DEA Coastlines ============== Geoscience Australia Landsat Coastlines Collection 3 ----------------------------------------------------- **Authored on**: 2020-08 **Updated on**: 2023-08 **Author/s**: Robbi Bishop-Taylor, Rachel Nanson, Stephen Sagar, Leo Lymburner **License**: CC BY Attribution 4.0 International License View the [original metadata page](https://cmi.ga.gov.au/data-products/dea/581/dea-coastlines) for the most up-to-date information on this product. Abstract -------- 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**](https://maps.dea.ga.gov.au/story/DEACoastlines) 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 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 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](http://narrabeen.wrl.unsw.edu.au/) (Turner et al., 2016) - [NSW Beach Profile Database](http://www.nswbpd.wrl.unsw.edu.au/) (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](http://www.tasmarc.info/) (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](/sites/default/files/inline-images/Figure1_validation_temporal%20%281%29.png) ###### 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 (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](https://www.sciencedirect.com/science/article/pii/S0034425721004545).* #### Rates of change points 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](https://www.sciencedirect.com/science/article/pii/S0034425721004545).* ![Validation results](/sites/default/files/inline-images/Figure4_subpixel%20%281%29.png) ###### 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 points - Rates of change points 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 points 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 shoreline 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 - Annual 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 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](/sites/default/files/inline-images/Figure10_intertidal%20%281%29.png) ###### 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. 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: 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: 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: 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: 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: 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: 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: 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](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. Lyard, F.H., Allain, D.J., Cancet, M., Carrère, L. and Picot, N., 2021. FES2014 global ocean tide atlas: design and performance. Ocean Science, 17(3), pp.615-649. 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: Seifi, F., Deng, X. and Baltazar Andersen, O., 2019. Assessment of the accuracy of recent empirical and assimilated tidal models for the Great Barrier Reef, Australia, using satellite and coastal data. Remote Sensing, 11(10), p.1211. 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: h[ttp://www.tasmarc.info/](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 data*, *3*(1), 1-13. Available: Contacts -------- For questions or more information about this product, email [DEA Support](mailto:dea@ga.gov.au?subject=Data%20Products%20support%20for%20DEA%20Coastlines&cc=earthobservation@ga.gov.au).