Terrestrial Laser Scanning Scans Research Articles

Heritage Building Information Modelling (HBIM): A Review of Published Case Studies

Abstract. This paper presents a systematic review of published case studies on Heritage Building Information Modelling (HBIM) since 2018, and identifies research gaps in the subject matter. Building upon the foundational work of Ewart and Zuecco (2019), this research aims to reveal the latest trends in HBIM implementation, identify recent developments of HBIM technologies, changes in the purpose of HBIM programs and stakeholder roles and responsibilities, and uncover knowledge gaps that provide avenues for future research. Utilizing the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach, two major academic databases, Scopus and Web of Science (WOS), were searched, resulting in a rich and diverse dataset for analysis. The paper reports findings on the status of reality capture techniques used to acquire data for HBIM development, focusing on terrestrial laser scanning (TLS) technology. The review highlights the benefits and limitations of TLS for data acquisition in HBIM, as well as the integration of TLS with other reality capture technologies, such as Structure from Motion (SfM) and photogrammetry. The paper further outlines the typical workflow for processing TLS scan data and explores the integration of multiple point clouds for comprehensive heritage site modeling. In addition to the state of the art, this systematic review also uncovers several research gaps in the field of HBIM that offer opportunities for future research and innovation, including the lack of guidelines for data acquisition in HBIM programs, the predominantly manual development process of HBIM from TLS point cloud data, and the under-utilized capacity of TLS for long-term monitoring and change detection. This comprehensive review provides valuable insights into the current landscape of HBIM, offering guidance for future research and development in the heritage sector and highlighting areas in need of further investigation to advance the field.

COMPARATIVE ANALYSIS OF POINT CLOUDS ACQUIRED FROM A TLS SURVEY AND A 3D VIRTUAL TOUR FOR HBIM DEVELOPMENT

Abstract. This paper presents a comparative analysis of point clouds acquired from a terrestrial laser scanner (TLS) survey and a 3D virtual tour using Matterport technology for heritage building information modeling (HBIM) development. The study uses the Cloister of the Royal College of Corpus Christi Seminary, an important cultural heritage site in Valencia, Spain, as a case study. The point clouds from the TLS survey and Matterport scans were compared both quantitively in CloudCompare software and visually to assess their accuracy and quality. The Matterport point cloud data was found to be slightly lower in quality and accuracy compared to the TLS data, but still sufficient for developing some low-tolerance geometry in the HBIM model. The study shows that Matterport point cloud data has potential to supplement TLS scans, particularly in areas missed during the TLS survey due to range limitations. Matterport technology is accessible, affordable, and easy to use, making it a feasible option for heritage sites with limited resources. Moreover, Matterport technology captures high-quality visual data, including color and texture, providing a detailed representation of the heritage site. Thus, Matterport technology can be a valuable contribution to cultural heritage documentation and preservation, particularly for sites requiring a quick and efficient surveying process. The findings of this research offer insights into the relative advantages and limitations of these two reality capture techniques for cultural heritage documentation and preservation, and could potentially inform decision-making processes for future heritage preservation projects.

Qualitative Analysis of Tree Canopy Top Points Extraction from Different Terrestrial Laser Scanner Combinations in Forest Plots

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively.

A New Approach for Structural Monitoring Based on Terrestrial Laser Scan Data Using Control Planes

Laser scanning is a survey method that enables the obtaining of several data points on surfaces through the observation of horizontal and vertical angles and electronic distance measurements. The result of these observations is a set of 3D points named point clouds, which can be obtained from scans using total stations or the Terrestrial Laser Scanner (TLS). Currently, the use of TLS in geodetic structural deformation monitoring activities is under evaluation, since this technique can provide a greater number of points in a shorter period, when compared to scans performed by total stations. In this research, the feasibility of using control planes for geodetic monitoring was investigated in a controlled laboratory environment, exploring the current trend of parameterization of point clouds. From the development of an Experimental Plane Control (EPC) containing a flat surface that could be inclined in a controlled way, simultaneous scans with TLS and total station were carried out at different inclinations of the control plane. Both surveys were done in frontal scan mode (minimum plane inclination of 0º53’ and maximum plane inclination of 3º28’) and in oblique mode (plane inclination of 1º09’), where the plane inclination angles were generated by the rotation in EPC base, considering the first plane position as reference to inclination calculating. From parametrization of point clouds, a new approach was developed to test the significance of the estimated inclination angle of the plane in relation to a reference position. The results showed that it is possible to confirm that the inclined angles were statistically significant, but that the TLS scanning position can interfere in the determination of these data for monitoring purposes.

Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models.

Multiobjective Optimization-Based Terrestrial Laser Scanning Layout Planning for Landslide Monitoring

Terrestrial laser scanning (TLS) is an important means to monitor landslides, and the layout is the key to guarantee captured point clouds with a high quality and low cost. Nevertheless, TLS layout in landslide monitoring is currently determined by user’s subjective experience, and shows a poor performance in reliability and point accuracy. Therefore, we propose multi-objective optimization based TLS layout planning for landslide monitoring. Three-dimensional (3D) scanning simulation of candidate scan positions is first carried out. A multi-objective optimization problem (MOP) is then constructed according to requirements of landslide TLS monitoring in the coverage, cost, and point accuracy. Finally, the combination of an improved non-dominated sorting genetic algorithm Ⅱ (NSGA-II) and the Pareto-Edgeworth-Grierson (PEG) algorithm is introduced to solve the MOP and obtain the optimal layout. To test the proposed method, experiments were performed in two landslide sites in Wuhan, China. Results showed that the planned TLS layouts reached a trade-off among coverage, point accuracy, and the number of TLS scan positions. Comparison experiments based on simulation demonstrated that the constraints fused into NSGA-II dramatically improved the reliability of the planned TLS layout, and the proposed method improved the coverage and point accuracy by 11.62% and 1.52 times compared to the user’s experience. Real scanning demonstrated the proposed method improved the coverage by 10.48%, and reduced the deformation error by 0.78 mm~7.66 mm compared to the user’s experience. In summary, the proposed method is an efficient approach for TLS layout planning, which can improve the ability of landslide TLS monitoring.

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (≈136 points·m−2) of the initial point cloud density (≈37,240.86 points·m−2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs.

3D Graph-Based Individual-Tree Isolation (Treeiso) from Terrestrial Laser Scanning Point Clouds

Using terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-level to enable fine-scale forest management. However, there are technical barriers to converting point clouds into individual-tree features or objects aligned with forest inventory standards due to noise, redundancy, and geometric complexity. A practical model treeiso based on the cut-pursuit graph algorithm was proposed to isolate individual-tree points from plot-level TLS scans. The treeiso followed the local-to-global segmentation scheme, which grouped points into small clusters, large segments, and final trees in a hierarchical manner. Seven tree attributes were investigated to understand the underlying determinants of isolation accuracy. Sensitivity analysis based on the PAWN index was performed using 10,000 parameter combinations to understand the treeiso’s parameter importance and model robustness. With sixteen reference TLS plot scans from various species, an average of 86% of all trees were detected. The mean intersection-over-union (mIoU) between isolated trees and reference trees was 0.82, which increased to 0.92 within the detected trees. Sensitivity analysis showed that only three parameters were needed for treeiso optimization, and it was robust against parameter variations. This new treeiso method is operationally simple and addresses the growing need for practical 3D tree segmentation tools.

Estimation of vertical plant area density from single return terrestrial laser scanning point clouds acquired in forest environments

Plant area density (PAD in m2·m−3) defines the total one-sided total plant surface area within a given volume. It is a key variable in characterizing exchange processes between the atmosphere and land surface. Terrestrial laser scanning (TLS) provides unprecedented detail of the 3D structure of forest canopies. Yet, signal occlusion and uneven sampling density of the TLS point clouds limit our capacity to characterize the 3D distribution of canopy components. Recent studies have made use of statistical estimators of PAD that are applied to TLS point clouds subdivided into three-dimensional (3D) cubes, or voxels. Computation of such metrics under actual field conditions with point clouds containing several millions of returns is challenging. Moreover, rigorous assessment of the estimated PAD and effects of occlusions in forests remain unclear due to laborious, time-consuming, and inaccurate field measurements. In the present study, we present L-Vox, a software that computes PAD per voxel for TLS scans acquired in forest environments, which is based upon recent development of unbiased estimators derived from maximum likelihood. Two applications are presented. First, the software is evaluated for virtual forest plots, which are detailed 3D models of individual trees with corresponding simulated TLS scans, for which reference data are known. Second, L-Vox is applied to actual scans that were acquired in hardwood and coniferous plots in New Brunswick and Newfoundland, Canada. Both test cases were used to investigate the effects of occlusion and the uneven sampling in estimating PAD. The test cases were also used to assess the influence of voxel size and the number of scans per plot on PAD estimates. Our results showed strong correlations between the estimated PAD profile from L-Vox and simulated PAD for virtual forest plots, with a mean R2 = 0.98 and a mean coefficient of variation (CV) = 15.6%. We demonstrated that comparing multi-scan to single scan TLS acquisitions in real forest plots substantially reduced signal occlusion, resulting in an increase up to 50% in PAD values. Effects of voxel size on PAD estimates greatly depended upon the relative size of foliar and woody elements, with an optimal size around 10 cm in coniferous plots. L-Vox proved to be an efficient and accurate tool for computing 3D distributions of PAD from TLS measurements in natural forest environments.

OPTIMUM NUMBER AND POSITIONS OF TERRESTRIAL LASER SCANNER TO DERIVE DTM AT FOREST PLOT LEVEL

Abstract. Remote sensing of forest landscape is sometimes quite challenging, considering the rugged terrain, remote areas and presence of dense canopy. LiDAR is capable of generating dense three-dimensional (3D) structure of the forest. But when research is focused on a small area that needs dense information Terrestrial Laser Scanner (TLS) proves to be an efficient way of acquiring the information. TLS is an instrument used to acquire data from the ground surface level. Ground information and under canopy structure are acquired with more accuracy and precession with TLS when compared to aerial remote sensing techniques. When a TLS survey is done, scan positions and the number of scans are important parameters to consider to acquire datasets of forest plots. In this research, an analysis is carried out to determine the optimum number of TLS scans and positions that are needed to sufficiently generate the number of ground points to generate a Digital Terrain Model (DTM). The research considers two forest plots of 25m × 25m with different tree densities. Different TLS scanning combinations are used to determine the best scanning pattern for DTM generation at the forest plot level.

Volumetric overestimation of small branches in 3D reconstructions of <i>Fraxinus excelsior</i>

Terrestrial laser scanning (TLS) has been applied to estimate forest wood volume based on detailed 3D tree reconstructions from point cloud data. However, sources of uncertainties in the point cloud data (alignment and scattering errors, occlusion, foliage...) and the reconstruction algorithm type and parameterisation are known to affect the reconstruction, especially around finer branches. To better understand the impacts of these uncertainties on the accuracy of TLS-derived woody volume, high-quality TLS scans were collected in leaf-off conditions prior to destructive harvesting of two forest-grown common ash trees ( L.; diameter at breast height ~28 cm, woody volume of 732 and 868 L). We manually measured branch diameters at 265 locations in these trees. Estimates of branch diameters and tree volume from Quantitative Structure Models (QSM) were compared with these manual measurements. The accuracy of QSM branch diameter estimates decreased with smaller branch diameters. Tree woody volume was overestimated (+336 L and +392 L) in both trees. Branches measuring < 5 cm in diameter accounted for 80% and 83% of this overestimation respectively. Filtering for scattering errors or improved coregistration approximately halved the overestimation. Range filtering and modified scanning layouts had mixed effects. The small branch overestimations originated primarily in limitations in scanner characteristics and coregistration errors rather than suboptimal QSM parameterisation. For TLS-derived estimates of tree volume, a higher quality point cloud allows smaller branches to be accurately reconstructed. Additional experiments need to elucidate if these results can be generalised beyond the setup of this study.Fraxinus excelsior

A new index of forest structural heterogeneity using tree architectural attributes measured by terrestrial laser scanning

The maintenance or enhancement of structural heterogeneity (SH) of forest stands is considered a key element of sustainable forest management in central Europe, making the monitoring of stand structure an important task. However, existing approaches that enable the quantification of SH usually neglect the three-dimensional (3D) representation of forest structures. To overcome this limitation, terrestrial laser scanning (TLS) can be applied to provide an efficient, non-destructive and high resolution record of 3D structures. In this study, we present a new structural heterogeneity index (SHITLS) at the plot level which is based on 3D point clouds of individual trees that were derived from multiple scans. Because tree crowns are of particular importance for the 3D SH of forests, we focus particularly on outer and inner crown attributes. To test the robustness and the explanatory power of our index we sampled data on 84 plots across 12 different forest stand types in two study areas of Germany. We analyzed whether our index is able to explain differences between the different stand types. In addition, we compared our SHITLS to an existing stand structural complexity index (SSCI) that is based purely on single TLS scans. We found different trends of SHITLS between stand types dominated by Fagus sylvatica compared to those dominated by coniferous tree species (mainly Picea abies and Pinus sylvestris). Correlation analyses showed a significant, but weak, correlation with SSCI. The advantage of the SHITLS is the use of multiple scans from several scan positions. Thus, a better and very high resolution coverage of the forest structures is achieved. Due to its TLS-based nature this method provides many benefits in terms of reproducibility, objectivity and simplicity of data evaluation.

Assessing vertical structure of an endemic forest in succession using terrestrial laser scanning (TLS). Case study: Guadalupe Island

Endemic species comprise 16% of the floral species on Guadalupe Island, including three arboreal species. Almost 96% of the forest coverage was lost due to impacts related to feral goats and wildfires. To date, goats have been eradicated and the restoration of the native vegetation communities is underway. The purpose of this study was to develop a 3D Structural Classification Method (3D-SCM) using Terrestrial Laser Scanner (TLS) for mapping the vertical structure of the forest and to automatically characterize its physical attributes. Several TLS scans were performed in July 2016 to assess this recovery, particularly the succession progression. The 3D-SCM used the Forest Condition Classification (FCC) based on radiometric intensity and height values, and the Individual Shape Index (ISI) based on geometry and intensity parameters. It was designed to classify vegetation by stratum using a trial-and-error model to semi-automatically identify impacted areas and succession. The 3D-SCM was able to differentiate the vegetation strata by each forest community at the stand level with a precision of 93% as well as the tree structure parts (stem, branches, and leaves) with a precision of 97%, it also fits the best characteristic shape for the Guadalupe cypress (decagon) and for the pine (droplet). The resulting 3D-SCM provide precise physical specifications, demonstrates a high correlation R2 = 0.949 for (Diameter Breast Height) DBH, R2 = 0.974 for the crown and R2 = 0.97 for heights between maximum laser pulse of tree heights and crowns with respect to field measurements. Mapping 3D vertical forest measurements are important for quantifying the success of management practices and to assist future restoration actions, as it will allow quantification of forest dynamics and carbon sequestration on this island.

Deep Localization of Static Scans in Mobile Mapping Point Clouds

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

Urban Engineered Slope Collapsed in Rome on February 14th, 2018: Results from Remote Sensing Monitoring

On February 14th, 2018, in the North-Western sector of the Municipality of Rome (Central Italy), in the framework of an excavation for building construction, a portion of a piling wall piling wall collapsed in an already densely urbanized area. Soil behind the collapsed piling wall slipped inside the excavation site dragging seven cars parked on one side of the road running parallel to the piling wall and affecting some residential buildings located on the opposite side of the road. Fortunately, no injuries were counted but the 22 families living in the buildings next to the damaged wall were evacuated. Following the piling wall collapse, the Civil Protection of Rome, thanks to the technical support of the Research Centre on Geological Risks (CERI) of the Sapienza University of Rome, started a continuous monitoring of the affected area through remote sensing techniques. In the first hours following the collapse, a Terrestrial Synthetic Aperture Radar Interferometer (TInSAR) and a terrestrial laser scanner (TLS) were installed with the aim to control the evolution of the process, to support the local authority to manage the associated residual risk, and to ensure the safety of workers during emergency operations. In this paper we discuss some of the results obtained by the monitoring of the involved area. Thanks to the comparisons between different surveys and the reconstruction of the pre-event geometries, the total volume involved in the failure was estimated around 850 m3. In addition, through the analysis of data acquired by the 18 multi-temporal TLS scans and the three and a half months of continuous TInSAR monitoring, the movement involving a portion of the filling material used for stabilization works was observed and described. Such movement, reaching a total displacement of about 270–300 mm, was monitored and reported in real time.

A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments

Terrestrial laser scanning (TLS) has been recognized as an accurate means for non-destructively deriving three-dimensional (3D) forest structural attributes. These attributes include but are not limited to tree height, diameter at breast height, and leaf area density. As such, TLS has become an increasingly important technique in forest inventory practices and forest ecosystem studies. Multiple TLS scans collected at different locations are often involved for a comprehensive characterization of 3D canopy structure of a forest stand. Among which, multi-scan registration is a critical prerequisite. Currently, multi-scan TLS registration in forests is mainly based on a very time-consuming and tedious process of setting up hand-crafted registration targets in the field and manually identifying the common targets between scans from the collected data. In this study, a novel marker-free method that automatically registers multi-scan TLS data is presented. The main principle underlying our method is to identify shaded areas from the raw point cloud of a single TLS scan and to use them as the key features to register multi-scan TLS data. The proposed method is tested with 17 pairs of TLS scans collected in six plots across China with various vegetation characteristics (e.g., vegetation type, height, and understory complexity). Our results showed that the proposed method successfully registered all 17 pairs of TLS scans with equivalent accuracy to the manual registration approach. Moreover, the proposed method eliminates the process of setting up registration targets in the field, manually identifying registration targets from TLS data, and processing raw TLS data to extract individual tree attributes, which brings it the advantages of high efficiency and robustness. It is anticipated that the proposed algorithms can save time and cost of collecting TLS data in forests, and therefore improves the efficiency of TLS forestry applications.

A novel skyline context descriptor for rapid localization of terrestrial laser scans to airborne laser scanning point clouds

By utilizing the airborne laser scanning (ALS) and terrestrial laser scanning (TLS), the land surface information from both top view and side view can be captured rapidly. However, due to the different perspective views, resolutions, and ranges, the automatic localization of multiple TLS scans to ALS is challenging. To address this issue, this paper proposes a novel skyline context-based method. First, the ground points in ALS are extracted and used as potential TLS locations, and the corresponding skyline contexts are generated. After that, a 3D skyline-based k-d tree is built for searching the corresponding coarse localizations of TLS scans. The final refinement is done by the trimmed iterative closest point algorithm (T-ICP). 5 datasets with different ALS sizes and over one hundred TLS scans are undertaken to evaluate the performance of the proposed method. For one ALS data with mean point distance of 0.1 m, the average localization accuracy reached about 0.13 m. The experimental results indicate that the proposed method performs well for automatic localization of TLS scans to ALS point clouds, with advantages in both precision and adaptability.

Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS)

Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide.

Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.