Assessing digital elevation model uncertainty using GPS survey data

Dimitrios Bolkas, G. Fotopoulos, A. Braun, I. N. Tziavos

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The use of terrain and elevation data is critical for a number of applications in science and engineering. Typically, the quality of digital elevation models (DEMs) is assessed using external and independent point data sources to arrive at an overall RMS value for the errors. The utility of such a single-valued overall assessment depends on the spatial extent of the area under consideration and the terrain variability (both over time and space), as well as the application requirements. This paper aimed to understand the suite of parameters that are important to consider in deriving a DEM error budget. Specifically, terrain slope, land-cover type, information loss, and data measurement schemes were investigated. A region in western Canada spanning the Rocky Mountains was used to numerically quantify errors using two Global Positioning System (GPS) datasets: precise point positioning (PPP) profiles and GPS on benchmarks. Three digital elevation models [Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM2), Shuttle Radar Topography Mission 1 Arc-Second Digital Elevation Model Version 3 (SRTM1v3), and Canadian Digital Elevation Model (CDEM)] were assessed. Results highlight the importance of selecting ground-control points based on the region's characteristics (e.g., slope, tree cover). This leads to more representative RMS values that improve DEM uncertainty estimations. Finally, a mathematical method [projection onto convex sets (POCS)] for filling data gaps in the GPS data profiles was implemented, and results demonstrate the utility of this approach over conventional interpolation schemes.

Original languageEnglish (US)
Article number04016001
JournalJournal of Surveying Engineering
Volume142
Issue number3
DOIs
StatePublished - Aug 1 2016

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering

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