Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Ravaglia, Joris; Fournier, Richard; Bac, Alexandra; Vega, Cédric; Côté, Jean-François; Piboule, Alexandre; Rémillard, Ulysse

doi:10.3390/f10070599

Cited by 23 publications

(21 citation statements)

References 58 publications

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“…Bienert et al [24] and Pitkänen et al [32] also used an individual taper function, but they employed it in the final step of the DBH estimation procedure with the goal of adjusting the diameters of the neighboring circles. The presented algorithm possesses the advantage of omitting computationally intensive operations such as point-level outlier filtering, randomized sampling, or the estimation of normal vectors, which are effective yet computationally challenging tasks for outlier filtering [48].…”

Section: Estimation Of Single Tree Parametersmentioning

confidence: 99%

Voxel-Based Automatic Tree Detection and Parameter Retrieval from Terrestrial Laser Scans for Plot-Wise Forest Inventory

et al. 2021

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This paper presents a fully automatic method addressing tree mapping and parameter extraction (tree position, stem diameter at breast height, stem curve, and tree height) from terrestrial laser scans in forest inventories. The algorithm is designed to detect trees of various sizes and architectures, produce smooth yet accurate stem curves, and achieve tree height estimates in multi-layered stands, all without employing constraints on the shape of the crown. The algorithm also aims to balance estimation accuracy and computational complexity. The method’s tree detection combines voxel operations and stem surface filtering based on scanning point density. Stem diameters are obtained by creating individual taper models, while tree heights are estimated from the segmentation of tree crowns in the voxel-space. Twenty-four sample plots representing diverse forest structures in the south boreal region of Finland have been assessed from single- and multiple terrestrial laser scans. The mean percentages of completeness in stem detection over all stand complexity categories are 50.9% and 68.5% from single and multiple scans, respectively, while the mean root mean square error (RMSE) of the stem curve estimates ranges from ±1.7 to ±2.3 cm, all of which demonstrates the robustness of the algorithm. Efforts were made to accurately locate tree tops by segmenting individual crowns. Nevertheless, with a mean bias of −2.9 m from single scans and −1.3 m from multiple scans, the algorithm proved conservative in tree height estimates.

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Section: Estimation Of Single Tree Parametersmentioning

confidence: 99%

Voxel-Based Automatic Tree Detection and Parameter Retrieval from Terrestrial Laser Scans for Plot-Wise Forest Inventory

et al. 2021

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“…Ravaglia et al used the maximum extraction height that is defined as the height above the ground of the highest diameter of the stem to evaluate three tree stem diameter algorithms [50]. In our study, the integrity of stem extraction was calculated further to evaluate the ability of algorithms for stem extraction by the maximum extraction height, H max , and tree height, H tree , in the scanned points cloud.…”

Section: Evaluation Methodsmentioning

confidence: 98%

Improved 3D Stem Mapping Method and Elliptic Hypothesis-Based DBH Estimation from Terrestrial Laser Scanning Data

Qian

Tang

et al. 2020

Remote Sensing

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The detailed structure information under the forest canopy is important for forestry surveying. As a high-precision environmental sensing and measurement method, terrestrial laser scanning (TLS) is widely used in high-precision forestry surveying. In TLS-based forestry surveys, stem-mapping, which is focused on detecting and extracting trunks, is one of the core data processing tasks and the basis for the subsequent calculation of tree attributes; one of the most basic attributes is the diameter at breast height (DBH). This article explores and improves the methods for stem mapping and DBH estimation from TLS data. Firstly, an improved 3D stem mapping algorithm considering the growth direction in random sample consistency (RANSAC) cylinder fitting is proposed to extract and fit the individual tree point cloud section. It constructs the hierarchical optimum cylinder of the trunk and introduces the growth direction into the establishment of the backbone buffer in the next layer. Experimental results show that it can effectively remove most of the branches and reduce the interference of the branches to the discrimination of trunks and improve the integrity of stem extraction by about 36%. Secondly, a robust least squares ellipse fitting method based on the elliptic hypothesis is proposed for DBH estimation. Experimental results show that the DBH estimation accuracy of the proposed estimation method is improved compared with other methods. The mean root mean squared error (RMSE) of the proposed estimation method is 1.14 cm, compared with other methods with a mean RMSE of 1.70, 2.03, and 2.14 cm. The mean relative accuracy of the proposed estimation method is 95.2%, compared with other methods with a mean relative accuracy of 92.9%, 91.9%, and 90.9%.

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“…To further understand if the proposed scheme sufficiently preserves the original characteristics of a single tree, we quantify the morphological attributes of the reconstructed trees at different compression ratios. The morphological attributes considered include crown size (east-west, south-north, height) [4], diameter at breast height (DBH, diameter of the trunk at 1.30 m above the ground-surface) [2][3][4], ground diameter (diameter of the trunk at 0.20 m above the ground-surface, only for Papaya tree), tree height [2,4,5], which are important parameters for tree growth [1][2][3][4][5]. These morphological attributes are estimated with the software CloudCompare [42], a professional tool for processing point cloud.…”

Section: Experiments On Morphological Attributesmentioning

confidence: 99%

“…For the Papaya tree point cloud, we estimate the ground diameter rather than DBH due to its special branch structures (Table 6). For simplicity, we assume that the cross section of the trunk is circular and apply least-squares circle fitting [3] to estimate DBH and the ground diameter. The relative error statistics compared to measurements from uncompressed point clouds are also given in Table 7.…”

Section: Experiments On Morphological Attributesmentioning

confidence: 99%

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Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Yun

Cao

et al. 2020

Forests

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The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

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Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Cited by 23 publications

References 58 publications

Voxel-Based Automatic Tree Detection and Parameter Retrieval from Terrestrial Laser Scans for Plot-Wise Forest Inventory

Voxel-Based Automatic Tree Detection and Parameter Retrieval from Terrestrial Laser Scans for Plot-Wise Forest Inventory

Improved 3D Stem Mapping Method and Elliptic Hypothesis-Based DBH Estimation from Terrestrial Laser Scanning Data

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

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