Journal of Surveying Engineering

Ellipsoidal and Australian Height Datum heights have been determined for a 60-point control network from five base stations by three separate users using three different makes of real-time kinematic (RTK) global positioning system (GPS) equipment. This was to determine whether these RTK GPS surveys (i.e. equipment and users combined) can meet vertical accuracy specifications for contract work with Main Roads Western Australia. A comparison of the differences between the RTK GPS ellipsoidal heights and the control indicates 51 mm (95% confidence) accuracy, accounting for errors in the control network. Likewise, the difference between the RTK GPS–derived Australian Height Datum heights and the control indicates 53 mm (95% confidence) accuracy. However, there was an increasing number of failed RTK GPS position solutions with increasing baseline length, which had to be omitted from these statistics.

High-resolution aircraft or satellite data often require ground or image control for rectification of images, and this is best performed using control with a similar level of spatial resolution. To demonstrate the use of fine-resolution control, advanced fine-resolution (4 m × 4 m pixel) aircraft scanner data were geometrically corrected using high-order ground control points collected from a differential global positioning system (DGPS) experiment. A combination of corrections supplied by the aircraft data acquisition team, and DPGS points collected two years earlier allowed for a very high quality (root-mean square ≈ 1.000 m), fine-resolution image product.

Volume estimation packages often lack tools for measuring and analyzing the effect of input data uncertainty and possible error propagation in the volume estimation process. This paper proposes and develops an empirical procedure for error modeling in volume estimation using spatial simulation techniques. The procedure can be used to study the stability of interpolation methods in the presence of error, in other words finding a method that predicts best surface fit and volume estimates from imperfect data. The Monte Carlo simulation technique is used to construct an effective algorithm to study the effect of input data uncertainty on volumes estimated with perturbed Kriged surfaces and triangulated surfaces. The results are compared for consistency with a volume generated from a triangulated surface that holds the location points fixed.