ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 125–132, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-125-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 125–132, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-125-2021

  17 Jun 2021

17 Jun 2021

EELGRASS MAPPING WITH SENTINEL-2 AND UAV DATA IN PRINCE EDWARD ISLAND (CANADA)

E. Gallant1, A. LaRocque1, B. Leblon1, and A. Douglas2 E. Gallant et al.
  • 1Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton (NB), E3B 5A3, Canada
  • 2Southern Gulf of St. Lawrence Coalition on Sustainability, Stratford (PEI), C1B 1L1, Canada

Keywords: Eelgrass, Sentinel-2, UAV, Micasense Red-Edge, Random Forests, Acolite, Pix4D

Abstract. Eelgrass (Zostera marina L.) is a marine angiosperm that grows throughout coastal regions in Atlantic Canada. Eelgrass beds provide a variety of important ecosystem services, and while it is considered an important marine species, little research has been done to understand its distribution and location within Atlantic Canada. The purpose of this study was to assess the capability of Sentinel-2 and UAV imagery to map the presence of eelgrass beds within the Souris River in Prince Edward Island. Both imageries were classified using the non-parametric Random Forests (RF) supervised classifier and the resulting classification was validated using sonar data. The Sentinel-2 classified image had a lower validation accuracy at 77.7%, while the UAV classified image had a validation accuracy of 90.9%. The limitations of the study and recommendations for future work are also presented.