A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data

Zhang, Wuming; Wan, Peng; Wang, Zhihui; Cai, Shuqun; Chen, Yiming; Jin, Xiuliang; Yan, Guangjian

doi:10.3390/rs11020211

Cited by 102 publications

(100 citation statements)

References 43 publications

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“…In comparison, several other studies propose a range of RMSE for DBH estimates: 0.7 to 7 cm [15] [69]. In addition to DBH estimation, algorithms were compared for their ability to estimate the tree rate detection [24,70], or above ground biomass [56,57,71,72]. For example, Ohtmani et al [24] evaluated the CCF algorithm on stem detection rate and DBH, and Hackenberg et al [40,58] evaluated SimpleTree on DBH and aboveground biomass estimation.…”

Section: Discussionmentioning

confidence: 99%

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

Ravaglia

Fournier

Bac

et al. 2019

Forests

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Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.

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Section: Discussionmentioning

confidence: 99%

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

Ravaglia

Fournier

Bac

et al. 2019

Forests

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“…It utilizes the nature of cloth and modifies the physical process of cloth simulation to adapt to point cloud filtering [41]. Zhang et al [31] used the cloth simulation filter method to filter the ground. The ground points and non-ground points were also segmented by the cloth simulation filter method in our study.…”

Section: Co-registration Ground Filtering and Tls Data Thinningmentioning

confidence: 99%

“…After stem extraction, the DBH can be estimated from the stem points at breast height. There have been many DBH methods proposed, such as linear least square (Landau algorithm) circle fitting [3,10], nonlinear least squares (Gauss Newton) circle fitting [7], crescent moon method proposed by Kiraly and Brolly [47], RANSAC circle detection [39], Hough transform, and random Hough transform [26,31,40]. However, most of them are based on the assumption that the stem section is circular.…”

Section: Robust Least Squares Elliptic Fitting For Dbh Estimationmentioning

confidence: 99%

“…The main task is to estimate the optimal DBH parameters from the point cloud of the trunk at the corresponding height. Many articles have proposed many DBH estimation methods, such as linearized or nonlinear least square circle fitting [3,7,24], Hough-transform [26], cylinder fitting [15,38], random sample consensus (RANSAC) algorithm [8,39], and random Hough transform [31,40]. Most of these methods model the stem profiles as a circle and fit the diameter parameter from the stem points at the breast height.…”

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confidence: 99%

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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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“…Terrestrial laser scanning (TLS) has proven to non-destructively provide three-dimensional (3D) information on tree stems (Liang et al 2014, Kankare et al 2013, Raumonen et al 2013, Saarinen et al 2017 that has not been possible with calipers or measurement tape. Individual trees can be detected from a TLS-based point cloud through identification of circular shapes (Aschoff et al 2004, Maas et al 2008 or clusters of points (Cabo et al 2018, Zhang et al 2019. Points from individual trees can then be utilized in reconstructing the entire architectural structure of a tree (Raumonen et al 2013, Hackenberg et al 2014 or only the stem (Liang et al 2011, Heinzel & Huber 2017.…”

Section: Introductionmentioning

confidence: 99%

Assessing the effects of thinning on stem growth allocation of individual Scots pine trees

Saarinen

Kankare

Yrttimaa

et al. 2020

Preprint

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Forest management alters the growing conditions and thus further development of trees. However, quantitative assessment of forest management on tree growth has been demanding as methodologies for capturing changes comprehensively in space and time have been lacking. Terrestrial laser scanning (TLS) has shown to be capable of providing three-dimensional (3D) tree stem reconstructions required for revealing differences between stem forms and growth allocation. In this study, we used TLS-based 3D reconstructions of tree stems to investigate how forest management (i.e., varying thinning treatments and the following growth effects) has affected stem size and form of Scots pine (Pinus sylvestris L.) trees. The results showed that intensive thinning resulted in more stem volume and therefore total biomass allocation and carbon uptake compared to the moderate thinning. Scots pines growing in plots from which suppressed trees were removed (i.e., thinning from below) had larger bottom part of stems (i.e., volume accumulated more below 50% of tree height) compared to Scots pines growing in plots from which part of the dominant trees had been removed (i.e., thinning from above). Furthermore, intensive thinning, especially from below, produced less variation in relative stem attributes characterizing stem size and form. Thus, it can be concluded that thinning intensity, type, and the following growth effects have an impact on post-thinning stem growth as well as stem form of individual Scots pine trees. Our study presented detailed measurements on postthinning growth of Scots pines that have been laborious or impracticable before the emergence of TLS technology. Moreover, TLS-based stem reconstructions provided variety of attributes characterizing stem growth allocation that have not traditionally been feasible to obtain. The study demonstrated that TLS technology can be used to generate new knowledge for supporting forest management and silviculture as well as improving ecological understanding of boreal forests.

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A Novel Approach for the Detection of Standing Tree Stems from Plot-Level Terrestrial Laser Scanning Data

Cited by 102 publications

References 43 publications

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

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

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

Assessing the effects of thinning on stem growth allocation of individual Scots pine trees

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