Intertidal environments support important ecological habitats (e.g. sandy beaches and shores, tidal flats and rocky shores and reefs), and provide many valuable benefits such as storm surge protection, carbon storage and natural resources for recreational and commercial use. However, intertidal zones are faced with increasing threats from coastal erosion, land reclamation (e.g. port construction), and sea level rise. Accurate elevation data describing the height and shape of the coastline is needed to help predict when and where these threats will have the greatest impact. However, this data is expensive and challenging to map across the entire intertidal zone of a continent the size of Australia.

The rise and fall of the ocean can be used to describe the three-dimensional shape of the coastline by mapping the land-sea boundary (or 'waterline') across a range of known tides (e.g. low tide, high tide). Assuming that these waterlines represent lines of constant height relative to mean sea level (MSL), elevations can be modelled for the area of coastline located between the lowest and highest observed tide. To model the elevation of Australia's entire intertidal zone, 30 years of satellite images of the coastline (between 1986 and 2016 inclusive) were obtained from the archive of spatially and spectrally calibrated Landsat observations managed within the Digital Earth Australia (DEA) platform. Using the improved tidal modelling framework of the Intertidal Extents Model v2.0 (ITEM 2.0; Sagar et al. 2017, 2018), each satellite observation in the 30 year time series could be accurately associated with a modelled tide height using the global TPX08 ocean tidal model. These satellite observations were converted into a water index (NDWI), composited into discrete ten percent intervals of the observed tide range (e.g. the lowest 10% of observed tides etc), and used to extract waterlines using a spatially consistent and automated waterline extraction procedure. Triangulated irregular network (TIN) interpolation was then used to derive elevations relative to modelled mean sea level for each 25 x 25 m Landsat pixel across approximately 15,387 sq. km of intertidal terrain along Australia's entire coastline.

NIDEM differs from previous methods used to model the elevation of the intertidal zone which have predominately focused on extracting waterlines from a limited selection of satellite images using manual digitisation and visual interpretation (e.g. Chen and Rau 1998; Zhao et al. 2008; Liu et al. 2013; Chen et al. 2016). This manual process introduces subjectivity, is impractical to apply at a continental-scale, and has inherent restrictions based on the availability of high quality image data at appropriate tidal stages. By developing an automated approach to generating satellite-derived elevation data based on a 30 year time series of observations managed within the Digital Earth Australia (DEA) platform, it was possible to produce the first continental-scale three-dimensional model of the intertidal zone.

The National Intertidal Digital Elevation Model (NIDEM) consists of four raster datasets (NIDEM, unfiltered NIDEM, NIDEM mask, NIDEM uncertainty) and two vector datasets (NIDEM waterline contours and ITEM v2.0 tidal modelling polygons). For convenience, the four raster datasets are provided both as individual geotiff/NetCDF files, and continental-scale geotiff mosaics.

NIDEM

The NIDEM dataset provides elevation in metre units relative to modelled Mean Sea Level for each pixel of intertidal terrain across the Australian coastline. The DEMs have been cleaned by masking out non-intertidal pixels and pixels where tidal processes poorly explain patterns of inundation (see NIDEM mask below). This is the primary output product, and is expected to be the default product for most applications. The dataset consists of 306 raster files corresponding to the 306 ITEM v2.0 tidal modelling polygons, and a continental-scale mosaic.

Unfiltered NIDEM

The unfiltered NIDEM dataset provides un-cleaned elevation in metre units relative to modelled Mean Sea Level for each pixel of intertidal terrain across the Australian coastline. Compared to the default NIDEM product, these layers have not been filtered to remove noise, modelling artefacts or invalid elevation values (see NIDEM mask below). This supports applying custom filtering methods to the raw NIDEM data. The dataset consists of 306 raster files corresponding to the 306 ITEM v2.0 tidal modelling polygons, and a continental-scale mosaic.

NIDEM mask

The NIDEM mask dataset flags non-intertidal terrestrial pixels with elevations greater than 25 m, and sub-tidal pixels with depths greater than -25 m relative to Mean Sea Level. Pixels where tidal processes poorly explain patterns of inundation are also flagged by identifying any pixels with ITEM confidence NDWI standard deviation greater than 0.25. The NIDEM mask was used to filter and clean the NIDEM dataset to remove modelling artefacts and noise (e.g. intertidal pixels in deep water or high elevations) and invalid elevation estimates caused by coastal change or poor model performance. The dataset consists of 306 raster files corresponding to the 306 ITEM v2.0 tidal modelling polygons, and a continental-scale mosaic.

NIDEM uncertainty

The NIDEM uncertainty dataset provides a measure of the uncertainty (not to be confused with accuracy) of NIDEM elevations in metre units for each pixel. The range of Landsat observation tide heights used to compute median tide heights for each waterline contour can vary significantly between tidal modelling polygons. To quantify this range, the standard deviation of tide heights for all Landsat images used to produce each ITEM interval and subsequent waterline contour was calculated. These values were interpolated to return an estimate of uncertainty for each individual pixel in the NIDEM datasets: larger values indicate the waterline contour was based on a composite of images with a larger range of tide heights. The dataset consists of 306 raster files corresponding to the 306 ITEM v2.0 tidal modelling polygons, and a continental-scale mosaic.

NIDEM waterline contours

The NIDEM waterline contour dataset provides a vector representation of the boundary of every ten percent interval of the observed intertidal range. These contours were extracted along the boundary between each ITEM v2.0 tidal interval, and assigned the median and standard deviation (see NIDEM uncertainty above) of tide heights from the ensemble of corresponding Landsat observations. These datasets facilitate re-analysis by allowing alternative interpolation methods (e.g. kriging, splines) to be used to generate DEMs from median tide heights. The dataset consists of 306 shapefiles corresponding to the 306 ITEM v2.0 tidal modelling polygons.

ITEM v2.0 tidal modelling polygons

The ITEM v2.0 tidal model polygon dataset describes the multi-resolution tidal framework developed by Sagar et al. (2018). The framework uses partitioning methods to allow spatial variability in the tidal model to drive the size and locations of a Voronoi polygon mesh. The 306 resulting tidal modelling polygons are then used as analysis units for tidal modelling, with tide height predictions defined at the nodes of each Voronoi cell. To evaluate the representativeness of NIDEM data compared to the full tidal range, the spread of tidal heights coincident with the input Landsat imagery was compared against the full range of modelled tide heights present within each tidal modelling polygon. Three indices were calculated: spread (the proportion of the full modelled tidal range observed by Landsat), low tide offset (the proportion of the lowest tidal heights not observed by Landsat) and high tide offset (the proportion of the highest tidal heights not observed by Landsat). This evaluation of the observed tidal range at a particular location provides valuable information to users about the "fitness for purpose" of NIDEM at a given location for their specific application (see Bishop-Taylor et al. 2019).

The product differs from previous methods used to model the elevation of the intertidal zone which have predominately focused on extracting waterlines from a limited selection of satellite images using manual digitisation and visual interpretation (e.g. Chen and Rau 1998; Zhao et al. 2008; Liu et al. 2013; Chen et al. 2016). This manual process introduces subjectivity, is impractical to apply at a continental-scale, and has inherent restrictions based on the availability of high quality image data at appropriate tidal stages. By developing an automated approach to generating satellite-derived elevation data based on a 30 year time series of observations managed within the Digital Earth Australia (DEA) platform, we were able to produce the first continental-scale three-dimensional model of the intertidal zone.

By providing an important ‘missing link’ between existing terrestrial topographic and marine bathymetric datasets, we anticipate elevation data from NIDEM will contribute to unified coastal terrain models that combine best-available topography and bathymetry datasets into single continuous ‘topobathy’ datasets. This would provide coastal managers with near-seamless elevation data extending from inland to the ocean floor, and support a holistic approach to understanding the physical and ecological processes influencing the coastal zone. This may include providing valuable baseline elevation data for predicting the impact of coastal hazards such as storm surges or tsunami inundation, investigating mechanisms of coastal erosion and sediment transport, or improving modelling of sea-level rise under future climate change scenarios.

Access to accurate intertidal elevation data is also critical for studying and conserving coastal ecosystems, particularly given the important role intertidal topography and tidal dynamics play in regulating environmental stress and spatial patterns of species richness, abundance and productivity. Data from NIDEM will facilitate whole-of-landscape approaches to ecological modelling and three-dimensional habitat mapping in the intertidal zone that are not artificially restricted to either marine or terrestrial ecosystems.