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Remote Sensing
Other literature type . Article . 2020 . Peer-reviewed
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Remote Sensing
Article . 2020
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Remote Sensing
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Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

Authors: Evelyn Uuemaa; Sander Ahi; Bruno Montibeller; Merle Muru; Alexander Kmoch;

Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

Abstract

Freely available global digital elevation models (DEMs) are important inputs for many research fields and applications. During the last decade, several global DEMs have been released based on satellite data. ASTER and SRTM are the most widely used DEMs, but the more recently released, AW3D30, TanDEM-X and MERIT, are being increasingly used. Many researchers have studied the quality of these DEM products in recent years. However, there has been no comprehensive and systematic evaluation of their quality over areas with variable topography and land cover conditions. To provide this comparison, we examined the accuracy of six freely available global DEMs (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM) in four geographic regions with different topographic and land use conditions. We used local high-precision elevation models (Light Detection and Ranging (LiDAR), Pleiades-1A) as reference models and all global models were resampled to reference model resolution (1m). In total, 608 million 1x1 m pixels were analyzed. To estimate the accuracy, we generated error rasters by subtracting each reference model from the corresponding global DEM and calculated descriptive statistics for this difference (e.g., median, mean, root-mean-square error (RMSE)). We also assessed the vertical accuracy as a function of the slope, slope aspect, and land cover. We found that slope had the strongest effect on DEM accuracy, with no relationship for slope aspect. The AW3D30 was the most robust and had the most stable performance in most of the tests and is therefore the best choice for an analysis of multiple geographic regions. SRTM and NASADEM also performed well where available, whereas NASADEM, as a successor of SRTM, showed only slight improvement in comparison to SRTM. MERIT and TanDEM-X also performed well despite their lower spatial resolution.

Country
Estonia
Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Mean squared error Land cover Shuttle Radar Topography Mission Digital elevation model Aster (genus) Remote sensing Pixel biology Elevation biology.organism_classification Lidar Geology

Library of Congress Subject Headings: lcsh:Science lcsh:Q

Keywords

aspect, land cover, topography, accuracy assessment, validation, DEM; validation; accuracy assessment; slope; aspect; topography; land cover, DEM, slope, General Earth and Planetary Sciences

81 references, page 1 of 9

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15. Rizzoli, P.; Martone, M.; Gonzalez, C.; Wecklich, C.; Borla Tridon, D.; Bräutigam, B.; Bachmann, M.; Schulze, D.; Fritz, T.; Huber, M.; et al. Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogramm. Remote Sens. 2017, 132, 119-139. [CrossRef]

16. Hu, Z.; Peng, J.; Hou, Y.; Shan, J. Evaluation of recently released open global digital elevation models of Hubei, China. Remote Sens. 2017, 9, 262. [CrossRef]

17. Ariza-Villaverde, A.B.; Jiménez-Hornero, F.J.; Gutiérrez de Ravé, E. Influence of DEM resolution on drainage network extraction: A multifractal analysis. Geomorphology 2015, 241, 243-254. [CrossRef]

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19. Walczak, Z.; Sojka, M.; Wrózy n´ski, R.; Laks, I. Estimation of polder retention capacity based on ASTER, SRTM and LIDAR DEMs: The case of Majdany Polder (West Poland). Water 2016, 8, 230. [CrossRef]

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    106
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
106
Top 1%
Top 10%
Top 1%
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GLOMODAT
Enhancing data fusion, parallelisation for hydrological modelling and estimating sensitivity to spatial parameterization of SWAT to model nitrogen and phosphorus runoff at local and global scale
  • Funder: European Commission (EC)
  • Project Code: 795625
  • Funding stream: H2020 | MSCA-IF-EF-RI
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Assessing the socio-economic impact of digitalisation in rural areas
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