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

• Published in: Remote Sensing
• Main Authors: Brigitte Légaré, Simon Bélanger, Rakesh Kumar Singh, Pascal Bernatchez, Mathieu Cusson
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: vegetation phenology; spectral signature; intertidal coastal ecosystem; remote sensing; eelgrass ( Zostera marina L.); saltmarsh; Science; Q; Subarctic
• Online Access: https://doi.org/10.3390/rs14133000; https://doaj.org/article/feb771a227f94fb593acbe8dfd117814

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 ( <semantics> R r s ( λ ) </semantics> ). The results indicate that even when substantial vegetation growth is observed, the variation in <semantics> R r s ( λ ) </semantics> 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.