm and 620 nm respectively.
Comments
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