Intertidal seagrass extent from Sentinel-2 time-series show distinct trajectories in Western Europe

Davies, BFR, Oiry, S, Rosa, P, Zoffoli, ML, Sousa, AI, Thomas, OR, Smale, DA and Austen, MC 2024 Intertidal seagrass extent from Sentinel-2 time-series show distinct trajectories in Western Europe. Remote Sensing of Environment, 312. https://doi.org/10.1016/j.rse.2024.114340

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Abstract/Summary

Intertidal areas, which emerge during low tide, form a vital link between terrestrial and marine environments. Seagrasses, a well-studied intertidal habitat, provide a multitude of different ecosystem goods and services. However, owing to their relatively high exposure to anthropogenic impacts, seagrasss meadows and other intertidal habitats have seen extensive declines. Remote sensing methods that can capture the spatial and temporal variation of marine habitats are essential to best assess the trajectories of seagrass ecosystems. An advanced machine learning method has been developed to map intertidal vegetation from satellite-derived surface reflectance at a 12-band multispectral resolution and distinguish between similarly pigmented intertidal macrophytes, such as seagrass and green algae. The Intertidal Classification of Europe: Categorising Reflectance of Emerged Areas of Marine vegetation with Sentinel-2 (ICE CREAMS v1.0), a neural network model trained on over 300,000 Sentinel-2 pixels to identify different intertidal habitats, was applied to the open-access long term archive of systematically collected Sentinel-2 imagery to provide 7 years (2017–2023) worth of intertidal seagrass dynamics in 6 sites across Western Europe (471 Sentinel-2 Images). A combination of independently collected visually inspected Uncrewed Aerial Vehicle imagery and in situ quadrat images were used to validate ICE CREAMS. Having achieved a high seagrass classification accuracy (0.82 over 12,000 pixels) and consistent conversion into cover (19% RMSD), the ICE CREAMS model outputs provided evidence of site specific variation in trajectories of seagrass extent, when appropriate consideration of intra-annual variation has been considered. Inter-annual dynamics of sites showed some instances of consistent change, some indicated stability, while others indicated instability over time, characterised by increases and decreases across the time-series in seagrass coverage. This methological pipeline has helped to create up-to-date monitoring data that, with the planned continuation of the Sentinel missions, will allow almost real-time monitoring of these habitats into the future. This process, and the data it provides, could aid management practitioners from regional to international levels, with the ability to monitor intertidal seagrass meadows at both high spatial and temporal resolution, over continental scales. The implementation of Earth Observation for high-resolution monitoring of intertidal seagrasses could therefore allow for gap-filling seagrass datasets, and sustain specific and rapid management measures

Item Type:	Publication - Article
Additional Keywords:	intertidal seagrass, habitat monitoring, trajectory analysis, neural network, bayesian general additive model
Subjects:	Earth Observation - Remote Sensing Ecology and Environment Marine Sciences Technology
Divisions:	Marine Biological Association of the UK > Coastal Ecology
Depositing User:	Ms Kristina Hixon
Date made live:	08 Aug 2024 10:21
Last Modified:	08 Aug 2024 10:21
URI:	https://plymsea.ac.uk/id/eprint/10263

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