Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning

Self Cite

Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80–87.13% for bias, 38.82–57.30% for mean absolute error (MAE), and 27.17–56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation.

Section: Point Cloud Datamentioning

confidence: 99%

“…The circle fitting algorithm used in ANPDA is the Landau algorithm [42], which was implemented by Sumith [43] and proved to be reliable for DBH estimation based on TLS point cloud data by Chang et al [19]. The Landau algorithm is a non-iterative linear least square algorithm.…”

Section: Circle Fittingmentioning

confidence: 99%

Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation

Duanmu

Xing

2020

Self Cite

“…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.…”

mentioning

confidence: 99%

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

Qian

Tang

et al. 2020

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%.

“…With the rapid development of laser scanner, remote sensing is widely used in 3D reconstruction and recognition in different fields due to the advantages of non-contact, high precision and high efficiency [4]. In the past few decades, 3D reconstruction from point clouds has gained great attention, especially for parameter measurement for different types of trees [5][6][7]. Yun et al [8] proposed a leaf area measurement method based on 3D point cloud reconstruction using ground-based light detection and ranging (LiDAR).…”

Section: Introductionmentioning

confidence: 99%

A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

Zhou

2019

Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm2. The MAE values for small leaves, medium leaves and large leaves were 0.92 cm2, 1.05 cm2 and 3.39 cm2, respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.