Between the tides: modelling the elevation of Australia’s exposed intertidal zone at continental scale

The intertidal zone represents a critical transition between marine and terrestrial ecosystems, supporting a complex mosaic of highly productive and biologically diverse habitats. However, our understanding of these important coastal environments is limited by a lack of spatially consistent topographic data, which can be extremely challenging and costly to obtain at continental-scale. Satellite remote sensing represents an important resource for monitoring extensive coastal zones. Previous approaches to modelling the elevation of the intertidal zone using earth observation (EO) data have been restricted to small study regions or have relied on manual image interpretation, thus limiting their ability to be applied consistently over large geographic extents. In this study, we present an automated open-source approach to generate satellite-derived elevation data for over 15,387 km2 of intertidal terrain across the entire Australian coastline. Our approach combines global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. The resulting National Intertidal Digital Elevation Model (NIDEM) dataset provides an unprecedented three-dimensional representation of Australia's vast exposed intertidal zone at 25 m spatial resolution. We validate our model against LiDAR, RTK GPS and multibeam bathymetry datasets, finding that modelled elevations are highly accurate across sandy beach (±0.41 m RMSE) and tidal flat environments (±0.39 m RMSE). Model performance was least accurate (±2.98 m RMSE) within rocky shores and reefs and other complex coastal environments with extreme and variable tidal regimes. We discuss key challenges associated with modelling intertidal elevation including tidal model performance and biased observations from sun-synchronous satellites, and suggest future directions to improve the accuracy and utility of continental-scale intertidal elevation modelling. Our model can be applied to tidally-influenced coastal environments globally, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data

Assessment of Changes of Complex Shoreline from Medium‑Resolution Satellite Imagery

The imagery collected by medium-resolution earth-observing satellites is a powerful and cost-effective tool for the quantitative assessment of shoreline dynamics for water bodies of different spatial scales. In this study, we utilize imagery collected in 1984–2021 on the Middle Peninsula, Virginia, bordering the Chesapeake Bay, USA, by medium-resolution (10–30 m) satellites Landsat-5/7/8 and Sentinel-2A/B. The data was managed in the Earth Analytics Interoperability Lab (EAIL) Data Cube built and configured by the Commonwealth Scientific and Industrial Research Organization (CSIRO, Australia and Chile). The assessments of shoreline change demonstrate adequate agreement with assessments based on aerial photography collected during 1937–2009 by the Virginia Institute of Marine Science, with reasonable disagreement attributed to the differences in the analyzed periods and in the accuracy of land/ water edge detection. Most of the studied coastline was subject to land loss (erosion), in some locations exceeding 3 m year− 1, usually along low-lying sandy beaches. The shoreline segments with man-made structures such as marinas, bulkheads, revetments, and offshore breakwaters demonstrated a significantly lower range of changes as compared to natural reaches. Regular analysis of medium resolution satellite imagery appears to be an effective method for routine assessment of shoreline changes along the land/water edge

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu’s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50–3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space

Data from: Surface-water dynamics and land use influence landscape connectivity across a major dryland region

Landscape connectivity is important for the long-term persistence of species inhabiting dryland freshwater ecosystems, with spatiotemporal surface-water dynamics (e.g., flooding) maintaining connectivity by both creating temporary habitats and providing transient opportunities for dispersal. Improving our understanding of how landscape connectivity varies with respect to surface-water dynamics and land use is an important step to maintaining biodiversity in dynamic dryland environments. Using a newly available validated Landsat TM and ETM+ surface-water time series, we modelled landscape connectivity between dynamic surface-water habitats within Australia's 1 million km2 semi-arid Murray Darling Basin across a 25-year period (1987 to 2011). We identified key habitats that serve as well-connected ‘hubs’, or ‘stepping-stones’ that allow long-distance movements through surface-water habitat networks. We compared distributions of these habitats for short- and long-distance dispersal species during dry, average and wet seasons, and across land-use types. The distribution of stepping-stones and hubs varied both spatially and temporally, with temporal changes driven by drought and flooding dynamics. Conservation areas and natural environments contained higher than expected proportions of both stepping-stones and hubs throughout the time series; however, highly modified agricultural landscapes increased in importance during wet seasons. Irrigated landscapes contained particularly high proportions of well-connected hubs for long-distance dispersers, but remained relatively disconnected for less vagile organisms. The habitats identified by our study may serve as ideal high-priority targets for land-use specific management aimed at maintaining or improving dispersal between surface-water habitats, potentially providing benefits to biodiversity beyond the immediate site scale. Our results also highlight the importance of accounting for the influence of spatial and temporal surface-water dynamics when studying landscape connectivity within highly variable dryland environments

Local graph theory connectivity metrics from Bishop-Taylor et al. 2017

Graph theory local connectivity metrics data for two dispersal distances and each seasonal time-step in the 1987-2011 Landsat-derived surface-water time series (Tulbure et al. 2016). Betweenness and degree centrality results were used to assess the distribution of important “stepping-stone” and “hub” habitats across Australia’s Murray-Darling Basin

Surface water networks across space and time: a graph theory approach

Landscape connectivity – the capacity of organisms to move or disperse through their environment – is increasingly threatened by changing land use and increasing hydroclimatic variability driven by climate change. These impacts are likely to be particularly severe within dynamic dryland environments where many organisms rely on unpredictable flooding events for dispersal between fragmented habitats. In this thesis, I use graph theory network analysis to explore how surface water dynamics (i.e. flooding and drought) affect landscape connectivity through time and across large spatial extents. I focus on Australia’s Murray-Darling Basin (MDB), a globally significant and ecologically threatened river basin subject to extreme spatiotemporal habitat variability. In Chapter 2, I use static habitat data and modelled flooding scenarios for the entire MDB to assess how flooding affects landscape connectivity for two amphibian species. I identify ‘hub’ and ‘stepping stone’ habitats important for local and network-scale connectivity, and reveal that changes in movement conditions associated with flooding can greatly impact connectivity through entire amphibian habitat networks. In Chapter 3, I expand this approach by identifying high-priority targets for future conservation management using an unprecedented 25-year remotely sensed surface water time series. Although important habitats for connectivity exhibited extreme variability over time, I show that more of these habitats were located within protected areas than expected by chance. In Chapter 4, I use advanced habitat availability metrics to assess how periods of extreme hydroclimatic conditions (e.g. the 1999–2009 Millennium Drought and the 2010–2011 La Niña floods) affect connectivity at a sub-continental scale, finding that surface water network structure in the MDB provides resistance to drought. Finally, in Chapter 5 I compare habitat prioritisations based the static approach of Chapter 2 against the dynamic approach of Chapters 3 and 4, revealing that accounting for spatiotemporal habitat dynamics can result in large differences in connectivity estimates that vary by study region, spatial scale and hydroclimatic conditions. The computationally efficient graph theory methodology presented in this thesis is directly applicable to other spatiotemporally dynamic regions globally, enabling landscape connectivity to be assessed consistently across long temporal extents and at regional or subcontinental scales

Digital Earth Australia notebooks and tools repository

The Digital Earth Australia notebooks and tools repository ('DEA notebooks') hosts Jupyter Notebooks, Python scripts and workflows for analysing Digital Earth Australia (DEA) satellite data and derived products. The repository is intended to provide a guide to getting started with DEA, and to showcase the wide range of geospatial analyses that can be achieved using DEA data and open-source software including Open Data Cube and xarray.If you use any of the notebooks, code or tools in this repository in your work, please reference them using the following citation