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.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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).
Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Current State and Trends (Island, Washington DC, 2005).
Deegan, L. A. et al. Coastal eutrophication as a driver of salt marsh loss. Nature 490, 388–392 (2012).
Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).
Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl Acad. Sci. USA 106, 12377–12381 (2009).
De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl Acad. Sci. USA 109, 17995–17999 (2012).
Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl Acad. Sci. USA 113, 13785–13790 (2016).
Healy, T., Wang, Y. & Healy, J. Muddy Coasts of the World: Processes, Deposits, and Function (Elsevier Science, Amsterdam, 2002).
Blum, M. D. & Roberts, H. H. Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nat. Geosci. 2, 488–491 (2009).
Syvitski, J. P. M., Vörösmarty, C. J., Kettner, A. J. & Green, P. Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308, 376–380 (2005).
Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2, 681–686 (2009).
Nicholls, R. J. et al. in Climate Change 2007: Impacts, Adaptation and Vulnerability (Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change) (eds. Parry, M. et al.) 315–356 (Cambridge Univ. Press, Cambridge, 2007).
Arkema, K. K. et al. Coastal habitats shield people and property from sea-level rise and storms. Nat. Clim. Change 3, 913–918 (2013).
Passeri, D. L. et al. The dynamic effects of sea level rise on low-gradient coastal landscapes: a review. Earths Future 3, 159–181 (2015).
Lovelock, C. E., Feller, I. C., Reef, R., Hickey, S. & Ball, M. C. Mangrove dieback during fluctuating sea levels. Sci. Rep. 7, 1680 (2017).
Murray, N. J., Clemens, R. S., Phinn, S. R., Possingham, H. P. & Fuller, R. A. Tracking the rapid loss of tidal wetlands in the Yellow Sea. Front. Ecol. Environ. 12, 267–272 (2014).
Murray, N. J., Phinn, S. R., Clemens, R. S., Roelfsema, C. M. & Fuller, R. A. Continental scale mapping of tidal flats across East Asia using the Landsat archive. Remote Sens. 4, 3417–3426 (2012).
Goodbred, S. L. & Saito, Y. in Principles of Tidal Sedimentology (eds Davis, R. A. Jr & Dalrymple, R. W.) 129–149 (Springer, New York, 2012).
Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159 (2011).
Fan, D. in Principles of Tidal Sedimentology (eds Davis, R. A. Jr & Dalrymple, R. W.) 187–229 (Springer, New York, 2012).
Wilson, C. A. & Goodbred, S. L. Jr. Construction and maintenance of the Ganges-Brahmaputra-Meghna delta: linking process, morphology, and stratigraphy. Annu. Rev. Mar. Sci. 7, 67–88 (2015).
Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563 (2015).
Thomas, N. et al. Distribution and drivers of global mangrove forest change, 1996–2010. PLoS ONE 12, e0179302 (2017).
MacKinnon, J., Verkuil, Y. I. & Murray, N. J. IUCN Situation Analysis on East and Southeast Asian Intertidal Habitats, with Particular Reference to the Yellow Sea (Including the Bohai Sea). (IUCN, Cambridge, 2012).
Neumann, B., Vafeidis, A. T., Zimmermann, J. & Nicholls, R. J. Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLoS ONE 10, e0118571 (2015).
Naylor, R. L. et al. Effect of aquaculture on world fish supplies. Nature 405, 1017–1024 (2000).
Kirwan, M. L. & Megonigal, J. P. Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504, 53–60 (2013).
Spencer, T. et al. Global coastal wetland change under sea-level rise and related stresses: the DIVA wetland change model. Glob. Planet Change 139, 15–30 (2016).
Rodríguez, J. F., Saco, P. M., Sandi, S., Saintilan, N. & Riccardi, G. Potential increase in coastal wetland vulnerability to sea-level rise suggested by considering hydrodynamic attenuation effects. Nat. Commun. 8, 16094 (2017).
Kirwan, M. L., Temmerman, S., Skeehan, E. E., Guntenspergen, G. R. & Fagherazzi, S. Overestimation of marsh vulnerability to sea level rise. Nat. Clim. Change 6, 253–260 (2016).
Keith, D. A. et al. The IUCN Red List of Ecosystems: motivations, challenges, and applications. Conserv. Lett. 8, 214–226 (2015).
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. (US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Geophysical Data Center, Marine Geology and Geophysics Division, Boulder, 2009).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Dhanjal-Adams, K. et al. Distribution and protection of intertidal habitats in Australia. Emu 116, 208–214 (2016).
Sagar, S., Roberts, D., Bala, B. & Lymburner, L. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sens. Environ. 195, 153–169 (2017).
Ryu, J. H. et al. Detecting the intertidal morphologic change using satellite data. Estuar. Coast. Shelf Sci. 78, 623–632 (2008).
Ryu, J. H., Won, J. S. & Min, K. D. Waterline extraction from Landsat TM data in a tidal flat – a case study in Gomso Bay, Korea. Remote Sens. Environ. 83, 442–456 (2002).
Liu, Y., Li, M., Zhou, M., Yang, K. & Mao, L. Quantitative analysis of the waterline method for topographical mapping of tidal flats: a case study in the Dongsha sandbank, China. Remote Sens. 5, 6138–6158 (2013).
Liu, Y., Li, M., Cheng, L., Li, F. & Chen, K. Topographic mapping of offshore sandbank tidal flats using the waterline detection method: a case study on the Dongsha sandbank of Jiangsu radial tidal sand ridges, China. Mar. Geod. 35, 362–378 (2012).
Zhao, B., Guo, H., Yan, Y., Wang, Q. & Li, B. A simple waterline approach for tidelands using multi-temporal satellite images: a case study in the Yangtze Delta. Estuar. Coast. Shelf Sci. 77, 134–142 (2008).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
US Geological Survey. Product Guide: Landsat 4–7 Surface Reflectance (LEDAPS) Product https://landsat.usgs.gov/sites/default/files/documents/ledaps_product_guide.pdf (2018).
US Geological Survey. Product Guide: Landsat 8 Surface Reflectance Code (LASRC) Product https://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf (2018).
Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).
McFeeters, S. K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).
Feyisa, G. L., Meilby, H., Fensholt, R. & Proud, S. R. Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 140, 23–35 (2014).
Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033 (2006).
Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning (Springer, Boca Raton, 2013).
Congalton, R. G. & Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (CRC, London, 2008).
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).
Efron, B. & Tibshirani, R. Improvements on cross-validation: the 632+ bootstrap method. J. Am. Stat. Assoc. 92, 548–560 (1997).
Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J. & Elith, J. A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sens. Environ. 208, 145–153 (2018).
Foody, G. M. Sample size determination for image classification accuracy assessment and comparison. Int. J. Remote Sens. 30, 5273–5291 (2009).
Tukey, J. W. Exploratory Data Analysis (Addison-Wesley, Reading, 1977).
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, 2013).
US Geological Survey. Landsat 7 Data Users Handbook. Version 1.0 https://landsat.usgs.gov/sites/default/files/documents/LSDS-1927_L7_Data_Users_Handbook.pdf (USGS Publication LSDS-1927, 2018).
US Geological Survey. Landsat 8 Data Users Handbook. Version 2.0 https://landsat.usgs.gov/sites/default/files/documents/Landsat8DataUsersHandbook.pdf (USGS Publication LSDS-1574, 2016).
Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
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.
Nature thanks L. Lymburner, R. Nicholls and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
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.
a–k, 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.
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.
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.
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.
About this article
Cite this article
Murray, N.J., Phinn, S.R., DeWitt, M. et al. The global distribution and trajectory of tidal flats. Nature 565, 222–225 (2019). https://doi.org/10.1038/s41586-018-0805-8
Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series
Remote Sensing of Environment (2021)
Mapping Large-Scale Mangroves along the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model and Landsat Data
Remote Sensing (2021)
Science Bulletin (2021)
Agricultural and Forest Meteorology (2021)
Mismatch between watershed effects and local efforts constrains the success of coastal salt marsh vegetation restoration
Journal of Cleaner Production (2021)