LIDAR altimeter conception for HERA spacecraft

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).

Chapter 7 - Landslide analysis using laser scanners

A sensor network based on the LIDAR (LIght Detection And Ranging) principle is investigated in order to track persons inside a surveillance area and be able to identify security-relevant behavior. In order to minimize the overall sensor network complexity, power consumption and costs, we recently investigated the network topology based on a quality measure in terms of the number of nodes, measurement distance, width of the LIDAR beams and localization as well as classification performance. As a result, stationary beams with rather small opening angles of up to a few degrees are a good compromise. Since certain regions of the surveillance area are not directly assessed by the LIDAR beams, the accurate tracking of a target throughout the entire area of surveillance is challenging. We demonstrated that tracking based on a Kalman filter approach can nevertheless deliver satisfactory results for a single person inside the surveillance area. In this paper we focus on the task of reliably tracking two persons inside the surveillance area at the same time. The tracking of multiple moving targets is carried out by applying a Multiple Hypothesis Tracking filter approach. The localization and tracking performances are derived from simulations and experiments carried out with commercial laser scanners. In the near future, the rather expensive laser scanners will be replaced by appropriate LIDAR range finders.

The two main categories of light detection and ranging (LIDAR) for mapping and monitoring landslides are static and mobile devices. Static devices are commonly named ground-based LIDAR or terrestrial laser scanners (TLSs). Regarding mobile devices, the different instruments are normally classified depending on the platform configuration: either boats, cars or flying devices. There are three main types of LIDAR, corresponding to three different ways of measuring the range, for instance, the distance along the line of sight (LOS) between the sensor and the terrain. The types include: pulse LIDAR measures the time of flight (TOF) of a laser pulse; phase LIDAR uses the phase shift between the emitted and received signals; and triangulation LIDAR uses a camera to locate the laser spot on the scanned surface. Georeferencing is the process of coordinate transformation from an internal referential system relative to the LIDAR device into a real-world system of reference.

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.

Light Detection and Ranging (LIDAR) scanning can be used to safely and remotely provide intelligence on the interior of dangerous structures for use by first responders that need to enter these structures. By scanning into structures through windows and other openings or moving the LIDAR scanning into the structure, in both cases carried by a remote controlled robotic crawler, the presence of dangerous items or personnel can be confirmed or denied. Entry and egress pathways can be determined in advance, and potential hiding/ambush locations identified. This paper describes an integrated system of a robotic crawler and LIDAR scanner. Both the scanner and the robot are wirelessly remote controlled from a single laptop computer. This includes navigation of the crawler with real-time video, self-leveling of the LIDAR platform, and the ability to raise the scanner up to heights of 2.5 m. Multiple scans can be taken from different angles to fill in detail and provide more complete coverage. These scans can quickly be registered to each other using user defi ned 'pick points', creating a single point cloud from multiple scans. Software has been developed to deconstruct the point clouds, and identify specific objects in the interior of the structure from the point cloud. Software has been developed to interactively visualize and walk through the modeled structures. Floor plans are automatically generated and a data export facility has been developed. Tests have been conducted on multiple structures, simulating many of the contingencies that a first responder would face. Keywords Hazards, laser scanning, LIDAR, model, segmentation Language: en

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.

Measurement and management of vegetation biomass accumulation in ecosystems typically involves extensive field data collection, which can be expensive and time consuming, while leaving the user with relatively crude inputs to intricate biomass models. Light detection and ranging (LiDAR) remote sensing, which provides extensive height measurements of terrain and vegetation, has become an effective alternative to characterize vegetation structure. In this paper, we report on ongoing efforts at developing signal processing approaches to model herbaceous biomass using a new generation of airborne laser scanners, namely full-waveform LiDAR systems. Structural and statistic-based feature metrics are directly derived from LiDAR waveforms at the pixel level and related to plot-level field data. Initial results reveal a definite correlation between the LiDAR waveform and herbaceous biomass. Ongoing research focuses on the links between fractional cover estimated from imaging spectroscopy and woody biomass.

LiDAR technology is reshaping cave surveying by providing detailed 3D models that enhance the accuracy of morphological and rock art digitisation and reduce subjective interpretation. This technology, in its varied forms and solely or combined with other remote sensing techniques such as photogrammetry, enriches the documentation and supports multidisciplinary research by enabling spatial analyses and virtual exploration, thus opening new possibilities in various fields such as archaeology, geology, speleology, tourism or education. In this sense, this research aimed to democratise the use of low-cost mobile LiDAR (Light Detection and Ranging) 3D scanning, subjected to fewer accessibility limitations than tripod-mounted Terrestrial Laser Scanners (TLS), in cave archaeology and speleology. For this purpose, La Pileta Cave in Málaga (Spain) was chosen as a case study. Declared a Spanish National Monument in 1924, the cave boasts one of the greatest collections of prehistoric art in Europe and, therefore, a reference in South Iberia, and outstands out for its varied karstic morphologies. The research methodology involved a systematic process to ensure clarity and accuracy. First, the main itinerary in La Pileta was scanned using the smartphone LiDAR technique. This was followed by a Terrestrial Laser Scanning (TLS) survey of a specific sector within the same itinerary, with numerous morphological details of its karstic environment and important Palaeolithic rock art samples. Both the smartphone LiDAR and TLS spatial data were then validated against a Ground Control Points (GCPs) network previously established using a total station. Given the higher accuracy of TLS for graphical documentation, it was further employed as a benchmark to validate the accuracy of smartphone LiDAR. Despite its limitations, this research revealed smartphone LiDAR as a suitable technique for geometric data recording in cave archaeology and speleology. Solely or combined with TLS, mobile LiDAR can be used to document rock art panels in karstic environments, surpassing the latter technique in terms of texture quality. In addition to the accurate graphic documentation carried out in the cave sector, this research broke down the advantages and disadvantages of the smartphone LiDAR technique and provided a series of recommendations for its use in this context. • Suitability evaluation of built-in smartphone LiDAR for cave digitisation. • The accuracy of three different mobile LiDAR applications was analysed. • Accurate graphic documentation of a sector of great relevance in La Pileta Cave. • A series of recommendations for using mobile LiDAR in cave archaeology are offered.

Geometric integration of high-resolution satellite imagery and airborne LiDAR data for improved geopositioning accuracy in metropolitan areas

The point density is a preeminent parameter on airborne laser scanner surveys. It is not only related to accuracy but costs and savings. The lack of uniformity of the point density across the survey is well-known in the scientific community. This paper analyzes the behaviour of the point density derived by an oscillating mirror laser scanner on different single strips on flat bare ground in order to estimate a meaningful mean density value. The variation of the point density at both extreme ends of the oscillating mirror scan is meaningful. It will be demonstrated that excluding the extreme sectors across the strip corresponding to 1/8 of the swath width (12.5% of the sampling area, half in each side) for the computation of the mean density value is enough to satisfy light detection and ranging (LiDAR) specifications for national level surveys.

High resolution 3-dimensional documentation of archaeological monuments &amp; landscapes using airborne LiDAR

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through the genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. These findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.

Continual monitoring of civil structures (e.g., bridges) is essential to maintenance and ensuring safety and integrity. Non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) have been used in the past to study both the external and internal physical dimensions of objects and structures [1]. Light Detection and Ranging (LiDAR) technology has also been used in infrastructure monitoring to capture structural 3D information and to detect displacements in surfaces with millimeter accuracy [2]. Some other technologies, such as the Ground-Based Interferometric Radar (GBIR), suffer from precise target detection when monitoring objects and require installation of dedicated reflectors. Scanning structures using these existing state-of-the-art technologies can be expensive and time-consuming. Recently, visualization technologies such as Augmented Reality (AR) have been utilized with GBIR to solve target location uncertainties by making the radar&amp;#8217;s beam of radiation interact with the investigated structure [3].This work proposes the use of head-mounted Augmented Reality (AR) to visualize and support the monitoring of bridge structures. First, to overcome limitations of the HoloLens depth sensing technology, we used smartphone-based LiDAR (Apple iPhone 14 Pro) to capture and export a 3D model of the shape of the structure of interest. We then imported this model into the HoloLens application so that it could be overlaid and adjusted to match the physical bridge structure. Second, a digital component model was aligned with the position and orientation of the antenna. The beam of radiation is estimated in the visualization application using the method described in our previous work [3]; then, it is displayed as a frustrum determined by an equation according to this method. Since this method does not rely on real-time LiDAR or depth mapping, we are able to visualize the projected beam of radiation beyond the usual range limitations of up to 7 meters. Furthermore, this method can be used effectively in outdoor locations, which can be challenging for infrared-based depth mapping technology.The system can provide a relatively low-cost structural monitoring and assessment solution, which can allow researchers and surveyors to accurately visualize survey areas of interest and inform the decision-making process regarding maintenance of crucial civil structures.&amp;#160;Acknowledgments: Sincere thanks to the following for their support: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.&amp;#160;References[1] Alani A. et al., Non-destructive assessment of a historic masonry arch bridge using ground penetrating radar and 3D laser scanner. IMEKO International Conference on Metrology for Archaeology and Cultural Heritage Lecce, Italy, October 23-25, 2017.[2] Lee, J et al., Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit. 2019; 26:e2428.[3] Sotoudeh, S. et al. "A study into the integration of AR-based data collection and multi-dimensional signal processing methods for GB-SAR target detection." Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023). Vol. 12797. SPIE, 2023.

Laser scanning of forested areas helps in analyzing and understanding various aspects of forest conditions, including distribution of plants and trees, height distribution of trees, tree density, size and volume of wood, as well as ground surface properties. However, laser scanning of forest areas is also very challenging for many reasons. The best time for scanning is before trees leaf out in the spring or after trees cast their leaves in autumn before snowfall so an unmanned aerial vehicle (UAV) laser scanner can penetrate the forest from the tops of the trees down to the ground surface. To receive highly accurate laser data and high point density, the flight planning must be adjusted judiciously. Flight planning will be even more complex in steep terrain where the UAV cannot operate at a constant altitude. This paper discusses a UAV-based 3D laser data recording — light detection and ranging (LiDAR) scanning — of a forestry area with high accuracy and point cloud resolution. In addition, the point cloud of airborne laser scanning is compared with local terrestrial laser-scanning results. The forest area consists of mixed forests containing varying tree sizes and branch deformation. This paper summarizes our latest results in UAV-based LiDAR acquisition over a forest area to extract detailed forest and ground information and finds that UAV-based laser scanning is well suited for provision of both high-quality forest structural and terrain elevation information.