Light Detection And Ranging Scanning Research Articles

Accurate Calibration of a Self‐Developed Vehicle‐Borne LiDAR Scanning System

To obtain high‐precision measurement data using vehicle‐borne light detection and ranging (LiDAR) scanning (VLS) systems, calibration is necessary before a data acquisition mission. Thus, a novel calibration method based on a homemade target ball is proposed to estimate the system mounting parameters, which refer to the rotational and translational offsets between the LiDAR sensor and inertial measurement unit (IMU) orientation and position. Firstly, the spherical point cloud is fitted into a sphere to extract the coordinates of the centre, and each scan line on the sphere is fitted into a section of the sphere to calculate the distance ratio from the centre to the nearest two sections, and the attitude and trajectory parameters of the centre are calculated by linear interpolation. Then, the real coordinates of the centre of the sphere are calculated by measuring the coordinates of the reflector directly above the target ball with the total station. Finally, three rotation parameters and three translation parameters are calculated by two least‐squares adjustments. Comparisons of the point cloud before and after calibration and the calibrated point clouds with the point cloud obtained by the terrestrial laser scanner show that the accuracy significantly improved after calibration. The experiment indicates that the calibration method based on the homemade target ball can effectively improve the accuracy of the point cloud, which can promote VLS development and applications.

Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments.

In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement unit (IMU). The proposed algorithm’s main novelty is that, unlike in most algorithms, we apply an extended Kalman filter (EKF) to each LiDAR scan point and calculate the location relative to a triangular mesh. We also introduce three implementations of the algorithm: serial, parallel, and parallel-serial. The first implementation verifies the correctness of our innovative approach, but is too slow for real-time execution. The second approach implements a well-known parallel data fusion approach, but is still too slow for our application. The third and final implementation of the presented algorithm along with the state-of-the-art GPU data structures achieves real-time performance. According to our experimental findings, our algorithm outperforms the reference Gaussian mixture model (GMM) localization algorithm in terms of accuracy by a factor of two.

Trajectory via-point generation for autonomous mobile manipulation using 3D LiDAR data

In this work, an approach to generating a set of via points for use in manipulator trajectory path planning is presented. The approach was developed for use on a robotic underground mining system, particularly for the task of autonomous application of a sprayable concrete called shotcrete. A LiDAR (light detection and ranging) scanner on a nodding head produces point clouds that are used as the input for the via-point selection algorithm. The algorithm generates a set of position and orientation via points that the manipulator must follow to perform the shotcreting task. The developed algorithm has been successfully tested on an autonomous mobile-manipulator system in a scaled mock-up of an underground mine. The main advantage of this algorithm is the ability to generate via points for any section of an underground mine in any position relative to the robot.

Pallet Handling System with an Autonomous Forklift for Outdoor Fields

In Japan, the aging and depopulation of its workforce are issues. Therefore, the development of autonomous agricultural robots is required for saving manpower and labor. In this paper, we described an autonomous pallet handling system for forklift, which can automatically unload and convey pallets for harvesting vegetables outdoors. Because of inserting the forks into a narrow pallet hole, accurate pallet posture estimation and accurate control of a forklift and the forks are required. The system can detect the pallet by deep learning based object detection from an image. Based on the results of object detection and measurement by horizontal 3D light detection and ranging (LiDAR), the system accurately estimates a distance as well as horizontal and vertical deviation between the forklift and the pallet in the outside field. The forklift is controlled by sliding mode control (SMC) which is robust to disturbances. Furthermore, the vertical LiDAR scans the pallet for precisely adjusting the height of the fork. The system requires the environment with no or little preparation for the automation process. We confirmed the effectiveness of the system through an experiment. The experiment is assumed that the forklift unloads the pallet from the vehicle as the real task of agriculture. The experimental results indicated the suitability of the system in real agricultural environments.

UAV-Borne LiDAR Crop Point Cloud Enhancement Using Grasshopper Optimization and Point Cloud Up-Sampling Network

Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model; meanwhile, the density of the crop point cloud was quadrupled.

Dynamic wake tracking and characteristics estimation using a cost-effective LiDAR

Recent developments in Light Detection And Ranging (LiDAR) systems provide the possibility of remote measurement of the upcoming wind speed. Despite significant advances in remote sensing, extracting useful inflow characteristics from a limited number of line-of-sight measurements still requires assumptions of the inflow. Typically, the wind direction is derived based on the assumption of horizontal homogeneous inflow that is well satisfied in flat terrain and over sufficiently large time averages. However, such an assumption is violated if the wake from a neighbouring turbine impinges the inflow, and the velocity deficit in the wake causes a bias on the wind direction, misinterpreting as yaw misalignment by the downstream turbine. The actual yaw misalignment can be recovered by isolating the effect of the wake velocity deficit from the ambient inflow. A scanning LiDAR can easily track and characterise the wake; however, it is non-trivial for a cost-effective LiDAR with only a few fixed laser beams. Therefore, this paper presents a dynamic wake tracking and characteristics estimation algorithm for a cost-effective LiDAR. The proposed algorithm provides estimates of the wake centre location and other wake characteristics by exploiting the nature of wake meandering dynamics and state estimation theory. Assuming neutral stratification of the atmospheric boundary layer, the simulation results show that the wake position and its characteristics estimation is achievable in full and partial wake situations, thus presenting an estimation framework for potential applications, including yaw misalignment control and wake steering control.

AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD

Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.

Background Point Filtering of Low-Channel Infrastructure-Based LiDAR Data Using a Slice-Based Projection Filtering Algorithm.

A light detection and ranging (LiDAR) sensor can obtain richer and more detailed traffic flow information than traditional traffic detectors, which could be valuable data input for various novel intelligent transportation applications. However, the point cloud generated by LiDAR scanning not only includes road user points but also other surrounding object points. It is necessary to remove the worthless points from the point cloud by using a suitable background filtering algorithm to accelerate the micro-level traffic data extraction. This paper presents a background point filtering algorithm using a slice-based projection filtering (SPF) method. First, a 3-D point cloud is projected to 2-D polar coordinates to reduce the point data dimensions and improve the processing efficiency. Then, the point cloud is classified into four categories in a slice unit: Valuable object points (VOPs), worthless object points (WOPs), abnormal ground points (AGPs), and normal ground points (NGPs). Based on the point cloud classification results, the traffic objects (pedestrians and vehicles) and their surrounding information can be easily identified from an individual frame of the point cloud. We proposed an artificial neuron network (ANN)-based model to improve the adaptability of the algorithm in dealing with the road gradient and LiDAR-employing inclination. The experimental results showed that the algorithm of this paper successfully extracted the valuable points, such as road users and curbstones. Compared to the random sample consensus (RANSAC) algorithm and 3-D density-statistic-filtering (3-D-DSF) algorithm, the proposed algorithm in this paper demonstrated better performance in terms of the run-time and background filtering accuracy.

Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds

Due to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, efficient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation to higher order 3D models based on polygons or polyhedral surfaces, which is not a trivial process. The study presents a newly developed algorithm for correcting 3D point cloud data from airborne LiDAR surveys of regular 3D buildings. The proposed approach assumes the application of a sequence of operations resulting in 3D rasterization, i.e., creation and processing of a 3D regular grid representation of an object, prior to applying a regular Poisson surface reconstruction method. In order to verify the accuracy and quality of reconstructed objects for quantitative comparison with the obtained 3D models, high-quality ground truth models were used in the form of the meshes constructed from photogrammetric measurements and manually made using buildings architectural plans. The presented results show that applying the proposed algorithm positively influences the quality of the results and can be used in combination with existing surface reconstruction methods in order to generate more detailed 3D models from LiDAR scanning.

A Survey on LiDAR Scanning Mechanisms

In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

MEMS Mirrors for LiDAR: A review.

In recent years, Light Detection and Ranging (LiDAR) has been drawing extensive attention both in academia and industry because of the increasing demand for autonomous vehicles. LiDAR is believed to be the crucial sensor for autonomous driving and flying, as it can provide high-density point clouds with accurate three-dimensional information. This review presents an extensive overview of Microelectronechanical Systems (MEMS) scanning mirrors specifically for applications in LiDAR systems. MEMS mirror-based laser scanners have unrivalled advantages in terms of size, speed and cost over other types of laser scanners, making them ideal for LiDAR in a wide range of applications. A figure of merit (FoM) is defined for MEMS mirrors in LiDAR scanners in terms of aperture size, field of view (FoV) and resonant frequency. Various MEMS mirrors based on different actuation mechanisms are compared using the FoM. Finally, a preliminary assessment of off-the-shelf MEMS scanned LiDAR systems is given.

Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.

Coastal Wind Measurements Using a Single Scanning LiDAR

A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

Human-Leg Detection in 3D Feature Space for a Person-Following Mobile Robot Using 2D LiDARs

People detection is an essential technique for person-following mobile robots in applications of human-friendly services and collaborative tasks. 2D light detection and ranging (LiDAR) sensors are useful for these applications, especially for applications that detect and follow people while maintaining a suitable distance, at a close range, using accurate range measurements. In this study, we propose a method of human-leg detection in 3D feature space for a person-following mobile robot equipped with a 2D LiDAR sensor. We also propose an improved LiDAR scan segmentation technique to extract segments of human leg candidates. The newly proposed method generates a feature vector with the attributes of leg shapes and learns a classification boundary in 3D feature space. Experimental results indicate that the proposed method successfully describes the target dataset and provides accurate leg detection. This study demonstrates that human legs can be detected with improved accuracy by learning the classification boundary in 3D feature space.

Efficient Coarse Registration of Pairwise TLS Point Clouds Using Ortho Projected Feature Images

The degree of automation and efficiency are among the most important factors that influence the availability of Terrestrial light detection and ranging (LiDAR) Scanning (TLS) registration algorithms. This paper proposes an Ortho Projected Feature Images (OPFI) based 4 Degrees of Freedom (DOF) coarse registration method, which is fully automated and with high efficiency, for TLS point clouds acquired using leveled or inclination compensated LiDAR scanners. The proposed 4DOF registration algorithm decomposes the parameter estimation into two parts: (1) the parameter estimation of horizontal translation vector and azimuth angle; and (2) the parameter estimation of the vertical translation vector. The parameter estimation of the horizontal translation vector and the azimuth angle is achieved by ortho projecting the TLS point clouds into feature images and registering the ortho projected feature images by Scale Invariant Feature Transform (SIFT) key points and descriptors. The vertical translation vector is estimated using the height difference of source points and target points in the overlapping regions after horizontally aligned. Three real TLS datasets captured by the Riegl VZ-400 and the Trimble SX10 and one simulated dataset were used to validate the proposed method. The proposed method was compared with four state-of-the-art 4DOF registration methods. The experimental results showed that: (1) the accuracy of the proposed coarse registration method ranges from 0.02 m to 0.07 m in horizontal and 0.01 m to 0.02 m in elevation, which is at centimeter-level and sufficient for fine registration; and (2) as many as 120 million points can be registered in less than 50 s, which is much faster than the compared methods.

Functions and applications of Multi-Tower Platform of Qingyuan Forest Ecosystem Research Station of Chinese Academy of Sciences

The relationship between the structure and function of forest ecosystems is the main intere-sts in the research area of forest ecology and management. However, over complex terrains in particular, these studies had been challenged as uneasy tasks due to the limitations in the forest survey and measurement techniques and other supporting technologies. Chinese Academy of Sciences (CAS) funded "Multi-Tower LiDAR/ECFlux Platform for Monitoring the Structure and Function of Secondary Forest Ecosystems" (Multi-Tower Platform, MTP) as a field station network corner-stone research infrastructure project, which was completed by Qingyuan Forest CERN (Chinese Ecosystem Research Network). In a distinctively-bounded and monitored-outlet watershed, the MTP was integrated by light detection and ranging (LiDAR) scanners, eddy covariance (EC) flux instrument systems, whole- and sub-watershed hydrology station network, long-term forest plot arrays, and live data center. Using LiDAR scanning, the MTP can get cloud data for holographic information on canopy structure. The EC-flux instrument system and hydrology station network along with forest plot arrays could ensure the reliability of water and carbon observations over this complex terrain, which allows to verify the studies on flux measurement technologies and methods, as well as to understand the processes of ecohydrology and CO2 exchange between forest ecosystem and the atmosphere. Further, we can also assess the primary ecosystem services, including water conservation and carbon sequestration. All the data from "tower-station" were streamed through wireless network, which would facilitate data monitoring, management, and sharing. There are three tasks of MTP team: 1) defining innovative methods and descriptors to quantify three-dimensional forest structure; 2) developing theories and techniques to measure CO2/H2O fluxes and other trace gases over complex terrains; 3) understanding the relationship between structure and function of forest ecosystems, providing information and rationales for forest management practices to assure broad and sustainable benefits from forests.

A Comparative Study to Evaluate Accuracy on Canopy Height and Density Using UAV, ALS, and Fieldwork

Accurate measurement of the tree height and canopy cover density is important for forest biomass and management. Recently, Light Detection and Ranging (LIDAR) and Unmanned Aerial Vehicle (UAV) images have been used to estimate the tree height and canopy cover density for a forest stands. More so, UAV systems with autopilot functions, affordable Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) have created new possibilities, aided by available photogrammetric programs. In this study, we investigated the possibility of data collection methods using an Aerial LIDAR Scanner (ALS) and an UAV together with a fieldworks to evaluate accurate the tree standard metrics in Singyeri, Gyeongjusi, and Gyeongsangbukdo province. The derived metrics via statistical analyses of the ALS and UAV data and validated by field measurements were compared to a published forest type map (scale 1:5000) by the Korea Forest Service; geared towards improving the forest attributes. We collected data and analyzed and compared them with existent the forest type map produced from an aerial photographs and a digital stereo plotter. The ALS data of around 19.5 points·m–2 were collected by an airplane, then processed and classified using the LAStools; while about 362 images of the UAV were processed via Structure from Motion algorithm in the Agisoft Metashape Pro. Thus, we calculated the metrics using the point clouds of both an ALS and an UAV, and then verified their similarity. The fieldwork was manually done on 110 sampled trees. Calculated heights of the UAV were 3.8~5.8 m greater than those for the ALS; and when correlated with the fieldwork, the UAV data overestimated, while the maximum height of the ALS data was more accurate. For the canopy cover, the ALS computed canopy cover was 10%~30% less than that of the UAV. However, the canopy cover above 2 m by an UAV was the best measurement for a forest canopy. Therefore, these results assert that the examined techniques are robust and can significantly complement methods of the conventional data acquisition for the forest type map.

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System.

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

Direct estimation of leaf area index of tropical forests using LiDAR point cloud

Light Detection and Ranging (LiDAR) remote sensing is a promising method for accurate estimation of various forest structural parameters. A ground based LiDAR scanner, terrestrial laser scanner (TLS) has been used for the reference field measurements for the estimation of structural parameters such as tree height, DBH across large area forests sites. Leaf Area Index (LAI) is important forest structural parameter which has recently been estimated from TLS point cloud by geometrical/statistical modeling with tree height and DBH as proxy variables. In this work we present a method for direct estimation of LAI of tropical forests from TLS point cloud proposing a new algorithmic approach named as Point Spatial Density (PSD) algorithm. As an intermediate processing, the proposed method involves filtering and 3D reconstruction of individual trees using super voxel and min-cut segmentation approaches. Reconstructed trees are then extracted and geometrically modelled for estimating the LAI by the proposed PSD algorithm. The proposed PSD algorithm establishes a relationship between TLS point density, point spacing, and height of trees. Based on the number of points and the point spacing, the value of LAI found has been observed to be varying in sparse and dense canopy tree stands. Validation of the results with reference LAI measurements obtained using a LAI meter (LiCOR LAI Meter 2200) indicates a consistent correlation (R2 = 0.96) between the estimated and reference LAI measurements. The results suggest that the LAI in dense heterogeneous forests could be accurately estimated by the proposed PSD algorithm based approach.

Active 3D Imaging of Vegetation based on Multi-Wavelength Fluorescence LiDAR.

Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system’s performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.