Volume 11, Issue 2 - Special Issue: Deformation Monitoring

In this article first ideas are presented to extend the classical concept of geodetic network adjustment by introducing a new method for uncertainty assessment as two-step analysis. In the first step the raw data and possible influencing factors are analyzed using uncertainty modeling according to GUM (Guidelines to the Expression of Uncertainty in Measurements). This approach is well established in metrology, but rarely adapted within Geodesy. The second step consists of Monte-Carlo-Simulations (MC-simulations) for the complete processing chain from raw input data and pre-processing to adjustment computations and quality assessment. To perform these simulations, possible realizations of raw data and the influencing factors are generated, using probability distributions for all variables and the established concept of pseudo-random number generators. Final result is a point cloud which represents the uncertainty of the estimated coordinates; a confidence region can be assigned to these point clouds, as well. This concept may replace the common concept of variance propagation and the quality assessment of adjustment parameters by using their covariance matrix. It allows a new way for uncertainty assessment in accordance with the GUM concept for uncertainty modelling and propagation. As practical example the local tie network in “Metsähovi Fundamental Station”, Finland is used, where classical geodetic observations are combined with GNSS data.

Modelling correlations within laser scanning point clouds can be achieved by using synthetic covariance matrices. These are based on the elementary error model which contains different groups of correlations: non-correlating, functional correlating and stochastic correlating. By applying the elementary error model on terrestrial laser scanning several groups of error sources should be considered: instrumental, atmospheric and object based. This contribution presents calculations for the Leica HDS 7000. The determined variances and the spatial correlations of the points are estimated and discussed. Hereby, the mean standard deviation of the point cloud is up to 0.6 mm and the mean correlation is about 0.6 with respect to 5 m scanning range. The change of these numerical values compared to previous publications as Kauker and Schwieger [17] is mainly caused by the complete consideration of the object related error sources.

The benefits of terrestrial radar interferometry (TRI) for deformation monitoring are restricted by the influence of changing meteorological conditions contaminating the potentially highly precise measurements with spurious deformations. This is especially the case when the measurement setup includes long distances between instrument and objects of interest and the topography affecting atmospheric refraction is complex. These situations are typically encountered with geo-monitoring in mountainous regions, e.g. with glaciers, landslides or volcanoes. We propose and explain an approach for the mitigation of atmospheric influences based on the theory of intrinsic random functions of order k (IRF-k) generalizing existing approaches based on ordinary least squares estimation of trend functions. This class of random functions retains convenient computational properties allowing for rigorous statistical inference while still permitting to model stochastic spatial phenomena which are non-stationary in mean and variance. We explore the correspondence between the properties of the IRF-k and the properties of the measurement process. In an exemplary case study, we find that our method reduces the time needed to obtain reliable estimates of glacial movements from 12 h down to 0.5 h compared to simple temporal averaging procedures.

The technique of Image Assisted Total Stations (IATS) has been studied for over ten years and is composed of two major parts: one is the calibration procedure which combines the relationship between the camera system and the theodolite system; the other is the automatic target detection on the image by various methods of photogrammetry or computer vision. Several calibration methods have been developed, mostly using prototypes with an add-on camera rigidly mounted on the total station. However, these prototypes are not commercially available. This paper proposes a calibration method based on Leica MS50 which has two built-in cameras each with a resolution of 2560 × 1920  px : an overview camera and a telescope (on-axis) camera. Our work in this paper is based on the on-axis camera which uses the 30-times magnification of the telescope. The calibration consists of 7 parameters to estimate. We use coded targets, which are common tools in photogrammetry for orientation, to detect different targets in IATS images instead of prisms and traditional ATR functions. We test and verify the efficiency and stability of this monitoring method with multi-target.

A 330 m calibration baseline was established at the Universitat Politècnica de València (UPV) in 2007. Absolute scale was subsequently transferred in 2012 from the Nummela Standard Baseline in Finland and distances between pillars were determined with uncertainties ranging from 0.1 mm to 0.3 mm. In order to assess the long-term stability of the baseline three field campaigns were carried out from 2013 to 2015 in a co-operative effort with the Universidad Complutense de Madrid (UCM), which provided the only Mekometer ME5000 distance meter available in Spain. Since the application of the ISO17123-4 full procedure did not suffice to come to a definite conclusion about possible displacements of the pillars, we opted for the traditional geodetic network approach. This approach had to be adapted to the case at hand in order to deal with problems such as the geometric weakness inherent to calibration baselines and scale uncertainty derived from both the use of different instruments and the high correlation between the meteorological correction and scale determination. Additionally, the so-called the maximum number of stable points method was also tested. In this contribution it is described the process followed to assess the stability of the UPV submillimetric calibration baseline during the period of time from 2012 to 2015.

In Hungary, the high loess bank of the River Danube in Dunaszekcső has been moving with varying rate since 2007. On the high bank a geodetic monitoring network was established in September 2007. At the same time two borehole tiltmeters and later two ground water level sensors were also installed. The high-sensitive tiltmeters made it possible to study the relationships between the small tilts of the high bank and the ground water levels and the water level of the River Danube. Results of the multiple regression analysis between tilt components and water levels showed that the temporal variation of the regression coefficients is in close connection with the stability of the high bank. The investigations also showed that the movements are in very strong connection with the variation of the ground water level and less depend on the variation of the water level of the River Danube. The characteristic tilt processes, 3–4 weeks before large movements, and the slope stability changes inferred from the relationships between tilts and water level variations can be useful for early warning of landslides.