Search Results (433)

This research advances the integration of terrestrial laser scanning (TLS) in heritage documentation, targeting the development of holistic and practical guidance for practitioners to adopt the technology effectively. Acknowledging the pivotal role of TLS in capturing detailed and accurate representations of cultural heritage, the study emerges against a backdrop of technological progression and the evolving needs of heritage conservation. Through a comprehensive literature review, critical case studies of heritage sites in the U.S., expert interviews, and the development of a TLS for Heritage Documentation Best Practice Guide (the guide), the paper addresses the existing gaps in streamlined practices in the domain of TLS’s applications in heritage documentation. While recognizing and building upon foundational efforts such as international guidelines developed over the past decades, this study contributes a practice-oriented perspective grounded in field experience and case-based analysis. The developed guide seeks to equip practitioners with structured methods and practical tools to optimize the use of TLS, ultimately enhancing the quality and accessibility of heritage documentation. It also sets a foundation for integrating TLS datasets with other technologies, such as Building Information Modeling (BIM), virtual reality (VR), and augmented reality (AR) for heritage preservation, tourism, education, and interpretation, ultimately enhancing access to and engagement with cultural heritage sites. The paper also critically situates this guidance within the evolving theoretical discourse on digital heritage practices, highlighting its alignment with and divergence from existing methodologies. Full article

Accurate assessments of aboveground biomass (AGB) stocks and their changes in extensive Miombo forests are challenging due to the lack of site-specific allometric equations (AEs). Terrestrial Laser Scanning (TLS) is a non-destructive method that enables the calibration of AEs and has recently been validated by the IPCC guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the wet Miombo forest of Katanga, in the Democratic Republic Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented strong performance metrics (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between sites and between plots within sites. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from −35.04 to −19.97% was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga. Full article

Developing accurate activity data on tree biomass using remote sensing tools such as LiDAR and drone-mounted sensors is essential for improving carbon accounting in the agricultural sector. However, direct biomass measurements of perennial fruit trees remain limited, especially for validating remote sensing estimates. This study evaluates the potential of terrestrial laser scanning (TLS) and drone-mounted RGB sensors (Drone_RGB) for estimating biomass in two major perennial crops in South Korea: apple (‘Fuji’/M.9) and citrus (‘Miyagawa-wase’). Trees of different ages were destructively sampled for biomass measurement, while volume, height, and crown area data were collected via TLS and Drone_RGB. Regression analyses were performed, and the model accuracy was assessed using R², RMSE, and bias. The TLS-derived volume showed strong predictive power for biomass (R² = 0.704 for apple, 0.865 for citrus), while the crown area obtained using both sensors showed poor fit (R² ≤ 0.7). Aboveground biomass was reasonably estimated (R² = 0.725–0.865), but belowground biomass showed very low predictability (R² < 0.02). Although limited in scale, this study provides empirical evidence to support the development of remote sensing-based biomass estimation methods and may contribute to improving national greenhouse gas inventories by refining emission/removal factors for perennial fruit crops. Full article

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article

In recent decades, climate change has led to more frequent episodes of extreme rainfall, increasing the risk of river flooding. Streams and rivers characterized by short flow times are subject to rapid and impressive floods; for this reason, the modeling of their beds is of fundamental importance for the execution of hydraulic calculations capable of predicting the flow rates and identifying the points where floods may occur. In the context of studies conducted on three watercourses in Calabria (Italy), different survey and restitution techniques were used (aerial LiDAR, terrestrial laser scanner, GNSS, photogrammetry). By integrating these methodologies, multi-resolution models were generated, featuring a horizontal accuracy of ±16 cm and a vertical accuracy of ±15 cm. These models form the basis for the hydraulic calculations performed. The results demonstrate the feasibility of producing accurate models that are compatible with the memory and processing capabilities of modern computers. Furthermore, the technique set up and implemented for the refined representation of both the models and the effects predicted by hydraulic calculations in the event of exceptional rainfall (such as flow, speed, flooded areas, and critical points along riverbanks) serves as a valuable tool for improving hydrogeological planning, designing appropriate defense works, and preparing evacuation plans in case of emergency, all with the goal of mitigating hydrogeological risk. Full article

The Amazon rainforest hosts a diverse array of forest types, including those where Brazil nut (Bertholletia excelsa) occurs, which plays a crucial ecological and economic role. The Brazil nut is the second most important non-timber forest product in the Amazon, a symbol of development and sustainable use in the region, promoting the conservation of the standing forest. Understanding the vertical structure of these forests is essential to assess their ecological complexity and inform sustainable management strategies. We used terrestrial laser scanning (TLS) to assess the vertical structure of Amazonian forests with the occurrence of Brazil nut (Bertholletia excelsa) at regional (Amazonas, Mato Grosso, Pará, and Amapá) and local scales (forest typologies in Amapá). TLS allowed high-resolution three-dimensional characterization of canopy layers, enabling the extraction of structural metrics such as canopy height, rugosity, and leaf area index (LAI). These metrics were analyzed to quantify the forest vertical complexity and compare structural variability across spatial scales. These findings demonstrate the utility of TLS as a precise tool for quantifying forest structure and highlight the importance of integrating structural data in conservation planning and forest monitoring initiatives involving B. excelsa. Full article

This study aims to segment individual tree structures (stem, crown, and ground) from terrestrial LiDAR-derived point cloud data (PCD) and to compare the segmentation accuracy between two models: XGBoost (machine learning) and PointNet++ (deep learning). A total of 17 input features were categorized into spatial coordinates and normals, geometric structure features, and local distribution features. These were combined into four input configurations and evaluated under three downsampling conditions (2048, 4096, and 8192 points), resulting in 12 experimental setups. XGBoost achieved the highest stem segmentation F₁-score of 87.8% using all features with 8192 points, whereas Point-Net++ reached 92.1% using only spatial coordinates and normals with 4096 points. The analysis of missegmentation patterns showed that XGBoost frequently confused structures near stem-to-ground boundaries and around branch junctions, while PointNet++ occasionally missegmented complex regions between stems and crowns. Regarding processing time, XGBoost required 10 to 47 min across all conditions, whereas Point-Net++ required 49 min for the 2048-point condition and up to 168 min for 8192 points. Overall, XGBoost provided advantages in computational efficiency and in generating feature-importance scores, while PointNet++ outperformed XGBoost in segmentation accuracy and the recognition of structurally complex regions. Full article

The Marine Atmospheric Boundary Layer (MABL), as a critical component of Earth’s climate system, governs the exchange of matter and energy between the ocean surface and the lower atmosphere. This study presents shipborne Doppler lidar observations conducted during 12 January to 3 February 2024, along the southeastern Chinese coast. Employing a Coherent Doppler Wind Lidar (CDWL) system onboard the R/V “Yuezhanyu” research vessel, we investigated the spatiotemporal variability of MABL characteristics through integration with ERA5 reanalysis data. The key findings reveal a significant positive correlation between MABL height and surface sensible heat flux in winter, underscoring the dominant role of sensible heat flux in boundary layer development. Through the Empirical Orthogonal Function (EOF) analysis of the ERA5 regional boundary layer height, sensible heat flux, and sea level pressure, we demonstrate MABL height over the coastal seas typically exceeds the corresponding terrestrial atmospheric boundary layer height and exhibits weak diurnal variation. The CDWL observations highlight complex wind field dynamics influenced by synoptic conditions and maritime zones. Compared to onshore regions, the MABL over offshore areas further away from land has lower wind shear changes and a more uniform wind field. Notably, the terrain of Taiwan, China, induces significant low-level jet formations within the MABL. Low-level jets and low boundary layer height promote the pollution episode observed by CDWL. This research provides new insights into MABL dynamics over East Asian marginal seas, with implications for improving boundary layer parameterization in regional climate models and advancing our understanding of coastal meteorological processes. Full article

The sawn timber production process generates up to 63% of residues during primary processing in sawmills. For this industry, the devaluation and disposal of these residues remain significant challenges; proper management requires a more accurate quantification of the volume. This study evaluates and compares two indirect methods for estimating the volume of stacked residues: one based on image processing and the other on terrestrial LiDAR technology. Residues of Pinus spp. from a sawmill were used, with their actual volume determined using a xylometer. The image-based method, which uses threshold-based segmentation, achieved a R² = 0.64 and RMSE = 0.006 m³. In contrast, the LiDAR-based method, which derives measurements directly from 3D reconstruction, obtained an R² = 0.506 and RMSE = 0.009 m³. Despite these differences, ANOVA testing (p > 0.05) indicated no statistically significant differences between the methods. The results suggest that both approaches may serve as preliminary tools for forest residue quantification and provide a solid foundation for future research aimed at developing field-applicable technological solutions. Full article

Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and −0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring. Full article

The use of UAVs for analyzing soil degradation processes, particularly erosion, has become a crucial tool in environmental monitoring. However, the use of LiDAR (Light Detection and Ranging) or TLS (Terrestrial Lasser Scanner) may not be affordable for many researchers because of the elevated costs and difficulties for cloud processing to present a valuable option for rapid landscape assessment following extreme events like Mediterranean storms. This study focuses on the application of drone-based remote sensing with only an RGB camera in geomorphological mapping. A key objective is the removal of vegetation from imagery to enhance the analysis of erosion and sediment transport dynamics. The research was carried out over a cereal cultivation plot in Málaga Province, an area recently affected by high-intensity rainfalls exceeding 100 mm in a single day in the past year, which triggered significant soil displacement. By processing UAV-derived data, a Digital Elevation Model (DEM) was generated through geostatistical techniques, refining the Digital Surface Model (DSM) to improve topographical change detection. The ability to accurately remove vegetation from aerial imagery allows for a more precise assessment of erosion patterns and sediment redistribution in geomorphological features with rapid spatiotemporal changes. Full article

The structural characteristics of hickory trees exhibit a significant correlation with their fruit yield. As a distinctive high-quality nut of Zhejiang Province, hickory is a unique high-end dry fruit and woody oil plant in China. However, the long growth cycle and extended maturation period make their management particularly challenging, especially in the absence of high-precision 3D digital models. This study aims to optimize hickory tree management and identify trees with the most optimal structural features. It employs gradient-boosted machine learning modeling based on 23 key tree characteristics, transforming the experiential knowledge of forest farmers into quantifiable parameters. The consensus model achieved an LOOCV average accuracy of 87%, a training set accuracy of 100%, and a test set accuracy of 78%. Through this approach, three structural parameters that significantly impact the hickory tree were identified: the number of branches, the total length of all branches, and the crown base height from the ground. These parameters were used to select trees with superior structural traits. Furthermore, a novel method based on distance metrics was developed to assess the structural similarity of trees. This research not only highlights the importance of incorporating tree structural characteristics into forest management practices but also demonstrates how modern technological tools can enhance the productivity and economic returns of hickory forests. Through this integration, both the sustainability and economic viability of hickory forests are improved. Full article

Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering dynamics. Validated via single-photon LiDAR field tests on diverse coastal terrains at Jiajing Island, China, BNR-B reveals the following: (1) Solar zenith/azimuth angles non-uniformly modulate noise fields—higher solar zenith angles reduce noise intensity and homogenize spatial distribution; (2) surface reflectivity linearly correlates with noise rate (R² > 0.99), while roughness governs scattering directionality through micro-facet redistribution. BNR-B achieves 28.6% higher noise calculation accuracy than Lambertian models, with a relative phase error < 2% against empirical data. As the first BRDF-derived solar noise correction framework for coastal LiDAR, it addresses critical limitations of isotropic assumptions by resolving directional noise modulation. The model’s adaptability to marine–terrestrial interfaces enhances precision in coastal monitoring and submarine mapping, offering transformative potential for geospatial applications requiring photon-counting LiDAR in complex environments. Key innovations include dynamic coupling of geometric optics and surface scattering physics, enabling robust spatiotemporal noise quantification, critical for high-resolution terrain reconstruction. Full article

The degree of tree curvature exerts a significant influence on the utilization of forestry resources. This study proposes an enhanced quantitative structural modeling (QSM) method, founded upon terrestrial laser scanning (TLS) point cloud data, for the precise extraction of 3D curvature characteristics of tree trunks. The conventional approach operates under the assumption that the tree trunk constitutes an upright rotating body, thereby disregarding the tree trunk’s true curvature morphology. The proposed method is founded on the classical QSM algorithm and introduces two zoom factors that can dynamically adjust the fitting parameters. This improvement leads to enhanced accuracy in the representation of tree trunk curvature and reduced computational complexity. The study utilized 146 sample trees from 13 plots in Jixi, Anhui Province, which were collected and pre-processed by TLS. The study combines point cloud segmentation, manual labeling of actual curvature and dual-factor experiments, and uses quadratic polynomials and simulated annealing algorithms to determine the optimal model factors. The validation results demonstrate that the enhanced method exhibits a greater degree of concordance between the predicted and actual curvature values within the validation set. In the regression equation, the coefficient of the two-factor method for fitting a straight line is 0.95, which is substantially higher than the 0.75 of the one-factor method. Furthermore, the two-factor model has an R² of 0.21, indicating that the two-factor optimization method generates a significantly smaller error compared to the one-factor model (with an R² of 0.12). In addition, this study discusses the possible reasons for the error in the results, as well as the shortcomings and outlook. The experimental results demonstrate the augmented method’s capacity to accurately reconstruct the 3D curvature of tree trunks in most cases. This study provides an efficient and accurate method for conducting fine-grained forest resource measurements and tree bending studies. Full article

Occupational Health and Safety (OHS) in agriculture is a critical concern worldwide, with self-propelled machinery accidents, particularly tip/roll-overs, being a leading cause of injuries and fatalities. In such a context, while great attention has been paid to machinery safety improvement, a major challenge is the lack of studies addressing the analysis of the work environment to provide farmers with precise information on field slope steepness. This information, merged with an awareness of machinery performance, such as tilt angles, can facilitate farmers in making decisions about machinery operations in hilly and mountainous areas. To address this gap, the Italian Compensation Authority (INAIL) launched a research programme to integrate georeferenced slope data with the tilt angle specifications of common self-propelled machinery, following EN ISO 16231-2:2015 standards. This study presents the first results of this research project, which was focused on vineyards in the alpine region of the Autonomous Province of Trento, where terrestrial LiDAR technology was used to analyze slope steepness. The findings aim to provide practical guidelines for safer machinery operation, benefiting farmers, risk assessors, and manufacturers. By enhancing awareness of tip/roll-over risks and promoting informed decision-making, this research aims to contribute to improving OHS in agriculture, particularly in challenging terrains. Full article