Volume 19, Issue 3 - Special Issue: Joint International Symposium on Deformation Monitoring 2025

Close-range photogrammetry offers a wide range of industrial applications in the field of large volume metrology. The object coordinates are derived from captured images using a bundle adjustment. Even if the observations are assumed to be stochastically independent within the adjustment procedure, the estimated object coordinates are correlated. In subsequent applications such as surface fitting or deformation analysis, these estimated object coordinates are usually treated as independent and even identically distributed observations, neglecting stochastic information of the prior bundle adjustment. However, simplifications in stochastic modelling lead to misinterpretations of the adjustment results in terms of precision and reliability. Based on the estimates of a bundle adjustment, the impact of neglected correlations in subsequent applications is investigated. It is demonstrated that the chosen stochastic model affects the resulting standard deviations significantly. In surface fitting the derived standard deviations of datum-independent form parameters are two to five times overestimated when neglecting stochastic dependencies. Applying hypothesis tests to the estimates as part of quality assurance, for instance, lead to incorrect decisions, because the test statistics are biased. Analogously, in deformation analysis the risk of type I decision errors increases when in fact stable networks are falsely detected as deformed. This contribution indicates the advantage of the fully-populated dispersion matrix because the identified discrepancies cannot be compensated by introducing simple stochastic models, such as a diagonal variance matrix or a point-based block-diagonal matrix.

When it comes to the significance assessment of interepochal displacements, great reliance on the stochastic information related to the point clouds introduced in the estimation of the geometric representation of the analysed surface is present. Possible uncertainty sources in point clouds are instrument-, environment- or object sided. Further, the chosen mathematical model for point cloud approximation may introduce an uncertainty budget ascribed as model uncertainty. The present contribution employs variance component estimation (VCE) in the course of geometric point cloud approximation with tensor product B-spline surfaces. A method using the BIQUE-estimation of the variance components is used. It enables considering overlapping variance components. Here, those are related to measurement and model uncertainties. The aim of the article is to investigate whether a realistic estimation of the components can be achieved by means of the VCE in particular with regard to the separation between measurement and model uncertainty. The former include the distance and angular components whilst the model uncertainty is set up using covariance functions. For that, a B-spline surface with comparably superior complexity, whilst describing an identical geometric surface course as the functionally employed surface representation, is generated. Artificial altering of the more complex surface establishes the model uncertainty as distance between equally parametrized sampled points. Results based on simulated data show that variance components are separable and estimable if the model uncertainty exceeds measurement uncertainty, and only points affected by model deviations are included in the VCM setup.

Terrestrial laser scanners (TLS) are well-suited for conducting area-based deformation analysis of infrastructures. Unlike common point-based geodetic sensors, TLS can measure millions of points across the environment without requiring pre-defined, signalized measurement locations. However, TLS point clouds are affected by both random variations and residual systematic errors. These uncertainty components are often addressed using only probabilistic approaches, which may inadequately or overly optimistically represent the remaining systematic errors. To overcome these limitations, this study introduces an alternative framework based on interval mathematics to bound uncertainties arising from systematic errors. The proposed methodology includes a sensitivity analysis of TLS observation correction models, examining the variability of key input parameters. Unlike the quadratic approach for variance propagation, the interval-based method enables linear uncertainty propagation, effectively characterizing residual systematic uncertainties and their maximum effects. This paper details the methodology and presents typical interval values validated through simulations and real-data experiments. The findings highlight the potential of interval-based methods to enhance the TLS uncertainty model.

Terrestrial Laser Scanning (TLS) has proven to be an advanced method for areal deformation analysis, offering high-resolution, contactless data acquisition by capturing millions of data points on a structure’s surface. To deterministically model deformations based on subsequently acquired point clouds, corresponding data points are usually required. In order to avoid the cumbersome construction of these corresponding points, this paper aims to enhance TLS-based deformation analysis by further developing a stochastic modeling approach. The main concept of the approach is to model the measured object by means of three components using a collocation-based approach: trend (undistorted object), signal (deformation), and noise (measurement noise). To demonstrate the ability of stochastic relationships to describe deformations, typical temporal deterministic deformation processes, like step, impulse, linear, and periodic responses are simulated by means of non-stationary stochastic processes in this contribution. To accurately model deformation patterns, different types of non-stationary Gaussian temporal processes such as locally stationary, piecewise stationary, and modulated stochastic processes are investigated with respect to their ability to represent typical deformation patterns. When modeling deformations in TLS point clouds as correlated stochastic processes, it is essential to distinguish the correlated deformation signal from the correlated measurement noise. Filtering techniques are employed to separate correlated noise from the deformation signal. Although the proposed method effectively separates signal and noise, the filtering results greatly influence the deformation model results. Future research will focus on alternative solutions for seperating correlated signal and noise and extending the methodology to spatio-temporal point clouds, enabling a more comprehensive analysis of deformations in complex structures across both temporal and spatial dimensions.

In this contribution a load test of a wooden dome, consisting of 12 connected timber beams, is presented. The test comprises five different load levels, during which the dome was measured by means of a scanning total station. In addition, four signalized points on the dome are tracked by means of a tacheometer. The focus of this contribution is on the development of an automated processing chain for the deformation analysis of the complex structure. For this purpose, initially, a RANSAC-based segmentation is conducted to segment the individual beams. The estimation of B-spline curves provides the basis for a curve-to-curve comparison. To optimally model the beam’s behaviour by means of B-spline curves, different parameterization strategies are investigated and compared, enabling the definition of corresponding points on models of different epochs. The influence of constraints that include prior knowledge of the beams’ relative behaviour is investigated. Displacement vectors are derived based on corresponding curve points, which are defined by the curve parameter. The derived displacement vectors are analysed and their behaviour is compared to the movement of the signalized tacheometrically measured points. The curves resulting from the investigated parameterization strategies differ w.r.t. the optimal number of control points, the sum of squared residuals as well as the sum of squared Euclidean distances. The comparison with the tacheometric measurements further reveals the importance of an appropriate parameterization strategy when using B-spline curves in a model-to-model-based deformation analysis. With the curve parameters enabling the definition of point correspondences, displacement vectors that reflect the beams’ actual behaviour can be derived in case the point clouds have been appropriately parameterized.

This study focuses on classifying height differences in the area of the Marcel mine in the Upper Silesian Coal Basin, using UAV photogrammetry, UAV laser scanning, and InSAR coherence data. The random forest (RF) algorithm was selected for this purpose. The RF classifier used features calculated based on the digital terrain model of differences, InSAR coherence data, digital terrain, and surface models with their derivatives. Besides the 6-day coherence to capture temporal coherence variability, key statistical parameters of the time series were calculated. The model achieved an accuracy of 79 % and an F1-score of 81 %, effectively distinguishing mining-related displacements from other classes. The results indicate that incorporating InSAR coherence improved the separation of classes, but certain limitations, such as data resolution and the temporal similarity of features, suggest the need for further research.

Terrestrial Radar Interferometry (TRI) has established as the reference technology for slope monitoring in the open-pit mining sector. Along with the many benefits this technique provides, there remain some limitations that have not yet been fully resolved by current technology. One of the most curious disturbance factors, is signal interference caused by the reflection of the electromagnetic waves on a water surface. This phenomenon is well known in radio communications and is called multipath propagation. The presence of multipath effects in open-pit mining is not uncommon due to the presence of ponds at the bottom of the pit. In this context, a simulation algorithm has been developed for the evaluation of TRI water multipath, to be able to predict its presence and help monitoring campaign planning. In this work the general characteristics of TRI water multipath are described together with standard mitigation techniques. Furthermore, the simulation algorithm is presented and its application to some real case study is discussed.

Very slow-moving landslides threaten infrastructures and safety, yet detecting their subtle, long-term displacements remains challenging. The eastern slope of Mount Amiata, Tuscany, Italy, where high-precision geodetic monitoring is required to control the very slow-moving landslides affecting the area, represents an example of this issue. Namely, in this study we assess whether the joint exploitation of multi-temporal GNSS (continuous and periodic), Robotic Total Stations (RTS), and Persistent Scatterer InSAR techniques allows us to provide a coherent picture of very slow-moving landslide areas. While GNSS provides useful long-term displacement data, it may be affected by low signal-to-noise ratio, limiting precision to cm-mm level. Hence, to refine the outputs, we implement statistical time-series analysis, including weighted linear regression and 95 % confidence intervals. RTS delivered high-precision 3D data associated to local-scale measurements, while InSAR extended coverage to wider areas, resolving East-West and Vertical components from Sentinel-1 data. We highlight that, since 2019, the monitoring network has recorded horizontal displacements of ca. 10–15 mm/year, while vertical movements reached −7 mm/year, well correlating with local morphology and bedrock geology characteristics. We also perform a comparison of GNSS and RTS measurements with InSAR datasets, highlighting the limitations of InSAR in vegetated and geometrically complex terrains, while also confirming its value in capturing large scale displacement trends. The findings demonstrate that, despite their individual limitations, the joint exploitation of GNSS, RTS, and InSAR allow to provide a comprehensive framework for very slow-moving landslide areas. The scalable, cost-effective multi-source system here developed represents a robust approach for monitoring very slow-moving landslides, supporting risk mitigation strategies in landslide-prone regions.

The ionosphere plays a critical role in radio wave propagation, impacting satellite-based communication and navigation systems. This study evaluates near-real-time ionosphere maps (NRTIMs) derived from dual-frequency Global Positioning System (GPS) observations and validates them against established ionosphere models. Using dual-frequency Global Navigation Satellite System (GNSS) technology, the research mitigates ionospheric errors by measuring phase delays at L1 and L2 frequencies. Global ionosphere maps (GIMs) generated by the International GNSS Service (IGS) provide essential ionospheric corrections. Our approach combines accurate GPS observations with regional modeling to enhance GNSS positioning accuracy. The results demonstrate the effectiveness of the developed MATLAB algorithm in estimating ionospheric delays, showing strong convergence with GIMs. The results show a significant convergence between the Regional Ionosphere Modeling of RIM, IGS (Final Ionosphere Product), IGU (Ultra Rapid Ionosphere Product), and IGR (Rapid Ionosphere Product), as the highest average values during the 77th DOY of winter 2020 at the CPVG station were 14.753 TECU for RIM and 14.736, 14.7373 and 14.731 TECU at the CPVG station for IGS, IGU, and IGR while the average was for RIM, IGS, IGU, and IGR are respectively lower, with the lowest average values during the 190th DOY of autumn 2020 at station IZMI with a value of 3.5472 TECU for RIM, 3.5541, 3.5421 and 3.5624 TECU at IZMI station for IGS, IGU, and IGR respectively. By achieving strong agreement with existing GIMs and providing high-frequency results, the algorithm improves the reliability of GPS systems by effectively monitoring envelope disturbances. Ionic and dilute.

In this paper, current alternative techniques and sensors embedded in smart mobile devices for localization are reviewed. Firstly, the evolution of smartphone development since the first IBM phone in 1992 and the introduction of the term ‘smart-phone’ in 2000 until present is discussed. The main part of the paper deals then with the sensors and positioning techniques where an overview about the topologies and positioning methods and their applicability for smartphone positioning is provided. The most important sensors and techniques are then elaborated and discussed in detail. They include MEMS-based sensors and wireless positioning options. The wireless options include Bluetooth, Wi-Fi and UWB which may be employed together with MEMS-based inertial sensors. Also the use of the smartphone camera and LiDAR is briefly discussed. Moreover, the most applicable sensor fusion approaches including Kalman filtering, particle filter and map matching are also analyzed. In concluding the paper, new ways in using smartphones for science based on different application examples are showcased.

Egypt, for its location in the northeastern corner of Africa, is exposed to continuous tectonic activities. To the North, the subduction of the oceanic African plate under the Eurasian plate provides a seismic activity along the Hellenic and Cyprian arcs. To the East, Egypt is affected by the divergence between the African and Arabian plates along the Red Sea. The E-W fault trend is one of the three main fault trends in Egypt and it starts at the northern tip of the Gulf of Suez towards Cairo. Moreover, the government is planning to expand economically development along the Cairo-Suez Province. Most of the new housing and industrial cities are located between Cairo and Suez. To estimate of crustal deformation in the region, ∼140 Sentinel-1 SAR images were collected and distributed equally over 7 years (2015–2021). The PSI processing is applied to calculate a Line-of-sight velocity field. A GNSS station that exists in the area of study “SUEZ” as well as a close station “KATA” are analyzed using Bernese software V. 5 to validate InSAR results. The estimated horizontal velocities show the northwestern part of the study area is moving westward with 3–4 mm/yr while the southeastern zone is moving eastward with 2–4 mm/yr. The configuration of motion indicates a strike-slip motion along the local E-W fault. The velocity rate obtained from Bernese software for SUEZ station is 2 ± 0.35 mm/yr in the northeastern direction. Vertically, the northwestern part of the study area shows uplift rate of 2 mm/yr while the southeastern part along the coast of the Gulf of Suez subsides with 4–5 mm/yr. For GNSS, SUEZ station is shown to have an uplift rate of 1.55 ± 0.40 mm/yr. A remarkable agreement is noticed between the results of InSAR and GNSS for both horizontal and vertical components. Decision makers shall take such results into consideration while the large planning of investments in promising areas.

The increasing accuracy of the recently released Global Geopotential Models (GGMs) make them a reasonable geoid models, particularly in developing countries. Incorporating local geodetic datasets into a GGM could enhance its performance significantly. However, such integration requires appropriate mathematical modelling. The current research investigates the factors influencing the fitting of a GGM to heterogeneous geodetic data over local areas. The Multiple Linear Regression (MLR) approach is performed with variable independent factors to model the GGM discrepancies over two study areas in Egypt. Observed Global Navigation Satellite Systems (GNSS)/levelling and measured terrestrial gravity anomalies are investigated, among other independent variables, in the regression modelling. Based on the available data and attained findings, it has been demonstrated that MLR approach could produce a good fitting of a specific GGM’s geoid undulations, namely the XGM2019e_2159 model, locally with a coefficient of determination of more than 0.99. The regression equation has decreased the standard deviation of the investigated GGM-based undulations from ±0.130 m to ±0.046 m. Accordingly, the accuracy of a particular GGM has been enhanced considerably with improvements achieved 99 % and 64 % over the investigated two case study regions in Egypt.

The main objective of the work was to evaluate positioning performance of low-cost GNSS receiver u-blox ZED-F9P with patch antenna in GIS mapping scenarios. The whole mapping kit consisted of the mentioned receiver, a smartphone and two mobile applications. Thirty testing points with different levels of sky view were temporarily set up in an urban environment. Real-Time Kinematic (RTK) technique was used to estimate their position with the mentioned low-cost receiver and geodetic grade Trimble R10 receiver. Coordinates obtained from two independent measurement campaigns were compared to reference positions computed by rapid static relative technique. Both receivers provided a similar level of positioning correctness except two testing points where the geodetic grade GNSS receiver showed large errors in the first campaign. With an exclusion of these two points, both receivers delivered mean horizontal distances from the reference positions slightly exceeding 0.04 m and standard deviations oscillating around 0.05 m. In case of height estimates, mean differences from the reference values were at the level of 0.02 m for the Trimble R10 receiver and 0.07 m for the u-blox receiver, with standard deviations around 0.08 m reached by both tested devices. Secondly, areas of four polygons in the same urban environment were measured. Relative differences from reference values of areas ranged from 0.01 % to 0.46 %, with the Trimble receiver being slightly better in this task.

With the increasing use and popularity of GNSS, there is a growing emphasis on understanding the characteristics of the signals and the impact on their outputs. This article provides an analysis of the daily solution of Iceland permanent GNSS stations daily time series in near proximity to tectonic plate boundaries, aiming to investigate seasonal changes in coordinate values. As a part of the study, the data are prepared, and a function is fitted using the method of least squares, providing for further analysis coefficients and the quality of fit. The research reveals no unequivocal correlation between location and the height of annual amplitudes, except for the stations situated on the Vatnajökull ice cap. It consistently demonstrates higher seasonal changes compared to others, which indicates the influence of snow and water load. Excluding these results, the annual displacement for horizontal components is approximately 1 mm, while the average yearly amplitude for vertical components is almost 5 mm. The results concerned with the Up generally exhibit larger values compared to the other components. It is observed that the mean station variations are higher for the Eurasian plate. The quality of the fit, with regard to outliers and RMSE, does not demonstrate a correlation between the duration of the time series. Furthermore, the average percentage of detected outlier observations is higher for the North American plate.

This paper employs time-frequency analysis on time series with a sampling interval of 10 s of the E, N, H components of a single permanent GNSS station in Krakow which uses three different Ground-Based Augmentation Systems (GBAS). Time-frequency analysis was conducted using the Fourier transform band-pass filter, which segments the time series into frequency components. Through the examination of these coordinates and their discrepancies, it was observed that the correlation between results from different GBAS solutions is negligible. Additionally, the analysis of power-law noise in the E, N and H components reveals that the spectrum index of these time series, computed through robust estimation and a linear regression model, ranges from α = 0.90 to 1.22. This suggests their resemblance to pink noise or flicker noise.

In this paper, the surface deformation of Abu-Dabbab area in Egypt is analyzed. This area is appealing for a variety of investments and tourism because of its temperate climate and pristine surroundings. The ascending Sentinal-1A and Sentinal-1B data of 204 scenes from September, 2016 to December, 2021 and the descending Sentinal-1A data of 116 scenes from May, 2017 to December, 2021 are used for analysis. The surface deformation results in time series that are extracted by the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) approach. According to the InSAR monitoring data, Abu-Dabbab’s overall deformation rate is between approximately −10 mm/yr, 10 mm/yr and approximately −3 mm/yr, 3 mm/yr for vertical v U and horizontal (East-West) v E velocity respectively. These results indicate that the area is in a stable state. Field research confirms the validity of the SBAS-InSAR approach for surface deformation monitoring. By using Global Positioning System (GPS) techniques, the crustal deformation data are collected in this area. The more exact positions of the GPS station have been obtained by the recording, processing, and adjustment of GPS measurements. Within the same timeframe, there was a correlation between the GPS measurements of Marsa Alam’s station (MARS) and the InSAR results. The MARS station location’s estimated vertical and horizontal (East-West) measurements were 1.5 ± 0.5 mm/yr and −1.7 ± 0.6 mm/yr, respectively, with a 95 % confidence level from GPS data. The decomposition of the descending and ascending LOS tracks of the MARS GPS station yielded a vertical component and a horizontal component of 2.01 ± 0.4 mm/yr and −2.42 ± 0.7 mm/yr, respectively. The factors affecting the development of geological disasters are also discussed.