Global tidal marshes 2020 dataset
DOI10.5281/zenodo.8420753Zenodo8420753MaRDI QIDQ6693190
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 9 October 2023
Copyright license: No records found.
Tidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes in tidal marsh extent, and identify opportunities for their conservation and restoration, it is critical to develop a spatial knowledge base of their global occurrence. Here, we develop a globally consistent tidal marsh distribution map for the year 2020 at 10-m resolution.To map the location of the worlds tidal marshes we applied a random forest classification model to earth observation data from the year 2020. We trained the classification model with a reference dataset developed to support distribution mapping of coastal ecosystems, and predicted the spatial distribution of tidal marshes between 60N to 60S. We validated the tidal marsh map using standard accuracy assessment methods, with our final map having an overall accuracy score of 0.852.We estimate the global extent of tidal marshes in 2020 to be 52,880 km2 (95% CI: 32,030 to 59,780 km2) distributed across 120 countries and territories. Tidal marsh distribution is centred in temperate and Arctic regions, with nearly half of the global extent of tidal marshes occurring in the temperate Northern Atlantic (45%) region. At the national scale, over a third of the global extent (18,510 km2; CI: 11,200 20,900) occurs within the USA.Our analysis provides the most detailed spatial data on global tidal marsh distribution to date and shows that tidal marshes occur in more countries and across a greater proportion of the worlds coastline than previous mapping studies. Our map fills a major knowledge gap regarding the distribution of the worlds coastal ecosystems and provides the baseline needed for measuring changes in tidal marsh extent and estimating their value in terms of ecosystem services. This dataset accompanies the preprinthttps://doi.org/10.1101/2023.05.26.542433
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