Assessment of the metrological properties of a handheld MLS point cloud for geodetic field surveys

Terrestrial Laser Scanner (TLS) has become a familiar instrument to be used in wide range of engineering application. It can be used for the rapid capture of accurate and highly detailed 3D point cloud datasets. The advantage of laser scanner is that it can record huge number of points in a short period of time. The main idea in this contribution assesses the accuracy of TLS relative to other traditional surveying instruments. This is done throughout four different case studies. In all case studies the 3D coordinates, obtained using total station (TS) are assumed the reference coordinates. First, a control point network, that consists of nine points, is measured using TS, TLS, and real time kinematic global navigation satellite system (RTK-GPS). The precision of each instrument is investigated considering the standard deviation (SD) of measurements. In addition, the accuracy of TLS and RTK-GPS is investigated considering the measurements RMS. Secondly, a grid levelling for a 30,000m2 ground terrain was performed using TS and TLS. After words, the RMS of TLS measurements is computed and a grid of 5mx5m is generated from both surfaces; formed using TS and TLS measurements. Thirdly, the effect of incidence angle on TLS measurements is assessed by measuring fifty-six points fixed on a building facade using different incident angles. Those points were measured using both TS and TLS, and then the absolute height differences between TS and TLS measurements were calculated to figure out the effect of decreasing the incidence angle on measurements. In the fourth case study, the accuracy of TLS on steep-vertical cut measurements is investigated by surveying a downhill area of 500m2 by both TS and TLS, the RMS of TLS measurements was calculated. Finally, based on the obtained results, it was found that TLS produces a higher vertical accuracy than RTK-GPS in measuring control point networks. The RMS of TLS measurements was about 5cm. Moreover, TLS incidence angle is not preferable to be less than 45 degrees as the accuracy degrades significantly after this value. In steep-vertical cut measurements, TLS obtained RMS almost of 6mm discrepancies with a lower measurement period. Eventually, despite the fact that TLS is more expensive than traditional surveying techniques, it is more beneficial in terms of time and effort saving. In addition, it can figure out acceptable accuracy ranges with more detailed surveyed data.

Transmission towers on high-voltage power lines serve as supporting structures for electrical conductors and insulators, requiring routine maintenance to ensure safety and reliability. This study aims to analyze the 3D coordinates of transmission towers using Terrestrial Laser Scanner (TLS) and Airborne Laser Scanning (ALS) methods. The calculation of the Root Mean Square Error (RMSE) against Total Station (TS) measurements showed that TLS achieved higher accuracy, with an RMSE of 0.0037 m, compared to ALS at 0.0136 m. Statistical testing using the t-distribution on 21 data points showed that the t-values for TLS and ALS were 1.967255 and -0.385437, respectively, both of which fall within the critical value range at a 5% significance level. It was therefore concluded that there was no significant difference compared to the Total Station (TS) measurements. The confidence interval analysis at a 95% confidence level indicated that 95% of the TLS data and 61% of the ALS data fell within the acceptable range. In terms of visualization, TLS produced a denser and precise point cloud with texture details, while ALS excelled in point cloud color representation. Each method has its advantages, with TLS being superior in detailed accuracy and ALS being efficient for large-area data acquisition. Keywords: Airborne Laser Scanning, Point Cloud, Terrestrial Laser Scanner, Transmission Tower

Natural disasters and human interference have endangered heritage structures around the world. Therefore, 3D modeling of buildings is important for historical preservation, particularly in low-income and war-affected countries. The majority of 3D structure surveying acquisition approaches, terrestrial laser scanning (TLS), total station measurements, or traditional photogrammetry require either high-cost technologies or professional user supervision. Structure from motion (SfM) approaches address both of these issues by allowing a non-expert user to produce a dense point cloud for real structures by taking a few 2D photographs with a digital camera and processing them with highly automated and freely available data processing tools. The state of the art for the SfM technique is presented in this paper. Agisoft Metashape, VisualSFM, and Regard3D, three well-known types of SfM software, were examined and compared. The 3D point cloud was scaled and transformed into a local coordinates system using total station instruments that were used to obtain some ground control points (GCPs). Ninety-six 2D digital photographs for the historical Emara Palace in Najran, Saudi Arabia, were obtained as data input, and the image matching, bundle adjustment (BA), completeness, and accuracy of three used packages were calculated and compared.

Dense three-dimensional (3D) point cloud data sets generated by Terrestrial Laser Scanning (TLS) and Unmanned Aircraft System based Structure-from-Motion (UAS-SfM) photogrammetry have different characteristics and provide different representations of the underlying land cover. While there are differences, a common challenge associated with these technologies is how to best take advantage of these large data sets, often several hundred million points, to efficiently extract relevant information. Given their size and complexity, the data sets cannot be efficiently and consistently separated into homogeneous features without the use of automated segmentation algorithms. This research aims to evaluate the performance and generalizability of an unsupervised clustering method, originally developed for segmentation of TLS point cloud data in marshes, by extending it to UAS-SfM point clouds. The combination of two sets of features are extracted from both datasets: “core” features that can be extracted from any 3D point cloud and “sensor specific” features unique to the imaging modality. Comparisons of segmented results based on producer’s and user’s accuracies allow for identifying the advantages and limitations of each dataset and determining the generalization of the clustering method. The producer’s accuracies suggest that UAS-SfM (94.7%) better represents tidal flats, while TLS (99.5%) is slightly more suitable for vegetated areas. The users’ accuracies suggest that UAS-SfM outperforms TLS in vegetated areas with 98.6% of those points identified as vegetation actually falling in vegetated areas whereas TLS outperforms UAS-SfM in tidal flat areas with 99.2% user accuracy. Results demonstrate that the clustering method initially developed for TLS point cloud data transfers well to UAS-SfM point cloud data to enable consistent and accurate segmentation of marsh land cover via an unsupervised method.

<p>Mountainous areas bring unique challenges for surveying and natural hazard monitoring – inaccessibility, dangerous terrain, snow coverage and line-of-sight problems often make it next to impossible to perform ground-based monitoring or even to provide a good vantage point for close-range sensing (e.g. terrestrial laser scanning (TLS) or terrestrial photogrammetry). Airborne or satellite-based methods are often the only way to gain information about geodynamically active sites. Here, structure-from-motion (SfM) photogrammetry from unmanned aerial vehicle (UAV) imagery in particular can provide an inexpensive and easily implemented monitoring option. The Vigilans research project attempts to evaluate the feasibility of UAV-photogrammetry against more established surveying methods (e.g. in situ data from extensometers or total stations).</p><p>Our study site Marzellkamm is located in the Central Ötztal Alps of Western Austria. The active rock slope deformation we are monitoring in Vigilans lies at 2450-2850 m asl. on a SE-facing slope. Annual displacement rates of up to 1.5 m/year in the early 2010’s triggered monitoring and research interest. Due to the remote location, mitigation methods were not implemented, but a hiking trails was relocated. Orthoimage photogrammetry and ground-based monitoring instrumentation (extensometers, terrestrial laser scanning, total station measurements combined with GNSS and geodetic surveys) collected data 1971-2019.</p><p>In the last years, movement along the slope has slowed down considerably. The rather slow current movements provide a valuable challenge for detection, with rates of <0.05 m/year occurring in the more stable upper sections, while the NW section in particular still shows pronounced movement of up to 0.3 m/year. For this reason, Marzellkamm provides excellent evaluation for new methods such as UAV-SfM.</p><p>In three separate missions between summer 2018 to fall of 2019, UAV-SfM 3D-models of the site were created for displacement rate evaluations; it is planned to continue this monitoring for a total of three years as part of the Vigilans project. Photogrammetric missions were performed in conjunction with total station measurements of more than 30 ground control points.</p><p>The required level of precision is becoming achievable and affordable with new RTK/PPK-equipped (Real-Time-Kinematics/Post-Processed Kinematics) UAVs. However, evaluating the resulting 3D-- model in terms of movement rates remains non-trivial. The most common algorithm for change detection in point clouds, M3C2, is not well-suited to detect a laterally moving surface as a whole, as it detects changes along the normal orientation of a surface (such as subsidence). Therefore, the point cloud needs to be very selectively reduced, requiring complex filtering operations and expert input as well as expensive software packages.</p><p>This contribution will present a workflow to simplify such evaluation, based on 2.5D (DEM-based) algorithms such as IMCORR and DoD (Difference-of-DEMs), in comparison with the more complex 3D-pointcloud based processing. The presented workflow is based on Agisoft Metashape and Open-Source software tools QGIS and Saga GIS. It aims to streamline UAV-based surveying work, 3D-model generation and simplified change detection into a repeatable and easily automatable framework. Special emphasis will be put on estimating the quality of the recorded data.</p>

For some time past, terrestrial laser scanning has been adopted as one of the data acquisition techniques for e.g. deformation measurements, documentation of historical monuments and civil engineering projects. Using terrestrial laser scanning, millions of 3D points can be obtained with a high accuracy in a time span of minutes or even seconds depending on the type of laser scanner. However, processing the data still remains a time consuming process. As a result, total station measurements are often preferred over laser scanning for high frequency deformation measurements when time for data processing is limited. In the research at hand, the applicability of terrestrial laser scanning in time critical situations was assessed based on the case of a recent monitoring project on a sewage purification plant. Deformations had to be assessed twice a day in order to prevent accidents from happening. The total station measurements were executed and processed by a specialized company (Teccon bvba, Belgium). At the same time, terrestrial laser scanning was used by researchers of University College Ghent and Ghent University to acquire deformation data. The data obtained by laser scanning was processed independently and then georeferenced to the same coordinate system as used for the total station measurements to enable comparison of the resulting deformations. In order to be able to meet the time requirements, a “quick and dirty” method was developed to process the laser scan data. This method implied no cleaning up of the point clouds and only primitive modeling of parts of the structure. Although a complete 3D model of the whole structure could not be generated in the limited time frame between two scans, the objective, namely an accurate assessment of deformations almost in real time, was accomplished. Furthermore, the degree of detail that can be reached through the use of laser scanning surpasses the possibilities of total stations. Moreover, even with a “quick and dirty method”, visualization is much more comprehensive than can be obtained by using a total station.

<p>Technical advancements have widened the limits of remote sensing and Terrestrial Laser Scanning technology in studying underground cavities. Furthermore, the use of Unmanned Aerial Systems has proven to be a significant advantage in the study of caves, as under certain circumstances during the data processing, it is plausible to combine TLS and UAS data for generating a complete 3D model, representing surface and subsurface simultaneously. The use of state-of-the-art laser scanning equipment either terrestrial or handheld accompanied by total station measurements on a series of ground control points, has resulted in the scanning and detailed mapping the entire Melissani and Drogarati caves, in Cephalonia Island, in Greece, including hidden cavities. This study attempted not only to delineate a new methodology for compiling a highly detailed cave map, but also to identify structural discontinuities and faults for further investigation of the influence of rock failures in causing rock falls and further damage in the caves. Both show caves attract many visitors and since they are located at an area of very high seismicity, where large earthquakes occur very frequently, the risk is rather high.</p><p>The methodology is based on the synergy of equipment in different working levels, since the cave environment is by far one of the most difficult cases to survey, led to hazard identification in high detail and accuracy throughout the cavity. The fieldwork includes the generation of a unified point cloud for the underground cavity, generated by scanning at several bases inside the cave and by entering smaller cavities by holding the mobile scanner. The bundling of the partial point clouds is possible since the proposed methodology includes the establishment of a dense network of Ground Control Points, which are measured with Total Station equipment for gaining actual coordinates. After the merging of the partial scans were combined into a single point cloud, the methodology continues with further processing including filtering and noisy points removal. Moreover, the final product is combined with the point cloud that was generated after the photogrammetric processing and the methodology is completed with exporting the results in file formats that can be imported in several geotechnical or discontinuity recognition software for further interpretation. The results along with the produced 3D models could be utilized to determine areas susceptible to different failure types. The assessment of rock stability within a cave by combined innovative equipment, techniques, and research methods could be considered by the management authorities for the maintenance and/or re-design the tourist routes.</p>

Summary Quantitative measurements of above‐ground vegetation biomass are vital to a range of ecological and natural resource management applications. Remote‐sensing techniques, such as terrestrial laser scanning (TLS) and image‐based point clouds, are potentially revolutionary techniques for measuring vegetation biomass and deriving other related, structural metrics for these purposes. Surface vegetation biomass (up to 25 cm) in pasture, forest, and woodland environments is estimated from a 3D point cloud derived from a small number of digital images. Volume is calculated, using the 3D cloud and regressed against dry weight to provide an estimate of biomass. Assessment of the method is made through comparison to 3D point clouds collected through TLS surveys. High correlation between destructively sampled biomass and vegetation volume derived from TLS and image‐based point clouds in the pasture (TLS , image based ), dry grassy forest (TLS , image based ) and lowland forest (TLS , image based ) environments was found. Occlusion caused by standing vegetation in the woodland environment resulted in moderate correlation between TLS derived volume and biomass (). The effects of surrounding vegetation on the image‐based technique resulted in 3D point clouds being resolved for only 40% of the samples in this environment. The results of this study demonstrate that image‐based point cloud techniques are highly viable for the measurement of surface biomass. In contrast to TLS, volume and biomass data can be captured using low‐cost equipment and relatively little expertise.

This study investigates the applicability of a compact terrestrial laser scanner (Leica BLK360) for assessing the verticality of a tall industrial chimney, and compares its performance with high-precision total station measurements. In the first phase, a reference control network was established and observed using the tangential envelope method with a total station, providing a precise benchmark. Terrestrial laser scanning was then carried out, and circles were fitted to horizontal cross-sections extracted from the point cloud. A least-squares approach was used to calculate chimney-axis deviations and evaluate verticality along the height of the structure. The results of both methods revealed a consistent trend of deviations increasing with height. Maximum differences between the total station and TLS measurements did not exceed 5 mm, which remains within acceptable geodetic tolerance. This demonstrates that the BLK360 is capable of providing sufficiently accurate data for preliminary deformation monitoring of tall engineering structures. The main advantage of the BLK360 scanner lies in its rapid and automated data acquisition, which allows for more frequent observations, reduced fieldwork time, and early detection of structural irregularities. However, limitations such as a reduced measurement range, lower sensitivity under unfavorable conditions, and dependency on surface reflectivity were also identified. Despite these constraints, the study confirms that the BLK360 can serve as a valuable supplementary tool to conventional total station surveys, offering practical support for ongoing monitoring, and contributing to improved safety in engineering practice.

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

Analysis of the displacements of pipeline overpasses based on geodetic monitoring results

Terrestrial laser scanning (TLS) is a powerful tool for generating detailed 3D models of elevated structures such as bridges, towers, and buildings. However, the quality of the resulting models heavily depends on the setup configuration of the TLS system. This research evaluates the precision of 3D point cloud data of elevated structures acquired through TLS at different distances. The data processing was performed using Cyclone Register360 software. The study aimed to evaluate the accuracy of point cloud data obtained from various TLS setup locations and compare it with the measurements obtained from a Total Station. Four different distances were used to set the TLS to scan the three elevated structure piers. The acquired data was then processed using Cyclone Register360 software to eliminate noise, visually align, and precisely register the point clouds. The results indicated that shorter distances between TLS setups resulted in more accurate point cloud data, with reduced error rates, highlighting the need to locate the scanner effectively. The study also highlighted the capabilities of Cyclone Register360 in improving the precision of point cloud data through effective data processing techniques. The findings demonstrate the significance of precise scanning distance evaluation in TLS applications to ensure high-quality data capture. It is vital for comprehensive 3D modeling and analysis of elevated structures. These valuable insights apply to specialists in surveying, engineering, and architecture. It offers guidance on the best practices for TLS setups, which can improve the accuracy and reliability of measurements. Further studies should examine the influence of other factors, such as scanning angles and environmental conditions, on the precision of TLS data.

Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.

Over the last two decades, terrestrial light detection and ranging (LiDAR), also known as terrestrial laser scanning (TLS) has become a valuable tool in assessing the woody structure of trees, in a method that is accurate, non-destructive, and replicable. This technique provides the ability to scan an area, and utilizes specialized software to create highly detailed 3D point cloud representations of its surroundings. Although the original usage of LiDAR was for precision survey applications, researchers have begun to apply LiDAR to forest research. Tree metrics can be extracted from TLS tree point clouds, and in combination with structure modelling, can be used to extract tree volume, aboveground biomass (AGB), growth, species, and to understand ecological questions such as tree mechanics, branching architecture, and surface area. TLS can provide a robust and rapid assessment of tree characteristics. These characteristics will improve current global efforts to measure forest carbon emissions, understand their uncertainties, and provide new insight into tropical forest ecology. Thus, the main objective of this PhD is to explore the use of 3D models from terrestrial laser scanning point clouds to estimate biomass and architecture of tropical trees. TLS-derived biomass and TLS-derived architecture can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests.

In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. For this purpose, the external calibration parameters between the TLS and digital camera needs to be determined precisely. The camera calibration and internal TLS calibration are commonly carried out in advance in the laboratory environments. The focus of this research is to highly accurately and robustly estimate the external calibration parameters between the fused sensors using signalised target points. The observables are the image measurements, the 3D point clouds, and the horizontal angle reading of a TLS. In addition, laser tracker observations are used for the purpose of validation. The functional models are determined based on the space resection in photogrammetry using the collinearity condition equations, the 3D Helmert transformation and the constraint equation, which are solved in a rigorous bundle adjustment procedure. Three different adjustment procedures are developed and implemented: (1) an expectation maximization (EM) algorithm to solve a Gauss-Helmert model (GHM) with grouped t-distributed random deviations, (2) a novel EM algorithm to solve a corresponding quasi-Gauss-Markov model (qGMM) with t-distributed pseudo-misclosures, and (3) a classical least-squares procedure to solve the GHM with variance components and outlier removal. The comparison of the results demonstrates the precise, reliable, accurate and robust estimation of the parameters in particular by the second and third procedures in comparison to the first one. In addition, the results show that the second procedure is computationally more efficient than the other two.