Airborne light detection and ranging (LiDAR) point density analysis

Recent expansion in data sharing has created unprecedented opportunities to explore structure–function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data's potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30‐year time frame, including discrete‐return light detection and ranging (LiDAR) from an airborne observation platform. LiDAR is a well‐established and increasingly available remote sensing technology for measuring three‐dimensional characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 to 25+ pt/m2 depending on the instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the United States and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve of 16 structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7–8 pt/m2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and that users of these open‐source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

The variation in point cloud density is driven by many different factors. This variation is expected to affect the quality of the information extracted from the point clouds, however, the extent to which these variations impact the ability to accurately extract and assess geometric features of highways from point cloud data are unknown. This paper investigates the impacts of point density reduction on the extraction and assessment of four critical geometric features. The density of light detection and ranging (LiDAR) data was first reduced and the different features were extracted at varying levels of point density and on a selection of different highway segments in Alberta, Canada. The information obtained at lower point density was then compared to what was obtained at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e., reducing the point density to as low as 10% of the original data (30 ppm2 on the pavement surface) yielded results comparable to what was obtained at 100% density (300 ppm2) In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed. These findings are critical for transportation agencies considering the adoption of LiDAR technology to manage elements of their infrastructure and for researchers developing data processing tools and algorithms for the semantic segmentation of transportation features from remotely sensed point clouds.

Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied. We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared. Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06. We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests.

Topographical technology by Airborne LIDAR (Light Detection and Ranging) generates a precise points cloud with a density of several points per square meter, LIDAR data processing is a crucial step to be used. Extraction of 3D information in automatic way and especially in urban areas from LIDAR data is one of the most difficult problems in computer vision; it is also a necessary step for implementation of several applications that require a high level interpretation of LASER data. Therefore, there is recently an increased interest in this research field and a vast literature. The problematic discussed in this article lies in the differentiation between the sets of points that represent a specified layer of information (construction, vegetation, roads, lines, etc.). This step is called segmentation. The aim of this study is to provide a set of automatic segmentation techniques tailored to different types of 3D data and proposes a methodology to classify LIDAR data with a maximum degree of automaticity using only point cloud data.

Modelling vertical error in LiDAR-derived digital elevation models

Light detection and ranging (LiDAR) is an active remote sensing technology that can be used to directly acquire three-dimensional forest information, and it has great potential to replace traditional time-consuming field surveys. We aimed to determine the accuracy with which enhanced airborne LiDAR with high density (mean, 47 points m−2) can replicate detailed vertical foliage arrangement and three forest structural indices, i.e. number of layers (NSTRAT), sum of the percent vegetation cover (PCVC), and foliage height diversity (FHD), all of which are useful for characterizing avian habitats. Thirteen 40-m-long lines were set in an urban forest area, and LiDAR-based and field-based cross-sectional profiles with 1 × 1-m meshes were compared. In addition, we performed random LiDAR point thinning to determine whether the replications were possible with lower point densities. The 1 × 1-m mesh vertical foliage arrangements were replicated with high accuracy by using LiDAR (mean producer's accuracy, 0.756; mean user's accuracy, 0.810) before the point reduction. All the three indices were also replicated with high accuracy (<0.12 of the relative root mean square error for the 1:1 relationship). Even when the mean point density was reduced to 2.4 points m−2, the three indices could be quantitatively estimated with high accuracy by using simple linear regression analyses (>0.8 in R 2). For NSTRAT and FHD, numerical replication was possible with high accuracy (<0.2 and <0.1, respectively, of the relative root mean square error for the 1:1 relationship), even when the mean point density was reduced to 2.4 points m−2. In conclusion, enhanced high-density LiDAR seems to be highly suitable as a substitute for field surveys for delineating detailed vertical forest structure in order to characterize forest avian habitats. Moreover, lower-density LiDAR data could replicate the forest structural indices, suggesting its useful low-cost application in quantitative avian habitat models.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical &amp; digital imagery information for various applications.

The concept of the laser guide star was proposed by Foy and Labeyrie in 1985. The Laser Guide Star (LGS) depends on the abundance and distribution of sodium in the mesosphere. The mesospheric sodium often appears to consist of more than one layer, each of which exhibits time dependent variations in density and altitude. The non-zero thickness and finite range of the layers results in elongation of the LGS image resulting in a LGS defocus on extremely large telescopes such as TMT and VLT. Three methods have been proposed to measure the focus changes: direct imaging of the sodium plume, LIDAR (Light Detection and Ranging), and averaging of the LGS WFS focus error over 30-60 s. Direct imaging of the plume requires an additonal telescope. The principal advantage of LIDAR is the accurate height measurement. With LGS WFS focus error averaging, there are problems with the initial focus setting. Sodium resonance-fluorescence LIDAR has been undertaken by a number of groups: Purple Crow LIDAR (University of Western Ontario), Colorado State University LIDAR, and UBC (University of British Columbia) LIDAR. A number of means have been developed by TMT, SOR and VLT to correct Na variability. Na variability will be examined for its determination, analysis and impact on AO aberation correction.

PurposeThis paper aims to report the first iteration on the Light Detection and Ranging (LIDAR) Engineering Model altimeter named HELENA. HELENA is a Time of Flight (TOF) altimeter that provides time-tagged distances and velocity measurements. The LIDAR can be used for support near asteroid navigation and provides scientific information. The HELENA design comprises two types of technologies: a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km. Thermal-mechanical and radiometric simulations of the HELENA telescope are reported in this paper. The design is subjected to vibrational, static and thermal conditions, and it was possible to conclude by the results that the telescope is compliant with the random vibration levels, the static load and the operating temperatures.Design/methodology/approachThe Asteroid Impact & Deflection Assessment (AIDA) is a collaboration between the NASA DART mission and ESA Hera mission. The aim scope is to study the asteroid deflection through a kinetic collision. DART spacecraft will collide with Didymos-B, while ground stations monitor the orbit change. HERA spacecraft will study the post-impact scenario. The HERA spacecraft is composed by a main spacecraft and two small CubeSats. HERA will monitor the asteroid through cameras, radar, satellite-to-satellite doppler tracking, LIDAR, seismometry and gravimetry.FindingsThe HELENA design comprises two types of technologies: a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km.Originality/valueIn this paper is reported the first iteration on the LIDAR Engineering Model altimeter named HELENA. HELENA is a TOF altimeter that provides time-tagged distances and velocity measurements. The LIDAR can be used for support near asteroid navigation and provides scientific information. The HELENA design comprises two types of technologies: a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km.

This article describes how Light Detection and Ranging (LIDAR) has been established as a mainstream tool for the acquisition of three dimensional point data over the past few years. Besides the conventional mapping missions, LIDAR has also proved to be very effective for a wide range of applications such as forestry, urban planning, structural deformation analysis, and reverse engineering. In the context of a national dataset, it is safe to assume that multiple laser scanners are under different conditions in order to collect data. Current registration and segmentation algorithms assume homogeneity in the local point density and accuracy, which is an invalid assumption that cannot be tolerated. As a consequence of the wide range of LIDAR sensors that are currently available, it is becoming crucial to develop algorithms for the registration and segmentation of LIDAR data with significantly varying characteristics, for example, varying point density and accuracy. A methodology for the optimal registration and segmentation of heterogeneous LIDAR data is presented in this article. An example of integrating airborne and terrestrial laser scans is also presented, which is followed by a discussion of the pros and cons of the integration process.

Background The creation of defensible space, an area around the house where fuel is managed according to specific guidelines, can reduce ignition and fire spread, mitigating wildfire threat to communities. Remote sensing approaches can facilitate assessments of defensible spaces. Aims To develop a protocol to evaluate which defensible space hazards can be accurately assessed using Light Detection and Ranging (LiDAR). Methods We used high and low point density airborne LiDAR to measure hazards in 31 gardens in southeast Tasmania (Australia) based on alignment with defensible space guidelines. We compared the resulting ‘garden hazard scores’ with those generated from field observations to identify garden characteristics that can be estimated using LiDAR, as well as the best analysis resolution to do so. Key results We found a good association between field data and LiDAR measures of vegetation cover and spatial arrangement, but not plant flammability, inferred using Normalised Difference Vegetation Index (NDVI), or flammable objects. Low-density LiDAR consistently underestimated cover metrics compared with High-density LiDAR. Nonetheless, garden hazard scores calculated using either high- or low-density LiDAR yielded results similar to those obtained using only field observations. Implications LiDAR can be used to conduct semi-automatic, large-scale measurements of some, albeit not all, critical garden hazards.

3D LIDARs (Light Detection and Ranging) with 1.5μm nanosecond pulse lasers have been increasingly used in different applications. The main reason for their popularity is that these LIDARs have high performance while at the same time can be made eye-safe. Because the laser hazard effect on eyes or skin at this wavelength region (<1.4μm) is mainly from the thermal effect accumulated from many individual pulses over a period of seconds, scanning can effectively reduce the laser beam hazard effect from the LIDARs. Neptec LIDARs have been used in docking to the International Space Station, military helicopter landing and industrial mining applications. We have incorporated the laser safety requirements in the LIDAR design and conducted laser safety analysis for different operational scenarios. While 1.5μm is normally said to be the eye-safe wavelength, in reality a high performance 3D LIDAR needs high pulse energy, small beam size and high pulse repetition frequency (PRF) to achieve long range, high resolution and high density images. The resulting radiant exposure of its stationary beam could be many times higher than the limit for a Class 1 laser device. Without carefully choosing laser and scanning parameters, including field-of-view, scan speed and pattern, a scanning LIDAR can’t be eye- or skin-safe based only on its wavelength. This paper discusses the laser safety considerations in the design of eye-safe scanning LIDARs, including laser pulse energy, PRF, beam size and scanning parameters in two basic designs of scanning mechanisms, i.e. galvanometer based scanner and Risley prism based scanner. The laser safety is discussed in terms of device classification, nominal ocular hazard distance (NOHD) and safety glasses optical density (OD).

National and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average F 1 score = 96%), closely followed by the hybrid method (average F 1 score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average F 1 score = 82%) and depended on landscape context and vegetation composition (e.g. the F 1 score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.

Chapter 7 - Landslide analysis using laser scanners