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

Abstract

The monitoring of Earth's and planetary surface elevations at larger and finer scales is rapidly progressing through the increasing availability and resolution of digital elevation models (DEMs). Surface elevation observations are being used across an expanding range of fields to study topographical attributes and their changes over time, notably in glaciology, hydrology, volcanology, seismology, forestry and geomorphology. However, DEMs frequently contain large-scale instrument noise and varying vertical precision that lead to complex patterns of errors. Here, we present a validated statistical workflow to estimate, model, and propagate uncertainties in DEMs. We review the state-of-the-art of DEM accuracy and precision analyses, and define a conceptual framework to consistently address those. We show how to characterize DEM precision by quantifying the heteroscedasticity of elevation measurements, i.e. varying vertical precision with terrain-or sensor-dependent variables, and the spatial correlation of errors that can occur across multiple spatial scales. With the increasing availability of high-precision observations, our workflow based on independent elevation data acquired on stable terrain can be applied almost anywhere on Earth. We illustrate how to propagate uncertainties for both pixel-scale and spatial elevation derivatives, using terrain slope and glacier volume changes as examples. We find that uncertainties in DEMs are largely underestimated in the literature, and advocate that new metrics of DEM precision are essential to ensure the reliability of future Earth and planetary surface elevation assessments.

Six Consecutive Seasons of High‐Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery

“…In addition to the above snow depth evaluation metrics, we computed the median and NMAD of residuals over exposed stable terrain (paved roads, Figure S2 in Supporting Information ), where snow depth should be 0 m (e.g., Hugonnet et al., 2022; Pelto et al., 2019). We are unable to report similar metrics for the ASO snow depth products, as all values over exposed road surfaces are manually set to 0 m (Deschamps‐Berger et al., 2020).…”

Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data

“…For each ICESat-2 derived snow depth, the snow depth value of the closest ASO snow depth map in time was extracted. We use the term accuracy to describe biases in snow depth while precision is used for random errors (Hugonnet et al, 2022). The accuracy of the ICESat-2 derived snow depths was evaluated with the median of the residual (e.g.…”

Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes

“…Topographic data in the form of gridded digital elevation models (DEMs) are fundamental data for planetary science, geology, geomorphology, hydrology, glaciology, urban studies, and many other fields. DEMs have been developed from a variety of data sources, since the early days of digitized contour maps [1][2][3]. One of the most popular methods for modern DEM generation at <5 m spatial resolution for local (i.e., 10-10 4 km 2 ) study areas is stereogrammetry from optical satellite imagery (e.g., [4][5][6]), although these data can be extended to regional studies [7].…”

“…The accuracy and precision of DEMs are vital statistics for any use case [2,3,11,12]. Thus, researchers strive for higher quality (high accuracy and precision) DEMs from avail-able data sources.…”