Publication Abstracts

Scott et al. 2025, submitted

Scott, S.R., C. Karl, H. Plaas, M. H. Bergin, L.L. Satterwhite, and D. Carlson, 2025: Identifying cyanobacterial harmful algal blooms using high resolution satellite imagery and deep learning: A case study in the Albemarle Sound tributaries. IEEE Trans. Geosci. Remote Sens., submitted.

Chronic exposure to Cyanobacterial harmful algal blooms (CyanoHABs) and their cyanotoxins is an emerging global health threat. Acute exposure can cause severe neurological, gastrointestinal and respiratory illness in people and animals. Chronic exposure is associated with increased risk of liver disease, neurodegenerative diseases including amyotrophic lateral sclerosis (ALS), and cancer, although molecular mechanisms are unknown. The frequency, duration, and intensity of CyanoHABs across the globe has risen due to increased precipitation, excess nutrient enrichment, and warming temperatures, and recurrent CyanoHABs have been an ongoing concern in the Albemarle Sound region of North Carolina. To support early detection and enable system-wide CyanoHAB detection, techniques utilizing remote sensing data are essential to complement field-based methods. However, existing remote sensing approaches were primarily developed for large open-water systems and are poorly suited for dynamic, shallow environments with large land-water boundaries such as the Albemarle Sound. In this work, we developed a deep learning model using 3-band RGB Planet Lab satellite imagery collected over six sites along the Chowan River, a major tributary of the Albemarle Sound. We first validated the model on human-labeled imagery, achieving 84.09% accuracy and a 0.92 Area Under the Curve (AUC). We further validated our model by showing that predictions correlated to in situ water samples containing high concentrations of bloom-forming cyanobacterial cells. The model identified blooms in days prior to sampling, suggesting potential use for early detection and early identification of CyanoHAB events, and demonstrated statistically significant differences between interpolated cell counts during bloom events. These results show that the high resolution CyanoHAB predictive model developed here offers an approach for proactive monitoring over large rural coastal estuarine systems where local communities are more likely to experience chronic exposure to CyanoHABs.

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BibTeX Citation

@unpublished{sc07900t,
  author={Scott, S. R. and Karl, C. and Plaas, H. and H. Bergin, M. and Satterwhite, L. L. and Carlson, D.},
  title={Identifying cyanobacterial harmful algal blooms using high resolution satellite imagery and deep learning: A case study in the Albemarle Sound tributaries},
  year={2025},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - sc07900t
AU  - Scott, S. R.
AU  - Karl, C.
AU  - Plaas, H.
AU  - H. Bergin, M.
AU  - Satterwhite, L. L.
AU  - Carlson, D.
PY  - 2025
TI  - Identifying cyanobacterial harmful algal blooms using high resolution satellite imagery and deep learning: A case study in the Albemarle Sound tributaries
JA  - IEEE Trans. Geosci. Remote Sens.
JO  - IEEE Transactions on Geoscience and Remote Sensing
ER  -

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