A method for obtaining information on missing seedlings in artificial seedling forests

Abstract

The present invention discloses a method for obtaining information on missing seedlings in an artificial nursery forest, comprising obtaining an orthophoto of the nursery, processing the image to obtain a crown binary image; using a grid algorithm to calculate the inclination angle of a nursery row, and according to the calculation result of the inclination angle, rotating the crown binary image clockwise to make the nursery row in a horizontal position; using a centerline method to calculate the number of missing seedlings in each row and the position of missing seedlings in each row, obtaining data on missing seedlings in each row; summarizing the data on missing seedlings in each row, that is, obtaining the overall number of missing seedlings and overall missing seedling position data. The grid-variance method proposed in the present invention can correctly calculate the inclination angle of the nursery row, so that the inclined image of the nursery row can be rotated to the horizontal position of the nursery row, which is convenient for forestry workers to find trees by row position, and then facilitates seedling replacement, ensures the growth quality of eucalyptus forests, and increases farmers' income.

Description

Technical Field

[0001] The invention relates to the technical field of seedling shortage information acquisition, and in particular to a method for acquiring seedling shortage information of an artificial seedling forest. Background Art

[0002] During the period of tending seedlings in artificial forests, forestry workers are more accustomed to determining the location of seedlings based on the rows in which they are located and replanting them, rather than finding the location of trees based on their geographical coordinates.

[0003] Eucalyptus has the characteristics of straight trunk, fast growth, drought resistance, high yield and strong adaptability. It is an important source of pulp and is widely planted, with an area of ​​4.5 million hectares. The premise for realizing refined management of eucalyptus, rapid and accurate replanting, reducing resource waste and increasing production is to quickly and accurately identify the number and location of missing eucalyptus seedlings. The traditional way to obtain information on missing eucalyptus forests is through field visits, which is time-consuming and laborious.

[0004] With the development of technology, UAV remote sensing has developed rapidly in recent years due to its low cost, strong real-time data acquisition, simple operation and high ground resolution. UAV remote sensing combined with digital image processing technology has shown great advantages in forestry single tree identification and forest structure information extraction, and is increasingly used in the acquisition of seedling shortage information.

[0005] However, it is common for eucalyptus to have multiple trees growing on one stump, and the crowns are generally cross-overlapping, so it is not suitable to use the method of segmenting the crown to extract the number of trees and indirectly obtain the number of missing seedlings. In addition, compared to finding the location of trees based on the geographic coordinates of the seedlings, farmers are more accustomed to finding the location of seedlings by row.

[0006] How to use modern methods to obtain information on eucalyptus seedling shortages is of great significance to ensuring the growth quality of eucalyptus forests and increasing farmers' income. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] The present invention can solve the problem that the existing statistics on missing seedlings are generally conducted manually, which is time-consuming and labor-intensive, and that it is difficult to conduct statistics using drone remote sensing when multiple tree seedlings grow on a single stump.

[0009] (II) Technical solution

[0010] In order to achieve the above object, the present invention adopts the following technical scheme, a method for obtaining information on missing seedlings in an artificial seedling forest, specifically comprising the following steps:

[0011] S1: Obtain orthophotos of seedlings and process the images to obtain crown binarization images;

[0012] S2: Calculate the inclination angle of the seedling row using the grid algorithm, and rotate the crown binary image clockwise according to the calculation result of the inclination angle to make the seedling row in a horizontal position;

[0013] S3: Calculate the number of missing seedlings in each row and the position of missing seedlings in each row using the center line method to obtain missing seedling data in each row;

[0014] S4: Summarize the seedling missing data in each row, that is, obtain the overall seedling missing quantity and overall seedling missing position data.

[0015] As a preferred technical solution of the present invention, in S1, the specific steps of obtaining the tree crown binary image are:

[0016] S101: Use drones to obtain several adjacent visible light images of young forests in artificial forest farms;

[0017] S102: stitching a number of adjacent visible light images to generate an orthophoto, where the ground resolution of the orthophoto is 0.5 cm to 1.5 cm;

[0018] S103: performing denoising and image feature analysis on the original orthophoto image;

[0019] S104: construct a three-layer neural network to classify the orthophoto into five categories: tree crown, weeds, shadow, bare land and others. After classification by the BP neural network, the tree crown is extracted to obtain a tree crown image.

[0020] S105: Binarization processing is performed on the tree crown image, and then small area deletion and hole filling processing are performed to obtain a tree crown binary image.

[0021] As a preferred technical solution of the present invention, in S102, several adjacent visible light images are spliced ​​to generate an orthophoto, and the tool used is Pix4Dmapper software; in S103, the original orthophoto is denoised and image feature analyzed, and the tool used is ENVI software; in S104, a three-layer neural network is constructed, and the tool used is NeuralNetClassification provided by ENVI software; in S105, the crown image is binarized, and the tool used is MATLAB software.

[0022] As a preferred technical solution of the present invention, in S2, the specific steps of calculating the inclination angle of the seedling row are:

[0023] S201: Rotate the tree crown binary image clockwise by a certain angle θ within 0°-180°;

[0024] S202: Count the maximum X coordinate of the crown pixel in the crown binary image, recorded as Xmax, and the minimum X coordinate, recorded as Xmin, divide the interval Xmin, Xmax into horizontal grids with an interval of L, the number of intervals is Num, and the intervals are [Xmin, Xmin+L), [Xmin+L, Xmin+2L)…[Xmin+(Num-1)*L, Xmax];

[0025] S203: Let the number of crown pixels in each row of grids be Ni, the average number of crown pixels in each row of grids be N, and calculate the variance of crown pixel samples to measure the uniformity of crown pixel distribution. The calculation expression is:

[0026]

[0027] S204: Repeat steps S201-S203 until the crown binary image is rotated 180°, and obtain the crown pixel distribution sample variance of the crown binary image in different directions The rotation angle corresponding to the maximum variance is the inclination angle of the seedling row.

[0028] As a preferred technical solution of the present invention, in the steps S201-S204, the weight of the number of pixels of each tree crown is taken as 0.01 during the calculation process.

[0029] As a preferred technical solution of the present invention, in step S202, the grid spacing L is less than 1 times the width of the space between rows.

[0030] As a preferred technical solution of the present invention, in step S3, the specific steps of obtaining the seedling missing data of each row are:

[0031] S301: When the rows of seedlings are all in a horizontal state, project each crown pixel onto the X-axis;

[0032] S302: using K-means clustering to obtain the center point of each cluster of projection points on the X-axis;

[0033] S303: Draw horizontal parallel center lines at each center point;

[0034] S304: Find the position of missing seedlings in each row and the number of missing seedlings in each row on the parallel center line.

[0035] As a preferred technical solution of the present invention, in S302, the number of clusters of K-means clustering is the total number of seedling rows.

[0036] As a preferred technical solution of the present invention, in step S304, the specific steps of searching for the position of missing seedlings in each row and the number of missing seedlings in each row on the parallel center line are:

[0037] S3041: starting from the left end of the first row of trees, find the number of non-vegetation pixels in each column of pixels whose distance from the central axis of the row of trees is less than D on both sides. If none of them are vegetation pixels, it is considered that there is no vegetation at that location in the row. Otherwise, it is considered that there is vegetation. When the continuous length S of non-vegetation pixels reaches a certain threshold S0, it is determined that there is no seedling at that location.

[0038] S3042: The value of the threshold S0 is calculated according to the following formula:

[0039] S0=2R-d②

[0040] ②In the formula, R is the tree spacing, and d is the crown diameter;

[0041] S3043: Calculate the total length S of each row of continuous non-crown pixels T , the number of missing seedlings in each row can be obtained according to the following formula:

[0042]

[0043] ③In the formula, R is the tree spacing.

[0044] (III) Beneficial effects

[0045] 1. The method for obtaining information on missing seedlings in an artificial nursery forest provided by the present invention can correctly calculate the inclination angle of a nursery row by using the grid-variance method, thereby being able to rotate the inclined image of the nursery row to the horizontal position of the nursery row, making it convenient for forestry workers to find trees by row position, thereby facilitating seedling replenishment, ensuring the growth quality of eucalyptus forests, and increasing farmers' income.

[0046] 2. The method for obtaining information on missing seedlings in an artificial nursery forest provided by the present invention adopts a centerline method to detect the position and number of missing seedlings, and the missing seedling detection rate is above 90%, achieving a good detection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 It is a schematic diagram of the process of the present invention;

[0049] Figure 2 It is a schematic diagram of the orthophoto and the test area location of the present invention;

[0050] Figure 3 It is a schematic diagram of the tree crown binarization image of the present invention;

[0051] Figure 4 This is a schematic diagram of the grid pixel distribution of the original tree crown image of the present invention;

[0052] Figure 5 This is a schematic diagram of the distribution of tree crown pixels after the rotation tilt angle of the present invention;

[0053] Figure 6 This is a schematic diagram of the variance when L is 0.3 times the width of the inter-row gap when the inclination angle of the seedling row of the present invention is 90°;

[0054] Figure 7 This is a schematic diagram of the variance when L is 0.6 times the width of the inter-row gap when the inclination angle of the seedling row of the present invention is 90°;

[0055] Figure 8 This is a schematic diagram of the variance when L is 1 times the width of the inter-row gap when the inclination angle of the seedling row of the present invention is 90°;

[0056] Fig. 9 This is a schematic diagram of the variance when L is 1.3 times the width of the inter-row gap when the inclination angle of the seedling row of the present invention is 90°;

[0057] Fig.10 This is a schematic diagram of the variance when L is 1.6 times the width of the inter-row gap when the inclination angle of the seedling row of the present invention is 90°;

[0058] Fig.11 This is a schematic diagram of the variance when L is twice the width of the inter-row gap when the inclination angle of the seedling row is 90°;

[0059] Fig.12 This is a schematic diagram of the variance when the test area L is 0.6 times the inter-row gap width of the present invention;

[0060] Fig.13 It is a schematic diagram of the centerline position of a seedling row of the present invention;

[0061] Fig.14 It is a schematic diagram of searching for missing seedling positions of the present invention;

[0062] Fig.15 Schematic diagram of the value difference of the threshold value S0 of the present invention;

[0063] Fig.16 This is a schematic diagram of the position of missing seedlings in the first test rectangular area of ​​the present invention;

[0064] Fig.17 It is a schematic diagram of the position of missing seedlings in the second test rectangular area of ​​the present invention.

[0065] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0067] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0068] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0069] In the description of the present invention, it is necessary to understand that the orientations or positional relationships indicated by terms such as “longitudinal”, “up”, “down”, “left”, “right”, “top”, “bottom”, “inside” and “outside” are based on the orientations or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0070] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0071] Example 1

[0072] like Figures 1 to 16 As shown, a method for obtaining information on missing seedlings in an artificial seedling forest specifically comprises the following steps:

[0073] S1: Obtain orthophotos of seedlings and process the images to obtain crown binarization images;

[0074] S2: Calculate the inclination angle of the seedling row using the grid algorithm, and rotate the crown binary image clockwise according to the calculation result of the inclination angle to make the seedling row in a horizontal position;

[0075] S3: Calculate the number of missing seedlings in each row and the position of missing seedlings in each row using the center line method to obtain missing seedling data in each row;

[0076] S4: Summarize the seedling missing data in each row, that is, obtain the overall seedling missing quantity and overall seedling missing position data.

[0077] Specifically, in S1, the specific steps of obtaining the tree crown binary image are:

[0078] S101: Use drones to obtain several adjacent visible light images of young forests in artificial forest farms;

[0079] S102: Pix4Dmapper software is used to stitch several adjacent visible light images to generate orthophotos. The ground resolution of the orthophotos is 0.5cm-1.5cm and is within the orthophoto impact;

[0080] S103: Using ENVI software, the original orthoimage is subjected to denoising and image feature analysis;

[0081] S104: Using the NeuralNetClassification function in ENVI software, a three-layer neural network was constructed to classify the orthophoto into five categories: tree crown, weeds, shadow, bare land, and others. After classification by the BP neural network, the tree crown was extracted to obtain the tree crown image.

[0082] S105: Binarization processing is performed on the tree crown image, and then small area deletion and hole filling processing are performed to obtain a tree crown binary image.

[0083] It should be noted that, in this embodiment, a light UAV DJI Phantom 4 was used to obtain high-resolution visible light images of a young forest in an artificial forest farm. The visible light camera carried by the UAV has a pixel of 20 million, an equivalent focal length of 24mm, a shooting heading overlap of 80%, a lateral overlap of 75%, a flat terrain, a shooting altitude of 100m, and a photo resolution of 5472*3648.

[0084] Specifically, in S2, the specific steps of calculating the inclination angle of the seedling row are:

[0085] S201: Rotate the tree crown binary image clockwise by a certain angle θ within 0°-180°;

[0086] S202: Count the maximum X coordinate of the crown pixel in the crown binary image, recorded as Xmax, and the minimum X coordinate, recorded as Xmin, divide the interval Xmin, Xmax into horizontal grids with an interval of L, the number of intervals is Num, and the intervals are [Xmin, Xmin+L), [Xmin+L, Xmin+2L)…[Xmin+(Num-1)*L, Xmax];

[0087] S203: Let the number of crown pixels in each row of grids be Ni, the average number of crown pixels in each row of grids be N, and calculate the variance of crown pixel samples to measure the uniformity of crown pixel distribution. The calculation expression is:

[0088]

[0089] S204: Repeat steps S201-S203 until the crown binary image is rotated 180°, and obtain the crown pixel distribution sample variance of the crown binary image in different directions The rotation angle corresponding to the maximum variance is the inclination angle of the seedling row.

[0090] See also Figure 2 and Fig.12 In this embodiment, two rectangular areas are selected as test areas for research. Considering that trees are distributed in rows rather than uniformly distributed in the entire rectangular area, it is assumed that there is a set of parallel horizontal grids in the area. When the image is rotated to different directions, the crown pixels will present different distribution states in the grids. When the direction of the seedling row in the image is parallel to the grid line, there will be an extreme situation where there are many crown pixels in some grids, while there are few or even no crown pixels in some grids. That is, the distribution of crown pixels in the entire image in the grid is extremely uneven. Based on the distribution analysis of crown pixels in horizontal grids, we can use the sample variance of the sequence composed of the number of crown pixels in each grid (the number of crown pixels in each row of grids is Ni, and the average number of crown pixels in each row of grids is N) to measure whether the distribution of crown pixels is uniform.

[0091] It can be seen from the above calculation content that the interval size L of the horizontal grid will affect the effectiveness of this method. The interval size is related to the width of the inter-row gap (the number of pixels corresponding to the width of the inter-row gap can be determined according to the pixel spatial resolution and the actual width of the inter-row gap). Therefore, in order to explore the appropriate value of L, this application tested the crown images with different seedling row inclination angles. When each image L was 0.3 times the inter-row gap width, 0.6 times the inter-row gap width, 1 times the inter-row gap width, 1.3 times the inter-row gap width, 1.6 times the inter-row gap width and 2 times the inter-row gap width, the sample variance S2 corresponding to different rotation angles in the range of 0°-180° was calculated respectively. In order to prevent the sample variance value from being too large, the weight of each crown pixel number was taken as 0.01 during the calculation process. According to the experimental results, in order to make the variance map obtain the maximum value at the seedling row inclination angle position, the general grid interval L should be less than 1 times the inter-row gap width.

[0092] Specifically, in step S3, the specific steps of obtaining the seedling missing data of each row are:

[0093] S301: When the rows of seedlings are all in a horizontal state, project each crown pixel onto the X-axis;

[0094] S302: using K-means clustering to obtain the center point of each cluster of projection points on the X-axis;

[0095] S303: Draw horizontal parallel center lines at each center point;

[0096] S304: Find the position of missing seedlings in each row and the number of missing seedlings in each row on the parallel center line.

[0097] Specifically, in S302, the number of clusters of K-means clustering is the total number of seedling rows.

[0098] Specifically, in step S304, the specific steps of searching for the position of missing seedlings in each row and the number of missing seedlings in each row on the parallel center line are:

[0099] S3041: starting from the left end of the first row of trees, find the number of non-vegetation pixels in each column of pixels whose distance from the central axis of the row of trees is less than D on both sides. If none of them are vegetation pixels, it is considered that there is no vegetation at that location in the row. Otherwise, it is considered that there is vegetation. When the continuous length S of non-vegetation pixels reaches a certain threshold S0, it is determined that there is no seedling at that location.

[0100] S3042: The value of the threshold S0 is calculated according to the following formula:

[0101] S0=2R-d②

[0102] ②In the formula, R is the tree spacing, and d is the crown diameter;

[0103] S3043: Calculate the total length S of each row of continuous non-crown pixels T , the number of missing seedlings in each row can be obtained according to the following formula:

[0104]

[0105] ③In the formula, R is the tree spacing.

[0106] See also Fig.13 and Fig.17 It should be noted that, in this embodiment, the above operation is performed on two rectangular test areas, and the information obtained is as described in the following table:

[0107] (1) Number of missing seedlings per row in test area 1

[0108]

[0109] (2) Number of missing seedlings per row in test area 2

[0110]

[0111]

[0112] In summary, the overall detection rate of missing seedlings is greater than 90%

[0113] In summary, the grid-variance method proposed in this application can correctly calculate the inclination angle of the seedling row, so that the inclined image of the seedling row can be rotated to the horizontal position of the seedling row, which is convenient for forestry workers to find trees by row position. The grid-variance method is directly affected by the size of the grid spacing when calculating the inclination angle of the row of trees. Through the experimental tests in this article, it can be seen that when using the grid-variance method, the grid size should be less than 1 times the width of the gap between rows, in order to obtain a more obvious sample variance peak. In addition, it should be pointed out that the grid-variance method is only suitable for situations where the trees are arranged in a straight line and in parallel.

[0114] Finally, it should be noted that when the row of trees is rotated to a horizontal state, the centerline method proposed in this application can correctly detect the position and number of missing seedlings in each row. The missing seedling detection rate is over 90%. The detection result depends on the accuracy of distinguishing the crown from the background. In the eucalyptus sprout forest study area selected in this application, some areas with missing seedlings were not detected because grass grew there, and the spectral characteristics of the grass and the crown were extremely similar, resulting in the image being misjudged as the crown. Therefore, in later research, it is necessary to further study the method of distinguishing the crown from other vegetation in order to better extract the crown and detect more accurate missing seedling areas.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for obtaining information on seedling shortage in an artificial seedling forest, characterized in that: The specific steps include: S1: Obtain orthophotos of seedlings and process the images to obtain crown binarization images; S2: Calculate the inclination angle of the seedling row using the grid algorithm, and rotate the crown binary image clockwise according to the calculation result of the inclination angle to make the seedling row in a horizontal position; In S2, the specific steps of calculating the inclination angle of the seedling row are: S201: Rotate the tree crown binary image clockwise by a certain angle θ within 0°-180°; S202: Count the maximum X coordinate of the crown pixel in the crown binary image, recorded as Xmax, and the minimum X coordinate, recorded as Xmin, divide the interval Xmin, Xmax into horizontal grids with an interval of L, the number of intervals is Num, and the intervals are [Xmin, Xmin+L), [Xmin+L, Xmin+2L)…[Xmin+(Num-1)*L, Xmax]; S203: Let the number of crown pixels in each row of grids be Ni, the average number of crown pixels in each row of grids be N, and calculate the variance of crown pixel samples to measure the uniformity of crown pixel distribution. The calculation expression is: S204: Repeat steps S201-S203 until the crown binary image is rotated 180°, and obtain the crown pixel distribution sample variance of the crown binary image in different directions The rotation angle corresponding to the maximum variance is the tilt angle of the seedling row; S3: Calculate the number of missing seedlings in each row and the position of missing seedlings in each row using the center line method to obtain missing seedling data in each row; S4: Summarize the seedling missing data in each row, that is, obtain the overall seedling missing quantity and overall seedling missing position data.

2. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 1, characterized in that: In S1, the specific steps of obtaining the tree crown binary image are: S101: Use drones to obtain several adjacent visible light images of young forests in artificial forest farms; S102: stitching a number of adjacent visible light images to generate an orthophoto, where the ground resolution of the orthophoto is 0.5 cm to 1.5 cm; S103: performing denoising and image feature analysis on the original orthophoto image; S104: construct a three-layer neural network to classify the orthophoto into five categories: tree crown, weeds, shadow, bare land and others. After classification by the BP neural network, the tree crown is extracted to obtain a tree crown image. S105: Binarization processing is performed on the tree crown image, and then small area deletion and hole filling processing are performed to obtain a tree crown binary image.

3. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 2, characterized in that: In S102, several adjacent visible light images are spliced ​​to generate an orthophoto, and the tool used is Pix4Dmapper software; in S103, the original orthophoto is denoised and image feature analyzed, and the tool used is ENVI software; in S104, a three-layer neural network is constructed, and the tool used is NeuralNetClassification provided by ENVI software; in S105, the crown image is binarized, and the tool used is MATLAB software.

4. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 1, characterized in that: In the steps S201-S204, the weight of the number of pixels of each tree crown is taken as 0.01 during the calculation process.

5. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 1, characterized in that: In step S202, the grid interval L is set to be less than 1 times the width of the space between rows.

6. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 1, characterized in that: In step S3, the specific steps of obtaining the seedling shortage data of each row are as follows: S301: When the rows of seedlings are all in a horizontal state, project each crown pixel onto the X-axis; S302: using K-means clustering to obtain the center point of each cluster of projection points on the X-axis; S303: Draw horizontal parallel center lines at each center point; S304: Find the position of missing seedlings in each row and the number of missing seedlings in each row on the parallel center line.

7. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 6, characterized in that: In S302, the number of clusters of K-means clustering is the total number of seedling rows.

8. The method for obtaining information on missing seedlings in an artificial seedling forest according to claim 6, characterized in that: In step S304, the specific steps of searching for the position of missing seedlings in each row and the number of missing seedlings in each row on the parallel center line are: S3041: starting from the left end of the first row of trees, find the number of non-vegetation pixels in each column of pixels whose distance from the central axis of the row of trees is less than D on both sides. If none of them are vegetation pixels, it is considered that there is no vegetation at that location in the row. Otherwise, it is considered that there is vegetation. When the continuous length S of non-vegetation pixels reaches a certain threshold S0, it is determined that there is no seedling at that location. S3042: The value of the threshold S0 is calculated according to the following formula: S0=2R-d② ②In the formula, R is the tree spacing, and d is the crown diameter; S3043: Calculate the total length S of each row of continuous non-crown pixels T , the number of missing seedlings in each row can be obtained according to the following formula: ③In the formula, R is the tree spacing.

Images