DEA Intertidal

Geoscience Australia Sentinel-2 Landsat Intertidal Calendar Year Collection 3

Version:: 1.0.0 (Latest)
Product types:: Derivative, Raster
Time span:: 2016 – 2022
Update frequency:: Yearly
Product ID:: ga_s2ls_intertidal_cyear_3

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Overview

About

The DEA Intertidal product suite maps the changing elevation, exposure and tidal characteristics of Australia’s exposed intertidal zone, the complex zone that defines the interface between land and sea.

Incorporating both Sentinel-2 and Landsat data, the product suite provides an annual 10 m resolution elevation product for the intertidal zone, enabling users to better monitor and understand some of the most dynamic regions of Australia’s coastlines. Utilising an improved tidal modelling capability, the product suite includes a continental scale mapping of intertidal exposure over time, enabling scientists and managers to integrate the data into ecological and migratory species applications and modelling.

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Key details

Parent product(s)	Sentinel-2A and -2B and Landsat 7, 8, and 9 NBART and Observational Attributes
Collection	Geoscience Australia Landsat Collection 3; Geoscience Australia Sentinel-2 Collection 3
DOI	10.26186/149403
Licence	Creative Commons Attribution 4.0 International Licence

Cite this product

Data citation

Bishop-Taylor, R., Phillips, C., Newey, V., Sagar, S.(2024). Digital Earth Australia Intertidal. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/149403

Publications

Bishop-Taylor, R., Sagar, S., Lymburner, L., Beaman, R.L., 2019. Between the tides: modelling the elevation of Australia’s exposed intertidal zone at continental scale. Estuarine, Coastal and Shelf Science. Available: https://doi.org/10.1016/j.ecss.2019.03.006

Sagar, S., Phillips, C., Bala, B., Roberts, D., Lymburner, L., 2018. Generating continental scale pixel-based surface reflectance composites in coastal regions with the use of a multi-resolution tidal model. Remote Sensing. 10, 480. Available: https://doi.org/10.3390/rs10030480

Sagar, S., Roberts, D., Bala, B., Lymburner, L., 2017. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sensing of Environment 195, 153-169. Available: https://doi.org/10.1016/j.rse.2017.04.009

Details

Background

Intertidal environments contain many important ecological habitats such as sandy beaches, tidal flats, rocky shores, and reefs. These environments also provide many valuable benefits such as storm surge protection, carbon storage, and natural resources.

Intertidal zones are being increasingly faced with threats including coastal erosion, land reclamation (e.g. port construction), and sea level rise. These regions are often highly dynamic, and accurate, up-to-date elevation data describing the changing topography and extent of these environments is needed.

The intertidal zone also forms a critical habitat and foraging ground for migratory shore birds and other species. An improved characterisation of the exposure patterns of these dynamic environments is important to support conservation efforts and to gain a better understanding of migratory species pathways. However, this data is expensive and challenging to map across the entire intertidal zone of a continent the size of Australia.

The DEA Intertidal product suite provides annual continental-scale elevation and exposure product layers for Australia’s exposed intertidal zone, mapped at a 10 m resolution from DEA’s archive of open-source Landsat and Sentinel-2 satellite data. The exposed intertidal zone consists of coastal regions periodically inundated by tidal flows, not including areas obscured by vegetation cover such as mangroves. These intertidal products enable users to better monitor and understand some of the most dynamic regions of Australia’s coastlines.

Applications

Integration with existing topographic and bathymetric data to seamlessly map the elevation of the coastal zone.
Providing baseline elevation data to assist coastal hazard impact assessment from extreme weather and inundation events.
Investigating coastal erosion and sediment transport processes.
Supporting habitat mapping and modelling for coastal ecosystems extending across the terrestrial to marine boundary.
Characterise the spatio-temporal exposure patterns of the intertidal zone to support migratory species studies and applications.

Technical Information

DEA Intertidal features

The DEA Intertidal product suite contains 3 core product layers, 7 tidal attribute (ta) layers, and 2 quality assessment (qa) layers, all provided as continental 10 m resolution GeoTIFFs for the Australian coastal and intertidal region.

All datasets are produced annually from a 3-year composite of input data from the combined Sentinel-2 and Landsat 7, 8, and 9 DEA Collection 3 surface reflectance products. The product time series commences from 2016, with datasets labelled by the middle year of data. For example, the 2017 layer combines data from 2016, 2017, and 2018. Updates to the product suite are scheduled annually.

What this product offers

The DEA Intertidal product suite is the next generation of intertidal products developed in DEA. It improves on the DEA Intertidal Elevation Model (also known as the National Intertidal Digital Elevation Model or NIDEM) (Bishop-Taylor et al., 2019) and adds several new features and products to help users better understand the intertidal environment.

NIDEM was the first 3D model of Australia’s intertidal zone — the area of coastline exposed and flooded by ocean tides. The DEA Intertidal suite fundamentally changes and improves the way in which the exposed intertidal zone is modelled compared to the original NIDEM elevation model:

The addition of Sentinel-2 data improves the spatial resolution of the model to 10 m, compared to the 25m of the original NIDEM.
Incorporation of a new pixel-based method supports a reduction in the temporal epoch of the product to 3 years (in comparison to 28 years in NIDEM), improving the ability to capture the current state of dynamic coastal environments and enabling ‘change over time’ applications using annual epochs.
Quantification of the vertical uncertainty of the elevation model.
An Intertidal Exposure model at 10 m resolution to examine the spatiotemporal patterns of exposure and inundation across the intertidal zone, supporting migratory species studies and habitat mapping applications.
Tidal metrics to enable users to understand the varied ranges and distributions of tidal stages observed by the Landsat and Sentinel-2 satellites across Australia, and how this information can be used to better understand and interpret the products.
The implementation of an ensemble tidal modelling approach, acknowledging the wide range of global and regional tide models available and their varying performance across different regions of Australia. See Ensemble Tidal Modelling.

File Naming Convention

File names use the format:

{product_name}_{x tile code}{y tile code}_{year--frequency}_{dataset maturity}_{layer_name}.tif

For example:

ga_s2ls_intertidal_cyear_3_x082y139_2022--P1Y_final_elevation.tif

Code repositories

DEA Intertidal Github — A codebase for DEA Intertidal product generation workflows
DEA Tools Github — Parallelised tide modelling and data manipulation tools
PyTMD Github — Python-based tidal prediction software

Core Product Layers

DEA Intertidal Elevation

Name	`elevation`
Data type	`float32`
Units	`metres above MSL`

DEA Intertidal Elevation (Figure 1) provides elevation in metre units relative to modelled Mean Sea Level for each pixel of the satellite-observed exposed intertidal zone across the Australian coastline. The elevation model is generated from DEA Landsat and Sentinel-2 surface reflectance data from each 3-year composite period, utilising a pixel-based approach based on Ensemble Tidal Modelling. For every pixel, the time series of surface reflectance data is converted to the Normalised Difference Water Index (NDWI) and each observation tagged with the tidal height modelled at the time of acquisition by the satellite. A rolling median is applied from low to high tide to reduce noise (such as white water, sunglint, and non-tidal water level variability), then analysed to identify the tide height at which the pixel transitions from dry to wet. This tide height represents the elevation of the pixel.

DEA Intertidal Elevation layer — Figure 1 — DEA Intertidal Elevation, with low elevation values shown in dark colours and high elevation shown in light colours.

DEA Intertidal Elevation Uncertainty

Name	`elevation_uncertainty`
Data type	`float32`
Units	`metres`

DEA Intertidal Elevation Uncertainty (Figure 2) provides a measure of the quality of each modelled elevation value in metre units. Uncertainty is calculated by assessing how cleanly the modelled elevation separates satellite observations into dry and wet observations. This is achieved by identifying satellite observations that were misclassified by the modelled elevation (for instance, pixels that were observed as wet at tide heights lower than the modelled elevation, or alternately, observed as dry at higher tide heights). The spread of tide heights from these misclassified observations is summarised using a robust Median Absolute Deviation (MAD) statistic, and reported as \(0.5 \times MAD\) to represent one-sided uncertainty bounds (i.e. ± uncertainty on either side of the pixel’s elevation). Common causes of high elevation uncertainty can be poor tidal model performance, rapidly changing intertidal morphology, or noisy underlying satellite data.

DEA Intertidal Elevation Uncertainty layer — Figure 2 — DEA Intertidal Elevation Uncertainty, with high uncertainty shown in light colours.

DEA Intertidal Exposure

Name	`exposure`
Data type	`uint8`
Units	`percent`
‘No data’ value	`255`

DEA Intertidal Exposure (Figure 3) models the percentage of time that any intertidal pixel of known elevation is exposed from tidal inundation. Exposure is calculated by comparing the pixel elevation back against a high temporal resolution model of tide heights for that location, based on the Ensemble Tidal Modelling approach. Exposure percentage is calculated as the fraction of exposed observations relative to the total number of observations generated in the high temporal resolution tidal model for the 3-year product epoch.

DEA Intertidal Exposure layer — Figure 3 — DEA Intertidal Exposure, with low exposure values (i.e. rarely exposed pixels) shown in dark colours.

Tidal Attribute Layers (ta)

DEA Intertidal tidal spread

Name	`ta_spread`
Data type	`uint8`
Units	`percent`
‘No data’ value	`255`

The tidal spread dataset provides the percentage of the full astronomical tidal range observed by the time series of satellite observations at each pixel (see Figure 4a). DEA Intertidal Spread takes the concept of satellite tide bias, introduced in Bishop-Taylor et al (2019), and applies it at a pixel scale to demonstrate the fraction of the full tide range that was sensor observed during the analysis epoch at that location. In this work, the astronomical tide range is defined as that modelled by the Ensemble Tidal Modelling approach.

DEA Intertidal low tide offset

Name	`ta_offset_low`
Data type	`uint8`
Units	`percent`
‘No data’ value	`255`

The low tide offset dataset quantifies the proportion of the lowest tides not observed at any time during the analysis epoch by satellites at each pixel (as a percentage of the astronomical tide range). It is calculated by measuring the offset between the lowest astronomical tide (LAT) and the lowest satellite-observed tide (LOT; see Figure 4b). A high value indicates that DEA Intertidal datasets may not map the lowest regions of the intertidal zone.

DEA Intertidal high tide offset

Name	`ta_offset_high`
Data type	`uint8`
Units	`percent`
‘No data’ value	`255`

The high tide offset dataset quantifies the proportion of the highest tides not observed at any time during the analysis epoch by satellites at each pixel (as a percentage of the astronomical tide range). It is calculated by measuring the offset between the highest astronomical tide (HAT) and the highest satellite-observed tide (HOT; see Figure 4c). A high value indicates that DEA Intertidal datasets may not map the highest regions of the intertidal zone.

Tidal Attributes Description Figure — Figure 4 — Illustration of the concept of observed tide heights (dots corresponding to satellite acquisition time) compared to the full modelled tidal range (blue lines). Descriptions of Spread (a), Lowtide offset (b), and Hightide offset (c) are detailed in the text.

DEA Intertidal lowest observed tide

Name	`ta_lot`
Data type	`float32`
Units	`metres above MSL`

The lowest observed tide dataset maps the lowest satellite-observed tide (LOT) of the satellite time series at each pixel during the analysis epoch, based on Ensemble Tidal Modelling.

DEA Intertidal highest observed tide

Name	`ta_hot`
Data type	`float32`
Units	`metres above MSL`

The highest observed tide dataset maps the highest satellite-observed tide (HOT) of the satellite time-series at each pixel during the analysis epoch, based on Ensemble Tidal Modelling.

DEA Intertidal lowest astronomical tide

Name	`ta_lat`
Data type	`float32`
Units	`metres above MSL`

The lowest astronomical tide dataset maps the lowest astronomical tide (LAT) for each pixel, as modelled by the Ensemble Tidal Model for the analysis epoch. Note that the LAT modelled for each individual analysis epoch may differ from the LAT modelled across ‘all time’ for any given location.

DEA Intertidal highest astronomical tide

Name	`ta_hat`
Data type	`float32`
Units	`metres above MSL`

The highest astronomical tide dataset maps the highest astronomical tide (HAT) for each pixel, as modelled by the Ensemble Tidal Model for the analysis epoch. Note that the HAT modelled for each individual analysis epoch may differ from the HAT modelled across ‘all time’ for any given location.

Quality Assessment Layers (qa)

DEA Intertidal NDWI frequency

Name	`qa_ndwi_freq`
Data type	`uint8`
Units	`percent`
‘No data’ value	`255`

This quality assessment band provides the inundation frequency of each pixel across the analysis epoch, as measured by NDWI. High values indicate that a pixel was observed as being inundated regularly in satellite observations.

DEA Intertidal NDWI correlation

Name	`qa_ndwi_corr`
Data type	`float32`
Units	`correlation`

This quality assessment dataset provides pixel-level Pearson correlations between NDWI satellite observations and tide heights from the Ensemble Tidal Model over the analysis epoch. High values indicate that patterns of inundation were positively correlated with tide, indicating that the pixel was likely to be tidally influenced.

Ensemble Tidal Modelling

The Ensemble Tidal Modelling approach was implemented to account for the varying performance and biases of 7 commonly used global tidal models when applied to the various regions and tidal regimes of continental Australia (Figure 5).

The tidal models listed below were implemented within the DEA environment using the pyTMD and DEA Tools Python packages:

FES2014 (Lydard et al., 2021)
FES2012 (Carrère et al., 2012)
TPXO8-atlas-v1 (Egbert & Erofeeva, 2002)
TPXO9-atlas-v5  (Egbert & Erofeeva, 2002)
EOT20 (Hart-Davis et al., 2021)
HAMTIDE11 (Taguchi et al., 2014)
GOT4.10 (Ray, 2013)

The ensemble process utilises ancillary data (in this case, the correlation of the NDWI values to each individual tidal model output) to weight and selects the top 3 tidal models for a given location. A single ensemble tidal output from these 3 optimal models is then generated for use in all downstream product workflows.

Figure 5 — Global tide models validated at Australian Baseline Sea Level Monitoring Project (ABSLMP) and Global Extreme Sea Level Analysis (GESLA) tide gauges

Lineage

The DEA Intertidal product suite extends the concepts developed in the DEA Intertidal Elevation (NIDEM) product, integrating higher resolution 10 m Sentinel-2 data with the original 30 m Landsat data to create annual elevation models and exposure product layers for Australia’s intertidal zone.

This shift to a more dynamic product suite is achieved through a pixel-based algorithm, replacing the waterline interpolation methods of NIDEM, and an improved tidal modelling process to better leverage the increased data resolution and density provided by the inclusion of Sentinel-2 data.

Processing Steps

Satellite data from Sentinel-2A and -2B, Landsat 7, 8, and 9 are loaded.
Satellite data cloud masked and converted to NDWI.
Tides modelled for every satellite pixel using Ensemble Tidal Modelling.
Satellite data filtered to probable intertidal pixels using NDWI inundation frequency and tide correlation and masked to remove deep ocean water using bathymetry from GA Australian 250m Bathymetry and Topography Grid (Beaman, 2023).
Pixel-based rolling median applied from low to high tide.
Intertidal elevation modelled based on tide height at which a rolling median transitions from dry to wet.
Intertidal elevation uncertainty modelled based on how cleanly modelled elevation divides satellite observations into dry and wet.
Intertidal exposure calculated by comparing Intertidal elevation against high frequency modelled tides.
Tidal metrics calculated by comparing satellite-observed tides against high frequency modelled tides.

References

Beaman, R. 2023. AusBathyTopo 250m (Australia) 2023 Grid - A High-resolution Depth Model for Australia (20230004C). Geoscience Australia, Canberra.

Bishop-Taylor, R., Sagar, S., Lymburner, L., Beaman, R.J., 2019. Between the tides: Modelling the elevation of Australia’s exposed intertidal zone at continental scale. Estuarine, Coastal and Shelf Science 23, 115–128.

Carrère L., F. Lyard, M. Cancet, A. Guillot, L. Roblou, 2012. FES2012: A new global tidal model taking advantage of nearly 20 years of altimetry, Proceedings of meeting “20 Years of Altimetry”, Venice 2012

Egbert, G.D., Erofeeva, S.Y., 2002. Efficient Inverse Modeling of Barotropic Ocean Tides. J. Atmospheric Ocean. Technol. 19, 183–204.

Hart-Davis, M.G., Piccioni, G., Dettmering, D., Schwatke, C., Passaro, M., Seitz, F., 2021. EOT20: a global ocean tide model from multi-mission satellite altimetry. Earth System Science Data 13, 3869–3884.

Lyard, F.H., Allain, D.J., Cancet, M., Carrère, L., Picot, N., 2021. FES2014 global ocean tide atlas: design and performance. Ocean Science 17, 615–649.

Ray, R. D. (2013). Precise comparisons of bottom-pressure and altimetric ocean tides. Journal of Geophysical Research: Oceans, 118(9), 4570–4584.

Taguchi, E., Stammer, D., and Zahel, W. (2014), Inferring deep ocean tidal energy dissipation from the global high-resolution data-assimilative HAMTIDE model, J. Geophys. Res. Oceans, 119, 4573– 4592.

Quality

Accuracy

Product accuracy was validated against high resolution external Digital Elevation Model (DEM) data sources from across the Australian coastline, including aerial LiDAR and multibeam bathymetry datasets. These included datasets from the following sources:

Aerial LiDAR hosted on Geoscience Australia’s ELVIS platform
Aerial LiDAR and multibeam datasets hosted on the WA Bathymetry Portal
Aerial LiDAR over Gulf of Carpentaria tidal flats, Northern Territory Government
Aerial LiDAR over the Dampier Archipelago, University of Western Australia
Aerial LiDAR over Gulf of Carpentaria, Airborne Research Australia

To create a consistent basis for comparison against DEA Intertidal outputs, data from each validation data source was mosaicked and reprojected into 10 m DEM rasters. These rasters were then combined into annual continental-scale rasters corresponding to each year of data in the DEA Intertidal product suite. Because these DEMs had varied coverage of the intertidal zone due to being acquired across a range of tidal conditions, true exposed intertidal pixels were identified via a combination of manual digitisation using underlying high-resolution basemap data as a reference, and automated filtering to pixels with elevations between local Lowest and Highest Astronomical Tide. Additional areas of ocean or ‘no data’ contamination were masked using a simple slope rule, considering only pixels with a non-zero slope as viable candidates for intertidal pixels.

To evaluate the accuracy of DEA Intertidal Elevation, we calculated RMSE, MAE, correlation, bias, and R-squared statistics by comparing modelled DEA Intertidal Elevation outputs with validation data from the relevant year (e.g. 2021 DEA Intertidal Elevation data was compared against 2021 validation data). To provide insights into product performance across different coastal environments, this analysis was conducted separately on microtidal (tide range < 2 m), mesotidal (2–4 m) and macrotidal (> 4 m) coastlines (Table 1, Figure 6).

Table 1 — DEA Intertidal Elevation validation statistics comparing performance across microtidal, mesotidal, and macrotidal coastlines.
	Microtidal	Mesotidal	Macrotidal
Correlation	0.6	0.86	0.96
R-squared	0.36	0.74	0.92
RMSE (m)	0.28	0.27	0.28
MAE (m)	0.21	0.21	0.20
Bias (m)	0.14	0.16	0.12
Slope	0.44	0.80	1.03

Validation at different tidal ranges — Figure 6 — DEA Intertidal Elevation validation heatmaps comparing performance across microtidal, mesotidal and macrotidal coastlines.

Caveats and limitations

DEA Intertidal covers the exposed intertidal zone which includes sandy beaches and shores, tidal flats and rocky shores and reefs. The model excludes intertidal vegetation communities such as mangroves.
Model performance improves with increasing tide range, being lowest in microtidal coastlines and highest in mesotidal and macrotidal coastlines. Due to the limited spatial extent of the intertidal zone in microtidal environments and the dominance of non-tidal water level influences (e.g. storm surge and ocean waves in high energy wave dominated environments), DEA Intertidal should be used with caution in microtidal environments.
DEA Intertidal relies on accurate tide modelling for reliable results. Although the Ensemble Tidal Modelling approach used in this product attempts to obtain the best local tide modelling data for any given location, areas of poor quality tide modelling still remain. This is particularly the case in areas of complex and unpredictable tide dynamics, such as embayments and estuaries where global ocean tide modelling results may produce highly inaccurate outputs. In these environments, modelled elevations and exposure should be used with caution and evaluated with reference to modelled elevation uncertainty data. Examples of areas affected by poor quality tide modelling inputs include:
- Western Port, Victoria
- Corner Inlet, Victoria
- Broad Sound and Shoalwater Bay, Queensland
- Van Diemen Gulf, Northern Territory
- Torres Strait Islands, Queensland
Due to biases in the tidal coverage of satellite sensors like Landsat and Sentinel-2, DEA Intertidal outputs are unlikely to cover the full extent of the intertidal zone from Lowest to Highest Astronomical tide (e.g. Figure 5). These tidal biases can be evaluated using the product’s Tidal Attribute Layers which highlight regions where DEA Intertidal will underestimate the lower, upper or full extent of the intertidal zone.
Areas of false positive intertidal data over water exist in areas with low satellite coverage and high levels of environmental or sensor noise. Areas affected include:
- Recherche Archipelago, Western Australia
- South-eastern Eyre Peninsula, South Australia
- Torres Strait Islands, Queensland
Areas of highly turbid water can be incorrectly mapped as intertidal terrain, particularly if temporal patterns of turbidity correlate with tidal dynamics. Areas affected by turbidity include:
- King Sound, Western Australia
- Cambridge Gulf, Western Australia
- Joseph Bonaparte Gulf, Northern Territory
- Daly River, Northern Territory
- Broad Sound, Queensland

Quality Assurance

Code used to generate DEA Intertidal is run against automated integration tests to ensure that product quality is maintained as updates and improvements are made. These tests verify that the entire product generation workflow is performing as expected, and track changes in product accuracy over time.

Access

Access the data

See it on a map	See it on a map	Learn how to use DEA Maps
Explore data availability	Data explorer	Learn how to use the DEA Explorer
Get the data online	Access the data on AWS Download the data from ELVIS Access the data on NCI	Learn how to access the data via AWS
Code sample	Code examples GitHub repository	Learn how to use the DEA Sandbox
Get via web service	Web Map Service (WMS)	Learn how to use DEA’s web services

History

Old versions

View previous versions of this data product.

1.0.0: DEA Intertidal Elevation (Landsat)

Credits

Acknowledgments

The authors would like to sincerely thank the following individuals and organisations for validation data and valuable feedback on preliminary versions of this product:

Mick O’Leary/Ryan Lowe — University of Western Australia
Jenna Hounslow/Adrian Gleiss — Murdoch University
Mitchell Baum — University of Melbourne
Lucya Roncevich — Western Australia Department of Transport
Duncan Moore — GA Maritime Jurisdiction Advice
Ben Ford — Department of Climate Change, Energy, Environment and Water
Maria Zann — QLD Department of Environment and Science
Rob Beaman — James Cook University
Kathy Murray — WA Department of Biodiversity, Conservation and Attractions
Hannah Power — University of Newcastle
Mitch Lyons — University of New South Wales
Nick Murray — James Cook University
Airborne Research Australia
Northern Territory Government

License and copyright

Released under Creative Commons Attribution 4.0 International Licence.