Seamless composite high resolution Digital Elevation Model (DEM) for the Murray Darling Basin Australia

Full description

This collection provides a seamlessly merged, hydrologically robust Digital Elevation Model (DEM) for the Murray Darling Basin (MDB), Australia, at 5 m and 25 m grid cell resolution.

This composite DEM has been created from all the publicly available high resolution DEMs in the Geoscience Australia (GA) elevation data portal Elvis (https://elevation.fsdf.org.au/) as at November 2022. The input DEMs, also sometimes referred to as digital terrain models (DTMs), are bare-earth products which represent the ground surface with buildings and vegetation removed. The DEMs were either from lidar (0.5 to 2 m resolution) or photogrammetry (5 m resolution) and totalled 852 individual DEMs.

The merging process involved ranking the DEMs, pairing the DEMs with overlaps, and adjusting and smoothing the elevations of the lower ranked DEM to make the edge elevations compatible with the higher-ranked DEM. This method is adapted from Gallant 2019 with modifications to work with hundreds of DEMs and have a variable number of gaussian smoothing steps.

Where there were gaps in the high-resolution DEM extents, the Forests and Buildings removed DEM (FABDEM; Hawker et al. 2022), a bare-earth radar-derived, 1 arc-second resolution global elevation model was used as the underlying base DEM. FABDEM is based on the Copernicus global digital surface model.

Additionally, hillshade datasets created from both the 5 m and 25 m DEMs are provided.

Note: the FABDEM dataset is available publicly for non-commercial purposes and consequently the data files available with this Collection are also available with a Creative Commons NonCommercial ShareAlike 4.0 Licence (CC BY-NC-SA 4.0). See https://data.bris.ac.uk/datasets/25wfy0f9ukoge2gs7a5mqpq2j7/license.txt
Lineage: For a more detailed lineage see the supporting document Composite_MDB_DEM_Lineage.

DATA SOURCES
1. Geoscience Australia elevation data (https://elevation.fsdf.org.au/) via Amazon Web Service s3 bucket. Of the 852 digital elevation models (DEMs) from the GA elevation data portal, 601 DEMs were from lidar and 251 were from photogrammetry. The latest date of download was Nov 2022. The oldest input DEM was from 2008 and the newest from 2022.

2. FABDEM - Forests and buildings removed DEM based on the 1 arc-second Copernicus global digital surface model. Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17, 024016. https://doi.org/10.1088/1748-9326/ac4d4f

METHODS
Part I. Preprocessing
The input DEMs were prepared for merging with the following steps:
1. Metadata for all input DEMs was collated in a single file and the DEMs were ranked from finest resolution/newest to coarsest resolution/oldest
2. Tiled input DEMs were combined into single files
3. Input DEMs were reprojected to GA LCC conformal conic projection (EPSG:7845) and bilinearly resampled to 5 m
4. Input DEMs were shifted vertically to the Australian Vertical Working Surface (AVWS; EPSG:9458)
5. The input DEMs were stacked (without any merging and/or smoothing at DEM edges) based on rank so that higher ranking DEMs preceded the lower ranking DEMs, i.e. the elevation value in a grid cell came from the highest rank DEM which had a value in that cell
6. An index raster dataset was produced, where the value assigned to each grid cell was the rank of the DEM which contributed the elevation value to the stacked DEM (see Collection Files - Index_5m_resolution)
7. A metadata file describing each input dataset was linked to the index dataset via the rank attribute (see Collection Files - Metadata)

Vertical height reference surface
https://icsm.gov.au/australian-vertical-working-surface

Part II. DEM Merging
The method for seamlessly merging DEMs to create a composite dataset is based on Gallant 2019, with modifications to work with hundreds of input DEMs. Within DEM pairs, the elevations of the lower ranked DEM are adjusted and smoothed to make the edge elevations compatible with the higher-ranked DEM. Processing was on the CSIRO Earth Analytics and Science Innovation (EASI) platform. Code was written in python and dask was used for task scheduling.

Part III. Postprocessing
1. A minor correction was made to the 5 m composite DEM in southern Queensland to replace some erroneous elevation values (-8000 m a.s.l.) with the nearest values from the surrounding grid cells
2. A 25 m version of the composite DEM was created by aggregating the 5m DEM, using a 5 x 5 grid cell window and calculating the mean elevation
3. Hillshade datasets were produced for the 5 m and 25 m DEMs using python code from https://github.com/UP-RS-ESP/DEM-Consistency-Metrics

Part IV. Validation
Six validation areas were selected across the MDB for qualitative checking of the output at input dataset boundaries. The hillshade datasets were used to look for linear artefacts. Flow direction and flow accumulation rasters and drainage lines were derived from the stacked DEM (step 5 in preprocessing) and the post-merge composite DEM. These were compared to determine whether the merging process had introduced additional errors.

OUTPUTS
1. seamlessly merged composite DEMs at 5 m and 25 m resolutions (geotiff)
2. hillshade datasets for the 5 m and 25 m DEMs (geotiff)
3. index raster dataset at 5 m resolution (geotiff)
4. metadata file containing input dataset information and rank (the rank column values link to the index raster dataset values)
5. figure showing a map of the index dataset and 5m composite DEM (jpeg)

DATA QUALITY STATEMENT
Note that we did not attempt to improve the quality of the input DEMs, they were not corrected prior to merging and any errors will be retained in the composite DEM.