Daniel Tomčík

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.

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices.

Forestry machinery plays a key role in forest management, but its increasing weight significantly impacts soil condition. Machinery passage causes soil compaction, which alters the physical, chemical, and biological properties of the soil and affects CO₂ concentration. This study aimed to measure the impact of soil compaction on the evolution of CO₂ concentrations over three years. Research was conducted near Zvolen, Slovakia, where soil was compacted in 2020 by a forestry skidder. The intensity of compaction was evaluated using a digital penetrometer. CO₂ concentrations were measured with a Vaisala MI70 device, with 22 measurements taken post-compaction at an average interval of 52 days. Soil compaction was characterized by a derived penetration index. At a depth of 10 cm, the average penetration index was 119%, indicating a 19% increase in resistance. The highest index, 134%, was found at a depth of 3 cm. A correlation of 0.4 was found between the number of passes and CO₂ concentration at 30 cm depth, and 0.8 between penetration index and CO₂ concentration. Results showed a significant impact of forestry machinery on soil CO₂ concentration, even three years later. Therefore, operating practices should minimize machinery impact on forest soils.

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.

Soil disturbance and compaction are inherent in ground-based harvesting operations. These changes are affected by numerous factors, related mainly to the technical parameters of the machines, soil conditions, and the technology used. This study aimed to analyze the changes of surface layers of soil caused by skidder traffic without loads on the Cambisols of Western Carpathians. We observed changes in the soil bulk density and penetration resistance. The results showed that only machine traffic caused a 0.32 to 0.35 (g cm⁻³) increase in soil bulk density. Besides machine traffic, bulk density was affected by soil moisture content. Penetration resistance of soil increased by 0.15 to 1.04 (MPa) after traffic of 40 machines. Penetration resistance showed a lower increase after traffic, and regression and correlation analysis proved a relationship between penetration resistance, skeleton content, and penetration depth, besides the number of machine passes (r = 0.33–0.55). Observing the changes in the physical properties of soils caused by machine traffic allows for a more detailed view of the effects of forest harvesting machinery on forest soils.