Abstract Quantifying the distribution of Spanish moss ( Tillandsia usneoides L.) is challenging because it grows suspended from high tree branches, limiting manual sampling. Terrestrial laser scanning (TLS) provides a non-destructive means of capturing vegetation structure in three dimensions. However, no established methods exist for identifying Spanish moss from TLS data. We evaluated five classification methods for distinguishing Spanish moss in TLS-derived point cloud data: Graph, DBSCAN, Random Forest (RF), Kernel Point Convolution (KPConv), and PointNet++. PointNet++ achieved the highest accuracy (81%), followed by DBSCAN (70%), KPConv (61%), RF (54%), and Graph (52%). Unsupervised methods required minimal computational resources (2–3 min, 8–16 GB memory) without training. RF required 3 h for training, 8 for prediction with 1024 GB memory. Deep learning methods required substantially more: KPConv needed 60 h for training, 4 for prediction (256 GB), while PointNet++ required 48 h for training, 1 for prediction (128 GB). Agreement was lowest in the central and upper canopy due to occlusion. Surface variation, PCA1, and verticality contributed most to accurate predictions. These results demonstrate the feasibility of using TLS and advanced classification methods for non-destructive Spanish moss mapping and highlight the accurate classification ability of PointNet++ for future biomass estimation at landscape scales.

In recent years, the use of terrestrial laser scanning (TLS) technology in the mining industry has grown in popularity because of its many benefits, including non-contact measurement, high precision, rapid data acquisition, and large-scale coverage. This study provides a thorough overview of the application of the TLS method in underground, open-pit, and closed mines, using data from 56 publications over the past fourteen years, from 2010 to May 2024. The reviewed literature shows that the TLS approach can be applied in five main operations in mining areas, particularly for monitoring deformation, displacement, subsidence, and landslides, as well as generating maps, 3D models, and point clouds. The findings reveal that TLS is an excellent technology for multitasking in any type of mine. In addition, future development trends for the mining industry based on the integration of TLS with machine learning and AI technologies are also discussed in this study. The results of this study may suggest directions for creating the specifications required for TLS deployment on mine sites.

Abstract. Modern trends in geospatial data acquisition are increasingly focused on efficiency, automation, and cost-effectiveness while maintaining sufficient accuracy for a wide range of applications. This paper evaluates the performance of several modern scanning devices, including terrestrial laser scanning (TLS) systems and SLAM-based or photogrammetry-LiDAR based solutions. Measurements were carried out in interior and exterior environments to assess not only positional accuracy but also practical aspects such as acquisition time, post-processing requirements, and overall costs. Results show that while SLAM-based scanners significantly reduce acquisition time and required manpower, their accuracy is lower compared to static TLS methods. Hybrid approaches offer a compromise, balancing speed with improved precision. Cloud-based solutions, such as the Matterport Pro3, provide user-friendly workflows but exhibit significant noise and registration errors, making them unsuitable for high-accuracy surveying tasks.This study confirms that no single scanning technology is universally optimal; instead, the balance between accuracy, efficiency, cost, and operator expertise must guide the choice of device for each specific application.

Abstract. Industrial facilities regularly require reconstruction or replacement of individual elements. To solve such problems, design organisations are increasingly using digital 3D models, which are created using data from terrestrial laser scanning, among other sources. s an example, several facilities of “Kazzinc” LLP were examined, covering the entire workflow from field surveys to the final results. During laser scanning, the average error in point cloud alignment was no more than 7 mm, which meets the requirements for data accuracy between stations of no more than 15 mm.The article highlights the advantages of terrestrial laser scanning (TLS), such as high measurement accuracy and reduced human error. The analysis of deviations from design specifications revealed potential conflicts, which helps to prevent delays and additional costs.Despite these achievements, the main limiting factor remains the lack of regulatory and technical documentation in Kazakhstan governing the use of TLS. In conclusion, it is emphasized that the further development and standardization of TLS methods could significantly enhance the quality and safety of engineering works.

ABSTRACT We present an efficient workflow to update existing ship documentation with as-measured ship geometry, accounting for modifications and age-related deformations. The goal is to provide geometric data in a 2D lines plan that can instantly update a compatible 3D digital model. This method is demonstrated through a case study involving a vessel requiring a replacement of its aft section. We used a low-cost photogrammetry procedure to capture the full-field geometry of the new section and integrate it with existing documentation. Agisoft Metashape software and consumer-grade equipment facilitated the photogrammetry, while Rhinoceros software helped capture a polyline-based 2D lines plan from the polygon mesh model. The polylines were then processed into splines to create a NURBS-based 3D model of the measured hull. By ensuring continuity and smoothness, we successfully integrated the newly measured stern shape into the existing 2D and 3D documentation. This method is cost-effective, reproducible, accurate, and time-efficient, offering a viable alternative to more expensive Terrestrial Laser Scanning methods. A skilled individual can complete the measurements and processing in two days. Our adaptable workflow supports the repair, modification, and retrofitting of ships, regardless of the specific software or hardware used.

Abstract. This paper presents a comparative analysis of photogrammetric and Terrestrial Laser Scanning (TLS) workflows for geo-monitoring alpine rock slope failures, focusing on the quality of the resulting point clouds and 3D displacement vectors. We compare two tracking workflows, a patch-based approach for TLS point clouds and a 2D feature-based method for photogrammetry. We evaluate both methods using data from the Hochvogel Mountain test site, collected across two measurement epochs. The results demonstrate that both TLS and photogrammetry effectively detect ongoing movement patterns down to a few millimeters, with similar accuracy despite slight differences in noise levels. Both methods offer high spatial resolution and sensitivity to small-scale displacements, showing strong agreement with reference measurements from tachymetry. Differences mainly arise in the spatial distribution: the TLS method is sparse in planar areas but very regular, while the photogrammetric method yields a significantly higher number of vectors, concentrated in regions with strong texture variation, but also includes more outliers.

Abstract Advanced measurement techniques, such as Terrestrial Laser Scanning (TLS), play a vital role in documenting cultural heritage and civil engineering structures. A key aspect of these applications is the accurate registration of point clouds. Conventional TLS methods often rely on manual or semi-automated correspondence detection, which can be inefficient for large or complex objects. Structure-from-Motion Terrestrial Laser Scanning (SfM-TLS) offers an alternative methodology, comprising two primary phases: correspondence search and incremental reconstruction. Descriptor matching in SfM-TLS typically employs the L 2 norm to measure Euclidean distances between features, valued for its simplicity and compatibility with algorithms like SIFT. This study investigates the influence of various distance metrics on descriptor matching during the correspondence search stage of SfM-TLS. Eight metrics were analysed: Bray-Curtis, Canberra, Correlation, Cosine, L 1, L 2, Squared Euclidean, and Standardised Euclidean. Synthetic data experiments highlighted challenges in keypoint detection and matching due to measurement angles, material characteristics, and 3D-to-2D transformations. Simulations incorporating Gaussian noise demonstrated that image rotation and skew significantly affected tie-point accuracy, more so than variations in intensity. In field applications involving cultural heritage sites and building interiors, the L 1 and Squared Euclidean metrics yielded higher accuracy, while the Canberra metric underperformed. Metric selection was found to have a greater impact on complex geometries, such as historical structures, compared to simpler forms. Consequently, this study recommends the L 1 and Squared Euclidean metrics for pairwise SfM-TLS registration, as they exhibit robustness in maintaining high accuracy and completeness across a variety of architectural scenarios.

Common beech (Fagus sylvatica L.) is one of Europe's most widespread forest tree species. In the actual context of climate change, this species has responded through its self-regulation mechanisms, proving a high plasticity. It is important to explore the specific mechanisms driving its response to climate change, taking into account silvicultural, phenological, and genetic perspectives and their interaction. Here, we tested for association between adaptive and growth traits and within-individual genetic diversity measured as individual heterozygosity (proportion of heterozygous loci per sampled individual), based on six genomic microsatellite markers (gSSRs, genomic simple sequence repeats) and six expressed sequence tag microsatellites (EST-SSRs) for 55 beech trees. We evaluated the spring and autumn phenology of beech trees sampled along an altitudinal gradient (560 - 1150 m) and the architectural traits using a non-destructive terrestrial laser scanning method (TLS). The effect of stand density at the onset of the growing season was evaluated by quantifying the competition through the Hegyi index. The onset of bud burst and senescence, as well as the length of the growing season, varied significantly and inversely proportionally with the altitudinal gradient. There was a difference of 14 days between the individuals located at the extremities of the altitudinal gradient in the onset of bud burst, 15 days in the onset of senescence, and 30 days in the length of the growing season. We obtained a very significant and positive correlation between altitude and bud burst and a very significant but negative one between altitude and the length of the growing season. An increase in tree competition directly implied a decrease in DBH and crown dimensions, especially by neighbours closer than 4 m. Stem's slenderness significantly increased with higher competition. Our results revealed a positive relationship between individual heterozygosity and the length of the growing season, as well as with the trunk volume and DBH. Higher individual heterozygosity was associated with a longer growing season, and a precocious onset of bud burst in beech. Higher heterozygosity was also associated with considerably higher total tree biomass. The genetic diversity was inversely proportional to stem forking. TLS shows great potential in extracting beech tree biomass indicators, but we still recommend using the conventional method as a complementary method for data validation, although it is time-consuming.

Our work aimed to compare the chip pile volumes calculated by laser ground scanning, UAV technology, and laser ground measurement and also to determine the accuracy, speed, and economic efficiency of each method. The large chip pile was measured in seven different ways: band measurement, laser measurement with Vertex, global navigation satellite system, handheld mobile laser scanner, terrestrial laser scanner, drone, and smartphone with a light detection and ranging sensor. All the methods were compared in terms of accuracy, price, user-friendliness, and time required to obtain results. The calculated pile volume, depending on the method, varied from 2588 to 3362 m3. The most accurate results were provided by the terrestrial laser scanning method, which, however, was the most expensive and the most demanding in terms of collecting and evaluating the results. From a time and economic point of view, the most effective methods were UAVs and smartphones with LiDAR.

Discontinuous deformations, such as sinkholes, pose significant challenges in post-mining areas due to their unpredictable nature and potential hazard to surface development and the safety of local communities. Therefore, monitoring the post-mining regions should be treated as a continuing task. This study addresses the ongoing problem of sinkhole formation in the former “Przyjaźń Narodów – Szyb Babina” (Babina) lignite mine located in the glaciotectonic region of Muskau Arch in western Poland. The research uses airborne and terrestrial laser scanning methods to identify and monitor discontinuous deformations, focusing on a newly discovered sinkhole. The methodology involves differential analysis of Digital Elevation Models (DEMs) and their derivatives obtained from airborne laser scanning (ALS) and periodic terrestrial laser scanning (TLS) measurements. The results of ALS DEM analysis allowed the successful identification of 75 confirmed sinkholes, the largest measuring 12.8 m in diameter and 4.8 m deep. Whereas, differential DEM analysis indicated new sinkholes that developed between 2011 and 2020 in the area of shallow underground mining. Two-year TLS monitoring of the new sinkhole showed no progression in its dimensions. However, localised erosion processes associated with water transport were detected. The study shows that sinkhole formation processes are active 5 decades after the end of mining and highlights the importance of continuous monitoring of post-mining areas with advanced laser scanning methodss.

Terrestrial laser scanning technology is becoming increasingly common for automated spatial data acquisition and digitization in the fields of surveying, civil engineering and architecture. The data from measurements made with terrestrial laser scanners are a huge array of points in space, called a point cloud, which describes the captured surface of the object under study. The point cloud processing is performed in specialized software products for handling measurements from laser scanners, which provide different possibilities for manipulating the point cloud and forming different results. The software available on the market differs according to its data processing capabilities and functionalities, application areas, methods used, manufacturer and cost. To be able to perform spatial data processing and analysis correctly and with high quality, it is important to understand the available functionalities of the different software products and their advantages and disadvantages compared to others. A comparison is made for three software packages for point cloud processing - Autodesk ReCap Pro, CloudCompare and Trimble RealWorks. The different functionalities available in the products are described and presented on small building measurements along with their performance accuracy and efficiency. The strengths and weaknesses of the different software products are identified through the comparison performed. The first section describes the basic principles of the terrestrial laser scanning method. In section two, the different point cloud processing software products on the market are presented, together with a description of the different file formats for data exchange and a theoretical section on point cloud registration, filtering, and modelling. The third section contains a presentation of the main functions and processing capabilities in Autodesk ReCap Pro, CloudCompare and Trimble RealWorks software. The fourth section describes the data used for the study, the measurements performed, their processing and results in the three software, together with an assessment of accuracy by control measurements. Section five contains conclusions and implications.

<p>Project "Devils’ town Erosion MONITORing - DEMONITOR" involves the monitoring of accessible earth pillars in the area of Devil’s town, by using a combination of several non‐invasive methods. Terrestrial laser scanning (TLS) and photogrammetric imaging with unmanned aircraft (UAV) as a platform showed as great solutions for 3D modeling of this site and erosion monitoring. In this work it is shown that using manual free flight mode for imaging with UAV gave much better results than the missions performed with predefined flight plans. The desired long‐term effect from this research should have a significant part in the overall socioeconomic development of the municipality of Kuršumlija, and the entire Toplica district.</p>

Thanks to the development of geodetic methods and equipment, there has been a transition from conventional methods to modern technologies, which can efficiently and accurately acquire a large amount of data in a short time without the need for direct contact with the measured object. Combined technologies such as Structure from Motion (SfM), Multi-View Stereo (MVS) photogrammetry using Unmanned Aerial Systems (UAS), and terrestrial laser scanning (TLS) are often used for monitoring geohazards and documenting objects in quarries to obtain detailed and accurate information about their condition and changes. This article deals with the analysis of point clouds obtained with different settings in terms of average absolute point distance, average point density, and time range for surveying and office work. The numerical and graphical results of the research lead to conclusions for scientific and practical applications for activities in the mining industry.

In continuous cover forestry, plenter silviculture is regarded as an elaborated system for optimizing the sustainable production of high-quality timber maintaining a constant but heterogeneous canopy. Its complexity necessitates high silvicultural expertise and a detailed assessment of forest stand structural variables. Terrestrial laser scanning (TLS) can offer reliable techniques for long-term tree mapping, volume calculation, and stand variables assessment in complex forest structures. We conducted surveys using both automated TLS and conventional manual methods (CMM) on two plots with contrasting silvicultural regimes within the Black Forest, Germany. Variations in automated tree detection and stand variables were greater between different TLS surveys than with CMM. TLS detected an average of 523 tree stems per hectare, while CMM counted 516. Approximately 9.6% of trees identified with TLS were commission errors, with 6.5% of CMM trees being omitted using TLS. Basal area per hectare was slightly higher in TLS (38.9 m3) than in CMM (38.2 m3). However, CMM recorded a greater standing volume (492.7 m3) than TLS (440.5 m3). The discrepancy in stand volume between methods was primarily due to TLS underestimating tree height, especially for taller trees. DBH bias was minor at 1 cm between methods. Repeated TLS inventories successfully matched an average of 424 tree positions per hectare. While TLS adequately characterizes complex plenter forest structures, we propose enhancing this methodology with personal laser scanning to optimize crown coverage and efficiency and direct volume measurements for increased accuracy of wood volume estimations. Additionally, utilizing 3D point cloud data-derived metrics, such as structural complexity indices, can further enhance plenter forest management.

The article explores the methods and potential applications of terrestrial laser scanning technology. It also presents the preliminary results of scanning the Bijambare cave, where a georeferenced three-dimensional model of the cave's interior was created. This model, based on available data, stands as the first accurate 3D representation of a speleological object in Bosnia and Herzegovina. The first section of the paper provides a brief overview of the Bijambare cave area and the laser scanning technology employed, highlighting its advantages over other geodetic measurement techniques. Subsequently, the text delves into the stages of the measurement processing, followed by an examination of the various products generated. The practical significance of these products is illustrated using the example of the Bijambare cave.

The aim of this work is to investigate the process of obtaining necessary information about the metric parameters of small-area arrays, linearly arranged and individual green plantings on predominantly urbanized territories, and to apply the results of data processing in the compilation of topographic and special maps from the corresponding scanning materials. Methodology. For this purpose, terrestrial laser scanning methods, dynamic laser scanning as a data source for tree-level mapping of the territory, and as an information base for filling in the respective cadastres are subject to research. The possibilities of using data from these methods to obtain information about green plantings using modern software tools have been explored. Based on terrestrial laser scanning data performed in accordance with the requirements of regulatory spatial reference documents, data processing of terrestrial laser scanning was carried out using automated methods, namely the Terrasolid software suite. The need for more than 40% coverage of the tree trunk with a point cloud obtained from laser scanning to eliminate possible errors in determining the relevant parameters due to the heterogeneity of the structure of different tree trunks has been confirmed. Preliminary processing of scanning materials was carried out using FARO Scene 2020 software. Scientific novelty and practical significance. An experiment was conducted to analyze the creation of both a plan-altitude and an information base regarding green plantings on selected objects within the Zakarpattia region. The process of collecting data on green plantings was improved by using terrestrial laser scanning and partial GNSS measurements, instead of traditional topographic-geodetic methods. A table containing information on green planting data has been created for the studied objects' territory. Automated methods were used to gather this information, including details about their location in the adopted coordinate system and the trunk diameter at a height of 1.3 meters.

Abstract Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees , an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees : these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m 3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees , is required. To facilitate improvements to TLS2trees , as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open‐source software.

The traditional leveling, total station, and global navigation satellite system (GNSS) and the new differential interferometric synthetic aperture radar (DInSAR) and terrestrial laser scanning (TLS) systems have their own advantages and limitations in the deformation monitoring of mining areas. It is difficult to obtain accurate deformation information only using single-source measurement data. In this study, we propose an LOS deformation correction method for DInSAR in mining areas by fusing ground data without control points. Based on free space data, small deformations at the edges of mining influence areas accurately obtained using DInSAR. By combining leveling/GNSS and TLS methods, it was possible to obtain large deformations in central areas without the need for control points located outside the mining influence range. For overcoming the non-uniform coordinates of the “space–ground” data and the limited overlap of the effective measurement ranges, the subsidence prediction model was employed to assist in its fusion. In addition, in LOS deformation correction, we retained the non-full cycle phase of DInSAR and replaced the full cycle phase with the one from the data fusion. Engineering experiments have shown that the correction results preserve the differences in the LOS deformations at the edge areas of the mine influence range, and they recover the lost LOS deformations at the center areas. Using the difference in the LOS deformation before and after correction as the verification indicator, the maximum absolute value of the errors after correction was 143 mm, which was approximately 6.4% of the maximum LOS deformation. In addition, there were still two errors that were large (−112 mm and −89 mm, respectively), and the absolute values of errors were not more than 75 mm. For all errors, the mean absolute value was 36 mm. Compared with 399 mm before correction, the error was reduced by 91%. This study provides technical support and theoretical reference for deformation monitoring and control in mining areas.

Vegetation structure is a crucial component of habitat suitability for wildlife, but methods traditionally used to quantify it can be costly, subjective, or difficult to replicate. There have been substantial advancements, and growing interest, in the use of terrestrial laser scanning (TLS) methods to measure fine-scale vegetation attributes in ecological research, but limited opportunities for ecologists and practitioners to learn how to incorporate these into applied research. Here, we provide a starting point for those who want to incorporate TLS methods into ecological research and monitoring, using a case study from a temperate forest in South-Eastern Australia. We scanned 24 sites at different stages of post-fire recovery as part of a larger project measuring the impact of fire on forest dwelling bats, using a FARO Focus 3DS. Using the data from these scans, we present an example workflow with accompanying R code to demonstrate how to process the point clouds and extract a range of vegetation structure metrics relevant to habitat structure for wildlife, including vegetation height, cover, density, and heterogeneity. We discuss how three-dimensional data obtained through TLS can be valuable to wildlife studies and opens up the potential to explore advanced ecological and conservation questions around how vegetation structure influences wildlife behaviours, distribution patterns, and responses to disturbance.

The accuracy of 3D indoor reconstructed models is critical in various applications such as indoor navigation, virtual reality (VR), and building information modelling (BIM). This research study aims to evaluate the accuracy of Unmanned Aerial Vehicles (UAV) and Terrestrial Laser Scanning (TLS) for 3D indoor modelling in large-scale building environments. To achieve this, several evaluations were made towards the number of point clouds, estimated costs and accuracy of the 3D indoor reconstructed model generated from dense point clouds acquired by Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanner (TLS). A small indoor classroom was selected for this study approximately 100m2. In UAV data acquisition, three (3) flight missions were set up at the front, left and right views. Meanwhile, five (5) scanning stations were placed on-site for the TLS method. Due to various different flight mission views in the UAV dataset, the number of point clouds was quite higher compared to the TLS method. However, a better-quality visualization of the TLS model has been obtained as opposed to the UAV 3D model. For the required time to generate a 3D model, it showed that UAV processing time was more consuming lots of time than the TLS method, especially when georeferencing the overlapping photographs. In terms of accuracy, the RMSE value from TLS was better than UAV at 0.003m compared to UAV at 0.021m. Overall, this study provides insights into the accuracy and suitability of UAV and TLS for 3D indoor modelling in large-scale building environments. The results can inform decision-making processes in various industries such as architecture, engineering, and construction, where accurate and reliable 3D models are crucial for design, planning, and management purposes.