Orthometric, normal and geoid heights in the context of the Brazilian altimetric network
The extensive use of GNSS positioning, combined with the importance of precise geoid heights for transformation between geodetic and orthometric heights, brings up the discussion of the influence of data uncertainties and the use of variable density values on these estimates. In this sense, we analy...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Federal do Paraná (UFPR) |
| Repositorio: | Boletim de Ciências Geodésicas |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/86198 |
| Acceso en línea: | https://revistas.ufpr.br/bcg/article/view/86198 |
| Access Level: | acceso abierto |
| Palabra clave: | Density Geoid Normal Heights Orthometric Heights Uncertainty |
| Sumario: | The extensive use of GNSS positioning, combined with the importance of precise geoid heights for transformation between geodetic and orthometric heights, brings up the discussion of the influence of data uncertainties and the use of variable density values on these estimates. In this sense, we analyze the influence of the topographic masses density distribution and the data uncertainty on the computation of orthometric and geoid heights in stations of the High Precision Altimetric Network of Brazil, considering the Helmert and Mader methods. For this, we use 569 stations whose values of geodetic and normal heights, gravity, and geopotential numbers are known. The results indicate that orthometric heights are more sensitive to density values and to greater heights than to the Helmert and Mader methods applied. Also, we verify that the normal and orthometric heights present significant differences for the analyzed stations, considering the high correlation between the heights, which provide small values of uncertainty. However, our analyses show that the use of the Mader method, along with variable density values, provides either more rigorous or more reliable results. |
|---|