Light Detection And Ranging Scanning Research Articles

Developing kNN forest data imputation for Catalonia

The combined use of LiDAR (Light Detection And Ranging) scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics. This information can be used in many ways in forest mapping, scenario analyses, and forest management planning. This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation (kNN stands for “k nearest neighbors”). In this method, data are imputed to a certain location from k field-measured sample plots, which are the most similar to the location in terms of LiDAR metrics and topographic variables. Weighted multidimensional Euclidean distance was used as the similarity measure. The study tested two different methods to optimize the distance measure. The first method optimized, in the first step, the set of LiDAR and topographic variables used in the measure, as well as the transformations of these variables. The weights of the selected variables were optimized in the second step. The other method optimized the variable set as well as their transformations and weights in one single step. The two-step method that first finds the variables and their transformations and subsequently optimizes their weights resulted in the best imputation results. In the study area, the use of three to five nearest neighbors was recommended. Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation. The optimal distance measure always included both LiDAR metrics and topographic variables. The study showed that the optimal similarity measure may be different for different regions. Therefore, it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.

A new method for detailed discharge and volume measurements of debris flows based on high-frequency 3D LiDAR point clouds; Illgraben, Switzerland

Debris flows are one of the most dangerous landslide types in mountainous regions. Their destructiveness is strongly controlled by their high peak discharge, which can be orders of magnitude larger than for floods. In order to reduce the associated hazards, detailed field measurements of natural debris flows are required to better constrain discharge and volume of these events. In this study, we used a high-frequency 3D LiDAR (light detection and ranging) scanner in combination with video cameras to measure key properties of a debris flow that occurred in the Illgraben catchment (Switzerland). Based on the LiDAR and video data, we directly measured i) front velocity ii) surge velocity iii) surface velocity and iv) cross-sectional area at sub-second intervals. We then estimated discharge and volume using these direct measurements and considering different channel bed geometry scenarios. We compared our results to estimates based on conventional methods and found that these more established methods substantially underestimate the (peak) discharge and volume for this event. Our results will be assessed in the future by analyzing more events, but our LiDAR-based method has the potential to provide much more detailed information on hazard-related debris-flow parameters, which will have important implications for understanding debris-flow processes and ultimately reducing their risk.

INVESTIGATING THE COMPLEMENTARY USE OF RADAR AND LIDAR FOR POSITIONING APPLICATIONS

Abstract. In the realm of Autonomous Vehicles (AVs), accurate, reliable and uninterrupted positioning capabilities are vital to ensure successful operations. Light Detection And Ranging (LiDAR) technology, capable of providing a high-fidelity 3D representation of the surrounding environment, has enabled numerous odometry-based positioning algorithms. These algorithms utilize a registration process to estimate relative motion from two successive 3D scans. However, the accuracy of the registration process can be compromised by the presence of dynamic objects, leading to significant translational and rotational deviations. On the other hand, Radar technology provides spatial and speed information. However, it is limited by spatial sparsity and susceptibility to noise. In this paper, we propose combining the complementary LiDAR and Electronic Scanning Radar (ESR) measurements, along with onboard motion sensors for improved navigation performance in complex and dynamic environments. This is achieved by employing a radar-based filtering mechanism that refines the LiDAR’s point cloud mitigating the impact of dynamic objects. This results in a more robust registration process, which in turn enhances the LiDAR Inertial Odometry (LIO) solution. The proposed method was verified using real data collected from onboard motion sensors, a 3D LiDAR, and four ESRs from road tests conducted in downtown Calgary, Alberta, Canada. Our approach achieved an improved average horizontal positioning and heading RMSE of 0.43 meters and 0.25 degrees, respectively, compared to the 0.66 meters and 0.39 degrees observed with the standalone LIO solution. Moreover, submeter-level and lane-level accuracies were enhanced to 95% and 100% of the time, respectively, up from 85.7% and 94.9%.

Velocity Estimation from LiDAR Sensors Motion Distortion Effect

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.

Modelling Small Artefact for Preservation – A Case Study of Perlis Heritage

Heritage preservation is essential for preserving historical sites and cultural artefact for future generations. However, they are prone to damages and destructions due to weather conditions and other factors. 3D models and reconstructions can aid in the conservation of historical sites and artefact. LiDAR (light detection and ranging) technology can be utilized to obtain accurate 3D representation of object or area of interest. This project aims to preserve one of the historical artefact in Perlis, Malaysia which is known as ‘Batu Nisan Acheh’ or the Acheh Gravestone by using 3D model and reconstruction. iPhone 13 Pro Max LiDAR scanner is used to collect the raw dataset of the artefact with Scaniverse application. MATLAB is employed for data processing which includes data filtering, noise reduction, downsampling and 3D surface reconstruction. In addition, a GUI application is also developed in enabling users to upload their desired point cloud files and produce its 3D model for future usage. Results show that the accuracy, effectiveness, and usability of heritage preservation initiatives are improved by combining iPhone 13 Pro Max LiDAR scanning with MATLAB processing, which is useful for virtual displays, restoration, and future study.

Automated Method for SLAM Evaluation in GNSS-Denied Areas

The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allow efficient automated mapping. Engineering structures like bridges shadow global navigation satellite system (GNSS), which complicates precise localization. Simultaneous localization and mapping (SLAM) algorithms offer a sufficient solution, since they do not require GNSS. However, testing and comparing SLAM algorithms in GNSS-denied areas is difficult due to missing ground truth data. This work presents an approach to measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner Leica RTC360 and a tachymeter to acquire point cloud and trajectory information. The proposed method is independent of time synchronization between robot and tachymeter and also works on sparse SLAM point clouds. For the evaluation of the proposed method, three LiDAR-based SLAM algorithms called KISS-ICP, SC-LIO-SAM, and MA-LIO are tested using a UGV equipped with two light detection and ranging (LiDAR) sensors and an inertial measurement unit (IMU). KISS-ICP is based solely on a single LiDAR scanner and SC-LIO-SAM also uses an IMU. MA-LIO, which allows multiple (different) LiDAR sensors, is tested on a horizontal and vertical one and an IMU. Time synchronization between the tachymeter and SLAM data during post-processing allows calculating the root mean square (RMS) absolute trajectory error, mean relative trajectory error, and the mean point cloud to reference point cloud distance. It shows that the proposed method is an efficient approach to measure the performance of SLAM in GNSS-denied areas. Additionally, the method shows the superior performance of MA-LIO in four of six test tracks with 5 to 7 cm RMS trajectory error, followed by SC-LIO-SAM and KISS-ICP in last place. SC-LIO-SAM reaches the lowest point cloud to reference point cloud distance in four of six test tracks, with 4 to 12 cm.

Coupling wind LiDAR fixed and volumetric scans for enhanced characterization of wind turbulence and flow three‐dimensionality

SummaryOver the last decades, pulsed light detection and ranging (LiDAR) anemometry has gained growing attention in probing the marine atmospheric boundary layer (MABL) due to its ease of use combined with compelling spatio‐temporal resolution. Among several scanning strategies, fixed scans represent the most prominent choice when high‐frequency resolution is required; however, no information is provided about the spatial heterogeneity of the wind field. On the other hand, volumetric scans allow for the characterization of the spatial variability of the wind field with much lower temporal resolution than fixed scans. In this work, the recently developed “LiDAR Statistical Barnes Objective Analysis” (LiSBOA) algorithm for the optimal design of LiDAR scans and retrieval of wind velocity statistics is tailored for applications in the MABL. The LiDAR data, collected during a recent experimental campaign over Lake Lavon in Texas, show a good consistency of mean velocity profiles between fixed and LiSBOA‐interpolated volumetric data, thus further encouraging the use of coupled fixed and volumetric scans for simultaneous characterizations of wind turbulence statistics along the vertical direction and volumetric heterogeneity of the wind field.

Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey.

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.

Scalable hybrid adjustment of images and LiDAR point clouds

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼3000 images were matched in <1.5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment.

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering.

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match) of tree tops is up to 0.92, the coefficient of determination (R 2) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

Asynchronous Multiple LiDAR-Inertial Odometry Using Point-Wise Inter-LiDAR Uncertainty Propagation

In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common practice is to transform points among sensors while requiring strict time synchronization or approximating transformation among sensor frames. Unlike existing methods, we elaborate the inter-sensor transformation using continuous-time (CT) inertial measurement unit (IMU) modeling and derive associated ambiguity as a point-wise uncertainty. This uncertainty, modeled by combining the state covariance with the acquisition time and point range, allows us to alleviate the strict time synchronization and to overcome FOV difference. The proposed method has been validated on both public and our datasets and is compatible with various LiDAR manufacturers and scanning patterns. We open-source the code for public access at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/minwoo0611/MA-LIO</uri>

LiDAR Point Cloud Data Combined Structural Analysis Based on Strong Form Meshless Method Using Essential Boundary Condition Capturing.

This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques.

Development of Cloud Point Data Processing Program for 3D BIM and 2D Cross Section Needs

The need for technological developments is needed to facilitate performance, accuracy, and effectiveness of work, especially in the field of civil engineering, is needed. With the emergence of innovative LiDAR (Light Detection and Ranging) technology scanners that are popularly used for 3D printing, developed into LiDAR Scanners for real field scanning. The result of using a LiDAR Scanner is in the form of point cloud data in a certain format, with a large enough memory. The purpose of this research is to use field point cloud data as 3D BIM data and then form a cross-section of the object. For this purpose, a special program is needed that functions to process cloud point data complexly, and is easy to use to change the shape of cloud point data to 3D data surface and 2D cross sections. The method used in this study is by creating a special program to process data point clouds using script code with the python language and several data point cloud processing libraries. In the program, 2 sub-menus will be created with certain functions: 1) Point Cloud (voxel downsampling, outlier reduction, normalize); 2) 3D model (ball pivoting/poisson surface, reduce vertex, slice mesh, transform mesh). In each data processing, the created program can only process on a specific file format; for point cloud processing in .xyz, .xyzn, .xyzrgb, .pts, .ply, .pcd formats; while for 3D data processing models are in .ply, .stl, .obj, .off , .gltf/glb format. The result of data processing using the created program can be a 3D surface with .ply /.obj format, and for cross-section generated 2D data with .jpg / .png format, and can be in the form of .dxf data for Autocad software. 3D surface data can be used as BIM data, while 2D cross-section data can be used as built 2D.

Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion

Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed.

Measuring tree diameter using a LiDAR-equipped smartphone: a comparison of smartphone- and caliper-based DBH

Tree diameter measurement is one of the most important stages of forest inventories to assess growing stock, aboveground biomass, and landscape restoration options, among others. This study investigates the accuracy of measuring tree diameters using a Light Detection and Ranging (LiDAR)-equipped smartphone vs. a normal caliper (reference data) and the opportunity to use low-cost smartphone-based applications in forest inventories. To estimate the diameter at breast height (DBH) of single trees, we used a smartphone with a third-party app that automatically analyzed three-dimensional (3D) point clouds. For two different measurement techniques, we compared the two measurement techniques based on DBH data from 55 Calabrian pine (Pinus brutia Ten.) and 50 oriental plane (Platanus orientalis L.) trees using the paired-sample t-test and Wilcoxon signed-rank test. Mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), percent bias (PBIAS), and coefficient of determination (R2) were used as precision and error statistics. Statistical differences were observed between the reference and smartphone-based DBH according to the paired-sample t-test and Wilcoxon signed-rank test. The R2 values obtained were determined as 0.91, 0.88, and 0.88 for Calabrian pine, oriental plane, and all tree species (105 trees), respectively. In addition to the overall accuracy performance of the comparison between reference and estimations, MAE, MSE, RMSE, and PBIAS values for the DBH of 105 tree stems were calculated as 1.56cm, 5.42cm, 2.33cm, and - 5.10%, respectively. The estimation accuracies increased in regular stem forms compared with forked stems particularly observed on plane trees. Further experiments are needed to investigate the uncertainties associated with trees of different stem forms, species (coniferous or deciduous), different work environments, and different types of LiDAR and LiDAR-based app scanners.

Phase-Modulated Continuous-Wave Coherent Ranging Method and Anti-Interference Evaluation

Light detection and ranging (LiDAR) has been applied in many areas because of its excellent performance. An easily achievable, cost-effective, and high-performance ranging method is a major challenge of LiDAR. Meanwhile, with the increasing applications of LiDAR, numerous LiDARs can be made to operate simultaneously, and potential interference is inevitable. Therefore, immunity against interference is paramount in LiDAR systems. In this paper, we demonstrated a ranging method referred to as phase-modulated continuous-wave (PhMCW). A detection range of 50 m and a ranging error of 2.2 cm are achieved. A one-dimensional scanning LiDAR system that is capable of detecting targets at 28 m is built, demonstrating the validation of the PhMCW method. Moreover, we propose a quantitative method for evaluating the anti-interference capability of lidar systems. The p-values of the Ljung–Box test were 0.0589 and 0.6327 for ToF and coherent LiDAR interferences, respectively, indicating that the PhMCW system is immune to interference. The proposed method can be applied to all types of LiDAR systems, regardless of the ranging method or beam-steering technique used.

AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical “correction-then-registration” motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry.

Feasibility of Low-Cost LiDAR Scanner Implementation in Forest Sampling Techniques

Despite the growing impact of remote sensing technology in forest inventories globally, there is a continuous need for ground measurements on sample plots. Even though the newest volume assessment methodology requires fewer sample plots, the accuracy of ground-recorded data influences the final accuracy of forest stand modeling. Therefore, effective and economically justified tools are in the continuous interest of foresters. In the presented research, a consumer-grade light detection and ranging (LiDAR) sensor mounted on iPad was used for forest inventory sample plot data collection—including tree location and diameter breast height. In contrast to other similar research, feasibility and user-friendliness were also documented and emphasized. The study was conducted in 63 real sample plots used for the inventory of Polish forests. In total, 776 trees were scanned in 3 types of forest stands: pine, birch, and oak. The root mean square error was 0.28 m for tree locations and 0.06 m for diameter breast height. Various additional analyses were performed to describe the usage of an iPad in tree inventories. It was contended that low-cost LiDAR scanners might be successfully used in real forest conditions and can be considered a reliable and easy-to-implement tool in forest inventory measurements.

The effects of sampling and instrument orientation on LiDAR data from crop plots.

Wheat is one of the most widely consumed grains in the world and improving its yield, especially under severe climate conditions, is of great importance to world food security. Phenotyping methods can evaluate plants according to their different traits, such as yield and growth characteristics. Assessing the vertical stand structure of plants can provide valuable information about plant productivity and processes, mainly if this trait can be tracked throughout the plant's growth. Light Detection And Ranging (LiDAR) is a method capable of gathering three-dimensional data from wheat field trials and is potentially suitable for providing non-destructive, high-throughput estimations of the vertical stand structure of plants. The current study considers LiDAR and focuses on investigating the effects of sub-sampling plot data and data collection parameters on the canopy vertical profile (CVP). The CVP is a normalized, ground-referenced histogram of LiDAR point cloud data representing a plot or other spatial domain. The effects of sub-sampling of plot data, the angular field of view (FOV) of the LiDAR and LiDAR scan line orientation on the CVP were investigated. Analysis of spatial sub-sampling effects on CVP showed that at least 144000 random points (600 scan lines) or an area equivalent to three plants along the row were adequate to characterize the overall CVP of the aggregate plot. A comparison of CVPs obtained from LiDAR data for different FOV showed that CVPs varied with the angular range of the LiDAR data, with narrow ranges having a larger proportion of returns in the upper canopy and a lower proportion of returns in the lower part of the canopy. These findings will be necessary to establish minimum plot and sample sizes and compare data from studies where scan direction or field of view differ. These advancements will aid in making comparisons and inform best practices for using close-range LiDAR in phenotypic studies in crop breeding and physiology research.

Design of a circum-scanning light detection and ranging emission system

The need for imaging and detecting in various instrument areas has brought light detection and ranging (LiDAR) to the forefront of consumer technology. Among different LiDAR, microelectromechanical system (MEMS) LiDAR has more appeal due to its small size and high integration. However, due to its low resolution and slight scanning angle, MEMS LiDAR does not apply to all scenes. Thus we presented a 360-deg scanning LiDAR emission optical system. Design formulas were deduced from theoretical formulas. In this system, the aspheric lens collimated the beam, MEMS micromirrors tracked the concentric circular beams, and the converging lens compensated for the divergence of the outgoing beam. By doing so, the target was scanned 360 deg at high resolution. With Zemax, the fast-axis divergence angle was 0.2484 mrad, the slow-axis divergence angle was 0.1546 mrad, and the system energy utilization rate was 84.16% with an angular resolution of 0.0142 deg at a distance of 10,000 mm. We have provided a potential solution for improving the scanning angle and resolution of MEMS LiDAR.