A method, system, device and medium for calculating visible light images of Suaeda salsa biomass from unmanned aerial vehicles

Abstract

The present invention discloses a method, system, device and medium for calculating a UAV visible light image of Suaeda salsa biomass, and relates to the technical field of remote sensing image processing. The method comprises: obtaining a visible light image taken by a UAV in a target area; performing reflectance calculation on the color values ​​of red, green and blue bands in the visible light image based on a diffuse reflection standard plate to obtain a reflectance image of each band; performing chromaticity angle α calculation on the reflectance image, and extracting biomass information according to a chromaticity angle threshold and a remote sensing inversion model of Suaeda salsa biomass to obtain a remote sensing inversion result of Suaeda salsa biomass in the target area; using ENVI software to calculate the total area of ​​the target area, the coverage area of ​​Suaeda salsa and the corresponding Suaeda salsa biomass according to the remote sensing inversion result of Suaeda salsa biomass in the target area, and using ArcGIS software to draw a remote sensing thematic map of Suaeda salsa biomass. The present invention can realize the accurate extraction of Suaeda salsa biomass from the visible light image taken by a UAV.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and in particular to a method, system, equipment and medium for calculating a visible light image of a drone of Suaeda salsa biomass. Background Art

[0002] Suaeda salsa is a typical vegetation type in the ecological environment of coastal wetlands in northern China. It has many important functions, such as improving soil salinization, maintaining normal succession of wetland ecosystems, and preventing wind and consolidating embankments. Coastal wetlands are distributed over a wide area with complex landforms. Many areas are inaccessible to human resources, and traditional manual monitoring methods (visual estimation, probability method, and grid method, etc.) are difficult to meet monitoring requirements.

[0003] Remote sensing technology can achieve large-scale, dynamic and continuous monitoring of Suaeda salsa, save time and cost, and largely make up for the shortcomings of traditional measurement methods. Based on the obvious purple-red characteristics of Suaeda salsa in remote sensing images, some researchers use manual interpretation methods to delineate the distribution range of Suaeda salsa, but cannot achieve refined extraction of Suaeda salsa information. In addition, based on the characteristics of Suaeda salsa, although the normalized vegetation index, ratio vegetation index, enhanced vegetation index, etc. can be used for remote sensing extraction of Suaeda salsa information, the thresholds of various vegetation indices are usually affected by many factors, and there are cases where the threshold intervals of Suaeda salsa overlap with other vegetation and cannot be effectively distinguished.

[0004] In addition, the currently widely used satellite image calculations are still limited by their low spatial resolution. There is still a certain degree of uncertainty in the calculation of areas with low coverage of Suaeda salsa, edge areas, and mixed pixel areas containing other vegetation, making it impossible to accurately extract the coverage area of ​​Suaeda salsa and effectively calculate the biomass of Suaeda salsa. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, device and medium for calculating the biomass of Suaeda salsa by using a drone visible light image, which can realize the accurate extraction of the biomass of Suaeda salsa from the visible light image taken by drone.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for calculating the biomass of Suaeda salsa by using a drone visible light image, comprising:

[0008] Acquire visible light images taken by drones in the target area;

[0009] Based on the diffuse reflection standard plate, the reflectance of the color values ​​of the red, green and blue bands in the visible light image is calculated to obtain a reflectance image of each band;

[0010] Calculating the chromaticity angle α of the reflectance image, and extracting biomass information according to the chromaticity angle threshold and the Suaeda salsa biomass remote sensing inversion model to obtain the Suaeda salsa biomass remote sensing inversion result of the target area;

[0011] The total area of ​​the target area, the coverage area of ​​the Suaeda salsa and the corresponding biomass of the Suaeda salsa were calculated using ENVI software based on the remote sensing inversion results of the Suaeda salsa biomass in the target area, and the remote sensing thematic map of the Suaeda salsa biomass was drawn using ArcGIS software.

[0012] Optionally, it is characterized in that the process of drone aerial photography is: setting the equipment parameters of the drone's onboard camera, using a "single scene" or "planned route" mode to perform aerial photography of the target area, and diffuse reflection standard plates with different reflectivity are arranged in the target area.

[0013] Optionally, it is characterized in that the formula for calculating the reflectivity includes:

[0014] ,

[0015] In the formula, R R , R G , R B They represent the reflectivity of the red, green, and blue bands in the drone aerial images, respectively; V R , V G , V B They represent the color values ​​of the red, green and blue bands in the aerial images respectively; a, b, c, d, e and f represent the conversion coefficients of the red, green and blue bands in the aerial images, which are obtained by fitting the color values ​​and reflectivity scatter points of the diffuse reflection standard plate in each band in the aerial images through a quadratic function.

[0016] Optionally, it is characterized in that the calculation formula of the Suaeda salsa biomass remote sensing inversion model is:

[0017] Biomass = f ( α )

[0018] In the formula, Biomass represents the biomass of Suaeda salsa, f ( α ) represents a fitting function with the chromaticity angle α as the independent variable, and the fitting function adopts any one of a linear function, a quadratic function, an e exponential function and a power function.

[0019] Optionally, it is characterized in that the process of extracting biomass information is:

[0020] According to the formula if α > α threshold then Biomass = f ( α ) else 0 remote sensing extraction of the Suaeda salsa biomass of each pixel in the target area is performed to determine the pixels with Suaeda salsa information and the Suaeda salsa biomass; wherein if, then, else are conditional functions, indicating that the Suaeda salsa biomass remote sensing inversion model is used to calculate the Suaeda salsa biomass for the image pixels that meet the conditional threshold, and the image pixels that do not meet the conditional threshold are assigned 0; α threshold is the chromaticity angle threshold.

[0021] The present invention also provides a UAV visible light image calculation system for Suaeda salsa biomass, comprising:

[0022] An image acquisition unit, used to obtain a visible light image taken by a drone in a target area;

[0023] A reflectivity calculation unit, used to calculate the reflectivity of the color values ​​of the red, green and blue bands in the visible light image based on the diffuse reflection standard plate to obtain a reflectivity image of each band;

[0024] A biomass information extraction unit is used to calculate the chromaticity angle α of the reflectance image, and extract biomass information according to the chromaticity angle threshold and the Suaeda salsa biomass remote sensing inversion model to obtain the Suaeda salsa biomass remote sensing inversion result of the target area;

[0025] The thematic map drawing unit is used to calculate the total area, the coverage area and the corresponding biomass of Suaeda salsa of the target area according to the remote sensing inversion results of the biomass of Suaeda salsa in the target area using ENVI software, and draw the remote sensing thematic map of the biomass of Suaeda salsa using ArcGIS software.

[0026] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned drone visible light image calculation method for Suaeda salsa biomass.

[0027] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for calculating the UAV visible light image of Suaeda salsa biomass as described above.

[0028] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] The present invention discloses a method, system, device and medium for calculating a UAV visible light image of Suaeda salsa biomass, the method comprising obtaining a visible light image taken by a UAV in a target area; performing reflectance calculation on the color values ​​of red, green and blue bands in the visible light image based on a diffuse reflection standard plate to obtain a reflectance image of each band; performing chromaticity angle α calculation on the reflectance image, and extracting biomass information according to a chromaticity angle threshold and a remote sensing inversion model of Suaeda salsa biomass to obtain a remote sensing inversion result of Suaeda salsa biomass in the target area; using ENVI software to calculate the total area of ​​the target area, the coverage area of ​​Suaeda salsa and the corresponding Suaeda salsa biomass according to the remote sensing inversion result of Suaeda salsa biomass in the target area, and drawing a remote sensing thematic map of Suaeda salsa biomass. The present invention can expand the application of UAV remote sensing technology in ecological environment monitoring, and realize accurate extraction of Suaeda salsa biomass from visible light images taken by UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0031] Figure 1 It is a schematic flow chart of the method for calculating the biomass of Suaeda salsa L. of the present invention;

[0032] Figure 2 It is a technical flow chart of this embodiment;

[0033] Figure 3 The visible light image of the drone of the target area in this embodiment 1;

[0034] Figure 4 The remote sensing thematic map of Suaeda salsa biomass in Example 1;

[0035] Figure 5 The visible light image of the target area by the UAV in this embodiment 2;

[0036] Figure 6 This is the remote sensing thematic map of Suaeda salsa biomass in Example 2. DETAILED DESCRIPTION

[0037] 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.

[0038] The purpose of the present invention is to provide a method, system, device and medium for calculating the biomass of Suaeda salsa by using a drone visible light image, which can realize the accurate extraction of the biomass of Suaeda salsa from the visible light image taken by drone.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] like Figure 1-Figure 2 As shown, the present invention provides a method for calculating the biomass of Suaeda salsa by using a drone visible light image, comprising:

[0041] Step 100: Obtain a visible light image of the target area taken by a drone.

[0042] Step 200: Based on the diffuse reflection standard plate, the reflectance of the color values ​​of the red, green and blue bands in the visible light image is calculated to obtain a reflectance image of each band.

[0043] Step 300: Calculate the chromaticity angle α of the reflectance image, and extract biomass information according to the chromaticity angle threshold and the Suaeda salsa biomass remote sensing inversion model to obtain the Suaeda salsa biomass remote sensing inversion result of the target area.

[0044] Step 400: using ENVI software to calculate the total area of ​​the target area, the coverage area of ​​Suaeda salsa and the corresponding Suaeda salsa biomass according to the remote sensing inversion results of the Suaeda salsa biomass in the target area, and using ArcGIS software to draw a remote sensing thematic map of the Suaeda salsa biomass.

[0045] Based on the above technical process, a more detailed processing flow for each step is provided:

[0046] As a specific implementation of step 100, a drone with a positioning function is selected in the target area for aerial photography, and the specific steps are as follows:

[0047] a) Before taking aerial photos, fix the exposure factors such as the aperture, shutter, ISO (sensitivity) of the drone’s onboard camera (do not set it to automatic exposure).

[0048] b) When taking aerial photos, use the "single scene" or "planned route" mode to take aerial photos of the target area, and diffuse reflection standard plates with different reflectivity must be arranged in the target area (no less than 3 plates).

[0049] c) After aerial photography, a visible light image of the target area is obtained.

[0050] As a specific implementation of step 200, the aerial visible light image data is processed:

[0051] Reflectivity calculation: After obtaining the aerial visible light image, the color values ​​of the red, green, and blue bands in the image are converted into reflectivity. The formula is as follows:

[0052] ,

[0053] In the formula, R R , R G , R B They represent the reflectance of the red, green, and blue bands in the drone aerial images, respectively; V R , V G , V B Respectively represent the color values ​​of the red, green, and blue bands in the aerial image; a, b, c, d, e, and f represent the conversion coefficients of the red, green, and blue bands in the aerial image, which are obtained by fitting the color values ​​of the diffuse reflection standard plate in each band and its reflectivity scatter points in the aerial image through a quadratic function (through the "0" point). It should be noted that the conversion coefficient changes with the lighting conditions, so each image needs to be fitted and analyzed to obtain the conversion coefficient.

[0054] As a specific implementation of step 300, the process of extracting the biomass information of Suaeda salsa includes chromaticity angle calculation, chromaticity angle threshold determination, inversion model construction and Suaeda salsa biomass information extraction, each of which consists of:

[0055] (1) Chromaticity angle calculation

[0056] After obtaining the reflectance of the blue, green, and red bands, calculate the X, Y, and Z primary color stimulus values. The formula is as follows:

[0057] ,

[0058] In the formula, X , Y , Z Represents the three primary color stimulus values; R R , R G , R B Respectively represent the reflectivity of the red, green and blue bands in the drone aerial images.

[0059] After obtaining the three primary color stimulus values, calculate the x, y chromaticity coordinates, the formula is as follows:

[0060] ,

[0061] In the formula,x , y Indicates the chromaticity coordinate value.

[0062] get x , y After the chromaticity coordinates are obtained, the chromaticity angle is calculated α , the formula is as follows:

[0063] ,

[0064] In the formula, α represents the chromaticity angle; arctan2 represents the bivariate inverse tangent function.

[0065] (2) Determination of chromaticity angle threshold

[0066] There are obvious differences in the colors of Suaeda salsa, mudflats, green vegetation and water bodies, that is, the chromaticity angles show large differences. The chromaticity angle of Suaeda salsa is the largest, followed by mudflats, and the green vegetation and water bodies are smaller. Therefore, a statistical analysis of the chromaticity angle images taken by drones is performed to determine the chromaticity angle threshold for Suaeda salsa identification. The specific steps are as follows:

[0067] a) Import the chromaticity angle image obtained in the above process into ENVI software.

[0068] b) Crop out some pure mudflat areas in the drone aerial image and save them.

[0069] c) Count the distribution of pixels with different chromaticity angles in the cropped mudflat area, and determine a chromaticity angle after the cumulative proportion of chromaticity angle pixels exceeds 99.9% as the minimum chromaticity angle threshold for identifying Suaeda salsa α threshold , that is, when it is greater than the threshold, it is judged as a Suaeda pterocarpa pixel.

[0070] (3) Inversion model construction

[0071] Use square plots (not less than 0.25m 2 ) to conduct a simultaneous measurement experiment of Suaeda salsa biomass and drone aerial photography. The specific steps are as follows:

[0072] a) Before measurement, the most densely populated area of ​​Suaeda salsa was selected as the sample plot with the maximum initial biomass. The sample plot was fixed and diffuse reflectance standard plates with different reflectivity were placed in the open area.

[0073] b) Select the "single scene" shooting mode to carry out drone aerial photography, obtain the visible light image when the biomass of Suaeda salsa in the sample plot is the largest, and refer to the above-mentioned image data processing method to calculate the corresponding chromaticity angle α1 (the mean value of all pixels in the sample plot).

[0074] c) Evenly pluck out some of the Suaeda salsa in the sample plot to reduce its biomass, and take aerial photos with a drone to obtain a visible light image when the biomass of the Suaeda salsa in the sample plot is the second largest. Similarly, refer to the above-mentioned image data processing method to obtain the corresponding chromaticity angle α2, and put the plucked Suaeda salsa into sealed bags for storage, numbering them.

[0075] d) Remove the Suaeda salsa in the sample plot evenly (until all the plants are removed), take drone photos simultaneously, and calculate the chromaticity angle α corresponding to different biomasses x , and put the plucked winged saltwort into sealed bags and number them for storage.

[0076] e) After the aerial photography is completed, the samples of Suaeda salsa were brought back to the laboratory as soon as possible. The mud at the roots of Suaeda salsa in each sealed bag was cleaned with clean water. The Suaeda salsa in each sealed bag was wrapped with tin foil and placed in an oven to dry. The dry weight was weighed and recorded by number.

[0077] Finally, the biomass of Suaeda salsa in each sample plot at each time of aerial photography was calculated based on the sampling sequence (kg / m 2 ); ensure that the number of modeling samples of Suaeda salsa biomass and chromaticity angles with different gradients is no less than 5 groups.

[0078] After obtaining the modeling sample data of Suaeda salsa biomass and corresponding chromaticity angles at different gradients, the biomass of Suaeda salsa ( Biomass ) and chromaticity angle α Scatter plot; select linear function, quadratic function, e exponential function and power function for fitting analysis, and determine the best one as the remote sensing inversion model of Suaeda salsa biomass in the target area ( Biomass = f ( α )).

[0079] (4) Extraction of biomass information of Suaeda salsa

[0080] Combined with the obtained "chromaticity angle threshold α threshold ” and “Remote sensing inversion model of Suaeda salsa biomass” can realize the remote sensing extraction of Suaeda salsa biomass information. The extraction results of Suaeda salsa biomass information are expressed as “ Biomass " indicates that the extraction algorithm is as follows:

[0081] if α > α threshold then Biomass = f ( α ) else 0

[0082] In the formula, if, then, else represent conditional functions, that is, the chromaticity angle that meets the conditions α The chromaticity angle of pixels that do not meet the conditions is calculated. αThe pixel value is 0 (representing the biomass information of Suaeda wingless). It should be noted that some bright objects (white, etc.) also have a high chromaticity angle, but their actual color is obviously different from that of Suaeda winged and should be removed during the extraction process.

[0083] As a specific implementation of step 400, the biomass calculation and thematic map drawing of Suaeda salsa are as follows:

[0084] (1) Calculation of Suaeda salsa biomass

[0085] ENVI software was used to calculate the Biomass The total number of pixels and the number of Suaeda salsa pixels in the remote sensing inversion results can be multiplied by the area of ​​a single pixel to obtain the total area of ​​the target area (m 2 )、Sophora salsa coverage area(m 2 ), and calculated the mean biomass of Suaeda salsa in the target area (kg / m 2 ).

[0086] (2) Drawing of thematic maps

[0087] Will" Biomass The remote sensing inversion results were imported into ArcGIS software to draw the remote sensing thematic map of Suaeda salsa biomass, which can more intuitively show the spatial distribution of Suaeda salsa biomass. When drawing the thematic map, the pixel value of 0 is represented by white, representing no Suaeda salsa; when the pixel value is not 0, it is represented by different colors, representing the high and low biomass of Suaeda salsa. Example 1

[0088] On October 14, 2024, the target area was the distribution area of ​​Suaeda salsa in Xidianzi, Wafangdian City, Dalian, and the biomass of Suaeda salsa was calculated based on the visible light images taken by drones. First, diffuse reflectance standard plates (reflectivity of 1.2%, 5%, 10%, and 25%) were set up in the open space of the target area; then, multiple drones were controlled to fly over the target area at an altitude of 100m and a spatial resolution of 0.0278m, and visible light images of the target area were taken vertically ( Figure 3 ); Fit the color value of each diffuse reflection standard plate to the scattered points of its reflectivity with a quadratic function (through the "0" point), and obtain the formula for converting the color value of each band of red, green, and blue into reflectivity, as follows:

[0089] ,

[0090] Using the above formula, the color values ​​of the red, green, and blue bands in the true color image are converted into reflectance, and the reflectance images of the red, green, and blue bands of the target area are obtained. Based on the above red, green, and blue band reflectance images, the chromaticity angle image of the target area is calculated. After statistically analyzing the distribution of the number of pixels at different chromaticity angles in the mudflat area, the minimum chromaticity angle threshold for identifying Suaeda salsa is determined to be "246.70", which can remove more than 99.9% of non-Suaeda salsa pixels.

[0091] Through the biomass plot measurement test of Suaeda salsa biomass, 6 groups of modeling sample data of different gradients of Suaeda salsa biomass and corresponding chromaticity angle were obtained. Scatter plots were drawn and linear functions, quadratic functions, e-exponential functions and power functions were used for fitting analysis. The fitting results are shown in Table 1.

[0092] Table 1 Comparison of fitting results of different function forms

[0093] ,

[0094] By comparison, it was found that the e-exponential function performed better than other functions, and the remote sensing inversion model of Suaeda salsa biomass was obtained as follows:

[0095] ,

[0096] The above “chromatic angle threshold 246.70” and “remote sensing inversion model of Suaeda salsa biomass” were used for the chromatic angle image of the target area, and the remote sensing inversion results of Suaeda salsa biomass in the target area were calculated.

[0097] The total area, coverage area and biomass of Suaeda salsa were calculated by ENVI software and were 16142.80 m 2 、1242.81 m 2 , 0.0171 kg / m 2 , and used ArcGIS software to draw the remote sensing thematic map of Suaeda salsa biomass ( Figure 4 ). It can be found that the use of this technology to process UAV aerial visible light images can accurately and intuitively display the spatial distribution of Suaeda pterocarpa biomass. Example 2

[0098] On October 15, 2024, the target area was the distribution area of ​​Suaeda salsa near the Changxing Island Bridge in Wafangdian City, Dalian, and the biomass of Suaeda salsa was calculated based on the visible light images taken by drones. First, diffuse reflection standard plates (reflectivity of 1.2%, 5%, 10%, and 25%) were placed in the open space of the target area; then, multiple drones were controlled to fly over the target area at an altitude of 60m and a spatial resolution of 0.0167m, and visible light images of the target area were taken vertically ( Figure 5); Fit the color value of each diffuse reflection standard plate to the scattered points of its reflectivity with a quadratic function (through the "0" point), and obtain the formula for converting the color value of each band of red, green, and blue into reflectivity, as follows:

[0099] ,

[0100] Using the above formula, the color values ​​of the red, green, and blue bands in the true color image are converted into reflectance to obtain the reflectance images of the red, green, and blue bands of the target area. Based on the above red, green, and blue band reflectance images, the chromaticity angle image of the target area is calculated. After statistically analyzing the distribution of the number of pixels at different chromaticity angles in the mudflat area, the minimum chromaticity angle threshold for identifying Suaeda salsa is determined to be "245.22", which can remove more than 99.9% of non-Suaeda salsa pixels.

[0101] In this example, the target area is in the distribution area of ​​Suaeda salsa in Wafangdian City, and the remote sensing inversion model of Suaeda salsa biomass has regional applicability, so the inversion model is the same as Example 1. The above "chromatic angle threshold 245.22" and "Suaeda salsa biomass remote sensing inversion model" are used for the chromatic angle image of the target area, and the remote sensing inversion result of Suaeda salsa biomass in the target area is calculated.

[0102] The total area, coverage area, and biomass of Suaeda salsa were calculated using ENVI software and were 5825.36 m 2 、130.25 m 2 , 0.0043 kg / m 2 , and used ArcGIS software to draw the remote sensing thematic map of Suaeda salsa biomass ( Figure 6 ). It was also found that the use of this technology to process UAV visible light images can accurately and intuitively display the spatial distribution of Suaeda salsa biomass.

[0103] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0104] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for calculating the biomass of Suaeda salsa by using an unmanned aerial vehicle visible light image, characterized in that: include: Acquire visible light images taken by drones in the target area; Based on the diffuse reflection standard plate, the reflectance of the color values ​​of the red, green and blue bands in the visible light image is calculated to obtain a reflectance image of each band; Calculating the chromaticity angle α of the reflectance image, and extracting biomass information according to the chromaticity angle threshold and the Suaeda salsa biomass remote sensing inversion model to obtain the Suaeda salsa biomass remote sensing inversion result of the target area; The total area of ​​the target area, the coverage area of ​​the Suaeda salsa and the corresponding biomass of the Suaeda salsa were calculated using ENVI software according to the remote sensing inversion results of the Suaeda salsa biomass in the target area, and a remote sensing thematic map of the Suaeda salsa biomass was drawn using ArcGIS software; The formula for calculating the reflectivity includes: ， In the formula, R R , R G , R B They represent the reflectance of the red, green, and blue bands in the drone aerial images, respectively; V R , V G , V B Respectively represent the color values ​​of the red, green and blue bands in the aerial image; a, b, c, d, e and f represent the conversion coefficients of the red, green and blue bands in the aerial image, which are obtained by fitting the color values ​​and reflectivity scatter points of the diffuse reflection standard plate in each band in the aerial image through a quadratic function; The calculation formula of the remote sensing inversion model of Suaeda salsa biomass is: Biomass = f ( α ) In the formula, Biomass represents the biomass of Suaeda salsa, f ( α ) represents a fitting function with the chromaticity angle α as an independent variable, wherein the fitting function adopts any one of a linear function, a quadratic function, an e exponential function and a power function; The process of extracting biomass information is as follows: According to the formula if α > α threshold then Biomass = f ( α ) else 0 remote sensing extraction of the Suaeda salsa biomass of each pixel in the target area is performed to determine the pixels with Suaeda salsa information and the Suaeda salsa biomass; wherein if, then, else are conditional functions, indicating that the Suaeda salsa biomass remote sensing inversion model is used to calculate the Suaeda salsa biomass for the image pixels that meet the conditional threshold, and the image pixels that do not meet the conditional threshold are assigned 0; α threshold is the chromaticity angle threshold.

2. The method for calculating the biomass of Suaeda salsa by unmanned aerial vehicle visible light image according to claim 1, characterized in that: The process of the drone aerial photography is as follows: setting the equipment parameters of the drone's onboard camera, adopting the "single scene" or "planned route" mode to take aerial photography of the target area, and distributing diffuse reflection standard plates with different reflectivity in the target area.

3. A UAV visible light image computing system for Suaeda salsa biomass, applied to the method as described in any one of claims 1-2, characterized in that: include: An image acquisition unit, used to obtain a visible light image taken by a drone in a target area; A reflectivity calculation unit, used to calculate the reflectivity of the color values ​​of the red, green and blue bands in the visible light image based on the diffuse reflection standard plate to obtain a reflectivity image of each band; A biomass information extraction unit is used to calculate the chromaticity angle α of the reflectance image, and extract biomass information according to the chromaticity angle threshold and the Suaeda salsa biomass remote sensing inversion model to obtain the Suaeda salsa biomass remote sensing inversion result of the target area; The thematic map drawing unit is used to calculate the total area, the coverage area and the corresponding biomass of Suaeda salsa of the target area according to the remote sensing inversion results of the biomass of Suaeda salsa in the target area using ENVI software, and draw the remote sensing thematic map of the biomass of Suaeda salsa using ArcGIS software.

4. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the drone visible light image calculation method for Suaeda salsa biomass according to any one of claims 1-2.

5. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for calculating the visible light image of Suaeda salsa biomass by an unmanned aerial vehicle according to any one of claims 1 to 2.

Images