Terrestrial Laser Scanning Research Articles

Various scenarios of measurements using a smartphone with a LiDAR sensor in the context of integration with the TLS point cloud

Abstract Smartphones with Light Detection and Ranging (LiDAR) sensors are increasingly used for engineering measurements. Although the processing of the acquired point clouds seems similar to the processing of point clouds measured with, for example, a terrestrial laser scanner, processing data from a smartphone requires a special approach, first of all, when it comes to methods of obtaining and registering point clouds to obtain one complete metric point cloud. The research consisted of comparing various scenarios of measuring using a smartphone with a LiDAR sensor (a smartphone held in hand, a smartphone on a selfie stick, and a smartphone mounted on a gimbal), two acquisition strategies (one direction and zigzag) and two registration methods (point to point and cloud to cloud). The aim of the study was to find the best solution for registering the obtained point cloud with referenced terrestrial laser scanning (TLS) point cloud. It turns out that how we obtain field data using a smartphone with a LiDAR sensor is important and affects the accuracy of point cloud integration. The results showed that the use of additional devices such as a gimbal supports the data acquisition process and has an impact on the point cloud registration. In the analysed case, the RMSE registration error was the smallest and amounted to 0.012 m and 0.019 m, while the largest registration error was 0.060 m and 0.065 m, for object 1 and object 2, respectively. The result obtained using the proposed methodology can be considered satisfactory.

Assessment of Minaret Inclination and Structural Capacity Using Terrestrial Laser Scanning and 3D Numerical Modeling: A Case Study of the Bjelave Mosque

Terrestrial laser scanners (TLS) are widely employed in structural health monitoring (SHM) of large objects due to their superior capabilities compared to traditional geodetic methods. TLS provides rapid and detailed data on the geometric properties of objects, enabling various types of analyses. In this study, TLS was utilized to examine the minaret of the Bjelave Mosque, located in Sarajevo, Bosnia and Herzegovina. The inclination of the minaret was assessed using principal component analysis (PCA) and linear regression (LR) applied to sampled data from four edges of the minaret’s body. The geodetically determined inclination values were used as input data for subsequent static and pushover analyses conducted in DIANA FEA, where the minaret was modeled. The analyses indicate that the inclination increases stress and strain, leading to larger cracks and reduced structural capacity, as demonstrated by the pushover analysis curves. This study highlights the combined impact of structural inclination, water infiltration, and settlement on the minaret’s integrity and proposes these findings as a basis for future maintenance and strengthening measures.

An Integrated Method for Inverting Beach Surface Moisture by Fusing Unmanned Aerial Vehicle Orthophoto Brightness with Terrestrial Laser Scanner Intensity

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a single method has inherent limitations. Passive remote sensing is challenged by complex beach illumination and sediment grain size variability. Active remote sensing represented by LiDAR (light detection and ranging) exhibits high sensitivity to moisture, but requires cumbersome intensity correction and may leave data holes in high-moisture areas. Using machine learning, this research proposes a BSM inversion method that fuses UAV (unmanned aerial vehicle) orthophoto brightness with intensity recorded by TLSs (terrestrial laser scanners). First, a back propagation (BP) network rapidly corrects original intensity with in situ scanning data. Second, beach sand grain size is estimated based on the characteristics of the grain size distribution. Then, by applying nearest point matching, intensity and brightness data are fused at the point cloud level. Finally, a new BP network coupled with the fusion data and grain size information enables automatic brightness correction and BSM inversion. A field experiment at Baicheng Beach in Xiamen, China, confirms that this multi-source data fusion strategy effectively integrates key features from diverse sources, enhancing the BP network predictive performance. This method demonstrates robust predictive accuracy in complex beach environments, with an RMSE of 2.63% across 40 samples, efficiently producing high-resolution BSM maps that offer values in studying aeolian sand transport mechanisms.

Quantitative analysis of different SLAM algorithms for geo-monitoring in an underground test field

Geo-monitoring provides quantitative and reliable information to identify hazards and adopt appropriate measures timely. However, this task inherently exposes monitoring staff to hazardous environments, especially in underground settings. Since 2000s, robots have been widely applied in various fields and many studies have focused on establishing autonomous mobile robotic systems as well as solving the issue of underground navigation and mapping. However, only a few studies have conducted quantitative evaluations of these methods, and almost none have provided a systematic and comprehensive assessment of the suitability of mapping robots for underground geo-monitoring. In this study, a methodology for objective and quantitative assessment of the applicability of SLAM methods in underground geo-monitoring is proposed. This involves the development of an underground test field and some specific metrics, which allow detailed local accuracy analysis of point measurements, line segments, and areas using artificial targets. With this proposed methodology, a series of repeated experimental measurements has been performed with an autonomous driving robot and the selected LiDAR- and visual-based SLAM methods. The resulting point cloud was compared with the reference data measured by a total station and a terrestrial laser scanner. The accuracy and precision of the selected SLAM methods as well as the verifiability and reliability of the results are evaluated and discussed by analysing quantities such as the deviations of the control points coordinates, cloud-to-cloud distances between the test and reference point cloud, normal vector, centre point coordinates and area of the planar objects. The results demonstrate that the HDL Graph SLAM achieves satisfactory precision, accuracy, and repeatability with a mean cloud-to-cloud distance of 0.12 m (with a standard deviation of 0.13 m) in an 80 m closed-loop measurement area. Although RTAB-Map exhibits better plane-capturing capabilities, the measurement results reveal instability and inaccuracies.

Gypsum cave notches and their palaeoenvironmental significance: A combined morphometric study using terrestrial laser scanning, traditional cave mapping, and geomorphological observations

Gypsum cave notches and their palaeoenvironmental significance: A combined morphometric study using terrestrial laser scanning, traditional cave mapping, and geomorphological observations

Benchmarking tree species classification from proximally sensed laser scanning data: Introducing the FOR‐species20K dataset

Abstract Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single‐tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. To address the above limitations, we compiled the FOR‐species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR‐species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP‐Mixer, DGCNNs) and multi‐view 2D‐based methods (SimpleView, DetailView, YOLOv5). 2D Image‐based models had, on average, higher overall accuracy (0.77) than 3D point cloud‐based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top‐scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. The FOR‐species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state‐of‐the‐art of point cloud tree species classification methods.

Contrasting responses of bats and macro-moths to structural complexity in forest borders

Habitat fragmentation increases the proportion of forest borders in the landscape and many forest borders lose their structural complexity due to modern forestry practices. However, remnants of structurally complex deciduous forests can remain as ecotones between plantations and agricultural fields. In this study we used terrestrial laser scanning to measure structural complexity of different forest borders, measured microclimate, and surveyed bats and macro-moths to understand how these taxa are affected. Our aim is to disentangle the main drivers, direct or indirect, that influence bat and moth assemblages. We studied 79 forest borders, and surrounding landscapes and compared them with adjacent agricultural fields and coniferous plantations. Overall, we found less bat activity and lower macro-moth diversity in simple compared to complex borders. Using structural equation modelling, we show the contrasting responses of forest-specialist bats and moths to structural complexity; with bats responding positively and moths negatively. We found similar divergent results in relation to understorey openness; with increasing forest-specialist bat activity but a lower diversity of forest-specialist moths in more open borders. Understorey vegetation also appears to regulate microclimate with more open borders being warmer and less humid. This has a potential knock-on effect for bats as they favoured borders that were warmer and more humid. Surrounding land-cover was more important than structural complexity for generalist species; with increasing generalist bat activity due to a higher proportion of local deciduous forest cover and increasing generalist moth diversity in landscapes with more forest borders. Overall, these complex relationships between forest structure, microclimate and landscape factors, coupled with divergent responses of both taxa highlight their diverse ecological needs. Therefore, we highlight the importance of managing forest borders to retain complexity and connectivity within multifunctional landscapes.

Evaluating the Accuracy of 3D Point Cloud Data of Elevated Structures Using Terrestrial Laser Scanners at Various Distances

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.

Automated 3D Modeling vs. Manual Methods: A Comparative Study on Historic Timber Tower Structure Assessment

The present study focuses on the preservation of historic timber constructions, crucial cultural heritage assets that demand effective structural health monitoring (SHM) to ensure safety and integrity. SHM aims to detect and evaluate potential structural deviations that may compromise performance, requiring both detailed geometric data acquisition and 3D modeling. For this purpose, contactless tools such as photogrammetry, laser scanning, and other topographic methods are employed to gather point cloud data. This research utilizes a terrestrial laser scanner (TLS) to generate 3D models of the historic timber tower of St. Michaeler church in Vienna. A novel automated modeling method is compared with two manual modeling approaches. The first is a traditional as-designed structural model created in Dlubal RSTAB software, and the second is a manually generated as-built model created using a scan-to-BIM application in Revit. While the first model is based on 2D plan documents created from the TLS point cloud, the second and automated models use the point cloud as direct input. The findings demonstrate that this automated model significantly enhances early-stage structural assessment efficiency, providing reliable insights into structural conditions with minimal processing time. This research underscores the potential of automated 3D modeling in preliminary structural assessments of historic timber structures.

TLSynth: A Novel Blender Add-On for Real-Time Point Cloud Generation from 3D Models

Point clouds are a crucial element in the process of scanning and reconstructing 3D environments, such as buildings or heritage sites. They allow for the creation of 3D models that can be used in a wide range of applications. In some cases, however, only the 3D model of an environment is available, and it is necessary to obtain point clouds with the same characteristics as those captured by a laser scanner. For instance, point clouds may be required for surveys, performance optimization, site scan planning, or validation of point cloud processing algorithms. This paper presents a new terrestrial laser scanner (TLS) simulator, designed as a Blender add-on, that produces synthetic point clouds from 3D models in real time. The simulator allows users to adjust a set of parameters to replicate real-world scanning conditions, such as noise generation, ensuring the synthetic point clouds closely mirror those produced by actual laser scanners. The target meshes may be derived from either a real-world scan or 3D designs created using design software. By replicating the spatial distributions and attributes of real laser scanner outputs and supporting real-time generation, the simulator serves as a valuable tool for scan planning and the development of synthetic point cloud repositories, advancing research and practical applications in 3D computer vision.

Examining low-intensity surface fires in boreal forests using sentinel-2 time series and bitemporal terrestrial laser scanning

ABSTRACT In Fennoscandia, wildfires are typically low-intensity surface fires that burn vegetation on the forest floor. Multispectral remote sensing, particularly the normalized burn ratio (NBR) index, is widely used to assess burn severity, but its effectiveness in identifying surface fires under dense canopies is less studied. This study examined the characteristics of surface fires across seven one-hectare test sites in Scots pine-dominated boreal forests in Finland. Fire-induced spectral changes, measured as difference normalized burn ratio (dNBR) values from Sentinel-2 data, were compared with structural changes on the forest floor measured using terrestrial laser scanning (TLS). Breakpoint analysis of NBR time series identified most surface fires, with a noticeable decline in NBR values. Fires not identified with the breakpoint analysis had less burned ground vegetation and denser canopy cover. A moderate correlation (r = –0.5) was observed between spectral and volumetric changes, with higher dNBR values corresponding to greater vegetation reduction. However, dense canopies lowered burn severity despite similar ground vegetation volume changes. NBR changes were explained by vegetation volume, canopy cover, and site conditions (R² = 84%). These findings improve understanding of the applicability of multispectral remote sensing in detecting fires, assessing burn area, and estimating burn severity in boreal forests.

Dynamic displacement measurement of a wind turbine tower using accelerometers: tilt error compensation and validation

Abstract. For vibration-based structural health monitoring (SHM) of wind turbine support structures, accelerometers are often used. Besides the structural acceleration, the measured quantity also contains the acceleration component due to gravity, which is known as tilt error. This tilt error must be quantified and taken into account; otherwise it can lead to incorrect evaluations, especially in the fatigue estimation or the dynamic displacement estimation using accelerometers. The standard solution is to explicitly measure the tilt angle, which requires an additional sensor for each measurement point and is not applicable for already recorded measurements without tilt information. Therefore, a novel tilt error compensation method is presented by using the static bending line. As a result the influence of the tilt error can be estimated in advance, and no additional sensors for tilt measurement are needed. The compensation method is applied to accelerometer measurements of an onshore wind turbine tower and validated with contactless absolute distance measurements from a terrestrial laser scanning (TLS) system. The position and frequency-dependent tilt error of the investigated tower has a significant influence on the quasi-static motion below 0.2 Hz with a minimum amplitude error of 9 %, whereas the normalised bending mode shapes around 0.3 Hz are only slightly affected.

Application of mobile laser scanner to augment stationary collected point clouds

ABSTRACT Acquiring precise and comprehensive representations of architectural structures or industrial installations is crucial for a variety of monitoring and cadastral applications. A number of tools have been developed for this purpose, but they often face the problem with balancing accuracy and acquisition time. Terrestrial laser scanners typically provide the highest precision in point clouds, as they perform scanning while motionlessly mounted on rigid tripods. Careful pre-planning is needed for scanner locations on the object under investigation. Limited number of scanning locations often compromises point clouds at inaccessible locations, especially in complex environments. In this paper, we propose a solution to this issue by employing an autonomous quadrupedal robot-based laser scanner to augment the point clouds obtained with high-precision stationary scanners. We demonstrate this approach in an auditorium consisting of stairs, multi-row seats and additional complex-shaped acoustic diffusors.

A Reconstruction of the Shrine of the Prophet Nahum: An Analysis of 3D Documentation Methods and Data Transfer Technology for Virtual and Augmented Realities

This article focuses on modern methods of documentation and visualization for a historic object. Digital photogrammetry and terrestrial laser scanning (TLS), which are essential tools for documenting cultural heritage in view of their rapid development in recent years, were used, compared, and analyzed. Furthermore, the use of available 3D computer graphics technologies for visualization is described and an optimal procedure for converting the object into VR and AR is proposed and implemented. The technologies presented in this article were tested within the context of a project on the reconstruction of the shrine of the Prophet Nahum in the city of Alqosh in northern Iraq, taking the shrine as a case study. Funded by ARCH Int. and provided by GemaArt Int., the restoration project started in 2018 and was completed in 2021. The ongoing documentation was prepared by the CTU and it used the materials for research purposes. Accurate documentation using photogrammetry, drones, and TLS was key to the restoration. Leica BLK360, Faro Focus S150, and GeoSlam laser scanners were used, as well as photogrammetric methods. In particular, the documentation process involved the creation of 3D textured models from the photogrammetry, which were compared to the TLS data to ensure accuracy. These models were necessary to track changes during the reconstruction phases and to calculate the volumes of rubble removed and materials added. Our data analysis revealed significant differences between the construction logs and the analysis of the accurate 3D models; the results showed an underestimation of the displaced material statements by 13.4% for removed material and 4.6% for added material. The use of heat maps and volumetric analyses helped to identify areas of significant change that guided the reconstruction and documented significant changes to the building for the investor. These findings are important for use in the construction industry with respect to historic sites as well as for further research focused on visualization using VR (virtual reality) and AR (augmented reality). The conversion of existing 3D models into VR and AR is rapidly evolving and significant progress was made during this project. The Unreal Engine (UE) game engine was used. Despite the significantly improved performance of the new UE 5 version, the data for conversion to VR and AR needs to be decimated to reduce the amount—in our case, this was by up to 90%. The quality appearance of the objects is then ensured by textures. An important outcome of this part of the research was the debugged workflow developed to optimize the 3D models for VR, which was essential for creating a virtual museum that shows the restoration process.

NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest

Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE (2.81%), which consistently outperformed standard cylinder modeling approaches.

A Method to Evaluate Orientation-Dependent Errors in the Center of Contrast Targets Used with Terrestrial Laser Scanners.

Terrestrial laser scanners (TLS) are portable dimensional measurement instruments used to obtain 3D point clouds of objects in a scene. While TLSs do not require the use of cooperative targets, they are sometimes placed in a scene to fuse or compare data from different instruments or data from the same instrument but from different positions. A contrast target is an example of such a target; it consists of alternating black/white squares that can be printed using a laser printer. Because contrast targets are planar as opposed to three-dimensional (like a sphere), the center of the target might suffer from errors that depend on the orientation of the target with respect to the TLS. In this paper, we discuss a low-cost method to characterize such errors and present results obtained from a short-range TLS and a long-range TLS. Our method involves comparing the center of a contrast target against the center of spheres and, therefore, does not require the use of a reference instrument or calibrated objects. For the short-range TLS, systematic errors of up to 0.5 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) and resolutions (30 points-per-degree (ppd) and 90 ppd) considered for this TLS. For the long-range TLS, systematic errors of about 0.3 mm to 0.8 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) at low resolution (28 ppd). Errors of under 0.3 mm were observed in the target center as a function of the angle for the two distances at high resolution (109 ppd).

A new approach for quantification of total above-ground heartwood and sapwood volume of trees

Key messageTerrestrial laser scanning data of trees combined with models of heartwood content proportion of woody disks can provideprecise characterization of total aboveground tree sapwood and heartwood volume.Quantifying sapwood and heartwood content of trees is challenging. Previous studies have primarily characterized main stem wood composition, while branches have rarely been studied. Terrestrial Laser Scanning (TLS) can provide precise representations of the entire above-ground tree structure, non-destructively, to help estimate total tree sapwood and heartwood volume. In this study, we used TLS to scan above-ground portions of twenty-four open-grown, urban Gleditsia triacanthos trees on Michigan State University campus. TLS data were used to generate quantitative structure models that provided comprehensive characterizations of the total tree woody surface area (WSA) and volume. A subsample of trees was harvested (after scanning) and main stem and branch woody disks were collected to build models of heartwood content proportion. Models were applied to measurements from TLS to quantify complete heartwood and sapwood volume of each tree, including main stem and branches. From the base to the top of the trees, the largest portion of stem vertical cumulative volume was heartwood, whereas vertical cumulative volume of branches showed the opposite pattern. Absolute heartwood volume declined monotonically toward zero from stem base to stem top, while absolute sapwood volume declined sharply from stem base up to near the crown base and then remained relatively constant within crown. We also found that tree WSA increased with sapwood volume for both branches and main stem. This study developed a novel, general method for quantifying total aboveground sapwood and heartwood volume of trees and provided new insights into urban tree growth and structure.

Three-Dimensional Digital Documentation for the Conservation of the Prambanan Temple Cluster Using Guided Multi-Sensor Techniques

The Prambanan Temple cluster is a world heritage site that has significant value for humanity, a multiple zone cluster arrangement of highly ornamented towering temples, and a Hindu architectural pattern design. It lies near the Opak Fault, at the foothills of Mount Merapi, on an unstable ground layer, and is surrounded by human activities in Yogyakarta, Indonesia. The site’s vulnerability implies the necessity of 3D digital documentation for its conservation, but its complexity poses difficulties. This work aimed to address this challenge by introducing the utilization of architectural pattern design (APD) to guide multi-sensor line-ups for documentation. First, APDs were established from the literature to derive the associated multiple detail levels; then, multiple sensors and modes of light detection and ranging (Lidar) scanners and photogrammetry were utilized according to their detail requirements and, finally, point cloud data were processed, integrated, assessed, and validated by the proof of the existence of an APD. The internal and external qualities of each sensor result showed the millimeter- to centimeter-range root mean squared error, with the terrestrial laser scanner (TLS) having the best accuracy, followed by aerial close-range and terrestrial-mode photogrammetry and nadiral Lidar and photogrammetry. Two relative cloud distance analyses of every point cloud model to the reference model (TLS) returned the millimeter and centimeter ranges of the mean distance values. Furthermore, visually, every point cloud model from each sensor successfully complemented each other. Therefore, we can conclude that our approach is promising for complex heritage documentation. These results provide a solid foundation for future analyses, particularly in assessing structural vulnerabilities and informing conservation strategies.

Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning

The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns.

Assessing the number and placement of ground control points in low-cost UAV photogrammetry

This study examines the impact of the number and placement of Ground Control Points (GCPs) on the accuracy of photogrammetric results using low-cost UAVs. The relevance of this research stems from the growing popularity and applicability of UAV photogrammetry, particularly in areas with diverse terrains and requirements for precise spatial data. The experiment leverages modern photogrammetric techniques, including Structure from Motion (SfM) algorithms, to analyze how GCP configurations affect error distribution and model accuracy. The study's first phase focused on evaluating the influence of GCP placement at varying heights. A photogrammetric survey was conducted at the Kyiv Hippodrome, utilizing terrestrial laser scanning (TLS) to establish high-precision coordinates for control points. The data acquisition involved the DJI Phantom 4 Pro V2 UAV, with multiple flight missions capturing images at 30-degree camera deviations from the nadir. Points were systematically analyzed by alternating their roles as GCPs and Control Points (CPs). The results demonstrated that errors significantly increased when CPs were located further from the UAV camera, emphasizing the need for proximity in GCP placement. The second phase analyzed how GCP configurations and quantities influence photogrammetric model accuracy. By forming 12 groups of GCPs, each varying in distribution and number, the study identified optimal setups for minimizing errors. Groups with evenly distributed points across the survey area, comprising at least eight GCPs, exhibited the lowest root mean square errors (RMSE). Conversely, configurations with GCPs concentrated along a single side or solely on the survey area's edges resulted in substantial inaccuracies. Key findings reveal that an effective GCP placement strategy involves prioritizing proximity to the UAV camera and achieving even distribution across the surveyed area. Additionally, configurations with fewer than eight GCPs tend to suffer from sharp declines in accuracy. The research underscores the importance of balancing GCP quantity and placement for achieving reliable photogrammetric outputs in low-cost UAV applications