Mine ecological restoration effect evaluation method and system

Abstract

The invention discloses a mine ecological restoration effect evaluation method comprising the following steps: determining a mine ecological restoration area, obtaining a remote sensing image, and preprocessing the remote sensing image; calculating texture features of the remote sensing image based on wavelet decomposition; calculating the average value of the vegetation coverage change indexes; calculating the average gain of the biomass in the ecological restoration process according to the change of the vegetation biomass in the sampling area along with time; calculating the average purification benefit of the soil metal concentration in the ecological restoration process; and evaluating the ecological restoration effect of the mine according to the average purification benefit of the soil metal concentration, the average gain of the biomass and the average value of the vegetation coverage change indexes. The comprehensive influence of the mining area soil conditions on the vegetation biomass is accurately quantified, a scientific basis is provided for evaluation of the mine ecological restoration effect, and key evaluation parameters for evaluation of the mine ecological restoration effect are determined from ecological factors in an ecological system after mine ecological restoration.

Description

Technical Field

[0001] The present invention relates to the field of ecological restoration, and in particular to a method and system for evaluating the effectiveness of mine ecological restoration. Background Art

[0002] Rapidly and accurately acquiring and identifying vegetation coverage information after mine ecological restoration facilitates scientific assessment of mine ecological restoration effectiveness. A search revealed that Chinese patent number CN116258406A discloses a technical method and system for evaluating the effectiveness of ecological restoration in plateau open-pit mining areas. This method collects, organizes, and analyzes data on the mining area's physical geography and regional background, specialized geological surveys, remote sensing and drone imagery, and data on the survey, design, construction, and supervision of ecological restoration projects, as well as final acceptance inspections. This method then integrates site stability and surveys, sampling, and monitoring of ecological elements such as soil, surface water, groundwater, permafrost, landscape, grassland, and wetlands to assess the current state of the mining area's ecological environment and the effectiveness of its ecological restoration. The assessment cycle, milestones, and scope are determined based on the timing and region of the ecological protection and restoration project implementation. Nodes are primarily used to determine the timing of phased assessments. Based on the characteristics of each ecological element in the ecosystem, the survey and monitoring content and indicators are determined, and an assessment indicator system and method are established. This evaluation method is also applicable to post-evaluation within a certain period of operation and maintenance after acceptance. However, this method cannot conduct real-time evaluation of vegetation coverage in mining areas. It lacks dynamic monitoring of changes in mining ecosystems and timely feedback on changes in ecosystem vegetation coverage. It cannot provide an accurate basis for vegetation coverage evaluation in mining areas. In addition, remote sensing images often have brightness distortion problems caused by the nonlinearity of the sensor's own response curve. This distortion not only affects the image quality, but may also affect subsequent image processing and analysis. Summary of the Invention

[0003] In response to the above-mentioned deficiencies in the prior art, the present invention provides a method and system for evaluating the effectiveness of mine ecological restoration, which comprehensively evaluates the effectiveness of mine ecological restoration by rationally processing remote sensing images and combining them with field survey and collection data.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for evaluating the effectiveness of mine ecological restoration is provided, which includes the following steps: S1: Determine the mine ecological restoration area, obtain remote sensing images of the mine ecological restoration area changing over time during the ecological restoration process, and preprocess the remote sensing images; S2: performing wavelet decomposition on the pre-processed remote sensing image, calculating the wavelet coefficients, and calculating the texture features of the remote sensing image based on the wavelet decomposition; S3: Calculate the average value of vegetation coverage change index based on the texture characteristics of remote sensing images that change over time during the ecological restoration process; S4: During the ecological restoration process, M Biological sampling points are set up, and sampling areas with set ranges are planned at the sampling points to calculate the vegetation biomass within the sampling areas; the average biomass gain during the ecological restoration process is calculated based on the changes in vegetation biomass in the sampling areas over time; S5: Calculate the pollution index of the target metals on the soil based on the concentration of target metals in the mining area within the sampling area, and calculate the average purification benefit of the soil metal concentration during the ecological restoration process; S6: Evaluate the effect of mine ecological restoration based on the average purification benefit of soil metal concentration, the average gain of biomass and the average value of vegetation cover change index.

[0005] Furthermore, step S1 includes: S11: Determine the mine ecological restoration area and obtain remote sensing images of the mine ecological restoration area changing over time during the ecological restoration process to obtain a remote sensing image set; S12: using a bilinear interpolation method to process the remote sensing image, processing the remote sensing image into a fixed-pixel remote sensing image, thereby forming a new remote sensing image set; ; in, are the pixel values ​​of the four nearest points in the remote sensing image, is the pixel coordinate in the remote sensing image, m is the length direction of the remote sensing image, n is the width direction of the remote sensing image, is the interpolation weight in the width direction, is the interpolation weight in the length direction, is the pixel coordinate in the new remote sensing image The corresponding pixel value; S13: Grayscale the new remote sensing image to form a grayscale image, and then calculate the grayscale threshold value. Binarize the pixels in the grayscale image; ; in, is the pixel coordinate in the grayscale image The corresponding grayscale value.

[0006] Furthermore, step S2 includes: Based on the grayscale image after binarization, wavelet analysis is used to extract the texture features of green plant cover in the grayscale image. The specific steps include: S21: perform binary processing on the grayscale image N Level wavelet decomposition, to obtain the wavelet coefficients after wavelet decomposition; ; in, is the pixel coordinate in the grayscale image after binarization The corresponding pixel The approximate coefficient of level, is the series of wavelet coefficients, For the Level wavelet coefficients, is the pixel coordinate after wavelet decomposition, I is the number of pixels in the length direction of the grayscale image, J is the number of pixels in the width direction of the grayscale image, is the low-pass filter function, is the high-pass filter function; S22: Calculate the texture features of the binarized grayscale image based on wavelet decomposition. The texture features include: Energy coefficients of wavelet coefficients E ; ; in, is the pixel after wavelet decomposition u 、 v The corresponding wavelet coefficients; The entropy value that reflects the texture complexity of the grayscale image after binarization S ; ; in, Pixels u 、 v The probability distribution of the corresponding wavelet coefficients; Contrast Ratio CON ; ; in, Pixels u 、 v Gray value of Correlation coefficient COR ; ; in, are the mean grayscale values ​​of rows and columns in the grayscale image after binarization, are the standard deviations of the grayscale values ​​of rows and columns in the grayscale image after binarization.

[0007] Furthermore, step S3 includes: S31: Obtain the texture features corresponding to each remote sensing image in the remote sensing image set, and calculate the vegetation coverage change index corresponding to two adjacent remote sensing images at the image acquisition time based on the remote sensing images that change over time. ; ; in, are the energy coefficients corresponding to two adjacent remote sensing images, are the entropy values ​​corresponding to two adjacent remote sensing images, are the contrast coefficients corresponding to two adjacent remote sensing images, are the correlation coefficients corresponding to two adjacent remote sensing images, are the energy coefficient change, entropy value change, contrast coefficient change, and the weight of the impact of contrast coefficient change on vegetation coverage change index. t is the acquisition time of the remote sensing image; S32: Calculate the average value of vegetation coverage change index during ecological restoration based on vegetation coverage change index ; .

[0008] Furthermore, step S4 includes: S41: During the ecological restoration process, M Biological sampling points are set up, and sampling areas with set ranges are planned at the sampling points to calculate the vegetation biomass within the sampling areas; Vegetation biomass includes aboveground biomass of trees B 1. Shrub aboveground biomass B 2 and herbaceous aboveground biomass B 3; ; in, is the average diameter at breast height of trees in the sampling area, is the average height of trees in the sampling area, are the biomass calculation coefficients related to tree diameter at breast height and tree height, is the regression coefficient for tree biomass calculation, is the biomass calculation coefficient related to shrub height, is the regression coefficient calculated for shrub biomass, is the average area of ​​shrub crowns in the sampling area, N 3 is the herbaceous plant coverage area in the sampling area, s is the area of ​​the sampling region, w 3 is the average dry weight per unit area of ​​herbaceous plants; S42: Calculate the total vegetation biomass within the sampling areaB : ; According to the total vegetation biomass in each sampling area B m Calculate the average total vegetation biomass in the mine ecological restoration area at the current moment B k , k To calculate the sampling times of vegetation biomass, one sampling time corresponds to one sampling moment. m is the number of the sampling area; ; S43: Obtain the average total vegetation biomass data at different times during the ecological restoration process , B K For the K The average total vegetation biomass calculated from the subsampling; S44: Based on the average total vegetation biomass data Calculate the average biomass gain during ecological restoration ; ; in, B k-1 For the k -The average total vegetation biomass calculated from 1 sampling.

[0009] Furthermore, step S5 includes: S51: Based on the concentration of target metals in the soil of the mining area within the sampling area, calculate the pollution index data of the target metals on the soil at different times during the ecological restoration process , For the K The pollution index calculated by subsampling; ; in, e is the type of target metal, g is the number of target metal types, is the concentration of the target metal sampled in the soil, is the limit value of the target metal, is the toxicity weight of the target metal on plant growth; S52: Using pollution index data Calculate the average purification benefit of soil metal concentrations during ecological restoration ; ; in, For the k The pollution index of the sub-sampling.

[0010] Furthermore, step S6 includes: Calculating the mine ecological restoration coefficient F ; ; in, is the purification benefit threshold of soil metal concentration, is the biomass gain threshold, is the threshold of vegetation cover change index; These are the impact weights of soil metal concentration purification, biomass gain, and vegetation coverage change on mine ecological restoration; Setting the threshold of mine ecological restoration coefficient F 0; if , then the ecological restoration effect of the mine is judged to be good, otherwise, the ecological restoration effect of the mine is poor.

[0011] The beneficial effects of the present invention are as follows: This solution utilizes remote sensing image data combined with regional field survey sampling data to achieve a comprehensive assessment of the effectiveness of mine ecological restoration. By calculating the changes in vegetation coverage during the mine ecological restoration period, the purification efficiency of target metals remaining in the developed mine in the soil, and biomass gain, the effectiveness indicators of mine ecological restoration are quantified, achieving an accurate assessment of mine ecological restoration. The comprehensive impact of soil conditions in the mining area on vegetation biomass can be accurately quantified, providing a scientific basis for evaluating the effectiveness of mine ecological restoration. Starting from the ecological factors in the ecosystem after mine ecological restoration, the key evaluation parameters for evaluating the effectiveness of mine ecological restoration are determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of the mine ecological restoration effectiveness evaluation method. DETAILED DESCRIPTION

[0013] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0014] like Figure 1 As shown, a method for evaluating the effectiveness of mine ecological restoration includes the following steps: S1: Determine the mine ecological restoration area, obtain remote sensing images of the mine ecological restoration area changing over time during the ecological restoration process, and pre-process the remote sensing images. Step S1 specifically includes: S11: Determine the mine ecological restoration area and obtain remote sensing images of the mine ecological restoration area changing over time during the ecological restoration process to obtain a remote sensing image set; S12: using a bilinear interpolation method to process the remote sensing image, processing the remote sensing image into a fixed-pixel remote sensing image, thereby forming a new remote sensing image set; ; in, are the pixel values ​​of the four nearest points in the remote sensing image, is the pixel coordinate in the remote sensing image, m is the length direction of the remote sensing image, n is the width direction of the remote sensing image, is the interpolation weight in the width direction, is the interpolation weight in the length direction, is the pixel coordinate in the new remote sensing image The corresponding pixel value; S13: Grayscale the new remote sensing image to form a grayscale image, and then calculate the grayscale threshold value. Binarize the pixels in the grayscale image; ; in, is the pixel coordinate in the grayscale image The corresponding grayscale value.

[0015] S2: Perform wavelet decomposition on the pre-processed remote sensing image, calculate the wavelet coefficients, and calculate the texture features of the remote sensing image based on the wavelet decomposition. Step S2 specifically includes: Based on the grayscale image after binarization, wavelet analysis is used to extract the texture features of green plant cover in the grayscale image. The specific steps include: S21: perform binary processing on the grayscale image N Level wavelet decomposition, to obtain the wavelet coefficients after wavelet decomposition; ; in, is the pixel coordinate in the grayscale image after binarization The corresponding pixel The approximate coefficient of level, is the series of wavelet coefficients, For the Level wavelet coefficients, is the pixel coordinate after wavelet decomposition, I is the number of pixels in the length direction of the grayscale image, J is the number of pixels in the width direction of the grayscale image, is the low-pass filter function, is the high-pass filter function; S22: Calculate the texture features of the binarized grayscale image based on wavelet decomposition. The texture features include: Energy coefficients of wavelet coefficients E ; ; in, is the pixel after wavelet decomposition u 、 v The corresponding wavelet coefficients; The energy coefficient transformation reflects the uniformity of the image's grayscale distribution and the coarseness and fineness of its texture. If the grayscale values ​​of pixels in a grayscale image are similar, the energy coefficient is small, indicating a fine texture. If some pixels have large grayscale values ​​and others have small grayscale values, the energy coefficient is large. A large energy coefficient indicates a relatively uniform and regularly varying texture pattern.

[0016] The entropy value that reflects the texture complexity of the grayscale image after binarization S ; ; in, Pixels u 、 v The probability distribution of the corresponding wavelet coefficients; The entropy value is used to measure the randomness of the information contained in the image. When all gray values ​​are equal or the pixel gray values ​​show the greatest randomness, the entropy is the largest; therefore, the entropy value indicates the complexity of the gray distribution of the image. The larger the entropy value, the more complex the image.

[0017] Contrast Ratio CON ; ; in, Pixels u 、 v Gray value of The contrast coefficient measures the distribution of grayscale values ​​and the extent of local variation within an image, reflecting image clarity and the depth of texture grooves. A higher contrast coefficient indicates deeper texture grooves, greater contrast, and a clearer effect. Conversely, a lower contrast coefficient indicates shallower grooves and a more blurred effect.

[0018] Correlation coefficient COR ; ; in, are the mean grayscale values ​​of rows and columns in the grayscale image after binarization, are the standard deviations of the grayscale values ​​of rows and columns in the grayscale image after binarization.

[0019] The correlation coefficient is used to measure the similarity of the grayscale of the image in the row or column direction. Therefore, the size of the correlation coefficient reflects the local grayscale correlation. The larger the value, the greater the correlation.

[0020] S3: Calculate the average value of the vegetation coverage change index based on the texture features of the remote sensing image that changes over time during the ecological restoration process. Step S3 specifically includes: S31: Obtain the texture features corresponding to each remote sensing image in the remote sensing image set, and calculate the vegetation coverage change index corresponding to two adjacent remote sensing images at the image acquisition time based on the remote sensing images that change over time. ; ; in, are the energy coefficients corresponding to two adjacent remote sensing images, are the entropy values ​​corresponding to two adjacent remote sensing images, are the contrast coefficients corresponding to two adjacent remote sensing images, are the correlation coefficients corresponding to two adjacent remote sensing images, are the energy coefficient change, entropy value change, contrast coefficient change, and the weight of the impact of contrast coefficient change on vegetation coverage change index. t is the acquisition time of the remote sensing image; S32: Calculate the average value of vegetation coverage change index during ecological restoration based on vegetation coverage change index ; .

[0021] S4: During the ecological restoration process, M Biological sampling points are set, and sampling areas with set ranges are planned at the sampling points to calculate the vegetation biomass within the sampling areas; the average biomass gain during the ecological restoration process is calculated based on the change of vegetation biomass in the sampling areas over time. Step S4 specifically includes: S41: During the ecological restoration process, M Biological sampling points are set up, and sampling areas with set ranges are planned at the sampling points to calculate the vegetation biomass within the sampling areas; Vegetation biomass includes aboveground biomass of trees B 1. Shrub aboveground biomass B 2 and herbaceous aboveground biomass B 3; ; in, is the average diameter at breast height of trees in the sampling area, is the average height of trees in the sampling area, are the biomass calculation coefficients related to tree diameter at breast height and tree height, is the regression coefficient for tree biomass calculation, is the biomass calculation coefficient related to shrub height, is the regression coefficient calculated for shrub biomass, is the average area of ​​shrub crowns in the sampling area, N 3 is the herbaceous plant coverage area in the sampling area, s is the area of ​​the sampling region, w 3 is the average dry weight per unit area of ​​herbaceous plants; S42: Calculate the total vegetation biomass within the sampling area B : ; According to the total vegetation biomass in each sampling area B m Calculate the average total vegetation biomass in the mine ecological restoration area at the current moment B k , k To calculate the sampling times of vegetation biomass, one sampling time corresponds to one sampling moment. m is the number of the sampling area; ; S43: Obtain the average total vegetation biomass data at different times during the ecological restoration process , B K For the K The average total vegetation biomass calculated from the subsampling; S44: Based on the average total vegetation biomass data Calculate the average biomass gain during ecological restoration ; ; in, B k-1 For the k -The average total vegetation biomass calculated from 1 sampling.

[0022] S5: Based on the concentration of target metals in the soil of the mining area within the sampling area, calculate the pollution index of the target metals on the soil, and calculate the average purification benefit of the soil metal concentration during the ecological restoration process. Step S5 specifically includes: S51: Based on the concentration of target metals in the soil of the mining area within the sampling area, calculate the pollution index data of the target metals on the soil at different times during the ecological restoration process , For the K The pollution index calculated by subsampling; ; in, e is the type of target metal, g is the number of target metal types, is the concentration of the target metal sampled in the soil, is the limit value of the target metal, is the toxicity weight of the target metal on plant growth; S52: Using pollution index data Calculate the average purification benefit of soil metal concentrations during ecological restoration ; ; in, For the k The pollution index of the sub-sampling.

[0023] S6: Average purification benefits based on soil metal concentrations , average biomass gain and the average value of vegetation cover change index Evaluate the effectiveness of mine ecological restoration; Calculating the mine ecological restoration coefficient F ; ; in, is the purification benefit threshold of soil metal concentration, is the biomass gain threshold, is the threshold of vegetation cover change index; These are the impact weights of soil metal concentration purification, biomass gain, and vegetation coverage change on mine ecological restoration; Setting the threshold of mine ecological restoration coefficient F 0; if , then the ecological restoration effect of the mine is judged to be good, otherwise, the ecological restoration effect of the mine is poor.

[0024] This method combines remote sensing image data with regional field survey sampling data to achieve a comprehensive assessment of the effectiveness of mine ecological restoration. By calculating changes in vegetation coverage during the restoration period, the purification efficiency of target metals remaining in developed mines in the soil, and biomass gain, it quantifies indicators of mine ecological restoration effectiveness and enables accurate assessment of mine ecological restoration. It can accurately quantify the comprehensive impact of mining area soil conditions on vegetation biomass, providing a scientific basis for evaluating the effectiveness of mine ecological restoration. It also determines key evaluation parameters for evaluating the effectiveness of mine ecological restoration based on the ecological factors in the ecosystem after mine ecological restoration.

Claims

1. A method for evaluating the effectiveness of mine ecological restoration, characterized in that: The following steps are involved: S1: Determine the mine ecological restoration area, obtain remote sensing images of the mine ecological restoration area changing over time during the ecological restoration process, and preprocess the remote sensing images; S2: performing wavelet decomposition on the pre-processed remote sensing image, calculating the wavelet coefficients, and calculating the texture features of the remote sensing image based on the wavelet decomposition; S3: Calculate the average value of vegetation coverage change index based on the texture characteristics of remote sensing images that change over time during the ecological restoration process; S4: During the ecological restoration process, M Biological sampling points are set up, and sampling areas with set ranges are planned at the sampling points to calculate the vegetation biomass within the sampling areas; the average biomass gain during the ecological restoration process is calculated based on the changes in vegetation biomass in the sampling areas over time; S5: Calculate the pollution index of the target metals on the soil based on the concentration of target metals in the mining area within the sampling area, and calculate the average purification benefit of the soil metal concentration during the ecological restoration process; S6: Evaluate the effect of mine ecological restoration based on the average purification benefit of soil metal concentration, the average gain of biomass and the average value of vegetation cover change index.

2. The method for evaluating the effectiveness of mine ecological restoration according to claim 1, characterized in that: The step S1 comprises: S11: Determine the mine ecological restoration area and obtain remote sensing images of the mine ecological restoration area changing over time during the ecological restoration process to obtain a remote sensing image set; S12: using a bilinear interpolation method to process the remote sensing image, processing the remote sensing image into a fixed-pixel remote sensing image, thereby forming a new remote sensing image set; ； in, are the pixel values ​​of the four nearest points in the remote sensing image, is the pixel coordinate in the remote sensing image, m is the length direction of the remote sensing image, n is the width direction of the remote sensing image, is the interpolation weight in the width direction, is the interpolation weight in the length direction, is the pixel coordinate in the new remote sensing image The corresponding pixel value; S13: Grayscale the new remote sensing image to form a grayscale image, and then calculate the grayscale threshold value. Binarize the pixels in the grayscale image; ； in, is the pixel coordinate in the grayscale image The corresponding grayscale value.

3. The method for evaluating the effectiveness of mine ecological restoration according to claim 2, wherein: The step S2 comprises: Based on the grayscale image after binarization, wavelet analysis is used to extract the texture features of green plant cover in the grayscale image. The specific steps include: S21: perform binary processing on the grayscale image N Level wavelet decomposition, to obtain the wavelet coefficients after wavelet decomposition; ； in, is the pixel coordinate in the grayscale image after binarization The corresponding pixel The approximate coefficient of level, is the series of wavelet coefficients, For the Level wavelet coefficients, is the pixel coordinate after wavelet decomposition, I is the number of pixels in the length direction of the grayscale image, J is the number of pixels in the width direction of the grayscale image, is the low-pass filter function, is the high-pass filter function; S22: Calculate the texture features of the binarized grayscale image based on wavelet decomposition. The texture features include: Energy coefficients of wavelet coefficients E ; ； in, is the pixel after wavelet decomposition u 、 v The corresponding wavelet coefficients; The entropy value that reflects the texture complexity of the grayscale image after binarization S ; ； in, Pixels u 、 v The probability distribution of the corresponding wavelet coefficients; Contrast Ratio CON ; ； in, Pixels u 、 v Gray value of Correlation coefficient COR ; ； in, are the mean grayscale values ​​of rows and columns in the grayscale image after binarization, are the standard deviations of the grayscale values ​​of rows and columns in the grayscale image after binarization.

4. The method for evaluating the effectiveness of mine ecological restoration according to claim 3, wherein: The step S3 comprises: S31: Obtain the texture features corresponding to each remote sensing image in the remote sensing image set, and calculate the vegetation coverage change index corresponding to two adjacent remote sensing images at the image acquisition time based on the remote sensing images that change over time. ; ； in, are the energy coefficients corresponding to two adjacent remote sensing images, are the entropy values ​​corresponding to two adjacent remote sensing images, are the contrast coefficients corresponding to two adjacent remote sensing images, are the correlation coefficients corresponding to two adjacent remote sensing images, are the energy coefficient change, entropy value change, contrast coefficient change, and the weight of the impact of contrast coefficient change on vegetation coverage change index. t is the acquisition time of the remote sensing image; S32: Calculate the average value of vegetation coverage change index during ecological restoration based on vegetation coverage change index ; 。 5. The method for evaluating the effectiveness of mine ecological restoration according to claim 4, wherein: The step S4 comprises: S41: During the ecological restoration process, M Biological sampling points are set up, and sampling areas with set ranges are planned at the sampling points to calculate the vegetation biomass within the sampling areas; Vegetation biomass includes aboveground biomass of trees B 1. Shrub aboveground biomass B 2 and herbaceous aboveground biomass B 3; ； in, is the average diameter at breast height of trees in the sampling area, is the average height of trees in the sampling area, are the biomass calculation coefficients related to tree diameter at breast height and tree height, is the regression coefficient for tree biomass calculation, Calculate the biomass coefficient for shrub height, is the regression coefficient calculated for shrub biomass, is the average area of ​​shrub crowns in the sampling area, N 3 is the herbaceous plant coverage area in the sampling area, s is the area of ​​the sampling region, w 3 is the average dry weight per unit area of ​​herbaceous plants; S42: Calculate the total vegetation biomass within the sampling area B : ; According to the total vegetation biomass in each sampling area B m Calculate the average total vegetation biomass in the mine ecological restoration area at the current moment B k , k To calculate the sampling times of vegetation biomass, one sampling time corresponds to one sampling moment. m is the number of the sampling area; ； S43: Obtain the average total vegetation biomass data at different times during the ecological restoration process , B K For the K The average total vegetation biomass calculated from the subsampling; S44: Based on the average total vegetation biomass data Calculate the average biomass gain during ecological restoration ; ； in, B k-1 For the k -The average total vegetation biomass calculated from 1 sampling.

6. The method for evaluating the effectiveness of mine ecological restoration according to claim 5, characterized in that: The step S5 comprises: S51: Based on the concentration of target metals in the soil of the mining area within the sampling area, calculate the pollution index data of the target metals on the soil at different times during the ecological restoration process , For the K The pollution index calculated by subsampling; ； in, e is the type of target metal, g is the number of target metal types, is the concentration of the target metal sampled in the soil, is the limit value of the target metal, is the toxicity weight of the target metal on plant growth; S52: Using pollution index data Calculate the average purification benefit of soil metal concentrations during ecological restoration ; ； in, For the k The pollution index of the sub-sampling.

7. The method for evaluating the effectiveness of mine ecological restoration according to claim 6, characterized in that: The step S6 comprises: Calculating the mine ecological restoration coefficient F ; ； in, is the purification benefit threshold of soil metal concentration, is the biomass gain threshold, is the threshold of vegetation cover change index; These are the impact weights of soil metal concentration purification, biomass gain, and vegetation coverage change on mine ecological restoration; Setting the threshold of mine ecological restoration coefficient F 0; if , then the ecological restoration effect of the mine is judged to be good, otherwise, the ecological restoration effect of the mine is poor.

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