Terrestrial Laser Scanning Research Articles

Efficient in-site laser scanning scheme with adaptive angular resolution for long-span bridge geometry measurement

For geometrical measurements on long-span bridges, Terrestrial Laser Scanning (TLS) using conventional scanning approach yields the model with over-dense points in close regions and point redundancy in background. This not only hampers the efficiency of on-site scanning but also complicates data post-processing. This study introduces an optimised scanning scheme incorporating automatic identification of bridge key components and fine scan planning with adaptable angular resolutions. A novel method for TLS pose calibration, leveraging geometric feature partitioning and image template matching is proposed, which aids in projecting bridge component labels from the design model to the scan model in the TLS’s view. Considering the substantial disparities in scanning distances across different regions, a multi-segment scan planning is proposed through the clustering of spherical angles and desired angular resolutions. The effectiveness of the proposed method is demonstrated through a field test on a long-span bridge under construction, specifically for deck height measurements.

Analysis of structural integrity through the combination of non-destructive testing techniques in heritage inspections: The study case of San Segundo's hermitage (Ávila, Spain)

The establishment of proper conservation and preservation actions has become one of the major concerns for historic cities. Given that possible damage affecting these buildings can be fast-acting and irreversible, the evaluation of the damage origin must be faced from the application of different prospecting possibilities adapted to the specific conditions of the approach. In this regard, the application of non-destructive testing techniques has become one of the most common practices for the characterization of pathologies in cultural heritage. The present research focuses on the analysis of the structural integrity of the Hermitage of San Segundo (center of Spain) through the application of these prospective methodologies that do not pose any damage to the asset. More specifically, the work combines geophysical and optical techniques (ground-penetrating radar, terrestrial laser scanner and drone with RGB camera, and infrared thermography) to provide a complete characterization of the analyzed building. Results show that the combination of the three non-destructive techniques allows detecting defects, defining their possible causes and providing a solid basis for future decision-making regarding maintenance and restoration actions. Methodological integration has shown that certain pathologies can only be detectable with specific techniques while others have been corroborated through the results obtained with the three testing methodologies.

Application and comparison of remote sensing techniques for data-driven disaster debris quantification

ABSTRACT Effective disaster debris management requires reasonable predictions pre-hazard and estimates post-hazard of debris for a community to get back to normal sooner. However, there is a lack of data related to post-disaster waste quantities that could validate and improve debris predictions. This knowledge gap can be addressed by using remote sensing technology to quantify disaster debris promptly following a hazard. This study aimed to demonstrate and compare multiple remote sensing tools available for quantifying disaster debris using post-disaster data collected following Hurricane Ida. The tools used in this study are satellite imagery, emergency response airborne imagery, unmanned aerial vehicles (UAVs), and terrestrial laser scanning (TLS). We found that satellite imagery is useful for quantifying vegetative debris generation and transportation across vast areas, however it is often limited by spatial and temporal resolutions. Emergency response airborne imagery, collected within days following the hazard, is well-suited for quantifying transported vegetative debris and can assist debris clearance of emergency service routes across large areas, although it can be limited spatially and temporally to the interests of the acquiring agency. UAVs and TLS can provide precise volumes of debris but UAVs may be a better option due to their lower cost and computational demand. Guidance is provided for selecting a remote sensing tool based on the desired application and available resources, which can assist decision making for disaster waste managers.

Flatness detection of super-flat emery floor based on terrestrial laser scanning

Traditional flatness detection method with 2-m ruler and feeler gauge can only manually measure to obtain data at sparse locations. Terrestrial Laser Scanning (TLS) has been widely used in the detection task of small-scale components. To overcome the time-consuming and laborious problem of traditional method and extend the application of TLS-based method, an efficient, full-coverage and intelligent TLS-based flatness detection method applicable to large-scale floors is presented in this paper to help with the acceptance of emery floor. An algorithm is designed to find the optimal position of ruler above the ground profile and measure the deviation between ruler and ground. The results of two comparison experiments demonstrates that the TLS-based method proposed in this paper has a high detection accuracy. In the detection task of a super-flat emery floor, floor plan labeled with pass rates can be easily obtained. Color maps marked with unqualified areas can be used as a guide for concrete polishing. By comparing row detection and column detection results, the pass rate is significantly directional in some conditions.

Design and Construction Aspects of Concrete Block Paved Vertical Traffic-Calming Devices Located in Home Zone Areas

In recent years, speed reduction measures have been increasingly used, especially in heavily urbanised areas. If local conditions allow, traffic-calming schemes are implemented, which include a variety of traffic-calming measures (TCM). Some of the most common traffic-calming measures are concrete block paved vertical traffic-calming devices (VTCDs), including speed tables, speed humps, speed bumps and raised pedestrian crossings. Different design and construction recommendations apply to the respective VTCD types. The aim of this article is to examine the effectiveness of VTCDs in speed reduction and the impact of horizontal forces on pavement conditions, both within and beyond VTCDs, after over a dozen years of exposure to traffic. For this purpose, speed surveys were carried out on selected two-way streets running through home zones in Poland. The pavements on selected VTCDs were identified and visually assessed for damage, and subsidence areas were estimated using the terrestrial laser scanning (TSL) technique. The analysis resulted in the development of the Deviation Model 3D (DM3D), showing local deviations from the theoretical surface, obtained by superimposing the two models, the Real Surface Digital Terrain Model (RS DTM) and the Theoretical Surface Digital Terrain Model (TS DTM). A comparative analysis of the pavement surface condition and the magnitudes of horizontal forces allowed us to identify the locations of critical spots in VTCDs. The results were used as the basis for developing recommended pavement structures and deriving engineering recommendations for concrete block paving in VTCDs.

Nonmatrix pores form high-permeability zones in carbonate layers: The Furna Feia cave in the Potiguar Basin, Brazil

Studying nonmatrix porosity in reservoirs is challenging, especially between core samples and seismic scales. To fill this gap, this work studied a karst system as an analog for a nonmatrix carbonate reservoir. We used a multidisciplinary approach, analyzing host rock data (petrography, petrophysics, and tectonic history) and porosity geometry (terrestrial laser scanner, ground penetrating radar, and field analysis). The karst system occurs in Cretaceous carbonates of the Jandaíra Formation along a regional subaerial unconformity that forms the boundary between lagoonal facies (bed package II) and tidal bar facies (BP-III). BP-I (tidal bar 1) is observed at the base of the cave, while BP-II is in the middle of the cave walls, and BP-III (tidal bar 2) occurs at the ceiling. We find that matrix pores are more pervasive in BP-I. Nonmatrix pores predominate in BP-II and BP-III but mostly in the boundary between them. Horizontal dissolution (decametric-cavernous pores) is more intense along the subaerial unconformity. Metric nonmatrix pores, such as gigapores (pockets) and cavernous pores (cupolas), occur within dolomitic lagoonal facies but predominate in tidal bar facies at the cave ceiling. Most of these metric pores are elongated, and their principal axes are preferentially oriented along NW–SE-striking fractures, indicating tectonic control of the Poço Verde Fault Zone. In this way, we conclude that nonmatrix porosity follows tectonic features below a subaerial unconformity, forming high-permeability zones in carbonate layers. Dolomitization guides the fractures intensity, leading to their consequent enlargement. The features studied here led to cave evolution. These findings can guide karst reservoir prediction.

Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Branch architecture plays an important role in forest physical and ecological processes, greatly affecting forest spatial structure and the accumulation, production and distribution of organic carbon. Very few studies have quantified the branch architecture of large-scale forest plots, due to the lack of high-resolution large-scale three-dimensional data. The emergence of unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) provides great potential to overcome this limitation. However, due to insufficient quality of UAV-LiDAR data for branch reconstruction, existing algorithms remain tremendously challenging. We propose an effective branch reconstruction algorithm for UAV-LiDAR data to quantify branch architecture. First, individual branches are extracted using a region growth method that is guided by the branch growth direction and local smoothness constraint. Second, incorrect branches are eliminated based on three pieces of branch features, i.e., specific angles between branches at the same whorl, specific height differences between branches at different whorls, and the growth pattern of branches from the stem outward, reaching maximum distances at tips. Finally, branches are reconstructed using polynomial fitting, and branch architecture parameters are extracted based on branch models. The proposed algorithm simplifies branch reconstruction by skipping the separation of photosynthetic and non-photosynthetic components. It also adapts well to UAV-LiDAR point clouds, generating more realistic reconstructions. The algorithm successfully identified non-photosynthetic components in experiments involving 240 trees, including Scots pine, Norway spruce, and Radiata pine. The average overall accuracy for these three species was 0.88, 0.76, and 0.74, respectively. The proposed algorithm was tested for branch reconstruction using UAV-LiDAR data from a two-hectare Larix principis-rupprechtii plot. The accuracy of branch identification and parameter extraction accuracy were evaluated branch by branch based on the individual branches manually identified in terrestrial laser scanning data. Results showed the F-score of branch and stem identification was 0.58 and 1. The relative RMSE of branch length and angle was 36.87% and 18.3%, and ones of stem length and diameter were 1.46% and 6.16%. The proposed algorithm outperformed the well-known reconstruction algorithms, including TreeQSM, AdTree and Laplacian-based algorithms.

Побудова твердотілої моделі історичного об’єкту: на прикладі форту «Марушка Ліс»

Purpose of this work is to use terrestrial laser scanning for analysis and construction of a digital model of the fort “Marushka Lis” focusing on the process of data collection, processing, and their further use, particularly for web mapping of post-military sites. In the modern world, the preservation and restoration of historical structures is an important task that requires the application of advanced technologies. Methodology. Terrestrial laser scanning serves as an indispensable tool in this process, providing accurate and detailed spatial data. A technological scheme for creating a digital model of a historical object has been proposed. The study of historical objects using various methods of information collection was considered, namely: topographic surveying, terrestrial digital photogrammetry, terrestrial laser scanning, ground-penetrating radar studies, tachymetric surveying, GNSS. The methodology for creating a digital model of a historical object and the presented technological scheme, which includes data collection, processing of the obtained results, and output data, are described. Results. A comprehensive approach was applied to create the model, which included studying the history of the object, terrestrial laser scanning, terrestrial digital photogrammetry, and conducting geodetic measurements. A key aspect was the accurate and detailed reproduction of all architectural and structural elements of the fort. The use of software such as Leica Cyclone REGISTER 360, Autodesk ReCap, and Autodesk Revit allowed the integration of collected data into one accurate and detailed model. The sequential execution of work according to the proposed methodology enabled the creation of a digital model of the fort “Marushka Lis.” Scientific novelty and practical significance. The scientific novelty lies in the use of terrestrial laser scanning and 3D modeling for the reconstruction of a destroyed post-military object. The obtained digital model of the fort “Marushka Lis” will be used in web mapping of post-military sites in Lviv region.

Comparison of As-Built Surveys Using Handheld Laser Scanner and Conventional Method

With the advent of laser scanners in general, handheld laser scanners have become a tool used by surveyors to capture point clouds. The handheld laser scanner is useful for tight and confined spaces where a terrestrial laser scanner or a human cannot enter. Many surveyors still rely on the conventional method, which involves using a distometer and measuring tape for their strata as-built surveys. This is due to the simplicity and cost-effectiveness of the equipment. However, the conventional method is highly susceptible to human errors, unlike the laser scanner, which captures all features in point clouds without human intervention. In this research, data collected from both handheld laser scanners and conventional methods are compared. The handheld laser scanner captured a total of three levels. After collecting data at the site, the GeoSLAM Hub software processed the point clouds. This software helps convert the 3D point clouds into a 2D plan, with dimensions listed for the subsequent comparison process. The comparison focuses on the X and Y dimensions of the unit, with a tolerance of ±0.1m as per the circular KPUP-1-2015-JILID-I-v2016 provided by JUPEM. Following the comparison, the Root Mean Square Error (RMSE) is determined, with the lowest and highest values being 0.018m and 0.024m, respectively. In conclusion, the results show a minimum difference of 0.001m and a maximum difference of 0.049m, which falls within the provided tolerance. The handheld laser scanner offers visual aids to surveyors, enabling them to identify discrepancies' locations quickly. However, its drawback lies in the equipment's cost. Ultimately, the strata as-built plan is prepared for the three surveyed levels.

Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.

Digital 3D Reconstruction of Ancient Chinese Great Wild Goose Pagoda by TLS Point Cloud Hierarchical Registration

Digital reconstruction of ancient buildings is very challenging due to its architectural complexity and structural delicacy. Therefore, how to apply new Earth Observation (EO) technology to digitally reconstruct the complete and real model of ancient buildings without damage has become an urgent issue. This article proposes a multi-scan point cloud hierarchical registration method for 3D reconstruction of the ancient Chinese Great Wild Goose Pagoda based on Terrestrial Laser Scanner (TLS) data. First, a multi-scan point cloud hierarchical registration method is elaborately designed for 3D reconstruction. Then, the outline feature of the ancient Chinese Great Wild Goose Pagoda is automatically extracted by point cloud slicing. Finally, fine modeling is carried out according to the outline feature extracted from the reconstructed point cloud, and after that block combination and texture mapping are conducted for 3D digitalization and documentation. The experiments indicate that the Root Mean Square Error (RMSE) of multi-scan hierarchical registration of the translation and rotation are less than 3 cm and 0.862 ∘ , respectively, and the complete and real model of the Great Wild Goose Pagoda is obtained by fine modeling and texture mapping, which provides an important reference for 3D digital protection of ancient buildings.

A Broadly Applicable Coarse Alignment Framework for the Point Cloud of Hull Blocks

_ The terrestrial laser scanning (TLS) technology, which has been widely used in recent years, provides a new approach to control the construction quality of hull blocks in shipyards. As the cloud-model registration problem plays the most significant role in the data processing stage of utilizing the TLS devices, this paper concentrates on developing a broadly applicable coarse alignment framework for the point cloud of hull blocks. This framework involves two steps: the recognition of the connection regions of the point clouds and the coarse registration method according to the classification results. To detect the connection regions automatically, a hand-crafted simple model of the connection regions of hull blocks is built and its supporting detection method is proposed, besides, considering the recent overwhelming success of deep learning methods, a deep learning network suitable for large hull block datasets is constructed according to the Point-Net, and convolutional neural networks paradigms. Then, the coarse alignment method based on the detected connection regions is proposed. Experimental results illustrate the good performance of the proposed framework. Keywords computers in construction; shipbuilding; automation

Neighborhood competition modulates the link between crown structure and tree ring variability in monospecific and mixed forest stands

3D crown shape and tree ring development are autobiographies of the growth conditions. With advancements in terrestrial laser scanning, modeling growth from 3D crown structure offers insights into trees’ structural and functional responses in a non-destructive and repetitive way. However, how the trees in different growing conditions respond in 3D structure and manifest their responses into tree rings remains unexplored, which would help to better explain tree-level growth dynamics and forest management. To enhance this understanding, we tested a set of hypotheses: (HI) that 3D crown shape (CS) and tree ring (width) patterns (TRP) are correlated across different growing conditions or forest stands like monospecific, provenances trials, and mixed forest stands; (HII) that stand types influence the CS-TRP link; and (HⅢ) local neighborhood competition (LNC) modulates the CS-TRP link. (HⅣ) 3D crowns manifest local growth conditions; therefore, 3D crown structures can be used to predict tree ring growth. We assessed these hypotheses by employing terrestrial laser scanning-based 3D crown shape and dendrochronology-based tree ring width patterns from Norway spruce (Picea abies [L.] Karst.) trees growing in pure spruce (unthinned and thinned), provenances trial, and mixed-species trial (with European Beech, Fagus sylvatica [L.]) stands covering a large-scale competition gradient. We first show that 3D crown shape and TRP metrics differed significantly across forests (p<0.05) but were correlated (p<0.05). Neighborhood competition among the forest types influences the link between 3D crowns and tree rings. Pathway-based analyses revealed that neighborhood competition indirectly influences ring variability by modifying crown structure (p<0.05), suggesting local growth conditions are mostly manifested into crown shapes, leading to 3D crown shape-based low-error growth predictions (0.44 mm) across forest types. However, incorporating competition legacy information (competition over the last 30 years) in the model slightly improved the prediction performance (error reduced to 0.41 mm), further explaining that the crown likely loses growth information due to growing conditions (competition it faces and species with it is growing). This study reveals how trees in different growing conditions differed structurally and mechanized their responses in tree rings, providing crucial insights into tree-level growth dynamics and management.

Accuracy comparison of terrestrial and airborne laser scanning and manual measurements for stem curve-based growth measurements of individual trees

Monitoring forest growth accurately is important for assessing and controlling forest carbon stocks that impact, for example, the atmospheric CO2 concentration and, consequently, the climate change. In prior studies, forest growth monitoring with laser scanning methods has resulted in relatively high errors. However, the contribution of reference measurement error to uncertainty in growth resolution has rarely been analysed, and the reference measurements are usually considered mostly flawless. In this study, a seven-year-long growth of individual trees was estimated using both airborne and terrestrial laser scanning (ALS, TLS) that have emerged as potential candidates for digital forest reference measurements. The growth values were derived for diameter at breast height (DBH) and stem volume between the years 2014 and 2021 using an indirect approach. The values obtained with laser scanning were paired with manual field measurements and also with each other to study pairwise errors. The pairwise comparison showed that even though all the three measurement methods produced good Pearson correlation coefficients for one-time measurements (all above 0.88), the coefficients for growth measurements were significantly lower (0.19–0.44 for DBH and 0.47–0.66 for stem volume). The best correlation and root mean squared deviation (RMSD) for DBH growth (ρ = 0.44, RMSD = 0.98 cm) and stem volume growth (ρ = 0.66, RMSD = 0.052 m3) was observed between the manual field measurements and the ALS-based growth measurement method, in which the tree stem curve was obtained from the 2021 point cloud, and the stem curve was predicted backwards for the year 2014 according to height growth. The ALS method suffered less from outlying values than the TLS-based growth measurement method, in which the growth was computed based on the difference of stem curves derived separately for the years 2014 and 2021. The study showed that observing the stem curve is a potential method for short-period growth monitoring. Using the pairwise comparison results, we further derived estimates for the mean and standard deviation of measurement error of each individual measurement method. For the manual measurements, the standard deviation of error was found to be approximately 0.4 cm for DBH growth and 0.03 m3 for volume growth, which were the lowest of the three methods but not by a large margin. This highlights the need for more accurate reference data as the accuracy of laser scanning-based growth estimation methods continues to approach the accuracy of manual measurements.

Degradation of the 2014–2015 Holuhraun vent-proximal edifice in Iceland

AbstractThe earliest stages of volcanic vent degradation are rarely measured, leaving a gap in the knowledge that informs landform degradation models of cinder cones and other monogenetic vent structures. We documented the initial degradation of a 500-m-long spatter rampart at the primary vent of the 2014–2015 Holuhraun eruption in northern Iceland with high-resolution topographic change maps derived from terrestrial laser scanning (TLS) and photogrammetric surveys using an unoccupied aircraft system (UAS). Topographic differencing shows a total negative volume change of 42,637 m3, and a total positive volume change (basal deposition) of 10,304 m3 (primarily as deposition at the base of steeply sloping surfaces). Two distinct styles of volume changes were observed on the interior and exterior of the spatter rampart. Material on the interior of the vent was removed from oversteepened slopes by discrete rockfalls, while diffusive processes were qualitatively evident on the exterior slopes. We propose a novel conceptual landform evolution model for spatter ramparts that combines rockfall processes on the interior walls, diffusive gravitational sliding on the exterior slopes, and incorporates cooling contraction and compaction over the entire edifice to describe the observed modes of topographic change during the onset of degradation. Potential hazards at fresh spatter ramparts are rockfalls at high slope areas of the vent interior walls where contacts between spatter clasts are prone to weakening by fumarolic activity, weathering, and settling. To capture such hazards, our data suggest a cadence for monitoring changes yearly for the first few years post-eruption, and at longer intervals thereafter.

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.

Forest in situ observations through a fully automated under-canopy unmanned aerial vehicle

ABSTRACT Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory. Each solution has its advantages and disadvantages in terms of accuracy, completeness, and efficiency. For a forest area, Terrestrial Laser Scanning (TLS) has the highest data quality, but is limited to static perspectives and lack the efficiency. Mobile Mapping Systems (MMS) systems gain on the efficiency but compromise the data quality. More recently, under-canopy UAV caught attentions for its potential to leverage the advantages of both TLS and MMS systems. This study demonstrates the feasibility of autonomous forest in situ investigation using an autonomous under-canopy UAV Laser Scanning (ULS) system, and evaluates the performance of such system in deriving key forest and tree attributes through a comparison with other close-range sensing systems such as the TLS and the Personal Laser Scanning (PLS). The under-canopy ULS system uses an onboard LiDAR sensor to aid its self-traverse in an unknown forest environment and to collect point cloud data during its movement inside the forest. Key factors influencing the systems’ overall performance were investigated via various experiments. The point cloud data collected by the under canopy autonomous ULS system deliver similar stem capturing capacity as TLS in single layer forest stands with less undergrowth. The RMSEs of the DBH estimates were 0.81 cm (3.80%), 4.12cm (19.92%), and 5.13cm (22.01%), respectively. The RMSEs of the stem curve estimates were 1.27 cm (5.48%), 3.97 cm (17.63%), and 5.18 cm (22.49%), respectively. The geometric accuracy and the completeness of the point cloud significantly improved when the trajectory was densified. More studies on autonomous route planning in complex unknown forest is required to improve the system mobility, data quality, and the applicability of such systems in future practical forest in situ observations.

Estimation of Diameter at Breast Height in Tropical Forests Based on Terrestrial Laser Scanning and Shape Diameter Function

Estimating forest carbon content typically requires the precise measurement of the trees’ diameter at breast height (DBH), which is crucial for maintaining the health and sustainability of natural forests. Currently, Terrestrial Laser Scanning (TLS) systems are commonly used to acquire forest point cloud data for DBH estimation. However, traditional circular fitting methods face challenges such as a reliance on forest elevation normalization and underfitting of large trees. This study explores a novel approach, the Shape Diameter Function (SDF) algorithm model, leveraging the advantages of three-dimensional point cloud information to replace traditional circular fitting methods. This study employed a parallel approach, combining the Digital Elevation Model (DEM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to segment tree point clouds at breast height. Additionally, a point cloud SDF algorithm based on an octree structure was proposed to accurately estimate individual tree DBH. The research data were obtained from tropical secondary forests located in Cameroon, Peru, Indonesia, and Guyana, with forest ground point cloud data acquired via TLS. The experimental results demonstrated the superior performance of the SDF algorithm in estimating DBH. Compared with the Random Sample Consensus (RANSAC) and Hough transform methods, the Root Mean Square Error (RMSE) decreased by 28.1% and 47.8%, respectively. Particularly in estimating DBH for large trees, the SDF algorithm exhibited smaller errors, indicating a closer alignment between the estimated individual tree DBH values and those obtained from manual measurements. This study presented a more accurate DBH estimation algorithm, contributing to the exploration of improved forest carbon content estimation methods.

Conservation-oriented integrated approach for structural stability assessment of complex historic masonry structures

The paper presents a multidisciplinary approach for diagnosing the structural stability of the Takiyya of Ibrahim al-Gulshani in Historic Cairo using geometric and architectural documentation, field survey, laboratory tests, and numerical analyses under static and seismic conditions. The Takiyya, built during the Mamluk period, is an important medieval architectural complex in Egypt and has undergone multiple renovations. The field-testing includes geotechnical and geophysical investigations to evaluate the properties of the bearing soil beneath the monument. A 3D survey using Terrestrial Laser Scanning was employed to record Takiyya’s geometry. Terrestrial photogrammetry and high-resolution images accompanied by an in-situ damage survey were used to document cracking levels, stone weathering, and identification of decay patterns. Laboratory tests were conducted to assess the mineral composition, physical and mechanical properties of the construction materials to enable structural assessments. Data appraisal and integration into architectural drawings was used for mapping degradation patterns and the preservation state of the building. The documentation procedures combined with experiments were then employed to develop a model for structural analysis. Three-dimensional finite element numerical simulations employing macro-modelling approaches were performed for the Takiyya structures under various loading conditions, including modelling the bearing soil, resulting in good agreement with in-situ recorded structure damage patterns. The static and seismic nonlinear analyses helped identify the causes and effects of the current structural deficiencies. The adopted methodologies provided large amounts of information, permitting an improved understanding of existing damage and the assessment of the preservation state of the masonry structure. This project exists thanks to the Egyptian Ministry of Tourism and Antiquities (MoTA) and is funded by World Monument Fund (WMF) through various donors.

Giant sequoia (Sequoiadendron giganteum) in the UK: carbon storage potential and growth rates.

Giant sequoias (Sequoiadendron giganteum) are some of the UK's largest trees, despite only being introduced in the mid-nineteenth century. There are an estimated half a million giant sequoias and closely related coastal redwoods (Sequoia sempervirens) in the UK. Given the recent interest in planting more trees, partly due to their carbon sequestration potential and also their undoubted public appeal, an understanding of their growth capability is important. However, little is known about their growth and carbon uptake under UK conditions. Here, we focus on S. giganteum and use three-dimensional terrestrial laser scanning to perform detailed structural measurements of 97 individuals at three sites covering a range of different conditions, to estimate aboveground biomass (AGB) and annual biomass accumulation rates. We show that UK-grown S. giganteum can sequester carbon at a rate of 85 kg yr-1, varying with climate, management and age. We develop new UK-specific allometric models for S. giganteum that fit the observed AGB with r 2 > 0.93 and bias < 2% and can be used to estimate S. giganteum biomass more generally. This study provides the first estimate of the growth and carbon sequestration of UK open-grown S. giganteum and provides a baseline for estimating their longer-term carbon sequestration capacity.