Band 17, Heft 2 - Special Issue: Deformation Monitoring

High-precision electro-optic distance measurement (EDM) is essential for deformation monitoring. Although sub-ppm instrumental accuracy is already feasible with state-of-the-art commercial technology, the practically attainable accuracy on distances over more than a few hundred meters is limited by uncertainties in estimating the integral refractive index along the propagation path, which often results in measurement errors of several ppm. This paper presents a new instrumental basis for high-accuracy multispectral EDM using an optical supercontinuum to enable dispersion-based inline refractivity compensation. Initial experiments performed on two spectrally filtered bands of 590 and 890 nm from the supercontinuum show measurement precision better than 0.05 mm over 50 m for an acquisition time of around 3 ms on the individual bands. This represents a comparable performance to our previously reported results on 5 cm by over a range of 3 orders of magnitude longer, which can still be improved by increasing the acquisition time. The preliminary results indicate a relative accuracy of about 0.1 mm at 50 m on each wavelength. Improvement is possible by calibration and by implementing a self-reference scheme that mitigates slow drifts caused by power-to-phase coupling. The results reported herein thus indicate that the presented approach can be further developed for achieving sub-ppm accuracy of refractivity compensated distance measurements on practically useful ranges and under outdoor conditions.

At the Pieniny Klippen Belt in Poland, the novel primary reference baseline EURO5000 is required as part of the European Research project GeoMetre to both validate refractivity-compensated EDM prototypes and investigate the metrological traceability of GNSS-based distances. Since the aimed uncertainty is 1 mm at 5 km ( k = 2), the design, construction, and validation must be carefully prepared to fulfil the high standards of the GeoMetre field campaigns which are planned to be carried out in May 2022. This contribution describes the main features of the EURO5000 and presents the results of the preliminary validation which includes a first comparison between the results obtained by using precise currently available EDMs as well as GNSS techniques following the standard GNSS geodetic processing algorithms, on the one hand, and the improved GNSS-Based Distance Meter (GBDM+) approach developed at UPV, on the other hand. The preliminary validation presented in this contribution also permits (1) to detect potential problems in the use of the baseline such as potential geodynamic problems, atmospheric refraction or multipath limitations, (2) to produce a set of reliable results, and (3) to pave the way for the final field comparisons between the novel EDMs and the GBDM+ approach. The result of this metrological experiment may significantly contribute to overcome the limitations of current high-precision deformation monitoring applications that require their scale to be consistent with the SI-metre within 0.1 ppm in several km.

As part of the structural inspection, compliance with the specified flatness tolerances according to DIN (in particular DIN 18202:2019-07) has to be checked. Today, the monitoring of the flatness is carried out mainly with levelling instruments and tacheometers. However, these measuring methods are time-consuming, as stacking out the measuring grid and capturing the heights are performed manually. In addition, the data evaluation must be done in a separate work step. Therefore, we developed the mobile robot RITA for height measurement in combination with a stationary tacheometer. Now, the entire process of flatness control is carried out automatically, and heights are recorded reliably in real-time. For practicality, we developed a compact design of the mobile robot in combination with hardware modules. Our reflector tracking unit makes it possible to follow the reflector on the robotic platform in order to maintain the line of sight to the tacheometer. Furthermore, our mechanical pendulum unit ensures that the height measurement is always carried out vertically, even if the robot itself is tilted. Initial practical tests have shown that the high demands on the robotic platform are met and that the implementation of the flatness control can be automated. For that, investigations concerning the location accuracy of the robot were carried out, and the height measurement was validated. It turns out, that demands in the lowest millimeter range are fulfilled. Overall, these tests showed the enormous gain in performance due to the newly developed height measurement robot compared to the previous slow, complex, and tiring manual process.

Today, rapid growth in infrastructure development and urbanisation process increases the attention for accurate deformation monitoring on a relatively large-scale. Furthermore, such deformation monitoring is of great importance in the assessment and management of natural hazard processes like landslides, earthquakes, and floods. In this study, the Persistent Scatterer Interferometry (PSI) technique is applied using open-source Synthetic Aperture Radar (SAR) data from the satellite Sentinel-1. It allows point-wise deformation monitoring based on time series analysis of specific points. It also enables performing spatio-temporal area-based deformation monitoring. Currently, these data do not have a sophisticated quality assurance process to judge the significance of deformations. To obtain different quality classes of the Persistent Scatterer (PS) data points, the first step is to classify them into buildings and ground types using LoD2 building models. Next, time series analysis of the PS points is performed to model systematic and random errors. It allows estimation of the offset and the deformation rate for each point. Finally, spatio-temporal modelling of neighbourhood relations of the PS points is carried out using local geometric patches which are approximated with a mathematical model, such as, e.g., multilevel B-Splines. Subsequently, the quality of SAR data from temporal and spatial neighbourhood relations is checked. Having an appropriate spatio-temporal quality model of the PS data, a deformation analysis is performed for areas of interest in the city of Hamburg. In the end, the results of the deformation analysis are compared with the BodenBewegungsdienst Deutschland (Ground Motion Service Germany) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany.

A rubble mound breakwater (BW) is a coastal engineering structure built for purposes of harbor protection in areas with severe wave regimes. Block displacements can lead to a structure weakening, making the harbor more vulnerable to waving hazard. During the phase of BW design/rehabilitation the effectiveness evaluation of both the shape and the protective elements is made throughout 3D scale models, built inside wave basins or wave flumes. The experiment consists of simulating the impact of the swell on the model. During the process, 3D point cloud (PC) data is recorded with depth sensors, or later generated by photogrammetry from image data. Surface changes are obtained by comparing PCs, with the estimation of depth differences. However, inaccurate face delimitation on PCs may lead to less conclusive results of blocks’ location and motion. In this study, it is proposed an edge-based image segmentation method, based on the Hough transformation algorithm, in order to obtain the straight edges of the block’s faces in images of breakwater models. By crossing each resulting polygonal region with the PC depth data of the block, a regular plane surface can be adjusted to the 3D points, from which the block’s center point can be geometrically estimated. For this study RGB and PC data regions were selected from a scanned BW model, built with small cubic concrete blocks. The results obtained reinforce the positive contribution that the proposed image segmentation methodology can provide, regarding the improvement of displacement accuracy and spending time analysis, in BW monitoring work.

Performing deformation analyses with high accuracy demands using terrestrial laser scanners is very challenging due to insufficient knowledge about the error budget and correlations. Terrestrial laser scans suffer from random and systematic errors that degrade the quality of the point cloud. Even though the vast majority of systematic errors can be calibrated, remaining errors or errors that vary with time or temperature influence spatially neighboring points in the same way. Hence, correlations between the measurements exist. Considering area-based deformation analyses, these correlations have two effects: On the one hand, they reduce the effective number of measurements in the point cloud, which mainly influences the decision of whether the movement is significant or not. On the other hand, correlations caused by systematic errors in the scanner can lead to a misinterpretation as a deformation of the object. Within this study, we analyze the deformation of a concrete wall (9.50 m height, 50 m width), and we develop a workflow that avoids the misinterpretation of correlated measurements as deformations of the object. Therefore, we first calibrate the scanner to reduce the influence of systematic errors. Afterwards, we use the average of two-face measurements from several scanner stations to eliminate remaining systematic errors and correlated measurements. This study demonstrates that systematic effects can lead to errors of a few millimeters that are likely to be interpreted as small deformations, and it provides a strategy to avoid misinterpretation. Hence, it is inevitable either to model or to eliminate systematic errors of the scanner while performing a precise deformation analysis with a magnitude of a few millimeters.

One approach to estimate space-continuous deformation from point clouds is the parameter-based epochal comparison of approximating surfaces. This procedure allows a statistical assessment of the estimated deformations. Typically, holistic geometric models approximate the scanned surfaces. Regarding this, the question arises on how discontinuities of the object’s surface resulting from e.g. single bricks or concrete blocks, influence the parameters of the approximating continuous surfaces and in further consequence the derived deformation. This issue is tackled in the following paper. B-spline surfaces are used to approximate the scanned point clouds. The approximation implies solving a Gauss–Markov-Model, thus allowing accounting for the measurements’ stochastic properties as well as propagating them on the surfaces’ control points. A parametric comparison of two B-spline surfaces can be made on the basis of these estimated control points. This approach is advantageous with regard to the transition of the space-continuous deformation analysis to a point-based task, thus ensuring the applicability of the well-established congruency model. The influence of the structure’s geometry on the surfaces’ control points is investigated using terrestrial laser scans of a clinker facade. Points measured in the joints are eliminated using an own developed segmentation approach. A comparison of the results obtained from segmented as well as from unsegmented laser scans for the B-spline approximation and the subsequent deformation analysis provides information about the structure-related influence. An aqueduct arc is used as measuring object in this study. For the intended comparison, data sets, which contain possible influences due to changes of the mechanical loads, are analysed.

Accurate and robust 3D point cloud registration is the crucial part of the processing chain in terrestrial laser scanning (TLS)-based deformation monitoring that has been widely investigated in the last two decades. For the scenarios without signalized targets, however, automatic and robust point cloud registration becomes more challenging, especially when significant deformations and changes exist between the sequence of scans which may cause erroneous registrations. In this contribution, a fully automatic registration algorithm for point clouds with partially unstable areas is proposed, which does not require artificial targets or extracted feature points. In this method, coarsely registered point clouds are firstly over-segmented and represented by supervoxels based on the local consistency assumption of deformed objects. A confidence interval based on an approximate assumption of the stochastic model is considered to determine the local minimum detectable deformation for the identification of stable areas. The significantly deformed supervoxels between two scans can be detected progressively by an efficient iterative process, solely retaining the stable areas to be utilized for the fine registration. The proposed registration method is demonstrated on two datasets (both with two-epoch scans): An indoor scene simulated with different kinds of changes, including rigid body movement and shape deformation, and the Nesslrinna landslide close to Obergurgl, Austria. The experimental results show that the proposed algorithm exhibits a higher registration accuracy and thus a better detection of deformations in TLS point clouds compared with the existing voxel-based method and the variants of the iterative closest point (ICP) algorithm.

Important infrastructure such as highways or railways traverse unstable terrain in many mountainous and scenic parts of the world. Rockfalls and landslides result in frequent maintenance needs, system unreliability due to frequent closures and restrictions, and safety hazards. Seismic activity significantly amplifies these negative economic and community impacts by generating large rockfalls and landslides as well as weakening the terrain. This paper interrogates a rich database of repeat terrestrial lidar scans collected during the Canterbury New Zealand Earthquake Sequence to document geomorphic processes as well as quantify rockfall activity rates through time. Changes in the activity rate (spatial distribution) and failure depths (size) were observed based on the Rockfall Activity Index (RAI) morphological classification. Forecasting models can be developed from these relationships that can be utilized by transportation agencies to estimate increased maintenance needs for debris removal to minimize road closures from rockfalls after seismic events.