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Satellite remote sensing can provide semi-automated monitoring to aid coastal decision-making

Agate, Joseph, Ballinger, Rhoda ORCID: and Ward, Raymond D. 2024. Satellite remote sensing can provide semi-automated monitoring to aid coastal decision-making. Estuarine, Coastal and Shelf Science 298 , 108639. 10.1016/j.ecss.2024.108639

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Coastlines are projected to face unprecedented pressures over the next century due to climate change-induced changes in sea level, storm, wave, and tidal regimes. This projection of increasing pressure is driving a reappraisal of existing shoreline management practices, with both science and policy calling for future strategies to work with the natural protection provided by coastal habitats such as salt marshes. However, we currently lack the understanding of long-term ecosystem dynamics required to incorporate these habitats into the definitive predictions of risk relied on in coastal protection planning. Satellite remote sensing has the potential to provide data that could address this knowledge gap with its frequent repeat times and global coverage facilitating the production of high temporal frequency time-series over large areas. This study sought to explore this potential in one of the largest coastal plain estuaries the in the UK, the Severn Estuary. The Random Forest machine learning algorithm was used to develop a time-series of marsh extents across the estuary from 1985 to 2020 in Google Earth Engine, with widths also extracted as a proxy for the marshes’ protective capacity. These changes were monitored in six areas that contained the most significant areas of salt marsh across the estuary. This analysis revealed a significant increasing trend in extent and widths (p < 0.05), and therefore natural coastal protection, in three of the six areas over the study period, with validation testing finding an overall accuracy for the classification of >90% and a strong agreement found between the detected widths and those found in previous surveys. These findings demonstrate that satellite remote sensing combined with machine learning has the potential to provide valuable insights into changes in the extents of marshes and therefore their protective capacity. This information can be useful in the coastal planning process, allowing decision-makers to assess the sustainability of existing defences fronted by marshes, as well as allowing them to make informed decisions about the location of restoration schemes.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Environmental Sciences
Publisher: Elsevier
ISSN: 0272-7714
Date of First Compliant Deposit: 1 February 2024
Date of Acceptance: 12 January 2024
Last Modified: 09 Feb 2024 11:30

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