Monitoring saltmarsh restoration in the upper Bay of Fundy using multi-temporal Sentinel-2 imagery and Random Forests classifier

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University of New Brunswick


Saltmarshes provide important ecosystem services, including coastline protection, but face decline due to human activities and climate change. There are increasing efforts to conserve and restore saltmarshes worldwide. My thesis evaluates the effectiveness of Sentinel-2 satellite imagery to monitor landcover changes using a saltmarsh restoration project in New Brunswick, undergoing its 9th to 12th year of recovery. Specifically, from 2019–2022, five satellite images per growing season were acquired. Random Forests classification for 13 landcover classes (ranging from bare mud to various plant communities) achieved high overall classification accuracy, peaking at 96.43% in 2021. Field validation points confirmed this, with high validation accuracies reaching 93.02%. Classification results successfully distinguished ecologically significant classes such as Spartina alterniflora–S. patens mix. My results revealed the appearance of high marsh species in restoration sites and elevational-based zonation patterns, indicating progression. They demonstrate the potential of Sentinel-2 imagery for monitoring saltmarsh restoration projects, aiding management efforts.