Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment

Abstract

Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to debias the global-extent datasets. Recent advances in satellite platforms coupled with an increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Amongst the more challenging landscapes to quantify surface elevations are dense forested mountain catchments where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms has resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against an up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing de-forested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.

A GIS Automated Tool for Morphometric Flood Analysis Based on the Horton–Strahler River Classification System

“…FABDEM (Forest and Buildings removed Copernicus DEM) is a processed layer from ESA's Copernicus program (derived from the COPDEM layer), which was chosen due to the fact that the forests and buildings of the raw layer were filtered out, and it is an elevation model that has received improvements with hydrological applications in mind. Furthermore, previous studies on floods have proven the suitability of the FABDEM layer for hydrological analysis in large-scale areas [47][48][49][50][51][52]. In addition, FABDEM has global coverage, thereby facilitating the replicability of the current results in other areas as well.…”

Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas

“…Thus, the removal of forests and buildings achieved by FABDEM is beneficial for our study to optimize the classification of surface objects. In addition, the comparison of FABDEM, LiDAR data, Copernicus DEM, ICESat-2 and other DEMs demonstrates the utility of the FABDEM dataset [50,60,61]. In addition, to assess the transferability of the proposed method across different DEMs, we introduce Shuttle Radar Topography Mission (SRTM) [62] and ALOS World 3D-30 m (AW3D30) [63] DEMs as additional datasets.…”

A 30 m global flood inundation model for any climate scenario

“…Switching the underlying DEM data used by the model from the 3 arc second (~90 m) resolution SRTM-based MERIT data set (Yamazaki et al, 2017) to the new 1 arc second (~30 m) FABDEM (Hawker et al, 2022). FABDEM is a corrected version of the Copernicus DEM with forests and buildings removed and has been shown to offer significant advantages over MERIT (Hawker et al, 2023;Marsh et al, 2023).…”