High Density Point Cloud Data Research Articles

Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys

When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primitives and object primitives, respectively. In this study, a densely populated area was chosen as the study area. Light airborne LIDAR mounted on a delta wing was used to collect point cloud data at a low altitude (170 m) over a dense cross-course. The point cloud data were then combined, corrected, and enhanced with texture data, and the houses were vectorized on the point cloud. The accuracy of the results was then assessed. With a median inaccuracy of 4.8 cm and a point cloud data collection rate of 83.3%, using airborne LIDAR to measure house corners can significantly lighten the labour associated with external house corner measurements.This test extracts the texture information of point cloud data through the efficient processing of high-density point cloud data, providing a reference for the application of texture information of airborne LIDAR data and a clear understanding of its accuracy.

Tree Species Classification Based on Upper Crown Morphology Captured by Uncrewed Aircraft System Lidar Data

The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m2 and an aggregate pulse density of 556 pulses/m2. We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas.

Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling

ABSTRACT Three-dimensional models of trees that correspond to the real-world forms of the trees on the ground are used in urban planning, solar power estimation, and other disciplines. Previous studies have focused on generating 3D tree models from high-density point cloud data such as Terrestrial Laser Scanning (TLS) data, which is expensive and limited to small spatial extents. However, there has been limited exploration of inexpensive solutions to model trees over large spatial extents. The goal of this study is to use widely available discrete return Airborne Laser Scanning (ALS) data along with field-captured tree photographs and Google Street View (GSV) images to develop 3D equivalents of trees over larger spatial extents. To this end, we designed a process to assign representative 3D models for individual trees in discrete return ALS point clouds. This study demonstrates the use of a Convolutional Neural Network (CNN) model and 3D models generated with Structure from Motion (SfM) for the realistic modelling of deciduous non-overlapping trees from discrete return ALS data. We classified and labelled the crown shapes of deciduous trees in a study area into four classes based on GSV images of trees. We delineated and segmented non-overlapping deciduous trees from ALS data and reduced them to 2D images using voxel point counts. Next, we trained a CNN architecture to match the 2D images to the corresponding classes observed from GSV images. For each class, we created a representative 3D tree model using field-captured circumnavigational photos of trees and SfM. To demonstrate 3D visualization using the 3D tree models, we created a 3D visualization of the trees surrounding a parking lot. The trained CNN model from this study can be used to classify non-overlapping deciduous trees from discrete return ALS data and subsequently visualize near-realistic 3D tree models of trees.

ROAD INFRASTRUCTURE MAPPING BY USING IPHONE 14 PRO: AN ACCURACY ASSESSMENT

Abstract. Vital aspects of transportation networks, such as the extraction of road information and analysis of road conditions, have become increasingly important research topics as they outline the foundation of many applications such as high-precision mapping, infrastructure planning and maintenance, intelligent transportation, or road design analysis. Therefore, regularly obtaining accurate high-density point cloud data of infrastructures supports many transportation-based applications and provides up-to-date information for smart cities or digital twins. Low-cost smartphone platforms equipped with a variety of sensors provide new and powerful data acquisition capabilities that can be exploited in the geospatial field. For example, mobile phones are now capable of collecting valuable data to generate accurate models to support digital reconstruction of infrastructures. These platforms can provide simple and effective data acquisition, while offering useful geospatial data that can be an alternative to traditional measurement techniques. However, the sensor performance with respect to spatial accuracy of point clouds generated in different applications have not yet been fully investigated. Thus, this paper evaluates the feasibility of using the point clouds generated by the built-in camera and LiDAR sensors integrated into iPhone 14 Pro for extracting road-related information. Additionally, the use of the viDoc RTK Rover on the iPhone 14 Pro increases the platform positioning accuracy, consequently improving the georeferencing accuracy of the point clouds. To validate the performance of the point clouds obtained by the iPhone 14 Pro, a reference dataset of the road features was obtained by measuring with a single-point RTK-GNSS receiver, receiving corrections from the Turkish CORS network (TUSAGA-Aktif) which provides two to three centimetres of accuracy. In addition, reference point cloud data over the same area was obtained from different platforms such as Mobile LiDAR and UAS, and the road features were extracted from these dataset and performance validated. The data acquired by the iPhone 14 Pro was processed and evaluated with respect to the reference datasets. The advantages and disadvantages of using iPhone 14 Pro are analysed in detail and the findings are reported.

A Geometric-Feature-Based Method for Automatic Extraction of Anchor Rod Points from Dense Point Cloud

As the technology of high-precision 3D laser scanning becomes increasingly prevalent in the fields of hydraulic building modeling and deformation monitoring, the quality of point clouds plays an increasingly crucial role in data processing. This paper investigates an automatic extraction method of anchor rod points based on geometric features, which focuses on the influence of anchor rod points and mixed pixels in the data of an underground powerhouse of a pumped storage power station on modeling and deformation monitoring during the construction period. This workflow consists of two steps that can automatically extract anchor rod points from high-density point cloud data. Triangular mesh features in the local neighborhood and the parameters of the anchor rods are used to locate the anchor rod in downsampled data, and curvature features are used to extract anchor rod points precisely. The experiment of extracting anchor rods shows that the accuracy of this method of initial identification is 97.2%. Furthermore, precise extraction based on curvature curve fitting is applicable. This method can accurately separate the three types of anchor rods from the dense point cloud on the rough surface of a cavern roof; the false-extraction rate of anchor rod points is about 0.11% to 5.09%. This method can provide high-quality and dependable data sources for the precise registration, modeling and deformation analysis of point clouds in a construction cavern.

A new point cloud simplification method with feature and integrity preservation by partition strategy

The advancement of 3D scanning technology has gradually brought to light the issue of complex and time-consuming processing of high-density point cloud data. To address this need, this article proposes a method for point cloud simplification based on the concept of partitioning, which divides the point cloud's points into edge points, feature points, and non-feature points. On the basis of the normal angular difference, the edge points are extracted from the entire point cloud. The point cloud is then segmented by curvature into feature and non-feature regions using a region growing segmentation method. The feature points are determined by calculating the information entropy of the points in the feature region, whereas the non-feature points are extracted by voxel down-sampling the non-feature points. The experiment demonstrates that the proposed method effectively preserves the features and integrity of the point cloud while requiring less computational effort.

Estimating Individual Tree Above-Ground Biomass of Chinese Fir Plantation: Exploring the Combination of Multi-Dimensional Features from UAV Oblique Photos

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the important tree species in plantation in southern China. Rapid and accurate acquisition of individual tree above-ground biomass (IT-AGB) information is of vital importance for precise monitoring and scientific management of Chinese fir forest resources. Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology can simultaneously obtain high-density point cloud data and high spatial resolution spectral information, which has been a main remote sensing source for obtaining forest fine three-dimensional structure information and provided possibility for estimating IT-AGB. In this study, we proposed a novel approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach. The results demonstrated that the intensity variables derived from the color space were strongly correlated with the IT-AGB and obviously improved the estimation accuracy. The model constructed by the combination of point cloud variables, vegetation index and RGB spatial intensity variables had high accuracy (R2 = 0.79; RMSECV = 44.77 kg; and rRMSECV = 0.25). Comparing the performance of estimating IT-AGB models with different spatial resolution images (0.05, 0.1, 0.2, 0.5 and 1 m), the model was the best at the spatial resolution of 0.2 m, which was significantly better than that of the other four. Moreover, we also divided the individual tree canopy into four directions (East, West, South and North) to develop estimation models respectively. The result showed that the IT-AGB estimation capacity varied significantly in different directions, and the West-model had better performance, with the estimation accuracy of 67%. This study indicates the potential of using oblique photogrammetry technology to estimate AGB at an individual tree scale, which can support carbon stock estimation as well as precision forestry application.

3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing

Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.

Development of structured light 3D-scanner with high spatial resolution and its applications for additive manufacturing quality assurance

Digital three-dimensional (3D) scanning is a cutting-edge metrology method that can digitally reconstruct surface topography with high precision and accuracy. Such metrology can help traditional manufacturing processes evolve into a smart manufacturing paradigm, which can ensure product quality by automated sensing and control. However, due to limitations with the spatial resolution, scanning speed, and size of the focusing area, commercially available systems cannot be directly used for in-process monitoring in smart manufacturing. For example, a metal 3D printer requires a scanner with second-level sensing, micron-level spatial resolution, and a centimeter-scale scanning region. Among the 3D scanning technologies, structured light 3D scanning can meet the scanning speed criteria but not the spatial resolution and scanning region criteria. This work addresses these challenges by reducing the field of view of a structured light scanner system while increasing the image sensor pixel resolution. Improvements to spatial resolution and accuracy are achieved by establishing hardware selection criteria, integrating the proper hardware, designing a scale-appropriate calibration target, and developing noise reduction procedures during calibration. An additively manufactured Ti-6Al-4V part was used to validate the effectiveness of the proposed 3D scanner. The scanning result shows that both melt pool geometry and overall shape can be clearly captured. In the end, the scanning accuracies of the proposed scanner and a professional-grade commercial scanner are validated with a nanometer-level accuracy white light interferometer using high-density point cloud data. Compared to the commercial scanner, the proposed scanner improves the spatial resolution from 48 to 5 μm and the accuracy from 108.5 to 0.5 μm. Compared to the white light interferometer, the proposed scanner improves the scanning and processing speed from 2 to 20 s.

Three-Dimensional Slope Imaging Method for Ground-Based Real-Aperture Radar.

Traditional two-dimensional radar images can only reflect the target azimuth and slant range and thus suffer problems of geometric deformation and overlapping. The unique three-dimensional (3D) imaging capability of ground-based real-aperture radar can more accurately and directly achieve correlation between the radar image and the slope monitoring scenarios, thus providing reliable information for the early warning and forecasting of landslides and collapse disasters. The latest method of selecting a slope target from a high-resolution range profile includes two indexes: maximum amplitude and coherence, which will affect the accuracy of displacement measurement when there is an interference target. We present a three-dimensional slope imaging method based on smoothness constraints. On the basis of the latest method, the objective fact of the practically smooth and continuous distribution of slope surfaces is considered. This method can be used for image interpretation on strongly scattered targets within the slope. The independently developed ground-based real-aperture slope radar system was deployed in the Heidaigou Open-Pit Coal Mine in Inner Mongolia to carry out 3D slope imaging experiments. The effectiveness of this method in slope monitoring and imaging was confirmed by comparing the surface roughness and the spatial positions of the targets with the high-density point cloud data in the projective plane obtained during the same time period. We used RMSE function and roughness as two measures. It shows that the method presented in this paper is more suitable for actual three-dimensional slope imaging.

MODELLING PERMAFROST TERRAIN USING KINEMATIC, DUAL-WAVELENGTH LASER SCANNING

Abstract. In this paper we introduce the first dual-wavelength, kinematic backpack laser scanning system and its application on high resolution 3D terrain modelling of permafrost landforms. We discuss the data processing pipeline from acquisition to preparation, system calibration and terrain model process. Topographic information is vital for planning and monitoring tasks in urban planning, road construction for mass calculations, and mitigation of flood and wind related risks by structural design in coastal areas. 3D data gives possibility to understand natural processes inducing changes in the terrain, such as the cycles of thaw-freeze in permafrost regions. Through an application case on permafrost landforms in the Arctic we present the field practices and data processing applied, characterize the data output and discuss the precision and accuracy of the base station, trajectory and point cloud data. Two pulsed time of flight ranging, high performance mobile laser scanners were used in combination with a near navigation grade GNSS-IMU positioning on a kinematic backpack platform. The study shows that with a high-end system 15 mm absolute accuracy of 3D data could be achieved using PPP processing for the GNSS base station and multi-pass differential trajectory post-processing. The PPP solution shows millimetre level agreement (Easting 6 mm, Northing 4 mm, and elevation 8 mm standard deviations) for the base station coordinates over an 11 day period. The point cloud residual standard deviation for angular boresight misalignment was 27 mm. The absolute distance between ground surfaces from interactive analysis was 17 mm with 13 mm standard deviation (n = 64). The proposed backpack laser scanning provides accurate and precise 3D data and performance over considerable land surface area for detailed elevation modelling and analysis of the morphology of features of interest. The high density point cloud data permits fusion of the dual-wavelength lidar reflectance data into spectral products.

Application of 3D laser scanning technology for preservation and monitoring of Thai pagoda: A case study of Wat Krachee Ayutthaya

3D laser scanning technology is one of the current technologies for developing a sustainable and smart city. This technology is applied to obtain the three-dimensional information of building in digital format. To preserve the historic structure, the current structural information is required for structural assessment, monitoring, and conservation planning. The data obtained by the 3D laser scanner is normally used to generate the three-dimensional point cloud data of the building surface. The high-density point cloud data represented the dimension, shape, and size of the building in the current state can be acquired in more accurate and faster than another technique. This paper aims to present the application of 3D laser scanning technology for preservation and monitoring of heritage pagoda in Thailand. The pagoda of Wat Krachee located in Ayutthaya province of Thailand was selected as a case study. The results obtained in this study are a part of Wat Krachee conservation project, which is led by UNESCO Bangkok and the Fine Arts Department, Thailand. One of the challenges in Wat Krachee project is how to preserve the masonry pagoda with two intertwined trees growing inside and become a part of pagoda structure. The terrestrial 3D laser scanner was applied to develop the digital data documentation and 3D model of pagoda in the current state. The 3D survey work has been started since February 2018. The analysis of data obtained in February 2018, Mar 2018, July 2018, and July 2019 was carried out. The significant dimensions and deformation of pagoda are monitored. According to the results, it is found that the 3D laser scanning technology is very effective for assessment and monitoring. The 3D point cloud model obtained in this study can be used to develop the assume original model of the Wat Krachee pagoda, which is the inverted bell-shape with a square base.

Factors to be considered in establishing a scanning laboratory for testing the accuracy of terrestrial laser scanning technologies

Laser scanning is a 21st century mapping technique that is used to generate high density point cloud data in three dimensions (3D). The 3D scans can be used for surveying, mapping and monitoring of rock mass movement in mining. For high precision and reliable work, the instrument must be used correctly, regularly calibrated and checked to ensure that it constantly performs according to manufacturer's specifications. In this study, a Leica Total Station TCR 1201+ and a Trimble S6 Total Station were used to establish accurate coordinates for a control beacon and scanning targets. Thereafter, the scanning of the targets was carried out using a FARO Focus XD 130 terrestrial laser scanner. A comparison was conducted using the coordinates from the terrestrial laser scanner and total station to examine the point accuracy of the FARO scanner. The result from the comparison between the laser scanners coordinates and the total station coordinates showed that the FARO Focus is capable of producing accurate point cloud data that can be used for the purpose of monitoring underground rock mass movements. Lessons learnt during the development of the short-range scanning laboratory are summarised as factors to be considered when establishing such a laboratory. These include, operator knowledge, skill and work flow, robust target design, scanning geometry, full target visibility during scanning, calibration of instruments, laser scanner field of view and application of systematic error propagation during measurements.

Detection and monitoring of defects on three-dimensional curved surfaces based on high-density point cloud data

The surface quality of three-dimensional (3-D) curved surfaces is one of the most important factors that can directly influence the performance of the final product. This paper presents a systematic approach for detection and monitoring of defects on 3-D curved surfaces based on high-density point cloud data. Firstly, an algorithm to remove outliers and a boundary recognition algorithm are proposed to divide the entire 3-D curved surface including millions of measured points into multiple sub-regions. Secondly, two new evaluation indexes based on wavelet packet entropy and normal vector are explored to represent the features of the multiple sub-regions to determine whether the sub-regions are out-of-limit (OOL) of specifications. Thirdly, three quality parameters representing quality characteristics of a curved surface are presented and their values are calculated based on the clusters of OOL sub-regions. Finally, three individual control charts are presented to monitor the three quality parameters. As long as any quality parameter is out of the control range, the manufacturing process of the curved surface is determined to be out-of-control (OOC). The results of a case study show that the proposed approach can effectively identify the OOC manufacturing process and detect defects on 3-D curved surfaces.

VBRT: A novel voxel-based radiative transfer model for heterogeneous three-dimensional forest scenes

Modeling the radiative transfer (RT) in heterogeneous forest scenes is important for understanding biophysical processes, as well as retrieving information from remotely sensed data. LiDAR (Light Detection and Ranging) is capable of providing highly detailed three-dimensional (3D) canopy structural information that can be used to parameterize RT models. In previous studies, point cloud data (such as terrestrial LiDAR data) are often voxelized with coarse resolutions, and the foliage voxels are often assumed to be turbid medium. In this study we propose a new voxel-based RT model, namely VBRT, that uses high resolution solid voxels to approximate 3D structure of forest more accurately than coarse resolution turbid medium voxels used in previous studies. Parallel computing techniques are used to speed up computation and the model can run on high performance computing (HPC) platforms. VBRT was tested in four virtual forest scenes, using the well-known physically based ray tracer (PBRT) as a benchmark. The Discrete Anisotropic Radiative Transfer (DART) model, which is based on turbid medium voxels, was also compared. Experimental results show that simulated digital imagery and bi-directional reflectance factor (BRF) by VBRT and PBRT are in good agreement, and the difference in simulation results can be reduced by using higher resolution voxels or larger number of samples per pixel. According to our test, parameterizing VBRT using high resolution terrestrial LiDAR data with 0.02m voxels can produce more accurate results than DART with turbid medium voxels (0.1m), although VBRT is more computation-intensive due to the use of higher resolution voxels. Our results indicate that VBRT has good potential in modeling radiation transfer in forests, as it is possible to parameterize the model using high density point cloud data such as terrestrial LiDAR data.

Development of Classification Technique of Point Cloud Data Using Color Information of UAV Image

This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that ...

3D BUILDING FAÇADE RECONSTRUCTION USING HANDHELD LASER SCANNING DATA

Abstract. 3D The three dimensional building modelling has been an interesting topic of research for decades and it seems that photogrammetry methods provide the only economic means to acquire truly 3D city data. According to the enormous developments of 3D building reconstruction with several applications such as navigation system, location based services and urban planning, the need to consider the semantic features (such as windows and doors) becomes more essential than ever, and therefore, a 3D model of buildings as block is not any more sufficient. To reconstruct the façade elements completely, we employed the high density point cloud data that obtained from the handheld laser scanner. The advantage of the handheld laser scanner with capability of direct acquisition of very dense 3D point clouds is that there is no need to derive three dimensional data from multi images using structure from motion techniques. This paper presents a grammar-based algorithm for façade reconstruction using handheld laser scanner data. The proposed method is a combination of bottom-up (data driven) and top-down (model driven) methods in which, at first the façade basic elements are extracted in a bottom-up way and then they are served as pre-knowledge for further processing to complete models especially in occluded and incomplete areas. The first step of data driven modelling is using the conditional RANSAC (RANdom SAmple Consensus) algorithm to detect façade plane in point cloud data and remove noisy objects like trees, pedestrians, traffic signs and poles. Then, the façade planes are divided into three depth layers to detect protrusion, indentation and wall points using density histogram. Due to an inappropriate reflection of laser beams from glasses, the windows appear like holes in point cloud data and therefore, can be distinguished and extracted easily from point cloud comparing to the other façade elements. Next step, is rasterizing the indentation layer that holds the windows and doors information. After rasterization process, the morphological operators are applied in order to remove small irrelevant objects. Next, the horizontal splitting lines are employed to determine floors and vertical splitting lines are employed to detect walls, windows, and doors. The windows, doors and walls elements which are named as terminals are clustered during classification process. Each terminal contains a special property as width. Among terminals, windows and doors are named the geometry tiles in definition of the vocabularies of grammar rules. Higher order structures that inferred by grouping the tiles resulted in the production rules. The rules with three dimensional modelled façade elements constitute formal grammar that is named façade grammar. This grammar holds all the information that is necessary to reconstruct façades in the style of the given building. Thus, it can be used to improve and complete façade reconstruction in areas with no or limited sensor data. Finally, a 3D reconstructed façade model is generated that the accuracy of its geometry size and geometry position depends on the density of the raw point cloud.

BENCHMARKING MOBILE LASER SCANNING SYSTEMS USING A PERMANENT TEST FIELD

Abstract. The objective of the study was to benchmark the geometric accuracy of mobile laser scanning (MLS) systems using a permanent test field under good coverage of GNSS. Mobile laser scanning, also called mobile terrestrial laser scanning, is currently a rapidly developing area in laser scanning where laser scanners, GNSS and IMU are mounted onboard a moving vehicle. MLS can be considered to fill the gap between airborne and terrestrial laser scanning. Data provided by MLS systems can be characterized with the following technical parameters: a) point density in the range of 100-1000 points per m2 at 10 m distance, b) distance measurement accuracy of 2-5 cm, and c) operational scanning range from 1 to 100 m. Several commercial, including e.g. Riegl, Optech and others, and some research mobile laser scanning systems surveyed the test field using predefined driving speed and directions. The acquired georeferenced point clouds were delivered for analyzing. The geometric accuracy of the point clouds was determined using the reference targets that could be identified and measured from the point cloud. Results show that in good GNSS conditions most systems can reach an accuracy of 2 cm both in plane and elevation. The accuracy of a low cost system, the price of which is less than tenth of the other systems, seems to be within a few centimetres at least in ground elevation determination. Inaccuracies in the relative orientation of the instruments lead to systematic errors and when several scanners are used, in multiple reproductions of the objects. Mobile laser scanning systems can collect high density point cloud data with high accuracy. A permanent test field suits well for verifying and comparing the performance of different mobile laser scanning systems. The accuracy of the relative orientation between the mapping instruments needs more attention. For example, if the object is seen double in the point cloud due to imperfect boresight calibration between two scanners, this will make especially the automatic modelling of the object much more challenging.

Estimating surface roughness of terrestrial laser scan data using orthogonal distance regression

AbstractWith the increasing accessibility of terrestrial light detection and ranging scanners (LiDAR), generating tools to elicit meaningful information from high-density point cloud data has become of paramount importance. Surface roughness is one metric that has gained popularity, largely due to the accuracy and density of LiDAR-derived point cloud data. Surface roughness is typically defined as a spread of point distances from a reference datum, the standard deviation of point distances from a model surface being a commonly employed model. Unfortunately, a recent literature review has found that existing surface roughness models are far from standardized and may be prone to error resulting from underlying surface topography. In the research presented here, we develop a surface roughness model that is robust to underlying topographic variability by segmenting the point cloud with a three-dimensional regular grid, establishing local (grid cell) reference planes by orthogonal distance regression, and estimating the surface roughness of each grid cell as the standard deviation of orthogonal point-to-plane distances. This surface roughness model is employed to identify fracture and rubble zone distributions within a terrestrial LiDAR scan from a basalt outcrop in southeast Idaho, and the results are compared to a more common model based on ordinary least-squares plane fitting. Results indicate that the orthogonal regression model is robust to outcrop orientation and that the ordinary least-squares model systematically overestimates surface roughness by contaminating estimates with spatially correlated errors that increase with decreasing grid size.