Environmental spatial data within dense tree cover: exploiting multi-frequency GNSS signals to improve positional accuracy

Abstract

Environmental monitoring tasks over large spatial coverage often necessitate acquiring sample/reference positions using the global navigation satellite systems in order to optimise operational costs. Often, such tasks occur within dense tree coverage where the navigation signals are blocked. For tasks requiring accurate positions under limited resources, this becomes undesirable, especially if the operation is to be carried out while in motion, i.e. “on the fly” or “real-time kinematic”. Even with this realisation, numerous studies investigating the potential of combining the constellations of these navigation systems mostly focus on their structural aspects, leaving the exploitation of the multi-signal constellation under dense tree cover largely untested. Using a test experiment of a station declared unusable due to dense tree cover at Curtin University (Australia), this study evaluates whether sample positions can be improved using multi-constellation global navigation satellite systems where poor sky visibility exist due to tree coverage. Positioning improvement measures are (1) geometrical gain measured by position dilution of precision, (2) horizontal and vertical uncertainty estimates and (3) positional accuracies determined through the comparison of the obtained control positions and their known values. The results indicate significant positioning improvement when all constellations are utilised in comparison with using Global Positioning System alone in dense tree cover environments, i.e. geometrical gain of as much as 72%, horizontal precisions by about 40%, vertical precisions of up to 50% and 94% accuracy improvement. This study thus opines that utilising full global navigation satellite’s constellation would benefit environmental monitoring tasks carried out under dense tree cover.