Multi-source DEM accuracy evaluation based on ICESat-2 in Qinghai-Tibet Plateau, China

ABSTRACT

Digital Elevation Models (DEMs) are critical datasets in the field of Earth sciences, essential for accurate measurement and analysis of the Earth's surface. We used ICESat-2 to quantitatively evaluate 5 DEMs (ALOS PALSAR, ASTER GDEM V3, COPERNICUS, NASADEM and TanDEM-X) on the Tibetan Plateau. The research findings indicate that the ALOS exhibits the highest level of accuracy, as evidenced by its root mean square error (RMSE) value of 5.05m. It is closely followed by NASA and COP, which have RMSE values of 6.23m and 8.10m, respectively. In contrast, the ASTER and TDX90 demonstrate comparatively lower levels of accuracy, as indicated by their respective RMSE values of 11.47m and 12.32m. It is worth mentioning that the accuracy of DEMs is significantly influenced by land cover, especially during the transition from areas with low vegetation to those with high vegetation. This transition often results in a decrease in accuracy. Furthermore, the accuracy of DEMs tends to decrease with increasing slope values. Aspect exhibits a notable spatial distribution pattern characterized by a "low in the Southwest direction, high in all other directions" phenomenon. This study offers significant contributions to the evaluation of DEMs accuracy and its suitability in the Qinghai-Tibet Plateau.

KEYWORDS:

• Digital elevation models
• ICESat-2 ATL08
• vertical accuracy assessment
• Qinghai-Tibet Plateau

Acknowledgements

The authors would like to thank three anonymous reviewers for their constructive comments and suggestions, which greatly improved the quality of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the data used in this study are for free and publicly available.

Additional information

Funding

This work was supported by the Second Qinghai-Tibet Plateau Scientific Research Program under Grant [grant number 2019QZKK0307] and the National Key Research and Development Program of China under Grant [Project No. 2019YFE0115200].