A quantitative analysis method and system for the relationship between groundwater depth and vegetation development

Abstract

The present invention relates to the field of groundwater resource technology in coal mining, and in particular to a quantitative analysis method and system for the relationship between groundwater depth and vegetation development. The present invention combines the analytic hierarchy process with the entropy method to determine different ecological sensitivity types, and adopts the method of setting statistical characteristic values ​​to quantitatively analyze the changing trends of groundwater depth and NDVI. The present invention analyzes the distribution characteristics of vegetation ecological development as the groundwater level changes with the depth, establishes the relationship between surface vegetation ecology and groundwater depth under different ecological geological environments, and provides a scientific basis for achieving sustainable mining of coal resources in mining areas and ecological environmental protection decisions, which is of great significance to water-saving coal mining in mining areas.

Description

Technical Field

[0001] The invention relates to the technical field of groundwater resources in coal mining, and in particular to a quantitative analysis method and system for the relationship between groundwater depth and vegetation development. Background Art

[0002] The mining of coal resources has a huge impact on the distribution of groundwater resources in mining areas. In particular, once the key impermeable layer of the coal seam roof is damaged during coal mining, a large amount of sand layer groundwater will flow into the mine tunnel, causing mine water inrush accidents, causing the groundwater level in the mining area to drop, and aggravating ecological and environmental geological problems. Therefore, quantitative analysis of the quantitative relationship between groundwater depth and vegetation development will help mine production in the following ways: 1. Predict the trend of changes and development of the ecological environment of groundwater and surface vegetation after coal resource mining; 2. Evaluate the risk of surface vegetation evolution under future groundwater development conditions, and ultimately provide a scientific basis for the sustainable mining of coal resources and ecological and environmental protection decisions in mining areas. Summary of the invention

[0003] The present invention overcomes the shortcomings of the prior art and provides a quantitative analysis method and system for the relationship between groundwater depth and vegetation development.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention discloses a quantitative analysis method for the relationship between groundwater depth and vegetation development, comprising the following steps:

[0006] Step 1: Collect and organize the characteristic data of the mining area, including vegetation ecology, geology and hydrology data;

[0007] Step 2: Select a single factor of ecological sensitivity and conduct an ecological sensitivity analysis on the research area of ​​the mining area based on the single factor of ecological sensitivity;

[0008] Step 3: Construct a judgment matrix, and use the analytic hierarchy process and entropy method to calculate the weights of each factor in the judgment matrix according to the analysis results in step 2, and use the comprehensive weighting formula to integrate the weights to obtain the indicator weights of the evaluation factors of the study area, and obtain the ecological sensitivity factor according to the indicator weights of the evaluation factors of the study area;

[0009] Step 4: Divide the single ecological sensitivity factor into high sensitivity factor, medium sensitivity factor and low sensitivity factor according to the degree of influence, and assign values ​​of 7, 5 and 3 respectively to obtain the ecological sensitivity analysis results; divide the study area into three sensitive areas according to the ecological sensitivity analysis results, namely high sensitivity area, medium sensitivity area and low sensitivity area;

[0010] Step 5: Exploring a large number of exploration boreholes in the mining area, obtaining observation data and data on groundwater phreatic levels, and statistically analyzing the phreatic depths of different ecological sensitivity types to obtain groundwater burial depth data for the study area;

[0011] Step 6: On the AicGIS software platform, the groundwater depth data of the study area in step 5 is used to perform Kriging spatial difference analysis to obtain raster data representing the distribution of groundwater depth; based on the raster data representing the distribution of groundwater depth, a statistical map of groundwater depths of three different ecological sensitivities in the study area is made;

[0012] Step 7: Establish a quantitative analysis between the ecological development of surface vegetation and the groundwater depth, and use the method of setting statistical characteristic values ​​to quantitatively analyze the changing trends of groundwater depth and NDVI, so as to analyze the degree of influence of the change of groundwater depth on vegetation growth and development;

[0013] Step 8: Based on step 6, carry out vegetation ecological sensitivity zoning based on groundwater depth;

[0014] The selection of a single ecological sensitivity factor and the analysis of ecological sensitivity of the research area of ​​the mining area are specifically as follows:

[0015] Three factors, namely elevation, slope and aspect, were extracted from topographic factors; two factors, namely vegetation coverage and water environment, were extracted from habitat resource factors; and land use type factors were extracted from human activity factors. A total of six single factors of ecological sensitivity were obtained.

[0016] An ecological sensitivity evaluation system was established based on six single ecological sensitivity factors, and an ecological sensitivity analysis of the study area was conducted based on the ecological sensitivity evaluation system.

[0017] Furthermore, in a preferred embodiment of the present invention, the step 2 further comprises the following steps:

[0018] Determine the four main control indicators of surface elevation, terrain slope, surface water resource distribution and vegetation ecological status development, and establish the overall goal of ecological geological environment classification;

[0019] Set the goals and decisions as the highest level, the surface elevation and terrain slope factors as the middle level, and the ecological sensitivity assessment system as the lowest level;

[0020] The overall goal is divided according to the four main control indicators and the ecological geological environment types, and combined with the layer-by-layer construction method, the relationship between each layer of index and the previous layer of index is determined; and the comparison matrix between each ecological sensitivity single factor is determined.

[0021] Furthermore, in a preferred embodiment of the present invention, the following steps are also included:

[0022] Obtain data on surface high-rise buildings, terrain slope, and surface water resource distribution in the digital high-rise model;

[0023] Mask and crop the acquired data based on ArcGIS software;

[0024] ArcGIS software was used to perform slope analysis on the digital high-rise model, extract slope factors and terrain factors, unify the coordinate system and projection system of the digital high-rise model, and unify the grid pixel size to 30mx30m through raster data resampling.

[0025] Furthermore, in a preferred embodiment of the present invention, in said step three, the following steps are also included:

[0026] Construct the judgment matrix D and calculate the maximum eigenvalue λ of the judgment matrix D max and the corresponding eigenvector W i , normalize the obtained eigenvectors; the maximum eigenvalue λ max represents the weight distribution of each ecological sensitivity factor, while the vector component Wi represents the relative weight of each single ecological sensitivity factor relative to the factor in the previous layer. Through this process, the importance of the single ecological sensitivity factor can be ranked;

[0027] The consistency of the matrix D is evaluated by calculating the consistency index CI and the consistency ratio CR; when the order n≤2, the matrix always has complete consistency; when the order n>2, if the consistency ratio CR≤0.1, the matrix has satisfactory consistency;

[0028] Among them, the calculation formula of consistency index CI is: CI=(λ max ) / (n-1); the calculation formula of consistency ratio CR is, CR=CI / RI, RI is the average random consistency index of the judgment matrix.

[0029] Furthermore, in a preferred embodiment of the present invention, a quantitative analysis between the ecological development status of surface vegetation and the groundwater depth is established, and the changing trend of groundwater depth and NDVI is quantitatively analyzed by setting statistical characteristic values, so as to analyze the degree of influence of the change of groundwater depth on vegetation growth and development, specifically:

[0030] If the vegetation index P is the probability that the NDVI is less than the preset value, P = 50% means the median value in the NDVI statistical interval, which is expressed as NDVI (p = 50%). Since the vegetation index P is affected by the change of groundwater depth, NDVI (p = 50%) is used as an indicator to reflect the change of vegetation index of medium development degree of vegetation in the statistical interval of regional groundwater depth; The NDVI value of the vegetation index of P = 98%, that is, NDVI (p = 98%) is used as an indicator to reflect the change of vegetation index of high development degree of vegetation in the statistical interval of regional groundwater depth;

[0031] Among them, the expression of the nonlinear change trend of NDVI (p=50%) is:

[0032] ;

[0033] Where x is the groundwater depth in meters; 1 and λ 2 is the gain factor of NDVI (p=50%), in m -1 ;k 1 and k 2 is the soil erodibility factor of NDVI (p=50%);

[0034] Relative to NDVI （p=50%） , NDVI （p=98%） The expression of the nonlinear change process is:

[0035] ;

[0036] Where x is the groundwater depth in meters; 3 and λ 4 is the gain factor of NDVI (p=98%), in m -1 ;k 3 and k 4 is the soil erodibility factor of NDVI (p=98%);

[0037] Based on the changes in NDVI (p=50%) and NDVI (p=98%), we can analyze the extent to which the changes in groundwater depth affect the growth and development of vegetation.

[0038] The second aspect of the present invention discloses a quantitative analysis system for the relationship between groundwater depth and vegetation development, the quantitative analysis system for the relationship between groundwater depth and vegetation development comprises a memory and a processor, the memory stores a program for a quantitative analysis method for the relationship between groundwater depth and vegetation development, and when the program for the quantitative analysis method for the relationship between groundwater depth and vegetation development is executed by the processor, the steps of any one of the methods for quantitative analysis of the relationship between groundwater depth and vegetation development are implemented.

[0039] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the present invention can quickly divide different ecological sensitivity levels according to the existing hydrogeological data, determine the impact of different types of ecological geological environments on the groundwater depth and its sensitivity analysis on coal resource mining activities, thereby providing a scientific basis for protecting the groundwater resources in the mining area and maintaining the ecological environment of the mining area, which is of great significance for the subsequent realization of more scientific and environmentally friendly coal development. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0041] Figure 1 Flow chart for implementing the method of the present invention;

[0042] Figure 2 It is the normalized thematic map of vegetation index in ecological geological environment type;

[0043] Figure 3 It is a thematic map of land use classification in ecological geological environment types;

[0044] Figure 4 It is the thematic map of groundwater depth in ecological geological environment type;

[0045] Figure 5 It is the thematic map of terrain slope in the ecological geological environment type;

[0046] Figure 6 This is a thematic map of ecological sensitivity classification. DETAILED DESCRIPTION

[0047] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0049] The first aspect of the present invention discloses a quantitative analysis method for the relationship between groundwater depth and vegetation development, comprising the following steps:

[0050] Step 1: Collect and organize the characteristic data of the mining area, including vegetation ecology, geology, and hydrology data;

[0051] It should be noted that the vegetation ecology index is derived from Landsat satellite remote sensing data. According to the scope of the study area, two data sets were selected and mosaicked. When the satellite passed over to collect data, the weather in the study area was clear and there was no large area of ​​cloud in the sky. Therefore, the cloud cover of the two images was low, the imaging quality was high, and the images were clear.

[0052] Step 2: Select a single factor of ecological sensitivity, conduct an ecological sensitivity analysis on the research area of ​​the mining area based on the single factor of ecological sensitivity, and obtain the analysis results;

[0053] Step 3: Construct a judgment matrix, and use the analytic hierarchy process and entropy method to calculate the weights of each factor in the judgment matrix according to the analysis results in step 2, and use the comprehensive weighting formula to integrate the weights to obtain the indicator weights of the evaluation factors of the study area, and obtain the ecological sensitivity factor according to the indicator weights of the evaluation factors of the study area;

[0054] Step 4: Divide the single ecological sensitivity factor into high sensitivity factor, medium sensitivity factor and low sensitivity factor according to the degree of influence, and assign values ​​of 7, 5 and 3 respectively to obtain the ecological sensitivity analysis results; divide the study area into three sensitive areas according to the ecological sensitivity analysis results, namely high sensitivity area, medium sensitivity area and low sensitivity area;

[0055] If the ecological sensitivity factor is greater than 7, it is classified as a high sensitivity factor; if the ecological sensitivity factor is between 5 and 7, it is classified as a medium sensitivity factor; if the ecological sensitivity factor is between 3 and 5, it is classified as a low sensitivity factor;

[0056] Among them, the statistical distribution of NDVI of three different ecological sensitivity types can be analyzed by determining the NDVI value interval of vegetation distribution in the study area. NDVI is the normalized vegetation index;

[0057] The ecological sensitivity factor is greater than or equal to 7, which is a high sensitivity factor; the ecological sensitivity factor is less than 7 and greater than or equal to 5, which is a medium sensitivity factor; the ecological sensitivity factor is less than 5 and greater than or equal to 3, which is a low sensitivity factor;

[0058] Step 5: Exploring a large number of exploration boreholes in the mining area, obtaining observation data and data on groundwater phreatic levels, and statistically analyzing the phreatic depths of different ecological sensitivity types to obtain groundwater burial depth data for the study area;

[0059] Among them, by exploring a large number of exploration boreholes in the mining area, we obtained relatively rich observation data and data on groundwater phreatic levels, and statistically analyzed the phreatic depths of different ecological sensitivity types to obtain the groundwater level depth data of the study area;

[0060] Step 6: On the AicGIS software platform, the groundwater depth data of the study area in step 5 is used to perform Kriging spatial difference analysis to obtain raster data representing the distribution of groundwater depth; based on the raster data representing the distribution of groundwater depth, a statistical map of groundwater depths of three different ecological sensitivities in the study area is made;

[0061] It should be noted that making statistical maps of groundwater depths of three different ecological sensitivities in the study area based on the raster data representing the distribution of groundwater depth specifically includes the following steps: obtaining raster data representing groundwater depth in the study area to ensure the accuracy and completeness of the data. Performing dimensionality reduction processing on the obtained raster data to obtain processed raster data. According to the range of three different ecological sensitivities in the study area, extracting groundwater depth data in the corresponding area from the raster data. Performing statistical analysis on the extracted groundwater depth data, and calculating the average value, maximum value, minimum value and other statistical indicators of groundwater depth within each ecological sensitivity range. According to the statistical analysis results, selecting appropriate statistical chart types (such as bar charts, line charts, etc.), drawing statistical maps of groundwater depths of three different ecological sensitivities in the study area. According to the drawn statistical charts, the distribution of groundwater depths in different ecological sensitivity areas is compared and analyzed, and the relationship between groundwater depth and ecological sensitivity is explored.

[0062] The obtained raster data is subjected to dimensionality reduction processing to obtain the processed raster data, which specifically includes the following steps: first, the raster data is converted into a matrix form to obtain an original matrix, wherein each raster cell of the original matrix corresponds to an element in the matrix, the row represents different raster cells, and the column represents different attributes or variables. The correlation coefficients between the raster data are calculated, and a correlation coefficient matrix is ​​constructed according to the correlation coefficients between the raster data. The correlation coefficient matrix is ​​subjected to eigenvalue decomposition to decompose the relationship number matrix into the product of eigenvectors and eigenvalues ​​to obtain a decomposition result; the eigenvectors and corresponding eigenvalues ​​are extracted from the decomposition result, wherein the eigenvectors describe the projection of the data in the direction of the principal component, and the eigenvalues ​​represent the variance size in the direction of these principal components. The extracted eigenvalues ​​are sorted according to the size, and the eigenvectors corresponding to the first N eigenvalues ​​are selected as the principal components according to the sorted eigenvalues. According to the selected principal components, the raster data is projected into a blank matrix to obtain a projection matrix; the projection matrix is ​​multiplied with the original matrix to obtain an initial raster data matrix after dimensionality reduction. Calculate the variance contribution rate of each element in the initial raster data matrix after dimensionality reduction; compare the variance contribution rate of each element in the initial raster data matrix after dimensionality reduction with a preset threshold one by one; if the variance contribution rate of each element in the initial raster data matrix after dimensionality reduction is greater than the preset threshold, output the initial raster data matrix after dimensionality reduction as the final raster data matrix; if there is an element in the initial raster data matrix after dimensionality reduction whose variance contribution rate is not greater than the preset threshold, select the eigenvectors corresponding to the first N+K eigenvalues ​​as the principal components of the raster data, and perform reprojection and dimensionality reduction processing on the raster data until the variance contribution rate of this element is greater than the preset threshold, and then output the initial raster data matrix after dimensionality reduction as the final raster data matrix.

[0063] Step 7: Establish a quantitative analysis between the ecological development of surface vegetation and the groundwater depth, and use the method of setting statistical characteristic values ​​to quantitatively analyze the changing trends of groundwater depth and NDVI, so as to analyze the degree of influence of the change of groundwater depth on vegetation growth and development;

[0064] Step 8: Based on step 6, carry out vegetation ecological sensitivity zoning based on groundwater depth.

[0065] Furthermore, in a preferred embodiment of the present invention, the single factor of ecological sensitivity is selected, and an ecological sensitivity analysis is performed on the research area of ​​the mining area according to the single factor of ecological sensitivity to obtain the analysis results, which are specifically:

[0066] Three factors, namely elevation, slope and aspect, were extracted from topographic factors; two factors, namely vegetation coverage and water environment, were extracted from habitat resource factors; and land use type factors were extracted from human activity factors. A total of six single factors of ecological sensitivity were obtained.

[0067] An ecological sensitivity evaluation system was established based on six single ecological sensitivity factors, and an ecological sensitivity analysis of the study area was conducted based on the ecological sensitivity evaluation system to obtain the analysis results.

[0068] It should be noted that the ecological sensitivity assessment system is a tool used to assess the sensitivity of the ecological environment in the study area to external disturbances. Through ecological sensitivity analysis, the factors that have the greatest impact on the ecosystem can be identified to help formulate effective ecological protection and restoration measures. The steps of ecological sensitivity analysis of the study area according to the ecological sensitivity assessment system include: first, determine the indicator system for ecological sensitivity assessment, which usually includes indicators of natural environmental conditions, biodiversity, ecosystem service functions, human activities, etc. Collect relevant data of the study area, including topography, soil type, vegetation cover, climate conditions, water resources, human activity intensity and other data. Standardize the collected data and unify data of different scales and units under the same evaluation criteria. Determine the weight of each indicator according to the importance of the evaluation indicator. Apply the ecological sensitivity assessment model to calculate the ecological sensitivity index of the study area. Usually, the scores of various indicators are combined to obtain a comprehensive ecological sensitivity assessment result. According to the results of the ecological sensitivity assessment, the ecologically vulnerable areas and areas with high ecological sensitivity in the study area are identified to provide a scientific basis for ecological protection and planning. Through the above steps, the ecological sensitivity of the study area can be comprehensively evaluated, providing a basis for scientific and reasonable ecological protection and management.

[0069] Furthermore, in a preferred embodiment of the present invention, the step 2 further comprises the following steps:

[0070] Determine the four main control indicators of surface elevation, terrain slope, surface water resource distribution and vegetation ecological status development, and establish the overall goal of ecological geological environment classification;

[0071] Set the goals and decisions as the highest level, the surface elevation and terrain slope factors as the middle level, and the ecological sensitivity assessment system as the lowest level;

[0072] The overall goal is divided according to the four main control indicators and the ecological geological environment types, and combined with the layer-by-layer construction method, the relationship between each layer index and the previous layer index is determined; and the comparison matrix between each ecological sensitivity single factor is determined; the comparison matrix is ​​a preset standard matrix, which is set by the user.

[0073] Furthermore, in a preferred embodiment of the present invention, the following steps are also included:

[0074] Obtain data on surface high-rise buildings, terrain slope, and surface water resource distribution in the digital high-rise model;

[0075] Mask and crop the acquired data based on ArcGIS software;

[0076] ArcGIS software was used to perform slope analysis on the digital high-rise model, extract slope factors and terrain factors, and unify the coordinate system and projection system of the digital high-rise model. In order to ensure the accuracy of raster calculation, the raster pixel size was uniformly adjusted to 30x30m in the raster calculation.

[0077] In ArcGIS software, for the accuracy of raster calculation, the unified raster pixel size in raster calculation is 30x30m.

[0078] It should be noted that the data of surface elevation, terrain slope, surface water, etc. are all from digital elevation model (DEM) data (30mx30m). Based on the ArcGIS platform, the data are masked and clipped, and the slope analysis of DEM is performed using ArcGIS software to extract slope factors and terrain factors, unify the coordinate system and projection system, and unify the raster pixel size to 30mx30m by resampling the raster data. The factors such as surface elevation and terrain slope are set as the intermediate layer, and the ecological sensitivity analysis and evaluation are set as the final target layer. The step-by-step construction method is used to determine the relationship between each level index and the previous level index, and to determine the comparison matrix between the constructed factors. The importance of each factor is evaluated by processing the data by mutual comparison.

[0079] Furthermore, in a preferred embodiment of the present invention, in said step three, the following steps are also included:

[0080] Construct the judgment matrix D and calculate the maximum eigenvalue λ of the judgment matrix D max and the corresponding eigenvector W i , normalize the obtained eigenvectors; the maximum eigenvalue λ max represents the weight distribution of each ecological sensitivity factor, while the vector component Wi represents the relative weight of each single ecological sensitivity factor relative to the factor in the previous layer. Through this process, the importance of the single ecological sensitivity factor can be ranked;

[0081] The consistency of the matrix D is evaluated by calculating the consistency index CI and the consistency ratio CR; when the order n≤2, the matrix always has complete consistency; when the order n>2, if the consistency ratio CR≤0.1, the matrix has satisfactory consistency;

[0082] Among them, the calculation formula of consistency index CI is: CI=(λ max ) / (n-1); the calculation formula of consistency ratio CR is, CR=CI / RI, RI is the average random consistency index of the judgment matrix.

[0083] It should be noted that the judgment matrix D is constructed and its maximum eigenvalue λ is calculated. max and the corresponding eigenvector W i , and normalize the obtained eigenvector. The maximum eigenvalue λ max represents the weight distribution of each ecological sensitivity factor, and the vector component W i It represents the relative weight of each factor relative to the previous factor. This process can rank the importance of factors.

[0084] It should be noted that by calculating the consistency test standard, the satisfactory consistency of the K judgment matrices D and the satisfactory compatibility between the judgment matrices are tested. When the satisfactory consistency and satisfactory compatibility meet the conditions at the same time, it means that the weight allocation vector meets the preset requirements.

[0085] In order to evaluate the consistency of the matrix, the consistency index CI and the consistency ratio CR are calculated. When the order n≤2, the matrix always has complete consistency. When the order n>2, if the consistency ratio CR≤0.1, the matrix has satisfactory consistency. The calculation formula of the consistency index CI is: CI=(λ max ) / (n-1); the calculation formula of consistency ratio CR is, CR=CI / RI, RI is the average random consistency index of the judgment matrix.

[0086] It should be noted that the entropy method assigns weights based on the relative effects of each factor. First, the judgment matrix x is standardized. The standardized matrix data is: ;

[0087] In the formula, xij is the matrix data of the i-th row and j-th column after standardization; minxj is the data with the smallest value in the j-th column of the original matrix; maxxj is the data with the largest value in the j-th column of the original matrix.

[0088] For negative data, use the translation method to eliminate negative values: ;

[0089] Where H is the translation amplitude of the evaluation factor.

[0090] Calculate the weight of the jth evaluation factor in the i-th sample, and the standardized judgment matrix is: ;

[0091] Where Y ij is the weight of the jth evaluation factor in the i-th sample; n is the number of factor data of the factor; m is the number of factors in the judgment matrix; y ij is the value of the jth evaluation factor in the ith sample; Y is the standardized judgment matrix.

[0092] The entropy value and difference coefficient of the jth evaluation factor are obtained as follows: , ;

[0093] In the formula, e j is the entropy value of the j-th evaluation factor; g j is the coefficient of difference of the j-th evaluation factor; n is the number of factor data of the factor; m is the number of factors in the judgment matrix; y ij is the value of the j-th evaluation factor in the ith sample.

[0094] Calculate the evaluation factor indicator weights: ;

[0095] In the formula, w i is the indicator weight of the i-th evaluation factor; n is the number of factor data of the factor; g j is the coefficient of difference of the j-th evaluation factor.

[0096] Combining the analytic hierarchy process and entropy method to comprehensively weight each indicator, we can achieve dynamic and accurate calculation of each indicator and obtain an ecological sensitivity factor that is more in line with reality:

[0097] ;

[0098] ;

[0099] In the formula, w j is the comprehensive weight of the jth indicator; w j is the comprehensive weight of the jth indicator after weighting by the Lagrange multiplier method; minF is the ecological sensitivity factor; n is the number of factor data of the factor.

[0100] Furthermore, in a preferred embodiment of the present invention, a quantitative analysis between the ecological development status of surface vegetation and the groundwater depth is established, and the changing trend of groundwater depth and NDVI is quantitatively analyzed by setting statistical characteristic values, so as to analyze the degree of influence of the change of groundwater depth on vegetation growth and development, specifically:

[0101] If the vegetation index P is the probability that the NDVI is less than the preset value, P = 50% means the median value within the NDVI statistical interval, which is represented by NDVI (p = 50%). Since it is affected by the change of groundwater depth, NDVI (p = 50%) is used as an indicator to reflect the change of vegetation index of medium development degree of vegetation within the statistical interval of regional groundwater depth; in addition, the NDVI value of the vegetation index with P = 98%, that is, NDVI (p = 98%), is used as an indicator to reflect the change of vegetation index of high development degree of vegetation within the statistical interval of regional groundwater depth;

[0102] Among them, the expression of the nonlinear change trend of NDVI (p=50%) is:

[0103] ;

[0104] Where x is the groundwater depth in meters; 1 and λ 2 is the gain factor of NDVI (p=50%), in m -1 ;k 1 and k 2 is the soil erodibility factor of NDVI (p=50%); 1 , 2 , K 1 , K 2 The average correlation coefficient R of this nonlinear formula is obtained by fitting. 2 >0.5; where m -1 It is the reciprocal of the length unit meter;

[0105] Relative to NDVI （p=50%） , NDVI （p=98%） The expression of the nonlinear change process is:

[0106] ;

[0107] Where x is the groundwater depth in meters; 3 and λ 4 is the gain factor of NDVI (p=98%), in m -1 ;k 3 and k 4 is the soil erodibility factor of NDVI (p=98%); 3 , 4 , K 3 , K 4 The average correlation coefficient R of this nonlinear formula is obtained by fitting. 2 >0.5; where m -1 It is the reciprocal of the length unit meter;

[0108] Based on the changes in NDVI (p=50%) and NDVI (p=98%), we can analyze the extent to which the changes in groundwater depth affect the growth and development of vegetation.

[0109] Furthermore, in a preferred embodiment of the present invention, the vegetation ecological sensitivity zoning based on groundwater depth is carried out on the basis of step 6, specifically:

[0110] It should be noted that to analyze the relationship between groundwater depth changes and NDVI in the high-sensitivity, medium-sensitivity, and low-sensitivity ecological areas of the study area, the following steps can be followed:

[0111] Collect groundwater depth data and NDVI data in the study area to ensure that the temporal and spatial resolution and quality of the data meet the research requirements;

[0112] According to the results of ecological sensitivity assessment, the study area is divided into ecologically highly sensitive areas, ecologically moderately sensitive areas, and ecologically low sensitive areas, and the division is carried out according to the size of the ecological sensitivity index;

[0113] The groundwater depth data and NDVI data were associated with the ecological sensitivity area division results, and the data were spatially matched and sorted to ensure data consistency and comparability;

[0114] Statistical analysis was conducted on groundwater depth and NDVI data in ecologically highly sensitive areas, ecologically moderately sensitive areas, and ecologically low sensitive areas, and the average values ​​and changing trends of groundwater depth and NDVI in each area were compared;

[0115] Use statistical methods (such as correlation coefficient analysis) or spatial analysis methods (such as spatial autocorrelation analysis) to explore the correlation between groundwater depth and NDVI. Analyze the spatial distribution relationship and change trend of groundwater depth and NDVI in different sensitive areas;

[0116] Based on the analysis results, explain the relationship between groundwater depth and NDVI in ecologically highly sensitive areas, ecologically moderately sensitive areas, and ecologically low sensitive areas. Explore the impact of groundwater depth changes on vegetation growth and the health of ecosystems in different sensitive areas;

[0117] The analysis results are visualized by making maps and charts to show the relationship between groundwater depth changes and NDVI in different sensitive areas, providing a scientific basis for subsequent ecological and environmental management and protection;

[0118] Through the above steps, we can conduct an in-depth analysis on the relationship between groundwater depth changes and NDVI in the ecologically highly sensitive areas, ecologically moderately sensitive areas, and ecologically low sensitive areas of the study area, providing an important reference for ecological environmental monitoring and management.

[0119] Specific embodiments are listed below with reference to the accompanying drawings to further illustrate the present invention.

[0120] See also Figures 1 to 6This example takes a mine in Huainan as an example. The mine has been mining for a long time, which has a certain impact on the ecological environment of the mine and its surroundings. Long-term drainage work also has an impact on the groundwater environment of the mine. In order to quantitatively analyze the relationship between the groundwater depth and vegetation development of the mine, the steps are as follows:

[0121] Collect ecological, hydrological and geological data of the study area;

[0122] Select the single factor of ecological sensitivity, set the goal and decision as the highest level, set the major factors as the middle level, and set the decision object as the lowest level. Determine the logical relationship between each level;

[0123] The data were masked and clipped based on ArcGIS platform, and the slope analysis of DEM was performed using ArcGIS software to extract slope factors and terrain relief factors, unify the coordinate system and projection system, and unify the grid pixel size to 30mx30m through raster data resampling;

[0124] Among them, the construction of the indicator system of the study area comprehensively considers the environmental factors of the study area, selects land use type, high-rise, slope, and normalized vegetation index as evaluation factors, and uses the hierarchical analysis method to determine the indicator weights:

[0125] ;

[0126] The relationship between each index and the previous index is determined by using a step-by-step construction method. The importance of each factor in the evaluation system is processed by comparing the data with each other.

[0127] By using the constructed judgment matrix D, its maximum eigenvalue λ is calculated max represents the weight distribution of each ecological sensitivity factor, and the vector component W i It represents the relative weight of each factor relative to the previous factor. This process can rank the importance of factors. w =λ max W.

[0128] Among them, in order to evaluate the consistency of the matrix, the consistency index CI and the consistency ratio CR are calculated. When the order n≤2, the matrix always has complete consistency. When the order n>2, if the consistency ratio CR≤0.1, the matrix has satisfactory consistency. The calculation formula of the consistency index CI is: CI=(λ max ) / (n-1); The calculation formula of consistency ratio CR is CR=CI / RI, where RI is the average random consistency index of the judgment matrix. The RI value of the judgment matrix of order 1 to 9 is:

[0129] ;

[0130] After weight analysis using the analytic hierarchy process, entropy method and comprehensive weighting method, the following conclusions were finally drawn:

[0131] ;

[0132] Each ecological sensitivity factor is divided into high-sensitivity factor, medium-sensitivity factor and low-sensitivity factor according to the degree of influence, and assigned values ​​(7, 5, 3) respectively.

[0133] The results of ecological sensitivity analysis were obtained, and the region was divided into three sensitive areas, namely high sensitivity area, medium sensitivity area and low sensitivity area.

[0134] Using the method of statistical characteristic values, if P is the probability that the NDVI is less than the preset value, P=50% represents the median in the NDVI statistical interval, expressed as NDVI (p=50%). NDVI (p=50%) can be used as an indicator to reflect the changes in the vegetation index of medium-developed vegetation within the statistical interval of regional groundwater depth. In addition, p=98% is the NDVI value of the vegetation index, that is, NDVI (p=98%) can be used as an indicator to reflect the changes in the vegetation index of high-developed vegetation within the statistical interval of regional groundwater depth.

[0135] In the geological environment of ecologically highly sensitive areas, NDVI (p=50%) reaches a maximum value of 0.345 at a groundwater depth of 11m, and NDVI (p=98%) reaches a maximum value of 0.445 at a groundwater depth of about 15m. The trends of NDVI (p=50%) and NDVI (p=98%) are basically the same, both showing a nonlinear trend of increasing first and then decreasing with the increase of groundwater depth. NDVI (p=50%) begins to show an increasing trend in the range of 1-11m underground depth, and then shows a decreasing trend in the range of 11-22. At this time, the development of surface vegetation is basically not affected by the underground depth. NDVI (p=98%) begins to fluctuate and increase in the range of 1-15m groundwater depth, and then shows a decreasing trend in the range of 15-28m. But overall, with the decrease of underground depth, NDVI (p=50%) and NDVI (p=98%) do not change much.

[0136] According to the above statistical results, the linear change trend of NDVI (p=50%) in the ecological environment is expressed as:

[0137] ;

[0138] The expression of the linear change process of NDVI (p=98%) is:

[0139] ;

[0140] By comparing the change process of EVI (p=50%) and EVI (p=98%), it can be found that there is a critical groundwater depth x=12 m for the change of EVI. When the groundwater depth is less than 12 m, the EVI vegetation index generally increases with the increase of groundwater depth. When the groundwater depth exceeds 40 m, the attenuation of EVI (p=50%) and EVI (p=98%) gradually stabilizes, and the increase of groundwater depth no longer significantly affects the change of EVI.

[0141] In the geological environment of ecologically sensitive areas, the linear change trend of NDVI (p=50%) in the ecological environment is expressed as:

[0142] ;

[0143] The expression of the linear change process of NDVI (p=98%) is:

[0144] ;

[0145] By comparing the change process of EVI (p=50%) and EVI (p=98%), it can be found that there is a critical groundwater depth x=2 m for the change of EVI. When the groundwater depth is less than 2 m, the EVI vegetation index generally increases with the increase of groundwater depth. When the groundwater depth exceeds 40 m, the attenuation of EVI (p=50%) and EVI (p=98%) gradually stabilizes, and the increase of groundwater depth no longer significantly affects the change of EVI.

[0146] In the geological environment of the ecologically low-sensitive area, the linear change trend of NDVI (p=50%) in the ecological environment is expressed as:

[0147] ;

[0148] The expression of the linear change process of NDVI (p=98%) is:

[0149] ;

[0150] By comparing the change process of EVI (p=50%) and EVI (p=98%), it can be found that there is a critical groundwater depth x=12 m for the change of EVI. When the groundwater depth is less than 12 m, the EVI vegetation index generally increases with the increase of groundwater depth. When the groundwater depth exceeds 40 m, the attenuation of EVI (p=50%) and EVI (p=98%) gradually stabilizes, and the increase of groundwater depth no longer significantly affects the change of EVI.

[0151] In summary, the present invention establishes a quantitative relationship between the vegetation development index and the groundwater depth based on the classification of ecological environment types for coal resource development. It is of great significance to protect the precious underground groundwater resources and maintain the ecological geological environment under coal mining conditions, and to achieve ecological environmental protection and coal mining. It provides a new idea for quantifying the relationship between ecological sensitivity analysis and groundwater level. The method proposed in the present invention is simple in idea, economical and reliable, and can play a guiding role in the coal mining process, which is of great significance for water conservation and coal mining in areas with fragile ecological geological environment.

[0152] The second aspect of the present invention discloses a quantitative analysis system for the relationship between groundwater depth and vegetation development, the quantitative analysis system for the relationship between groundwater depth and vegetation development comprises a memory and a processor, the memory stores a program for a quantitative analysis method for the relationship between groundwater depth and vegetation development, and when the program for the quantitative analysis method for the relationship between groundwater depth and vegetation development is executed by the processor, the steps of any one of the methods for quantitative analysis of the relationship between groundwater depth and vegetation development are implemented.

[0153] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0154] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0155] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0156] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0157] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0158] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A quantitative analysis method for the relationship between groundwater depth and vegetation development, It is characterized in that The following steps are involved: Step 1: Collect and organize the characteristic data of the mining area, including vegetation ecology, geology and hydrology data; Step 2: Select a single factor of ecological sensitivity and conduct an ecological sensitivity analysis on the research area of ​​the mining area based on the single factor of ecological sensitivity; Step 3: Construct a judgment matrix, and use the analytic hierarchy process and entropy method to calculate the weights of each factor in the judgment matrix according to the analysis results in step 2, and use the comprehensive weighting formula to integrate the weights to obtain the indicator weights of the evaluation factors of the study area, and obtain the ecological sensitivity factor according to the indicator weights of the evaluation factors of the study area; Step 4: Divide the single ecological sensitivity factor into high sensitivity factor, medium sensitivity factor and low sensitivity factor according to the degree of influence, and assign values ​​of 7, 5 and 3 respectively to obtain the ecological sensitivity analysis results; divide the study area into three sensitive areas according to the ecological sensitivity analysis results, namely high sensitivity area, medium sensitivity area and low sensitivity area; Step 5: Exploring a large number of exploration boreholes in the mining area, obtaining observation data and data on groundwater phreatic levels, and statistically analyzing the phreatic depths of different ecological sensitivity types to obtain groundwater burial depth data for the study area; Step 6: On the AicGIS software platform, the groundwater depth data of the study area in step 5 is used to perform Kriging spatial difference analysis to obtain raster data representing the distribution of groundwater depth; based on the raster data representing the distribution of groundwater depth, a statistical map of groundwater depths of three different ecological sensitivities in the study area is made; Step 7: Establish a quantitative analysis between the ecological development of surface vegetation and the groundwater depth, and use the method of setting statistical characteristic values ​​to quantitatively analyze the changing trends of groundwater depth and NDVI, so as to analyze the degree of influence of the change of groundwater depth on vegetation growth and development; Step 8: Based on step 6, carry out vegetation ecological sensitivity zoning based on groundwater depth; The selection of a single ecological sensitivity factor and the analysis of ecological sensitivity of the research area of ​​the mining area are specifically as follows: Three factors, namely elevation, slope and aspect, were extracted from topographic factors; two factors, namely vegetation coverage and water environment, were extracted from habitat resource factors; and land use type factors were extracted from human activity factors. A total of six single factors of ecological sensitivity were obtained. An ecological sensitivity evaluation system was established based on six ecological sensitivity single factors, and ecological sensitivity analysis of the study area was conducted based on the ecological sensitivity evaluation system; Among them, according to the grid data representing the distribution of groundwater depth, a statistical map of groundwater depth of three different ecological sensitivities in the study area is made, which specifically includes the following steps: Obtain raster data representing the depth of groundwater level in the study area to ensure the accuracy and completeness of the data; The obtained raster data is processed by dimensionality reduction to obtain processed raster data; according to the three different ecological sensitivity ranges in the study area, the groundwater level depth data in the corresponding area is extracted from the raster data; Conduct statistical analysis on the extracted groundwater depth data and calculate the average, maximum and minimum statistical indicators of groundwater depth within each ecological sensitivity range; According to the statistical analysis results, select the statistical chart type and draw the statistical chart of the groundwater depth of three different ecological sensitivities in the study area; According to the statistical chart drawn, the distribution of groundwater depth in different ecological sensitivity areas was compared and analyzed, and the relationship between groundwater depth and ecological sensitivity was obtained; The obtained raster data is subjected to dimensionality reduction processing to obtain processed raster data, which specifically includes the following steps: The raster data is converted into a matrix form to obtain an original matrix, wherein each raster cell of the original matrix corresponds to an element in the matrix, rows represent different raster cells, and columns represent different attributes or variables; Calculating the correlation coefficients between the raster data, and constructing a correlation coefficient matrix according to the correlation coefficients between the raster data; performing eigenvalue decomposition on the correlation coefficient matrix to decompose the relationship number matrix into the product of eigenvectors and eigenvalues ​​to obtain a decomposition result; Extracting eigenvectors and corresponding eigenvalues ​​from the decomposition results, wherein the eigenvectors describe the projection of the data in the direction of the principal components, and the eigenvalues ​​represent the variances in the directions of the principal components; The extracted eigenvalues ​​are sorted by size, and according to the sorted eigenvalues, the eigenvectors corresponding to the first N eigenvalues ​​are selected as the principal components; According to the selected principal components, the raster data is projected into a blank matrix to obtain a projection matrix; the projection matrix is ​​multiplied by the original matrix to obtain an initial raster data matrix after dimensionality reduction; Calculating the variance contribution rate of each element in the initial grid data matrix after dimensionality reduction; comparing the variance contribution rate of each element in the initial grid data matrix after dimensionality reduction with a preset threshold one by one; If the variance contribution rate of each element in the initial raster data matrix after dimensionality reduction is greater than the preset threshold, the initial raster data matrix after dimensionality reduction is output as the final raster data matrix; If the variance contribution rate of a certain element in the initial raster data matrix after dimensionality reduction is not greater than the preset threshold, the eigenvectors corresponding to the first N+K eigenvalues ​​are selected as the principal components of the raster data, and the raster data is reprojected and dimensionality reduced until the variance contribution rate of this element is greater than the preset threshold, and the initial raster data matrix after dimensionality reduction is output as the final raster data matrix; Among them, a quantitative analysis between the ecological development of surface vegetation and the groundwater depth is established, and the changing trend of groundwater depth and NDVI is quantitatively analyzed by setting statistical characteristic values, so as to analyze the degree of influence of the change of groundwater depth on vegetation growth and development, specifically: If the vegetation index P is the probability that the NDVI is less than the preset value, P = 50% means the median value in the NDVI statistical interval, which is expressed as NDVI (p = 50%). Since the vegetation index P is affected by the change of groundwater depth, NDVI (p = 50%) is used as an indicator to reflect the change of vegetation index of medium development degree of vegetation in the statistical interval of regional groundwater depth; The NDVI value of the vegetation index of P = 98%, that is, NDVI (p = 98%) is used as an indicator to reflect the change of vegetation index of high development degree of vegetation in the statistical interval of regional groundwater depth; Among them, the expression of the nonlinear change trend of NDVI (p=50%) is: ； Where x is the groundwater depth in meters; 1 and λ 2 is the gain factor of NDVI (p=50%), in m -1 ;k 1 and k 2 is the soil erodibility factor of NDVI (p=50%); Relative to NDVI （p=50%） , NDVI （p=98%） The expression of the nonlinear change process is: ； Where x is the groundwater depth in meters; 3 and λ 4 is the gain factor of NDVI (p=98%), in m -1 ;k 3 and k 4 is the soil erodibility factor of NDVI (p=98%); Based on the changes in NDVI (p=50%) and NDVI (p=98%), we can analyze the extent to which the changes in groundwater depth affect the growth and development of vegetation.

2. A quantitative analysis method for the relationship between groundwater depth and vegetation development according to claim 1, It is characterized in that The step 2 also includes the following steps: Determine the four main control indicators of surface elevation, terrain slope, surface water resource distribution and vegetation ecological status development, and establish the overall goal of ecological geological environment classification; Set the goals and decisions as the highest level, the surface elevation and terrain slope factors as the middle level, and the ecological sensitivity assessment system as the lowest level; The overall goal is divided according to the four main control indicators and the ecological geological environment types, and combined with the layer-by-layer construction method, the relationship between each layer of index and the previous layer of index is determined; and the comparison matrix between each ecological sensitivity single factor is determined.

3. A quantitative analysis method for the relationship between groundwater depth and vegetation development according to claim 2, It is characterized in that The following steps are also included: Obtain data on surface high-rise buildings, terrain slope, and surface water resource distribution in the digital high-rise model; Mask and crop the acquired data based on ArcGIS software; ArcGIS software was used to perform slope analysis on the digital high-rise model, extract slope factors and terrain factors, unify the coordinate system and projection system of the digital high-rise model, and unify the grid pixel size to 30mx30m through raster data resampling.

4. A quantitative analysis method for the relationship between groundwater depth and vegetation development according to claim 1, It is characterized in that In the step three, the following steps are also included: Construct the judgment matrix D and calculate the maximum eigenvalue λ of the judgment matrix D max and the corresponding eigenvector W i , normalize the obtained eigenvectors; the maximum eigenvalue λ max represents the weight distribution of each ecological sensitivity factor, while the vector component Wi represents the relative weight of each single ecological sensitivity factor relative to the factor in the previous layer. Through this process, the importance of the single ecological sensitivity factor can be ranked; The consistency of the matrix D is evaluated by calculating the consistency index CI and the consistency ratio CR; when the order n≤2, the matrix always has complete consistency; when the order n>2, if the consistency ratio CR≤0.1, the matrix has satisfactory consistency; Among them, the calculation formula of consistency index CI is: CI=(λ max ) / (n-1); the calculation formula of consistency ratio CR is, CR=CI / RI, RI is the average random consistency index of the judgment matrix.

5. A quantitative analysis system for the relationship between groundwater depth and vegetation development, It is characterized in that The quantitative analysis system for the relationship between groundwater depth and vegetation development includes a memory and a processor. The memory stores a program for the quantitative analysis method for the relationship between groundwater depth and vegetation development. When the program for the quantitative analysis method for the relationship between groundwater depth and vegetation development is executed by the processor, the steps of the quantitative analysis method for the relationship between groundwater depth and vegetation development as described in any one of claims 1 to 4 are implemented.

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