A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation

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.interferometric signal and stereo images, as well as the post-processing methods, will affect the data accuracy [10].Consequently, the tradeoff between the spatial resolution, spatial coverage, and vertical accuracy means that the currently available datasets do not meet the demands for the large-scale geospatial applications [11]. On the one hand, more advanced imaging and processing technologies will be applied to Earth observation [12], and new products with a higher resolution and fine-edited quality are being generated and released. However, new data generation is not only highly cost and time-consuming, but is also inevitably limited by the imaging techniques [13]. Moreover, the editing of raw DEM data only using the single-source information, e.g. the noise filtering and voids interpolation methods, might fail in the cases where local data quality are poor [14][15][16][17][18]. Thus, considering the auxiliary information among the multi-source datasets, merging the currently available datasets through multi-source data fusion is a possible way to solve this problem. Different fusion techniques were developed to solve the quality issues of DEM data, such as voids, anomalies and different sources of noise [14,[19][20][21][22][23][24]. Based on the fusion methods, great efforts have been made to generate high-quality DEM products by merging the existing datasets [19,21,25].In this paper, we introduce a fused DEM dataset (82°S-82°N) mainly reconstructed from the 30-m unfilled SRTM1, AW3D30, ASTER GDEM v2 and ICESat GLAS data, which we refer to as GSDEM-30. The goal is to generate a global seamless DEM using the multi-source accuracy enhancement method and the regularized multi-scale fusion algorithm [22,26]. Considering the global data characteristics, the artificial neural networ...

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