Journal of Surveying Engineering

The detection of the discordant points, i.e., outliers, in linear regression models is a problem, which has been studied extensively. Huber’s $M$ -estimation is recommended not only for robust regression but also for detecting outliers. However, $M$ -estimation does not show high performance in detecting outliers for some cases. The aim of this paper is to propose a new method for improving the ability of $M$ -estimation in outlier detection. It consists of the iterative combination of the $M$ -estimator along with a scheme of reducing weights in some observations at random. The theorems proving contribution of the proposed algorithms have also been included. A series of Monte Carlo simulation experiments show that the performance of the new algorithm in the presence of outliers is better than $M$ -estimation alone. By using the new method, the results, on average, improved by about 7%.

In the network-based real-time kinematic (RTK) global positioning system approach, the rover positioning accuracy and reliability depends on the quality of the atmospheric corrections, which is largely a function of spatial and temporal variability of ionospheric and tropospheric parameters. The location of the rover receiver with respect to the reference network receivers is also a very important factor, especially for applications such as off-shore navigation, where favorable geometry cannot always be assured. The primary goal of this paper is to describe tests of the speed and reliability of the ambiguity resolution and the ultimate accuracy of kinematic positioning for two representative reference receiver geometries: (1) pentagonal reference receiver geometry, with network-rover separation up to $131\mathrm{km}$ , which represents a typical reference scenario where the rover is located inside the reference network; and (2) irregular geometry, simulating a shore-bound scenario where the reference network can support only extrapolation of the atmospheric corrections to an off-shore rover (outside the reference network), with network–rover separation up to $200\mathrm{km}$ . The latter scenario is of special interest here, as the objective is to investigate the maximum acceptable separation of the rover receiver from the shore-bound reference stations. The Ohio Continuously Operating Reference Stations (CORS) network is used to simulate both scenarios, and the MPGPS software developed at The Ohio State University Satellite Positioning and Inertial Navigation Laboratory is used to carry out the analyses over a $24\text{-}\mathrm{h}$ period of varying ionospheric activity. As a result of this study, the error budget associated with both network geometries is obtained, and the limitations of the network approach as a function of the network-rover geometry can be ascertained.

This paper presents a probabilistic approach to describe and visualize uncertainty and error in spatial data. The probabilistic approach assigns $n$ -dimensional probability zones to $n$ -dimensional measured feature locations. The size of each $n$ -dimensional zone depends on the uncertainty arising from imprecise measurements or derived inaccuracy values, and a user-selected probability threshold that the “true” feature location is to be found within this probabilistic space. The uncertainties relate to recorded measurement precision, accuracy of a linear network, and issues of scale and resolution. The confidence intervals are based on the ${\chi }^2$ distribution, where the probability that the measured point location is within the tabulated distance of the true point location can be tested. The error model computes the probability of the intersection of two features or data sources to determine whether they are compatible and if they should be used together. The error model allows the user to assess the potential quality implications of combining data from different sources and with different qualities. The error model is encapsulated in a software program that includes a graphic user interface that facilitates visualization of results. The ability to visualize the quality of spatial data at different significance levels of confidence provides a powerful tool for communicating the impacts of the quality of spatial data on applications of interest to users.

We investigate the use of multiepoch airborne and terrestrial lidar to detect and measure ground displacements of sufficient magnitude to damage buried pipelines and other water system facilities that might result, for example, from earthquake or rainfall-induced landslides. Lidar scans are performed at three sites with coincident measurements by total station surveying. Relative horizontal accuracy is evaluated by measurements of lateral dimensions of well defined objects such as buildings and tanks; we find misfits ranging from approximately $5\text{to}12\mathrm{cm}$ , which is consistent with previous work. The bias and dispersion of lidar elevation measurements, relative to total station surveying, is assessed at two sites: (1) a power plant site (PP2) with vegetated steeply sloping terrain; and (2) a relatively flat and unvegetated site before and after trenching operations were performed. At PP2, airborne lidar showed minimal elevation bias and a standard deviation of approximately $70\mathrm{cm}$ , whereas terrestrial lidar did not produce useful results due to beam divergence issues and inadequate sampling of the study region. At the trench site, airborne lidar showed minimal elevation bias and reduced standard deviation relative to PP2 $(6–20\mathrm{cm})$ , whereas terrestrial lidar was nearly unbiased with very low dispersion $(4–6\mathrm{cm})$ . Pre- and posttrench bias-adjusted normalized residuals showed minimal to negligible correlation, but elevation change was affected by relative bias between epochs. The mean of elevation change bias essentially matches the difference in means of pre- and posttrench elevation bias, whereas elevation change standard deviation is sensitive to the dispersion of individual epoch elevations and their correlation coefficient. The observed lidar bias and standard deviations enable reliable detection of damaging ground displacements for some pipelines types (e.g., welded steel) but not all (e.g., concrete with unwelded, mortared joints).

This paper presents the performance of the real-time kinematic (RTK) technique in very severe conditions. The measurements were conducted in forest conditions during two different experiments. Two Global Positioning System receivers were used: an Ashtech Z-Surveyor and a Z-Xtreme. Radiomodems were used for transmitting RTK corrections from a reference station to a rover. The RTK measurements were performed during quite good satellite configuration. The use of RTK technology, based on the Ashtech Z-Xtreme receiver as a rover in forest terrain, allowed centimeter accuracy to be obtained. However, practical experiments showed that there is a need for redundant independent RTK solutions based on repeated independent ambiguity reinitializations. Although gross errors might occur, the RTK technique can be very helpful technology for centimeter positioning in woodland areas.