Version 1
: Received: 4 June 2019 / Approved: 5 June 2019 / Online: 5 June 2019 (10:26:30 CEST)
How to cite:
Yue, L.; Shen, H.; Liu, L.; Yuan, Q.; Zhang, L. A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation. Preprints2019, 2019060036. https://doi.org/10.20944/preprints201906.0036.v1
Yue, L.; Shen, H.; Liu, L.; Yuan, Q.; Zhang, L. A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation. Preprints 2019, 2019060036. https://doi.org/10.20944/preprints201906.0036.v1
Yue, L.; Shen, H.; Liu, L.; Yuan, Q.; Zhang, L. A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation. Preprints2019, 2019060036. https://doi.org/10.20944/preprints201906.0036.v1
APA Style
Yue, L., Shen, H., Liu, L., Yuan, Q., & Zhang, L. (2019). A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation. Preprints. https://doi.org/10.20944/preprints201906.0036.v1
Chicago/Turabian Style
Yue, L., Qiangqiang Yuan and Liangpei Zhang. 2019 "A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation" Preprints. https://doi.org/10.20944/preprints201906.0036.v1
Abstract
The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.
Keywords
digital elevation models; multi-source fusion; multi-scale fusion; global evaluation; accuracy validation.
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
