Date of Completion

2022

Document Type

Honors College Thesis

Department

Department of Computer Science and Department of Mathematics and Statistics

Thesis Type

Honors College, College of Arts and Science Honors

First Advisor

Scott Hamshaw

Second Advisor

Donna Rizzo

Third Advisor

Andrew Schroth

Keywords

Cyanobacteria, Lake Champlain, Data Science, Satellite Imagery, Algae Bloom

Abstract

As Lake Champlain experiences an increase in cyanobacterial blooms and toxins, the need to understand and monitor this activity grows. This research sought to help address the problem of the lack of certainty of the spatial patterns of blooms in Lake Champlain. This uncertainty comes from the dependence on in-situ data that only represents specific points within the lake. Data derived from satellite imagery were analyzed and used to assess the spatiotemporal trends of cyanobacterial blooms. Three segments of Lake Champlain, Missisquoi Bay, St. Albans Bay, and the Northeast Arm were focused on in this research. A process was developed for defining and detecting blooms in these segments. It was found that the three segments of Lake Champlain, Missisquoi Bay, St. Albans Bay, and the Northeast Arm have similar annual trends, though seasonally, Missisquoi Bay and the Northeast Arm behave more similarly than St. Albans Bay.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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