288
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

A new quality validation of global digital elevation models freely available in China

, , , &
Pages 409-420 | Received 15 Feb 2015, Accepted 18 May 2015, Published online: 30 Mar 2016
 

Abstract

Global Digital Elevation Models (DEMs) are widely used in the study of natural hazards and environmental change on a global scale. This paper focuses on validation of the most widely used and newly released global DEMs (SRTM v4.1, NASA SRTM v3, SRTMX and ASTER GDEM v2) in China. Authors use independent and precise ground GPS observations to assess their absolute accuracies. The SRTMX DEM performs best with a height RMSE of 9.7 m, while the RMSE of the ASTER GDEM2 is slightly better than that of the SRTM v4.1, and SRTM v3 in these regions have the largest RMSE of 16.6 m. However, systematic negative bias still exists in all the global DEMs. Results of the raster-based comparisons between the DEMs are dependent on the knowledge of vegetation type, density and structure to a large extent as well as accurate co-registration. Slope comparisons exhibit a hierarchical slope difference of about 2° between the SRTMX DEM, ASTER GDEM2 and SRTM C-band DEM (v4.1 and v3). This paper provides the first direct evidence and measurement of the product quality of SRTMX and SRTM v3 DEM in China and also offers a benchmark for the future evaluation of following global DEM products.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 249.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.