In this study, we evaluated the performance of GeoSLAM ZEB Horizon and Stonex X120GO SLAM handheld mobile laser scanners (HMLS) to address two primary objectives. First, we aimed to assess and compare the accuracy of positioning achieved using HMLS instruments. Second, we sought to investigate the influencing factors and their impact on estimation accuracies. The factors influencing the accuracy of positioning in HMLS Simultaneous Localization and Mapping-aided solutions were defined, considering the scanner type, distance from the trajectory, forest structure, tree species, and Diameter at Breast Height. The same type of trajectory was tested in five different stand structures. The evaluation of GeoSLAM HMLS point clouds yielded an average positional RMSE of 17.91 cm, while the data extracted from the Stonex HMLS resulted in an average positional RMSE of 17.33 cm. These results underscore the significant potential of HMLS technology in addressing the critical need for precise positioning data in various applications, from forestry management to environmental monitoring, wildlife habitat assessment, and climate change studies. By harnessing the power of handheld mobile laser scanners, our research aims to enhance the accuracy and efficiency of geospatial data capture in challenging.

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.

Accurate and efficient forest inventory methods are crucial for monitoring forest ecosystems, assessing carbon stocks, and supporting sustainable forest management. Traditional field-based techniques, which rely on manual measurements such as diameter at breast height (DBH) and tree height (TH), remain labour-intensive and time-consuming. In this study, we introduce and validate a fully open-source, low-cost terrestrial laser scanning system (LCA-TLS) built from commercially available components and based on the Livox Avia sensor. With a total cost of €2050, the system responds to recent technological developments that have significantly reduced hardware expenses while retaining high data quality. This trend has created new opportunities for broadening access to high-resolution 3D data in ecological research. The performance of the LCA-TLS was assessed under controlled and field conditions and benchmarked against three reference devices: the RIEGL VZ-1000 terrestrial laser scanner, the Stonex X120GO handheld mobile laser scanner, and the iPhone 15 Pro Max structured-light device. The LCA-TLS achieved high accuracy for estimating DBH (RMSE: 1.50 cm) and TH (RMSE: 0.99 m), outperforming the iPhone and yielding results statistically comparable to the Stonex X120GO (DBH RMSE: 1.32 cm; p > 0.05), despite the latter being roughly ten times more expensive. While the RIEGL system produced the most accurate measurements, its cost exceeded that of the LCA-TLS by a factor of about 30. The hardware design, control software, and processing workflow of the LCA-TLS are fully open-source, allowing users worldwide to build, modify, and apply the system with minimal resources. The proposed solution thus represents a practical, cost-effective, and accessible alternative for 3D forest inventory and LiDAR-based ecosystem monitoring.

Recently, the cultivation of fast-growing tree (FGT) plantations has gained importance due to the growing energy and climate crisis. FGT plantations have the potential to reduce carbon footprints and lower greenhouse gas emissions by utilization of local renewable energy sources. Effective monitoring of above-ground biomass (AGB) is crucial for the successful management of these plantations. Standard methods for estimating AGB rely on easily measurable parameters, such as Diameter at Breast Height (DBH) and tree height, which are highly correlated with AGB. Traditional methods for measuring DBH include measuring tapes and calipers; however, these techniques can be labor-intensive, time-consuming, and limited when assessing large areas. Innovative approaches, such as photogrammetry, terrestrial laser scanning (TLS), mobile laser scanning (MLS), and iPhone LiDAR scanning, can complement these traditional methods by generating point clouds that can be used for extracting dendrometric parameters. This study evaluates the effectiveness of TLS (RIEGL VZ-1000), MLS (Stonex X120 GO), iPhone LiDAR (iPhone 13 Pro MAX), and terrestrial photogrammetry (iPhone 13 Pro MAX) for estimating DBH in a Paulownia plantation. Each technology has limitations: while TLS offers high accuracy, it is also expensive and time-consuming. Similarly, MLS is relatively costly. On the other hand, iPhone LiDAR and terrestrial photogrammetry are more affordable alternatives; however, the iPhone LiDAR has a limited scanning range, and photogrammetry requires considerable time and expertise for data collection and processing. The primary objective of this study was to evaluate these technologies based on their accuracy in DBH estimation, ease of use, data collection, processing time, and cost within the ideal conditions of a Paulownia plantation (characterized by the absence of understory, level ground, and uniform tree shape and spacing). The aim was to determine whether traditional methods could be replaced with more efficient, quicker, easier, and cost-effective alternatives. Results indicated that TLS, MLS, and photogrammetry provided similar DBH estimation accuracies, with root mean square error (RMSE) values between 0.7 and 0.72 cm and relative RMSE values between 2.87 % and 2.95 %. In contrast, the iPhone LiDAR was the least accurate, with an RMSE of 1.7 cm and an rRMSE of 6.96 %. This study demonstrates that all evaluated technologies offer sufficient accuracy for DBH estimation, although TLS and MLS capture additional parameters at a higher cost. Therefore, TLS is impractical for DBH estimation in plantation environments due to its high cost, time, and labor demands. While less expensive, terrestrial photogrammetry also requires significant time investment and operator expertise. Despite its cost, MLS achieved the best results among all the evaluated technologies and proved to be the fastest and relatively simple. If cost is a concern, the best solution for DBH estimation in an FGT plantation environment would be iPhone LiDAR scanning. It represents the most affordable option with satisfactory accuracy and ease of use.This abstract is based upon work from COST Action 3DForEcoTech, CA20118, supported by COST (European Cooperation in Science and Technology) and APVV 20 0391 Monitoring of forest stands in three-dimensional space and time by innovative close-range approaches.