A calculation method for the vegetation carbon sink in the restoration of a tailings pond

Abstract

The present invention discloses a calculation method for the vegetation carbon sink in the restoration of a tailings pond, comprising the following steps: A. Conduct remote sensing photography of the tailings pond area at two different time points to obtain two remote sensing images of the tailings pond; B. Divide the remote sensing images according to the distribution of vegetation species in the tailings pond; C. Determine the vegetation density in the image block according to the vegetation distribution and shadow distribution in the image blocks at the same position taken at different time points; D. Query the single-plant carbon sink data of the corresponding vegetation according to the vegetation species, then calculate the carbon sink data of the image block according to the vegetation density, and finally sum up the carbon sink data of all the image blocks to obtain the vegetation carbon sink in the restoration of the tailings pond. The present invention can improve the deficiencies of the prior art and improve the calculation accuracy of the vegetation carbon sink in the restoration of the tailings pond.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon sink calculation, and in particular to a method for calculating the carbon sink of rehabilitated vegetation in a tailings pond. Background Art

[0002] Carbon sink refers to the amount of carbon dioxide absorbed by vegetation. In order to accurately evaluate the impact of various rehabilitated vegetation on the environment, it is necessary to accurately calculate its carbon sink. The existing calculation method is to conduct field sampling and then estimate the carbon sink of the rehabilitated vegetation in the entire area. Due to the complex terrain and low vegetation distribution uniformity of the tailings pond, there is a large deviation between the estimated result and the actual situation due to the limitation of the sampling quantity. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for calculating the carbon sink of rehabilitated vegetation in a tailings pond, which can solve the deficiencies of the prior art and improve the calculation accuracy of the carbon sink of rehabilitated vegetation in a tailings pond.

[0004] To solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0005] A method for calculating the carbon sink of rehabilitated vegetation in a tailings pond includes the following steps:

[0006] A. Conduct remote sensing photography of the tailings pond area at two different time points to obtain two remote sensing images of the tailings pond;

[0007] B. Divide the remote sensing images according to the vegetation type distribution in the tailings pond;

[0008] C. Determine the vegetation density in the image block according to the vegetation distribution and shadow distribution in the image blocks at the same position taken at different time points;

[0009] D. Query the single-plant carbon sink data of the corresponding vegetation according to the vegetation type, then calculate the carbon sink data of the image block according to the vegetation density, and finally sum up the carbon sink data of all the image blocks to obtain the carbon sink of the rehabilitated vegetation in the tailings pond.

[0010] Preferably, in step A, the shooting times of the two remote sensing images are respectively from 9:00 to 10:00 in the morning and from 2:00 to 3:00 in the afternoon, and the shooting weather is selected to be sunny and cloudless with a wind force less than or equal to 3 levels.

[0011] Preferably, in step B, the number of vegetation types in each image block is less than or equal to 3, and the type with the largest number of vegetation accounts for more than 70% of the total number of vegetation in the image block.

[0012] Preferably, in step C, calculating the vegetation density includes the following steps,

[0013] C1. Obtain the estimated range of vegetation density according to the vegetation distribution;

[0014] C2. Simulate the estimated vegetation density range obtained in step C1, compare the simulation results with the shadow distribution in the two captured image blocks, and obtain the accurate value of the vegetation density.

[0015] Preferably, preset the corresponding table data of gray level and vegetation density and the gray level threshold; in step C1, extract the vegetation areas in the two captured image blocks, then perform image fusion on the extracted areas, calculate the average gray level of the fused image, use the preset corresponding table data of gray level and vegetation density to query the vegetation density range corresponding to the average gray level of the image, then traverse the fused image, mark the gray level points whose gray levels exceed the gray level threshold, and adjust the vegetation density range according to the number of marked gray level points. The vegetation density is proportional to the number of marked gray level points.

[0016] Preferably, in step C2, divide the shadow and vegetation areas into blocks respectively, associate the shadow area blocks corresponding to the vegetation area blocks, then calculate the average shadow brightness of the two shadow area blocks associated with the same vegetation area block, then calculate the average shadow brightness generated by the standard vegetation density according to the shooting time, calculate the deviation range of the actual vegetation density and the standard vegetation density according to the deviation between the actual shadow average brightness and the standard shadow average brightness, and finally determine the accurate vegetation density range in the estimated vegetation density range according to the calculated deviation range, and use the median value of the accurate vegetation density range as the accurate value of the vegetation density.

[0017] The beneficial effects brought by adopting the above technical solution are as follows: The present invention calculates the carbon sink data through the analysis of remote sensing graphics. Utilize the shadow changes of vegetation in remote sensing images at different times to effectively calculate the vegetation density, and then calculate the vegetation carbon sink data of the entire tailings pond through the vegetation density and the single-plant carbon sink data. This calculation method does not rely on field sampling and calculates the vegetation carbon sink data of the tailings pond accurately. Description of the Drawings

[0018] Figure 1 is a flowchart of a specific embodiment of the present invention. Detailed Embodiment

[0019] Refer to Figure 1 , a calculation method for the vegetation carbon sink in the restoration of a tailings pond, comprising the following steps:

[0020] A. Conduct remote sensing shooting on the tailings pond area at two different time points to obtain two remote sensing images of the tailings pond;

[0021] B. Divide the remote sensing image according to the vegetation type distribution of the tailings pond;

[0022] C. Determine the vegetation density in the image block based on the vegetation distribution and shadow distribution in the image blocks of the same location taken at different time points;

[0023] D. Query the single-plant carbon sink data of the corresponding vegetation according to the vegetation type, then calculate the carbon sink data of the image block based on the vegetation density, and finally sum up the carbon sink data of all image blocks to obtain the carbon sink of the restored vegetation in the tailings pond.

[0024] In step A, the shooting times of the two remote sensing images are respectively from 9 am to 10 am and from 2 pm to 3 pm, and the shooting weather is selected as sunny and cloudless with a wind force less than or equal to level 3.

[0025] In step B, the types of vegetation in each image block are less than or equal to 3, and the type with the largest number of vegetation accounts for more than 70% of the total number of vegetation in the image block.

[0026] In step C, calculating the vegetation density includes the following steps,

[0027] C1. Obtain the estimated range of vegetation density based on the vegetation distribution;

[0028] C2. Simulate the estimated range of vegetation density obtained in step C1, compare the simulation result with the shadow distribution in the image blocks taken twice, and obtain the accurate value of the vegetation density.

[0029] The corresponding table data of the preset gray level and vegetation density, and the gray level threshold; in step C1, extract the vegetation areas in the image blocks taken twice, then perform image fusion on the extracted areas, calculate the average gray level of the fused image, use the corresponding table data of the preset gray level and vegetation density to query the vegetation density range corresponding to the average gray level of the image, then traverse the fused image, mark the gray level points whose gray levels exceed the gray level threshold, and adjust the vegetation density range according to the number of marked gray level points. The vegetation density is proportional to the number of marked gray level points.

[0030] In step C2, divide the shadow and vegetation areas into blocks respectively, associate the shadow area blocks corresponding to the vegetation area blocks, then calculate the average shadow brightness of the two shadow area blocks associated with the same vegetation area block, then calculate the average shadow brightness generated by the standard vegetation density according to the shooting time, calculate the deviation range of the actual vegetation density and the standard vegetation density according to the deviation between the actual shadow average brightness and the standard shadow average brightness, and finally determine the accurate range of the vegetation density in the estimated range of the vegetation density according to the calculated deviation range, and use the median value of the accurate range of the vegetation density as the accurate value of the vegetation density.

[0031] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention.

[0032] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A calculation method for the vegetation carbon sink in the restoration of a tailings pond, characterized in that It includes the following steps: A. Conduct remote sensing photography of the tailings pond area at two different time points to obtain two remote sensing images of the tailings pond; B. Divide the remote sensing images according to the vegetation species distribution in the tailings pond; C. Determine the vegetation density in the image block based on the vegetation distribution and shadow distribution in the image blocks at the same position taken at different time points; D. Query the single-plant carbon sink data of the corresponding vegetation according to the vegetation species, then calculate the carbon sink data of the image block according to the vegetation density, and finally sum up the carbon sink data of all image blocks to obtain the carbon sink of the restored vegetation in the tailings pond; In step C, calculating the vegetation density includes the following steps: C1. Obtain the estimated range of vegetation density based on the vegetation distribution; C2. Simulate the estimated range of vegetation density obtained in step C1, compare the simulation results with the shadow distribution in the image blocks taken twice, and obtain the accurate value of the vegetation density; Preset the corresponding table data of gray level and vegetation density and the gray level threshold; In step C1, extract the vegetation areas in the image blocks taken twice, then fuse the extracted areas, calculate the average gray level of the fused image, use the preset corresponding table data of gray level and vegetation density to query the vegetation density range corresponding to the average gray level of the image, then traverse the fused image, mark the gray level points whose gray levels exceed the gray level threshold, and adjust the vegetation density range according to the number of marked gray level points. The vegetation density is proportional to the number of marked gray level points; In step C2, divide the shadow and vegetation areas into blocks respectively, associate the shadow area blocks corresponding to the vegetation area blocks, then calculate the average shadow brightness of the two shadow area blocks associated with the same vegetation area block, then calculate the average shadow brightness generated by the standard vegetation density according to the shooting time, calculate the deviation range of the actual vegetation density and the standard vegetation density according to the deviation between the actual shadow average brightness and the standard shadow average brightness, and finally determine the accurate range of vegetation density in the vegetation density estimated range according to the calculated deviation range, and use the median value of the accurate range of vegetation density as the accurate value of the vegetation density.

2. The calculation method of the vegetation carbon sink for tailings pond restoration according to claim 1, characterized in that: In step A, the shooting times of the two remote sensing images are respectively from 9:00 to 10:00 in the morning and from 2:00 to 3:00 in the afternoon, and the shooting weather is selected as clear and cloudless with the wind force less than or equal to 3 levels.

3. The calculation method for the vegetation carbon sink restoration of the tailings pond according to claim 2, characterized in that: In step B, the number of vegetation species in each image block is less than or equal to 3, and the species with the largest number of vegetation accounts for more than 70% of the total number of vegetation in the image block.

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