DEA Coastlines

Geoscience Australia Landsat Coastlines Collection 3

Version
1.1.0 flat-square
Program
Digital Earth Australia
Collection
Geoscience Australia Landsat Collection 3
Resource type
Derivative
Published Date
13/12/2021

About

A groundbreaking data product, DEA Coastlines combines satellite data with tidal modelling to map the typical location of the Australian coastline at mean sea level for every year since 1988. Resulting shorelines and detailed rates of change show how beaches, sandspits, river mouths, and tidal flats have grown and eroded over time.

Background

Australia has a highly dynamic coastline of over 30,000 km, with over 85% of its population living within 50 km of the coast. This coastline is subject to a wide range of pressures, including extreme weather and climate, sea level rise and human development. Understanding how the coastline responds to these pressures is crucial to managing this region, from social, environmental and economic perspectives. 

What this product offers

Digital Earth Australia Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire Australian coastline from 1988 to the present. 

The product combines satellite data from Geoscience Australia's Digital Earth Australia program with tidal modelling to map the most representative location of the shoreline at mean sea level tide for each year. The product enables trends of coastal retreat and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades. 

The ability to map shoreline positions for each year provides valuable insights into whether changes to our coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting our coastlines and potentially assist planning and forecasting for future scenarios. 

Applications

  • Monitoring and mapping rates of coastal erosion along the Australian coastline 
  • Prioritise and evaluate the impacts of local and regional coastal management based on historical coastline change 
  • Modelling how coastlines respond to drivers of change, including extreme weather events, sea level rise or human development 
  • Supporting geomorphological studies of how and why coastlines have changed across time 

Related products

Publications

Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery. Remote Sensing of Environment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734

Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/j.ecss.2021.107712

Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019). Sub-pixel waterline extraction: Characterising accuracy and sensitivity to indices and spectra. Remote Sensing, 11(24), 2984. Available: https://www.mdpi.com/2072-4292/11/24/2984

Data access

Link to data Digital Earth Australia - Public Data download (ESRI Shapefile and GeoPackage f…
Dataset technical metadata
Link to maps Interactive DEA Coastlines product on DEA Maps
Code examples Jupyter notebook
Github repository
Web services WMS (layer name: dea:DEACoastlines)
WFS (layer names: dea:coastlines, dea:coastlines_statistics)
eCat record 116268
DOI https://doi.org/10.26186/116268
CMI RESTful node ID 581
Security classification Unclassified
Update frequency annually
Product life span -

Access notes

Data download

DEA Coastlines data for the entire Australian coastline is available to download in two formats:

  • GeoPackage (recommended): suitable for QGIS; includes built-in symbology for easier interpretation
  • ESRI Shapefiles: suitable for ArcMap and QGIS

To download DEA Coastlines data:

  1. Click the Link to data link above
  2. Click on either the GeoPackage (DEACoastlines_gpkg_v1.1.0.zip) or ESRI Shapefile (DEACoastlines_shp_v1.1.0.zip) format to download the data to your computer.
  3. Unzip the zip file by right clicking on the downloaded file and selecting Extract all.

To load GeoPackage data in QGIS:

  1. Drag and drop the unzipped DEACoastlines_v1.1.0.gpkg file into the main QGIS map window, or select it using Layer > Add Layer > Add Vector Layer.
  2. When prompted to Select Vector Layers to Add, select all three layers and then OK.
  3. The DEA Coastlines layers will load with built-in symbology. By default, DEA Coastlines layers automatically transition based on the zoom level of the map. To deactivate this: right click on a layer in the QGIS Layers panel, click Set Layer Scale Visibility, and untick Scale visibility.

To load ESRI Shapefile data in ArcMap or QGIS:

  1. If using QGIS, double-click on the unzipped DEACoastlines.qgz file to open a QGIS project file with accompanying layer symbology.
  2. If using ArcMap, double-click on the unzipped DEACoastlines.mxd file to open an ArcMap map document with accompanying layer symbology.
  3. If you encounter slow performance in ArcMap, we recommend loading the shapefile data in QGIS or ArcGIS Pro instead. Alternatively, extract a smaller subset of the DEA Coastlines layers for your area of interest.
     

DEA Maps

To explore DEA Coastlines on the interactive DEA Maps platform, visit the link below:

https://maps.dea.ga.gov.au/story/DEACoastlines

To add DEA Coastlines to DEA Maps manually:

  1. Open DEA Maps.
  2. Select Explore map data on the top-left.
  3. Select Sea, ocean and coast > DEA Coastlines > DEA Coastlines
  4. Click the blue Add to the map button on top-right.

By default, the map will show hotspots of coastal change at continental scale. Red dots represent retreating coastlines (e.g. erosion), while blue dots indicate seaward growth. The larger the dots and the brighter the colour, the more coastal change that is occurring at the location. 

More detailed rates of change will be displayed as you zoom in. To view a time series chart of how an area of coastline has changed over time, click on any labelled point (press "Expand" on the pop-up for more detail):

DEA Maps zoom example

Zoom in further to view individual annual shorelines:

DEA Maps coastlines example

Note: To view a DEA Coastlines layer that is not currently visible (e.g. rates of change statistics at full zoom), each layer can be added to the map individually from the Coastal > Digital Earth Australia Coastlines > Supplementary data directory.


Loading DEA Coastlines data from the Web Feature Service (WFS) using Python

DEA Coastlines data can be loaded directly in a Python script or Jupyter Notebook using the DEA Coastlines Web Feature Service (WFS) and geopandas:
 

import geopandas as gpd

# Specify bounding box
ymax, xmin = -33.65, 115.28
ymin, xmax = -33.66, 115.30

# Set up WFS requests for annual shorelines & rates of change statistics
deacl_coastlines_wfs = f'https://geoserver.dea.ga.gov.au/geoserver/wfs?' \
                       f'service=WFS&version=1.1.0&request=GetFeature' \
                       f'&typeName=dea:coastlines&maxFeatures=1000' \
                       f'&bbox={ymin},{xmin},{ymax},{xmax},' \
                       f'urn:ogc:def:crs:EPSG:4326'
deacl_statistics_wfs = f'https://geoserver.dea.ga.gov.au/geoserver/wfs?' \
                       f'service=WFS&version=1.1.0&request=GetFeature' \
                       f'&typeName=dea:coastlines_statistics&maxFeatures=1000' \
                       f'&bbox={ymin},{xmin},{ymax},{xmax},' \
                       f'urn:ogc:def:crs:EPSG:4326'

# Load DEA Coastlines data from WFS using geopandas
deacl_coastlines_gdf = gpd.read_file(deacl_coastlines_wfs)
deacl_statistics_gdf = gpd.read_file(deacl_statistics_wfs)

# Ensure CRSs are set correctly
deacl_coastlines_gdf.crs = 'EPSG:3577'
deacl_statistics_gdf.crs = 'EPSG:3577'


Loading DEA Coastlines data from the Web Feature Service (WFS) using R

DEA Coastlines data can be loaded directly into R using the DEA Coastlines Web Feature Service (WFS) and the sf package:
 

library(magrittr)
library(glue)
library(sf)

# Specify bounding box
xmin = 115.28
xmax = 115.30
ymin = -33.66
ymax = -33.65

# Read in DEA Coastlines annual shoreline data, using `glue` to insert our bounding 
# box into the string, and `sf` to  load the spatial data from the Web Feature Service 
# and set the Coordinate Reference System to Australian Albers (EPSG:3577)
deacl_coastlines = "https://geoserver.dea.ga.gov.au/geoserver/wfs?service=WFS&version=1.1.0&request=GetFeature&typeName=dea:coastlines&maxFeatures=1000&bbox={ymin},{xmin},{ymax},{xmax},urn:ogc:def:crs:EPSG:4326" %>% 
  glue::glue() %>%
  sf::read_sf() %>% 
  sf::st_set_crs(3577)

# Read in DEA Coastlines rates of change statistics data
deacl_statistics = "https://geoserver.dea.ga.gov.au/geoserver/wfs?service=WFS&version=1.1.0&request=GetFeature&typeName=dea:coastlines_statistics&maxFeatures=1000&bbox={ymin},{xmin},{ymax},{xmax},urn:ogc:def:crs:EPSG:4326" %>% 
  glue::glue() %>%
  sf::read_sf() %>% 
  sf::st_set_crs(3577)

Technical information

2020 DEA Coastlines update

In December 2021, the DEA Coastlines product was updated to version 1.1.0. This includes the following key changes to the data and web services: 

Improvements and additions: 

  • Added annual shoreline data for 2020. The 2020 shoreline is an interim dataset that is subject to change, and will be updated to a final version in the following 2021 DEA Coastlines update. 

  • Implemented a new temporal filter that improves mapping performance across small islands and dynamic sandbars while reducing noise in areas with poor satellite coverage. 

  • Time series graphs are now available for non-significant rates of change points on DEA Maps (previously this was only available for retreating and growing points). 

  • Australian Coastal Geomorphology Smartline and Australian Coastal Sediment Compartments datasets are now available in the "Supplementary data" folder on DEA Maps to assist in interpreting observed patterns of coastal change. 

  • Added new valid_obs and valid_span fields to rates of change points that summarise the total number and temporal coverage of valid non-outlier shoreline observations. Results from valid_obs are now used to filter out unreliable rates of change points on DEA Maps by removing points with fewer than 25 valid observations. 

  • Climate indices (e.g. SOI) are now detrended prior to regression to remove false positives associated with trends in index values over time, and to allow climate index relationships to be modelled for coastlines with temporal erosional or progradation trends through time. 

  • Numerous minor improvements to inland water and estuary masking to fix areas of missing shoreline data and remove non-coastal features 

Backwards incompatible changes:

  • Removed experimental breaks field from rates of change points. 

  • Removed retreat, growth fields from rates of change points (this data is already captured in rate_time, with negative values indicating retreat and positive values indicating growth). 

  • Updated WMS layer group names from "dea:DEACoastLines" to "dea:DEACoastlines", and "dea:AnnualCoastlines" to "dea:AnnualShorelines" 

  • As part of this update, all annual shorelines were reprocessed. This may result in some minor changes to shoreline positions compared to version 1.0.0 of the dataset.

For a full summary of changes made in version 1.1.0, refer to Github


DEA Coastlines dataset

The DEA Coastlines product contains three layers: 

Annual shorelines

Annual shoreline vectors from 1988 that represent the median or ‘most representative’ position of the shoreline at approximately mean sea level tide (0 m Above Mean Sea Level) for each year (Figure 1).

  • Dashed shoreline have low certainty due to either few non-cloudy satellite observations, poor tidal modelling performance, or aerosol issues (see Caveats and limitations)
DEA CoastLines coastline layer

Figure 1: Annual coastlines from DEA Coastlines visualised on the interactive DEA Coastlines web map

Rates of change statistics

A point dataset providing robust rates of coastal change statistics for every 30 m along Australia’s non-rocky (clastic) coastlines (Figure 2). The most recent annual shoreline is used as a baseline for measuring rates of change.

DEA CoastLines rates of change statistics layer

Figure 2: Rates of change points from DEA Coastlines visualised on the interactive DEA Coastlines web map

On the interactive DEA Coastlines web map, points are shown for locations with statistically significant rates of change only (p-value < 0.01, see sig_time below). Each point shows annual rates of change (in metres per year; see rate_time below), and an estimate of uncertainty in brackets (95% confidence interval; see se_time). For example, there is a 95% chance that a point with a label -10.0 m (±1.0 m) is retreating at a rate of between -9.0 and -11.0 metres per year.

The rates of change statistics dataset contains the following attribute columns that can be accessed by clicking on labelled points in the web map: 

Annual shoreline distances
  • dist_1990, dist_1991 etc: Annual shoreline distances (in metres) relative to the most recent baseline shoreline. Negative values indicate that an annual shoreline was located inland of the baseline shoreline. By definition, the dist_2019 baseline column will always have a distance of 0 m.
Rates of change statistics
  • rate_time: Annual rates of change (in metres per year) calculated by linearly regressing annual shoreline distances against time (excluding outliers). Negative values indicate retreat and positive values indicate growth. 
  • sig_time: Significance (p-value) of the linear relationship between annual shoreline distances and time. Small values (e.g. p-value < 0.01 or 0.05) may indicate a coastline is undergoing consistent coastal change through time. 
  • se_time: Standard error (in metres) of the linear relationship between annual shoreline distances and time. This can be used to generate confidence intervals around the rate of change given by rate_time (e.g. 95% confidence interval = se_time * 1.96)
  • outl_time: Individual annual shoreline are noisy estimators of coastline position that can be influenced by environmental conditions (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal modelling results or limited clear satellite observations). To obtain reliable rates of change, outlier shorelines are excluded using a robust Median Absolute Deviation outlier detection algorithm, and recorded in this column. 
Climate driver statistics
  • rate_soi: The slope of any linear relationship between annual shoreline distances and the Southern Oscillation Index or SOI (in metres change per unit of SOI). Negative values indicate that coastlines have historically retreated during La Niña years. This comparison is made after first de-trending both the annual SOI values and annual shoreline distances to remove any trends of chronic shoreline growth or retreat. Please note: this comparison was made against SOI data from NOAA which does not apply a standard scaling factor to SOI values unlike Australia's BOM. Divide rate_soi and se_soi values by 10 to get rates of coastal change per increase in BOM-scaled SOI values.
  • sig_soi: Significance (p-value) of the linear relationship between annual shoreline distances and SOI. 
  • se_soi: Standard error (in metres) of the linear relationship between annual shoreline distances and SOI.
  • outl_soi: A list of any shorelines excluded from the SOI regression by the robust outlier detection algorithm.
Other derived statistics
  • sce: Shoreline Change Envelope (SCE). A measure of the maximum change or variability across all annual shorelines, calculated by computing the maximum distance between any two annual shoreline (excluding outliers). This statistic excludes sub-annual shoreline variability.
  • nsm: Net Shoreline Movement (NSM). The distance between the oldest (1988) and most recent annual shoreline (excluding outliers). Negative values indicate the coastline retreated between the oldest and most recent shoreline; positive values indicate growth. This statistic does not reflect sub-annual shoreline variability, so will underestimate the full extent of variability at any given location.
  • max_year, min_year: The year that annual shorelines were at their maximum (i.e. located furthest towards the ocean) and their minimum (i.e. located furthest inland) respectively (excluding outliers). This statistic excludes sub-annual shoreline variability.
  • breaks: An experimental list of any shorelines identified as non-linear breakpoints in the time series. This can be useful for verifying that a significant trend is indeed linear, or identifying areas of rapid non-linear change (e.g. associated with coastal development or management).

Coastal change hotspots

A point layer giving the average rate of change (in metres per year) for significant statistics points within a moving 5 km window along the coastline (Figure 3). This is useful for visualising regional or continental-scale patterns of coastal change. 

DEA CoastLines summary layer

Figure 3: Coastal change hotspots from DEA Coastlines visualised on the interactive DEA Coastlines web map

Accuracy and limitations

Annual shoreline accuracy and precision

An extensive validation against independent coastal monitoring datasets was conducted to evaluate the positional accuracy and precision of DEA Coastlines annual shorelines, and the accuracy of our modelled long-term rates of coastal change (i.e. metres retreat or growth per year). In total, 57,662 independent measurements of coastline position were acquired across coastal Australia from the following data sources (Figure 4):

  • City of Gold Coast ETA Lines (Strauss et al., 2017)
  • Moruya and Pedro Beach survey (Short et al. 2014)
  • Narrabeen-Collaroy Beach Survey Program (Turner et al., 2016)
  • NSW Beach Profile Database (Harrison et al., 2017)
  • South Australia Coastal Monitoring Profile Lines (South Australian Coast Protection Board, 2000)
  • Sunshine Coast Council ETA Lines (Griffith Centre for Coastal Management, 2016) 
  • Tasmanian Shoreline Monitoring and Archiving Project (TASMARC, 2021)
  • Victorian Coastal Monitoring Program (Pucino et al., 2021)
  • Western Australia Department of Transport (WA DoT) Coastline Movements (Department of Transport, 2009)

Validation sites

Figure 4: The spatial and temporal distribution of the independent validation data that was compared against DEA Coastlines annual shorelines and rates of change. 
 

Annual shoreline accuracy and precision

This validation assessed the ability of DEA Coastlines to reproduce a specific shoreline proxy: the median annual position of the shoreline at mean sea level tide (0 m Above Mean Sea Level; AMSL). Validations were performed using existing beach profile lines where possible. For each validation profile line, we identified the median annual position of the 0 m AMSL tide datum across all annual validation observations, and compared this to the position of the corresponding DEA Coastlines shoreline for each year (Figure 5).

To ensure a like-for-like comparison, we selected a subset of validation data with an annual survey frequency approximately equivalent to the Landsat satellite imagery used to generate DEA Coastlines data (i.e. 22 annual observations or greater based on a 16 day overpass frequency). Absolute mapping accuracy (i.e. how far the mapped shorelines were from the median annual position of the shoreline for each year, after correcting for tide) was assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE):

  • Absolute mapping accuracy: 7.3 metres MAE (10.3 metres RMSE) accuracy at mapping the median annual position of the shoreline after correcting for tide

Shoreline mapping bias and precision (i.e. how well modelled shorelines reproduced relative shoreline dynamics even when affected by substrate-specific seaward or landward biases) was evaluated by calculating the average of all individual errors, then subtracting these systematic biases from our results to produce bias-corrected MAE and RMSE. R-squared was also calculated to compare overall correlations between DEA Coastlines and validation shoreline positions:

  • Bias: 5.6 metre landward bias (i.e. shorelines mapped inland of their true position)
  • Precision: 6.1 metres bias-corrected MAE (8.7 metres bias-corrected RMSE)
  • R-squared: 0.92

For a more detailed breakdown of validation results by substrate, please refer to Bishop-Taylor et al. 2021.


Rates of coastal change accuracy

To evaluate our long-term rates of change, we identified 330 validation transects with an extensive (> 10 years) temporal record of coastal monitoring data, encompassing a total of 11,632 independent measurements of shoreline position. We computed linear regression-based annual rates of coastal change (metres per year) between 0 m AMSL shoreline positions and time, and compared these against rates calculated from DEA Coastlines for corresponding years of data to ensure a like-for-like comparison. Validation statistics were then calculated across all 330 transects regardless of statistical significance, and a smaller subset of 144 transects with statistically significant rates of retreat or growth (p < 0.01) in either the validation data or DEA Coastlines:

All transects:

  • Accuracy: 0.35 m / year MAE (0.60 m / year RMSE)
  • Bias: 0.08 m / year
  • R-squared: 0.90

Significant transects only:

  • Accuracy: 0.31 m / year MAE (0.52 m / year RMSE)
  • Bias: 0.08 m / year
  • R-squared: 0.95

For a more detailed discussion of rates of change validation results, please refer to Bishop-Taylor et al. 2021.

Validation results

Figure 5: DEA Coastlines annual shorelines compared against a) aerial photogrammetry-derived annual ~0 m AMSL shorelines from the Western Australian Department of Transport Coastline Movements dataset, and b) transect-based in-situ validation data for three example locations that demonstrate sub-pixel precision shoreline extraction: Narrabeen Beach, Tugun Beach, and West Beach. DEA Coastlines transect data in panel b represent the 0 m AMSL Median Annual Shoreline Position shoreline proxy, and have been corrected for consistent local inland biases to assess the ability to capture relative coastline dynamics through time.

Caveats and limitations

Annual shorelines
  • Annual shorelines from DEA Coastlines summarise the median (i.e. "dominant") position of the shoreline throughout the entire year, corrected to a consistent tide height (0 m AMSL). Annual shorelines will therefore not reflect shorter-term coastal variability, for example changes in shoreline position between low and high tide, seasonal effects, or short-lived influences of individual storms. This means that these annual shorelines will show lower variability than the true range of coastal variability observed along the Australian coastline.
Rates of change statistics
  • Rates of change do not assign a reason for change, and do not necessarily represent processes of coastal erosion or sea level rise. In locations undergoing rapid coastal development, the construction of new inlets or marinas may be represented as hotspots of coastline retreat, while the construction of ports or piers may be represented as hotspots of coastline growth. Rates of change points should therefore be evaluated with reference to the underlying annual coastlines and external data sources or imagery.
  • Rates of change statistics may be inaccurate or invalid within complex mouthbars, or other coastal environments undergoing rapid non-linear change through time. In these regions, it is advisable to visually assess the underlying annual coastline data when interpreting rates of change to ensure these values are fit-for-purpose. Regions significantly affected by this issue include:
    • Cambridge Gulf, Western Australia
    • Joseph Bonaparte Gulf, Western Australia/Northern Territory
Data quality issues
  • Annual shorelines may be less accurate in regions with complex tidal dynamics or large tidal ranges, and low-lying intertidal flats where small tidal modelling errors can lead to large horizontal offsets in coastline positions (Figure 6). Annual shoreline accuracy in intertidal environments may also be reduced by the influence of wet muddy substrate or intertidal vegetation, which can make it difficult to extract a single unambiguous coastline (Bishop-Taylor et al. 2019a, 2019b, 2021). It is anticipated that future versions of this product will show improved results due to integrating more advanced methods for waterline detection in intertidal regions, and through improvements in tidal modelling methods. Regions significantly affected by intertidal issues include:
    • The Pilbara coast, Western Australia from Onslow to Pardoo
    • The Mackay region, Queensland from Proserpine to Broad Sound
    • The upper Spencer Gulf, South Australia from Port Broughton to Port Augusta
    • Western Port Bay, Victoria from Tooradin to Pioneer Bay
    • Hunter Island Group, Tasmania from Woolnorth to Perkins Island
    • Moreton Bay, Queensland from Sandstone Bay to Wellington Point
  • Shorelines may be noisier and more difficult to interpret in regions with low availability of satellite observations caused by persistent cloud cover. In these regions it can be difficult to obtain the minimum number of clear satellite observations required to generate clean, noise-free annual shorelines. Affected regions include:
    • South-western Tasmania from Macquarie Heads to Southport
  • In some urban locations, the spectra of bright white buildings located near the coastline may be inadvertently confused with water, causing a land-ward offset from true shoreline positions. 
  • Some areas of extremely dark and persistent shadows (e.g. steep coastal cliffs across southern Australia) may be inadvertently mapped as water, resulting in a landward offset from true shoreline positions. 
  • 1991 and 1992 shorelines are currently affected by aerosol-related issues caused by the 1991 Mount Pinatubo eruption. These shorelines should be interpreted with care, particularly across northern Australia. 
Validation approach
  • To compare annual shorelines to validation datasets, multiple validation observations in a year were combined into a single median measurement of coastline position. In the case where only a single validation observation was taken for a year, this single observation may not be reflective of typical shoreline conditions across the entire year period. Because of this, validation results are expected to be more reliable for validation datasets with multiple observations per year.
  • The current validation approach was biased strongly towards Australia's south-western, southern and south-eastern coastlines due to the availability of historical coastal monitoring data. This bias prevented us from including more complex intertidal environments in our validation, which is likely to have inflated the accuracy of our results due to issues outlined above.

Intertidal issues

Figure 6: Potentially spurious shorelines in macrotidal coastal regions characterised by gently sloped tidal flat environments: a) Broad Sound and b) Shoalwater Bay, Queensland. Dashed shorelines indicate data that was flagged as affected by tidal modelling issues based on MNDWI standard deviation. In these locations, the TPXO 8 tidal model was unable to effectively sort satellite observations by tide heights, resulting in output shorelines that did not adequately suppress the influence of the tide.

Quality assurance

To allow problematic data to be accounted for or excluded from future analyses, all annual shorelines from DEA Coastlines are automatically screened for several potential data quality issues. These issues are flagged in the annual shoreline "certainty" field and symbolised by dashed lines on the interactive map version of DEA Coastlines. These flags include coastlines affected by:

  • Aerosol issues caused by the 1991 eruption of Mount Pinatubo.
  • Tidal modelling issues, potentially caused by errors in the tidal model used to constrain shorelines to mean sea level, or the influence of shallow intertidal topography that can lead to inaccurate coastline mapping (e.g. dashed lines in Figure 6 above).
  • Limited good quality satellite observations, which can reduce the quality of resulting shoreline features.

For more information, refer to Caveats and limitations above.

Software

The following software was used to generate this product: 

Relevant websites

References

Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery. Remote Sensing of Environment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734

Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/j.ecss.2021.107712

Bishop-Taylor, R., Sagar, S., Lymburner, L., & Beaman, R. J. (2019a). Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale. Estuarine, Coastal and Shelf Science, 223, 115-128. Available: https://doi.org/10.1016/j.ecss.2019.03.006

Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019b). Sub-pixel waterline extraction: Characterising accuracy and sensitivity to indices and spectra. Remote Sensing, 11(24), 2984. Available: https://doi.org/10.3390/rs11242984

DoT, (2018). Capturing the Coastline: Mapping Coastlines in WA over 75 Years. Department of Transport, Western Australia (2018). Available: https://www.transport.wa.gov.au/mediaFiles/marine/MAC_P_CapturingtheCoastline.pdf

Griffith Centre for Coastal Management, 2016. Sunshine Coast Beach Profile Database: Description of BPA Historical Database and Recommendations for Ongoing Monitoring Programs (No. 188), Griffith Centre for Coastal Management Research Report. 

Harrison, A.J., Miller, B.M., Carley, J.T., Turner, I.L., Clout, R., Coates, B., 2017. NSW beach photogrammetry: A new online database and toolbox. Australasian Coasts & Ports 2017: Working with Nature 565. 

Pucino, N., Kennedy, D.M., Carvalho, R.C., Allan, B., Ierodiaconou, D., 2021. Citizen science for monitoring seasonal-scale beach erosion and behaviour with aerial drones. Scientific Reports 11, 3935. https://doi.org/10.1038/s41598-021-83477-6 

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

Short, A.D., Bracs, M.A., Turner, I.L., 2014. Beach oscillation and rotation: local and regional response at three beaches in southeast Australia. Journal of Coastal Research 712–717. https://doi.org/10.2112/SI-120.1 

South Australian Coast Protection Board, 2000. Monitoring Sand Movements along the Adelaide Coastline. Department for Environment and Heritage, South Australia. 

Strauss, D., Murray, T., Harry, M., Todd, D., 2017. Coastal data collection and profile surveys on the Gold Coast: 50 years on. Australasian Coasts & Ports 2017: Working with Nature 1030. 

TASMARC, 2021. TASMARC (The Tasmanian Shoreline Monitoring and Archiving Project) (2019) TASMARC database. Available: http://www.tasmarc.info/

Turner, I. L., Harley, M. D., Short, A. D., Simmons, J. A., Bracs, M. A., Phillips, M. S., & Splinter, K. D. (2016). A multi-decade dataset of monthly beach profile surveys and inshore wave forcing at Narrabeen, Australia. Scientific data3(1), 1-13. Available: http://narrabeen.wrl.unsw.edu.au/

 

Data sources

Processing steps

  1. Load stack of all available Landsat 5, 7 and 8 satellite imagery for a location
  2. Convert satellite observations to a remote sensing water index (MNDWI)
  3. For each satellite image, model ocean tides into a 2 x 2 km grid based on exact time of image acquisition
  4. Interpolate tide heights into spatial extent of image stack
  5. Mask out high and low tide pixels by removing all observations acquired outside of 50 percent of the observed tidal range centered over mean sea level
  6. Combine tidally-masked data into annual median composites from 1988 to the present representing the coastline at approximately mean sea level
  7. Apply morphological extraction algorithms to mask annual median composite rasters to a valid coastal region
  8. Extract waterline vectors using subpixel waterline extraction
  9. Compute rates of coastal change at every 30 m along Australia's non-rocky coastlines using linear regression

Major algorithms

Schema / spatial extent

Digital Earth Australia Coastlines extent

Update frequency annually
Temporal extent
Min. longitude -4846590.00
Max. longitude -1887450.00
Min. latitude -1015650.00
Max. latitude 2121650.00
Coordinate reference system Australian Albers / GDA94 (EPSG: 3577)
Cell size X 30.00
Cell size Y 30.00

Example images

DEA CoastLines at Busselton, WA
DEA CoastLines at Cape Peron, Rockingham, WA
DEA CoastLines at Port Geographe, WA
DEA CoastLines at Point Stuart, NT
DEA CoastLines rates of change statistics at Perth, WA
DEA CoastLines rates of change statistics at Adelaide, SA
DEA CoastLines rates of change statistics at Long Bay, SA
DEA CoastLines at Corner Inlet, Vic
DEA CoastLines at Dutton Way, Vic
DEA CoastLines at Queenscliff, Vic
DEA CoastLines rates of change statistics at Old Bar, NSW
DEA CoastLines at Tweed Heads, NSW and QLD
DEA CoastLines at Jumpinpin Channel, QLD
DEA CoastLines at Barubbra Island, QLD

Owner

Commonwealth of Australia (Geoscience Australia)

Principal contributors

Robbi Bishop-Taylor, Rachel Nanson, Stephen Sagar, Leo Lymburner

Subject matter experts

Robbi Bishop-Taylor

License

CC BY Attribution 4.0 International License

Rights statement

© Commonwealth of Australia (Geoscience Australia) 2019. Creative Commons Attribution 4.0 International License.

Acknowledgments

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

  • Centre for Integrative Ecology, Deakin University 
  • Griffith Centre for Coastal Management, Griffith University 
  • Water Research Laboratory, University of New South Wales
  • Victorian Coastal Monitoring Program
  • TASMARC Project, Antarctic Climate & Ecosystems Cooperative Research Centre 
  • City of Gold Coast, Queensland 
  • Sunshine Coast Council, Queensland 
  • Western Australia Department of Transport
  • South Australia Department of Environment and Water
  • Queensland Department of Environment and Science
  • NSW Department of Planning, Industry and Environment
  • Andrew Short, University of Sydney
  • Colin Woodroffe, University of Wollongong

This research was undertaken with the assistance of resources from the National Computational Infrastructure High Performance Data (HPD) platform, which is supported by the Australian Government.