Na layer variability and implications for LGS adaptive optics: determination, analysis and impact on AO correction

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 & digital imagery information for various applications.

AIChE JournalVolume 44, Issue 2 p. 499-501 Letter to the Editor Letters to the editor Norman Epstein, Norman Epstein Dept. of Chemical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSearch for more papers by this authorJohn R. Grace, John R. Grace Dept. of Chemical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSearch for more papers by this authorJunping Zhang, Junping Zhang Dept. of Chemical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSearch for more papers by this authorLauren A. Briens, Lauren A. Briens Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this authorCedric L. Briens, Cedric L. Briens Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this authorJ. Hay, J. Hay Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this authorA. Margaritis, A. Margaritis Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this author Norman Epstein, Norman Epstein Dept. of Chemical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSearch for more papers by this authorJohn R. Grace, John R. Grace Dept. of Chemical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSearch for more papers by this authorJunping Zhang, Junping Zhang Dept. of Chemical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSearch for more papers by this authorLauren A. Briens, Lauren A. Briens Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this authorCedric L. Briens, Cedric L. Briens Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this authorJ. Hay, J. Hay Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this authorA. Margaritis, A. Margaritis Dept. of Chemical and Biochemical Engineering, University of Western Ontario, London, Ont. N6A 5B9, CanadaSearch for more papers by this author First published: 16 April 2004 https://doi.org/10.1002/aic.690440228AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume44, Issue2February 1998Pages 499-501 RelatedInformation

A second-generation Gamma Remote Sensing Portable RAdio Detection and Ranging (RADAR) Interferometer (GPRI-II) and a Lieca C-10 LIght Detection and Ranging (LIDAR) were employed to monitor the volume change behavior of two thin compacted clay test sections at the University of Arkansas Cato Springs Research Center (UACRSC). The viability of using remote sensing methods to accurately identify and quantify deformation associated with expansive clay behavior was evaluated. Two 464.52 m2 (5,000 ft2) sections consisting of 20.3 cm (8 inches) thick compacted clay layer underlain by a 5.1 cm (2 inches) thick sand drainage blanket were constructed. One test section was amended with three (Phase 1) and six (Phase 2) percent sodium bentonite. Four LIDAR and 355 RADAR observations were captured from the roof of the UACSRC building between January and July, 2012. Multiple RADAR images were captured on a weekly basis and after significant precipitation events. RADAR measurements, obtained by calculating the interferometric phase difference between coregistered temporally separated RADAR images, were used to determine topography and deformation. LIDAR measurements, as obtained from LIDAR point clouds, were also used to determine topography and deformation. To obtain accurate topography and deformation measurements the vegetation effects within the LIDAR data was removed. Deformation maps generated using both the RADAR and LIDAR methods are presented and compared. The results indicate both technologies are capable of detecting surface movements associated with expansive clay behavior. Moreover, the surface movements associated with expansive clay behavior can be accurately quantified using the LIDAR device.

LiDAR (Light Detection and Ranging) is a positive remote sensing technology. In this paper, with analyzed the characteristic of LiDAR data and discussed the advantages of LiDAR data, we present an improved data filtering algorithm for LiDAR data. It is basing on the last return laser pulses and tries to design an fast pre-processing LiDAR point data with the purpose of DEM Modeling from LiDAR based point cloud data. The algorithm contains two times of filtering with different aims and data process strategies. Experiments of real terrain area are presented for testing efficiency and accuracy of this filtering method.

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 detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

The continuous and reliable operation of subsea pipelines is of critical importance to the global oil and gas industry, as these pipelines are responsible for transporting vast quantities of hydrocarbons from offshore production fields to onshore facilities. However, the harsh underwater environment in which these pipelines operate presents numerous challenges, including exposure to corrosive seawater, extreme pressure variations, and mechanical stress from geological movements. These factors can cause significant structural degradation over time, leading to potential corrosion, material fatigue, and deformation. Without regular monitoring, such issues may go undetected, increasing the risk of leaks, spills, or catastrophic failures, which would have severe environmental, economic, and safety consequences. Traditionally, subsea pipeline monitoring has relied on periodic inspections using technologies such as remotely operated vehicles (ROVs), sonar, and visual assessments. While these methods have proven useful, they often suffer from limitations such as low resolution, infrequent data collection, and the high operational costs associated with deploying ROVs and divers for manual inspections. Moreover, these traditional methods may not be capable of providing the continuous, real-time data needed to make proactive maintenance decisions and ensure pipeline integrity. This research paper focuses on the application of LiDAR (Light Detection and Ranging) technology as a transformative solution for monitoring the structural health of subsea pipelines. LiDAR is a remote sensing technology that uses laser pulses to capture detailed, high-resolution 3D data about surfaces and environments. By adapting LiDAR for underwater use, this paper proposes a novel method for real-time, continuous monitoring of pipeline conditions. The use of LiDAR in subsea environments presents unique opportunities to detect early signs of corrosion, material degradation, and structural deformation with a level of precision that is difficult to achieve with conventional monitoring techniques. The key advantage of LiDAR technology lies in its ability to collect accurate and comprehensive data in challenging conditions. Underwater LiDAR systems can penetrate turbid water, providing clear images and measurements of pipeline surfaces, even in low-visibility environments. The resulting high-resolution 3D models allow for the detailed analysis of pipeline integrity, enabling operators to track changes in pipeline conditions over time and respond to potential issues before they escalate. This capability is particularly important for detecting corrosion and deformation, which can occur gradually and may not be immediately apparent using traditional methods.In this paper, we explore the current state of subsea pipeline monitoring technologies, comparing the strengths and limitations of existing approaches. We then introduce a LiDAR-based solution, discussing the technical adaptations necessary for using LiDAR in subsea conditions. This includes considerations for data acquisition, processing, and analysis, as well as the challenges posed by underwater environments, such as light absorption and scattering by water particles. Furthermore, mathematical models for calculating corrosion rates and predicting deformation based on LiDAR data will be presented. These models allow for quantitative assessments of the structural health of pipelines, enabling operators to make informed decisions about maintenance schedules and interventions. The use of real-time data not only improves the accuracy of these models but also provides the ability to forecast future pipeline behavior, reducing the likelihood of unexpected failures. This paper also includes case studies and diagrams to demonstrate the practical application of LiDAR in subsea pipeline monitoring. By presenting real-world examples of how this technology has been implemented, we aim to highlight its effectiveness in improving the safety and longevity of critical infrastructure in the oil and gas sector. LiDAR technology represents a significant advancement in the field of subsea pipeline monitoring, offering enhanced data accuracy, real-time monitoring capabilities, and cost-effective solutions for ensuring pipeline integrity. This research paper not only outlines the potential of LiDAR as a monitoring tool but also provides a roadmap for its implementation in the oil and gas industry, with the ultimate goal of reducing risks, minimizing environmental impacts, and ensuring the continued safe operation of subsea pipelines.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this paper reports a fundamental study that employs a previously developed novel wind tunnel testing methodology to investigate and quantify the effects of stress factors affecting LiDAR perception. It was found that soiling characteristics, such as raindrop size distribution and droplet impact kinematics, and material properties, such as surface roughness and wettability, all play critical roles in influencing LiDAR performance to different extents. The results suggest that although LiDAR is an optical type of sensor, its perception does not necessarily align with camera vision, which is closer to human perception. Specifically, hydrophilic surfaces show better advantages over hydrophobic surfaces for LiDAR sensor applications when driving in rain. The overall relationships between material properties, adherent raindrop dynamics, and LiDAR perceptions are summarized.&lt;/div&gt;&lt;/div&gt;

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.

Airborne light detection and ranging (LIDAR) system uses in many field such as military, agriculture, geology and transportation. LIDAR data acquisition consumes several gigabytes from the storage capacity of the system. So, it's necessary to reduce the on board storage capacity as well as its transmission rate. Compression of LIDAR data means reducing the amount of data required to represent the digital data acquired by LIDAR system. This is done by removing data redundancies to achieve compression. In this paper we explain LIDAR system theory and component, compare between lossy and lossless compression techniques then we discuss LIDAR data compression techniques in order to choose a suitable one for LIDAR system that gives high compression ratio with suitable processing time. In most LIDAR data compression techniques classification of LIDAR data is an important issue as a preprocessing step to simplify the data. Using more than one compression algorithms increase the compression ratio but increase the processing time as well.

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.

Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50–100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.

Green infrastructures (GI) are key elements in urban areas for heat mitigation, carbon capture and providing of aesthetic reasons. However, there is currently limited knowledge about the effects of various plant compositions, arrangements and varying density of plant cover, because traditional measuring methods are expensive / labour-intensive, imprecise, and tall buildings pose accessibility challenges. The presented study proposes applying LiDAR measurements on GI to gain in-depth understanding of plant growth, inventory of vegetation cover and thereby providing a useful tool for sustainable urban hazard management.The use of LiDAR (Light Detection and Ranging) technology has revolutionised forest monitoring by offering precise, efficient, and highly detailed spatial data for creating comprehensive 3D reconstructions of forest structures. The ability to capture fine details on both vegetation and structural surfaces is particularly advantageous for studying complex, vertical environments such as green fa&amp;#231;ades. This study used static ground-based LiDAR (RIEGL VZ-600i) to capture the 3D structure of a vertical greenery with wooden support structures before and after harvesting. Defined squares of 1 m&amp;#178; were fully harvested, the biomass collected and dry weight was obtained. Reference measurements for vegetation height (distance from wall to the outermost part of the plant) were recorded on a grid for 40 measurement points. The reference measurements were related to LiDAR alpha-hull volumetric analysis and predictions of growing biomass could be derived.By integrating point cloud analysis developed for forest monitoring into urban contexts, LiDAR facilitates a holistic analysis of natural and built environments. By analysis of LiDAR intensity and mapping further reference measurements for plant vitality and structural integrity, green wall health can be evaluated. Already established practices like alpha-hulling provide a successful tool to document green fa&amp;#231;ades comprehensively. Combining LiDAR with traditional measures enhances our understanding of the interactions between vegetation and architectural surfaces, enabling improved design and maintenance of GI and NBS to enable better planning and maintaining of NBS to reduce the effect of urban heat islands.&amp;#160;

Species richness in forest ecosystems largely depends on habitat structure and composition. These attributes can be assessed in field surveys, however, such data often lacks in spatial extent. Remote sensing technologies such as light detection and ranging (LiDAR) provide alternative tools to quantify structural elements across relatively broad areas at a fine resolution. To study the habitat requirements of hazel grouse (Bonasa bonasia), an indicator species of structurally rich forest stands, we assessed the structure and composition of Swiss mountain forests over three biogeographical regions. We designed a sample based field survey of forest structure and composition and a LiDAR based assessment of vertical and horizontal forest structures using a nationwide LiDAR dataset with a mean point density of 1.4 m2. The dependent variable consisted of hazel grouse presence/absence data at a resolution of 1 km2. Species distribution models were computed for both variable sets separately and in combination, using boosted regression trees, a statistical machine learning technique. Model performance assessment based on explained deviance and AUC showed that the combined model performed best, with over 55% explained deviance in the observed data, followed by the field and LiDAR models. The field model revealed that hazel grouse favored evenly distributed, rich ground vegetation, optimally with a substantial portion of bilberry (Vaccinium myrtillus). The abundance of tall rowans (Sorbus aucuparia), basal branched trees and a high percentage of resource trees in the shrub layer were found to be further essential habitat elements. LiDAR was powerful in detecting important structural features, whereby the horizontal forest structure explained more of the deviance than the vertical forest structure. The most influential LiDAR variable was a measure of canopy height heterogeneity. Apart from indicating structurally rich forest stands, it probably also served as a proxy of compositional aspects such as the abundance of light demanding resource trees and shrubs or of a well developed ground vegetation. To support habitat management, we derived variable thresholds at a relevant spatial scale (1 km2) for forest management. Our study showed that LiDAR provides adequate means to assess structural habitat elements area-wide, thus overcoming the difficulties associated with sample based field assessments. The best model fit, however, was obtained by combining LiDAR variables with compositional variables from the field survey. Hence, we successfully bridged the gap between different ecologically relevant scales, such as habitat configuration and structure at the regional scale and the abundance of habitat elements at the local scale. The methods applied in this study can also be used to identify hotspots of forest structural richness, a matter of interest in the light of emerging attempts to conserve biodiversity in forests.

Estimates of forest stand mean height using airborne LiDAR (light detection and ranging) instruments have been reported previously with accuracies comparable to traditional ground‐based measurements. However, the small area covered by a LiDAR sensor in a single aircraft overpass is a significant hindrance for large‐scale forest inventories. In comparison, airborne interferometric synthetic aperture radar (InSAR) systems are also able to make estimates of surface height, but the swath coverage is often far greater, typically five or ten times that of the LiDAR coverage. A set of interferometric data takes was acquired by the ESAR airborne sensor over a managed pine plantation at Thetford Forest, UK. Scattering phase centre height estimates were made from two single‐pass X‐band acquisitions and polarimetric repeat‐pass L‐band acquisitions and compared with height estimates made from a separate LiDAR acquisition. The relationship between the scattering phase centre heights and stand top heights estimated, and the accuracy of stand top height estimates estimated from InSAR and LiDAR is quantified by the root mean square error (rmse). General yield class models by the Forestry Commission (UK) were used to estimate stand top height from a GIS database used for forest management. The longer wavelength L‐band radiation penetrates deeper into the canopy than the X‐band, and the scattering phase centre height is affected by both forest structural parameters (canopy density, understorey, and gaps) and sensor parameters (look‐angle and reduced coherence through temporal and volume decorrelation). Consequently, a simple translation of scattering phase centre height into stand top height gives noisy results for L‐band, with observed rmse values between ±3.1 m in the near‐range and ±6.4 m in the far‐range. The X‐band based top height estimates are more accurate, with rmse between ±2.9 m in the near‐ and ±4.1 m in the far‐range, which can be further reduced by an empirical incidence angle correction. Stand top height estimates from LiDAR achieved an rmse of only ±2.0 m. The X‐band scattering phase centre heights have also been related to mean stand height and are comparable with heights observed from the LiDAR sensor and field measurements. An rmse of ±2.5 m for the mean stand height estimates based on the X‐band dataset was found. Finally, we briefly discuss error propagation from the use of a terrain model, here provided by the Ordnance Survey.