Vertical accuracy evaluation of aster gdem2 over a mountainous area based on UAV photogrammetry

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Abstract

Global digital elevation models (GDEM) provide elementary information on heights of the Earth's surface and objects on the ground. GDEMs have become an important data source for a range of applications. The vertical accuracy of a GDEM is critical for its applications. Nowadays UAVs has been widely used for large-scale surveying and mapping. Compared with traditional surveying techniques, UAV photogrammetry are more convenient and more cost-effective. UAV photogrammetry produces the DEM of the survey area with high accuracy and high spatial resolution. As a result, DEMs resulted from UAV photogrammetry can be used for a more detailed and accurate evaluation of the GDEM product. This study investigates the vertical accuracy (in terms of elevation accuracy and systematic errors) of the ASTER GDEM Version 2 dataset over a complex terrain based on UAV photogrammetry. Experimental results show that the elevation errors of ASTER GDEM2 are in normal distribution and the systematic error is quite small. The accuracy of the ASTER GDEM2 coincides well with that reported by the ASTER validation team. The accuracy in the research area is negatively correlated to both the slope of the terrain and the number of stereo observations. This study also evaluates the vertical accuracy of the up-sampled ASTER GDEM2. Experimental results show that the accuracy of the up-sampled ASTER GDEM2 data in the research area is not significantly reduced by the complexity of the terrain. The fine-grained accuracy evaluation of the ASTER GDEM2 is informative for the GDEM-supported UAV photogrammetric applications.

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APA

Liang, Y., Qu, Y., Guo, D., & Cui, T. (2018). Vertical accuracy evaluation of aster gdem2 over a mountainous area based on UAV photogrammetry. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 579–584). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-579-2018

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