23 Nov 2022
 | 23 Nov 2022
Status: this preprint is currently under review for the journal TC.

Co-registration and residual correction of digital elevation models: A comparative study

Tao Li, Yuanlin Hu, Bin Liu, Liming Jiang, Hansheng Wang, and Xiang Shen

Abstract. Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and Kääb (2011) and Rosenholm and Torlegard (1988) . The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using 23 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM pairs from the western margin of the Greenland Ice Sheet. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (4.6 % on average and 15.3 % maximum) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and Kääb considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals.

Tao Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-205', Anonymous Referee #1, 19 Dec 2022
    • AC1: 'Reply on RC1', Xiang Shen, 04 Mar 2023
  • RC2: 'Comment on tc-2022-205', Anton Schenk, 11 Jan 2023
    • AC2: 'Reply on RC2', Xiang Shen, 04 Mar 2023

Tao Li et al.

Tao Li et al.


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Short summary
Raw DEMs are often misaligned with each other due to georeferencing errors, and a co-registration process is required before DEM differencing. We present a comparative analysis of the two classical DEM co-registration and three residual correction algorithms. The experimental results show that rotation and scale biases should be considered in DEM co-registration. The new non-parametric regression technique can eliminate the complex systematic errors, which existed in the co-registration results.