LiDAR Ground Research Articles

Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models

AbstractTopography and the computational mesh grid are fundamental inputs to all two‐dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter‐scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC‐RAS model with varying mesh grid resolutions (1 and 2 m) and drone‐based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel‐floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.

Modeling structural traits of Aleppo pine (Pinus halepensis Mill.) forests with low-density LiDAR

ABSTRACT Mediterranean forests of Aleppo pine (Pinus halepensis Mill.) have a crucial role in climate change, as they are extremely adaptive and provide valuable timber or carbon stocks. However, greater detail quantifying those attributes is needed: although National Forest Inventories are acceptable, continuous cover maps are normally lacking. Here, we use the public Spanish low-density LiDAR flights to model above-ground biomass, volume, tree density, basal area and dominant height of naturally regenerated Mediterranean Aleppo pine forests, comparing individual-tree detection and area-based approach. We found R2 and RRMSE among 0.51–0.66 and 40–34% for above-ground biomass, 0.54–0.70 and 34–28% for volume, 0.23–0.45 and 33–28% for tree density, 0.48–0.62 and 32–27% for basal area, and 0.70–0.69 and 11–11% for dominant height. In all cases but dominant height, the area-based approach outperformed the individual-tree detection. Neither time difference between LiDAR flight and ground measurement or past land use affected the area-based approach models, yet the latter had a strong effect on observed productivity. The different definitions of dominant height were equivalent and did not influence the dominant height models. We believe these models, and their corresponding maps, will be a great asset for policymakers and different stakeholders for Aleppo pine forests throughout the Mediterranean basin.

Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

FMCW Radar on LiDAR map localization in structural urban environments

AbstractMultisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

Drivers of deciduous forest near-infrared reflectance: A 3D radiative transfer modeling exercise based on ground lidar

The fraction of near infrared (NIR) solar energy reflected by forest canopies has been shown to correlate with leaf nitrogen availability and photosynthesis. But the canopy properties behind this relation are unclear, partly because whole canopy reflectance and albedo results from multiple complex interactions between photons and tree parts, and also because modeling these interactions has been challenging due to difficulties in representing forest canopies with sufficient detail. Ground lidar technology has been applied to this problem in recent years; it is used here to provide highly detailed forest canopy representations as inputs into a ray tracing radiative transfer model. These structural representations are coupled with the best available field data on leaf optical properties to produce simulated images of canopy reflectance in the NIR, as well as the detailed 3D mapping of NIR energy absorption within the canopy. The simulations performed for two deciduous forests in the northeastern USA were first compared to airborne hyperspectral observations to assess their accuracy. The radiative transfer model was then used to quantify the sensitivity of whole canopy reflectance and vertical absorption of NIR to changes in model parametrization in an attempt to identify the main drivers of canopy reflectance. The results point to the arrangement of leaves in the topmost canopy layers, both in terms of leaf angles and leaf clumping within shoots, and to the leaf-level absorptance in all canopy layers as the dominant factors affecting canopy reflectance and albedo. These findings emphasize the need for improving our understanding of leaf arrangement in canopy tops – including what it covaries with – and for including measurements of leaf transmission when measuring leaf spectral properties in the field.

Investigation of wind veer characteristics on complex terrain using ground-based lidar

The wind direction shift with height significantly influences wind turbine performance, particularly in relation to terrain conditions. In this work, wind conditions at 12 measurement heights ranging from 40 m to 200 m using a ground lidar, Windcube V2, installed on a 16 m tall building were analysed to examine the characteristics of wind veer angles in complex terrain. The measurement campaign was carried out from January 1st to December 31st, 2022, in the southeastern part of South Korea. The terrain complexity around the ground lidar system was evaluated using the ruggedness index (RIX), whose result was 14.06 percent corresponding to complex terrain. The ground lidar measurements were compared with mesoscale data, EMD-WRF South Korea, for the data accuracy check. Wind veer frequencies and wind roses were derived to identify directional shifts with height. Furthermore, diurnal, monthly, and seasonal variations of wind veer characteristics were analysed. Wind shear exponent factor (WSE) and turbulence kinetic energy (TKE) were calculated, and wind veer profiles were constructed based on these parameters. The relative errors of wind speeds were analysed for rotor equivalent wind speed (REWS) and hub height wind speed (HHWS), with REWS with wind veer correction, REWSveer, as a reference. Additionally, atmospheric stability conditions were classified using WSE and TKE, and the vertical changes in wind veering were analysed according to the stability conditions. The findings reveal lower wind speeds exhibited larger wind veer values and fluctuations. The relative errors for the REWS and the HHWS were 0.04 % and 0.20 % on average, respectively. The study demonstrates that terrain conditions significantly impacted wind veer angles at heights below 100 m, whereas the influence diminished with increasing height above 100 m. The results could be helpful for wind farm developers to make decisions on the siting as well as the hub height of wind turbines on complex terrain

LiDAR Ground Detection-Based Dynamic Inverse Perspective Mapping of BEV Lanes

LiDAR Ground Detection-Based Dynamic Inverse Perspective Mapping of BEV Lanes

Ground Point Detection Method Based on Grid Division and Plane Fitting

Ground point detection is an important part of unmanned driving technology, and the detection results will affect the subsequent tasks. Aiming at the problem of under-segmentation and mis-segmentation of lidar ground point segmentation in unstructured scenes, a ground point recognition method based on grid division is proposed. This method first uses the constraint conditions to preprocess and extract the ROI region; secondly, the point cloud is divided by the method of grid division; finally, the plane fitting method is used to realize the ground point recognition. Based on the structured data set KITTI and the unstructured data set ORFD data set, this paper compares them with RANSAC, LF and HDL algorithms. The experimental results show that the algorithm in this paper can segment the ground points well, which is superior to the three algorithms compared in the evaluation index, and can detect the ground points quickly and accurately.

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

BENCHMARKING THE EXTRACTION OF 3D GEOMETRY FROM UAV IMAGES WITH DEEP LEARNING METHODS

Abstract. 3D reconstruction from single and multi-view stereo images is still an open research topic, despite the high number of solutions proposed in the last decades. The surge of deep learning methods has then stimulated the development of new methods using monocular (MDE, Monocular Depth Estimation), stereoscopic and Multi-View Stereo (MVS) 3D reconstruction, showing promising results, often comparable to or even better than traditional methods. The more recent development of NeRF (Neural Radial Fields) has further triggered the interest for this kind of solution. Most of the proposed approaches, however, focus on terrestrial applications (e.g., autonomous driving or small artefacts 3D reconstructions), while airborne and UAV acquisitions are often overlooked. The recent introduction of new datasets, such as UseGeo has, therefore, given the opportunity to assess how state-of-the-art MDE, MVS and NeRF 3D reconstruction algorithms perform using airborne UAV images, allowing their comparison with LiDAR ground truth. This paper aims to present the results achieved by two MDE, two MVS and two NeRF approaches levering deep learning approaches, trained and tested using the UseGeo dataset. This work allows the comparison with a ground truth showing the current state of the art of these solutions and providing useful indications for their future development and improvement.

Assessing the risk of slope failure to highway infrastructure using automated time-lapse electrical resistivity tomography monitoring

Electrical resistivity tomography (ERT) monitoring provides time-lapse images of the subsurface. These images can be used to assess spatiotemporal variation in moisture content, which is a key driver of slope failure, making ERT monitoring an effective tool to evaluate precursory conditions of failure. This work presents the results of ERT monitoring on a slope above a major highway located on the border between England and Wales. During highway construction in the 1960s the slope was subject to several large landslide events which resulted in the re-design of the carriageway and installation of engineered mitigation measures. A section of the slope known as the ‘partially slipped area’ exhibited partial displacement during this time but did not progress to full slope failure, and therefore presents an ongoing risk to the highway, even though it does not experience ongoing displacement. An ERT monitoring system was installed across this area to monitor subsurface variations in moisture content. The results show a complex pattern of subsurface moisture dynamics within the partially slipped area when compared to the adjacent area of stable slope. This is most likely a result of the uneven and hummocky terrain in the partially slipped area and its effects on rainfall infiltration, storage and drainage, combined with the displacement-induced jointing present in the underlying sandstone units. The ERT results are used to assess the volume of unstable ground, placing the volume at the upper end of estimates from previous studies. Furthermore, analysis of the ERT dataset for surface displacements shows no movement at the site, which is confirmed by analysis of differential LiDAR plots and ground motion data derived from InSAR. This study demonstrates the application of ERT monitoring on a low activity, high risk slope, highlighting the need to understand subsurface processes at the slope-scale to inform long-term slope management.

Wind power variation by wind veer characteristics with two wind farms

To clarify the wind veer characteristics with height and their effect on the wind turbine power outputs, an investigation was carried out at the wind farms with complex and simple terrains. A 2 MW and a 1.5 MW wind turbine were tested, each having an 80 m tall met mast and a ground lidar to capture wind veering. Wind veer conditions were divided into four types based on wind direction changes with height. The power deviation coefficient (PDC) and the revenue differences for the four types were derived from the estimated electric productions. As a result, the wind veer angle across turbine rotors were more significant at the complex site than at the simple site. For the two sites, the PDC values ranged from − 3.90 to 4.21% depending on the four types, which led to a 20-year revenue variation of − 274,750–423,670 USD/MW.

Determining the Flow State of Channels Under Vegetation With Airborne Lidar

AbstractHeadwater channels are vital to ecological health, water quality, and watershed connectivity. However, the geographic extent and the temporal wetting and drying dynamics of headwater catchments are not thoroughly understood, primarily due to occlusive vegetation. Light Detection and Ranging (lidar) data can be utilized to address this knowledge gap. Airborne lidar is capable of penetrating vegetation and has been used as a spatial and spectral tool for water body delineation. In the work presented here we: (a) develop a novel means of normalizing lidar ground return intensity to allow for comparison across data sets; (b) demonstrate a statistically significant reduction in median return intensity of wet stream reaches under dense vegetation relative to dry channel reaches; and (c) leverage this reduction to create classified maps of wet and dry channel networks in densely vegetated drainages. Across four study basins spanning over 100 km2 we observed an average reduction in median intensity of 41.7% and 72.2% for areas with and without dense vegetation, respectively. Optimal thresholds for delineating wet from dry stream reaches were determined using probability density functions. Resulting classified maps yielded overall accuracies ranging from 87.3% to 95.3% when compared to the National Hydrography Dataset via stratified random sampling. This study demonstrates that remote delineation of channel flow states in densely vegetated areas is possible, thus allowing for better consideration, designation, and conservation of headwater channels.

Lidar monitoring of the aerosol content in the near-ground atmospheric layers over Sofia

Lidar measurements provide possibility to determine and localize the sources of aerosol emissions in the air, to determine the presence of airborne transport of dust in the atmosphere, to track in real-time aerosol movement above vast territories [1-4]. Presently, along with building networks of ground lidar stations, such as the European Aerosol Research Lidar Network (EARLINET), it becomes more and more important to develop and refine the lidar methods of processing the data from measurements to retrieve the necessary information [5]. The present study comments on results of aerosols monitoring in the lowest atmospheric layers above Sofia by using lidar remote sensing. More than 20 lidar measurements were carried out in the period January – June 2022. The lidar system emitted laser pulses nearly horizontally within the planetary boundary layer (PBL); the reflected pulses were collected to calculate the range-corrected signals and to produce color-coded time-distance diagrams. These diagrams, superimposed on a map of the city to illustrate the location of the aerosol layers were published on the website of the Laser Radar Laboratory of the Institute of Electronics (IE-BAS) at: http://www.ie-bas.org/Departments/LidarData_Horizont/Quicklooks-Horizont.htm.

Point-based metric and topological localisation between lidar and overhead imagery

In this paper, we present a method for solving the localisation of a ground lidar using overhead imagery only. Public overhead imagery such as Google satellite images are readily available resources. They can be used as the map proxy for robot localisation, relaxing the requirement for a prior traversal for mapping as in traditional approaches. While prior approaches have focused on the metric localisation between range sensors and overhead imagery, our method is the first to learn both place recognition and metric localisation of a ground lidar using overhead imagery, and also outperforms prior methods on metric localisation with large initial pose offsets. To bridge the drastic domain gap between lidar data and overhead imagery, our method learns to transform an overhead image into a collection of 2D points, emulating the resulting point-cloud scanned by a lidar sensor situated near the centre of the overhead image. After both modalities are expressed as point sets, point-based machine learning methods for localisation are applied.

A Method for Forest Canopy Height Inversion Based on UAVSAR and Fourier–Legendre Polynomial—Performance in Different Forest Types

Mapping forest canopy height at large regional scales is of great importance for the global carbon cycle. Polarized interferometric synthetic aperture radar is an efficient and irreplaceable remote sensing tool. Developing an efficient and accurate method for forest canopy height estimation is an important issue that needs to be addressed urgently. In this paper, we propose a novel four-stage forest height inversion method based on a Fourier–Legendre polynomial (FLP) with reference to the RVoG three-stage method, using the multi-baseline UAVSAR data from the AfriSAR project as the data source. The third-order FLP is used as the vertical structure function, and a small amount of ground phase and LiDAR canopy height is used as the input to solve and fix the FLP coefficients to replace the exponential function in the RVoG three-stage method. The performance of this method was tested in different forest types (mangrove and inland tropical forests). The results show that: (1) in mangroves with homogeneous forest structure, the accuracy based on the four-stage FLP method is better than that of the RVoG three-stage method. For the four-stage FLP method, R2 is 0.82, RMSE is 6.42 m and BIAS is 0.92 m, while the R2 of the RVoG three-stage method is 0.77, RMSE is 7.33 m, and bias is −3.49 m. In inland tropical forests with complex forest structure, the inversion accuracy based on the four-stage FLP method is lower than that of the RVoG three-stage method. The R2 is 0.50, RMSE is 11.54 m, and BIAS is 6.53 m for the four-stage FLP method; the R2 of the RVoG three-stage method is 0.72, RMSE is 8.68 m, and BIAS is 1.67 m. (2) Compared to the RVoG three-stage method, the efficiency of the four-stage FLP method is improved by about tenfold, with the reduction of model parameters. The inversion time of the FLP method in a mangrove forest is 3 min, and that of the RVoG three-stage method is 33 min. In an inland tropical forest, the inversion time of the FLP method is 2.25 min, and that of the RVoG three-stage method is 21 min. With the application of large regional scale data in the future, the method proposed in this study is more efficient when conditions allow.

An Effect of Wind Veer on Wind Turbine Performance

An investigation was performed to identify the wind veer impact on wind turbine power performance at a wind farm located on Jeju Island, South Korea. A 2 MW wind turbine was used as a test turbine. An 80 m-tall met mast was located 220 m away from the test wind turbine and a ground lidar was installed close to the met mast. The wind veer conditions were divided into four types: veering in upper and lower rotor (VV), veering in upper and backing in lower rotor (VB), backing in upper and lower rotor (BB) and backing in upper and veering in lower rotor (BV). The frequency of the four types was identified at the wind farm. The characteristics of wind veer was analysed in terms of diurnal variation and wind speed. In addition, the power curves of the four types were compared with that under no veer condition. Also, the power deviation coefficient (PDC) derived from the power outputs was calculated to identify the effect of the four types on the turbine power performance. As a result, the frequencies of the types, VV, VB, BB and BV were 62.7 %, 4.9 %, 9.2 % and 23.1 %, respectively. The PDCs for the types VV and BV were 3.0 % and 4.2 %, respectively, meaning a power gain while those for the types VB and BB were -2.9 % and -3.9 %, respectively, meaning a power loss.

AN GROUND AND UNDER-GROUND URBAN ROADS SURVEYING APPROACH USING INTEGRATED 3D LIDAR AND 3D GPR TECHNOLOGY

Abstract. Digitalization of urban roads is an important part of smart city construction. In addition to having a basic understanding of the structure of the transportation network, we need to have a preliminary understanding of the information around the road, the current status of the road, and the impact that municipal projects may have on the road. At present, the three-dimensional information of the ground parts of roads can be obtained efficiently and accurately on a large scale by using three-dimensional scanning technology. However, there is a lack of comprehensive and intuitive understanding of the under-ground information and a lack of synergistic consideration of the ground and under-ground information. In this paper, a ground and under-ground urban road surveying system based on 3D LiDAR and 3D ground penetrating radar (GPR) is presented. The system covers multi-sensor coordinated control, time-space datum setup, and post-processing data. Experiments show that the system can realize the integrated ground and under-ground 3D surveying for urban roads, generate intuitive three-dimensional point cloud map model of ground and under-ground of urban roads, and provide effective technical support for smart city construction.

LILO: A Novel Lidar–IMU SLAM System With Loop Optimization

Light detection and ranging (Lidar) has become the core device in a simultaneous localization and mapping system, which has attracted much attention in recent years. However, the point clouds acquired by Lidar are sparse and mutually uncorrelated, making it difficult to calculate correspondences between consecutive frames and resulting in data drifting. In this situation, we propose a tightly coupled Lidar + IMU fusion system with loop optimization to address the above-mentioned problems. First, the ground Lidar points are segmented and removed. Second, under our framework, an inertial measurement unit is used to deskew the motion distortion of Lidar. Third, a voxel grid filtering process is implemented to further eliminate the redundant points, and feature matching is performed by identifying lines and planes. Finally, loop closure detection is realized to correct pose estimation and localization error. Experimental results on both public dataset and field test demonstrate the effectiveness of our algorithm.

Modeling Merchantable Wood Volume Using Airborne LiDAR Metrics and Historical Forest Inventory Plots at a Provincial Scale

So far, large-scale projects aiming to map forest attributes using aerial LiDAR data have been developed using ground sample plots acquired synchronously with LiDAR. No large projects have been developed using aerial LiDAR acquired independent of ground sample plot datasets. The goal of this study was to develop and validate large-scale parametric merchantable wood volume estimation models using existing historical ground sample plots. The models can be applied to large LiDAR datasets to map merchantable wood volume as a 10 × 10 m raster. The study demonstrated that a relative density index (RDI) based on a self-thinning equation and dominant height were suitable variables that can be calculated both for ground sample plots and LiDAR datasets. The resulting volume raster showed sound accuracy rates when compared to validation zones: R², 82.25%; RMSE, 13.7 m3/ha; and bias, −4.09 m3/ha. The results show that ground sample plot datasets acquired synchronously with LiDAR can be used to calculate the RDI and dominant height. These variables can consequently be used to map forest attributes over a large area with a high level of accuracy, thus not requiring the implementation of new costly sample plots.