A mine ecological environment governance assessment method based on forest resource data

Abstract

The present invention discloses a mine ecological environment management assessment method based on forest resource data, comprising the following steps: determining a target assessment area, obtaining the mining data during the mining period in the target assessment area, N The team used remote sensing images to determine the maximum mining area during the mining period; screened the minimum mining area exposed after ecological restoration; calculated the ecological restoration quality coefficient represented by remote sensing images; evenly selected several sub-areas within the mine ecological restoration area and calculated the ecological restoration quality coefficient represented by plant biomass; and used the ecological restoration quality coefficient represented by remote sensing images and plant biomass to comprehensively assess the quality of mine ecological environment governance. This method achieved high-precision assessment of the quality of mine ecological environment governance, effectively improved the scientific quantification level of mine ecological governance effects, and provided scientific decision-making support for ecological restoration projects.

Description

Technical Field

[0001] The present invention relates to the field of mine ecological environment governance, and in particular to a mine ecological environment governance assessment method based on forest resource data. Background Art

[0002] Mining can lead to ecological problems such as forest destruction and soil erosion in a region. To mitigate the impact of mining on the regional ecosystem, mine ecological remediation is typically implemented through human intervention, such as soil erosion control and artificial plant planting. Assessing the quality of mine ecological remediation is fundamental to ensuring its effectiveness. Traditional assessment methods rely on manual sampling and single-metric analysis, lacking a quantitative understanding of the ecosystem's overall integrity and dynamics. They also fail to fully utilize the spatiotemporal characteristics of forest resource data (such as remote sensing monitoring and IoT sensor data), making it difficult to accurately assess the quality of mine ecological remediation. Summary of the Invention

[0003] In response to the above-mentioned deficiencies in the prior art, the present invention provides a mine ecological environment management assessment method based on forest resource data, which uses a combination of remote sensing data and survey data to achieve a comprehensive assessment of the quality of mine ecological environment management.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A method for evaluating the ecological environment management of mines based on forest resource data is provided, which includes the following steps:

[0006] S1: Determine the target assessment area and obtain the mining data during the mining period within the target assessment area. N remote sensing images, and use the pixel data in the remote sensing images to determine the mining range reflected by each remote sensing image, and obtain the maximum mining range during the mining period;

[0007] S2: Obtain ecological restoration after mining in the target assessment area V remote sensing images, and use the pixel data in the remote sensing images to determine the remaining mining area corresponding to each remote sensing image, and obtain the minimum exposed mining area after ecological restoration;

[0008] S3: Calculate the ecological restoration quality coefficient represented by remote sensing images based on the difference in pixel value and pixel number between the maximum mining area and the minimum mining area;

[0009] S4: Evenly select several sub-areas within the mine ecological restoration area, collect the growth data of artificially planted plants and naturally grown plants in the sub-areas, calculate the biological contribution per unit area of ​​the artificially planted plants and naturally grown plants to the mine ecological restoration area, and then calculate the ecological restoration quality coefficient represented by plant biomass;

[0010] S5: Comprehensively evaluate the quality of mine ecological environment management using the ecological restoration quality coefficient represented by remote sensing images and the ecological restoration quality coefficient represented by plant biomass.

[0011] Furthermore, step S1 includes:

[0012] S11: Determine the target assessment area and obtain the target assessment area during mining. N Remote sensing images will N The remote sensing images are grayed out to obtain each remote sensing image. t n The grayscale value of each pixel h m ; n is the number of the remote sensing image, m Number the pixels;

[0013] S12: Acquire remote sensing images at the start of mining t 0, remote sensing image t Gray value range of pixels within 0 As the binarization standard range, For remote sensing images t 0 is the minimum grayscale value of the pixel, For remote sensing images t 0 is the maximum grayscale value of the pixels;

[0014] S13: Using the binarization standard range to analyze remote sensing images t n Perform binarization processing and perform remote sensing image t n The pixel values ​​within are interpolated according to the remote sensing image after binarization. t n The pixel value difference within the mining area is scanned to find the target pixel on the boundary line of the mining area and obtain the remote sensing image. t n The boundary line of the inner mining area.

[0015] S14: According to N The number of target pixels on the boundary line of the mining area in the remote sensing image , filter the number of target pixels The maximum value in , the maximum value The corresponding mining range is used as the maximum mining range during the mining period.

[0016] Furthermore, step S13 includes:

[0017] S131: Compare grayscale values h m Gray value range The size of , then the pixel m The pixel value is set to 255, otherwise, the pixel m The pixel value is set to 0 to obtain the remote sensing image after binarization processing t n ;

[0018] S132: Based on the remote sensing image after binarization t n Calculate the pixel difference between two adjacent pixels ,like , then determine the pixel m , pixels m -1 is a non-border pixel. , then determine the pixel m , pixels m -1 is the boundary pixel; Pixels m A pixel value of -1;

[0019] S133: Take one of the boundary pixels as the base pixel , scan the pixel values ​​of the 8 pixels adjacent to the basic pixel in a counterclockwise or clockwise direction to calculate the pixel value of the basic pixel and the scanned pixel values The difference , scan to the pixel adjacent to the base pixel to meet , then stop scanning and take the first scanned pixel as the first target pixel. x Number the 8 adjacent pixels;

[0020] S134: Return to step S133 and use the first target pixel as the base pixel , continue scanning with the base pixel 8 adjacent pixels, skipping the scanned pixels and basic pixels during the scanning process , a pixel is scanned only once, and the second target pixel is scanned;

[0021] S135: Repeat step S134 until the scan is complete. U consecutive target pixels and make the U The adjacent pixel scanning path corresponding to the target pixel and the basic pixel The corresponding adjacent pixel scanning paths overlap, U Continuous target pixel outputs, and sequentially U Connect consecutive target pixels to form a remote sensing image t n The boundary line of the inner mining area.

[0022] Furthermore, step S2 includes:

[0023] S21: Obtain ecological restoration after mining in the target assessment area V remote sensing images and V The remote sensing images are grayed out and steps S11-S14 are executed to filter out V The boundary lines representing the remaining mining area in the remote sensing image, and V The number of target pixels on the boundary line of the remaining mining area on the remote sensing image ;

[0024] S22: Set the target pixel number The minimum value in The corresponding mining area shall serve as the minimum mining area during the ecological restoration period.

[0025] Furthermore, step S3 includes:

[0026] S31: Calculate the ecological restoration efficiency coefficient for the area based on the pixel scale of the remote sensing image, based on the difference in the number of pixels between the minimum mining area and the maximum mining area. f 1;

[0027] ;

[0028] in, is the number of pixels within the maximum mining range, is the number of pixels within the minimum mining range, is the pixel scale, which indicates the surface area corresponding to a single pixel. T is the ecological restoration cycle, lg is the logarithmic function;

[0029] S32: Calculate the ecological restoration efficiency coefficient based on the vegetation density based on the pixel grayscale values ​​in the area between the minimum mining area and the maximum mining area f 2;

[0030] ;

[0031] in, b is the pixel number in the area between the minimum mining range and the maximum mining range, Pixels b The gray value of is the average grayscale value of pixels in the area between the minimum mining range and the maximum mining range, is the reference value of the pixel grayscale value;

[0032] S33: Utilize the ecological restoration efficiency coefficient f 1 and ecological restoration efficiency coefficient f 2 Calculate the ecological restoration quality coefficient represented by remote sensing images F ;

[0033] ;

[0034] in, They are the impact weights of the restoration efficiency regarding area and the restoration efficiency regarding density on the quality of ecological restoration.

[0035] Furthermore, step S4 includes:

[0036] S41: Evenly select several sub-areas within the mine ecological restoration area, collect the size of artificially planted trees in the sub-areas, and calculate the biomass of artificially planted trees ;

[0037] ;

[0038] in, V 1 is the trunk volume of artificially planted trees, The density of artificially planted trees, r 1 is the ratio of aboveground biomass to underground biomass of artificially planted trees, R Numbering of artificially planted trees;

[0039] S42: Calculate the total biomass of artificially planted trees in the sub-area , C is the number of artificially planted trees in the sub-area, R Number the artificially planted trees in the sub-area. x is the sub-region number;

[0040] S43: Calculate the average biomass of artificially planted trees in the area of ​​mine ecological restoration , as the biological contribution per unit area of ​​artificially planted trees to mine ecological restoration; X is the number of sub-regions, s is the area of ​​the sub-region;

[0041] S44: Similarly, collect the size of naturally grown trees in the sub-area and calculate the average biomass of naturally grown trees in the mine ecological restoration area. , as the biological contribution per unit area of ​​naturally grown trees to mine ecological restoration;

[0042] S45: Utilizing average biomass and average biomass Calculate the ecological restoration quality coefficient represented by plant biomass Z ;

[0043] ;

[0044] in, are the influence weights of artificially planted biomass and naturally grown biomass on the quality of ecological restoration.

[0045] The beneficial effects of the present invention are as follows: the present invention accurately identifies the mining range of a mine based on remote sensing images of a region, and screens the maximum mining range in the mining stage. The maximum mining range is used as the area with the greatest damage to the forest during the mining process, and at the same time obtains the minimum mining range in the process of mine ecological restoration. The mining range reduced between the maximum mining range and the minimum mining range is the area where ecological restoration is successful, and the quality of ecological restoration is evaluated in combination with the efficiency of ecological restoration. At the same time, the biomass of plants is measured and calculated by measuring the growth status of regional plants, thereby evaluating the biomass per unit area of ​​the area after ecological restoration, and then evaluating the quality of ecological restoration.

[0046] The present invention combines the efficiency of vegetation area restoration, the efficiency of vegetation growth restoration and the biomass per unit area to comprehensively evaluate the quality of mine ecological environment management. Starting from multiple forest resource data, it realizes high-precision evaluation of the quality of mine ecological environment management, effectively improves the scientific quantification level of mine ecological management effects, and provides scientific decision-making support for ecological restoration projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the mine ecological environment governance assessment method based on forest resource data.

[0048] Figure 2 Schematic diagram of filtering target pixels.

[0049] Figure 3 Schematic diagram of remote sensing image of the target area.

[0050] Figure 4 Schematic diagram of the remote sensing image after binarization. DETAILED DESCRIPTION

[0051] 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.

[0052] like Figure 1 As shown, a mine ecological environment governance assessment method based on forest resource data includes the following steps:

[0053] S1: Determine the target assessment area and obtain the mining data during the mining period within the target assessment area. N remote sensing images, and use pixel data in the remote sensing images to determine the mining range reflected by each remote sensing image, and obtain the maximum mining range during the mining period. Step S1 specifically includes:

[0054] S11: Determine the target assessment area and obtain the target assessment area during mining. N Remote sensing images will N The remote sensing images are grayed out to obtain each remote sensing image. t n The grayscale value of each pixel h m ; n is the number of the remote sensing image, m Number the pixels;

[0055] S12: Acquire remote sensing images at the start of mining t 0, remote sensing image t Gray value range of pixels within 0 As the binarization standard range, For remote sensing images t 0 is the minimum grayscale value of the pixel, For remote sensing images t 0 is the maximum grayscale value of the pixels;

[0056] Remote sensing image at the start of mining t 0 is a remote sensing image showing intact forest vegetation before mining, providing a reference for the subsequent assessment of mine ecological environment governance. The goal of mine ecological environment governance is to return to the vegetation cover state before mining.

[0057] S13: Using the binarization standard range to analyze remote sensing images t n Perform binarization processing and perform remote sensing image t n The pixel values ​​within are interpolated according to the remote sensing image after binarization. tn The pixel value difference within the mining area is scanned to find the target pixel on the boundary line of the mining area and obtain the remote sensing image. t n The boundary line of the inner mining range. Step S13 specifically includes:

[0058] S131: Compare grayscale values h m Gray value range The size of , then the pixel m The pixel value is set to 255, otherwise, the pixel m The pixel value is set to 0 to obtain the remote sensing image after binarization processing t n ;

[0059] like Figure 3 and Figure 4 As shown, Figure 3 This is a standard remote sensing image. The brown area in the image is the mining area. After binarization processing, Figure 4 As shown, the enclosed white area is the mining area, and the black area is the forest area.

[0060] S132: Based on the remote sensing image after binarization t n Calculate the pixel difference between two adjacent pixels ,like , then determine the pixel m , pixels m -1 is a non-border pixel. , then determine the pixel m , pixels m -1 is the boundary pixel; Pixels m A pixel value of -1;

[0061] S133: Take one of the boundary pixels as the base pixel , scan the pixel values ​​of the 8 pixels adjacent to the basic pixel in a counterclockwise or clockwise direction to calculate the pixel value of the basic pixel and the scanned pixel values The difference , scan to the pixel adjacent to the base pixel to meet , then stop scanning and take the first scanned pixel as the first target pixel. x Number the 8 adjacent pixels;

[0062] S134: Return to step S133 and use the first target pixel as the base pixel , continue scanning with the base pixel 8 adjacent pixels, skipping the scanned pixels and basic pixels during the scanning process , a pixel is scanned only once, and the second target pixel is scanned;

[0063] S135: Repeat step S134 until the scan is complete. U consecutive target pixels and make the U The adjacent pixel scanning path corresponding to the target pixel and the basic pixel The corresponding adjacent pixel scanning paths overlap, U Continuous target pixel outputs, and sequentially U Connect consecutive target pixels to form a remote sensing image t n The boundary line of the inner mining area.

[0064] like Figure 2 As shown, the pixels in the remote sensing image are magnified, and one grid represents one pixel. Figure 2 From the basic pixel Starting from the right adjacent pixel of the image, and scanning in counterclockwise order, the boundary line segment formed, the remote sensing image after binarization processing, only the two adjacent pixels on the boundary line meet , otherwise it satisfies .

[0065] S14: According to N The number of target pixels on the boundary line of the mining area in the remote sensing image , filter the number of target pixels The maximum value in , the maximum value The corresponding mining range is used as the maximum mining range during the mining period.

[0066] S2: Obtain ecological restoration after mining in the target assessment area V The remaining mining area corresponding to each remote sensing image is determined using the pixel data in the remote sensing image to obtain the minimum exposed mining area after ecological restoration. Step S2 specifically includes:

[0067] S21: Obtain ecological restoration after mining in the target assessment area V remote sensing images and V The remote sensing images are grayed out and steps S11-S14 are executed to filter out V The boundary lines representing the remaining mining area in the remote sensing image, and V The number of target pixels on the boundary line of the remaining mining area on the remote sensing image ;

[0068] S22: Set the target pixel number The minimum value in The corresponding mining area shall serve as the minimum mining area during the ecological restoration period.

[0069] S3: Calculate the ecological restoration quality coefficient represented by the remote sensing image based on the difference between the maximum mining area and the minimum mining area. Step S3 specifically includes:

[0070] S31: Calculate the ecological restoration efficiency coefficient for the area based on the pixel scale of the remote sensing image, based on the difference in the number of pixels between the minimum mining area and the maximum mining area. f 1;

[0071] ;

[0072] in, is the number of pixels within the maximum mining range, is the number of pixels within the minimum mining range, is the pixel scale, which indicates the surface area corresponding to a single pixel. T is the ecological restoration cycle, lg is the logarithmic function;

[0073] Regarding the ecological restoration efficiency coefficient of area f 1 represents the efficiency of reducing the mining area during the ecological restoration process. The greater the efficiency of reducing the mining area, the higher the quality of the ecological restoration. Conversely, the lower the quality of the ecological restoration. In this embodiment, the ecological restoration efficiency coefficient is calculated using a natural exponential function. The natural exponential function is an increasing function with an output greater than 0. As the efficiency of reducing the mining area increases, the calculated result of the natural exponential function increases, and the output ecological restoration efficiency coefficient is amplified.

[0074] S32: Calculate the ecological restoration efficiency coefficient based on the vegetation density based on the pixel grayscale values ​​in the area between the minimum mining area and the maximum mining area f 2;

[0075] ;

[0076] in, b is the pixel number in the area between the minimum mining range and the maximum mining range, Pixels b The gray value of is the average grayscale value of pixels in the area between the minimum mining range and the maximum mining range, is the reference value of the pixel grayscale value;

[0077] During mine ecological restoration, attention must be paid not only to the efficiency of restoring forest vegetation destroyed during mining, but also to the effectiveness of forest vegetation restoration. Remote sensing imagery can reflect this process based on the density or depth of green of the vegetation. The grayscale value of green ranges from 90 to 125, with 90 representing light green and 125 representing dark green. In remote sensing imagery, dense forest areas are represented by a dark green color to represent the tree canopy line and the canopy layer formed by trees growing to the ground. Shorter grasses, such as grassland and cultivated land, are generally represented by yellow-green or light green. Vigorous tree forests have strong resistance to natural disaster risks, while short grasses have weaker resistance to natural disaster risks.

[0078] For this example, the efficiency of green restoration in the ecological restoration process before and after mining is used to express the quality of ecological restoration from another dimension, and the green gray value of vegetation before mining is used as the standard. f 2 also uses the natural index function for calculation, and uses the output of the natural index function as the denominator, indicating that the smaller the green restoration difference, the higher the quality of ecological restoration, and the ecological restoration efficiency coefficient f 2The larger the value, the greater the green restoration difference, the lower the quality of ecological restoration, and the ecological restoration efficiency coefficient f 2The smaller the value.

[0079] S33: Utilize the ecological restoration efficiency coefficient f 1 and ecological restoration efficiency coefficient f 2 Calculate the ecological restoration quality coefficient represented by remote sensing images F ;

[0080] ;

[0081] in, They are the weights of the impact of the restoration efficiency of area and the restoration efficiency of density on the quality of ecological restoration, which are generally taken as .

[0082] S4: Evenly select several sub-areas within the mine ecological restoration area, collect the growth data of artificially planted plants and naturally grown plants in the sub-areas, calculate the biological contribution per unit area of ​​the artificially planted plants and naturally grown plants to the mine ecological restoration area, and then calculate the ecological restoration quality coefficient represented by plant biomass. Step S4 specifically includes:

[0083] S41: Evenly select several sub-areas within the mine ecological restoration area, collect the size of artificially planted trees in the sub-areas, and calculate the biomass of artificially planted trees ;

[0084] ;

[0085] in, V 1 is the trunk volume of artificially planted trees, in m 2 The volume of trees is obtained by collecting the diameter at breast height and the height of artificially planted trees using a one-dimensional or two-dimensional volume table. A one-dimensional volume table is usually a relationship table established based on the diameter at breast height data of a large number of trees of the same species and the corresponding trunk volume. When using it, you only need to know the diameter at breast height of the tree to find the corresponding trunk volume. The two-dimensional volume table takes into account both the diameter at breast height and the height of the tree, which is relatively more accurate. The trunk volume is obtained by collecting the diameter at breast height and the height of the tree. The density of artificially planted trees can be obtained by consulting relevant information on the artificially planted tree species. For example, the wood density of pine is generally 400-600kg / m 3 The density of hardwood such as oak may be between 600-900kg / m 3 between; r 1 is the ratio of above-ground biomass to underground biomass of artificially planted trees. The ratio can be obtained by consulting the data based on the type of artificially planted trees. r 1. Ratio of different tree species r 1The difference is large, such as the ratio of poplar r 1 is between 0.5-0.6, the ratio of spruce r 1 is between 0.4-0.5, R Number the artificially planted trees.

[0086] S42: Calculate the total biomass of artificially planted trees in the sub-area , C is the number of artificially planted trees in the sub-area, R Number the artificially planted trees in the sub-area. x is the sub-region number;

[0087] S43: Calculate the average biomass of artificially planted trees in the area of ​​mine ecological restoration , as the biological contribution per unit area of ​​artificially planted trees to mine ecological restoration; X is the number of sub-regions, s is the area of ​​the sub-region;

[0088] S44: Similarly, collect the size of naturally grown trees in the sub-area and calculate the average biomass of naturally grown trees in the mine ecological restoration area. , as the biological contribution per unit area of ​​naturally grown trees to mine ecological restoration;

[0089] S45: Utilizing average biomass and average biomass Calculate the ecological restoration quality coefficient represented by plant biomass Z ;

[0090] ;

[0091] in, They are the weights of the impact of artificial planting biomass and natural growth biomass on ecological restoration quality, generally taken as In the process of ecological restoration in mining areas, the initial ecological environment established based on artificially planted trees is more important as a basis for the growth of new plants and trees. The ecological environment management of mines should seek to form a complete plant ecosystem. Once the plant ecosystem is established, it can operate stably without human intervention. Therefore, naturally grown plant biomass is more important to the complete plant ecosystem.

[0092] S5: Using the Ecological Restoration Quality Factor Z and ecological restoration quality coefficient F Comprehensively evaluate the quality of mine ecological environment management;

[0093] like and , it indicates that the mine ecological environment governance meets the requirements, otherwise, the mine ecological environment governance does not meet the requirements. are the standard quality coefficients of vegetation cover and vegetation biomass, respectively.

[0094] The present invention accurately identifies the mining range of a mine based on remote sensing images of a region, and screens the maximum mining range during the mining stage. The maximum mining range is used as the area with the greatest damage to the forest during the mining process. At the same time, the minimum mining range during the mine ecological restoration process is obtained. The mining range reduced between the maximum mining range and the minimum mining range is the area where ecological restoration is successful, and the ecological restoration quality is evaluated in combination with the ecological restoration efficiency. At the same time, the biomass of plants is measured and calculated by measuring the growth status of regional plants, thereby evaluating the biomass per unit area of ​​the area after ecological restoration, and then evaluating the ecological restoration quality.

[0095] The present invention combines the efficiency of vegetation area restoration, the efficiency of vegetation growth restoration and the biomass per unit area to comprehensively evaluate the quality of mine ecological environment management. Starting from multiple forest resource data, it realizes high-precision evaluation of the quality of mine ecological environment management, effectively improves the scientific quantification level of mine ecological management effects, and provides scientific decision-making support for ecological restoration projects.

Claims

1. A mine ecological environment management assessment method based on forest resource data, characterized in that: The following steps are involved: S1: Determine the target assessment area and obtain the mining data during the mining period within the target assessment area. N remote sensing images, and use the pixel data in the remote sensing images to determine the mining range reflected by each remote sensing image, and obtain the maximum mining range during the mining period; S2: Obtain ecological restoration after mining in the target assessment area V remote sensing images, and use the pixel data in the remote sensing images to determine the remaining mining area corresponding to each remote sensing image, and obtain the minimum mining area exposed after ecological restoration; S3: Calculate the ecological restoration quality coefficient represented by remote sensing images based on the difference in pixel value and pixel number between the maximum mining area and the minimum mining area; S4: Evenly select several sub-areas within the mine ecological restoration area, collect the growth data of artificially planted plants and naturally grown plants in the sub-areas, calculate the biological contribution per unit area of ​​the artificially planted plants and naturally grown plants to the mine ecological restoration area, and then calculate the ecological restoration quality coefficient represented by plant biomass; S5: Comprehensively evaluate the quality of mine ecological environment management using the ecological restoration quality coefficient represented by remote sensing images and the ecological restoration quality coefficient represented by plant biomass; The step S3 comprises: S31: Calculate the ecological restoration efficiency coefficient for the area based on the pixel scale of the remote sensing image, based on the difference in the number of pixels between the minimum mining area and the maximum mining area. f 1; ； in, is the number of pixels within the maximum mining range, is the number of pixels within the minimum mining range, is the pixel scale, which indicates the surface area corresponding to a single pixel. T for the ecological restoration cycle; S32: Calculate the ecological restoration efficiency coefficient based on the vegetation density based on the pixel grayscale values ​​in the area between the minimum mining area and the maximum mining area f 2; ； in, b is the pixel number in the area between the minimum mining range and the maximum mining range, Pixels b The gray value of is the average grayscale value of pixels in the area between the minimum mining range and the maximum mining range, is the reference value of the pixel gray value, is the minimum grayscale value of the pixels in the remote sensing image at the starting moment, is the maximum grayscale value of the pixels in the remote sensing image at the starting moment; S33: Utilize the ecological restoration efficiency coefficient f 1 and ecological restoration efficiency coefficient f 2 Calculate the ecological restoration quality coefficient represented by remote sensing images F ; ； in, These are the weights of the impact of restoration efficiency regarding area and restoration efficiency regarding density on the quality of ecological restoration; The step S4 comprises: S41: Evenly select several sub-areas within the mine ecological restoration area, collect the size of artificially planted trees in the sub-areas, and calculate the biomass of artificially planted trees ; ； in, V 1 is the trunk volume of artificially planted trees, The density of artificially planted trees, r 1 is the ratio of aboveground biomass to underground biomass of artificially planted trees, R Numbering of artificially planted trees; S42: Calculate the total biomass of artificially planted trees in the sub-area , C is the number of artificially planted trees in the sub-area, R Number the artificially planted trees in the sub-area. x is the sub-region number; S43: Calculate the average biomass of artificially planted trees in the area of ​​mine ecological restoration , as the biological contribution per unit area of ​​artificially planted trees to mine ecological restoration; X is the number of sub-regions, s is the area of ​​the subregion; S44: Similarly, collect the size of naturally grown trees in the sub-area and calculate the average biomass of naturally grown trees in the mine ecological restoration area. , as the biological contribution per unit area of ​​naturally grown trees to mine ecological restoration; S45: Utilizing average biomass and average biomass Calculate the ecological restoration quality coefficient represented by plant biomass Z ; ； in, are the influence weights of artificially planted biomass and naturally grown biomass on the quality of ecological restoration.

2. The mine ecological environment management assessment method based on forest resource data according to claim 1 is characterized in that: The step S1 comprises: S11: Determine the target assessment area and obtain the target assessment area during mining. N Remote sensing images will N The remote sensing images are grayed out to obtain each remote sensing image. t n The grayscale value of each pixel h m ; n is the number of the remote sensing image, m Number the pixels; S12: Acquire remote sensing images at the start of mining t 0, remote sensing image t Grayscale value range of pixels within 0 As the binarization standard range; S13: Using the binarization standard range to analyze remote sensing images t n Perform binarization processing and perform remote sensing image t n The pixel values ​​within are interpolated according to the remote sensing image after binarization. t n The pixel value difference within the mining area is scanned to find the target pixel on the boundary line of the mining area and obtain the remote sensing image. t n Boundary lines of the inner mining area; S14: According to N The number of target pixels on the boundary line of the mining area in the remote sensing image , filter the number of target pixels The maximum value in , the maximum value The corresponding mining range is used as the maximum mining range during the mining period.

3. The mine ecological environment management assessment method based on forest resource data according to claim 2 is characterized in that: The step S13 includes: S131: Compare grayscale values h m Gray value range The size of , then the pixel m The pixel value is set to 255, otherwise, the pixel m The pixel value is set to 0 to obtain the remote sensing image after binarization processing t n ; S132: Based on the remote sensing image after binarization t n Calculate the pixel difference between two adjacent pixels ,like , then determine the pixel m , pixels m -1 is a non-border pixel. , then determine the pixel m , pixels m -1 is the boundary pixel; Pixels m A pixel value of -1; S133: Take one of the boundary pixels as the base pixel , scan the pixel values ​​of the 8 pixels adjacent to the basic pixel in a counterclockwise or clockwise direction to calculate the pixel value of the basic pixel and the scanned pixel values The difference , scan to the pixel adjacent to the base pixel to meet , then stop scanning and take the first scanned pixel as the first target pixel. x Number the 8 adjacent pixels; S134: Return to step S133 and use the first target pixel as the base pixel , continue scanning with the base pixel 8 adjacent pixels, skipping the scanned pixels and basic pixels during the scanning process , a pixel is scanned only once, and the second target pixel is scanned; S135: Repeat step S134 until the scan is complete. U consecutive target pixels and make the U The adjacent pixel scanning path corresponding to the target pixel and the basic pixel The corresponding adjacent pixel scanning paths overlap, U Continuous target pixel outputs, and sequentially U Connect consecutive target pixels to form a remote sensing image t n The boundary line of the inner mining area.

4. The mine ecological environment management assessment method based on forest resource data according to claim 3 is characterized in that: The step S2 comprises: S21: Obtain ecological restoration after mining in the target assessment area V remote sensing images and V The remote sensing images are grayed out and steps S11-S14 are executed to filter out V The boundary lines representing the remaining mining area in the remote sensing image, and V The number of target pixels on the boundary line of the remaining mining area on the remote sensing image ; S22: Set the target pixel number The minimum value in The corresponding mining area shall serve as the minimum mining area during the ecological restoration period.

Images