Influência da combinação de dados GPS e GLONASS no georreferenciamento de imóveis rurais

• Autor(a) principal: Pinto, Marcelo Solfa [UNESP]
• Data de Publicação: 2013
• Outros Autores: De Oliveira Camargo, Paulo [UNESP], Monico, João Francisco Galera [UNESP]
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1590/S1982-21702013000100009; http://hdl.handle.net/11449/75064
• topic: Georreferencing; GPS/GLONASS; Rural properties; System integration; data processing; GLONASS; GPS; land tenure; neighborhood; positioning system; rural area; Brazil

The land question has been a widely discussed topic in Brazil, regarding land tenure. Law No. 10.267/01 was a major breakthrough for the agrarian issue. Since then on all rural properties must be georeferenced to the Brazilian Geodetic System (BGS). Therefore, satellite positioning and conventional methods are extensively used. Changes have been occurring in satellite positioning systems due to the addition of new signals in GPS (Global System Positioning), restructuring of GLONASS (Global Orbiting Navigation Satellite System), and the new systems like Galileo and Compass as well. To evaluate the effects of combining GPS and GLONASS data, several batches of processings were performed on different configurations. The data processing was performed to determine the coordinates of points of basic support and those materializing the neighborhood of the rural properties. As a result, it was found that the use of accurate ephemeris in transporting coordinates to support points has no significant influence, since transportation with broadcast ephemeris also meets the accuracy requirements for the Standard Technique for Georreferencing Rural Properties. On the other hand, when GPS and GLONASS data were used, such combination provides the best results. In the case of neighboring points, the use of GPS and GLONASS data is also recommended because such data meet the precision requirement and showed better results than those from where data were processed separately.