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Article

Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats

1
Département de Biologie, Université du Québec à Rimouski, Chimie et Géographie, Québec-Océan et BORÉAS, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
2
Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Québec-Océan 555, Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Matteo Convertino and Jie Li
Remote Sens. 2022, 14(13), 3000; https://doi.org/10.3390/rs14133000
Received: 30 April 2022 / Revised: 17 June 2022 / Accepted: 19 June 2022 / Published: 23 June 2022
Intertidal vegetation provides important ecological functions, such as food and shelter for wildlife and ecological services with increased coastline protection from erosion. In cold temperate and subarctic environments, the short growing season has a significant impact on the phenological response of the different vegetation types, which must be considered for their mapping using satellite remote sensing technologies. This study focuses on the effect of the phenology of vegetation in the intertidal ecosystems on remote sensing outputs. The studied sites were dominated by eelgrass (Zostera marina L.), saltmarsh cordgrass (Spartina alterniflora), creeping saltbush (Atriplex prostrata), macroalgae (Ascophyllum nodosum, and Fucus vesiculosus) attached to scattered boulders. In situ data were collected on ten occasions from May through October 2019 and included biophysical properties (e.g., leaf area index) and hyperspectral reflectance spectra (Rrs(λ)). The results indicate that even when substantial vegetation growth is observed, the variation in Rrs(λ) is not significant at the beginning of the growing season, limiting the spectral separability using multispectral imagery. The spectral separability between vegetation types was maximum at the beginning of the season (early June) when the vegetation had not reached its maximum growth. Seasonal time series of the normalized difference vegetation index (NDVI) values were derived from multispectral sensors (Sentinel-2 multispectral instrument (MSI) and PlanetScope) and were validated using in situ-derived NDVI. The results indicate that the phenology of intertidal vegetation can be monitored by satellite if the number of observations obtained at a low tide is sufficient, which helps to discriminate plant species and, therefore, the mapping of vegetation. The optimal period for vegetation mapping was September for the study area. View Full-Text
Keywords: vegetation phenology; spectral signature; intertidal coastal ecosystem; remote sensing; eelgrass (Zostera marina L.); saltmarsh; classification vegetation phenology; spectral signature; intertidal coastal ecosystem; remote sensing; eelgrass (Zostera marina L.); saltmarsh; classification
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MDPI and ACS Style

Légaré, B.; Bélanger, S.; Singh, R.K.; Bernatchez, P.; Cusson, M. Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats. Remote Sens. 2022, 14, 3000. https://doi.org/10.3390/rs14133000

AMA Style

Légaré B, Bélanger S, Singh RK, Bernatchez P, Cusson M. Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats. Remote Sensing. 2022; 14(13):3000. https://doi.org/10.3390/rs14133000

Chicago/Turabian Style

Légaré, Brigitte, Simon Bélanger, Rakesh Kumar Singh, Pascal Bernatchez, and Mathieu Cusson. 2022. "Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats" Remote Sensing 14, no. 13: 3000. https://doi.org/10.3390/rs14133000

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