Ecological restoration effect evaluation method

Abstract

The invention discloses an ecological restoration effect evaluation method which comprises the following steps: obtaining various indexes of different areas in a target area, including original evaluation data of soil, water sources, animals, vegetation, ecological system landscapes and ecological system functions; performing dimensionless processing on the original evaluation data through a standardization method to obtain standard evaluation data; calculating the combination weight of each index of different areas in the target area; and performing weighted calculation on the standard evaluation data based on the combined weight to obtain evaluation values of the indexes before and after ecological restoration, and comparing the evaluation values of the indexes before and after ecological restoration to obtain an ecological restoration effect evaluation result. According to the invention, the indexes of the mountainous area are monitored, combined weighting is carried out according to the evaluation results of the modules, a comprehensive evaluation result is obtained, and a scientific evaluation method is provided for researchers to evaluate the ecological restoration effect of the mountainous area.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ecological environment restoration and evaluation, and particularly relates to an ecological restoration effect evaluation method. BACKGROUND

[0002] Mountainous areas, as an important barrier area of China's ecological security, concentrate multiple ecological functions such as water conservation, soil and water conservation, and biodiversity. However, the current ecological restoration project effect evaluation generally has problems such as coarse evaluation granularity, single index, and ignoring the comprehensive coupling relationship between the structure and function of the ecological system. The traditional weighting method cannot objectively and accurately reflect the weight distribution difference between different indexes. Therefore, it is urgent to build an ecological restoration effect evaluation system that integrates multi-dimensional indexes, considers the characteristics of different ecological function areas, and adopts a scientific weighting mechanism. SUMMARY

[0003] The purpose of the present application is to provide an ecological restoration effect evaluation method, which solves the problem that the weighting method in the existing ecological restoration effect evaluation system cannot objectively and accurately reflect the weight distribution difference between different indexes.

[0004] The technical scheme adopted by the present application is: an ecological restoration effect evaluation method, comprising the following steps: Step 1, obtaining various indexes of different regions in the target area, including soil, water source, animal, vegetation, original evaluation data of ecological system landscape and ecological system function; Step 2, carrying out dimensionless processing on the original evaluation data obtained in step 1 by a standardization method to obtain standard evaluation data; Step 3, calculating the combined weight of each index of different regions in the target area; Step 4, based on the combined weight obtained in step 3, carrying out weighted calculation on the standard evaluation data obtained in step 2 to obtain the evaluation value of each index before and after ecological restoration, and comparing the evaluation value of each index before and after ecological restoration to obtain the ecological restoration effect evaluation result.

[0005] The present application is also characterized in that, The original evaluation data acquisition method of the soil index in step 1 is specifically: Step 1.1, collecting soil samples and measuring soil physical and chemical properties including carbon content and nitrogen content in the treatment area and the untreated area of ecological restoration; Step 1.2, calculating the soil retention amount of the soil samples collected in step 1.1 by formula (1) Q sr : Q sr = R × K × L × S ×C × P (1) In formula (1), R represents rainfall erosivity factor, K represents soil erodibility factor, L represents slope length factor, S represents slope gradient factor, C represents vegetation cover and management factor, P represents water and soil conservation measures factor.

[0006] The original evaluation data of the water source index in step 1 is obtained in the following manner: The water source conservation amount is calculated by formula (2) W : (2) In formula (2), P represents precipitation, R represents surface runoff, ET represents evapotranspiration, Δ S represents water storage variable; The amount of non-point source pollution is calculated by formula (3) L : (3) In formula (3), A i represents the area of the first i class of land, C i represents the pollution output coefficient corresponding to the first i class of land.

[0007] The original evaluation data of the animal index in step 1 is obtained in the following manner: Step 1.1, in the treatment area and the untreated area of ecological restoration, the activities of medium and large mammals are monitored by setting up infrared cameras to obtain monitoring data; Step 1.2, the data monitored in step 1.1 is calculated by formula (4) to calculate the animal Shannon diversity index H′ : (4) In formula (4), p i represents the proportion of the number of individuals of the first i animal species to the total number of monitored animal individuals.

[0008] The original evaluation data of the vegetation index in step 1 is obtained in the following manner: Step 1.1, in the treatment area and the untreated area of ecological restoration, the vegetation coverage is monitored respectively to obtain the relative change rate of vegetation coverage in the treatment area and the untreated area. Step 1.2: Select representative quadrats in the ecological restoration watershed using the quadrat method, record the plant species names and number of individuals in each quadrat, and count the total number of all monitored plant species in the watershed. Step 1.3: Calculate the Shannon diversity index of plants using equation (5) based on the data monitored in step 1.2. H′ : (5) In equation (5), p i Indicates the first i The proportion of individuals of a plant species to the total number of individuals of all plant species in the watershed.

[0009] The specific method for obtaining the raw assessment data for the ecosystem landscape indicators in step 1 is as follows: Landscape connectivity is calculated using equation (6). FI : FI = PD / S (6) In equation (6), PD Indicates the number of plaques. S Indicates the area of ​​the landscape; Landscape heterogeneity is calculated using equation (7). SHDI : (7) In equation (7), p i Indicates the first i The area proportion of landscape types.

[0010] The specific method for obtaining the raw assessment data for ecosystem function indicators in step 1 is as follows: Step 1.1: Calculate the ecosystem resilience using equation (8). E : (8) In equation (8), Indicates the first i Ecosystem resilience score, X i Indicates the first i Land use area of ​​different types; Calculate the value of ecosystem services using equation (9) P : (9) In equation (9), ESV Indicates the value of ecosystem services. VC i Indicates the first i Ecosystem value coefficientX i represent the first i class land use area; ecosystem adaptability is calculated by formula (10) A : (10) In formula (10), FI represent landscape connectivity, SHDI represent landscape heterogeneity, FRAC represent patch fractal dimension; Step 1.2, based on the elasticity force of the ecosystem obtained in step 1.1 E ecosystem service value P ecosystem adaptability A ecosystem resilience is calculated by formula (11) R : (11).

[0011] In step 2, the obtained original evaluation data set is first defined as X ={ X ij}, wherein i is different regions, j is each index; then for each index j , the original evaluation data is converted into a dimensionless value in the interval [0, 1], i.e. standard evaluation data, by formula (12): (12) In formula (12), x ij is the original value of the first i index of the first j region, y ij is the dimensionless value of the first i index of the first j region.

[0012] Step 3 is specifically: Step 3.1, calculate the entropy weight Step 3.1.1, for the dimensionless standard evaluation data, the proportion of the value of each region under the first j index to the total value of all regions of the index is calculated by formula (13) p ij : (13) In formula (13), y ij is the first i region of the first jThe indicator is a dimensionless value. m Total number of regions; Step 3.1.2: For each indicator j Information entropy is calculated using equation (14). e i : (14) Step 3.1.3: Calculate redundancy using equation (15). d j Then, the entropy weight is calculated using equation (16). w E : (15) (16) In equation (16), n The total number of indicators; Step 3.2: Calculate the weights of the CRITIC method. Step 3.2.1: For each indicator j The standard deviation of the dimensionless value of this indicator for all regions is calculated using equation (17). σ j : (17) In equation (17), y ij For the first i Region 1 j The indicator is a dimensionless value. m For the total number of regions, For the first j The average value of the indicator across all regions; Step 3.2.2: For every two indicators j and k The Pearson correlation coefficient is calculated using equation (18). R jk And calculate the conflict using equation (19). C j : (18) (19) In equation (18), y j , y k The first j, k The dimensionless value of the indicator j, k =1, 2... n In equation (19), n The total number of indicators; Step 3.2.3. Calculate the information quantity by formula (20) S j , and then calculate the CRITIC method weight by formula (21) w C : (20) (21) Step 3.3. Combine the entropy value method weight w E and the CRITIC method weight w C to obtain the combined weight W i : (22).

[0013] The beneficial effects of the present application are: the ecological restoration effectiveness evaluation method of the present application monitors the indexes of mountain vegetation coverage, land use change, soil nitrogen content, soil carbon content, annual average rainfall, annual average evapotranspiration, species richness, landscape connectivity, etc., combines and weights the evaluation results of each module to obtain a comprehensive evaluation result, thereby providing researchers with a scientific evaluation method for the effectiveness of mountain ecological restoration. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a system architecture schematic diagram of the ecological restoration effectiveness evaluation method of the present application; Figure 2 is a flowchart of the ecological restoration effectiveness evaluation method of the present application. DETAILED DESCRIPTION

[0015] The present application will be described in detail below in combination with the drawings and specific embodiments.

[0016] The present application provides an ecological restoration effectiveness evaluation method, as shown in Figure 1 , which comprises a soil evaluation module for evaluating soil conservation effectiveness and soil nutrient effectiveness, a water source evaluation module for evaluating ecological restoration water conservation effectiveness and non-point source pollution control effectiveness, a vegetation evaluation module for evaluating ecological restoration vegetation restoration effectiveness, an animal evaluation module for evaluating ecological restoration animal protection effectiveness, an ecological system landscape evaluation module for evaluating ecological system connectivity and heterogeneity, an ecological system function evaluation module for evaluating ecological system elasticity, ecological system resilience and ecological system service function, and a comprehensive evaluation module for generating a comprehensive evaluation result according to the evaluation results of the soil evaluation module, the water source evaluation module, the vegetation evaluation module, the animal evaluation module, the ecological system landscape evaluation module and the ecological system function evaluation module.

[0017] Embodiment 1 The soil evaluation module is used for evaluating the organic matter content, nitrogen content and soil conservation amount in the ecological restoration soil layer. The soil evaluation module includes a soil conservation amount calculation submodule, a soil collection submodule, a soil organic matter calculation submodule and a soil nitrogen calculation submodule. The soil conservation amount calculation submodule uses the general soil erosion equation: Q sr = R × K × L × S × C × P (1) In formula (1), R represents the rainfall erosion factor, K represents the soil erodibility factor, L represents the slope length factor, S represents the slope factor, C represents the vegetation coverage and management factor, P represents the water and soil conservation measure factor.

[0018] The soil samples of the treated and untreated areas are obtained through the soil collection submodule, the carbon content of the organic soil samples is tested through the organic matter calculation submodule, and the nitrogen content of the organic soil samples is tested through the nitrogen content calculation submodule.

[0019] Embodiment 2 The water source evaluation module is used for evaluating the water source conservation amount and the treatment effect of non-point source pollution in the ecological restoration basin. The water source evaluation module includes a water source conservation amount submodule and a non-point source pollution submodule. The water source conservation amount submodule is calculated by the formula water balance method: (2) In formula (2), P represents the precipitation, R represents the surface runoff, ET represents the evapotranspiration, and Δ S represents the water storage variable.

[0020] The non-point source pollution submodule is calculated by the outlet coefficient method: (3) In formula (3), A i represents the area of the first i class of land, C i represents the pollution output coefficient corresponding to the first i class of land.

[0021] Embodiment 3 The animal evaluation module is used for evaluating the animal species diversity of the ecological restoration basin. The animal species monitoring submodule, the animal species diversity calculation submodule, through the animal species monitoring submodule, the infrared camera is arranged in the management and non-management area, the medium and large mammals are monitored, the time, the species, the activity behavior are recorded, and the species diversity calculation submodule is used for calculating the Shannon diversity index of the basin animal: (4) In formula (4), p i indicates the proportion of the number of individuals of the i th animal species in the total number of monitored animal individuals. i

[0022] Example 4 The vegetation evaluation module is used for evaluating the vegetation coverage and the vegetation diversity of the ecological restoration basin. The vegetation coverage submodule, the vegetation species investigation submodule and the vegetation diversity calculation submodule are included. The vegetation coverage submodule is used for monitoring the vegetation coverage change in the management and non-management area. The vegetation species investigation submodule is used for investigating the vegetation species and abundance in the basin by the quadrat method. The vegetation species diversity submodule is used for calculating the Shannon diversity index of the plants in the basin: (5) In formula (5), p i indicates the proportion of the number of individuals of the i th plant species in the total number of individuals of all plant species in the basin. i

[0023] Example 5 The ecosystem landscape evaluation module is used for evaluating the landscape connectivity and the landscape heterogeneity of the ecological restoration basin. The landscape connectivity submodule and the landscape heterogeneity submodule are included. The landscape connectivity is calculated by Fragstats (landscape fragmentation): FI FI PD / S (6) In formula (6), PD indicates the number of patches, S indicates the landscape area.

[0024] The ecosystem landscape heterogeneity submodule calculates the SHDI (Shannon diversity index) by Fragstats: (7) In formula (7), p i indicates the proportion of the area of the i th landscape type. i

[0025] Example 6 ​​​​​The ecosystem function evaluation module is used for evaluating the ecosystem elasticity, ecosystem resilience, ecosystem adaptability evaluation, and ecosystem service function evaluation of the ecological restoration basin. The ecosystem function evaluation module comprises an ecosystem elasticity submodule, an ecosystem adaptability submodule, an ecosystem resilience submodule, and an ecosystem service function submodule. The ecosystem elasticity submodule calculates elasticity scores for land use by using an analytic hierarchy process: (8) In formula (8), E represents an ecosystem elasticity force, represents a first-class ecosystem elasticity score, i represents a second-class ecosystem elasticity score, X i represents a first-class land use area. i

[0026] The ecosystem service function submodule is P (ecosystem resistance): (9) In formula (9), ESV represents an ecosystem service value, VC i represents a first-class ecosystem value coefficient, i i represents a second-class land use area. X i

[0027] The ecosystem adaptability submodule is A (ecosystem adaptability): (10) In formula (10), FI represents a landscape connectivity, SHDI represents a landscape heterogeneity, FRAC represents a patch fractal dimension.

[0028] The ecosystem resilience submodule is R (ecosystem resilience): (11) Example 7 ​​​The comprehensive assessment module generates a comprehensive assessment result based on the assessment results from the soil assessment module, water source assessment module, vegetation assessment module, animal assessment module, ecosystem landscape assessment module, and ecosystem function assessment module. It includes an entropy method submodule, a CRITIC method submodule, and a combined weighting submodule. It calculates weight indicators using both the entropy and CRITIC methods, and based on the principle of minimum relative information entropy, combines the entropy method weights and CRITIC method weights to obtain the combined weights of the indicators. The entropy method submodule's calculation logic includes establishing an indicator set. X ={ X ij}, Dimensionless representation of indicators: (12) In equation (12), x ij For the first i Region 1 j Original values ​​of the indicator y ij For the first i Region 1 j The indicator is dimensionless.

[0029] Calculate the first i The object in the first j Percentage of each indicator: (13) In equation (13), y ij For the first i Region 1 j The indicator is a dimensionless value. m This represents the total number of regions.

[0030] Calculate information entropy: (14) Calculate redundancy: (15) Calculate the weights: (16) In equation (16), n This represents the total number of indicators.

[0031] The CRITIC submodule's calculation logic includes a set of indicators. X ={ X ij The indicator is dimensionless using formula (12), and the standard deviation of the indicator is calculated: (17) In equation (17), yij For the first i Region 1 j The indicator is a dimensionless value. m For the total number of regions, For the first j The average value of the indicator across all regions.

[0032] Calculate the correlation of indicators: (18) In equation (18), y j , y k The first j, k The dimensionless value of the indicator j, k =1, 2... n .

[0033] Calculate conflict: (19) In equation (19), n This represents the total number of indicators.

[0034] Calculated information content: (20) Calculate the weights: (twenty one) The combined weights utilize the principle of minimum relative information entropy, combining the entropy method weights with the CRITIC method weights to obtain the combined weights: (twenty two) Example 8 This embodiment takes the Wangjia River basin in the Qinba Mountains as an example, such as... Figure 2 As shown, the effectiveness of ecological restoration in this watershed is evaluated.

[0035] S1. Using ArcGIS, calculate the following indicators for the soil assessment module, water source assessment module, vegetation assessment module, animal assessment module, ecosystem landscape assessment module, and ecosystem function assessment module before ecological restoration of the watershed: Table 1. Indicators of different areas before ecological restoration

[0036] S2. Normalize the data using the entropy method submodule of the comprehensive evaluation module: Table 2. Normalized Indicators for Different Regions

[0037] S3. Calculate the index weighting of sub-modules using the entropy value method for comprehensive evaluation: Table 3 Index Specific Gravity

[0038] S4, calculate information entropy value by the entropy value method sub-module of the comprehensive evaluation module: Table 4 Information Entropy Value

[0039] S5, calculate difference coefficient by the entropy value method sub-module of the comprehensive evaluation module: Table 5 Difference Coefficient

[0040] S6, calculate each index weight by the entropy value method sub-module of the comprehensive evaluation module: Table 6 Each Index Weight

[0041] S7, calculate each index standard deviation by the CRITIC method sub-module of the comprehensive evaluation module: Table 7 Each Index Standard Deviation

[0042] S8, calculate each index conflict by the CRITIC method sub-module of the comprehensive evaluation module: Table 8 Each Index Conflict

[0043] S9, calculate each index information quantity by the CRITIC method sub-module of the comprehensive evaluation module: Table 9 Each Index Information Quantity

[0044] S10, calculate each index weight by the CRITIC method sub-module of the comprehensive evaluation module: Table 10 Each Index Weight

[0045] S11, calculate each index combined weight by the combined weight sub-module of the comprehensive evaluation module: Table 11 Each Index Combined Weight

[0046] S12, calculate each index evaluation value by the combined weight sub-module of the comprehensive evaluation module: Table 12 Each Index Evaluation Value

[0047] S13, according to the above process, the evaluation value of each index after ecological restoration is calculated by the comprehensive evaluation module; S14, the ecological restoration effect is evaluated by comparing the evaluation values of each index before and after ecological restoration.

Claims

1. A method for assessing the effectiveness of ecological restoration, characterized in that, The method comprises the following steps: Step 1, obtaining various indexes of different regions in the target region, including original evaluation data of soil, water source, animal, vegetation, ecosystem landscape and ecosystem function; Step 2, carrying out dimensionless processing on the original evaluation data obtained in step 1 by a standardization method to obtain standard evaluation data; Step 3, calculating the combination weight of various indexes of different regions in the target region; Step 4, based on the combination weight obtained in step 3, the standard evaluation data obtained in step 2 is weighted and calculated to obtain the evaluation value of each index before and after ecological restoration, and the evaluation result of the effect of ecological restoration is obtained by comparing the evaluation value of each index before and after ecological restoration.

2. The method of claim 1, wherein, The original evaluation data of the soil index in step 1 is obtained in the following manner: Step 1.1, in the treatment area and the untreated area of ecological restoration, soil samples are collected and soil physical and chemical properties including carbon content and nitrogen content are measured; Step 1.

2. Calculate the soil retention amount by formula (1) for the soil sample collected in Step 1.1 Q sr : Q sr = R × K × L × S × C × P （1） in formula (1), R represents a rainfall erosivity factor, K represents a soil erodibility factor, L represents a slope length factor, S represents a slope gradient factor, C represents a vegetation cover and management factor, P represents a conservation practice factor.

3. The method of claim 1, wherein, The original evaluation data of the water source index in step 1 is obtained in the following manner: The water conservation amount is calculated by formula (2) W : （2） In formula (2), P represents the amount of precipitation, R represents the amount of surface runoff, ET represents the amount of evapotranspiration, Δ S represents the water storage variable; The surface pollution amount is calculated by formula (3) L : （3） In formula (3), A i indicates the first i class of land, C i indicates the first i class of land corresponds to the pollution output coefficient.

4. The method of claim 1, wherein, The original evaluation data of the animal index in step 1 is obtained in the following manner: Step 1.1, in the treatment area and the untreated area of ecological restoration, infrared cameras are arranged to monitor the activities of medium and large mammals to obtain monitoring data; Step 1.

2. Calculate the Shannon diversity index for the animals from the data monitored in step 1.1 by the formula (4) H′ : （4） In formula (4), p i indicates the proportion of the number of individuals of the i animal species to the total number of monitored animal individuals.

5. The method of claim 1, wherein, The original evaluation data of the vegetation index in step 1 is obtained in the following manner: Step 1.1, in the treatment area and the untreated area of ecological restoration, the vegetation coverage is monitored respectively to obtain the relative change rate of vegetation coverage in the treatment area and the untreated area; Step 1.2, representative quadrats are selected in the ecological restoration basin by using the quadrat method, the plant species name and individual number in each quadrat are recorded, and the total number of all monitored plant species in the basin is counted; Step 1.

3. Calculate plant Shannon diversity index from data monitored in Step 1.2 by formula (5) H′ : （5） In formula (5), p i indicates the proportion of the number of individuals of the i-th plant species in the total number of individuals of all plant species in the watershed. i indicates the proportion of the number of individuals of the i-th plant species in the total number of individuals of all plant species in the watershed.

6. The ecological restoration effectiveness assessment method of claim 1, wherein, The original evaluation data of the ecosystem landscape index in step 1 is obtained in the following manner: Landscape connectivity is calculated by equation (6) FI : FI = PD / S （6） In formula (6), PD represents the number of patches, S represents the landscape area; Landscape heterogeneity is calculated by equation (7) SHDI : （7） In formula (7), p i indicates the area proportion of the landscape type of the first i class.

7. The ecological restoration effectiveness assessment method of claim 6, wherein, The original evaluation data of the ecosystem function index in step 1 is obtained in the following manner: Step 1.

1. Calculate the ecosystem resilience force by equation (8) E : （8） In formula (8), representing the first i class ecosystem elasticity score, X i representing the first i class land use area; The ecosystem service value is calculated by formula (9) P : （9） In formula (9), ESV represents the value of the ecosystem service, VC i represents the value of the first i class of ecosystem value coefficient, X i represents the value of the first i class of land use area; Ecosystem adaptability is calculated by equation (10) A : （10） In formula (10), FI represents the landscape connectivity, SHDI represents the landscape heterogeneity, FRAC represents the patch fractal dimension; Step 1.2, ecosystem resilience force based on the ecosystem elasticity force resulting from step 1.1 E , ecosystem service value P , ecosystem adaptability A , calculating the ecosystem resilience by equation (11) R : （11）。 8. The ecological restoration effectiveness assessment method of claim 1, wherein, The first step in step 2 defines the obtained original evaluation data set as X ={ X ij}, wherein i is a different region, j is an index; and then for each index j , the original evaluation data is converted into a dimensionless value in the interval [0, 1], i.e. standard evaluation data, by formula (12): （12） In formula (12), x ij For the i Region the j Index raw value, y ij For the i Region the j Index dimensionless value.

9. The method of claim 1, wherein, The step 3 is specifically: Step 3.1, calculating the entropy value method weight Step 3.1.1: For the dimensionless standard evaluation data, calculate the value of each region in the first step using equation (13). j The proportion of the value under the indicator to the total value of all areas of the indicator. p ij : （13） In formula (13), y ij For the i Region the j Indicator dimensionless value, m For the total number of regions; Step 3.1.2, for each indicator j , the information entropy is calculated by equation (14) e i : （14） Step 3.1.

3. Calculate redundancy by equation (15) d j and further calculate entropy weight by equation (16) w E : （15） （16） In formula (16), n is the total number of indicators; Step 3.2, calculating the CRITIC method weight Step 3.2.1, for each indicator j The standard deviation of the dimensionless values of this indicator for all regions is calculated by equation (17) σ j : （17） In formula (17), y ij For the first i Region the first j Indicator dimensionless value, m For the total number of regions, For the first j Indicator average value of all regions; Step 3.2.

2. For each pair of indicators j and k the Pearson correlation coefficient is calculated by equation (18) R jk and the conflict is calculated by equation (19) C j : （18） （19） In formula (18), y j , y k respectively the first j, k dimensionless value of the index, j, k = 1, 2... n ; in formula (19), n is the total number of indices; Step 3.2.

3. Calculate information content by equation (20) S j and further calculate CRITIC method weights by equation (21) w C : （20） （21） Step 3.3, combining the entropy weights from equation (22) with the CRITIC weights w E w C W i :​​ （22）。

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