Ecological vulnerability evaluation method for resource development area

Abstract

The invention discloses a resource development area ecological vulnerability evaluation method, and belongs to the field of ecological environment research. Based on an SRP model, 14 evaluation indexes including elevation, gradient, topographic relief, annual average rainfall, annual average temperature, dryness, vegetation coverage, landscape restoration index, landscape fragmentation, biological abundance, population density, GDP density, per capita cultivated area and resource development intensity are selected from three dimensions including ecological sensitivity, restoring force and pressure degree; and meanwhile, on the basis of integrating the advantages and disadvantages of the subjective and objective weighting methods, a method based on game theory combination weighting is provided to calculate the index weight, so that the credibility and rationality of an evaluation result are enhanced. According to the method, the spatial relationship between the ecological vulnerability change and the evaluation indexes is analyzed by using the space-time geographic weighted regression model, and the correlation between the evaluation indexes is considered, so that the method has important significance for deeply knowing the complex cause of the ecological vulnerability of the resource development area and formulating differentiated ecological protection measures.

Description

Technical Field

[0001] The invention relates to an ecological vulnerability assessment method for a resource development zone, belonging to the field of ecological environment research. Background Art

[0002] Ecological vulnerability assessment is to evaluate and analyze the fragility of the ecological environment in a certain area to determine the stability and anti-disturbance capacity of the regional ecosystem. The ecological self-repair ability of resource development areas is poor, and its long-term accumulation effect can easily endanger the ecological security of the region, thereby causing adverse effects on the regional ecological environment. Therefore, understanding the current status and evolution of ecological vulnerability in resource development areas is the key to formulating ecological environmental protection countermeasures and achieving sustainable development.

[0003] At present, there is no internationally recognized and universal indicator system and evaluation method, but there are few studies on ecological vulnerability evaluation in resource development zones, and there is a lack of a systematic indicator system for ecological vulnerability evaluation in resource development zones. At the same time, the spatial correlation between evaluation indicators is not taken into account in the analysis of driving factors of ecological vulnerability. The existing technology includes: Patent No. CN202211248799.7 discloses a coastal tidal flat ecological vulnerability evaluation system and method, and the steps are as follows: first, based on exposure, sensitivity and fitness, a coastal tidal flat ecological vulnerability evaluation index system is constructed; then, according to the improved expert weighting of data variation, the evaluation index weight is obtained, the basic data of the study area is collected and preprocessed, and unified to the same grid pixel size; finally, the exposure, sensitivity and fitness evaluation are carried out respectively, and the exposure index, sensitivity index and fitness index are calculated, and the sum of the three is used for integrated ecological vulnerability evaluation. Although this invention can provide a technical basis for marine ecological protection, marine ecological red line demarcation and coastal tidal flat planning and management, this method cannot accurately assess the ecological vulnerability of resource development areas. At the same time, the method uses an improved expert weighting method based on data variation, which inevitably has subjectivity. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a resource development zone ecological vulnerability assessment method, which can take into account the impact of resource development on ecological vulnerability, and also proposes a method of determining the index weights based on game theory combined weighting on the basis of the advantages and disadvantages of subjective and objective weighting methods, so as to enhance the credibility and rationality of the assessment results. The spatial relationship between the change of ecological vulnerability and the evaluation index is analyzed by using the spatiotemporal geographic weighted regression model, taking into account the correlation between the evaluation indexes, which is of great significance for in-depth understanding of the complex causes of ecological vulnerability in resource development zones and the formulation of differentiated ecological protection measures.

[0005] To achieve the above technical objectives, the present invention provides a method for evaluating ecological vulnerability of resource development areas, comprising the following steps:

[0006] Step 1: Data collection and indicator selection: Collect basic data of resource development areas, including remote sensing data, meteorological station data, social and economic data, and resource development data;

[0007] Combined with the characteristics of the ecological environment in the study area, the ecological vulnerability index evaluation system (SRP) of the resource development zone was constructed using basic data. The ecological vulnerability index evaluation system of the resource development zone includes three evaluation indicators: ecological sensitivity (S), ecological resilience (R) and ecological pressure (P). At the same time, the index data was converted into spatial data, unified coordinate system and resolution, clipped according to the scope of the study area, and data standardized.

[0008] Step 2: Determine the indicator weights: First, use the analytic hierarchy process (AHP) to determine the subjective weight vector W1 of all evaluation indicators in the selected ecological vulnerability indicator evaluation system, then use the entropy weight method (EWM) to determine the objective weight W2 of the evaluation indicators, and finally use the game theory combined weighting method (GTCW) to combine the subjective weight vector W1 and the objective weight W2 to determine the optimal combination weight W of the ecological vulnerability evaluation indicators. * ;

[0009] Step 3, Ecological vulnerability assessment and vulnerability classification: According to linear weighting, the standardized evaluation index is multiplied by its corresponding optimal combination weight value to obtain the ecological vulnerability index EVI, and the ecological vulnerability index is divided into five levels of slight, mild, moderate, severe and strong vulnerability using the natural breakpoint method, thereby obtaining the spatial distribution map of the ecological vulnerability classification zoning of the resource development zone;

[0010] Step 4: Use the standard deviation ellipse model to quantitatively analyze the spatial shift of the slightly, slightly, moderately, severely, and strongly vulnerable areas in the resource development zone over time in the spatial distribution map of the ecological vulnerability classification zoning, and identify the specific trends and directions of these changes;

[0011] Step 5: Use the standardized evaluation indicators and ecological vulnerability index values ​​to establish a spatiotemporal geographic weighted regression model. Use the spatiotemporal geographic weighted regression model to analyze the spatial relationship between ecological vulnerability and evaluation indicators in resource development zones and the main evaluation indicators that drive ecological vulnerability.

[0012] Furthermore, the basic data in step 1 are specifically as follows: remote sensing data products include digital elevation data, NDVI data, and land use data; meteorological station data include annual average precipitation and annual average temperature data, and spatial interpolation processing is performed on the obtained rainfall and temperature station data; socioeconomic data include per capita GDP and population density data; resource development data include mine distribution data and mineral type data.

[0013] Furthermore, the evaluation indicators of the resource development zone ecological vulnerability index evaluation system include:

[0014] The specific ecological sensitivity evaluation indicators include: elevation, slope, terrain undulation, average annual precipitation, average annual temperature and dryness; the specific ecological resilience evaluation indicators include: vegetation coverage, landscape restoration index, landscape fragmentation and biological abundance; the specific evaluation indicators include: GDP density, population density, per capita cultivated land area and resource development intensity.

[0015] Furthermore, since the ecological vulnerability assessment involves the above-mentioned multiple evaluation indicators, the units of the indicators are different, and the range of the evaluation indicator values ​​is also different, the range method is used to normalize the positive evaluation indicators and negative evaluation indicators, so as to eliminate the differences between all evaluation indicator data. The specific formula is as follows:

[0016] Positive evaluation indicators: elevation, slope, terrain relief, dryness, landscape fragmentation, population density, GDP density, per capita cultivated land area, resource development intensity. The standardized formula used is:

[0017]

[0018] Negative evaluation indicators: Annual average precipitation, annual average temperature, vegetation coverage, biological abundance, and landscape restoration index use the standardized formula:

[0019]

[0020] Where: Y i represents the standardized value of index i, x i represents the original value of index i, x max 、x min They represent the maximum and minimum values ​​of the evaluation index respectively.

[0021] Furthermore, the steps of calculating the subjective weight vector W1 of the evaluation index using the analytic hierarchy process AHP are as follows:

[0022] a1. Construct a hierarchical structure model, which includes the target layer, criterion layer and program layer for evaluating ecological vulnerability; the target layer is ecological vulnerability assessment, the criterion layer is ecological sensitivity, ecological resilience and ecological pressure, and the program layer is elevation, slope, terrain undulation, average annual precipitation, average annual temperature, dryness, vegetation coverage, landscape fragmentation, landscape restoration index, biological abundance, population density, GDP density, per capita cultivated land area and resource development intensity;

[0023] a2. First, compare the three elements of ecological sensitivity, ecological resilience and ecological pressure in the criterion layer in pairs, and then evaluate the elements in the scheme layer corresponding to a certain element in the criterion layer in pairs, carry out hierarchical sorting, and use the 1-9 comparison scale method to judge according to the importance of the elements to the ecological vulnerability assessment, so as to construct the judgment matrix A;

[0024] a3. Use the square root method to calculate the subjective weight vector W1, and use the consistency index to check whether the constructed judgment matrix A is valid, so as to avoid a large deviation between the subjective judgment matrix and the objective facts. The steps are as follows:

[0025] ①Multiply each row of the judgment matrix and raise it to the nth power to obtain the vector

[0026] ② Vector C i Normalization processing obtains the subjective weight vector W1, that is,

[0027] ③ Based on the calculated W1, the maximum characteristic root λ of the judgment matrix A can be calculated max ;

[0028]

[0029] ④ The consistency ratio CR value is obtained by using the ratio of the consistency index CI and the average random consistency index RI value to determine whether the consistency is passed. The RI value is obtained by querying the matrix order n, where the CI calculation formula is as follows:

[0030]

[0031] λ max is the maximum characteristic root of the judgment matrix A, n is the number of elements in the solution layer corresponding to a certain element in the criterion layer;

[0032]

[0033] When the consistency ratio satisfies CR<0.1, the judgment matrix A satisfies the consistency, otherwise the judgment matrix A is readjusted.

[0034] Furthermore, the steps to determine the objective weight W2 of the evaluation index using the entropy weight method are as follows:

[0035] b1. Standardize the evaluation indicators;

[0036] b2. Calculate the weight value X of the i-th evaluation object in the j-th evaluation index ij , the specific formula is as follows:

[0037]

[0038] α ij It represents the value of the jth indicator in the i-th evaluation object;

[0039] b3. Calculate the entropy value of the evaluation index. The specific formula is as follows:

[0040]

[0041] n is the number of indicators;

[0042] b4. Calculate the objective weight W2 of the evaluation index using the following formula:

[0043]

[0044] Furthermore, the method of game theory combined weighting is used to regard the indicator weight W1 determined by the hierarchical analysis method and the indicator weight W2 determined by the entropy weight method as the two sides of the game, and the consistency between different weights is sought to obtain the best combined weight. The process is as follows:

[0045] c1. Combine the subjective weight vector W1 and the objective weight W2 of the evaluation index to obtain the combined weight vector W:

[0046] W=λ1W1+λ2W2

[0047] Where: λ1, λ2 are linear combination coefficients;

[0048] c2. Using the theoretical framework of game theory to find the best weight vector is essentially to optimize the linear combination coefficients λ1 and λ2 in the above formula to minimize the deviation between W and W1 and W2. The calculation formula is as follows:

[0049] min(||W-W1||2+||W-W2||2)=min(||λ1W1+λ2W2-W1||2+||λ1W1+λ2W2-W2||2)

[0050] Among them, λ1+λ2=1, λ1, λ2≥0;

[0051] c3. According to the differential principle, the first-order derivative condition for the minimum value of the sum of the combined weight and the deviation is obtained through the formula:

[0052]

[0053] c4, the linear combination coefficients λ1 and λ2 obtained from the above formula are further processed to obtain the optimal linear combination coefficients and

[0054]

[0055] c5. Therefore, the optimal combination weight vector W* is:

[0056]

[0057] Furthermore, the standardized values ​​of each evaluation index are multiplied by their corresponding optimal combination weight values ​​according to linear weighting to obtain the ecological vulnerability index EVI. The calculation formula is as follows:

[0058]

[0059] Among them, i represents the evaluation index, X i is the standardized value of the i-th evaluation index, is the optimal combination weight vector of the i-th evaluation index.

[0060] Furthermore, the standard deviation ellipse analysis model is used to track the changes in the ecological vulnerability level of the study area in different historical periods, to understand the spatial transfer of ecological vulnerability in the region over time and to identify the specific trends and directions of these changes. The parameters of the standard deviation ellipse analysis model include the center of gravity, azimuth, and major and minor axes, and the expressions are as follows:

[0061]

[0062]

[0063]

[0064]

[0065] Among them, G(x i ,y i ) is the coordinate of the center of gravity of the region, θ is the direction angle, σ x and σ y are the lengths of the X and Y axes of the ellipse respectively; and X i and Y i Difference from the center of gravity.

[0066] Furthermore, the specific steps of establishing a spatiotemporal geographic weighted regression model to analyze the spatial relationship between ecological vulnerability and evaluation indicators in resource development areas and the main evaluation indicators that drive ecological vulnerability are as follows:

[0067] d1. Use the Create Fishnet tool in the ArcToolbox software toolbox to create a 10km×10km fishing net based on the study area, extract each standardized evaluation index and ecological vulnerability index value to the center point of the fishing net, and then import it into SPSS software to use the variance inflation factor VIF value to test the multicollinearity of all evaluation indicators, and exclude evaluation indicators with significant collinearity. When VIF>10, it means that there is significant collinearity between the evaluation indicators, and these evaluation indicators need to be removed until the VIF values ​​of all rating indicators are <10;

[0068] d2. The evaluation indicators that meet the above conditions are used as independent variables, and the ecological vulnerability index EVI is used as the dependent variable to construct a spatiotemporal geographic weighted regression model. The specific formula is as follows:

[0069]

[0070] In the formula, y i is the ecological vulnerability index of resource development areas, β0(u i , v i , t i ) represents a constant term based on spatial position; (u i , v i , t i ) is the space-time dimension coordinate, β k The regression coefficient of the independent variable; ε i is the error term.

[0071] Beneficial effects: The present invention fully considers the impact of resource development on ecological fragility, and also proposes a method of determining indicator weights based on game theory combined weighting based on the advantages and disadvantages of subjective and objective weighting methods to enhance the credibility and rationality of the evaluation results. The spatial relationship between the ecological fragility of resource development areas and the evaluation indicators is analyzed using a spatiotemporal geographic weighted regression model, taking into account the correlation between the evaluation indicators, which is of great significance for in-depth understanding of the complex causes of ecological fragility in resource development areas and the formulation of differentiated ecological protection measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of the method for evaluating ecological vulnerability of resource development zones in an embodiment of the present invention.

[0073] Figure 2 It is a schematic diagram of a method and process for determining evaluation index weights in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0075] like Figure 1 As shown, the present invention discloses a method for evaluating ecological vulnerability of resource development areas, and the steps are as follows:

[0076] First, basic data were collected, including remote sensing data products such as digital elevation data, NDVI data, and land use data; meteorological data including annual average precipitation and annual average temperature data; spatial interpolation processing was performed on the obtained precipitation and temperature station data; socioeconomic data including per capita GDP and population density data; resource development data including mine distribution data and mineral type data; ecological vulnerability indicators of resource development areas were selected from three aspects: ecological sensitivity (S), ecological resilience (R), and ecological pressure (P), as follows: ecological sensitivity indicators are elevation, slope, terrain undulation, annual average precipitation, annual average temperature, and dryness; ecological resilience is vegetation coverage, landscape restoration index, landscape fragmentation, and biological abundance; ecological pressure is per capita GDP, population density, per capita cultivated land area, and resource development intensity; and all evaluation index data were converted into spatial data, unified coordinate system and resolution, clipped according to the scope of the study area, and data standardized; then the hierarchical analysis (AHP) method was used to determine the subjective weight vector W1 of the evaluation index, and the entropy weight method was used to determine the objective weight W2 of the evaluation index. Finally, game theory was used to group the evaluation indexes. The combined weighting method combines subjective and objective weights to determine the optimal combination weight W* of each evaluation index; secondly, the standardized value of the evaluation index is multiplied with its corresponding optimal combination weight value according to linear weighting to obtain the ecological vulnerability index EVI, and the EVI value is divided into five levels of slight, mild, moderate, severe and strong vulnerability using the natural breakpoint method; the standard deviation ellipse model is used to quantitatively analyze the spatial transfer of the slight, mild, moderate, severe and strong vulnerable areas in the resource development zone over time from a spatial perspective, and to identify the specific trends and directions of these changes; finally, the standardized evaluation index is then imported into SPSS software to use the variance inflation factor (VIF) value to test the multicollinearity of all indicators, and the evaluation indicators with significant collinearity are excluded. When VIF>10, it means that there is significant collinearity between the independent variables, and these evaluation indicators need to be removed until the VIF values ​​of all independent variables are <10. Then the evaluation indicators that meet the above conditions are used as independent variables, and the ecological vulnerability index EVI is used as the dependent variable to construct a spatiotemporal geographic weighted regression model, and then the spatial relationship between ecological vulnerability and evaluation indicators is analyzed.

[0077] like Figure 2 As shown in the figure, the method and process of determining the evaluation index weights, the steps of determining the index weights by analytic hierarchy process are as follows:

[0078] a1. Construct a hierarchical structure model, which includes the target layer, criterion layer and program layer for evaluating ecological vulnerability; the target layer is ecological vulnerability assessment, the criterion layer is ecological sensitivity, ecological resilience and ecological pressure, and the program layer is elevation, slope, terrain undulation, average annual precipitation, average annual temperature, dryness, vegetation coverage, landscape fragmentation, landscape restoration index, biological abundance, population density, GDP density, per capita cultivated land area and resource development intensity;

[0079] a2. First, compare the three elements of ecological sensitivity, ecological resilience and ecological pressure in the criterion layer in pairs, and then evaluate the elements in the scheme layer corresponding to a certain element in the criterion layer in pairs, carry out hierarchical sorting, and use the 1-9 comparison scale method to judge according to the importance of the elements to the ecological vulnerability assessment, so as to construct the judgment matrix A;

[0080] a3. The steps of calculating the subjective weight vector W1 using the square root method and the consistency test are as follows:

[0081] ①Multiply each row of the judgment matrix and raise it to the nth power to obtain the vector

[0082] ② Vector C i Normalization processing obtains the subjective weight vector W1, that is,

[0083] ③ Based on the calculated W1, the maximum characteristic root λ of the judgment matrix A can be calculated max ;

[0084]

[0085] ④ The consistency ratio CR value is obtained by using the ratio of the consistency index CI and the average random consistency index RI value to determine whether the consistency is passed. The RI value is obtained by querying the matrix order n, where the CI calculation formula is as follows:

[0086]

[0087] λ max is the maximum characteristic root of the judgment matrix A, and n is the number of elements in the solution layer corresponding to an element in the criterion layer

[0088]

[0089] When the consistency ratio satisfies CR<0.1, the judgment matrix satisfies the consistency, otherwise the judgment matrix is ​​readjusted.

[0090] The steps to determine the objective weight W2 of the evaluation index using the entropy weight method are as follows:

[0091] b1. Standardize the indicators;

[0092] b2. Calculate the weight value X of the i-th evaluation object in the j-th indicator ij , the specific formula is as follows:

[0093]

[0094] α ij It represents the value of the jth indicator in the i-th evaluation object;

[0095] b3. Calculate the index entropy value. The specific formula is as follows:

[0096]

[0097] n is the number of indicators;

[0098] b4. Determine the objective weight W2 of the evaluation index. The specific formula is as follows:

[0099]

[0100] The method of game theory combined weighting is used to regard the indicator weight W1 determined by the hierarchical analysis method and the indicator weight W2 determined by the entropy weight method as the two sides of the game, and the consistency between different weights is sought to obtain the best combined weight. The process is as follows:

[0101] c1. According to the weights of subjective and objective evaluation indicators, the combined weight vector W is:

[0102] W=λ1W1+λ2W2

[0103] Where: λ1, λ2 are linear combination coefficients;

[0104] c2. Using the theoretical framework of game theory to find the best weight vector is essentially to optimize the linear combination coefficients λ1 and λ2 in the above formula to minimize the deviation between W and W1 and W2. The calculation formula is as follows:

[0105] min(||W-W1||2+||W-W2||2)=min(||λ1W1+λ2W2-W1||2+||λ1W1+λ2W2-W2||2)

[0106] Among them, λ1+λ2=1, λ1, λ2≥0;

[0107] c3. According to the differential principle, the first-order derivative condition for the minimum value of the sum of the combined weight and the deviation is obtained through the formula:

[0108]

[0109] c4, the linear combination coefficients λ1 and λ2 obtained from the above formula are further processed to obtain the optimal linear combination coefficients and

[0110]

[0111] c5. Therefore, the optimal combination weight W* is:

[0112]

[0113] According to linear weighting, the standardized value of each evaluation index is multiplied by its corresponding optimal combination weight value to obtain the ecological vulnerability index EVI. The calculation formula is as follows:

[0114]

[0115] Among them, i represents the evaluation index, X i is the standardized value of the i-th evaluation index, is the optimal combination weight vector of the i-th evaluation index.

[0116] The standard deviation ellipse analysis model is used to track the changes in the ecological vulnerability level of the study area in different historical periods, understand the spatial transfer of ecological vulnerability in the region over time, and identify the specific trends and directions of these changes. The parameters of the standard deviation ellipse analysis model include the center of gravity, azimuth, and major and minor axes, and the expressions are as follows:

[0117]

[0118]

[0119]

[0120]

[0121] Among them, G(x i ,y i ) is the coordinate of the center of gravity of the region, θ is the direction angle, σ x and σ y are the lengths of the X and Y axes of the ellipse respectively; and X i and Y i Difference from the center of gravity.

[0122] The specific steps of establishing a spatiotemporal geographic weighted regression model to analyze the spatial relationship between ecological vulnerability and evaluation indicators in resource development areas and the main evaluation indicators that drive ecological vulnerability are as follows:

[0123] d1. Use the Create Fishnet tool in the ArcToolbox software toolbox to create a 10km×10km fishing net based on the study area, extract each standardized evaluation index and ecological vulnerability index value to the center point of the fishing net, and then import it into SPSS software to use the variance inflation factor VIF value to test the multicollinearity of all evaluation indicators, and exclude evaluation indicators with significant collinearity. When VIF>10, it means that there is significant collinearity between the evaluation indicators, and these evaluation indicators need to be removed until the VIF values ​​of all rating indicators are <10;

[0124] d2. The evaluation indicators that meet the above conditions are used as independent variables, and the ecological vulnerability index EVI is used as the dependent variable to construct a spatiotemporal geographic weighted regression model. The specific formula is as follows:

[0125]

[0126] In the formula, y i is the ecological vulnerability index of resource development areas, β0(u i , v i , t i ) represents a constant term based on spatial position; (u i , v i , t i ) is the space-time dimension coordinate, β k The regression coefficient of the independent variable; ε i is the error term.

Claims

1. A method for evaluating ecological vulnerability of resource development areas, characterized in that: The following steps are involved: Step 1: Data collection and indicator selection: Collect basic data of resource development areas, including remote sensing data, meteorological station data, social and economic data, and resource development data; Combined with the characteristics of the ecological environment in the study area, the ecological vulnerability index evaluation system (SRP) of the resource development zone was constructed using basic data. The ecological vulnerability index evaluation system of the resource development zone includes three evaluation indicators: ecological sensitivity (S), ecological resilience (R) and ecological pressure (P). At the same time, the index data was converted into spatial data, unified coordinate system and resolution, clipped according to the scope of the study area, and data standardized. Step 2: Determine the indicator weights: First, use the analytic hierarchy process (AHP) to determine the subjective weight vector W1 of all evaluation indicators in the selected ecological vulnerability indicator evaluation system, then use the entropy weight method (EWM) to determine the objective weight W2 of the evaluation indicators, and finally use the game theory combined weighting method (GTCW) to combine the subjective weight vector W1 and the objective weight W2 to determine the optimal combination weight W of the ecological vulnerability evaluation indicators. * ; Step 3, Ecological vulnerability assessment and vulnerability classification: According to linear weighting, the standardized evaluation index is multiplied by its corresponding optimal combination weight value to obtain the ecological vulnerability index EVI, and the ecological vulnerability index is divided into five levels of slight, mild, moderate, severe and strong vulnerability using the natural breakpoint method, thereby obtaining the spatial distribution map of the ecological vulnerability classification zoning of the resource development zone; Step 4: Use the standard deviation ellipse model to quantitatively analyze the spatial shift of the slightly, slightly, moderately, severely, and strongly vulnerable areas in the resource development zone over time in the spatial distribution map of the ecological vulnerability classification zoning, and identify the specific trends and directions of these changes; Step 5: Use the standardized evaluation indicators and ecological vulnerability index values ​​to establish a spatiotemporal geographic weighted regression model. Use the spatiotemporal geographic weighted regression model to analyze the spatial relationship between ecological vulnerability and evaluation indicators in resource development zones and the main evaluation indicators that drive ecological vulnerability.

2. The method for evaluating ecological vulnerability of resource development zones according to claim 1, characterized in that: The basic data in step 1 are as follows: remote sensing data products include digital elevation data, NDVI data, and land use data; meteorological station data include annual average precipitation and annual average temperature data, and spatial interpolation processing is performed on the obtained rainfall and temperature station data; socioeconomic data include per capita GDP and population density data; resource development data include mine distribution data and mineral type data.

3. The method for evaluating ecological vulnerability of resource development zones according to claim 1, characterized in that: The evaluation indicators of the resource development zone ecological vulnerability index evaluation system include: The specific ecological sensitivity evaluation indicators include: elevation, slope, terrain undulation, average annual precipitation, average annual temperature and dryness; the specific ecological resilience evaluation indicators include: vegetation coverage, landscape restoration index, landscape fragmentation and biological abundance; the specific evaluation indicators include: GDP density, population density, per capita cultivated land area and resource development intensity.

4. The method for assessing ecological vulnerability of resource development zones according to claim 3, characterized in that: Since the ecological vulnerability assessment involves the above-mentioned multiple evaluation indicators, the units of the indicators are different, and the range of the evaluation indicator values ​​is also different. The range method is used to normalize the positive evaluation indicators and negative evaluation indicators to eliminate the differences between all evaluation indicator data. The specific formula is as follows: Positive evaluation indicators: elevation, slope, terrain relief, dryness, landscape fragmentation, population density, GDP density, per capita cultivated land area, resource development intensity. The standardized formula used is: Negative evaluation indicators: Annual average precipitation, annual average temperature, vegetation coverage, biological abundance, and landscape restoration index use the standardized formula: Where: Y i represents the standardized value of index i, x i represents the original value of index i, x max 、x min They represent the maximum and minimum values ​​of the evaluation index respectively.

5. The method for assessing ecological vulnerability of resource development areas according to claim 4, characterized in that: The steps of calculating the subjective weight vector W1 of the evaluation index using the analytic hierarchy process AHP are as follows: a1. Construct a hierarchical structure model, which includes the target layer, criterion layer and program layer for evaluating ecological vulnerability; the target layer is ecological vulnerability assessment, the criterion layer is ecological sensitivity, ecological resilience and ecological pressure, and the program layer is elevation, slope, terrain undulation, average annual precipitation, average annual temperature, dryness, vegetation coverage, landscape fragmentation, landscape restoration index, biological abundance, population density, GDP density, per capita cultivated land area and resource development intensity; a2. First, compare the three elements of ecological sensitivity, ecological resilience and ecological pressure in the criterion layer in pairs, and then evaluate the elements in the scheme layer corresponding to a certain element in the criterion layer in pairs, carry out hierarchical sorting, and use the 1-9 comparison scale method to judge according to the importance of the elements to the ecological vulnerability assessment, so as to construct the judgment matrix A; a3. Use the square root method to calculate the subjective weight vector W1, and use the consistency index to check whether the constructed judgment matrix A is valid, so as to avoid a large deviation between the subjective judgment matrix and the objective facts. The steps are as follows: ①Multiply each row of the judgment matrix and raise it to the nth power to obtain the vector ② Vector C i Normalization processing obtains the subjective weight vector W1, that is, ③ Based on the calculated W1, the maximum characteristic root λ of the judgment matrix A can be calculated max ; ④ The consistency ratio CR value is obtained by using the ratio of the consistency index CI and the average random consistency index RI value to determine whether the consistency is passed. The RI value is obtained by querying the matrix order n, where the CI calculation formula is as follows: λ max is the maximum characteristic root of the judgment matrix A, n is the number of elements in the solution layer corresponding to a certain element in the criterion layer; When the consistency ratio satisfies CR<0.1, the judgment matrix A satisfies the consistency, otherwise the judgment matrix A is readjusted.

6. The method for assessing ecological vulnerability of resource development areas according to claim 4, characterized in that: The steps to determine the objective weight W2 of the evaluation index using the entropy weight method are as follows: b1. Standardize the evaluation indicators; b2. Calculate the weight value X of the i-th evaluation object in the j-th evaluation index ij , the specific formula is as follows: α ij It represents the value of the jth indicator in the i-th evaluation object; b3. Calculate the entropy value of the evaluation index. The specific formula is as follows: n is the number of indicators; b4. Calculate the objective weight W2 of the evaluation index using the following formula:

7. The method for assessing ecological vulnerability of resource development areas according to claim 6, characterized in that: The method of game theory combined weighting is used to regard the indicator weight W1 determined by the hierarchical analysis method and the indicator weight W2 determined by the entropy weight method as the two sides of the game, and the consistency between different weights is sought to obtain the best combined weight. The process is as follows: c1. Combine the subjective weight vector W1 and the objective weight W2 of the evaluation index to obtain the combined weight vector W: W=λ1W1+λ2W2 Where: λ1, λ2 are linear combination coefficients; c2. Using the theoretical framework of game theory to find the best weight vector is essentially to optimize the linear combination coefficients λ1 and λ2 in the above formula to minimize the deviation between W and W1 and W2. The calculation formula is as follows: min(||W-W1||2+||W-W2||2)=min(||λ1W1+λ2W2-W1||2+||λ1W1+λ2W2-W2||2) Among them, λ1+λ2=1,λ1,λ2≥0; c3. According to the differential principle, the first-order derivative condition for the minimum value of the sum of the combined weight and the deviation is obtained through the formula: c4, the linear combination coefficients λ1 and λ2 obtained from the above formula are further processed to obtain the optimal linear combination coefficients and c5, so the optimal combination weight vector W * for:

8. The method for assessing ecological vulnerability of resource development areas according to claim 4, characterized in that: According to linear weighting, the standardized value of each evaluation index is multiplied by its corresponding optimal combination weight value to obtain the ecological vulnerability index EVI. The calculation formula is as follows: Among them, i represents the evaluation index, X i is the standardized value of the i-th evaluation index, is the optimal combination weight vector of the i-th evaluation index.

9. The method for assessing ecological vulnerability of resource development areas according to claim 1, characterized in that: The standard deviation ellipse analysis model is used to track the changes in the ecological vulnerability level of the study area in different historical periods, understand the spatial transfer of ecological vulnerability in the region over time, and identify the specific trends and directions of these changes. The parameters of the standard deviation ellipse analysis model include the center of gravity, azimuth, and major and minor axes, and the expressions are as follows: Among them, G(x i ,y i ) is the coordinate of the center of gravity of the region, θ is the direction angle, σ x and σ y are the lengths of the X and Y axes of the ellipse respectively; and X i and Y i Difference from the center of gravity.

10. The method for assessing ecological vulnerability of resource development areas according to claim 1, characterized in that: The specific steps of establishing a spatiotemporal geographic weighted regression model to analyze the spatial relationship between ecological vulnerability and evaluation indicators in resource development areas and the main evaluation indicators that drive ecological vulnerability are as follows: d1. Use the Create Fishnet tool in the ArcToolbox software toolbox to create a 10km×10km fishing net based on the study area, extract each standardized evaluation index and ecological vulnerability index value to the center point of the fishing net, and then import it into SPSS software to use the variance inflation factor VIF value to test the multicollinearity of all evaluation indicators, and exclude evaluation indicators with significant collinearity. When VIF>10, it means that there is significant collinearity between the evaluation indicators, and these evaluation indicators need to be removed until the VIF values ​​of all rating indicators are <10; d2. The evaluation indicators that meet the above conditions are used as independent variables, and the ecological vulnerability index EVI is used as the dependent variable to construct a spatiotemporal geographic weighted regression model. The specific formula is as follows: In the formula, y i is the ecological vulnerability index of resource development areas, β0(u i ,v i ,t i ) represents a constant term based on spatial position; (u i ,v i ,t i ) is the space-time dimension coordinate, β k The regression coefficient of the independent variable; ε i is the error term.

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