Search Results (1,011)

Tree height and vertical forest structure are important attributes in forestry, but their traditional measurement or assessment in the field is expensive, time-consuming, and often inaccurate. One of the main advantages of using remote sensing data to estimate vertical forest structure is the ability to obtain accurate data for larger areas in a more time- and cost-efficient manner. Temporal changes are also important for estimating and analysing tree heights, and in many countries, national airborne laser scanning (ALS) surveys have been conducted either only once or at specific, longer intervals, whereas aerial surveys are more often arranged in cycles with shorter intervals. In this study, we reviewed all freely available national airborne remote sensing data describing three-dimensional forest structures in Slovenia and compared them with traditional field measurements in an area dominated by uneven-aged forests. The comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data, but they are still useful due to their temporally dense data. Up-to-date data are very important for forest management and the study of forest resilience and resistance to disturbance. Based on field measurements (2013 and 2023) and all remote sensing data, dominant and maximum heights are statistically significantly higher in uneven-aged forests than in mature, even-aged forests. Canopy height diversity (CHD) information, derived from lidar ALS and DAP data, has also proven to be suitable for distinguishing between even-aged and uneven-aged forests. The CHD_ALS 2023 was 1.64, and the CHD_CAS 2022 was 1.38 in uneven-aged stands, which were statistically significantly higher than in even-aged forest stands. Full article

Accurately quantifying fine-scale forest canopy-absorbed photosynthetically active radiation (APAR) is essential for monitoring forest growth and understanding ecological processes. The development of 3D radiative transfer models (3D RTMs) enables the precise simulation of canopy–light interactions, facilitating better quantification of forest canopy radiation dynamics. However, the complex parameters of 3D RTMs, particularly detailed 3D scene structures, pose challenges to the simulation of radiative information. While high-resolution LiDAR offers precise 3D structural data, the effectiveness of different tree crown reconstruction methods for APAR quantification using airborne laser scanning (ALS) data has not been fully investigated. In this study, we employed three ALS-based tree crown reconstruction methods: alphashape, ellipsoid, and voxel-based combined with the 3D RTM LESS to assess their effectiveness in simulating and quantifying 3D APAR distribution. Specifically, we used two distinct 3D forest scenes from the RAMI-V dataset to simulate ALS data, reconstruct virtual forest scenes, and compare their simulated 3D APAR distributions with the benchmark reference scenes using the 3D RTM LESS. Furthermore, we simulated branchless scenes to evaluate the impact of branches on APAR distribution across different reconstruction methods. Our findings indicate that the alphashape-based tree crown reconstruction method depicts 3D APAR distributions that closely align with those of the benchmark scenes. Specifically, in scenarios with sparse (HET09) and dense (HET51) canopy distributions, the APAR values from scenes reconstructed using this method exhibit the smallest discrepancies when compared to the benchmark scenes. For HET09, the branched scenario yields RMSE, MAE, and MAPE values of 33.58 kW, 33.18 kW, and 40.19%, respectively, while for HET51, these metrics are 12.74 kW, 12.97 kW, and 10.27%. In the branchless scenario, HET09′s metrics are 10.65 kW, 10.22 kW, and 9.79%, and for HET51, they are 2.99 kW, 2.65 kW, and 2.11%. However, differences remain between the branched and branchless scenarios, with the extent of these differences being dependent on the canopy structure. Our conclusion demonstrated that among the three tree crown reconstruction methods tested, the alphashape-based method has the potential for simulating and quantifying fine-scale APAR at a regional scale. It provides a convenient technical support for obtaining fine-scale 3D APAR distributions in complex forest environments at a regional scale. However, the impact of branches in quantifying APAR using ALS-reconstructed scenes also needs to be further considered. Full article

This paper explores the application of Airborne Laser Scanning (ALS) technology in the investigation of the medieval Norman site of Castel Fenuculus, in the lower Calore Valley, Southern Italy. This research aims to assess the actual potential of the ALS dataset provided by the Italian Ministry of the Environment (MATTM) for the detection and visibility of archaeological features in a difficult environment characterised by dense vegetation and morphologically complex terrain. The study focuses on improving the detection and interpretation of archaeological features through a systematic approach that includes the acquisition of ALS point clouds, the implementation of classification algorithms, and the removal of vegetation layers to reveal the underlying terrain and ruined structures. Furthermore, the aim was to test different classification and filtering techniques to identify the best one to use in complex contexts, with the intention of providing a comprehensive and replicable methodological framework. Finally, the Digital Elevation Model (DTM), and various LiDAR-derived models (LDMs), were generated to visualise and highlight topographical features potentially related to archaeological remains. The results obtained demonstrate the significant potential of LiDAR in identifying and documenting archaeological features in densely vegetated and wooded landscapes. Full article

Augmented reality (AR) offers several advantages in the commercialization of wood products, increasing both the efficiency and the attractiveness of the process of presenting and selling them. The digitization and virtualization of wood features/products for the purpose of their economic valorization represent a significant advance in technology and its application in traditional industries such as wood processing and trade. We present a concrete process of digitization and virtualization of wood features through AR for the purpose of its commercial valorization. Three methods of object scanning are tested: convergent photogrammetry, LiDAR scanning using an iPhone, and handheld scanners. Wood samples with different textures, shapes, and surface properties were used for the research, while each method was tested on a trio of models. The methods showed specific limitations: convergent photogrammetry is time-consuming and prone to human error, LiDAR iPhone scanning provides lower output quality and struggles with reflective surfaces, while handheld scanners are expensive and require additional tools for capturing color. Convergent photogrammetry was evaluated as the optimal and available method for the widest range of users. The 3D models were integrated into the Virtual Wood Market application, created in the Unreal Engine environment. The use of augmented reality in wood product commercialization offers significant benefits, including enhanced material efficiency, improved design and fabrication processes, better supply chain management, and increased customer engagement. These advantages can lead to more sustainable practices and higher customer satisfaction, ultimately driving the success of wood product commercialization. Full article

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through the genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. These findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements. Full article

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods. Full article

In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final versions translate into a high demand for disk space and therefore electricity. It is therefore obvious that in order to reduce energy consumption, lower the carbon footprint of the activity and sensitize sustainability in the digitization of the industry, lossless compression of the aforementioned datasets is a good solution. In this article, a new format for point clouds—3DL—is presented, the effectiveness of which is compared with 21 available formats that can contain LiDAR data. A total of 404 processes were carried out to validate the 3DL file format. The validation was based on four LiDAR point clouds stored in LAS files: two files derived from ALS (airborne laser scanning), one in the local coordinate system and the other in PL-2000; and two obtained by TLS (terrestrial laser scanning), also with the same georeferencing (local and national PL-2000). During research, each LAS file was saved 101 different ways in 22 different formats, and the results were then compared in several ways (according to the coordinate system, ALS and TLS data, both types of data within a single coordinate system and the time of processing). The validated solution (3DL) achieved CR (compression rate) results of around 32% for ALS data and around 42% for TLS data, while the best solutions reached 15% for ALS and 34% for TLS. On the other hand, the worst method compressed the file up to 424.92% (ALS_PL2000). This significant reduction in file size contributes to a significant reduction in energy consumption during the storage of LiDAR point clouds, their transmission over the internet and/or during copy/transfer. For all solutions, rankings were developed according to CR and CT (compression time) parameters. Full article

In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study evaluated the accuracy of forest resource assessment methods using terrestrial laser scanning (TLS) and backpack personal laser scanning (BPLS) under Leaf-on and Leaf-off conditions in the Gari Mountain Forest Management Complex, Hongcheon, Republic of Korea. The research was conducted across six sample plots representing low, medium, and high stand densities, dominated by Larix kaempferi and Pinus koraiensis. Conventional field survey methods and LiDAR technologies were used to compare key forest attributes such as tree height and volume. The results revealed that Leaf-off LiDAR data exhibited higher accuracy in capturing tree height and canopy structures, particularly in high-density plots. In contrast, during the Leaf-on season, measurements of understory vegetation and lower canopy were hindered by foliage obstruction, reducing precision. Seasonal differences significantly impacted LiDAR measurement accuracy, with Leaf-off data providing a clearer and more reliable representation of forest structures. This study underscores the necessity of considering seasonal conditions to improve the accuracy of LiDAR-derived metrics. It offers valuable insights for enhancing forest inventory practices and advancing the application of remote sensing technologies in forest management. Full article

The deterioration of historical heritage has underscored the need for precise documentation and accurate measurements in restoration and conservation efforts. Detailed documentation not only enhances understanding but also provides architects and engineers with the necessary tools to optimize these processes. However, limited funding has prompted researchers to develop low-cost geomatic tools and methodologies, such as multi-image photogrammetry, to generate 3D point clouds. Technologies like miniaturized Light Detection and Ranging (LiDAR) sensors, integrated into Apple devices such as the iPhone and iPad since 2020, have made these tools more accessible. These sensors deliver direct time-of-flight measurements, enabling accurate 3D data acquisition of historical structures. Despite the critical role that scan parameters—such as scanning speed, sensor angle, lighting, or the distance from the scanned object—may play, there is a lack of detailed studies examining their effects in the literature. To address this gap, this paper employs Taguchi’s orthogonal arrays to define the optimal scan parameters for the LiDAR sensor on the 2022 iPad Pro. The optimized parameters are then used to scan a historical building. Full article

This research seeks to develop and test a rapid mapping approach using unmanned aerial vehicles (UAVs) and terrestrial laser scanning to provide precise, high-resolution spatial data for urban areas right after disasters. This mapping aims to support efforts to protect the population and infrastructure while analyzing the situation in affected areas. It focuses on flood-prone regions lacking modern hydrological data and where regular monitoring is absent. This study was conducted in resort villages and adjacent catchments in Bulgaria’s southern Black Sea coast with leading maritime tourism features, after a flash flood on 5 September 2023 caused human casualties and severe material damage. The resulting field data with a spatial resolution of 3 to 5 cm/px were used to trace the effects of the flood on topographic surface changes and structural disturbances. Flood simulation using UAV data and a digital elevation model was performed. The appropriateness of contemporary land use forms and infrastructure location in catchments is discussed. The role of spatial data in the analysis of genetic factors in risk assessment is commented on. The results confirm the applicability of rapid mapping in informing the activities of responders in a period of increased vulnerability following a flood. The results were used by Bulgaria’s Ministry of Environment and Water to analyze the situation shortly after the disaster. Full article

The combination of UAV-LiDAR and LiDAR-SLAM (Simultaneous Localization and Mapping) technology can overcome the scanning limitations of different platforms and obtain comprehensive 3D structural information of forest stands. To address the challenges of the traditional registration algorithms, such as high initial value requirements and susceptibility to local optima, in this paper, we propose a high-precision, robust, NDT-VGICP registration method that integrates voxel features to register UAV-LiDAR and LiDAR-SLAM point clouds at the forest stand scale. First, the point clouds are voxelized, and their normal vectors and normal distribution models are computed, then the initial transformation matrix is quickly estimated based on the point pair distribution characteristics to achieve preliminary alignment. Second, high-dimensional feature weighting is introduced, and the iterative closest point (ICP) algorithm is used to optimize the distance between the matching point pairs, adjusting the transformation matrix to reduce the registration errors iteratively. Finally, the algorithm converges when the iterative conditions are met, yielding an optimal transformation matrix and achieving precise point cloud registration. The results show that the algorithm performs well in Chinese fir forest stands of different age groups (average RMSE—horizontal: 4.27 cm; vertical: 3.86 cm) and achieves high accuracy in single-tree crown vertex detection and tree height estimation (average F-score: 0.90; R² for tree height estimation: 0.88). This study demonstrates that the NDT-VGICP algorithm can effectively fuse and collaboratively apply multi-platform LiDAR data, providing a methodological reference for accurately quantifying individual tree parameters and efficiently monitoring 3D forest stand structures. Full article

Three-dimensional object detection is a key task in the field of autonomous driving that is aimed at identifying the position and category of objects in the scene. Due to the 3D nature of data generated by LiDAR, most models use it as input data for detection. However, the low scanning resolution of LiDAR for distant objects has inherent limitations to the method, and multimodal fusion 3D object detection methods have attracted widespread attention, mostly using both LiDAR and camera data as inputs for detection. Certainly, multimodal methods can also lead to many problems, the two main ones being the incomplete utilization of camera features and rough fusion methods. In this study, we proposed a novel multimodal 3D object detection method named VirtualFilter, which uses 3D point clouds and 2D images as inputs. In order to better utilize camera features, VirtualFilter utilizes the image semantic segmentation model to generate image semantic features and uses the semantic information to filter the virtual point cloud data during the virtual point cloud generation process to enhance the data accuracy of the virtual cloud. In addition, VirtualFilter utilizes a better RoI feature fusion strategy named 3D-DGAF (3D Distance-based Grid Attentional Fusion), which employs a attention mechanism based on distance gridding to better fuse the RoI features of the original and virtual point clouds. The experimental results on the authoritative autonomous driving dataset KITTI show that this multimodal 3D object detection method outperforms the baseline method in several evaluation metrics. Full article

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R²) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture. Full article

In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-edge modus operandi for the automated recognition of architectural elements within the palace’s inner courtyard. Employing the well-established Random Forest algorithm, implemented in a Python environment, the framework begins with a comprehensive evaluation of the geometric features identified in the LiDAR point cloud. This process employs the Mean Decrease in Impurity metric to evaluate the relevance of each variable. To boost the accuracy and efficiency of the final classifications, the features are refined post-assessment, enhancing both the training phase and the algorithm’s later evaluation. The research’s findings demonstrate significant potential, supporting advancements in CAD systems and HBIM that will enable more precise, automated modelling of architectural elements, thereby enhancing the accuracy of digital reconstructions and improving conservation planning for heritage sites. Full article

The sudden development of systems capable of rapidly acquiring dense point clouds has underscored the importance of data processing and pre-processing prior to modeling. This work presents the implementation of a denoising algorithm for point clouds acquired with LiDAR SLAM systems, aimed at optimizing data processing and the reconstruction of surveyed object geometries for graphical rendering and modeling. Implemented in a MATLAB environment, the algorithm utilizes an approximate modeling of a reference surface with Poisson’s model and a statistical analysis of the distances between the original point cloud and the reconstructed surface. Tested on point clouds from historically significant buildings with complex geometries scanned with three different SLAM systems, the results demonstrate a satisfactory reduction in point density to approximately one third of the original. The filtering process effectively removed about 50% of the points while preserving essential details, facilitating improved restitution and modeling of architectural and structural elements. This approach serves as a valuable tool for noise removal in SLAM-derived datasets, enhancing the accuracy of architectural surveying and heritage documentation. Full article