Leaf Orientation Retrieval From Terrestrial Laser Scanning (TLS) Data

Zheng, Guang; Moskal, L. Monika

doi:10.1109/tgrs.2012.2188533

Cited by 78 publications

(55 citation statements)

References 43 publications

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“…Segmenting the canopy into cubic volumes/voxels based on point clouds can provide a convenient mean of describing the spatial distribution of foliage area in the tree crown [38] and of estimating the leaf area density [21], LAI and LAD [20], etc. Béland et al [38] investigated the optimal voxel dimensions for estimating the spatial distribution of leaf area density within the crown.…”

Section: Cell Division and Leaf Placementmentioning

confidence: 99%

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Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

Xie

Wang

et al. 2018

Remote Sensing

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Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored or simplified because of their complexity. Therefore, we develop a voxel-based method to add leaves to a reconstructed 3D branches structure based on TLS point clouds. The location and size of each leaf depend on the spatial distribution and density of leaves points in the voxel. We reconstruct a small 3D scene with four realistic 3D trees and a virtual tree (including trunk, branches, and leaves), and validate the structure of each tree through the directional gap fractions calculated based on simulated point clouds of this reconstructed scene by the ray-tracing algorithm. The results show good coherence with those from measured point clouds data. The relative errors of the directional gap fractions are no more than 4.1%, though the method is limited by the effects of point occlusion. Therefore, this method is shown to give satisfactory consistency both visually and in the quantitative evaluation of the 3D structure.

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Section: Cell Division and Leaf Placementmentioning

confidence: 99%

“…The development of terrestrial laser scanner (TLS) technology enables the use of point clouds that were scanned from trees to estimate the structural parameters for single trees or forests, including LAI, LAD [20], and leaf area density [21]. Additionally, point clouds are also used to reconstruct 3D trees.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

Xie

Wang

et al. 2018

Remote Sensing

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“…In Section 2.4.4 and Figure 8b, we have stated a mean for predicting the threshold interval and shown the effect of threshold selection on the final individual tree leaf area estimate. Here, we also recommend incorporating additional thoughts, such as the ray tracing algorithm within the voxel [41] or the zenith and azimuth angle distribution of leaves in the canopy [49] for further assessing the threshold. In addition, 3D Delaunay triangulation has always seemed complex and requires a large amount of computation.…”

Section: Recommendationsmentioning

confidence: 99%

A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR

Yun

et al. 2016

Remote Sensing

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Abstract:Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD.

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“…In contrast to traditional passive optical approaches, point cloud data (PCD) generated from light detection and ranging (LiDAR) provide 3-D structural information of forest canopies (Alonzo et al, 2014;Antonarakis et al, 2010;Lefsky et al, 1999;Roberts et al, 2005;Zheng et al, 2013). In particular, terrestrial laser scanning (TLS) enables generating highly spatially resolved (< 5 mm point sampling space) PCD to vertically resolve forest structural information in great detail such as vertical plant profiles (Calders et al, 2015b), leaf orientation (Eitel et al, 2010;Zhao et al, 2015;Zheng and Moskal, 2012), and forest aboveground biomass (Calders et al, 2015a;Stovall and Shugart, 2014). There are three different types of information contained within the TLS-based PCD including the x, y, z geometric information, laser return intensity information (LRI), and red, green, and blue (RGB) color information.…”

Section: Introductionmentioning

confidence: 99%

Determining woody-to-total area ratio using terrestrial laser scanning (TLS)

Zheng

Eitel

et al. 2016

Agricultural and Forest Meteorology

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Accurately determining woody-to-total area ratio (WTA) is a key step to indirectly retrieve leaf area index (LAI) from terrestrial laser scanning (TLS) data. In this work, we first collected both individual tree and forest plot point cloud data (PCD) from broadleaf-and coniferous tree species and leaf characteristics using both side-lateral and full field-of-view TLS field setups with scan distances between 2.5 to 28 meters. Using a local geometrical feature-based algorithm, the generated PCD were automatically classified into three different categories including photosynthetic canopy components, non-photosynthetic canopy components, and bare earth. To convert each classified point into a surface area, we then developed and validated a novel approach that considers sampling space, laser incidence angle, and leaf orientation information. The estimated surface areas from this approach showed strong agreements with validation datasets for single leaf (91.44 %), photosynthetic (95.64 %), and non-photosynthetic canopy components (89.60 %) of an artificial tree and stems of an old-growth coniferous tree (93.53 %), two individual broadleaf trees (98.31 % and 97.46 %) and a broadleaf forest plot (90.26 %). By doing this, we computed the parameter WTA for an individual artificial tree (10.90 %), an old-growth coniferous tree (29.97 %), two individual broadleaf tree (14.83 % and 4.27 %) and four natural forest stands ranging from 7.74 % − 15.57 %, respectively. The proposed method can effectively improve the accuracy of retrieving true LAI by removing the effects of woody components and converting each point into a surface area.

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Leaf Orientation Retrieval From Terrestrial Laser Scanning (TLS) Data

Cited by 78 publications

References 43 publications

Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data

A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR

Determining woody-to-total area ratio using terrestrial laser scanning (TLS)

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