Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments

ABSTRACT

Flood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM).

KEYWORDS:

• topography
• DTM
• accuracy
• land cover
• slope
• aspect

Acknowledgments

For their help in acquiring and understanding the airborne-LiDAR DTMs used here as reference data, we are very grateful to Tristan Goulden, Nicholas Rollings, Lara Röttcher, Karim Sadr, Gregoire Vincent, Joe Mulligan, Debora Drucker, Chris Crook, Serene Ho, Rosario Ang and Tanel Hurt. Michael Meadows was supported by a RTP Stipend Scholarship from the Australian Government and a Postgraduate Research Scholarship from Natural Hazards Research Australia (NHRA).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The global DEMs assessed here are freely available online: SRTM from https://lpdaac.usgs.gov/products/srtmgl1v003, NASADEM from https://lpdaac.usgs.gov/products/nasadem_hgtv001, AW3D30 from https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm, GLO-30 from https://panda.copernicus.eu/web/cds-catalogue/panda, and FABDEM from https://data.bris.ac.uk/data/dataset/s5hqmjcdj8yo2ibzi9b4ew3sn. We used the Google Earth Engine Catalog to access both the MERIT DEM (https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3) and the Surface Water Occurrence layer (https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_3_GlobalSurfaceWater). The datasets used to indicate flood-prone areas are both available online: GFPLAIN250m at https://doi.org/10.6084/m9.figshare.6665165.v1 and the Low Elevation Coastal Zone (LECZ) raster at https://doi.org/10.7927/d1x1-d702. In addition to the MERIT DEM (used to derive slope), the other datasets used to evaluate the biophysical conditions in flood-prone areas globally are ESA WorldCover 2020 (https://worldcover2020.esa.int/downloader), present-day Köppen–Geiger zones (https://www.gloh2o.org/koppen) and GHSL Degree of Urbanisation rasters (https://data.jrc.ec.europa.eu/dataset/4606d58a-dc08-463c-86a9-d49ef461c47f). All (65) reference DTMs collated for this study are summarised in Table S1, including online access details wherever available.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.