Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset

Dataset of Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain. The data is composed of: For the Mont-Blanc case study: the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization); For the Northern Patagonian Icefield case study: the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac. The filenames correspond to those used in the associated GitHub repository: https://github.com/rhugonnet/dem_error_study. The shapefiles used for masking glaciers are available directly from the Randolph Glacier Inventory 6.0 at https://www.glims.org/RGI/. The date of the DEMs is in their original format: year-month-day for all but ASTER that has the original naming of AST L1A products. Units are meters for the DEMs and elevation differences, and percentages for the quality of stereo-correlation.