New Method to Correct Vegetation Bias in a Copernicus Digital Elevation Model to Improve Flow Path Delineation

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 22; p. 4332
• Main Authors: Brochado, Gabriel Thomé, Rennó, Camilo Daleles
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 20-11-2024
• Subjects: Algorithms; Bias; Datasets; Delineation; Digital Elevation Models; Drainage; Drainage patterns; Earth surface; Elevation; Flow paths; Forests; Hydrology; Machine learning; Methods; Qualitative analysis; Topographic mapping; Topographic maps; Topography; Vegetation; Vegetation cover; Water flow
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16224332

Digital elevation models (DEM) are widely used in many hydrologic applications, providing key information about the topography, which is a major driver of water flow in a landscape. Several open access DEMs with near-global coverage are currently available, however, they represent the elevation of the earth’s surface including all its elements, such as vegetation cover and buildings. These features introduce a positive elevation bias that can skew the water flow paths, impacting the extraction of hydrological features and the accuracy of hydrodynamic models. Many attempts have been made to reduce the effects of this bias over the years, leading to the generation of improved datasets based on the original global DEMs, such as MERIT DEM and, more recently, FABDEM. However, even after these corrections, the remaining bias still affects flow path delineation in a significant way. Aiming to improve on this aspect, a new vegetation bias correction method is proposed in this work. The method consists of subtracting from the Copernicus DEM elevations their respective forest height but adjusted by correction factors to compensate for the partial penetration of the SAR pulses into the vegetation cover during the Copernicus DEM acquisition process. These factors were calculated by a new approach where the slope around the pixels at the borders of each vegetation patch were analyzed. The forest height was obtained from a global dataset developed for the year 2019. Moreover, to avoid temporal vegetation cover mismatch between the DEM and the forest height dataset, we introduced a process where the latter is automatically adjusted to best match the Copernicus acquisition year. The correction method was applied for regions with different forest cover percentages and topographic characteristics, and the result was compared to the original Copernicus DEM and FABDEM, which was used as a benchmark for vegetation bias correction. The comparison method was hydrology-based, using drainage networks obtained from topographic maps as reference. The new corrected DEM showed significant improvements over both the Copernicus DEM and FABDEM in all tested scenarios. Moreover, a qualitative comparison of these DEMs was also performed through exhaustive visual analysis, corroborating these findings. These results suggest that the use of this new vegetation bias correction method has the potential to improve DEM-based hydrological applications worldwide.