Terrestrial Laser Scanning Data Research Articles

Static Terrestrial Laser Scanning (TLS) for Heritage Building Information Modeling (HBIM): A Systematic Review

Heritage Building Information Modeling (HBIM) is an essential technology for heritage documentation, conservation, and management. It enables people to understand, archive, advertise, and virtually reconstruct their built heritage. Creating highly accurate HBIM models requires the use of several reality capture tools, such as terrestrial laser scanning (TLS), photogrammetry, unmanned aerial vehicles (UAV), etc. However, the existing literature did not explicitly review the applications and impacts of TLS in implementing HBIM. This paper uses the PRISMA protocol to present a systematic review of TLS utilization in capturing reality data in order to recognize the status of applications of TLS for HBIM and identify the knowledge gaps on the topic. A thorough examination of the 58 selected articles revealed the state-of-the-art practices when utilizing static TLS technology for surveying and processing captured TLS data for developing HBIM models. Moreover, the absence of guidelines for using static TLS surveys for HBIM data acquisition, the lack of robust automated frameworks for producing/transferring 3D geometries and their attributes from TLS data to BIM entities, and the under-utilized application of TLS for long-term monitoring and change detection were identified as gaps in knowledge. The findings of this research provide stakeholders with a good grasp of static TLS for HBIM and therefore lay the foundation for further research, strategies, and scientific solutions for improving the utilization of TLS when documenting heritage structures and developing HBIM.

Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data

ABSTRACT Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views, resulting in an underestimation of tree height. Combining TLS with unmanned aerial vehicle laser scanning (ULS) is an effective way to overcome this limitation. Thus, it is vital to register multi-platform Light Detection and Ranging (LiDAR) data for various forestry applications. This study proposed three automated and nearly parameter-free optimized coarse-to-fine algorithms (i.e. FPFH-based optimized ICP (F-OICP), RANSAC-based optimized ICP (R-OICP), and NDT-based optimized ICP (N-OICP)) to accurately register TLS and ULS point data for individual tree crown delineation and parameters (diameter at breast height (DBH) and tree height) estimations in different forest types (i.e. coniferous, mixed broadleaf-coniferous, and broadleaf). Results showed that the proposed optimized algorithms had a good registration performance, with an average RMSE of about 8.3 cm for the transformation error; and obtained stable and high accuracies of individual tree crown delineation (ITCD) (F-score: 0.7), DBH (R2: 0.9, RMSE <1.85 cm), and tree height (R2: 0.8, RMSE <0.37 m) estimates for three forest types. F-OICP performed the best in tree height estimation, reducing the RMSE by 48%, 12%, and 12% compared to iterative closest point (ICP), R-OICP, and N-OICP, respectively. Stand type significantly impacted ITCD and individual tree parameter estimations. The ITCD and DBH estimation accuracy of coniferous forests were marginally higher than those of broadleaf forests (F-score: 0.78 vs. 0.78, DBH RMSE: 1.57 vs. 1.74), while those of mixed broadleaf-coniferous forests were the lowest (F-score: 0.71, DBH RMSE: 2.19). The accuracies of tree height estimates in coniferous forests were the highest (R2: 0.87, RMSE: 0.21 m), followed by mixed broadleaf-coniferous (R2: 0.84, RMSE: 0.37 m) and broadleaf (R2: 0.84, RMSE: 0.44 m) forests. This work developed automated, nearly parameter-free, and effective registration algorithms and recommended F-OICP to be the most appropriate for dense forests (i.e. natural secondary forests). The optimized registration algorithms facilitate the ability for the synergistic use of multi-platform LiDAR and offer appealing and promising approaches for future accurate quantification of individual tree parameters, efficient forest inventories, and sustainable forest management.

In a nutshell: exploring single tree parameters and above-ground carbon sequestration potential of common walnut (Juglans regia L.) in agroforestry systems

Although agroforestry systems (AFS) provide numerous ecosystem services and are a recognized strategy for climate change mitigation and adaptation, knowledge on the woody component is lacking. Single tree data could improve planning, management and optimization of AFS. One tree species which is of great interest due to its valuable timber and non-timber products is walnut (Juglans regia L.). We used terrestrial laser scanning data to fit quantitative structure models (QSMs) for 65 walnut trees in AFS with diameter at breast height (DBH) ranging from 1 to 77 cm. Based on the QSMs, volumetric information as well as height and crown parameters were derived. By combining the volumetric data with bark and wood density followed by carbon and nutrient concentration, whole tree biomass, nutrient and carbon content were derived. To enable the application of our results, we modeled allometric relationships based on the DBH. The maximum crown projection area of a tree was more than 340 m2, the maximum leafless above-ground dry biomass was 7.4 t and the maximum amount of stored carbon was 3.6 t (in metric tons). A modelled AFS comprising 15 trees per hectare with a target DBH of 60 cm projects at the end of its 60-year rotation period an above-ground tree volume of more than 100 m3, about 60 t of dry biomass and roughly 30 t of sequestered carbon. By producing allometric functions, we provide much needed information for small-scale modelling of AFS.

Optimal bucking of stems from terrestrial laser scanning data to maximize forest value

ABSTRACT An optimal bucking strategy that allocates cutting patterns to forest stands based on the individual characteristics of each stem is critical for maximizing value recovery. However, cutting patterns are usually excluded from bucking algorithms due to the difficulties associated with capturing tree quality features when collecting forest inventory data (e.g. branchiness and tree shape). This paper presents a non-destructive and fully automated methodology for the optimal bucking of stems based on terrestrial laser scanning (TLS) point clouds that aims to maximize the economic value of trees in a forest stand. It is based on the three-dimensional modelling of stems and includes the diameter and curvature of each log. The bucking algorithm also considers several timber products and calculates the most valuable log combination for each tree. The methodology was tested in a Pinus radiata plot with 120 trees, and the results were compared with those obtained with input data that do not take curvature into account: i.e. only diameters from TLS and taper equations. The analysis of the results suggests that not including curvature in the algorithm for optimal bucking results in an overestimation of the commercial value of timber products.

Registration of terrestrial laser scanning data in forest areas using smartphone positioning and orientation data

ABSTRACT Terrestrial laser scanning (TLS) is an effective tool in forest inventory, but multiple scans are commonly required to cover the whole plot. Thus, it is necessary to register different scans into uniform coordinate system. Including auxiliary information to simplify registration is an effective idea, e.g., levelling scanner and measuring initial scanning direction, but requires additional survey equipment. In this letter, a registration method for forest TLS data is proposed, using smartphone to obtain auxiliary information. Scanner position and initial scanning direction are measured by smartphone for each scan and then used to calculate pair-wise coarse transformation parameters. In fine registration, we construct a series of transformation parameters based on coarse registration result and evaluate them by two-level objective functions, using stem positions as input. In this stage, stems far away from origin are excluded with filtering threshold set as the horizontal distance between scanner positions. The proposed method is validated on six scan pairs, with distance between scan positions ranging from 19 m to 40 m. The mean rotation, horizontal and vertical translation errors are 4.3′, 8.3 mm and 35 mm. Comparison with previous study shows auxiliary information from smartphone is beneficial for registration and filtering distant stems can improve registration accuracy.

Estimating plant area density of individual trees from discrete airborne laser scanning data using intensity information and path length distribution

Plant area density (PAD) of individual trees is an important structural indicator related to tree growth status, stress levels due to pests and diseases, photosynthesis potential, and evapotranspiration. Airborne laser scanning (ALS) provides unprecedented 3D information for mapping forest canopy parameters. Previous studies mainly focused on mapping stand-level and 2D leaf area index. This study proposes a method to estimate PAD from discrete and multiple return ALS data at individual tree scales. The proposed method uses path length distribution to eliminate crown-shape-induced clumping, as well as intensity information to estimate crown transmittance from relative low-density points. The path length distribution is derived from the 3D crown boundary contours created by an alpha shape algorithm, which explicitly considers the non-uniform LiDAR pulse penetration distances. Pulse intensity is calibrated with the nearest pure-ground pulse to mitigate the need for prior leaf and ground reflectance information, which can be used in areas with a heterogeneous background. The proposed method was evaluated both in virtual experiments as well as with terrestrial laser scanning (TLS) data. The virtual experiments used the large-scale remote sensing data and image simulation model (LESS) to simulate virtual ALS scanning data based on abstract and realistic canopies. Results showed that the ALS-derived PAD is highly accurate, with RMSE less than 0.02 and R2 > 0.99 for the abstract sphere and cube crowns, and RMSE = 0.19 and R2 = 0.578 for the realistic crowns. The comparison with TLS of a birch plot shows that the ALS-derived PAD is consistent with those derived from TLS, with RMSE = 0.14 and R2 = 0.46. This study demonstrated that using the full intensity and geometry information of a point cloud is capable of generating high-resolution forest parameters from ALS data.

Forest Structure Measurement in Tropical Rainforest using Laser Scanning Technology

Numerous measurements can be made to extract the forest structure. Forest structure is one of the main aspects of forest management. The precise estimation of forest structure is vital for some forestry applications. Thus, this study presents a novel non-destructive approach for the measurement of forest structure using laser scanning technologies of airborne LiDAR and Terrestrial Laser Scanning (TLS). The study area was located at the forest campus of the Forest Research Institute of Malaysia (FRIM), Kepong, Selangor. The elements of forest structure that were measured: were canopy height; plant density and basal area. The field survey was conducted over 3 plots of 25 m radius circular shape with a total of 60 trees with diameter at breast height (DBH) of 10 cm and above. The tree canopy height was estimated based on canopy height model (CHM) of LiDAR data. For plant density, it was estimated based on crown delineation created from CHM. While, for TLS data, the extraction of individual trees was done using Cyclone algorithm. The forest structure measurement obtained from laser scanning technologies is proven to be reliable with the root mean square error (RMSE) of 5.415, t-test of 3.011, and p-value of 0.004 for canopy height. For basal area, the mean of RMSE, t-test and p-value was 0.22589, 0.620 and 0.324 for the overall 3 plots, respectively. The result obtained for plant density was one tree per meter². The final outputs were presented as the map of CHM, plant density and basal area map. In conclusion, laser scanning measurement improves and provides a precise technique for forest structure measurement.

A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds

Spatially continuous mapping forest aboveground biomass (AGB) is crucial for better understanding the capacities of carbon sequestration capacities of forest ecosystems at both individual tree and landscape levels. Collecting field data is one of the most labor-intensive and time-consuming tasks in biomass mapping using airborne laser scanning (ALS) data. Building on a LiDAR biomass index (LBI) developed for use with terrestrial laser scanning (TLS) data, we successfully developed an improved and robust LBI-based approach to estimate forest AGB at both individual tree and plot levels while minimizing the effort required for field data collection. This approach was tested for larch, birch, and eucalyptus over three forest farms in Northeast China and one in Southern China. The results showed that LBI was highly correlated with the diameter, height, and AGB of larch trees. AGB estimates derived using LBI-based models for the three tree species were close to ground measurements at the individual tree level. They explained 81% to 95% of the variance of independent test data not used to calibrate those models. Tree level AGB estimates are required by many applications, but they could not be provided by commonly used plot-based biomass mapping approaches like LiDAR metrics-based regression (LMR) or Random Forest (RF). Calibrated with small fractions of the trees needed to calibrate LMR and RF models, LBI-based biomass models produced plot level biomass estimates comparable to or better than those produced using the two plot-based methods. More importantly, the LBI-based models generalized far better than LMR and RF among the three larch forest farms located hundreds of kilometers apart. These promising results warrant more research on the effectiveness of the LBI-based approach for other forest types and tree species not considered in this study. As LiDAR technology and related algorithms are evolving rapidly, further improvements to this approach might be feasible. A robust LBI-based approach applicable to a wide range of tree species and forest types across the globe will greatly facilitate the use of increasingly better and more affordable ALS data to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) and other forest-based climate mitigation initiatives.

Semi-automated Generation of Geometric Digital Twin for Bridge Based on Terrestrial Laser Scanning Data

Digital twins (DTs) have a great potential for bridge operation and maintenance. Geometric digital twins (gDTs) are the key component of DTs. At present, a growing number of researchers are using high-precision 3D laser point clouds to generate gDTs. However, for large bridges, such as arch, cable-stayed, and suspension bridges, comprehensive point-cloud collection stations are difficult to set up due to their large span, narrow site, and limited field of vision. Consequently, the complete point clouds of these bridges cannot be easily obtained. Thus, knowing how to process absence point clouds and generate gDTs is an urgent problem. This study proposes a semiautomatic method of extracting geometric information of a bridge’s components in the absence of point clouds. First, an algorithm based on the combination of the iterative polynomial fitting curve and sliding window is developed to extract the arch ring accurately. Second, an improved random sample consensus (RANSAC) algorithm based on distribution density is adopted to extract the cross sections of the arch bridge components, except the arch ring. For cross sections that lack point clouds, a translation strategy is used to supplement the unknown line segment. Finally, for the T-beam, a model alignment method is proposed to best match the characteristic intersections extracted by the improved RANSAC algorithm and the points corresponding to the design model. The quality of the generated models is gauged using a point cloud deviation chromatogram. In addition, the stressed component piers are compared with its design parameters to verify the accuracy of the proposed method. Results show that our method can efficiently and accurately extract geometric information and generate gDT for the bridge.

Uncertainty assessment of a permanent long-range terrestrial laser scanning system for the quantification of snow dynamics on Hintereisferner (Austria)

A permanently installed terrestrial laser scanner (TLS) helps to investigate surface changes at high spatio-temporal resolution. Previous studies show that the annual and seasonal glacier volume, and subsequently the mass balance, can be measured by TLSs. This study systematically identifies and quantifies uncertainties and their sources of the permanent long-range TLS system at Hintereisferner glacier (Ötztal Alps, Austria) in order to assess its potential and limitations for detecting glaciologically relevant small-scale surface elevation changes, such as snowfall and redistribution events. Five uncertainty sources are analyzed: the registration method, the influence of the instrument and hardware limitations of the TLS, the effect of atmospheric conditions on the laser beam, the scanning geometry, and the uncertainty caused by rasterization. The instrument and hardware limitations cause the largest uncertainty to the TLS data, followed by the scanning geometry and influence of varying atmospheric conditions on the laser beam. The magnitude of each uncertainty source depends on the distance (range) between the TLS and the target surface, showing a strong decrease of the obtained spatial resolution and a concurrent increase in uncertainty with increasing distance. An automated registration method results in an uncertainty of ±0.50 m at grids of 100 by 100 m. After post-processing, a 0.1-m vertical accuracy can be obtained allowing the detection of surface changes of respective magnitudes and especially making it possible to quantify snow dynamics at Hintereisferner.

Impact of Reference Data Sampling Density for Estimating Plot-Level Average Shrub Heights Using Terrestrial Laser Scanning Data

Terrestrial laser scanning (TLS) data can offer a means to estimate subcanopy fuel characteristics to support site characterization, quantification of treatment or fire effects, and inform fire modeling. Using field and TLS data within the New Jersey Pinelands National Reserve (PNR), this study explores the impact of forest phenology and density of shrub height (i.e., shrub fuel bed depth) measurements on estimating average shrub heights at the plot-level using multiple linear regression and metrics derived from ground-classified and normalized point clouds. The results highlight the importance of shrub height sampling density when these data are used to train empirical models and characterize plot-level characteristics. We document larger prediction intervals (PIs), higher root mean square error (RMSE), and lower R-squared with reduction in the number of randomly selected field reference samples available within each plot. At least 10 random shrub heights collected in situ were needed to produce accurate and precise predictions, while 20 samples were ideal. Additionally, metrics derived from leaf-on TLS data generally provided more accurate and precise predictions than those calculated from leaf-off data within the study plots and landscape. This study highlights the importance of reference data sampling density and design and data characteristics when data will be used to train empirical models for extrapolation to new sites or plots.

Canopy structure: An intermediate factor regulating grassland diversity-function relationships under human disturbances.

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

Multi-sensor monitoring and data integration reveal cyclical destabilization of the Äußeres Hochebenkar rock glacier

Abstract. This study investigates rock glacier destabilization based on the results of a unique in situ and remote-sensing-based monitoring network focused on the kinematics of the rock glacier in Äußeres Hochebenkar (Austrian Alps). We consolidate, homogenize, and extend existing time series to generate a comprehensive dataset consisting of 14 digital surface models covering a 68-year time period, as well as in situ measurements of block displacement since the early 1950s. The digital surface models are derived from historical aerial imagery and, more recently, airborne and uncrewed-aerial-vehicle-based laser scanning (ALS and ULS, respectively). High-resolution 3D ALS and ULS point clouds are available at annual temporal resolution from 2017 to 2021. Additional terrestrial laser scanning data collected in bi-weekly intervals during the summer of 2019 are available from the rock glacier front. Using image correlation techniques, we derive velocity vectors from the digital surface models, thereby adding rock-glacier-wide spatial context to the point-scale block displacement measurements. Based on velocities, surface elevation changes, analyses of morphological features, and computations of the bulk creep factor and strain rates, we assess the combined datasets in terms of rock glacier destabilization. To additionally investigate potential rotational components of the movement of the destabilized section of the rock glacier, we integrate in situ data of block displacement with ULS point clouds and compute changes in the rotation angles of single blocks during recent years. The time series shows two cycles of destabilization in the lower section of the rock glacier. The first lasted from the early 1950s until the mid-1970s. The second began around 2017 after approximately 2 decades of more gradual acceleration and is currently ongoing. Both destabilization periods are characterized by high velocities and the development of morphological destabilization features on the rock glacier surface. Acceleration in the most recent years has been very pronounced, with velocities reaching 20–30 m a−1 in 2020–2021. These values are unprecedented in the time series and suggest highly destabilized conditions in the lower section of the rock glacier, which shows signs of translational and rotational landslide-like movement. Due to the length and granularity of the time series, the cyclic destabilization process at the Äußeres Hochebenkar rock glacier is well resolved in the dataset. Our study highlights the importance of interdisciplinary, long-term, and continuous high-resolution 3D monitoring to improve process understanding and model development related to rock glacier rheology and destabilization.

Estimation of Aboveground Biomass of Individual Trees by Backpack LiDAR Based on Parameter-Optimized Quantitative Structural Models (AdQSM)

Forest aboveground biomass (AGB) plays a key role in assessing forest productivity. In this study, we extracted individual tree structural parameters using backpack LiDAR, assessed their accuracy using terrestrial laser scanning (TLS) data and field measurements as reference values, and reconstructed 3D models of trees based on parameter-optimized quantitative structural models (AdQSM). The individual tree AGB was estimated based on individual tree volumes obtained from the tree model reconstruction, combined with the basic wood density values of specific tree species. In addition, the AGB calculated using the allometric biomass models was validated to explore the feasibility of nondestructive estimation of individual tree AGB by backpack LiDAR. We found that (1) the backpack LiDAR point cloud extracted individual tree diameter at breast height (DBH) with high accuracy. In contrast, the accuracy of the tree height extraction was low; (2) the optimal parameter values of the AdQSM reconstruction models for Larix gmelinii and Betula platyphylla were HS = 0.4 m and HS = 0.6 m, respectively; (3) the individual tree AGB estimated based on the backpack LiDAR and AdQSM fit well with the reference values. Our study confirms that backpack LiDAR can nondestructively estimate individual tree AGB, which can provide a reliable basis for further forest resource management and carbon stock estimation.

Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.

Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem

Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (&lt;2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management.

Maize Ear Height and Ear–Plant Height Ratio Estimation with LiDAR Data and Vertical Leaf Area Profile

Ear height (EH) and ear–plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R2 = 0.59 and RMSE = 16.90 cm for 2019, R2 = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R2 = 0.54 and RMSE = 18.00 cm for 2019, R2 = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R2 = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding.

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory.

The gap fraction (GF) of vegetative canopies is an important property related to the contained bulk of reproductive elements and woody facets within the tree crown volume. This work was developed from the perspectives of porous media theory and computer graphics techniques, considering the vegetative elements in the canopy as a solid matrix and treating the gaps between them as pores to guide volume-based GFvol calculations. Woody components and individual leaves were extracted from terrestrial laser scanning data. The concept of equivalent leaf thickness describing the degrees of leaf curling and drooping was proposed to construct hexagonal prisms properly enclosing the scanned points of each leaf, and cylinder models were adopted to fit each branch segment, enabling the calculation of the equivalent leaf and branch volumes within the crown. Finally, the volume-based GFvol of the tree crown following the definition of the void fraction in porous media theory was calculated as one minus the ratio of the total plant leaf and branch volume to the canopy volume. This approach was tested on five tree species and a forest plot with variable canopy architecture, yielding an estimated maximum volume-based GFvol of 0.985 for a small crepe myrtle and a minimal volume-based GFvol of 0.953 for a sakura tree. The 3D morphology of each compositional element in the tree canopy was geometrically defined and the canopy was considered a porous structure to conduct GFvol calculations based on multidisciplinary theory.

Imaging and Laser Scanning–Based Noncontact Deflection Monitoring Technique for Timber Railroad Bridges

This study developed a noncontact deflection monitoring technique using terrestrial laser scanner (TLS) and images for faster, safer, and efficient 2D/3D bridge deflection measurements. The technique was applied to assess the serviceability performance of two timber trestle railroad bridges located in Florida and validated based on bridge deflections from deflectometers. A key challenge includes extracting control points that are visible both in images and TLS data due to different modalities of the data. Hence, this study presents a method of extracting linear features from both images and TLS data. The camera pose was derived from images and TLS data by using a linear feature-based registration algorithm. The deformations in the structure were then detected by measuring the points of interest for different loads. There are several unique contributions of the study. First, there is no requirement for the use of targets which improves the safety of bridge engineers. Second, it is a relatively cost-effective technique for obtaining bridge deflections due to moving train loads, as the use of a laser scanner as part of the noncontact technique is only once in a lifetime for a given bridge. Alternatively, total stations can also be used to capture linear features. Finally, this technique produced accurate deflection measurements using a linear feature-based registration technique. This study found that the noncontact deflection monitoring technique agrees well with actual deformations observed from deflectometers.

Reconstruction of tunnel lining rebars from terrestrial laser scanning data

AbstractIncorrect rebar placement has led to serious concrete structural failures; thus, site inspectors must ensure that the rebars placed are compliant with as‐designed drawings before tunnel lining concreting. This paper aims to develop a methodology that automatically segments the semantics (e.g., main rebar and distribution rebar) and instances of curved rebars and reconstructs the tunnel lining rebars from the raw point clouds. There are five main parts in the proposed methodology: rebar mesh reshaping, rebar mesh extraction and refinement, semantic segmentation, instance labeling, and rebar mesh modeling. To validate the developed methodology, an experiment was conducted on the rebar point cloud of a high‐speed railway tunnel. The evaluation of the accuracies of segmentation was conducted by comparisons with the existing methods and manually labeled ground truth data. The results show that the rebar segmentation has high precision values with the acceptable recall values, and that the rebar models reconstructed achieve millimeter‐level (about 2 mm) accuracy.