A forestry monitoring system based on Internet

Abstract

The invention discloses an Internet-based forestry monitoring system, which relates to the technical field of forestry monitoring, and comprises a plane point cloud data acquisition module, a plane point cloud data preprocessing module, an elevation difference acquisition module, a crown diameter acquisition module, a benchmark value acquisition module and an abnormal growth tree marking module; by considering two parameters of the elevation difference and crown diameter of the trees, the growth conditions of the trees can be comprehensively evaluated from two dimensions, vertical and horizontal, and a plurality of trees can be analyzed separately, so that the growth abnormalities of individual trees can be discovered in time, providing a basis for targeted maintenance measures, reducing the workload of manual screening, and discovering trees with abnormal growth in time so that corresponding measures can be taken for intervention, such as pest control, fertilization, watering, etc., which is helpful to maintain the health and stability of the forest.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry monitoring, and in particular to a forestry monitoring system based on the Internet. Background Art

[0002] The monitoring and diagnosis of forestry trees is an important part of forest protection and urban greening management. With the acceleration of global climate change and urbanization, tree diseases are becoming increasingly serious, which has a significant impact on ecosystems and human life. At present, tree disease monitoring and diagnosis mainly rely on traditional visual observation and laboratory analysis methods, which are time-consuming, labor-intensive and easily affected by subjective factors.

[0003] Forests are habitats for many organisms and are vital to maintaining ecological balance. Through comprehensive monitoring of tree growth, changes in forest ecosystems can be discovered in a timely manner, providing a basis for protecting biodiversity. Forestry monitoring can provide a scientific basis for the rational use and protection of forest resources and ensure the sustainable development of forest resources. For example, through monitoring of tree growth, a reasonable logging intensity and renewal plan can be determined to avoid damage to forest ecosystems caused by excessive logging.

[0004] However, the traditional forestry monitoring system mainly focuses on the growth height of forestry trees, but ignores the important growth indicator of forestry trees, which leads to misjudgment of the health status of trees. At the same time, there is a lack of dynamic monitoring of the tree growth process and it is unable to timely discover abnormal conditions in tree growth, which affects the timeliness and effectiveness of forestry management. Based on this, a forestry monitoring system based on the Internet is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an Internet-based forestry monitoring system, which solves the technical problem of ignoring the important growth index of forestry trees, the growth crown width, which leads to misjudgment of the health status of trees.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A forestry monitoring system based on the Internet, comprising:

[0008] A plane point cloud data acquisition module is used to obtain plane point cloud data of multiple trees in front view and top view;

[0009] The plane point cloud data preprocessing module retains the valid points in the plane point cloud data of the tree in the front view and the top view, and removes the outliers;

[0010] The elevation difference acquisition module analyzes all valid points in the front-view plane point cloud data of multiple trees to obtain the elevation differences corresponding to the multiple trees;

[0011] The crown diameter acquisition module analyzes all valid points in the top-view plane point cloud data of multiple trees to obtain the crown diameters corresponding to the multiple trees;

[0012] The benchmark value acquisition module obtains the standard elevation difference and standard crown diameter corresponding to the trees according to the elevation difference and crown diameter corresponding to the multiple trees respectively;

[0013] The abnormal growth tree marking module conducts a comprehensive analysis of the elevation difference and crown diameter corresponding to each tree and the standard elevation difference and standard crown diameter, and marks the abnormal growth trees.

[0014] As a further solution of the present invention, the specific method of retaining the valid points and removing the outliers in the plane point cloud data of the tree in the front view and the top view is:

[0015] S1: Evenly divide the front view plane point cloud data of the tree into multiple point cloud area blocks, and then obtain multiple point cloud area blocks.

[0016] S2: randomly obtain one of the multiple point cloud area blocks as the analysis area block, calculate the mean value of the distance between each point cloud in the analysis area block and other point clouds, and mark it as Pi, calculate the mean value BA and standard deviation BB of Pi, establish the screening interval [BA-λBB, BA+λBB], and judge whether the mean value Pi of the distance between each point cloud in the analysis area block and other point clouds is within the screening interval [BA-λBB, BA+λBB]. If it is within the screening interval [BA-λBB, BA+λBB], the corresponding point cloud is retained as a valid point in the analysis area block; if it is not within the screening interval [BA-λBB, BA+λBB], the corresponding point cloud is removed as a discrete point in the analysis area block, where i refers to different point clouds in the area block, i=1, 2, ..., a, where a refers to the number of point clouds in the analysis area block, a is a positive integer, a≥1, and λ is the preset standard deviation coefficient;

[0017] S3: Repeat step S2 to obtain valid points corresponding to multiple point cloud area blocks of the front-view plane point cloud data of the tree, and then obtain all valid points in the front-view plane point cloud data of the tree;

[0018] S4: Analyze the top-view plane point cloud data of the tree using the same method as that used to analyze the front-view plane point cloud data of the tree, and then acquire all valid points in the top-view plane point cloud data of the tree.

[0019] As a further solution of the present invention, the specific method of obtaining the average value of the distance between each point cloud and other point clouds in the analysis area block is:

[0020] S21: obtaining one point cloud from each point cloud in the analysis area block as a target point cloud;

[0021] S22: Obtain the coordinates of the target point cloud and the coordinates of other point clouds, calculate the distance Je between the target point cloud and other point clouds in the analysis area block by using the distance calculation formula, and mark the average value of the distance Je as P1, where e refers to other different point clouds in the area block except the target point cloud, and e≥1;

[0022] S23: Repeat steps S21-S22 to obtain the mean value Pi of the distance between each point cloud and other point clouds in the analysis area block.

[0023] As a further solution of the present invention, a specific method for obtaining the elevation differences corresponding to a plurality of trees is as follows:

[0024] S01: randomly select one of the front-view plane point cloud data of multiple trees as the front-view calibration plane point cloud, define the distance between the highest point and the lowest point among the valid points of the front-view calibration plane point cloud as the elevation difference H1 of the front-view calibration plane point cloud, and take the point with the largest ordinate value among the valid points as the highest point, and the point with the smallest ordinate value as the lowest point;

[0025] S02: Repeat step S01 to obtain the elevation differences Hd corresponding to multiple trees, where d refers to different trees, d=1, 2, ..., c, where c refers to the number of trees, c is a positive integer, c≥1.

[0026] As a further solution of the present invention, a specific method for obtaining the crown diameters corresponding to a plurality of trees is as follows:

[0027] S11: randomly select one of the top-view plane point cloud data of multiple trees as the top-view calibration plane point cloud, obtain the closed contour line of the top-view calibration plane point cloud, use the center point of the closed contour line of the top-view calibration plane point cloud as the origin to make a two-dimensional coordinate system, mark the maximum point distance of the top-view calibration plane point cloud in the X-axis direction of the two-dimensional coordinate system as Wx, mark the maximum point distance in the Y-axis direction as Wy, and use the average of Wx and Wy as the crown diameter R1 of the top-view calibration plane point cloud;

[0028] S12: Repeat step S11 to obtain the crown diameters Rd corresponding to the multiple trees.

[0029] As a further solution of the present invention, a specific method for obtaining the standard elevation difference corresponding to the trees is:

[0030] Calculate the standard deviation K1 of the elevation difference Hd. When the standard deviation K1 is less than or equal to the preset value Y1, obtain the mean of Hd as the standard elevation difference MA corresponding to the tree. When the standard deviation K1 is greater than the preset value Y1, take the mean of the maximum and minimum values ​​in Hd as the standard elevation difference MA corresponding to the tree.

[0031] As a further solution of the present invention, the specific method of obtaining the standard crown diameter corresponding to the tree is:

[0032] Calculate the standard deviation K2 of the crown diameter Rd. When the standard deviation K2 is less than or equal to the preset value Y2, obtain the mean of Hd as the standard crown diameter MB corresponding to the tree. When the standard deviation K2 is greater than the preset value Y2, take the mean of the maximum and minimum values ​​in Rd as the standard crown diameter MB corresponding to the tree.

[0033] As a further solution of the present invention: abnormally growing trees are marked, specifically in the following manner:

[0034] Obtain the absolute values ​​FAd and FBd of the difference between the elevation difference and crown diameter corresponding to each tree and the standard elevation difference and standard crown diameter respectively. Calculate the deviation value Zd corresponding to each tree through the formula: Zd=FAd×θ1+FBd×θ2. When the deviation value Zd is greater than the preset value Y3, the corresponding tree is marked as an abnormal growth mark. When the deviation value Zd is less than or equal to the preset value Y3, no processing is performed. Among them, θ1 and θ2 are preset fixed coefficients, 1=θ1+θ2, θ1>θ2.

[0035] Beneficial effects of the present invention:

[0036] (1) The present invention uses a plane point cloud data preprocessing module to carefully process the front and top view plane point cloud data of trees, distinguish valid points from outliers, and avoid measurement errors caused by interference from invalid data;

[0037] (2) The present invention, by considering the two parameters of tree elevation difference and crown diameter, can comprehensively evaluate the growth status of trees from both vertical and horizontal dimensions, analyze multiple trees separately, and promptly discover the growth abnormalities of individual trees, providing a basis for targeted maintenance measures so that corresponding measures can be taken for intervention, such as pest control, fertilization, watering, etc., which helps to maintain the health and stability of the forest. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0040] Figure 2It is a schematic diagram of the framework structure of the method of the present invention;

[0041] Figure 3 It is a structural schematic diagram of the closed contour line of the planar point cloud when viewed from above according to the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1

[0044] See also Figure 1-Figure 3 As shown, the present invention is an Internet-based forestry monitoring system, comprising:

[0045] The plane point cloud data acquisition module uses ground-based laser radar technology to perform point cloud scanning of trees in the forest from the front and top, and then obtains plane point cloud data of multiple trees from the front and top;

[0046] The plane point cloud data preprocessing module preprocesses the plane point cloud data of the front view and the top view of the trees, and then obtains the valid points and outliers in the plane point cloud data of the front view and the top view of the trees in the forest. The valid points in the plane point cloud data of the front view and the top view of the trees are retained, and the outliers in the plane point cloud data of the front view and the top view are removed. The specific method is as follows:

[0047] S1: Evenly divide the front view plane point cloud data of the tree into multiple point cloud area blocks, thereby obtaining multiple point cloud area blocks;

[0048] S2: randomly obtain one of the multiple point cloud area blocks as the analysis area block;

[0049] Calculate the mean distance between each point cloud and other point clouds in the analysis area block, and mark it as Pi, where i refers to different point clouds in the analysis area block, i=1, 2, ..., a, where a refers to the number of point clouds in the analysis area block, a is a positive integer, a≥1;

[0050] The specific method of obtaining the mean value Pi of the distance between each point cloud and other point clouds in the analysis area block is:

[0051] S21: obtaining one point cloud from each point cloud in the analysis area block as a target point cloud;

[0052] S22: Mark the target point cloud coordinates as EA (EAx, EAy), and mark the other point cloud coordinates as EBe (EBex, EBey), where e refers to the other different point clouds in the area block except the target point cloud, e≥1, and the distance calculation formula is used. ; Calculate the distance Je between the target point cloud and other point clouds in the analysis area block, and mark the mean value of the distance Je as P1;

[0053] S23: Repeat steps S21-S22 to obtain the mean value Pi of the distance between each point cloud and other point clouds in the analysis area block;

[0054] By formula , calculate the mean BA and standard deviation BB of Pi, and establish the screening interval [BA-λBB, BA+λBB], where λ is the preset standard deviation coefficient, and the specific value is formulated by relevant personnel according to actual needs, a≥b≥1;

[0055] Determine whether the mean value Pi of the distance between each point cloud and other point clouds in the analysis area block is within the screening interval [BA-λBB, BA+λBB]. If so, the corresponding point cloud is retained as a valid point in the analysis area block. If so, the corresponding point cloud is removed as a discrete point in the analysis area block.

[0056] S3: Repeat step S2 to obtain valid points corresponding to multiple point cloud area blocks of the front-view plane point cloud data of the tree, and then obtain all valid points in the front-view plane point cloud data of the tree;

[0057] S4: Analyze the top-view plane point cloud data of the tree using the same method as that used to analyze the front-view plane point cloud data of the tree, and then obtain all valid points in the top-view plane point cloud data of the tree;

[0058] The elevation difference acquisition module analyzes all valid points in the frontal plane point cloud data of multiple trees, and obtains the elevation differences corresponding to multiple trees according to the analysis results. The specific method is as follows:

[0059] S01: randomly selecting one of the front-view plane point cloud data of multiple trees as the front-view calibration plane point cloud, obtaining the coordinates corresponding to each valid point in the front-view calibration plane point cloud, and defining the distance between the highest point and the lowest point in each valid point as the elevation difference H1 of the front-view calibration plane point cloud;

[0060] The specific method for calibrating the highest point and the lowest point among the valid points is to take the point with the largest ordinate value among the valid points as the highest point, and the point with the smallest ordinate value as the lowest point;

[0061] The specific method of obtaining the elevation difference H1 of the normal view calibration plane point cloud is:

[0062] Mark the coordinates of the highest point and the lowest point as C1 (C1x, C1y), C2 (C2x, C2y):

[0063] pass , calculate and obtain the elevation difference H1 of the normal view calibration plane point cloud;

[0064] S02: Repeat step S01 to obtain the elevation differences Hd corresponding to multiple trees, where d refers to different trees, d=1, 2, ..., c, where c refers to the number of trees, c is a positive integer, c≥1;

[0065] The elevation difference Hd can reflect the vertical growth of trees. A larger elevation difference may mean that the trees grow rapidly and upright and have a good growth trend, while a smaller elevation difference may indicate that the trees grow slowly or are subject to some growth restrictions, such as poor soil, lack of water, etc. By continuously monitoring the elevation difference value, abnormal conditions in the growth process of trees can be discovered in time so that corresponding maintenance measures can be taken.

[0066] The crown diameter acquisition module analyzes all valid points in the top-view plane point cloud data of multiple trees, and obtains the crown diameters corresponding to multiple trees according to the analysis results. The specific method is as follows:

[0067] S11: randomly select one of the top-view plane point cloud data of multiple trees as the top-view calibration plane point cloud, obtain the closed contour line of the top-view calibration plane point cloud, use the center point of the closed contour line of the top-view calibration plane point cloud as the origin to make a two-dimensional coordinate system, and use the average value of the north-south and east-west widths of the tree crown as the crown diameter, that is, mark the maximum point distance of the top-view calibration plane point cloud in the X-axis direction of the two-dimensional coordinate system as Wx, mark the maximum point distance in the Y-axis direction as Wy, and use the average value of Wx and Wy as the crown diameter R1 of the top-view calibration plane point cloud;

[0068] S12: Repeat step S11 to obtain the crown diameters Rd corresponding to multiple trees;

[0069] The crown diameter Rd reflects the lateral growth condition of trees. A larger crown diameter indicates that the trees are healthy and have a suitable growth environment. A smaller crown diameter may be due to factors such as high competitive pressure, insufficient light, or pests and diseases. Monitoring the crown diameter can help determine the health status of trees and their growth space requirements.

[0070] Embodiment 2

[0071] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes a reference value acquisition module and an abnormal growth tree mark acquisition module;

[0072] The benchmark value acquisition module analyzes and obtains the standard elevation difference and standard crown diameter corresponding to the trees according to the elevation difference Hd and crown diameter Rd corresponding to multiple trees. The specific method is as follows:

[0073] Calculate the standard deviation K1 of the elevation difference Hd. When the standard deviation K1 is less than or equal to the preset value Y1, obtain the mean value of Hd as the standard elevation difference MA corresponding to the tree. When the standard deviation K1 is greater than the preset value Y1, take the mean of the maximum and minimum values ​​in Hd as the standard elevation difference MA corresponding to the tree.

[0074] Calculate the standard deviation K2 of the crown diameter Rd. When the standard deviation K2 is less than or equal to the preset value Y2, obtain the mean value of Hd as the standard crown diameter MB corresponding to the tree. When the standard deviation K2 is greater than the preset value Y2, take the mean of the maximum and minimum values ​​in Rd as the standard crown diameter MB corresponding to the tree. The specific values ​​of the preset values ​​Y1 and Y2 are formulated by relevant personnel according to actual needs.

[0075] The abnormal growth tree marking module conducts a comprehensive analysis of the elevation difference and crown diameter corresponding to each tree and the standard elevation difference and standard crown diameter, and marks the abnormal growth trees according to the analysis results. The specific method is as follows:

[0076] Obtain the absolute values ​​of the difference between the elevation difference and crown diameter of each tree and the standard elevation difference and standard crown diameter, FAd and FBd, respectively. Calculate the deviation value Zd corresponding to each tree through the formula: Zd=FAd×θ1+FBd×θ2. When the deviation value Zd is greater than the preset value Y3, the corresponding tree is marked as an abnormal growth mark. When the deviation value Zd is less than or equal to the preset value Y3, no processing is performed. The specific value of the preset value Y3 is formulated by relevant personnel according to actual needs, where θ1 and θ2 are preset fixed coefficients, 1=θ1+θ2, θ1>θ2;

[0077] Through the plane point cloud data preprocessing module, the front and top view plane point cloud data of the trees are carefully processed to distinguish valid points from outliers, avoiding measurement errors caused by invalid data interference, making the subsequent calculated elevation difference and crown diameter more accurate, calculating and analyzing the mean distance between each point cloud and other point clouds in the regional block, and judging the valid points by establishing a screening interval. This method effectively removes noise points and improves the quality of the data. Considering the two parameters of the elevation difference and crown diameter of the trees at the same time, it can comprehensively evaluate the growth status of trees from both vertical and horizontal dimensions, analyze multiple trees separately, and timely discover the growth abnormalities of individual trees, providing a basis for targeted maintenance measures. The standard value is determined according to the elevation difference and crown diameter of multiple trees, and the abnormal growth trees are automatically marked by calculating the deviation value. This greatly improves the efficiency of forestry monitoring and reduces the workload of manual screening. It can timely discover trees with abnormal growth so that corresponding measures can be taken for intervention, such as pest control, fertilization, watering, etc., which helps to maintain the health and stability of the forest.

[0078] Embodiment 3

[0079] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0081] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A forestry monitoring system based on the Internet, characterized in that: include: A plane point cloud data acquisition module is used to obtain plane point cloud data of multiple trees in front view and top view; The plane point cloud data preprocessing module retains the valid points in the plane point cloud data of the tree in the front view and the top view, and removes the outliers; The elevation difference acquisition module analyzes all valid points in the front-view plane point cloud data of multiple trees to obtain the elevation differences corresponding to the multiple trees; The crown diameter acquisition module analyzes all valid points in the top-view plane point cloud data of multiple trees to obtain the crown diameters corresponding to the multiple trees; The benchmark value acquisition module obtains the standard elevation difference and standard crown diameter corresponding to the trees according to the elevation difference and crown diameter corresponding to the multiple trees respectively; The abnormal growth tree marking module conducts a comprehensive analysis of the elevation difference and crown diameter corresponding to each tree and the standard elevation difference and standard crown diameter, and marks the abnormal growth trees.

2. The Internet-based forestry monitoring system according to claim 1, characterized in that: The specific method of retaining the valid points and removing the outliers in the plane point cloud data of the tree in the front view and top view is as follows: S1: Evenly divide the front view plane point cloud data of the tree into multiple point cloud area blocks; S2: randomly obtain one of the multiple point cloud area blocks as the analysis area block, calculate the mean value of the distance between each point cloud in the analysis area block and other point clouds, and mark it as Pi, calculate the mean value BA and standard deviation BB of Pi, establish the screening interval [BA-λBB, BA+λBB], and judge whether the mean value Pi of the distance between each point cloud in the analysis area block and other point clouds is within the screening interval [BA-λBB, BA+λBB]. If it is within the screening interval [BA-λBB, BA+λBB], the corresponding point cloud is retained as a valid point in the analysis area block; if it is not within the screening interval [BA-λBB, BA+λBB], the corresponding point cloud is removed as a discrete point in the analysis area block, where i refers to different point clouds in the area block, i=1, 2, ..., a, where a refers to the number of point clouds in the analysis area block, a is a positive integer, a≥1, and λ is the preset standard deviation coefficient; S3: Repeat step S2 to obtain valid points corresponding to multiple point cloud area blocks of the front-view plane point cloud data of the tree, and then obtain all valid points in the front-view plane point cloud data of the tree; S4: Analyze the top-view plane point cloud data of the tree using the same method as that used to analyze the front-view plane point cloud data of the tree, and then acquire all valid points in the top-view plane point cloud data of the tree.

3. The Internet-based forestry monitoring system according to claim 2, characterized in that: The specific method for obtaining the mean distance between each point cloud and other point clouds in the analysis area block is: S21: obtaining one point cloud from each point cloud in the analysis area block as a target point cloud; S22: Obtain the coordinates of the target point cloud and the coordinates of other point clouds, calculate the distance Je between the target point cloud and other point clouds in the analysis area block by using the distance calculation formula, and mark the average value of the distance Je as P1, where e refers to other different point clouds in the area block except the target point cloud, and e≥1; S23: Repeat steps S21-S22 to obtain the mean value Pi of the distance between each point cloud and other point clouds in the analysis area block.

4. The Internet-based forestry monitoring system according to claim 3, characterized in that: The specific method of obtaining the elevation differences corresponding to multiple trees is: S01: randomly select one of the front-view plane point cloud data of multiple trees as the front-view calibration plane point cloud, define the distance between the highest point and the lowest point among the valid points of the front-view calibration plane point cloud as the elevation difference H1 of the front-view calibration plane point cloud, and take the point with the largest ordinate value among the valid points as the highest point, and the point with the smallest ordinate value as the lowest point; S02: Repeat step S01 to obtain the elevation differences Hd corresponding to multiple trees, where d refers to different trees, d=1, 2, ..., c, where c refers to the number of trees, c is a positive integer, c≥1.

5. The Internet-based forestry monitoring system according to claim 4, characterized in that: The specific method of obtaining the crown diameters corresponding to multiple trees is: S11: randomly select one of the top-view plane point cloud data of multiple trees as the top-view calibration plane point cloud, obtain the closed contour line of the top-view calibration plane point cloud, use the center point of the closed contour line of the top-view calibration plane point cloud as the origin to make a two-dimensional coordinate system, mark the maximum point distance of the top-view calibration plane point cloud in the X-axis direction of the two-dimensional coordinate system as Wx, mark the maximum point distance in the Y-axis direction as Wy, and use the average of Wx and Wy as the crown diameter R1 of the top-view calibration plane point cloud; S12: Repeat step S11 to obtain the crown diameters Rd corresponding to the multiple trees.

6. The Internet-based forestry monitoring system according to claim 5, characterized in that: The specific method for obtaining the standard elevation difference corresponding to the trees is: Calculate the standard deviation K1 of the elevation difference Hd. When the standard deviation K1 is less than or equal to the preset value Y1, obtain the mean of Hd as the standard elevation difference MA corresponding to the tree. When the standard deviation K1 is greater than the preset value Y1, take the mean of the maximum and minimum values ​​in Hd as the standard elevation difference MA corresponding to the tree.

7. The Internet-based forestry monitoring system according to claim 6, characterized in that: The specific method to obtain the standard crown diameter corresponding to the tree is: Calculate the standard deviation K2 of the crown diameter Rd. When the standard deviation K2 is less than or equal to the preset value Y2, obtain the mean of Hd as the standard crown diameter MB corresponding to the tree. When the standard deviation K2 is greater than the preset value Y2, take the mean of the maximum and minimum values ​​in Rd as the standard crown diameter MB corresponding to the tree.

8. The Internet-based forestry monitoring system according to claim 7, characterized in that: Mark trees with abnormal growth by: Obtain the absolute values ​​FAd and FBd of the difference between the elevation difference and crown diameter corresponding to each tree and the standard elevation difference and standard crown diameter respectively. Calculate the deviation value Zd corresponding to each tree through the formula: Zd=FAd×θ1+FBd×θ2. When the deviation value Zd is greater than the preset value Y3, the corresponding tree is marked as an abnormal growth mark. When the deviation value Zd is less than or equal to the preset value Y3, no processing is performed. Among them, θ1 and θ2 are preset fixed coefficients, 1=θ1+θ2, θ1>θ2.

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