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The global distribution and trajectory of tidal flats

Naturevolume 565, pages222–225 (2019)Cite this article

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Abstract

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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Fig. 1: Global distribution of tidal flat ecosystems.
Fig. 2: Regional subsets of extent of tidal flats, showing case studies of intertidal loss, instability and gain from 1984 to 2016.
Fig. 3: Global trajectory in the extent of tidal flats.

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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).

References

  1. Millennium Ecosystem Assessment.Ecosystems and Human Well-being: Current State and Trends (Island, Washington DC, 2005).

    Google Scholar 

  2. Deegan, L. A. et al. Coastal eutrophication as a driver of salt marsh loss.Nature490, 388–392 (2012).

    Article ADS CAS  Google Scholar 

  3. Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals.Science301, 958–960 (2003).

    Article ADS CAS  Google Scholar 

  4. Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems.Proc. Natl Acad. Sci. USA106, 12377–12381 (2009).

    Article ADS CAS  Google Scholar 

  5. 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. USA109, 17995–17999 (2012).

    Article ADS  Google Scholar 

  6. Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century.Proc. Natl Acad. Sci. USA113, 13785–13790 (2016).

    Article CAS  Google Scholar 

  7. Healy, T., Wang, Y. & Healy, J.Muddy Coasts of the World: Processes, Deposits, and Function (Elsevier Science, Amsterdam, 2002).

    Google Scholar 

  8. 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).

    Article ADS CAS  Google Scholar 

  9. 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.Science308, 376–380 (2005).

    Article ADS CAS  Google Scholar 

  10. Syvitski, J. P. M. et al. Sinking deltas due to human activities.Nat. Geosci.2, 681–686 (2009).

    Article ADS CAS  Google Scholar 

  11. Nicholls, R. J. et al. inClimate 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).

  12. Arkema, K. K. et al. Coastal habitats shield people and property from sea-level rise and storms.Nat. Clim. Change 3, 913–918 (2013).

    Article ADS  Google Scholar 

  13. Passeri, D. L. et al. The dynamic effects of sea level rise on low-gradient coastal landscapes: a review.Earths Future3, 159–181 (2015).

    Article ADS  Google Scholar 

  14. Lovelock, C. E., Feller, I. C., Reef, R., Hickey, S. & Ball, M. C. Mangrove dieback during fluctuating sea levels.Sci. Rep.7, 1680 (2017).

    Article ADS  Google Scholar 

  15. 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).

    Article  Google Scholar 

  16. 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).

    Article ADS  Google Scholar 

  17. Goodbred, S. L. & Saito, Y. inPrinciples of Tidal Sedimentology (eds Davis, R. A. Jr & Dalrymple, R. W.) 129–149 (Springer, New York, 2012).

  18. 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).

    Article  Google Scholar 

  19. Fan, D. inPrinciples of Tidal Sedimentology (eds Davis, R. A. Jr & Dalrymple, R. W.) 187–229 (Springer, New York, 2012).

  20. 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).

    Article ADS  Google Scholar 

  21. Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise.Nature526, 559–563 (2015).

    Article ADS CAS  Google Scholar 

  22. Thomas, N. et al. Distribution and drivers of global mangrove forest change, 1996–2010.PLoS ONE12, e0179302 (2017).

    Article  Google Scholar 

  23. 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).

    Google Scholar 

  24. 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 ONE10, e0118571 (2015).

    Article  Google Scholar 

  25. Naylor, R. L. et al. Effect of aquaculture on world fish supplies.Nature405, 1017–1024 (2000).

    Article ADS CAS  Google Scholar 

  26. Kirwan, M. L. & Megonigal, J. P. Tidal wetland stability in the face of human impacts and sea-level rise.Nature504, 53–60 (2013).

    Article ADS CAS  Google Scholar 

  27. Spencer, T. et al. Global coastal wetland change under sea-level rise and related stresses: the DIVA wetland change model.Glob. Planet Change139, 15–30 (2016).

    Article ADS  Google Scholar 

  28. 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).

    Article ADS  Google Scholar 

  29. 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).

    Article ADS  Google Scholar 

  30. Keith, D. A. et al. The IUCN Red List of Ecosystems: motivations, challenges, and applications.Conserv. Lett.8, 214–226 (2015).

    Article  Google Scholar 

  31. Farr, T. G. et al. The shuttle radar topography mission.Rev. Geophys.45, RG2004 (2007).

    Article ADS  Google Scholar 

  32. 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).

    Google Scholar 

  33. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone.Remote Sens. Environ.202, 18–27 (2017).

    Article ADS  Google Scholar 

  34. Dhanjal-Adams, K. et al. Distribution and protection of intertidal habitats in Australia.Emu116, 208–214 (2016).

    Article  Google Scholar 

  35. 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).

    Article ADS  Google Scholar 

  36. Ryu, J. H. et al. Detecting the intertidal morphologic change using satellite data.Estuar. Coast. Shelf Sci.78, 623–632 (2008).

    Article ADS  Google Scholar 

  37. 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).

    Article ADS  Google Scholar 

  38. 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).

    Article ADS  Google Scholar 

  39. 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).

    Article  Google Scholar 

  40. 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).

    Article ADS  Google Scholar 

  41. Breiman, L. Random forests.Mach. Learn.45, 5–32 (2001).

    Article  Google Scholar 

  42. US Geological Survey. Product Guide: Landsat 4–7 Surface Reflectance (LEDAPS) Producthttps://landsat.usgs.gov/sites/default/files/documents/ledaps_product_guide.pdf (2018).

  43. US Geological Survey. Product Guide: Landsat 8 Surface Reflectance Code (LASRC) Producthttps://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf (2018).

  44. Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products.Remote Sens. Environ.194, 379–390 (2017).

    Article ADS  Google Scholar 

  45. 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).

    Article ADS  Google Scholar 

  46. 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).

    Article ADS  Google Scholar 

  47. 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).

    Article ADS  Google Scholar 

  48. Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change.Trends Ecol. Evol.20, 503–510 (2005).

    Article  Google Scholar 

  49. James, G., Witten, D., Hastie, T. & Tibshirani, R.An Introduction to Statistical Learning (Springer, Boca Raton, 2013).

    Book  Google Scholar 

  50. Congalton, R. G. & Green, K.Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (CRC, London, 2008).

    Book  Google Scholar 

  51. Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change.Remote Sens. Environ.148, 42–57 (2014).

    Article ADS  Google Scholar 

  52. Efron, B. & Tibshirani, R. Improvements on cross-validation: the 632+ bootstrap method.J. Am. Stat. Assoc.92, 548–560 (1997).

    MathSciNet MATH  Google Scholar 

  53. 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).

    Article ADS  Google Scholar 

  54. Foody, G. M. Sample size determination for image classification accuracy assessment and comparison.Int. J. Remote Sens.30, 5273–5291 (2009).

    Article ADS  Google Scholar 

  55. Tukey, J. W.Exploratory Data Analysis (Addison-Wesley, Reading, 1977).

    MATH  Google Scholar 

  56. R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, 2013).

    Google Scholar 

  57. US Geological Survey. Landsat 7 Data Users Handbook. Version 1.0https://landsat.usgs.gov/sites/default/files/documents/LSDS-1927_L7_Data_Users_Handbook.pdf (USGS Publication LSDS-1927, 2018).

  58. US Geological Survey. Landsat 8 Data Users Handbook. Version 2.0https://landsat.usgs.gov/sites/default/files/documents/Landsat8DataUsersHandbook.pdf (USGS Publication LSDS-1574, 2016).

  59. Pekel, J. F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes.Nature540, 418–422 (2016).

    Article ADS CAS  Google Scholar 

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Acknowledgements

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

Authors and Affiliations

  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

Authors
  1. Nicholas J. Murray
  2. Stuart R. Phinn
  3. Michael DeWitt
  4. Renata Ferrari
  5. Renee Johnston
  6. Mitchell B. Lyons
  7. Nicholas Clinton
  8. David Thau
  9. Richard A. Fuller

Contributions

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.

Corresponding author

Correspondence toNicholas J. Murray.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

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

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.

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 sizen was calculated asn = z2P(1 − P)/h2, in whichP is the estimated proportion of training points that are likely to be allocated to the tidal flat class (estimated atP = 0.33),z is the desired significance level (z = 1.96) andh is the half-width of the desired confidence interval (corresponding toh = 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.

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

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Murray, N.J., Phinn, S.R., DeWitt, M.et al. The global distribution and trajectory of tidal flats.Nature565, 222–225 (2019). https://doi.org/10.1038/s41586-018-0805-8

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