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Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris


CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

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1862-9024
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Volume 13, Issue 3

Issues

Using direct transformation approach as an alternative technique to fuse global digital elevation models with GPS/levelling measurements in Egypt

Hossam Talaat Elshambaky
  • Corresponding author
  • Civil Dept., Misr Higher Institute for Engineering and Technology in Mansoura, Mansoura, Egypt
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Published Online: 2019-03-05 | DOI: https://doi.org/10.1515/jag-2018-0050

Abstract

Open global digital elevation models (GDEMs) represent a free and important source of information that is available to any country. Fusion processing between global and national digital elevation models is neither easy nor inexpensive. Hence, an alternative solution to fuse a GDEM (GTOPO30 or SRTM 1) with national GPS/levelling measurements is adopted. Herein, a transformation process between the GDEMs and national GPS/levelling measurements is applied using parametric and non-parametric equations. Two solutions are implemented before and after the filtration of raw data from outliers to assess the ability of the generated corrector surface model to absorb the effect of the outliers’ existence. In addition, a reliability analysis is conducted to select the most suitable transformation technique. We found that when both the fitting and prediction properties have equal priority, least-squares collocation integrated with a least-squares support vector machine inherited with a linear or polynomial kernel function exhibits the most accurate behavior. For the GTOPO30 model, before filtration of the raw data, there is an improvement in the mean and root mean square of errors by 39.31 % and 68.67 %, respectively. For the SRTM 1 model, the improvement in mean and root mean square values reached 86.88 % and 75.55 %, respectively. Subsequently, after the filtration process, these values became 3.48 % and 36.53 % for GTOPO30 and 85.18 % and 47.90 % for SRTM 1. Furthermore, it is found that using a suitable mathematical transformation technique can help increase the precision of classic GDEMs, such as GTOPO30, making them to be equal or more accurate than newer models, such as SRTM 1, which are supported by more advanced technologies. This can help overcome the limitation of shortage of technology or restricted data, particularly in developed countries. Henceforth, the proposed direct transformation technique represents an alternative faster and more economical way to utilize unfiltered measurements of GDEMs to estimate national digital elevations in areas with limited data.

Keywords: Least-Squares Collocation; Least-Squares Support Vector Machines; Artificial Neural Network; Global Digital Elevation Models; Support Vector Machines; Bursa–Wolf; Molodensky; SRTM; GTOPO30

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About the article

Received: 2018-12-25

Accepted: 2019-02-10

Published Online: 2019-03-05

Published in Print: 2019-07-26


Citation Information: Journal of Applied Geodesy, Volume 13, Issue 3, Pages 159–177, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/jag-2018-0050.

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