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The global distribution and trajectory of tidal flats

Naturevolume 565pages222225 (2019) | Download Citation


Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services1. As a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred1,2,3,4,5,6. However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats—one of the most extensive coastal ecosystems—remain unknown7. Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984–2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation7, occupy at least 127,921 km2 (124,286–131,821 km2, 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series—which included East Asia, the Middle East and North America—we estimate that 16.02% (15.62–16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development1, reduced sediment delivery from major rivers8,9, sinking of riverine deltas8,10, increased coastal erosion and sea-level rise11 signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems.

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Data availability

The Landsat archive imagery used for this analysis is available from the United States Geological Survey Earth Explorer (http://earthexplorer.usgs.gov), and via the Google Earth Engine data archive (http://earthengine.google.com). The tidal flat maps, data mask and pixel count layers generated in this study are available via the Google Earth Engine (http://earthengine.google.com) and at Intertidal Change Explorer (http://intertidal.app).

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


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This project was funded by a Google Earth Engine Research Award. Landsat data are courtesy of NASA Goddard Space Flight Center and the US Geological Survey. We thank Google for developing Google Earth Engine, and J. Wilshire, N. Hill, D. Keith, R. Kingsford, N. Mallot, C. Roelfsema, Z. Xie and R. Lucas for support throughout the project.

Reviewer information

Nature thanks L. Lymburner, R. Nicholls and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia

    • Nicholas J. Murray
    •  & Richard A. Fuller
  2. Centre for Ecosystem Science, School of Biological, Earth and Environmental Science, University of New South Wales, Sydney, New South Wales, Australia

    • Nicholas J. Murray
    •  & Mitchell B. Lyons
  3. Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Queensland, Australia

    • Stuart R. Phinn
  4. Google, Mountain View, CA, USA

    • Michael DeWitt
    • , Renee Johnston
    • , Nicholas Clinton
    •  & David Thau
  5. Australian Institute of Marine Science, Townsville, Queensland, Australia

    • Renata Ferrari


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N.J.M., S.R.P. and R.A.F. conceived the project and developed the remote sensing method. N.J.M., M.D., R.J., N.C. and D.T. ran the remote sensing classification. N.J.M., M.B.L. and R.F. analysed data. N.J.M. led the writing of the manuscript with contributions from all authors.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Nicholas J. Murray.

Extended data figures and tables

  1. Extended Data Fig. 1 The number of Landsat archive images used to map tidal flats globally for each time period in our analysis.

    The total number of Landsat images used in the random-forest classification was 707,528.

  2. Extended Data Fig. 2 Count of Landsat images used in the global tidal flat analysis.

    ak, Each panel shows the number of Landsat images used to map tidal flats for each time period: 2014–2016 (a), 2011–2013 (b), 2008–2010 (c), 2005–2007 (d), 2002–2004 (e), 1999–2001 (f), 1996–1998 (g), 1993–1995 (h), 1990–1992 (i), 1987–1989 (j) and 1984–1986 (k). The pixel-count layers provide details of how many Landsat pixels were available to compute the spectral image composite covariates.

  3. Extended Data Fig. 3 Distribution of randomly sampled points used for the independent accuracy assessment.

    The randomly sampled points (n = 1,358) were stratified between two classes (tidal flat and other) and by continent. Each point was assigned to a class by three independent observers with reference to a range of imagery, using an online validation application.

  4. Extended Data Fig. 4 Relationship between power and number of points used for validation.

    The plot shows the theoretical number of validation samples (n) required to achieve a desired confidence level. The minimum sample size n was calculated as n = z2P(1 − P)/h2, in which P is the estimated proportion of training points that are likely to be allocated to the tidal flat class (estimated at P = 0.33), z is the desired significance level (z = 1.96) and h is the half-width of the desired confidence interval (corresponding to h = 0.025)54. The vertical dashed line indicates the size of the validation set (n = 1,358) used to assess accuracy of the tidal flat dataset.

  5. Extended Data Fig. 5 Distribution of randomly sampled points used for assessing agreement between the global tidal flat data and an independently produced map of intertidal extent in Australia.

    The points (n = 4,000) were sampled using stratification between two classes (yellow, intertidal; purple, other) within the mapped area of our analysis.

  6. Extended Data Table 1 Predictor data layers used by the random-forest classifier to classify pixels as land, water or intertidal
  7. Extended Data Table 2 Date parameters used to filter the Landsat archive before developing image stacks for the classification of tidal flats
  8. Extended Data Table 3 Extent of tidal flats, per exclusive economic zone, in the top 50 countries, 2014–2016
  9. Extended Data Table 4 Error matrices from the three independent accuracy assessments and mode of all three assessments

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