Method and system for measuring aboveground biomass of sandy grassland plant population

Abstract

The invention discloses a sandy grassland plant population aboveground biomass measurement method and system, relates to the technical field of plant coverage measurement, and solves the related problem that sandy grassland aboveground biomass measurement is realized without combining the advantages of a remote sensing technology and an unmanned aerial vehicle technology. The method comprises the following steps: confirming a high-altitude image of a sandy grassland plant, carrying out spectrum confirmation on a point location on the confirmed high-altitude image, locking a spectrum curve associated with the corresponding point location, carrying out similarity analysis on the confirmed spectrum curve, confirming a similar point location, and locking a similar region based on a specific region covered by the corresponding similar point location. The method for determining the similar areas is fast and effective, can achieve a faster-progress area confirmation effect, and achieves a higher-efficiency and faster-speed processing effect.

Description

Technical Field

[0001] The invention relates to the technical field of plant coverage measurement, and in particular to a method and system for measuring aboveground biomass of plant populations in sandy grasslands. Background Art

[0002] In the study of grassland ecosystems, especially in sandy grassland environments, the aboveground biomass of plant populations is not only an important basis for measuring the health and productivity of ecosystems, but also a key parameter for evaluating the effectiveness of ecological restoration and formulating corresponding management strategies. Sandy grasslands, such as the area where dunes and meadows alternate in the Horqin Sandy Land, have weak ecosystem resistance to interference and are more sensitive to climate change and human activities. Therefore, accurate and efficient measurement of the aboveground biomass of sandy grassland plant populations is of great significance for maintaining ecosystem stability and promoting ecological restoration.

[0003] Traditional methods for measuring aboveground biomass of grassland plant populations often rely on manual field surveys, which are accurate but time-consuming and labor-intensive, and difficult to implement quickly over a large area. In addition, due to the particularity of the sandy grassland environment, such as loose soil and uneven vegetation distribution, traditional measurement methods face many difficulties in actual operation.

[0004] With the rapid development of remote sensing technology and drone technology, new means have been provided for measuring the aboveground biomass of grassland plant populations. By acquiring grassland images through high-altitude drones and analyzing them based on the spectral characteristics of different points in the images, rapid and non-destructive measurement of grassland plant population distribution and biomass can be achieved. However, most of the existing remote sensing measurement technologies are targeted at general grassland environments, and their applicability to the special environment of sandy grasslands still needs to be improved.

[0005] Therefore, the present invention proposes a method and system for measuring the aboveground biomass of plant populations in sandy grasslands, aiming to combine the advantages of remote sensing technology and unmanned aerial vehicle technology, and realize the rapid and accurate measurement of the aboveground biomass of grassland plant populations according to the particularity of the sandy grassland environment; this method obtains sandy grassland images through high-altitude unmanned aerial vehicles, and performs similarity analysis based on the spectral characteristics of different points in the image, thereby locking similar areas, and further confirming the area proportion of greening areas, providing a new solution for the measurement of aboveground biomass of sandy grassland plant populations. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method and system for measuring the aboveground biomass of plant populations in sandy grasslands, which solves the problem of not combining the advantages of remote sensing technology and unmanned aerial vehicle technology to achieve aboveground biomass measurement in sandy grasslands.

[0007] To achieve the above object, the present invention is implemented by the following technical scheme: A method for measuring the aboveground biomass of plant populations in sandy grasslands, comprising the following steps:

[0008] Step 1: Based on the determined sample area, use a high-altitude drone to obtain the regional image of the sample area, and based on the spectral characteristics of different points in the regional image, confirm the spectral curves associated with different points. The specific sub-steps are:

[0009] S11. Based on the confirmed regional image, confirm the brightness values ​​generated by different points in the regional image in different color bands, wherein the color bands are preset bands, and the color bands include: a red band of 600-700nm, a green band of 500-600nm, a blue band of 400-500nm, and an infrared band of 700-900nm, and calibrate the brightness values ​​generated by the corresponding points in different color bands as L i-k , where i represents different points and k represents different color bands;

[0010] S12, different brightness values ​​L confirmed at different points on different color bands i-k , confirm the spectrum curve belonging to this point, the horizontal coordinate axis of the spectrum curve is the band length, and the vertical coordinate axis is the brightness value. On the horizontal coordinate axis, based on the band length involved in the corresponding band, confirm the horizontal area confirmed by the corresponding band, and confirm the brightness value associated with the corresponding point in this horizontal area, so as to find the associated point in the coordinate system, and then connect several groups of associated points to confirm the spectrum curve of the point associated with this brightness value;

[0011] Step 2: Based on the spectral curves associated with different points in the regional image of the sample area, the spectral curves of several groups of points are analyzed for similarity, so as to lock similar points, and based on the coverage of several similar points of the same type, similar areas are locked. The specific sub-steps are as follows:

[0012] S21, randomly select a group of points, and then use the adjacent points of this point as the verification point, combine and analyze the spectral curves associated with this point and the verification point, and place them in the same two-dimensional coordinate system, and from the combined two-dimensional coordinate system, identify the two groups of points whose spectral curves are located at the farthest vertical distance from the same horizontal coordinate, and determine the brightness difference between the two groups of points: Cz = | brightness value of this point - brightness value of the verification point |, if Cz ≤ Y1, then the two groups of points are calibrated as similar points, if Cz > Y1, then no calibration is performed, where Y1 is a preset value;

[0013] The spectral curve associated with the calibrated similar points is taken as the pending curve, and based on the different brightness values ​​associated with the two sets of pending curves at the same horizontal coordinate, the middle value of the two sets of brightness values ​​is taken as the determined value, and based on the determined values ​​calibrated in sequence, the median curve of the two sets of pending curves is generated;

[0014] Then, the adjacent points around the similar point are used as check points, and the spectral curve of the check point is combined with the confirmed median curve for analysis to identify whether the check point belongs to a similar point. If so, the median curve is re-determined based on several groups of spectral curves associated with the similar point, and other adjacent points around are used as check points to confirm the similar points. If not, no calibration is performed.

[0015] Process each point in the regional image in turn, and confirm the existing similar points in turn;

[0016] S22, marking the area associated with the same type of similar points as a similar area, and confirming other similar areas one by one;

[0017] Step 3: For the confirmed groups of similar regions, determine the median curve of the corresponding similar region from the confirmed groups of spectral curves at different similar points in different similar regions, and then lock the same type of regions based on the similarity verification between the groups of median curves. The specific determination method is as follows:

[0018] S31, based on a number of similar points included in each similar region, confirming spectral curves associated with the number of similar points, identifying medians of a number of brightness values ​​associated with the same horizontal coordinate from the number of spectral curves, and generating a median curve of the similar region based on the confirmed medians;

[0019] S32. Based on different median curves confirmed by a number of different similar regions, randomly select median curves associated with two groups of similar regions to perform verification on similar regions:

[0020] The median curves associated with the two groups of similar areas are placed in the same two-dimensional coordinate system, and the median curves are divided into segments. The segment between the red band and the green band is marked as the red-green band, the segment between the green band and the blue band is marked as the green-blue band, and the segment between the blue band and the infrared band is marked as the blue-infrared band. It is confirmed whether the trend between the same bands marked in the two groups of median curves is the same.

[0021] If they are exactly the same: identify the area between the two median curves in the same two-dimensional coordinate system, connect the initial points of the two median curves to confirm the first connecting line, and then connect the end points of the two median curves to confirm the second connecting line. Based on the upper and lower associated median curves and the first connecting line and the second connecting line, lock the area associated between the two median curves, and lock the area M, and check M with the preset value Y2. If M≤Y2, the similar areas corresponding to the two median curves are marked as the same type of areas. If M>Y2, no calibration is performed.

[0022] If they are not exactly the same, the confirmation of the same type of area will not be carried out;

[0023] S33, using the same processing method as steps S31-S32 to specifically confirm and mark similar areas.

[0024] Step 4: Based on the confirmed similar areas where similar areas exist or do not exist, determine whether such similar areas belong to green areas by locking the RGB features associated with the corresponding similar areas, then confirm the area ratios of different green areas, and display the confirmed area ratios.

[0025] A system for measuring aboveground biomass of plant populations in sandy grasslands, comprising:

[0026] The image acquisition end uses a high-altitude drone to acquire regional images of the sample area based on the determined sample area, and confirms the spectral curves associated with different points based on the spectral characteristics of different points in the regional image;

[0027] The similar area confirmation end performs similarity analysis on the spectral curves of several groups of points based on the spectral curves associated with different points in the regional image of the sample area, thereby locking similar points, and locking similar areas based on the coverage of several similar points of the same type;

[0028] The same region calibration end determines the median curve of the corresponding similar region from the several groups of spectral curves confirmed at different similar points in different similar regions for the confirmed several groups of similar regions, and then locks the same region based on the similarity verification between the several groups of median curves;

[0029] The area ratio confirmation end determines whether such similar areas belong to green areas based on the confirmed similar areas with or without similar areas, by locking the RGB features associated with the corresponding similar areas, and then confirms the area ratios of different green areas and displays the confirmed area ratios.

[0030] The present invention provides a method and system for measuring aboveground biomass of plant populations in sandy grasslands. Compared with the prior art, it has the following beneficial effects:

[0031] The present invention confirms the aerial image of the sandy grassland plant, then performs spectral confirmation of the confirmed aerial image, locks the spectral curve associated with the corresponding point, and then performs similarity analysis on the confirmed spectral curve to confirm the similar points, and locks the similar area based on the specific area covered by the corresponding similar points. This method of determining the similar area is fast and effective, and can achieve a faster progress of area confirmation effect, and achieve a more efficient and faster processing effect.

[0032] For the different confirmed areas, based on the relevant performance characteristics of the corresponding areas, the same area is locked, and then based on the RGB characteristics of the corresponding areas, the green area is locked. Then, based on the area parameters of different green areas, the corresponding area ratios are determined to display the data, so that the relevant operators can timely understand the greening conditions of different greening varieties in the corresponding areas, and make timely response measures to further improve the management effect of sandy grassland plant populations in the corresponding green belts. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the process of the present invention;

[0034] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] First embodiment

[0037] See also Figure 1 The present application provides a method for measuring aboveground biomass of plant populations in sandy grasslands, comprising the following steps:

[0038] Step 1: Based on the determined sample area, a high-altitude drone is used to acquire the regional image of the sample area (the high-altitude drone is controlled by the operator and can return after acquiring the entire sample area. The sample area generally refers to a green area somewhere), and based on the spectral characteristics of different points in the regional image, the spectral curves associated with different points are confirmed. Specifically, in order to make relevant divisions for each different area in the corresponding image, it is necessary to divide the regional points according to the optical characteristics displayed by the corresponding points, so that different areas in the corresponding regional image are divided in turn, which is convenient for the subsequent specific confirmation of different populations of organisms;

[0039] The specific sub-steps of locking the spectral curve associated with the corresponding point are:

[0040] S11. Based on the confirmed regional image, confirm the brightness values ​​generated by different points in the regional image in different color bands, wherein the color bands are preset bands, which are prepared in advance by the operator, and the color bands include: red band (600-700nm), green band (500-600nm), blue band (400-500nm) and infrared band (700-900nm), which can also be understood as the RGB value associated with the corresponding point plus a group of infrared brightness values, and the RGB values ​​are three groups of values, corresponding to the red band, the green band and the blue band respectively, and the brightness values ​​generated by the corresponding points in different color bands are calibrated as L i-k , where i represents different points and k represents different color bands;

[0041] S12, different brightness values ​​L confirmed at different points on different color bands i-k , confirm the spectral curve belonging to this point, the horizontal coordinate axis of the spectral curve is the band length, and the vertical coordinate axis is the brightness value. On the horizontal coordinate axis, based on the band length involved in the corresponding band, confirm the horizontal area confirmed by the corresponding band, and confirm the brightness value associated with the corresponding point in this horizontal area, so as to find the associated point in the coordinate system, and then connect several groups of associated points to confirm the spectral curve of the point associated with this brightness value. Specifically, when confirming the horizontal coordinate axis of the corresponding point, the middle point of the corresponding band can be used as the horizontal coordinate of the brightness value of the corresponding band. For example, the brightness value generated by the corresponding point in the red band (600-700nm) is 100, then the coordinates confirmed in the two-dimensional coordinate system are (650, 100), and the brightness values ​​of other subsequent bands can also be confirmed in the same way, so as to interconnect several points and lock the spectral curve associated with this point;

[0042] Step 2: Based on the spectral curves associated with different points in the regional image of the sample area, the spectral curves of several groups of points are analyzed for similarity, so as to lock similar points, and based on the coverage of several similar points of the same type, similar areas are locked. Specifically, similar areas can be understood as areas covered by several similar points, that is, there are multiple segmented different areas in a green belt. By using the similarity analysis of similar points to confirm, they can be segmented and classified. Each different area classified belongs to a separate similar area.

[0043] The specific sub-steps for locking similar areas are:

[0044] S21, randomly select a group of points, and then use the adjacent points of this point as the verification point, combine and analyze the spectral curves associated with this point and the verification point, and place them in the same two-dimensional coordinate system, and identify the two groups of points whose vertical distances of the two groups of spectral curves are the farthest from the same horizontal coordinate (that is, the two points with the same horizontal coordinates and the farthest vertical coordinates) from the combined two-dimensional coordinate system, and determine the brightness difference between the two groups of points: Cz = | brightness value of this point - brightness value of the verification point |, if Cz ≤ Y1, the two groups of points are calibrated as similar points, if Cz > Y1, no calibration is performed, wherein Y1 is a preset value, and its specific value is determined by the operator based on experience, and generally does not exceed 30;

[0045] The spectral curve associated with the calibrated similar points is taken as the pending curve, and based on the different brightness values ​​associated with the two sets of pending curves at the same horizontal coordinate, the middle value of the two sets of brightness values ​​is taken as the determined value, and based on the determined values ​​calibrated in sequence, the median curve of the two sets of pending curves is generated;

[0046] Then, the adjacent points around the similar points are used as check points, and the spectral curve of the check points is combined with the confirmed median curve for analysis to identify whether the check point belongs to a similar point. If it does, the median curve is re-determined based on several groups of spectral curves associated with the similar points, and other adjacent points around are used as check points to confirm the similar points. If it does not belong, no calibration is performed. Specifically, there are several points in an area, starting from point A, and the surrounding of point A is point B. The spectral curves associated with point A and point B are re-determined. Combined analysis is performed to confirm the median curve associated with the two spectral curves, and then based on the determined median curve, the surrounding point C is checked to identify whether the distance value between the median curve and the spectral curve associated with point C meets the standard, so as to determine whether point C belongs to the similar points corresponding to point A and point B. If so, the median curve is reconfirmed, and other subsequently associated spectral curves are confirmed. By adopting this sequential confirmation method, different subsequently associated points can be confirmed in sequence to achieve a more standard similarity area confirmation effect;

[0047] Process each point in the regional image in turn, and confirm the existing similar points in turn;

[0048] S22, marking the area associated with the same type of similar points as a similar area, and confirming other similar areas one by one. Specifically, a similar area is an area commonly covered by a number of corresponding similar points, which belongs to the corresponding similar area;

[0049] Step 3: For the confirmed groups of similar regions, determine the median curve of the corresponding similar region from the confirmed groups of spectral curves at different similar points in different similar regions, and then lock the similar regions based on the similarity verification between the groups of median curves. The specific method for determining the similar regions is as follows:

[0050] S31, based on a number of similar points included in each similar region, confirming spectral curves associated with the number of similar points, identifying medians of a number of brightness values ​​associated with the same horizontal coordinate from the number of spectral curves, and generating a median curve of the similar region based on the confirmed medians;

[0051] S32. Based on different median curves confirmed by a number of different similar regions, randomly select median curves associated with two groups of similar regions to perform verification on similar regions:

[0052] Place the median curve associated with the two groups of similar areas in the same two-dimensional coordinate system, divide the median curve into segments, mark the segment between the red band and the green band as the red-green band, mark the segment between the green band and the blue band as the green-blue band, and mark the segment between the blue band and the infrared band as the blue-infrared band (for example: the proposed point associated with the red band is A (650, 100), and the proposed point associated with the green band is B (550, 80), then the red and green bands are the corresponding segments between point A and point B, so the confirmed infrared band is point A). The point associated with the blue band is point C, and the horizontal coordinate of point C is 450. Then, according to the specific segment confirmation process, the green and blue bands associated with it are the corresponding partial segment between point B and point C. Confirm whether the trend between the same bands marked in the two sets of median curves is the same (the trend includes a normal upward trend or a normal downward trend, and there will be no up and down situation, because the corresponding segment is the partial curve segment between the two points, so the corresponding curve has only one trend).

[0053] If they are exactly the same: identify the area between the two median curves in the same two-dimensional coordinate system, connect the initial points of the two median curves to confirm the first connecting line, and then connect the end points of the two median curves to confirm the second connecting line. Based on the upper and lower associated median curves and the first connecting line and the second connecting line, lock the area associated between the two median curves, and lock the area M, and check M with the preset value Y2, where the specific value of Y2 is determined by the operator based on experience. If M≤Y2, the similar areas corresponding to the two median curves are calibrated as similar areas. If M>Y2, no calibration is performed.

[0054] If they are not exactly the same, the confirmation of the same type of area will not be carried out;

[0055] S33. Use the same processing method as steps S31-S32 to specifically confirm and mark several similar areas as the same type of areas (if there are areas A, B and C, when confirming the same type of areas, areas A and B belong to the same type of areas, and areas B and C belong to the same type of areas, then A, B and C all belong to the same type of areas, and there is no need to confirm the same type of areas between areas A and C again).

[0056] Step 4: Based on the confirmed similar areas with or without similar areas, determine whether such similar areas belong to green areas by locking the RGB features associated with the corresponding similar areas, and then confirm the area ratios of different green areas, and display the confirmed area ratios. Specifically, different green areas have different area ratios. External relevant personnel can evaluate whether the area ratio of the corresponding green area meets the standard based on the specific display results, thereby completing the overall measurement effect of the corresponding plant population, so as to ensure the accuracy of the corresponding values;

[0057] The specific sub-steps for determining whether such similar areas belong to green areas are:

[0058] S41, for a similar region where no similar region exists, based on a median curve confirmed by the similar region, sequentially confirm brightness values ​​associated with a red band, a green band, and a blue band from the median curve, and determine an RGB feature of the similar region based on the confirmed brightness values;

[0059] For similar areas where there are similar areas of the same type, the same method as above is used to confirm the RGB features associated with each similar area, and then based on the RGB features associated with different similar areas, the brightness value features belonging to the same band are averaged, and the average value after processing is used as the RGB feature of the entire similar area (that is, there are two similar areas A and B belonging to the same type of area, and the corresponding area A has an R value associated with the RGB feature, that is, the brightness value associated with the red band, which corresponds to 100, and the corresponding area B has an R value associated with the RGB feature, that is, the brightness value associated with the red band, which corresponds to 80, then the R value confirmed by the similar area is 90);

[0060] S42, identifying whether the RGB features confirmed by the corresponding similar area or the same type of area belong to the preset green features, wherein the green features are preset features, which are prepared in advance by relevant operators based on experience, and are used to distinguish green areas from other areas, so as to facilitate the subsequent area ratio confirmation. If they do, the corresponding similar area is marked as a green area, otherwise, no marking is performed;

[0061] S43, based on the confirmed groups of green areas, locking the total area parameter Mz of the green areas;

[0062] For green areas where there are no similar areas, confirm the area parameter M1 of this green area to determine the area ratio of this type of green area, and its area ratio = M1 ÷ Mz;

[0063] For green areas with similar areas, the area parameters of several green areas belonging to the same area are summed to determine the total area parameter M2, and the area ratio of the same area is determined, and the area ratio = M2 ÷ Mz;

[0064] S44. Display the confirmed area percentages of different green areas for external personnel to view.

[0065] Second embodiment

[0066] Combination Figure 2 , a system for measuring aboveground biomass of plant populations in sandy grasslands, comprising:

[0067] The image acquisition end uses a high-altitude drone to acquire regional images of the sample area based on the determined sample area, and confirms the spectral curves associated with different points based on the spectral characteristics of different points in the regional image;

[0068] The similar area confirmation end performs similarity analysis on the spectral curves of several groups of points based on the spectral curves associated with different points in the regional image of the sample area, thereby locking similar points, and locking similar areas based on the coverage of several similar points of the same type;

[0069] The same region calibration end determines the median curve of the corresponding similar region from the several groups of spectral curves confirmed at different similar points in different similar regions for the confirmed several groups of similar regions, and then locks the same region based on the similarity verification between the several groups of median curves;

[0070] The area ratio confirmation end determines whether such similar areas belong to green areas based on the confirmed similar areas with or without similar areas, by locking the RGB features associated with the corresponding similar areas, and then confirms the area ratios of different green areas and displays the confirmed area ratios.

[0071] Some of the data in the above formulas are numerically calculated by removing their dimensions. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0072] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for measuring aboveground biomass of plant populations in sandy grasslands, characterized in that: The following steps are involved: Step 1: Based on the determined sample area, a high-altitude drone is used to acquire a regional image of the sample area, and based on the spectral characteristics of different points in the regional image, the spectral curves associated with different points are confirmed; Step 2: Based on the spectral curves associated with different points in the regional image of the sample area, the spectral curves of several groups of points are analyzed for similarity, so as to lock similar points, and based on the coverage of several similar points of the same type, similar areas are locked; Step 3: for the confirmed groups of similar regions, determine the median curve corresponding to the similar region from the confirmed groups of spectral curves at different similar points in different similar regions, and then lock the same type of regions based on the similarity verification between the groups of median curves; Step 4: Based on the confirmed similar areas where similar areas exist or do not exist, determine whether such similar areas belong to green areas by locking the RGB features associated with the corresponding similar areas, then confirm the area ratios of different green areas, and display the confirmed area ratios.

2. The method for measuring aboveground biomass of plant populations in sandy grasslands according to claim 1, characterized in that: In the step 1, the specific sub-steps of confirming the spectral curve associated with the corresponding point are: S11. Based on the confirmed regional image, confirm the brightness values ​​generated by different points in the regional image in different color bands, wherein the color bands are preset bands, and the color bands include: a red band of 600-700nm, a green band of 500-600nm, a blue band of 400-500nm, and an infrared band of 700-900nm, and calibrate the brightness values ​​generated by the corresponding points in different color bands as L i-k , where i represents different points and k represents different color bands; S12, different brightness values ​​L confirmed at different points on different color bands i-k , confirm the spectral curve belonging to this point, the horizontal coordinate axis of the spectral curve is the band length, and the vertical coordinate axis is the brightness value. On the horizontal coordinate axis, based on the band length involved in the corresponding band, confirm the horizontal area confirmed by the corresponding band, and confirm the brightness value associated with the corresponding point in this horizontal area, so as to find the associated point in the coordinate system, and then connect several groups of associated points to confirm the spectral curve of the point associated with this brightness value.

3. The method for measuring aboveground biomass of plant populations in sandy grasslands according to claim 2, characterized in that: In step 2, the specific sub-steps of locking similar areas are: S21, randomly select a group of points, and then use the adjacent points of this point as the verification point, combine and analyze the spectral curves associated with this point and the verification point, and place them in the same two-dimensional coordinate system, and from the combined two-dimensional coordinate system, identify the two groups of points whose spectral curves are located at the farthest vertical distance from the same horizontal coordinate, and determine the brightness difference between the two groups of points: Cz = | brightness value of this point - brightness value of the verification point |, if Cz ≤ Y1, then the two groups of points are calibrated as similar points, if Cz > Y1, then no calibration is performed, where Y1 is a preset value; The spectral curve associated with the calibrated similar points is taken as the pending curve, and based on the different brightness values ​​associated with the two sets of pending curves at the same horizontal coordinate, the middle value of the two sets of brightness values ​​is taken as the determined value, and based on the determined values ​​calibrated in sequence, the median curve of the two sets of pending curves is generated; Then, the adjacent points around the similar point are used as check points, and the spectral curve of the check point is combined with the confirmed median curve for analysis to identify whether the check point belongs to a similar point. If so, the median curve is re-determined based on several groups of spectral curves associated with the similar point, and other adjacent points around are used as check points to confirm the similar points. If not, no calibration is performed. Process each point in the regional image in turn, and confirm the existing similar points in turn; S22, marking the area associated with the same type of similar points as a similar area, and confirming other similar areas one by one.

4. The method for measuring aboveground biomass of plant populations in sandy grasslands according to claim 3, characterized in that: In step 3, the specific method for locking similar areas is as follows: S31, based on a number of similar points included in each similar region, confirming spectral curves associated with the number of similar points, identifying medians of a number of brightness values ​​associated with the same horizontal coordinate from the number of spectral curves, and generating a median curve of the similar region based on the confirmed medians; S32. Based on different median curves confirmed by a number of different similar regions, randomly select median curves associated with two groups of similar regions to perform verification on similar regions: The median curves associated with the two groups of similar areas are placed in the same two-dimensional coordinate system, and the median curves are divided into segments. The segment between the red band and the green band is marked as the red-green band, the segment between the green band and the blue band is marked as the green-blue band, and the segment between the blue band and the infrared band is marked as the blue-infrared band. It is confirmed whether the trend between the same bands marked in the two groups of median curves is the same. If they are exactly the same: identify the area between the two median curves in the same two-dimensional coordinate system, connect the initial points of the two median curves to confirm the first connecting line, and then connect the end points of the two median curves to confirm the second connecting line. Based on the upper and lower associated median curves and the first connecting line and the second connecting line, lock the area associated between the two median curves, and lock the area M, and check M with the preset value Y2. If M≤Y2, mark the similar areas corresponding to the two median curves as the same type of areas; If they are not exactly the same, the confirmation of the same type of area will not be carried out; S33, using the same processing method as steps S31-S32 to specifically confirm and mark similar areas.

5. The method for measuring aboveground biomass of plant populations in sandy grasslands according to claim 4, characterized in that: In the step S32, if M>Y2, no calibration is performed.

6. The method for measuring aboveground biomass of plant populations in sandy grasslands according to claim 4, characterized in that: In step 4, the specific method for confirming the area ratio of different green areas is: S41, for a similar region where no similar region exists, based on a median curve confirmed by the similar region, sequentially confirm brightness values ​​associated with a red band, a green band, and a blue band from the median curve, and determine an RGB feature of the similar region based on the confirmed brightness values; For similar areas where there are similar areas, the same method as above is used to confirm the RGB features associated with each similar area, and then based on the RGB features associated with different similar areas, the brightness value features belonging to the same band are averaged, and the average value after processing is used as the overall RGB feature of this similar area; S42, identifying whether the RGB feature confirmed by the corresponding similar area or the same type area belongs to the preset green feature, and the green feature is the preset feature. If it does, the corresponding similar area is marked as a green area, otherwise, no marking is performed; S43, based on the confirmed groups of green areas, locking the total area parameter Mz of the green areas; For green areas where there are no similar areas, confirm the area parameter M1 of this green area to determine the area ratio of this type of green area, and its area ratio = M1 ÷ Mz; For green areas with similar areas, the area parameters of several green areas belonging to the same area are summed to determine the total area parameter M2, and the area ratio of the same area is determined, and the area ratio = M2 ÷ Mz; S44. Display the confirmed area ratios of different green areas.

7. A system for measuring aboveground biomass of plant populations in sandy grasslands, the system being operated according to a method for measuring aboveground biomass of plant populations in sandy grasslands according to any one of claims 1 to 6, characterized in that: include: The image acquisition end uses a high-altitude drone to acquire regional images of the sample area based on the determined sample area, and confirms the spectral curves associated with different points based on the spectral characteristics of different points in the regional image; The similar area confirmation end performs similarity analysis on the spectral curves of several groups of points based on the spectral curves associated with different points in the regional image of the sample area, thereby locking similar points, and locking similar areas based on the coverage of several similar points of the same type; The same region calibration end determines the median curve of the corresponding similar region from the several groups of spectral curves confirmed at different similar points in different similar regions for the confirmed several groups of similar regions, and then locks the same region based on the similarity verification between the several groups of median curves; The area ratio confirmation end determines whether such similar areas belong to green areas based on the confirmed similar areas with or without similar areas, by locking the RGB features associated with the corresponding similar areas, and then confirms the area ratios of different green areas and displays the confirmed area ratios.

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