<i>R</i>Implementation of a Polyhedral Approximation to a 3D Set of Points Using the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>α</mml:mi></mml:mrow></mml:math>-Shape

Lafarge, Thomas; López, Beatriz Pateiro; Possolo, Antonio; Dunkers, Joy P.

doi:10.18637/jss.v056.i04

Cited by 32 publications

(28 citation statements)

References 22 publications

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“…The R programming language (Development Core Team, 2011) is perhaps the premier environment for statistical data analysis, is well supported by the integrated development environment RStudio and is a strong choice for data visualisation (Wickham, 2016). It already hosts a wealth of packages for general morphometric and graph theoretic analysis (Csardi and Nepusz, 2006;Duong, 2007;Lafarge et al, 2014;Schlager, 2017). An R package is a bundle of functions, documentation, data, tests and example code (Wickham, 2015).…”

Section: R Packages For Neuroanatomymentioning

confidence: 99%

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

Bates

Manton

Jagannathan

et al. 2020

eLife

189

159

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To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the <monospace>natverse</monospace>. The <monospace>natverse</monospace> allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the <monospace>natverse</monospace> enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The <monospace>natverse</monospace> also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The <monospace>natverse</monospace> is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.

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Section: R Packages For Neuroanatomymentioning

confidence: 99%

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

Bates

Manton

Jagannathan

et al. 2020

eLife

189

159

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“…The most suitable α index would be the one that encloses the smallest volume whilst maintaining a solid surface free from voids. To calculate the α-shape of the branches of each section, thus to compute the volume enclosed, a 3D algorithm available in the alphashape3d package from R [27] based on the original work of [24] was used. The resulting point clouds representing the vine shoots were strictly woody structures, since at that maturity stage, there were no leaves due to the winter season.…”

Section: Data Processingmentioning

confidence: 99%

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System

Moreno

Valero

Bengochea-Guevara

et al. 2020

Sensors

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Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

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“…The ATLS point clouds were transformed into convex hull structures approximating the shape and volume of the scanned trees using the "ashape3d" function in the alphashape3d R package [18]. The convex hull is fitted with Delauney Triangulation (drawing triangles between points so that there is no overlap between triangles).…”

Section: Lidar Volume Estimatesmentioning

confidence: 99%

“…A potential novel method could use terrestrial lidar (TLS) because three-dimensional lidar point clouds, based on the laser return-time/distance relationship, can be generated for any size tree. The point clouds can be transformed into a convex hull with a polyhedral surface approximating the shape of the tree, from which volumes can be calculated [17][18][19]. Intercepted snow volume can be estimated by subtracting snow-free tree volume from snow-on tree volume.…”

Section: Introductionmentioning

confidence: 99%

Toward a Novel Laser-Based Approach for Estimating Snow Interception

et al. 2020

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Forests reduce snow accumulation on the ground through canopy interception and subsequent evaporative losses. To understand snow interception and associated hydrological processes, studies have typically relied on resource-intensive point scale measurements derived from weighed trees or indirect measurements that compared snow accumulation between forested sites and nearby clearings. Weighed trees are limited to small or medium-sized trees, and indirect comparisons can be confounded by wind redistribution of snow, branch unloading, and clearing size. A potential alternative method could use terrestrial lidar (light detection and ranging) because three-dimensional lidar point clouds can be generated for any size tree and can be utilized to calculate volume of the intercepted snow. The primary objective of this study was to provide a feasibility assessment for estimating snow interception volume with terrestrial laser scanning (TLS), providing information on challenges and opportunities for future research. During the winters of 2017 and 2018, intercepted snow masses were continuously measured for two model trees suspended from load-cells. Simultaneously, autonomous terrestrial lidar scanning (ATLS) was used to develop volumetric estimates of intercepted snow. Multiplying ATLS volume estimates by snow density estimates (derived from empirical models based on air temperature) enabled the comparison of predicted vs. measured snow mass. Results indicate agreement between predicted and measured values (R2 ≥ 0.69, RMSE ≥ 0.91 kg, slope ≥ 0.97, intercept ≥ −1.39) when multiplying TLS snow interception volume with a constant snow density estimate. These results suggest that TLS might be a viable alternative to traditional approaches for mapping snow interception, potentially useful for estimating snow loads on large trees, collecting data in difficult to access terrain, and calibrating snow interception models to new forest types around the globe.

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RImplementation of a Polyhedral Approximation to a 3D Set of Points Using theα-Shape

Cited by 32 publications

References 22 publications

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System

Toward a Novel Laser-Based Approach for Estimating Snow Interception

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