Method and device for automatically identifying soil map branch shape map spots based on morphological features

Abstract

The application discloses a soil map branch shape map spot automatic distinguishing method and device based on morphological characteristics, and the method comprises the following steps: generating a spot set SP based on two-dimensional soil layer data, and generating a GeoDEM based on DEM data; screening soil map spots with a height difference greater than a preset threshold from the SP based on the GeoDEM and storing the soil map spots in a set SP1; screening soil map spots with a geometric circular rate less than a preset threshold and storing the soil map spots in SP2; screening soil map plates with a branch number greater than a preset threshold and storing the soil map plates in a set SP3; and outputting each soil map spot in the set SP3 to generate a soil branch shape map spot file. The application realizes automatic distinguishing of a large number of soil map branch shape map spots, and the accuracy and running efficiency of the distinguishing result are relatively high.

Description

Technical Field

[0001] The present invention relates to the field of soil survey and cartography, and in particular to a method and device for automatically distinguishing soil branch-shaped patches based on morphological characteristics. Background Art

[0002] Traditional soil mapping methods rely on field soil surveys to obtain knowledge about soil occurrence and distribution patterns within a region. The boundaries of different soil types are manually delineated on a geographic base map, such as a topographic map or remote sensing imagery, to create a soil map. These polygonal soil maps implicitly capture the knowledge of soil survey experts regarding the relationship between soil spatial distribution and geographic features. In the era of digital mapping, with the development of geographic information systems, data mining, and Earth observation technologies, emerging soil mapping methods are based on soil-landscape models and employ spatial data analysis and mathematical methods to predict soil spatial distribution. A key issue in digital soil mapping is how to capture the relationship between soil and environmental factors. The vast amount of expert knowledge implicit in traditional polygonal soil maps is generally not accessible through visual perception. However, because these maps are essentially visual soil spatial databases, data mining techniques can be used to extract this hidden soil-landscape knowledge.

[0003] Currently, research on soil-landscape knowledge mining based on traditional soil maps, both domestically and internationally, focuses on analyzing polygonal patch attributes. This involves overlaying or fusing multi-source geographic landscape spatial data onto digitized traditional soil maps to form multidimensional attribute tables, which are then analyzed for correlation to generate soil-landscape models. However, the geometry of polygonal patches is also closely related to pedogenic environmental conditions. Differences in geometry reflect regional differences in pedogenic characteristics. For example, soil patch assemblages in isolated hills or basins are often ring-shaped, while those in deeply incised mountains or hills are often dendritic and strip-like. Soil patches in coastal plains are often strip-shaped, while those in flood-alluvial fans are often fan-shaped. Soil patches in shallow lowlands and depressions, such as lagoons, are often elliptical. In fact, the cartographic quality of traditional soil maps primarily depends on the rationality of their patch geometry, that is, the consistency between soil distribution and geographic landscape units. Therefore, a possible development direction of digital soil mapping is to analyze the geometric shapes of various soil type polygons in traditional soil maps, find the soil-landscape patterns implicit in the patch shapes, and perform various spatial prediction analyses of soils through automatic recognition of the patch shapes of traditional soil maps and adaptive reasoning algorithms. Summary of the Invention

[0004] Purpose of the invention: To address the problems existing in the prior art, the present invention provides a method and device for automatically identifying soil branch-shaped patches based on morphological characteristics.

[0005] Technical solution: The method for automatically distinguishing soil branch-shaped patches based on morphological characteristics of the present invention includes:

[0006] (1) Extract each soil patch from the two-dimensional soil layer data and store it in the patch set SP, and store the DEM data in the raster dataset GeoDEM;

[0007] (2) Based on the raster dataset GeoDEM, soil patches with elevation differences greater than a preset threshold are selected from the patch set SP and stored in the set SP1;

[0008] (3) Calculate the geometric circularity of each soil patch in SP1 based on the perimeter and area, and select soil patches with a geometric circularity less than a preset threshold and store them in set SP2;

[0009] (4) Pick any patch sp2 from the set SP2 i , calculate sp2 i The horizontal distance between each boundary point and its centroid V is stored in the set PD;

[0010] (5) Find the position l of the maximum value element in set PD, and store the lth to the last elements of set PD in set PD1 in sequence, then store the first element to the l-1th element in set PD1 in sequence, and calculate the mean of each element in set PD1 and store it in set PD2;

[0011] (6) Calculate sp2 based on sets PD1 and PD2 i The number of branches, and when the number of branches is greater than the branch number threshold, sp2 i Store in set SP3;

[0012] (7) Execute steps (4) to (6) repeatedly until all soil patches in set SP2 are traversed to obtain set SP3;

[0013] (8) Output each soil patch in the set SP3 and generate a soil branch patch file.

[0014] Furthermore, step (1) includes:

[0015] (1-1) Read the two-dimensional soil layer data, and store each soil patch in the two-dimensional soil layer into the patch set SP = {sp i |i=1,2,…,SPI}; where sp i represents the i-th soil patch, and SPI represents the number of soil patches;

[0016] (1-2) Read DEM data and store it in the raster dataset GeoDEM.

[0017] Furthermore, step (2) includes:

[0018] (2-1) Obtain the elevation values of each boundary point of each patch in the patch set SP from GeoDEM, generate patches with elevation values, and store them in the patch set SP'.

[0019] (2-2) Extract any soil patch sp from the patch set SP'. i , and obtain the maximum value Hmax and the minimum value Hmin of the elevation values of each boundary point of sp. i If Hmax - Hmin > h, then store this sp i in the set SP1, where h is a preset elevation difference threshold.

[0020] (2-3) Loop and execute step (2-2) until the screening of all soil patches in the patch set SP' based on the elevation difference is completed, and obtain the set SP1 = {sp1 i | i = 1, 2,..., SPI1}; sp1 i represents the i-th soil patch after elevation difference screening, and SPI1 represents the number of soil patches after elevation difference screening.

[0021] Further, step (3) includes:

[0022] (3-1) Take any soil patch sp1 from the set SP1 i , and calculate the perimeter P of the soil patch sp1 i .

[0023] (3-2) Calculate the area A of the soil patch sp1 i .

[0024] (3-3) Based on the perimeter P and the area A, calculate the geometric circularity rate C of the soil patch sp1 i according to the following formula. If C < m, where m is a preset circularity rate threshold, then store this patch in the set SP2:

[0025] C = 4A / P 2

[0026] (3-4) Loop and execute steps (3-1) to (3-3) until the screening of all soil patches in SP1 based on the geometric circularity rate is completed, and obtain the set SP2 = {sp2 i | i = 1, 2,..., SPI2}; where SPI2 represents the number of soil patches after geometric circularity rate screening, and sp2 i represents the i-th soil patch after screening based on the geometric circularity rate.

[0027] Further, step (4) specifically includes:

[0028] (4-1) Take any soil patch sp2 from the set SP2 i, and get sp2 i The boundary point set P = {p j |j=1,2,…,PJ}, where p j For sp2 i The jth boundary point, PJ represents the number of boundary points in the boundary point set P;

[0029] (4-2) If the number of boundary points PJ>2k, k is the window width parameter for curve smoothing, then based on the boundary point set P, sp2 is calculated according to the following formula i The coordinates of the center of mass V (vx, vy); otherwise, execute step (6);

[0030]

[0031] Where, (p j,x ,p j,y ) represents the jth point p in the boundary point set P j coordinates;

[0032] (4-3) Calculate the horizontal distance between each boundary point in the boundary point set P and the centroid V, and store it in the set PD = {pd j |j=1,2,…,PJ}; where pd j Represents the jth point p in the boundary point set P j With sp2 i The horizontal distance from the center of mass V.

[0033] Furthermore, step (5) specifically includes:

[0034] (5-1) Find the element pd with the maximum value in the set PD l , get its position l, and move the lth element to the PJth element in the set PD, that is, [pd l ,…,pd PJ ] are stored in the set PD1 in sequence, and then the first element to the l-1th element in the set PD, that is, [pd1,…,pd l-1 ] are stored in the set PD1 in sequence, and the set PD1 = {pd1 p |p=1,2,…,PJ}, where pd1 p represents the pth element in the set PD1, and PJ represents the number of elements in the set PD;

[0035] (5-2) Take any element pd1 in the set PD1 p , according to the curve smoothing window width parameter k, based on the following formula to calculate the pd1 p The corresponding mean pd2 p , and pd2 p As the p-th element in the mean set PD2;

[0036]

[0037] (5-3) Loop through (5-2) until all elements in set PD1 are traversed and set PD2 = {pd2 p |p=1,2,…,PJ}.

[0038] Furthermore, step (6) specifically includes:

[0039] (6-1) Start from the first element to the last element in PD1 and PD2 and judge in sequence. If pd1 p >pd2 p , then t p The value of is recorded as 1, otherwise it is recorded as 0, and stored in the set T = {t p |p=1,2,…,PJ}, PJ represents the number of elements in set PD1;

[0040] (6-2) Traverse the elements in set T one by one. If there is a segment of elements [t p ,…,t s ] values ​​are all equal to 1, then it is considered that the soil pattern sp2 i The corresponding boundary point [p p ,…,p s ] is a branch, thus obtaining soil patch sp2 i The number of branches x;

[0041] (6-3) Clear the sets PD1, PD2 and P;

[0042] (6-4) If x>n, n is the preset branch number threshold, then sp2 i Stored in collection SP3.

[0043] The automatic identification device for soil branch-shaped patches based on morphological characteristics of the present invention includes a processor and a computer program stored in a memory and executable on the processor, and the above method is implemented when the processor executes the program.

[0044] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: it realizes the automatic identification of a large number of soil dendrite patterns, and the accuracy of the identification results and the operating efficiency are high. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the two-dimensional soil layer data used in this embodiment;

[0046] Figure 2 The DEM data used in this embodiment;

[0047] Figure 3 This is a flow chart of the method for automatically distinguishing soil branch-shaped patches based on morphological characteristics provided by the present invention;

[0048] Figure 4 This is the height difference screening result map;

[0049] Figure 5 This is the circularity screening result graph;

[0050] Figure 6 The distance map between each point on the edge of the patch and the centroid

[0051] Figure 7 The distance between each point on the edge of the patch and the centroid and the moving average is the graph starting from the point with the maximum distance.

[0052] Figure 8 This is the result of the branch pattern screening when the branch number threshold n=5;

[0053] Figure 9 This is the result of the branch pattern screening when the branch number threshold value n=7;

[0054] Figure 10 This is the result of the branch pattern screening when the branch number threshold n=9;

[0055] Figure 11 This is a partial enlarged view of the branch pattern screening results when the branch number threshold n=9. DETAILED DESCRIPTION

[0056] The following is a detailed description of the technical solution of the present invention in conjunction with the accompanying drawings. The experimental data of this embodiment uses 1:500,000 Jiangxi soil layer data ( Figure 1 ) and 30-meter resolution DEM data ( Figure 2 ), the projection coordinate system used in the experimental data is Krasovsky_1940_Albers. The following is further explained by describing a specific embodiment in conjunction with the accompanying drawings.

[0057] like Figure 3 As shown, the method for automatically identifying soil branch-shaped patches based on morphological characteristics provided in this embodiment specifically includes the following steps:

[0058] (1) Extract each soil patch from the two-dimensional soil layer data and store it in the patch set SP, and store the DEM data in the raster dataset GeoDEM.

[0059] This step specifically includes:

[0060] (1-1) Read the two-dimensional soil layer data, and store each soil patch in the two-dimensional soil layer into the patch set SP = {sp i |i=1,2,…,SPI}; where spi represents the i-th soil patch, and SPI represents the number of soil patches; in this embodiment, SPI=3685.

[0061] (1-2) Read DEM data and store it in the raster dataset GeoDEM.

[0062] (2) Based on the grid dataset GeoDEM, soil patches with elevation differences greater than a preset threshold are selected from the patch set SP and stored in the set SP1, such as Figure 4 shown.

[0063] This step specifically includes:

[0064] (2-1) Obtain the elevation values ​​of each boundary point of each patch in the patch set SP from GeoDEM, generate patches containing elevation values, and store them in the patch set SP′.

[0065] (2-2) Extract any soil patch sp from the patch set SP′ i , get sp i The maximum value Hmax and the minimum value Hmin of the elevation values ​​of each boundary point, if Hmax-Hmin>h, then the sp i Stored in set SP1, where h is a preset height difference threshold. In mountainous areas, h takes a larger value, and in plain areas, h can take a value of zero. In this embodiment, h=50 meters.

[0066] (2-3) Execute step (2-2) repeatedly until all soil patches in the patch set SP′ are screened based on height difference, and the set SP1 = {sp1 i |i=1,2,…,SPI1};sp1 i represents the i-th soil patch after height difference screening, and SPI1 represents the number of soil patches after height difference screening. In this embodiment, SPI1=2805, when i=1, Hmax-Hmin=268 meters.

[0067] (3) Calculate the geometric circularity of each soil patch in SP1 based on the perimeter and area, and select the soil patches with geometric circularity less than the preset threshold and store them in set SP2, such as Figure 5 shown.

[0068] This step specifically includes:

[0069] (3-1) Select any soil patch sp1 from the set SP1 i , and calculate the soil patch sp1 i Perimeter P; In this embodiment, the soil patch sp1 can be calculated based on the get_Length() function of GDAL i Perimeter P, when i=1, P=256383.9 meters.

[0070] (3 - 2) Calculate the area A of the soil patch sp1 i The area A of the soil patch sp1 can be calculated based on the GetArea() function of GDAL i The area A. In this embodiment, when i = 1, A = 132305799.9 square meters.

[0071] (3 - 3) Calculate the geometric circularity rate C of the soil patch sp1 according to the following formula based on the perimeter P and the area A i If C < m, where m is the preset circularity rate threshold, then store this patch in the set SP2.

[0072] C = 4A / P 2

[0073] In this embodiment, m = 0.1, SPI2 = 1159, when i = 1, C = 0.00805.

[0074] (3 - 4) Loop through steps (3 - 1) to (3 - 3) until all soil patches in SP1 are screened based on the geometric circularity rate, obtaining the set SP2 = {sp2 i |i = 1, 2, …, SPI2}; where SPI2 represents the number of soil patches screened by the geometric circularity rate, and sp2 i represents the i-th soil patch after screening based on the geometric circularity rate.

[0075] (4) Randomly select a patch sp2 from the set SP2 i , and calculate the horizontal distances between each boundary point of sp2 i and its centroid V, and store them in the set PD.

[0076] This step specifically includes:

[0077] (4 - 1) Randomly select a soil patch sp2 from the set SP2 i , and obtain the boundary point set P = {p i |j = 1, 2, …, PJ} of sp2 j , where p j is the j-th boundary point of sp2 i , and PJ represents the number of boundary points in the boundary point set P.

[0078] (4 - 2) If the number of boundary points PJ > 2k, where k is the window width parameter for curve smoothing, then calculate the coordinates (vx, vy) of the centroid V of sp2 i based on the boundary point set P according to the following formula; otherwise, execute step (6);

[0079]

[0080] Where, (p j,x ,p j,y ) represents the jth point p in the boundary point set P j coordinates; in this embodiment, when i=1, PJ=751, vx=1136465.582, vy=3018839.04.

[0081] (4-3) Calculate the horizontal distance between each boundary point in the boundary point set P and the centroid V, and store it in the set PD = {pd j |j=1,2,…,PJ}; where pd j Represents the jth point p in the boundary point set P j With sp2 i In this embodiment, the point number of the element in the set PD is used as the horizontal coordinate, pd j The point-centroid distance curve generated by the vertical coordinate is as follows Figure 6 shown.

[0082] (5) Find the position l of the maximum value element in set PD, and store the lth to the last elements of set PD in set PD1 in sequence, then store the first element to the l-1th element in set PD1 in sequence, and calculate the mean of each element in set PD1 and store it in set PD2.

[0083] This step specifically includes:

[0084] (5-1) Find the element pd with the maximum value in the set PD l , get its position l, and move the lth element to the PJth element in the set PD, that is, [pd l ,…,pd PJ ] are stored in the set PD1 in sequence, and then the first element to the l-1th element in the set PD, that is, [pd1,…,pd l-1 ] are stored in the set PD1 in sequence, and the set PD1 = {pd1 p |p=1,2,…,PJ}, where pd1 p Represents the pth element in set PD1, and PJ represents the number of elements in set PD.

[0085] (5-2) Take any element pd1 in the set PD1 p , according to the curve smoothing window width parameter k, based on the following formula to calculate the pd1 p The corresponding mean pd2 p , and pd2 p As the pth element in the mean value set PD2. In this embodiment, k=10.

[0086]

[0087] (5-3) Loop through (5-2) until all elements in set PD1 are traversed and set PD2 = {pd2 p |p=1,2,…,PJ}. In this embodiment, the point number of the element in the set PD1 is used as the horizontal coordinate, pd1 j The value of is the point-centroid distance curve generated by the ordinate and the point number of the element in the set PD2 is the abscissa, pd2 j The moving average generated by the vertical coordinate is Figure 7 shown.

[0088] (6) Calculate sp2 based on sets PD1 and PD2 i The number of branches, and when the number of branches is greater than the branch number threshold, sp2 i Stored in set SP3, in this embodiment, the number SPI3 in set SP3=818, and the preset threshold n=5.

[0089] This step specifically includes:

[0090] (6-1) Start from the first element to the last element in PD1 and PD2 and judge in sequence. If pd1 p >pd2 p , then t p The value of is recorded as 1, otherwise it is recorded as 0, and stored in the set T = {t p |p=1,2,…,PJ}, PJ represents the number of elements in set PD1;

[0091] (6-2) Traverse the elements in set T one by one. If there is a segment of elements [t p ,…,t s ] values ​​are all equal to 1, then it is considered that the soil pattern sp2 i The corresponding boundary point [p p ,…,p s ] is a branch, thus obtaining soil patch sp2 i The number of branches x;

[0092] (6-3) Clear the sets PD1, PD2 and P;

[0093] (6-4) If x>n, n is the preset branch number threshold, then sp2 i Stored in collection SP3.

[0094] (7) Repeat steps (4) to (6) until all soil patches in set SP2 are traversed to obtain set SP3.

[0095] (8) Output each soil patch in the set SP3 and generate a soil branch patch file, such as Figure 8 As shown in Figure 2, if the branch number threshold n is set to 7 or 9, the soil branch pattern file is as follows: Figure 9 、 10 , as shown in 11.

[0096] This embodiment also provides a device for automatically identifying soil branch-shaped patches based on morphological characteristics, including a processor and a computer program stored in a memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0097] The above disclosure is only a preferred embodiment of the present invention and cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for automatically identifying soil branch-shaped patches based on morphological characteristics, characterized in that The method includes: (1) Extract each soil patch from the two-dimensional soil layer data and store it in the patch set SP, and store the DEM data in the raster dataset GeoDEM; (2) Based on the raster dataset GeoDEM, soil patches with elevation differences greater than a preset threshold are selected from the patch set SP and stored in the set SP1; (3) Calculate the geometric circularity of each soil patch in SP1 based on the perimeter and area, and select soil patches with a geometric circularity less than a preset threshold and store them in set SP2; (4) Pick any patch sp2 from the set SP2 i , calculate sp2 i The horizontal distance between each boundary point and its centroid V is stored in the set PD; (5) Find the position l of the maximum value element in set PD, and store the lth to the last elements of set PD in set PD1 in sequence, then store the first element to the l-1th element in set PD1 in sequence, and calculate the mean of each element in set PD1 and store it in set PD2; (6) Calculate sp2 based on sets PD1 and PD2 i The number of branches, and when the number of branches is greater than the branch number threshold, sp2 i Store in set SP3; (7) Execute steps (4) to (6) repeatedly until all soil patches in set SP2 are traversed to obtain set SP3; (8) Output each soil patch in the set SP3 and generate a soil branch patch file.

2. The method for automatically identifying soil branch-shaped patches based on morphological characteristics according to claim 1, characterized in that: Step (1) includes: (1-1) Read the two-dimensional soil layer data, and store each soil patch in the two-dimensional soil layer into the patch set SP = {sp i |i=1,2,…,SPI}; where sp i represents the i-th soil patch, and SPI represents the number of soil patches; (1-2) Read DEM data and store it in the raster dataset GeoDEM.

3. The method for automatically identifying soil branch-shaped patches based on morphological characteristics according to claim 1, characterized in that: Step (2) includes: (2-1) Obtain the elevation value of each boundary point of each patch in the patch set SP from GeoDEM, generate patches containing elevation values, and store them in the patch set SP′; (2-2) Extract any soil patch sp from the patch set SP′ i , get sp i The maximum value Hmax and the minimum value Hmin of the elevation values ​​of each boundary point, if Hmax-Hmin>h, then the sp i Store in set SP1, where h is the preset height difference threshold; (2-3) Execute step (2-2) repeatedly until all soil patches in the patch set SP′ are screened based on height difference, and the set SP1 = {sp1 i |i=1, 2,...,SPI1}; sp1 i represents the i-th soil patch after height difference screening, and SP11 represents the number of soil patches after height difference screening.

4. The method for automatically identifying soil branch-shaped patches based on morphological characteristics according to claim 1, characterized in that: Step (3) includes: (3-1) Select any soil patch sp1 from the set SP1 i , and calculate the soil patch sp1 i Perimeter P; (3-2) Calculate soil pattern sp1 i The area A; (3-3) Based on the perimeter P and area A, calculate the soil patch sp1 according to the following formula i If C<m, where m is the preset circularity threshold, the spot is stored in the set SP2: C=4A / P 2 (3-4) Execute steps (3-1) to (3-3) repeatedly until all soil patches in SP1 are screened based on geometric circularity, and the set SP2 = {sp2 i |i=1,2,...,SPI2}; where SPI2 represents the number of soil patches after geometric circularity screening, sp2 i Represents the i-th soil patch after screening based on geometric circularity.

5. The method for automatically identifying soil branch-shaped patches based on morphological characteristics according to claim 1, characterized in that: Step (4) specifically includes: (4-1) Select any soil patch sp2 from the set SP2 i , and get sp2 i The boundary point set P = {p j |j=1, 2, ..., PJ}, where p j For sp2 i The jth boundary point, PJ represents the number of boundary points in the boundary point set P; (4-2) If the number of boundary points PJ>2k, k is the window width parameter for curve smoothing, then based on the boundary point set P, sp2 is calculated according to the following formula i The coordinates of the center of mass V (vx, vy); otherwise, execute step (6); Where, (p j，x , p j，y ) represents the jth point p in the boundary point set P j coordinates; (4-3) Calculate the horizontal distance between each boundary point in the boundary point set P and the centroid V, and store it in the set PD = {pd j |j=1,2,...,PJ};where, pd j Represents the jth point p in the boundary point set P j With sp2 i The horizontal distance from the center of mass V.

6. The method for automatically identifying soil branch-shaped patches based on morphological characteristics according to claim 1, characterized in that: Step (5) specifically includes: (5-1) Find the element pd with the maximum value in the set PD t , get its position l, and move the lth element to the PJth element in the set PD, that is, [pd l ,...,pd PJ ] are stored in the set PD1 in sequence, and then the first element to the l-1th element in the set PD, that is, [pd1, ..., pd l-1 ] are stored in the set PD1 in sequence, and the set PD1 = {pd1 p |p=1,2,...,PJ},where pd1 p represents the pth element in the set PD1, and PJ represents the number of elements in the set PD; (5-2) Take any element pd1 in the set PD1 p , according to the curve smoothing window width parameter k, based on the following formula to calculate the pd1 p The corresponding mean pd2 p , and pd2 p As the p-th element in the mean set PD2; (5-3) Loop through (5-2) until all elements in set PD1 are traversed and set PD2 = {pd2 p |p=1, 2, ..., PJ}.

7. The method for automatically identifying soil branch-shaped patches based on morphological characteristics according to claim 1, characterized in that: Step (6) specifically includes: (6-1) Start from the first element to the last element in PD1 and PD2 and judge in sequence. If pd1 p >pd2 p , then t p The value of is recorded as 1, otherwise it is recorded as 0, and stored in the set T = {t p |p=1,2,…,PJ}, PJ represents the number of elements in set PD1; (6-2) Traverse the elements in set T one by one. If there is a segment of elements [t p ,...,t s ] values ​​are all equal to 1, then it is considered that the soil pattern sp2 i The corresponding boundary point [p p ,...,p s ] is a branch, thus obtaining soil patch sp2 i The number of branches x; (6-3) Clear the sets PD1, PD2 and P; (6-4) If x>n, n is the preset branch number threshold, then sp2 i Stored in collection SP3.

8. A device for automatically identifying soil dendritic patches based on morphological characteristics, comprising a processor and a computer program stored in a memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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