Estimating diameter at breast height using personal laser scanning data based on stem surface nodes in polar coordinates

Duanmu, Jialong; Xing, Yunqi

doi:10.1080/2150704x.2020.1820613

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Poorly aligned point cloud segments and mixed point noise data in MLS affect DBH extraction accuracy. There have been some methods such as ANPDA [28] and SSN [31] aiming to minimize this effect, but there is still a lack of methods based on the conventional automatic DBH extraction framework without additional processing operations. Drawing on the idea of leveraging point cloud intensity information, as presented in [30], this study constructs a DBH estimation method without additional processing operations based on a conventional automatic DBH extraction framework, which effectively reduces the influence of poorly aligned point cloud segments and mixed point noise data.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have pointed out that poorly aligned point cloud segments and mixed point noise data in MLS affect the accuracy of DBH extraction [28][29][30] and that general point cloud preprocessing algorithms have difficulty in removing this type of low-quality point. Annular neighboring points distribution analysis (ANPDA) [28] and stem surface node (SSN) [31] have been proposed as preprocessing methods for DBH extraction, aiming to minimize the effect of poorly aligned point clouds on the DBH extraction. ANPDA identifies outliers by iteratively removing the outermost points and analyzing the distribution of neighboring points, using relative entropy to determine the termination criterion.…”

Section: Introductionmentioning

confidence: 99%

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Tree Diameter at Breast Height Extraction Based on Mobile Laser Scanning Point Cloud

Sheng,

Zhao,

Wang

et al. 2024

Forests

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The traditional measurement method (e.g., field survey) of tree diameter circumference often has high labor costs and is time-consuming. Mobile laser scanning (MLS) is a powerful tool for measuring forest diameter at breast height (DBH). However, the accuracy of point cloud registration seriously affects the results of DBH measurements. To address this issue, this paper proposes a new method for extracting tree DBH parameters; it achieves the purpose of efficient and accurate extraction of tree DBH by point cloud filtering, single-tree instance segmentation, and least squares circle fitting. Firstly, the point cloud data of the plantation forest samples were obtained by a self-constructed unmanned vehicle-mounted mobile laser scanning system, and the ground point cloud was removed using cloth simulation filtering (CSF). Secondly, fast Euclidean clustering (FEC) was employed to segment the single-tree instances, and the point cloud slices at breast height were extracted based on the point sets of single-tree instances, which were then fitted in two dimensions using the horizontally projected point cloud slices. Finally, a circle fitting algorithm based on intensity weighted least squares (IWLS) was proposed to solve the optimal circle model based on 2D point cloud slices, to minimize the impact of misaligned point clouds on DBH measures. The results showed that the mean absolute error (MAE) of the IWLS method was 2.41 cm, the root mean square error (RMSE) was 2.81 cm, and the relative accuracy was 89.77%. Compared with the random sample consensus (RANSAC) algorithm and ordinary least squares (OLS), the MAE was reduced by 36.45% and 9.14%, the RMSE was reduced by 40.90% and 12.26%, and the relative accuracy was improved by 8.99% and 1.63%, respectively. The R2 value of the fitted curve of the IWLS method was the closest to 1, with the highest goodness of fit and a significant linear correlation with the true value. The proposed intensity weighted least squares circle-fitting DBH extraction method can effectively improve the DBH extraction accuracy of mobile laser scanning point cloud data and reduce the influence of poorly aligned point clouds on DBH fitting.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tree Diameter at Breast Height Extraction Based on Mobile Laser Scanning Point Cloud

Sheng,

Zhao,

Wang

et al. 2024

Forests

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“…Ellipse fitting methods include adaptive ellipse fitting [8] and robust least squares fitting [9]. Circle fitting methods encompass image detection-based methods [10][11][12][13] (such as Hough transform, camera point clouds, wireframe models, and region segmentation), as well as algebraic and geometric methods [14][15][16][17][18][19][20][21][22](such as the least squares quadratic screening method, minimum variance iterative algorithm, and ringneighbor point method). Additionally, model-based methods include the tree height-DBH model and conical geometric model [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis of Street Trees in Vehicle-mounted LiDAR Point Clouds

Wang,

Hao,

2024

AJST

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Street trees, as key elements of urban road scenes, play a significant role in the overall ecological environment and aesthetics of city roads through their dynamic growth and changes. With the continuous development of urban roads, the growth conditions of street trees are also constantly changing. Therefore, studying how to infer the dynamic changes of roads through the variations in street trees is a highly valuable technical issue. This paper delves into several critical parameters of street trees, including diameter at breast height (DBH), tree height, trunk height, crown volume, and crown spread. By updating these parameters, determining transplantation and replanting decisions, and analyzing growth states, we comprehensively analyze the dynamic changes of street trees. To achieve this goal, we integrated multiple parameters into the analysis process of street trees and established a comprehensive analysis system centered on the DBH parameter. Combining this with data visualization technology, we provide an intuitive dynamic analysis method. Experimental results indicate that, compared to single-parameter analysis methods, the multi-parameter comprehensive analysis method demonstrates higher accuracy and stronger persuasive power in the study of dynamic changes in street trees, thereby validating the superiority and practicality of the proposed research algorithm. This research can provide more scientific and reliable references for urban road planning and management.

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