Studying the Development of Algal Blooms in Lake Erie
Academic Level at Time of Presentation
Junior
Major
Geology
List all Project Mentors & Advisor(s)
Robin Zhang PhD
Presentation Format
Event
Abstract/Description
The development of algal blooms in Lake Erie was identified using the characteristic spectral signature of algae over a ten-year period, from 2007-2017. Satellite imagery from Landsat OLI, Landsat MSS, and MODIS was gathered from the US Geological Survey website as a basis for the remote sensing of the blue-green algae. This data was edited to remove some of the effects of cloud cover. After running an unsupervised classification, the water pixels were isolated, and using regression models, the algae pixels were identified. Using in-situ data provided by NOAA, the imagery could be compared and rectified for accuracy. The pixels containing algae were counted and converted into acres to develop a spatial extent of the bloom. Running this process on ten images from roughly the same time every year allowed for the comparison of the extent of Lake Erie algae over a ten-year period. This study showed there was spikes in the surface area of the algae in 2011, 2015, and 2018. This data was compared to factors such as water temperature, air temperature, pH, and discharge from rivers feeding Lake Erie to explore causes of increased eutrophication. Understanding the fluctuating movement and development of these blooms can help better understand the patterns of the Lake Erie bloom and potentially take steps to mitigate the problem.
Fall Scholars Week 2018 Event
Earth and Environmental Sciences Poster Session
Studying the Development of Algal Blooms in Lake Erie
The development of algal blooms in Lake Erie was identified using the characteristic spectral signature of algae over a ten-year period, from 2007-2017. Satellite imagery from Landsat OLI, Landsat MSS, and MODIS was gathered from the US Geological Survey website as a basis for the remote sensing of the blue-green algae. This data was edited to remove some of the effects of cloud cover. After running an unsupervised classification, the water pixels were isolated, and using regression models, the algae pixels were identified. Using in-situ data provided by NOAA, the imagery could be compared and rectified for accuracy. The pixels containing algae were counted and converted into acres to develop a spatial extent of the bloom. Running this process on ten images from roughly the same time every year allowed for the comparison of the extent of Lake Erie algae over a ten-year period. This study showed there was spikes in the surface area of the algae in 2011, 2015, and 2018. This data was compared to factors such as water temperature, air temperature, pH, and discharge from rivers feeding Lake Erie to explore causes of increased eutrophication. Understanding the fluctuating movement and development of these blooms can help better understand the patterns of the Lake Erie bloom and potentially take steps to mitigate the problem.