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

Abstract

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%.

Extraction of Forestry Parameters Based on Multi-Platform LiDAR

“…The experimental results showed that the leastsquares ellipse fitting method is more suitable for the estimation of DBH. Compared with other methods, the estimation accuracy of DBH of this method was improved, with the RMSE of 1.14 cm [30]. Wu et al [19] used a combination of ALS and TLS to manage fruit tree structure, number of trees, diseases, fertilization, etc.…”

“…We used the data collected by TLS to extract the DBH. In earlier studies, it was necessary to segment all the trees in the entire study area [30,47,48]. Due to the large amount of TLS points, low efficiency and high computational costs may occur when single tree segmentation is performed.…”

“…In summary, obtaining high-precision forestry parameter requires combining the data collection from ALS and TLS [19,29,30]. Compared with MAVLS, UAVLS has the following advantages: (1) When equipped with the same type of LiDAR, the flying speed and altitude of the UAV are lower than that of a MAV.…”

Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning

“…TLS data have been used to estimate canopy characteristics outside of fire applications such as leaf area index [22], biomass [23,24], and canopy cover [25,26]. TLS collections have also targeted the mapping and measuring of individual trees [27,28]. More recently, TLS has been used to map fuels in three dimensions [29] and changes in forest structure following wildfire [30].…”

Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data

“…Most of the previous tree localization methods for TLS point clouds can be categorized into two classes: model-fitting methods and feature-based methods. The model-fitting methods treat the tree localization as the detection of stems from point clouds [11][12][13]. Maas et al [14] divide tree point clouds into slices along the vertical direction and the circle fitting is applied to the sliced points, then model inliers of fitted circles will be recognized as stems.…”