Tree Diameter at Breast Height Extraction Based on Mobile Laser Scanning Point Cloud
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1. Introduction
Forests are an important component of global carbon stocks and are critical to climate change and ecological balance [1]. The diameter at breast height (DBH) is the diameter of a forest tree at breast height (usually 1.3 m above the ground) [2] and is one of the most important measurement factors in forest resource surveys. DBH provides basic data for forest management, forest resources surveys and carbon cycle modelling, and it is of great significance for obtaining DBH data quickly in forest ecosystem monitoring.
With the advancement of remote sensing technology, the extraction of forest structural parameters using optical and light detection and ranging (LiDAR) data has become a research hotspot. Optical remote sensing techniques can establish the correlation between forest structural parameters and spectral information [3,4,5,6] but are affected by the shading of the forest canopy, making it difficult to obtain complete vertical structural information [7,8]. In contrast, LiDAR technology can penetrate the forest canopy and measure the vertical and horizontal structure of the forest [9], providing more accurate data. Airborne laser scanning (ALS) has been widely used to obtain information on tree height, stand density, and depression [10,11]; however, it is unable to provide detailed structural information under the canopy due to the shading problem caused by the dense canopy. Although unmanned aerial vehicle-based laser scanning (ULS) has a higher resolution and penetration capability, which is suitable for fine forest resource investigation [12,13], it is still affected by canopy shading and does not perform well in sub-canopy parameter extraction [14,15], which is not applicable to the study of DBH extraction.
With the development of understory laser scanning technology, the extraction of understory structure information has become more mature. Terrestrial laser scanning (TLS) for static work can obtain high-quality point cloud data with millimeter-level accuracy; it was first used for tree DBH extraction, and many scholars have already investigated how to efficiently and accurately extract DBH, tree height, and other structural parameters using TLS data [16,17,18]. The limitations of TLS are its measurement efficiency and data integrity. Mobile laser scanning (MLS), as an alternative technology to TLS, has been applied to the 3D mapping of forest environments since 2013 [19] and has already demonstrated application advantages and development potential for plot-level forest resource surveys and assessments [20,21,22]. Compared to TLS, MLS data acquisition is more efficient, and the occlusion effect is low, resulting in a higher tree detection rate in MLS point clouds. The results of Gollob et al. [23] showed that under the condition of a tree DBH threshold of 5 cm, the tree detection rate of MLS was 96%, while that of TLS was 78.5%. MLS systems typically achieve precise positioning and orientation through the Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU). New MLS systems also employ simultaneous localization and mapping (SLAM) techniques [24,25] to address GNSS access under the canopy. Although MLS acquires more complete point cloud data compared to TLS, it does not have a significant DBH extraction accuracy advantage [26]. SLAM-based MLS acquires point cloud data with a low accuracy (of centimeter level) and significantly higher noise [27]. MLS utilizes an IMU to compute the sensor’s positional information at different timestamps for real-time data collection and point cloud alignment. However, the drift characteristics of IMU result in errors accumulating over time, and although the SLAM algorithm can deal with drift-induced errors [28], the inaccuracy in the computation of the coordinate transformation matrices is unavoidable, which may lead to incorrect point cloud splicing [29]. Mis-splicing of the point cloud produces poorly aligned point cloud segments and mixed point noise data, which are the main errors during DBH extraction.
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. SSN converts horizontally projected points from Cartesian coordinates to polar coordinates and groups them based on polar angles to identify stem surface nodes. The surface nodes are then used to extract the DBH, replacing the use of the entire 2D projected point cloud in traditional methods. Moreover, there are also methods to enhance the accuracy of tree DBH extraction via MLS with the help of point cloud intensity information [30]. To the best of our knowledge, there is still a lack of methods for estimating the DBH without additional processing operations based on the conventional automated DBH extraction framework, which effectively reduces the effects of poorly aligned point cloud segments and mixed point noise data.
In summary, to improve the accuracy of MLS DBH extraction, this paper presents an improved method for tree DBH extraction based on the least squares method. The main research work of this paper is as follows: (1) An unmanned vehicle-mounted laser scanning system was constructed to realize the acquisition of understory structure information. (2) Combined with the least squares method to introduce the point cloud intensity information, an intensity-weighted least squares method is proposed to extract the tree DBH and to reduce the influence of poorly aligned point clouds on the DBH extraction.
3. Results
In order to verify the effectiveness of the proposed intensity weighted least squares (IWLS) DBH extraction method based on the point cloud intensity, the point cloud data of the same trees in the same area were used to carry out DBH extraction experiments by using the random sample consensus (RANSAC) algorithm and the ordinary least squares (OLS) algorithm. The three extraction results were compared in terms of accuracy. Based on the PCL point cloud library [41], the above three algorithms were implemented using the C++ programming language, and all of them conduct DBH extraction experiments according to the flow shown in Figure 5. The accuracy of the three algorithms for DBH extraction was evaluated by calculating and counting the absolute error AE, mean absolute error MAE, root mean square error RMSE, relative accuracy RA, and coefficient of determination .
The results in Table 4 show that the IWLS extraction method proposed in this study performed best among the three methods. In all four sample plots, the two accuracy evaluation metrics (AE and RMSE) used to reflect the error decreased, and the relative accuracy was the highest in all of them. In Sample Plot 1, the IWLS extraction method had an MAE of 2.99 cm and an RMSE of 3.65 cm, with a relative accuracy of 88.12%. Compared to the RANSAC and OLS extraction methods, the MAE decreased by 28.95% and 8.75%, the RMSE decreased by 33.28% and 14.55%, and the relative accuracy increased by 7.21% and 2.35%, respectively. In Sample Plot 2, the IWLS extraction method had a MAE of 1.28 cm and a RMSE of 1.48 cm, with a relative accuracy of 93.80%. Compared to the RANSAC and OLS extraction methods, the MAE was reduced by 54.84% and 13.53%, the RMSE was reduced by 62.74% and 13.75%, and the relative accuracy was improved by 12.51% and 1.06%, respectively. In Sample Plot 3, the MAE of the IWLS extraction method was 2.98 cm and the RMSE was 3.48 cm, with a relative accuracy of 88.00%. Compared to the RANSAC and OLS extraction methods, the MAE was reduced by 25.63% and 7.42%, the RMSE was reduced by 31.16% and 7.88%, and the relative accuracy was improved by 6.58% and 1.18%, respectively. In Sample Plot 4, the IWLS extraction method had a MAE of 2.39 cm and a RMSE of 2.63 cm, with a relative accuracy of 89.15%. Compared to the RANSAC and OLS extraction methods, the MAE was reduced by 41.99% and 9.59%, the RMSE was reduced by 41.93% and 13.80%, and the relative accuracy was improved by 9.63% and 1.99%, respectively.
From Table 4, it can be seen that the AE maximum values of the three extraction methods differed greatly, with an AE maximum value of 16.75 cm for the RANSAC extraction method in Sample Plot 1 and 2.89 cm for the IWLS extraction method in Sample Plot 2. The AE minimum values of the three extraction methods differed less: except for the AE minimum value of 0.72 cm for the RANSAC extraction method in Sample Plot 4, none of them exceeded 0.23 cm. In order to further analyze the absolute error values of the three extraction methods in the four sample plots, they were displayed in descending order, as shown in Figure 8. The distribution of the absolute error results shows that the absolute error of the RANSAC-based extraction method in the four sample plots is significantly larger than that of the OLS and IWLS methods, which are both based on all point data to solve the optimal model parameters, so the distribution of the two absolute errors is relatively similar but the absolute error between the extracted values of the DBH for the fitted circular model under the IWLS method and the measured values are smaller overall and have decreased. By further analyzing and comparing the absolute errors of the three methods, it can be proved that the IWLS extraction method outperforms both RANSAC and OLS methods.
The scatter plots of the extracted and measured values of DBH in each sample plot are shown in Figure 9. Comparing the linear regression scatter plots of different methods in the same sample plot, the scatter distribution of the IWLS extraction method is closer to the scatter regression line, the is closer to 1, and the goodness-of-fit is the highest, which indicates that the extraction results of the method have a significant linear correlation with the measured values. The scatter plots indicate that adjusting the importance of data points by intensity information can reduce the influence of low-quality point cloud data on the extraction accuracy of DBH and improve the accuracy of the DBH fitting results.
In this study, we compared and analyzed the accuracy of tree DBHs extracted from mobile laser scanning point cloud based on RANSAC, OLS, and IWLS. The experimental results show that the MAE of the IWLS method is 2.41 cm, which is better than the RANSAC method (3.79 cm) and the OLS (2.65 cm) method, with a reduction of 36.45% and 9.14%, respectively, in the tests of four sample plots; the average RMSE of the IWLS method is 2.81 cm, which is also better than the RANSAC method (4.76 cm) and the OLS methods (3.20 cm), which were reduced by 40.90% and 12.26%, respectively. In addition, the average relative accuracy of the IWLS method is 89.77%, which is higher than that of the RANSAC method (82.37%) and the OLS method (88.33%), with an improvement of 8.99% and 1.63%, respectively. In summary, the experimental results verified that the intensity-weighted least squares circular model fitting method can effectively improve the accuracy of tree DBH extraction.
Referring to the national standard “Forest Resources Planning and Design Survey Technical Specification” (GB/T 26424-2010) [2] in the DBH class division standard, the trees in the sample plot were divided into three diameter groups to analyze the performance of each method in different diameter groups; the data are shown in Table 5. In the table, A represents the medium diameter group with a DBH of 12–24 cm; B represents the large diameter group with a DBH of 24–36 cm; C represents the extra-large diameter group with a DBH of 36 cm or more. For the statistical data of different diameter groups in each sample site (provided in Table 5), the analysis is as follows: the average relative accuracy of the RANSAC method is higher in the C diameter group, the average relative accuracy of the A and B diameter groups is lower, and there is a certain fluctuation of accuracy between different diameter groups, so the overall adaptability is poor; the average relative accuracy of the OLS method is higher in the B diameter group, and the A and C diameter groups are slightly lower than that of the B diameter group, which is suitable for measuring the diameter of medium-sized and fine trees. The average relative accuracy of the IWLS method is higher in all three groups, and the fluctuation of accuracy between different groups is small, so the method has the widest adaptability and is suitable for the measurement of the diameter of trees of different thicknesses and finenesses. In conclusion, the IWLS method is the most adaptable method and has the best performance in different diameter groups.
4. Discussion
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.
The four sample plots differed in extraction accuracy. Through further analyses, this was found to be due to the different species and phenotypic characteristics of the trees in each sample plot, the choice of collection paths, and the terrain conditions. Different tree species in different sample plots have different trunk phenotypic characteristics, which affect the dispersion of LiDAR reflection data and data quality to some extent. In addition, differences in the diameter sizes of the trees also have an impact [42], which is reflected in differences in the shape and structure characteristics captured in the data. Differences in acquisition paths affect the angle and distance at which the LiDAR scans the trees, which in turn affects the density and quality of the point cloud data. Terrain differences [28], such as slope and undulation, affect the stability and scanning range of the scanning equipment, further affecting data accuracy. Combining these factors, each sample plot presents different characteristics during data acquisition and processing, leading to differences in final DBH extraction accuracy.
In addition, the experimental results show that the extraction accuracy of the RANSAC method is lower compared to the OLS and IWLS methods, mainly because of the irregular circular boundaries of the tree trunk point cloud data acquired by mobile laser scanning. The uneven distribution of contour points, with dense points in some regions and sparse points in others, makes different sampling subsets of RANSAC produce very different circle centers and radii, leading to difficulties in locating the circle center and unstable radius estimation, which in turn leads to lower extraction accuracies. The intensity-weighted least squares method proposed in this study ensures that highly geometrically consistent data play a dominant role in model fitting, thereby reducing the impact of low-quality alignment data and noise on the extraction accuracy of DBH.
In future research, there is a need to continue work in the following aspects: (1) The single-tree instance segmentation part requires human setting of parameter thresholds, which increases the factor of manual intervention. In order to improve the degree of automation, subsequent research can explore the adaptive setting of parameter thresholds based on point cloud features, to reduce the manual parameter tuning process. (2) Although the shape of the trunk cross-section resembles a circle, it is usually not a standard circle, and fitting the trunk DBH based on a circular model introduces errors into the fitting process to a certain extent. In subsequent studies, non-circular models such as polynomials can be attempted, to more accurately simulate the actual shape of the tree trunk cross-section, reduce the error introduced by the assumption of circular models, and improve the accuracy of the extraction of the chest diameter parameters.
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