Tree trunk direction extraction method and system based on hierarchical symmetry analysis

Abstract

The invention discloses a tree trunk direction extraction method and system based on hierarchical symmetry analysis. The method comprises the following steps: preprocessing tree point cloud; based on the canopy elevation model of the point cloud, performing single-tree segmentation on the preprocessed point cloud by adopting an image segmentation algorithm to obtain a single-tree point cloud; dividing the single-tree point cloud into a plurality of unequally spaced height layers in the Z-axis direction; searching the most symmetrical direction of the point cloud in each height layer; calculating an included angle between the most symmetrical direction of each height layer and the average direction, and removing the most symmetrical direction with the included angle greater than a set threshold value; and carrying out weighted averaging on the most symmetrical direction of each height layer to obtain the trunk direction of the tree. According to the method, the main direction is identified by using a method based on in-layer symmetry analysis, and the method is more suitable for natural postures of trees, so that the accuracy of trunk direction fitting is improved, and the method also has good robustness and generalization ability in a complex forest environment and under noise interference.

Description

Technical Field

[0001] This invention belongs to the fields of forestry remote sensing, computer vision and point cloud 3D reconstruction technology, and particularly relates to a method and system for extracting the trunk direction of trees based on hierarchical symmetry analysis. Background Technology

[0002] With the rapid development of LiDAR, UAV remote sensing, and 3D modeling technologies, forestry resource surveys are gradually transitioning from traditional manual methods to intelligent analysis based on 3D point cloud data. Point cloud technology can capture the spatial morphological information of each tree in a forest with high precision, providing basic data support for applications such as forest stand structure modeling, individual tree parameter estimation, and ecosystem simulation.

[0003] In point cloud-based forest resource management, the trunk direction (i.e., growth direction) of a single tree is a crucial structural parameter. It not only affects light acquisition, water transport, and wind resistance during tree growth, but also has significant application value in subsequent timber volume estimation, fallow risk prediction, and forestry machinery path planning. However, in natural or dense forest environments, due to factors such as complex tree morphology, severe branch and leaf interference, uneven point cloud density, and ground overlap caused by topographic undulations, traditional methods relying on regular height stratification, simplified columnar fitting, or manual interaction cannot accurately and stably extract the trunk direction of each tree. This is especially true in high-density forest areas or under noisy data conditions, where significant errors occur and processing efficiency is low.

[0004] The main shortcomings of existing technologies are as follows: 1) Lack of a complete preprocessing and segmentation process: Most methods do not perform systematic point cloud denoising and individual tree segmentation, resulting in impure fitting objects and seriously affecting fitting accuracy; 2) Unstable fitting accuracy of cylindrical models: Traditional least squares fitting methods are sensitive to outliers and are prone to divergence when there are branches, leaves, or point cloud interference; 3) Low degree of automation: Many methods require manual specification of initial axes or parameters, which cannot meet the needs of large-scale automated forest stand processing. Therefore, there is an urgent need for a robust, highly automated technology that can stably extract tree trunk orientation under noisy conditions to solve the above problems and promote the development of point cloud-based intelligent forestry survey technology towards high precision and high efficiency. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a method for extracting the trunk direction of trees based on hierarchical symmetry analysis. This method enables automatic batch extraction of tree growth directions even under high-noise conditions, and includes the following steps: Step 1: Preprocess the tree point cloud data collected by the UAV LiDAR. Step 2: Based on the canopy elevation model of the point cloud, an image segmentation algorithm is used to segment the tree point cloud after the preprocessing in Step 1 into individual tree point clouds. Step 3: Divide the single-tree point cloud into multiple non-equally spaced height layers along the Z-axis; Step 4: Find the most symmetrical direction of the point cloud in each altitude layer; Step 5: Calculate the angle between the most symmetrical direction and the average direction for each height layer, and remove the most symmetrical direction whose angle is greater than a set threshold. Step 6: Take a weighted average of the most symmetrical directions of each height level to obtain the trunk direction of the tree.

[0006] Furthermore, the preprocessing in step 1 includes removing height outliers and isolated points from the point cloud, then performing height normalization, filtering, and coordinate system unification, and removing points located at the bottom of the point cloud while retaining the point cloud of the main upper branches.

[0007] Furthermore, in step 2, a canopy elevation model is generated based on point cloud data, local maximum detection is used to determine the treetop position, and then an image segmentation algorithm is applied to the canopy elevation model image to divide the point cloud into multiple individual tree regions.

[0008] Furthermore, in step 3, kernel density estimation or local point count histogram analysis is used to analyze the density distribution of the point cloud along the Z-axis, and the points with significant density changes are taken as layer boundary points to obtain multiple non-equally spaced height layers.

[0009] Furthermore, in step 4, the point set in each height layer is plotted in a two-dimensional plane. XOY Starting from the X-axis, it traverses multiple directions. ,exist The projection of the point set onto a direction is performed, and the projection coordinates are calculated as follows: (1) In the formula, For point With the center of the point cloud within this height layer exist The projection difference in the direction; For all points within this height layer x The mean of the coordinates, For all points within this height layer y The mean of the coordinates.

[0010] The expression for the symmetry metric function is: (2) In the formula, It is a symmetry metric function. The median of all projection differences. This represents the variance of the projected difference relative to the median, i.e., the degree of dispersion.

[0011] Pick The value corresponding to the minimum The direction is the most symmetrical direction of this layer.

[0012] Furthermore, in step 5, the average direction The calculation method is as follows: (3) In the formula, The number of height layers to be divided, For the i The most symmetrical direction of each height layer.

[0013] Furthermore, in step 6, the three-dimensional direction vector of the most symmetrical direction for each height layer is first calculated, and the specific calculation method is as follows: (4) In the formula, For the i The three-dimensional direction vector of the most symmetrical direction of each height layer. For the i The most symmetrical direction of each height layer For the i The height of each floor.

[0014] Assume that after step 5 removes the most symmetrical direction with an excessive included angle, there are still... The formula for calculating the direction of a tree's trunk is as follows: (The formula is not provided in the original text.) (5) In the formula, The direction of the tree trunk. For the i The three-dimensional direction vector of the most symmetrical direction of each height layer. For the i The weight of the most symmetrical direction of each height layer is determined by the number of point clouds in each height layer. Indicates the modulus.

[0015] The present invention also provides a tree trunk direction extraction system based on hierarchical symmetry analysis, which is used to implement the tree trunk direction extraction method based on hierarchical symmetry analysis as described above.

[0016] Furthermore, it includes a processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute a tree trunk direction extraction method based on hierarchical symmetry analysis as described above.

[0017] Alternatively, it may include a readable storage medium storing a computer program that, when executed, implements a tree trunk direction extraction method based on hierarchical symmetry analysis as described above.

[0018] Compared with the prior art, the present invention has the following advantages: 1) This invention uses a method based on intra-layer symmetry analysis to identify the main direction, which better matches the natural posture of trees, thereby improving the accuracy of trunk direction fitting; 2) This invention uses an adaptive layering and direction anomaly removal mechanism to process point cloud structure, enhancing robustness and generalization ability under complex forest environments and noise interference; 3) This invention achieves efficient batch processing without manual intervention by designing a fully automated data processing flow (from preprocessing to direction-weighted fusion); 4) This invention uses a general geometric logic construction method framework, which does not depend on a specific point cloud platform, giving it good cross-regional and cross-device applicability and promotion potential; 5) This invention integrates point cloud preprocessing, single tree segmentation, direction estimation, and structured result output modules, providing a complete, continuous, and integrable analysis flow, facilitating subsequent statistics and modeling. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the tree trunk direction extraction method based on hierarchical symmetry analysis in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the projection of the point set in each height layer of an embodiment of the present invention onto the two-dimensional plane XOY.

[0022] Figure 3 This is a diagram illustrating the direction of the tree trunk obtained in an embodiment of the present invention.

[0023] Figure 4 A diagram illustrating the degree of tree tilt obtained from an embodiment of the present invention is provided.

[0024] Figure 5 This invention provides a diagram illustrating the distribution of tree growth direction along the slope obtained from an embodiment of the invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be further described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] Example 1 like Figure 1 As shown, this embodiment of the invention provides a method for extracting the trunk direction of a tree based on hierarchical symmetry analysis, including the following steps: Step 1: Preprocess the tree point cloud data collected by the UAV LiDAR.

[0027] Preprocessing includes removing height outliers and isolated points from the point cloud, followed by height normalization and filtering, unifying the coordinate system to the WGS 1984 coordinate system, and removing the bottom 20% of points in the point cloud, retaining the point cloud of the main upper branches. In this embodiment, removing height outliers refers to removing… The point, among which, Refers to the first i The height of each point The average height of all points. This refers to the standard deviation of the height of all points. (Used) k Neighborhood density estimation removes isolated points, i.e., removes points by radius. k Points whose number of interior points is less than a set threshold.

[0028] Step 2: Based on the canopy elevation model of the point cloud, an image segmentation algorithm is used to segment the tree point cloud after preprocessing in Step 1 into individual tree point clouds.

[0029] A canopy height model (CHM) is generated based on point cloud data. Local maximum detection is used to determine the treetop location. Then, a watershed segmentation algorithm is applied to the CHM image to divide the point cloud into multiple individual tree regions.

[0030] Step 3: Divide the single-tree point cloud into multiple non-equally spaced height layers along the Z-axis.

[0031] Kernel density estimation or local point count histogram analysis is used to analyze the density distribution of point cloud along the Z-axis, and points with significant density changes are used as layer boundary points to ensure that the layering can accurately characterize local geometric trends.

[0032] Step 4: Find the most symmetrical direction of the point cloud in each height layer and use it as the projection direction of the main direction onto the two-dimensional plane.

[0033] Because the degree of curvature of trees is not necessarily the same at different heights, and tree morphology theory suggests that even if a tree trunk is tilted, the left and right sides along the tilt direction should be approximately symmetrically distributed. Therefore, the most symmetrical direction of each layer of point cloud is found and taken as the projection direction of the trunk direction onto the two-dimensional plane.

[0034] like Figure 2 As shown, the red dots represent the center of the point cloud within this height layer. The point set within this height layer is represented in a two-dimensional plane. XOY In the middle, multiple directions are traversed starting from the X-axis. ,exist The projection of the point set onto a direction is performed, and the projection coordinates are calculated as follows: (1) In the formula, For point With the center of the point cloud within this height layer exist The projection difference in the direction; For all points within this height layer x The mean of the coordinates, For all points within this height layer y The mean of the coordinates.

[0035] The expression for the symmetry metric function is: (2) In the formula, It is a symmetry metric function; The median of all projection differences; This represents the variance of the projected difference relative to the median, i.e., the degree of dispersion.

[0036] Pick The value corresponding to the minimum The direction is the most symmetrical direction of this layer.

[0037] Step 5: Calculate the angle between the most symmetrical direction and the average direction for each height layer, and remove the most symmetrical direction whose angle is greater than a set threshold.

[0038] Average direction The calculation method is as follows: (3) In the formula, The number of height layers to be divided, For the first i The most symmetrical direction of each height layer.

[0039] Step 6: Take a weighted average of the most symmetrical directions of each height level to obtain the trunk direction of the tree.

[0040] No. i The three-dimensional direction vector of the most symmetrical direction of each height layer It can be represented as: (4) In the formula, For the i The most symmetrical direction of each height layer For the i The height of each floor.

[0041] Assume that after step 5 removes the most symmetrical direction with an excessive included angle, there are still... The formula for calculating the direction of a tree's trunk is as follows: (The formula is not provided in the original text.) (5) In the formula, The direction of the tree trunk; For the i The three-dimensional direction vector of the most symmetrical direction of each height layer; For the i The weight of the most symmetrical direction of each height layer is determined by the number of point clouds in each height layer. Indicates the modulus.

[0042] The geometric center (also known as the centroid or centroid) of a point cloud is a fundamental feature of point cloud data, representing the average position of the point cloud in space. Assume that the single-tree point cloud obtained after single-tree segmentation in step 3 has... There are points, and the coordinates of each point are... The geometric center coordinates of a single tree point cloud are obtained by weighted averaging of the coordinates in each dimension. ,Right now: (6) Calculate the equivalent radius of the tree trunk: (7) (8) In the formula, R is the equivalent radius of the tree trunk. This represents the number of points in a single tree point cloud. It is the horizontal projection distance from any point in the single-tree point cloud to the geometric center.

[0043] Output and save the trunk direction vector, geometric center coordinates, and equivalent radius of each tree as a structured CSV file. Calculate the trunk direction and vector of each individual tree. The degree of deviation is used to determine the degree of tree tilt.

[0044] Figure 3To process tree tilt direction maps obtained from airborne LiDAR data of a natural forest using the method proposed in this invention, the direction of the vector arrows indicates the calculated direction of the trunk of a single tree, and the color and length of the arrows indicate the degree of tilt of the single tree.

[0045] Figure 4 The image shows the processing results of laser point clouds of natural coniferous forests in mountainous terrain, collected by an airborne LiDAR in complex terrain, using the method proposed in this invention. The image contains 24,036 individual trees, with each point representing a tree. The color of the point indicates the direction and vector of the tree's trunk. The degree of deviation, i.e. the degree of tilt.

[0046] Figure 5 for Figure 4 Analysis of the leaning direction of trees in the same area: According to the slope distribution, the leaning direction of trees is divided into eight zones at 45° intervals: North, Northeast, East, Southeast, South, Southwest, West, and Northwest. Different colors represent the trunk direction of each individual tree.

[0047] Example 2 Based on the same inventive concept, the present invention also provides a tree trunk direction extraction system based on hierarchical symmetry analysis, including a processor and a memory. The memory is used to store program instructions, and the processor is used to call the program instructions in the memory to execute the tree trunk direction extraction method based on hierarchical symmetry analysis as described above.

[0048] Example 3 Based on the same inventive concept, the present invention also provides a tree trunk direction extraction system based on hierarchical symmetry analysis, including a readable storage medium on which a computer program is stored. When the computer program is executed, it implements the tree trunk direction extraction method based on hierarchical symmetry analysis as described above.

[0049] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.

[0050] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to replace them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for extracting the trunk direction of trees based on hierarchical symmetry analysis, characterized in that, Includes the following steps: Step 1: Preprocess the tree point cloud data collected by the UAV LiDAR. Step 2: Based on the canopy elevation model of the point cloud, an image segmentation algorithm is used to segment the tree point cloud after the preprocessing in Step 1 into individual tree point clouds. Step 3: Divide the single-tree point cloud into multiple non-equally spaced height layers along the Z-axis; Step 4: Find the most symmetrical direction of the point cloud in each altitude layer; Step 5: Calculate the angle between the most symmetrical direction and the average direction for each height layer, and remove the most symmetrical direction whose angle is greater than a set threshold. Step 6: Take a weighted average of the most symmetrical directions of each height level to obtain the trunk direction of the tree.

2. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 1, characterized in that: The preprocessing in step 1 includes removing height outliers and isolated points from the point cloud, then performing height normalization, filtering, and coordinate system unification, and removing points located at the bottom of the point cloud while retaining the point cloud of the main upper branches.

3. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 2, characterized in that: In step 2, a canopy elevation model is generated based on point cloud data. Local maximum detection is used to determine the treetop position. Then, an image segmentation algorithm is applied to the canopy elevation model image to divide the point cloud into multiple individual tree regions.

4. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 1, characterized in that: In step 3, kernel density estimation or local point count histogram analysis is used to analyze the density distribution of the point cloud along the Z-axis. Points with significant density changes are taken as layer boundary points to obtain multiple non-equidistant height layers.

5. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 1, characterized in that: In step 4, the point set in each height layer is plotted in a two-dimensional plane. XOY Starting from the X-axis, it traverses multiple directions. ,exist The projection of the point set onto a direction is performed, and the projection coordinates are calculated as follows: （1） In the formula, For point With the center of the point cloud within this height layer exist Projection difference in direction, For all points within this height layer x The mean of the coordinates, For all points within this height layer y The mean of the coordinates; The expression for the symmetry metric function is: （2） In the formula, It is a symmetry metric function. The median of all projection differences. This is the variance of the projected difference relative to the median, i.e., the degree of dispersion; Pick The value corresponding to the minimum The direction is the most symmetrical direction of this layer.

6. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 1, characterized in that: Average direction in step 5 The calculation method is as follows: （3） In the formula, The number of height layers to be divided, For the first i The most symmetrical direction of each height layer.

7. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 1, characterized in that: Step 6 first calculates the three-dimensional direction vector of the most symmetrical direction for each height layer. The specific calculation method is as follows: （4） In the formula, For the first i The three-dimensional direction vector of the most symmetrical direction of each height layer. For the first i The most symmetrical direction of each height layer For the first i The height of each floor.

8. The method for extracting the trunk direction of a tree based on hierarchical symmetry analysis as described in claim 7, characterized in that: In step 6, after step 5 removes the most symmetrical direction with an excessive included angle, there are still... The formula for calculating the direction of a tree's trunk is as follows: (The formula is not provided in the original text.) （5） In the formula, The direction of the tree trunk. For the first i The weight of the most symmetrical direction of each height layer is determined by the number of point clouds in each height layer. Indicates the modulus.

9. A tree trunk direction extraction system based on hierarchical symmetry analysis, characterized in that, It includes a processor and a memory, the memory being used to store program instructions, and the processor being used to call the program instructions in the memory to execute the tree trunk direction extraction method based on hierarchical symmetry analysis as described in any one of claims 1-8.

10. A tree trunk direction extraction system based on hierarchical symmetry analysis, characterized in that, The method includes a readable storage medium on which a computer program is stored, and when the computer program is executed, it implements a tree trunk direction extraction method based on hierarchical symmetry analysis as described in any one of claims 1-8.

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