LiDAR-derived digital holograms for automotive head-up displays.

A holographic automotive head-up display was developed to project 2D and 3D ultra-high definition (UHD) images using LiDAR data in the driver's field of view. The LiDAR data was collected with a 3D terrestrial laser scanner and was converted to computer-generated holograms (CGHs). The reconstructions were obtained with a HeNe laser and a UHD spatial light modulator with a panel resolution of 3840×2160 px for replay field projections. By decreasing the focal distance of the CGHs, the zero-order spot was diffused into the holographic replay field image. 3D holograms were observed floating as a ghost image at a variable focal distance with a digital Fresnel lens into the CGH and a concave lens.This project was funded by the EPSRC Centre for Doctoral Training in Connected Electronic and Photonic Systems (CEPS) (EP/S022139/1), Project Reference: 2249444

Leaf and wood classification framework for terrestrial LiDAR point clouds

Leaf and wood separation is a key step to allow a new range of estimates from Terrestrial LiDAR data, such as quantifying above-ground biomass, leaf and wood area and their 3D spatial distributions. We present a new method to separate leaf and wood from single tree point clouds automatically. Our approach combines unsupervised classification of geometric features and shortest path analysis. The automated separation algorithm and its intermediate steps are presented and validated. Validation consisted of using a testing framework with synthetic point clouds, simulated using ray-tracing and 3D tree models and 10 field scanned tree point clouds. To evaluate results we calculated accuracy, kappa coefficient and F-score. Validation using simulated data resulted in an overall accuracy of 0.83, ranging from 0.71 to 0.94. Per tree average accuracy from synthetic data ranged from 0.77 to 0.89. Field data results presented and overall average accuracy of 0.89. Analysis of each step showed accuracy ranging from 0.75 to 0.98. F-scores from both simulated and field data were similar, with scores from leaf usually higher than for wood. Our separation method showed results similar to others in literature, albeit from a completely automated workflow. Analysis of each separation step suggests that the addition of path analysis improved the robustness of our algorithm. Accuracy can be improved with per tree parameter optimization. The library containing our separation script can be easily installed and applied to single tree point cloud. Average processing times are below 10min for each tree

Persistent reduced ecosystem respiration after insect disturbance in high elevation forests

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no increase in ecosystem respiration from scales of several square metres up to an 84 km2 valley. Rather, we found comparable declines in both gross primary productivity and respiration suggesting little change in net flux, with a transitory recovery of respiration 6–7 years after mortality associated with increased incorporation of leaf litter C into soil organic matter, followed by further decline in years 8–10. The mechanism of the impact of tree mortality caused by these biotic disturbances is consistent with reduced input rather than increased output of carbon

TLS2trees: A scalable tree segmentation pipeline for TLS data

1. Above-ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. / 2. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. / 3. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. / 4. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible

A map of trees crowns in Camden, UK

Tree crown layer for Camden, UK produced by analysing LiDAR data provided by the UK Environment Agency

LiDAR Data: Malet Street London

A RIEGL VZ‐400 (RIEGL Laser Measurement Systems GmbH) was utilised for LiDAR data collection. The scanner used has a wavelength of 1,550 nm, a beam divergence of 0.35 mrad and a measuring range of around 600 m. The LiDAR data was obtained from scanning Malet street and Hampstead Heath in London. All text files uploaded represent the coordinates (x,y and z) of every point in the point cloud dataset. There datasets were collected from 11 different locations. The first column of each dataset in the text file represents the x coordinates, the second column the y coordinates and the third column the z coordinates of every point collected along the measurements. The data text files were read with a MATLAB algorithm by incorporating the x,y and z coordinates of each point to create a 3D image of the scanned scene. A 360-degree view was obtained in this way.Engineering and Physical Sciences Research Council (EP/S022139/1)

Assessing Approaches to 3D Tree Reconstruction from Terrestrial Laser Scanning Data

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Accelerated Augmented Reality Holographic 4k Video Projections Based on Lidar Point Clouds for Automotive Head‐Up Displays

Publication status: PublishedFunder: Stiftung der Deutschen Wirtschaft; doi: http://dx.doi.org/10.13039/501100015754Funder: European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska‐Curie grant agreement No 896410.AbstractIdentifying road obstacles hidden from the driver's field of view can ensure road safety in transportation. Current driver assistance systems such as 2D head‐up displays are limited to the projection area on the windshield of the car. An augmented reality holographic point cloud video projection system is developed to display objects aligned with real‐life objects in size and distance within the driver's field of view. Light Detection and Ranging (LiDAR) point cloud data collected with a 3D laser scanner is transformed into layered 3D replay field objects consisting of 400 k points. GPU‐accelerated computing generated real‐time holograms 16.6 times faster than the CPU processing time. The holographic projections are obtained with a Spatial Light Modulator (SLM) (3840×2160 px) and virtual Fresnel lenses, which enlarged the driver's eye box to 25 mm × 36 mm. Real‐time scanned road obstacles from different perspectives provide the driver a full view of risk factors such as generated depth in 3D mode and the ability to project any scanned object from different angles in 360°. The 3D holographic projection technology allows for maintaining the driver's focus on the road instead of the windshield and enables assistance by projecting road obstacles hidden from the driver's field of view.EPSR