ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Data Fusion and Assimilation
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1633491
This article is part of the Research TopicAdvanced Geospatial Data Analytics for Environmental Sustainability: Current Practices and Future ProspectsView all 5 articles
Monitoring Harmful Algae Blooms in Darlings Lake, New Brunswick, using K-means Clustering of Multi-spectral Satellite Imagery
Provisionally accepted
Catherine Evans1*
Romina Gehrmann2
Sarah Blenis3
Christopher Sean Greene4
Roxanne Mackinnon5
Jason Newport6Sarah Vela7Matthew Smith8
Zahra Sadeghi9
Stan Matwin9
Christopher Whidden9- 1DeepSense, Dalhousie University, Halifax, Canada
- 2Defence Research and Development Canada, Halifax, Canada
- 3Hammond River Angling Association, Nauwigewauk, Canada
- 4Department of Earth & Environmental Sciences, Dalhousie University, Halifax, Canada
- 5Atlantic Coastal Action Program Saint John, Saint John, Canada
- 6MERIDIAN, Dalhousie University, Halifax, Canada
- 7FishSounds, Victoria, Canada
- 8RadFormation, Toronto, Canada
- 9Faculty of Computer Science, Dalhousie University, Halifax, Canada
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USA) to understand the extent and severity of the blooms with a time series analysis of the normalized difference vegetation index (NDVI) and the normalized difference chlorophyll index (NDCI) over the lake using k-means clustering. We distinguish algae blooms from preexisting aquatic vegetation by creating a baseline map of mean aquatic vegetation extent, and subtracting this from each image in the time series. Additionally, results from a principal component analysis conducted on each year's imagery corroborate the k-means finding, and align with spatial trends of bloom events observed in the lake. In this study, NDCI values are observed to be more reliable for estimating the severity of algal blooms, while NDVI is more sensitive to glare, haze, thin clouds, and signal over-saturation caused by blooms, aligning with preexisting research findings. We successfully fit a linear regression between NDCI values and in situ measurements of phycocyanin concentrations surrounding AlgaeTracker™ buoys (R 2 :0.893). Furthermore we highlight bloom extent and severity for 2021 and 2022, revealing potential bloom hotspots in the lake. The methodology in this project can be extended to systematically analyze high-resolution satellite imagery in freshwater ecosystems to detect harmful algae blooms.
Keywords: Cyanobacteria, SuperDove, K-means, PCA, Land classification, algal blooms, freshwater, NDCI
Received: 22 May 2025; Accepted: 17 Jul 2025.
Copyright: © 2025 Evans, Gehrmann, Blenis, Greene, Mackinnon, Newport, Vela, Smith, Sadeghi, Matwin and Whidden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Catherine Evans, DeepSense, Dalhousie University, Halifax, Canada
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