Method for constructing ecological function evaluation model of three gorges reservoir area hydro-fluctuation belt

Abstract

The invention discloses a method for constructing an ecological function evaluation model of a three gorges reservoir area hydro-fluctuation belt, which is beneficial to protecting the ecological environment of the hydro-fluctuation belt by researching the ecological function of the three gorges reservoir area hydro-fluctuation belt. According to the method, the ecological environment key feature elements and interference factors of different types of hydro-fluctuation belts are screened, different hydro-fluctuation belt ecological system service function types are identified, a Three Gorges reservoir area hydro-fluctuation belt ecological function evaluation system is constructed, and the method has certain scientific value and economic and social and ecological environment practical significance.

Description

Technical Field

[0001] The present invention relates to the field of environmental governance, and in particular to a method for constructing an ecological function assessment model for the drawdown zone in the Three Gorges Reservoir area. Background Art

[0002] The drawdown zone of the Three Gorges Reservoir, an ecological transition zone connecting the land and water areas of the reservoir, serves as an indicator of ecological changes within the reservoir. Furthermore, pollutants such as domestic and industrial waste, residual fertilizers and pesticides from human activities along the reservoir banks enter the reservoir waters through the drawdown zone, causing water pollution and eutrophication. These pollutants accumulate and degrade in the drawdown zone due to the periodic fluctuations in the water level. The drawdown zone serves the important ecological functions of a buffer zone and revetment, and its effectiveness is crucial to maintaining reservoir water quality and its long-term safe operation.

[0003] Therefore, based on the characteristics of the ecosystem, basic functional features, and requirements and characteristics closely related to human welfare in the drawdown zone of the Three Gorges Reservoir, an ecological function assessment system for the drawdown zone of the Three Gorges Reservoir was established to determine its ecological function value, providing a theoretical basis, decision-making basis and scientific support for the subsequent dynamic monitoring of the ecological service value of the drawdown zone of the Three Gorges Reservoir, targeted remediation, and sustainable utilization of resources. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method for constructing an ecological function assessment model for the drawdown zone of the Three Gorges Reservoir area, which method can evaluate the ecological function of the drawdown zone area.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] It has certain scientific value, economic, social and ecological environmental significance.

[0007] The method for constructing an ecological function assessment model for the drawdown zone in the Three Gorges Reservoir area includes the following steps:

[0008] (1) Selection of ecological characteristic factors: From a comprehensive and integrated perspective, while adhering to the principles of comprehensiveness, feasibility, scientificity, and the combination of qualitative and quantitative factors, 11 indicators in five aspects were initially selected, including slope, elevation, topography, vegetation characteristics, resilience characteristics, water condition characteristics, and soil condition characteristics;

[0009] (2) Principal component analysis: 2020 was selected as the research base year. The data source was a fair and authoritative website. Principal component analysis was used to screen variables. The analysis steps are as follows:

[0010] a. Collect p-dimensional random vectors for the original indicator data and construct the sample matrix N:

[0011] N=(X1, X2, ..., X p ) T (1)

[0012] X i =(x i1 , x i2 ,…,x ip ) T , i=1,2,...,n>p (2)

[0013] In the formula: X1, X2..., X P Represents the characteristic factor index, T represents the transpose of the matrix, and Xi represents the sample value;

[0014] b. Normalize the sample matrix N to obtain the standardized matrix Z:

[0015]

[0016] In the formula: x ij Indicates that there are n samples, each sample has j values, represents the average value of j values, s j represents the variance of each column;

[0017] in,

[0018]

[0019] c. Calculate the correlation coefficient matrix for matrix Z:

[0020]

[0021] In the formula, R represents the correlation coefficient matrix describing the linear correlation between variables, r ij represents the i-th

[0022] The element in the jth row and column represents the correlation coefficient between the i-th variable and the j-th variable, p, z T , z represents the standardized data matrix (n×p), with the mean of each column being 0 and the standard deviation being 1;

[0023]

[0024] d. According to |R-λI p |=0 characteristic equation, eigenvalues and eigenvectors of the solver correlation matrix R;

[0025] e. Calculate the cumulative contribution rate:

[0026]

[0027] In the formula, E jIt represents the cumulative variance contribution rate of the first m principal components, that is, the proportion of original data information retained by the first m principal components, λ j It represents the eigenvalue of the jth principal component, reflecting the variance of the principal component;

[0028] And E j ≥0.8 to determine the m value, so that the information utilization rate is more than 70%. j , j = 1, 2, ..., m,

[0029] Solve equation R b =λ jb The unit eigenvector

[0030] In the formula, R b Indicates the load of variable b on a principal component or factor, reflecting the strength of the correlation between variable b and the principal component / factor, λ jb represents the eigenvalue (or eigenvector component) of the jth principal component / factor, which represents the contribution weight of the principal component / factor to the variable b;

[0031] f. Convert the standardized indicator variables into principal components:

[0032]

[0033] In the formula, Z i T The standardized feature vector of the ii-th sample, b j o , represents the jth basis vector or projection direction;

[0034] U1 is called the first principal component, U2 is called the second principal component, ..., U p It is called the pth principal component;

[0035] g. According to factor loading b ij Screening index, b ij is the factor loading of the jth principal component on the i-th indicator, according to the principal component F j Factor loading on the i-th indicator |b ij |Screening indicator,|b ij The larger the value of |, the more significant the impact of the i-th indicator on the results of the influencing factor evaluation system. ij The smaller | is, the less obvious the impact of the i-th indicator on the evaluation system results is. ij |Big indicator.

[0036] 2. The method for constructing an ecological function assessment model for the drawdown zone of the Three Gorges Reservoir area according to claim 1 is characterized in that the data is obtained from a geospatial data cloud, MOD17A3HGF product, Google Earth Engine inversion, the World Soil Database, Google Earth, or manual visual interpretation.

[0037] The beneficial effects of the present invention are as follows: the present invention screens the key characteristic elements and interference factors of the ecological environment of different types of drawdown zones through the characteristics of the ecological environment changes in the drawdown zone of the Three Gorges Reservoir, effectively identifies the ecological service function of the drawdown zone of the Three Gorges Reservoir, and then uses the principal component analysis method to build a complete ecological function evaluation system, which is successfully used in the drawdown zone of the Three Gorges Reservoir to understand the current status of the ecological function of the drawdown zone of the Three Gorges Reservoir, provide a basis for the ecological environment management of the drawdown zone of the Three Gorges Reservoir, propose corresponding environmental protection strategies for the specific ecological environment conditions of the drawdown zone of the Three Gorges Reservoir, and improve various plans for the ecological environment protection of the drawdown zone of the Three Gorges Reservoir. DETAILED DESCRIPTION

[0038] The present invention will be further described below with reference to specific examples so that those skilled in the art can better understand the present invention and implement it, but the examples are not intended to limit the present invention.

[0039] Example 1: Selection of characteristic factors

[0040] (1) Principles for selecting characteristic factors

[0041] When initially selecting the characteristic factors of the ecological environment in the drawdown zone of the Three Gorges Reservoir area, considering the complex internal relationships between the factors, we chose to start from a comprehensive and integrated perspective, and at the same time follow the principles of comprehensiveness, feasibility, scientificity, and the combination of qualitative and quantitative methods to initially select characteristic factor indicators.

[0042] ① Comprehensiveness

[0043] The selection of characteristic elements should be comprehensive, because any representation and description of the ecological environment should include a comprehensive level display of multiple aspects so as to reflect the changes in the ecological environment conditions in the drawdown zone of the Three Gorges Reservoir at a macro level.

[0044] ② Feasibility

[0045] The selection of characteristic elements of the drawdown zone in the Three Gorges Reservoir area should be feasible and take into account the current actual situation. The selection should take into account both how to represent the ecological environment and the availability of data for subsequent indicator screening.

[0046] ③Scientificity

[0047] The selection of characteristic elements of the drawdown zone in the Three Gorges Reservoir area should be scientific. The selected characteristic elements should be recognized and applied in a large number of previous studies. There should be sufficient data, a large number of formulated standards, and the selection of real, accurate and standard indicators that can objectively reflect the ecological environment of the drawdown zone.

[0048] ④ Combination of qualitative and quantitative

[0049] The selection of characteristic elements of the drawdown zone in the Three Gorges Reservoir area requires a combination of qualitative and quantitative methods. Indicators are selected based on the principle that qualitative is the basis of quantitative and quantitative is the refinement of qualitative.

[0050] (2) Characteristic factor analysis

[0051] ①Natural features

[0052] Like other large reservoirs in China, the Three Gorges Reservoir area has a distinct drawdown zone. Its ecological and environmental characteristics are primarily influenced and controlled by reservoir operation and the natural environmental base. The Three Gorges Reservoir area is a river-type reservoir primarily located within the Yangtze River reservoir section. A natural drawdown zone had already formed in this section before impoundment, and its ecological and environmental characteristics are primarily influenced and controlled by the natural dry and flood seasons. Compared to the natural drawdown zones of the Yangtze River and large reservoirs in China, the drawdown zone of the Three Gorges Reservoir area exhibits distinct ecological and environmental characteristics.

[0053] The main vegetation types in the area include evergreen broad-leaved forests, evergreen coniferous forests, deciduous coniferous forests, mixed evergreen and deciduous broad-leaved forests, bamboo forests, mountain shrubs and grasses, and economic forests. Subtropical evergreen broad-leaved forests are a valuable zonal vegetation in Chongqing, with high species density and significant ecological benefits. Therefore, this vegetation characteristic is considered one of the significant features of the Three Gorges Reservoir's drawdown zone.

[0054] The repeated cyclical changes in the water level in the reservoir area cause the soil in the drawdown zone to alternate between dry and wet. In the summer, the water level is low and the soil in the drawdown zone is in a dry state, which is significantly different from other areas. Therefore, the soil condition characteristics are one of the significant characteristic elements of the drawdown zone in the Three Gorges Reservoir area.

[0055] The study area has an average annual relative humidity of 76%, an average frost-free period of approximately 260 days, and concentrated precipitation from April to October, with an average annual precipitation of 1,100 to 1,200 mm. The storage and discharge cycles are opposite to the region's rainfall patterns and the hydrological cycles of natural rivers. Therefore, moisture conditions are a prominent characteristic of the drawdown zone in the Three Gorges Reservoir.

[0056] ②Economic characteristics

[0057] The Three Gorges Reservoir area has experienced rapid economic development in recent years, but this development has been uneven across regions. In 2009, the region's gross domestic product (GDP) reached approximately 504.827 billion yuan, with an average annual growth rate of 19.82%. Per capita GDP reached over 24,000 yuan. The primary, secondary, and tertiary industries accounted for a ratio of 7.4:45.4:47.2, with the secondary and tertiary industries predominating. Chongqing's GDP reached 298.625 billion yuan, accounting for approximately 60% of the reservoir area's GDP, with a per capita GDP of 44,787 yuan. Wanzhou, Fuling, Changshou, Yiling, and Kaixian counties all achieved GDPs exceeding 10 billion yuan, with Fuling, Yiling, and Wanzhou achieving above-average per capita GDPs of 34,517 yuan, 27,988 yuan, and 25,132 yuan, respectively. The GDPs of other counties ranged from 3.094 billion yuan to 9.369 billion yuan, with relatively low per capita GDPs. Furthermore, the urban-rural income gap in the reservoir area is significant, and urban and rural areas may have different needs for the development and utilization of the drawdown zone. In 2009, the per capita disposable income of rural residents in the Three Gorges Reservoir area was approximately 5,000 yuan, about one-third of the per capita disposable income of urban residents (approximately 14,000 yuan).

[0058] ③Social characteristics

[0059] In recent years, the population of the Three Gorges Reservoir area has generally stabilized. By the end of 2009, the total registered population in the Three Gorges Reservoir area reached 20.8118 million, with a population density of 367 people per square kilometer, more than double the national average of 138 people per square kilometer. Along the main and tributary rivers of the reservoir area are large and relatively densely populated cities with populations exceeding one million, such as Chongqing's main urban area, Wanzhou District, Kai County, Jiangjin District, Yunyang County, Fuling District, Fengjie County, and Zhong County. There are also smaller towns with relatively smaller populations, such as Xingshan and Wulong.

[0060] ④ Selection of ecological characteristic elements

[0061] This study comprehensively considers the overall ecological situation of the drawdown zone in the Three Gorges Reservoir area. Starting from the natural ecological level, 11 indicators in five aspects are selected, including slope, elevation, topographic position, vegetation characteristics, resilience characteristics, water condition characteristics, and soil condition characteristics. The characteristic factor indicators are shown in Table 1.

[0062] Table 1. Classification of ecological environment characteristic elements in the drawdown zone of the Three Gorges Reservoir area

[0063]

[0064] (2) Key feature element screening methods and principles

[0065] ①Source of sample data

[0066] This study uses the drawdown zone of the Three Gorges Reservoir as the scope and object of sample data acquisition, selects 2020 as the research base year, and the characteristic factor indicator data are all derived from major domestic and foreign portals. The data sources are fair and authoritative, which can ensure the representativeness and accuracy of this study (Table 2).

[0067] Table 2. Data sources of characteristic elements of the drawdown zone in the Three Gorges Reservoir area

[0068]

[0069]

[0070] ②Principle of principal component analysis

[0071] Principal Component Analysis (PCA), proposed by the foreign scholar Houtning in 1933, is a statistical method based on the idea of data dimensionality reduction. Using an orthogonal transformation, this method transforms an original random vector with correlated components into a new random vector with uncorrelated components. Algebraically, this transforms the covariance of the original random vector into a diagonal matrix. Geometrically, it transforms the original coordinate system into a new orthogonal coordinate system, aligning it with the p orthogonal directions that maximize the sample point distribution. This method then reduces the dimensionality of the multidimensional variable system.

[0072] One important aspect of principal component analysis is its use in selecting regression variables. To facilitate structural analysis, control, and forecasting of the model itself, and to facilitate the selection of optimal variables from a subset of the original variables, principal component analysis is used to select variables, simplifying the problem while also yielding more scientific and effective data results.

[0073] ③ Principal component analysis steps

[0074] a. Collect p-dimensional random vectors for the original indicator data and construct the sample matrix N:

[0075] N=(X1,X2,…,X p ) T (1)

[0076] X i =(x i1 ,x i2 ,…,x ip ) T i=1,2,...,n>p (2)

[0077] X1, X2..., X P Represents the characteristic factor index, T represents the transpose of the matrix, and Xi represents the sample value;

[0078] b. Normalize the sample matrix N to obtain the standardized matrix Z:

[0079]

[0080] In the formula: x ij Indicates that there are n samples, each sample has j values, represents the average value of j values, s j represents the variance of each column;

[0081] in,

[0082]

[0083] c. Calculate the correlation coefficient matrix for matrix Z:

[0084]

[0085] In the formula, R represents the correlation coefficient matrix describing the linear correlation between variables, r ij represents the i-th

[0086] The element in the jth row and column represents the correlation coefficient between the i-th variable and the j-th variable, p, z T , z represents the standardized data matrix (n×p), with the mean of each column being 0 and the standard deviation being 1;

[0087] Where:

[0088] d. According to |R-λI p |=0 characteristic equation, solve the eigenvalues and eigenvectors of the correlation matrix R.

[0089] e. Calculate the cumulative contribution rate:

[0090]

[0091] In the formula, E j It represents the cumulative variance contribution rate of the first m principal components, that is, the proportion of original data information retained by the first m principal components, λ j It represents the eigenvalue of the jth principal component, reflecting the variance of the principal component;

[0092] And E j ≥0.8 to determine the m value, so that the information utilization rate is more than 70%. j , j = 1, 2..., m, solve equation R b =λ jb The unit eigenvector

[0093] In the formula, R bIndicates the load of variable b on a principal component or factor, reflecting the strength of the correlation between variable b and the principal component / factor, λ jb It represents the eigenvalue (or eigenvector component) of the jth principal component / factor, and the contribution weight of the principal component / factor to the variable b.

[0094] f. Convert the standardized indicator variables into principal components:

[0095]

[0096] In the formula, Z i T The standardized feature vector of the ii-th sample, b j o , represents the jth basis vector or projection direction;

[0097] U1 is called the first principal component, U2 is called the second principal component, ..., U p It is called the pth principal component.

[0098] g. According to factor loading b ij Screening indicators. b ij is the factor loading of the jth principal component on the i-th indicator, according to the principal component F j Factor loading on the i-th indicator |b ij |Screening indicator,|b ij The larger the value of |, the more significant the impact of the i-th indicator on the results of the influencing factor evaluation system. ij The smaller | is, the less obvious the impact of the i-th indicator on the evaluation system results is. This study retains |b ij |Big indicator.

[0099] Example 3: Screening of key characteristic elements

[0100] (1) Screening steps

[0101] 7,000 random points were created based on the type of drawdown zone, and the index values of each characteristic factor were extracted to construct a raw data matrix of the ecological and environmental characteristic factors of different drawdown zone types. Analysis was performed using the factor analysis module of SPSS software. First, the data was standardized to eliminate the influence of different dimensions. KMO and Bartlett's sphericity tests were performed on the data, and principal component analysis was performed. The number of principal components was determined based on the cumulative contribution rate. The correlation matrix, total variance explained, and principal component loading matrix were calculated. Finally, the key characteristic factors of the ecological environment of the drawdown zone in the reservoir area were identified based on the principal component factor loadings.

[0102] (2) Screening results of key characteristic elements of different types of water-fluctuation zones

[0103] 1) Urban-type ebb and flow zone

[0104] ① KMO and Bartlett sphericity test: The test results are shown in Table 3. The KMO value is 0.582, which is greater than 0.5 and meets the requirements of factor analysis. The Bartlett sphericity test value is 0.000, P < 0.01. The test results indicate that there is a strong correlation between the variables, and principal component analysis can be performed on the 11 characteristic factor indicators.

[0105] Table 3. KMO and Bartlett test results for urban areas

[0106]

[0107] ② Correlation coefficient matrix analysis: From Table 4, we can see that there are different degrees of correlation between the 11 characteristic factor indicators of the ecological environment in the urban drawdown zone of the Three Gorges Reservoir area. Among them, the slope (X1) and the topographic position (X3) have a strong correlation, with a correlation coefficient of 0.975; the normalized vegetation index (X4) and the resilience coefficient (X7) have a strong correlation, with a correlation coefficient of 0.582; the soil texture (X9) and the soil pH (X 11 ) has a strong correlation, with a correlation coefficient of 0.679; slope (X1) and terrain moisture (X8) have a strong negative correlation, with a correlation coefficient of -0.541; terrain position (X3) and terrain moisture (X8) have a strong negative correlation, with a correlation coefficient of -0.599; soil texture (X9) and organic carbon content (X 10 ) have an obvious strong negative correlation with the correlation coefficient of -0.820; the organic carbon content (X 10 ) and soil pH (X 11 ) phase has an obvious strong negative correlation with a correlation coefficient of -0.904.

[0108] Table 4. Correlation coefficient matrix of characteristic elements of urban-type water-fluctuation zones

[0109] <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> <![CDATA[X9]]> <![CDATA[X 10 ]]> <![CDATA[X 11 ]]> <![CDATA[X1]]> 1.000 .079 .975 .117 .034 -.029 .148 -.541 -.146 .240 -.278 <![CDATA[X2]]> .079 1.000 .195 .026 -.001 -.031 -.041 -.225 .047 -.077 .069 <![CDATA[X3]]> .975 .195 1.000 .122 .042 -.026 .146 -.599 -.146 .239 -.281 <![CDATA[X4]]> .117 .026 .122 1.000 .159 -.247 .582 -.100 -.188 .199 -.160 <![CDATA[X5]]> .034 -.001 .042 .159 1.000 -.071 .166 -.047 -.077 .057 -.108 <![CDATA[X6]]> -.029 -.031 -.026 -.247 -.071 1.000 .172 .011 -.047 .010 -.008 <![CDATA[X7]]> .148 -.041 .146 .582 .166 .172 1.000 -.134 -.289 .278 -.266 <![CDATA[X8]]> -.541 -.225 -.599 -.100 -.047 .011 -.134 1.000 .144 -.202 .224 <![CDATA[X9]]> -.146 .047 -.146 -.188 -.077 -.047 -.289 .144 1.000 -.820 .679 <![CDATA[X 10 ]]> .240 -.077 .239 .199 .057 .010 .278 -.202 -.820 1.000 -.904 <![CDATA[X 11 ]]> -.278 .069 -.281 -.160 -.108 -.008 -.266 .224 .679 -.904 1.000

[0110] ③ Principal Component Contribution Analysis: Based on the principal component analysis, we obtained the total variance decomposition table 5. The eigenvalues of the first, second, third, and fourth principal components are all greater than 1, and the cumulative contribution of the first four principal components reaches 72.174%, exceeding 70%, indicating that the first four principal components contain a large amount of information. In summary, the first four principal components were extracted to obtain the correlation loading factor matrix between the principal components and each factor.

[0111] Table 5. Contribution rate of principal components of characteristic elements of urban-type water-fluctuation zones

[0112]

[0113]

[0114] ④ Principal component load factor matrix analysis: The principal component factor loads are shown in Table 6. The first principal component has a significant effect on slope (X1), terrain position (X3), terrain moisture (X8), soil texture (X9), organic carbon content (X1), and soil moisture content (X2). 10 ), soil pH (X 11 ) have large factor loading coefficients, which are 0.667, 0.679, 0.554, 0.705, 0.807, and 0.794, respectively. The first principal component mainly reflects terrain characteristics, water condition characteristics, and resilience characteristics. The second principal component still has large factor loading values for slope (X1), terrain position (X3), and terrain humidity (X8), which are 0.639, 0.667, and -0.539, respectively. The second principal component can be attributed to terrain characteristics and water condition characteristics. The third principal component has large factor loading values for normalized vegetation index (X4) and resilience coefficient (X7), which are 0.784 and 0.571, respectively. The third principal component mainly reflects vegetation characteristics and resilience characteristics. The fourth principal component has a large factor loading value for land cover type (X6), which is 0.895, and the fourth principal component mainly reflects resilience characteristics.

[0115] Table 6. Principal component factor loading values of characteristic elements of urban-type water-fluctuation zones

[0116]

[0117] ⑤ Results of screening of key characteristic elements of urban-type water-fluctuation zone: According to the maximum value of principal component factor loading greater than 0.8, the key characteristic elements of the urban-type water-fluctuation zone in the Three Gorges Reservoir area are screened as resilience characteristics and soil condition characteristics, as shown in Table 7, including characteristic indicators such as surface cover type (X6), organic carbon content (X 10 ).

[0118] Table 7. Identification results of key characteristic elements of urban-type water-fluctuation zones

[0119]

[0120] 2) Rural type water-fluctuation zone

[0121] ① KMO and Bartlett sphericity test: The test results are shown in Table 8. The KMO value is 0.528, which is greater than 0.5 and meets the requirements of factor analysis. The Bartlett sphericity test value is 0.000, P < 0.01. The test results indicate that there is a strong correlation between the variables, and principal component analysis can be performed on the 11 characteristic factor indicators.

[0122] Table 8. KMO and Bartlett test results for rural areas

[0123]

[0124] ② Correlation coefficient matrix analysis: From Table 9, we can see that there are different degrees of correlation between the 11 characteristic factor indicators of the ecological environment in the rural drawdown zone of the Three Gorges Reservoir area. Among them, the correlation between slope (X1) and topographic position (X3) is strong, with a correlation coefficient of 0.971; soil texture (X9) and soil pH (X 11 ) have a strong correlation with each other, with a correlation coefficient of 0.516; there is a strong negative correlation between slope (X1) and terrain moisture (X8), with a correlation coefficient of -0.603; there is a strong negative correlation between terrain position (X3) and terrain moisture (X8), with a correlation coefficient of -0.661; soil texture (X9) and organic carbon content (X 10 ) is -0.619, showing a strong negative correlation; organic carbon content (X 10 ) and soil pH (X 11 ) have a strong negative correlation, with a correlation coefficient of -0.840.

[0125] Table 9. Correlation coefficient matrix of characteristic elements of rural water-fluctuation zones

[0126]

[0127] ③ Principal Component Contribution Analysis: Based on the principal component analysis, we obtained the total variance decomposition table 10. The eigenvalues of the first, second, third, fourth, and fifth principal components were all greater than 1, and the cumulative contribution of the first five principal components reached 81.960%, exceeding 70%, indicating that the first five principal components contain a considerable amount of information. In summary, we extracted the first five principal components and derived the correlation loading matrix between the principal components and each factor.

[0128] Table 10. Contribution rate of principal components of characteristic elements in rural-type water-fluctuation zones

[0129]

[0130] ④ Principal component load factor matrix analysis: The principal component factor loads are shown in Table 11. The first principal component has a relatively large factor load coefficient for slope (X1), elevation (X2), terrain position (X3) and terrain moisture index (X8), which are 0.904, 0.573, 0.943 and 0.753 respectively. The first principal component can be attributed to terrain characteristics and water condition characteristics; the second principal component has a relatively large factor load coefficient for soil texture (X9), soil organic carbon content (X10), soil moisture content (X11) and soil moisture content (X12). 10 ) and soil pH (X 11) have factor loading coefficients with large absolute values, which are 0.804, 0.905, and 0.850, respectively. The second principal component can be attributed to soil condition characteristics; the third principal component has large factor loading values for surface cover type (X6) and resilience coefficient (X7), which are 0.729 and 0.913; the fourth principal component has high factor loading values for normalized vegetation index (X4) and surface cover type (X6), which are 0.870 and 0.580, respectively; the fifth principal component has a high factor loading value for net primary productivity of vegetation (X5), which is 0.877. The third, fourth, and fifth principal components mainly reflect vegetation and resilience characteristics.

[0131] Table 11. Principal component factor loading values of characteristic elements of rural-type water-fluctuation zones

[0132]

[0133] ⑤ Results of screening of key characteristic elements of rural water-fluctuation zone: According to the maximum value of principal component factor loading greater than 0.9, the key characteristic elements of rural water-fluctuation zone in the Three Gorges Reservoir area are screened as terrain characteristics, resilience characteristics, and soil condition characteristics, as shown in Table 12, including slope (X1), terrain position (X3), resilience coefficient (X7), organic carbon content (X8), and soil nutrient content (X9). 10 ).

[0134] Table 12. Identification results of key characteristic elements of rural-type water-fluctuation zones

[0135]

[0136]

[0137] 3) Submerged drawdown zone

[0138] ① KMO and Bartlett sphericity test: The test results are shown in Table 13. The KMO value is 0.598, which is greater than 0.5 and meets the requirements of factor analysis. The Bartlett sphericity test value is 0.000, P < 0.01. The test results indicate that there is a strong correlation between the variables, and principal component analysis can be performed on the 11 characteristic factor indicators.

[0139] Table 13. Submerged KMO and Bartlett test results

[0140]

[0141] ② Correlation coefficient matrix analysis: From Table 14, we can see that there are different degrees of correlation between the 11 characteristic factor indicators of the ecological environment in the inundated drawdown zone of the Three Gorges Reservoir area. Among them, there is a strong correlation between slope (X1) and topographic position (X3), with a correlation coefficient of 0.967; there is a strong correlation between normalized vegetation index (X4) and resilience coefficient (X7), with a correlation coefficient of 0.873; soil texture (X9) and soil pH (X1) have a strong correlation with each other, with a correlation coefficient of 0.873; soil texture (X1 ... 11 ) have a strong correlation with each other, with a correlation coefficient of 0.722; there is a strong negative correlation between slope (X1) and terrain moisture (X8), with a correlation coefficient of -0.588; there is a strong correlation between terrain position (X3) and terrain moisture (X8), with a correlation coefficient of -0.651; soil texture (X9) and organic carbon content (X 10 ) has a strong correlation, with a correlation coefficient of -0.592; organic carbon content (X 10 ) and soil pH (X 11 ) have a strong correlation with a correlation coefficient of -0.809.

[0142] Table 14. Correlation coefficient matrix of characteristic elements of submerged drawdown zone

[0143] <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> <![CDATA[X9]]> <![CDATA[X 10 ]]> <![CDATA[X 11 ]]> <![CDATA[X1]]> 1.000 .234 .967 -.080 .211 .005 -.080 -.588 -.026 .109 -.091 <![CDATA[X2]]> .234 1.000 .343 .071 .110 .016 .038 -.271 -.003 -.038 .030 <![CDATA[X3]]> .967 .343 1.000 -.056 .216 .002 -.068 -.651 -.030 .114 -.098 <![CDATA[X4]]> -.080 .071 -.056 1.000 .021 -.016 .873 -.004 -.095 .165 -.141 <![CDATA[X5]]> .211 .110 .216 .021 1.000 .008 .034 -.142 -.074 .089 -.144 <![CDATA[X6]]> .005 .016 .002 -.016 .008 1.000 .102 .001 .022 -.014 .003 <![CDATA[X7]]> -.080 .038 -.068 .873 .034 .102 1.000 .017 -.094 .154 -.133 <![CDATA[X8]]> -.588 -.271 -.651 -.004 -.142 .001 .017 1.000 .021 -.110 .115 <![CDATA[X9]]> -.026 -.003 -.030 -.095 -.074 .022 -.094 .021 1.000 -.592 .722 <![CDATA[X 10 ]]> .109 -.038 .114 .165 .089 -.014 .154 -.110 -.592 1.000 -.809 <![CDATA[X 11 ]]> -.091 .030 -.098 -.141 -.144 .003 -.133 .115 .722 -.809 1.000

[0144] ③ Principal Component Contribution Analysis: Based on the principal component analysis, we obtained the total variance decomposition table 15. The eigenvalues of the first, second, third, and fourth principal components were all greater than 1, and the cumulative contribution of the first four principal components reached 80.664%, exceeding 70%, indicating that the first four principal components contain a considerable amount of information. In summary, we extracted the first four principal components and derived the correlation loading matrix between the principal components and each factor.

[0145] Table 15. Contribution rate of principal components of characteristic elements of submerged drawdown zone

[0146]

[0147] ④ Principal component load factor matrix analysis: The principal component factor loads are shown in Table 16. The first principal component pair includes slope (X1), terrain position (X3), terrain moisture (X8), organic carbon content (X9), and soil moisture (X11). 10 ), soil pH (X 11 ) have factor loading coefficients with large absolute values, which are 0.771, 0.804, 0.675, 0.570 and 0.590 respectively. The first principal component can be attributed to terrain characteristics, water condition characteristics and soil condition characteristics; the second principal component has a significant impact on slope (X1), terrain position (X3), normalized vegetation index (X4), resilience coefficient (X7), soil texture (X9), organic carbon content (X1), soil moisture content (X2), soil moisture content (X3), soil moisture content (X4), soil moisture content (X5), soil moisture content (X6), soil moisture content (X7), soil moisture content (X8), soil moisture content (X9), soil moisture content (X10), soil moisture content (X11), soil moisture content (X12), soil moisture content (X13), soil moisture content (X14), soil moisture content (X15), soil moisture content (X16), soil moisture content (X17), soil moisture content (X18), soil moisture content (X19), soil moisture content (X21), soil moisture content (X22), soil moisture content (X23), soil moisture content (X24), soil moisture content (X25), soil moisture content (X26), soil moisture content (X27), soil moisture content (X28), soil moisture content (X29), soil moisture content (X3 10 ), soil pH (X 11) still have large factor loadings of 0.500, 0.510, 0.519, 0.523, 0.614, 0.622, and 0.652, respectively. The second principal component can be attributed to terrain characteristics, vegetation characteristics, resilience characteristics, and soil condition characteristics. The third principal component has large factor loadings of 0.795 and 0.800 for the normalized vegetation index (X4) and the resilience coefficient (X7), respectively, reflecting vegetation and resilience characteristics. The fourth principal component has a large factor loading of 0.987 for land cover type (X6), reflecting resilience characteristics.

[0148] Table 16. Principal component factor loading values of characteristic elements of the submerged drawdown zone

[0149]

[0150]

[0151] ⑤ Screening results of key characteristic elements of the submerged drawdown zone: Based on the maximum value of the principal component factor loading being greater than 0.8, the key characteristic elements of the submerged drawdown zone in the Three Gorges Reservoir area are screened out as terrain characteristics and resilience characteristics, as shown in Table 17. The characteristic indicator elements include terrain position (X3), surface cover type (X6), and resilience coefficient (X7).

[0152] Table 17. Identification results of key characteristic elements of submerged drawdown zone

[0153]

[0154] 4) Island-type ebb and flow zone

[0155] ① KMO and Bartlett sphericity test: The test results are shown in Table 18. The KMO value is 0.626, which is greater than 0.5 and meets the requirements of factor analysis. The Bartlett sphericity test value is 0.000, P < 0.01. The test results indicate that there is a strong correlation between the variables, and principal component analysis can be performed on the 11 characteristic factor indicators.

[0156] Table 18. Island KMO and Bartlett test results

[0157]

[0158] ② Correlation coefficient matrix analysis: From Table 19, we can see that there are different degrees of correlation between the 11 characteristic factor indicators of the ecological environment of the island-type drawdown zone in the Three Gorges Reservoir area. Among them, there is a strong correlation between slope (X1) and topographic position (X3), with a correlation coefficient of 0.953; there is a strong correlation between normalized vegetation index (X4) and resilience coefficient (X7), with a correlation coefficient of 0.520; surface cover type (X6) and organic carbon content (X7) are also closely related. 10 ) have a strong correlation with the correlation coefficient of 0.569; soil texture (X9) and soil pH (X 11 ) has a strong correlation with the correlation coefficient of 0.814; elevation (X2) and land cover type (X6) have a strong negative correlation with the correlation coefficient of -0.577; topographic position (X3) and topographic moisture (X8) have a strong negative correlation with the correlation coefficient of -0.510; land cover type (X6) and soil texture (X9) have a strong negative correlation with the correlation coefficient of -0.685; soil texture (X9) and organic carbon content (X 10 ) has a strong negative correlation, with a correlation coefficient of -0.848; organic carbon content (X 10 ) and soil pH (X 11 ) has a strong negative correlation with a correlation coefficient of -0.941.

[0159] Table 19. Correlation coefficient matrix of characteristic elements of island-type ebb and flow zones

[0160] <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> <![CDATA[X9]]> <![CDATA[X 10 ]]> <![CDATA[X 11 <!-- 14 -->]]> <![CDATA[X1]]> 1.000 -.062 .953 -.200 .213 -.079 -.146 -.446 .254 -.181 .167 <![CDATA[X2]]> -.062 1.000 .142 .462 .085 -.577 -.026 -.116 .486 -.463 .412 <![CDATA[X3]]> .953 .142 1.000 -.103 .204 -.192 -.146 -.510 .351 -.286 .255 <![CDATA[X4]]> -.200 .462 -.103 1.000 .176 -.264 .520 .195 -.021 -.022 -.063 <![CDATA[X5]]> .213 .085 .204 .176 1.000 .039 .258 -.087 -.054 -.075 .021 <![CDATA[X6]]> -.079 -.577 -.192 -.264 .039 1.000 .398 .060 -.685 .569 -.452 <![CDATA[X7]]> -.146 -.026 -.146 .520 .258 .398 1.000 .099 -.390 .249 -.302 <![CDATA[X8]]> -.446 -.116 -.510 .195 -.087 .060 .099 1.000 -.176 .188 -.164 <![CDATA[X9]]> .254 .486 .351 -.021 -.054 -.685 -.390 -.176 1.000 -.848 .814 <![CDATA[X 10 ]]> -.181 -.463 -.286 -.022 -.075 .569 .249 .188 -.848 1.000 -.941 <![CDATA[X 11 ]]> .167 .412 .255 -.063 .021 -.452 -.302 -.164 .814 -.941 1.000

[0161] ③ Principal Component Contribution Analysis: Based on the principal component analysis, we obtained the total variance decomposition table 20. The eigenvalues of the first, second, and third principal components were all greater than 1, and the cumulative contribution of the first three principal components reached 72.580%, exceeding 70%, indicating that the first three principal components contain a considerable amount of information. In summary, we extracted the first three principal components and derived the correlation loading matrix between the principal components and each factor.

[0162] Table 20. Contribution rate of principal components of characteristic elements of island-type ebb and flow zones

[0163]

[0164]

[0165] ④ Principal component load factor matrix analysis: The principal component factor loads are shown in Table 21. The first principal component has a significant effect on elevation (X2), terrain position (X3), land cover type (X6), soil texture (X9), organic carbon content (X10), soil urea content (X11), soil urea content (X12), soil urea content (X13), soil urea content (X14), soil urea content (X15), soil urea content (X16), 10 ), soil pH (X 11), with large factor loadings of 0.572, 0.562, 0.721, 0.912, 0.885, and 0.845, respectively. The first principal component can be attributed to topographic characteristics, resilience characteristics, and soil conditions. The second principal component also has large factor loadings of 0.781, 0.682, 0.579, and 0.543 for slope (X1), topographic position (X3), normalized difference vegetation index (X4), and topographic moisture (X8), respectively. The first two principal components can be attributed to topographic characteristics, vegetation characteristics, and moisture conditions. The third principal component has large factor loadings of 0.692, 0.636, and 0.700 for normalized difference vegetation index (X4), net primary productivity (X5), and resilience coefficient (X7), respectively. The third principal component primarily reflects vegetation characteristics and resilience.

[0166] Table 21. Principal component factor loading values of characteristic elements of island-type water-fluctuation zones

[0167]

[0168] ⑤ Screening results of key characteristic elements of island-type water-fluctuation zone: According to the maximum value of principal component factor loading greater than 0.8, the key characteristic elements of the island-type water-fluctuation zone in the Three Gorges Reservoir area are selected as soil condition characteristics, as shown in Table 22. The characteristic element indicators include soil texture (X9), organic carbon content (X 10 ), soil pH (X 11 ).

[0169] Table 22. Identification results of key characteristic elements of island-type ebb and flow zones

[0170]

[0171]

[0172] (3) Key feature element screening results

[0173] This summary combines the principal component analysis method to finally obtain the key characteristic elements of different types of water-fluctuation zones (Table 23).

[0174] Table 23. Characteristic elements of non-type water-fluctuation zones

[0175]

[0176] The above embodiments are merely preferred embodiments for the purpose of fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are within the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

Claims

1. A method for constructing an ecological function assessment model for the drawdown zone in the Three Gorges Reservoir area is characterized by: The steps include: (1) Selection of ecological characteristic factors: From a comprehensive and integrated perspective, while adhering to the principles of comprehensiveness, feasibility, scientificity, and the combination of qualitative and quantitative factors, 11 indicators in five aspects were initially selected, including slope, elevation, topography, vegetation characteristics, resilience characteristics, water condition characteristics, and soil condition characteristics; (2) Principal component analysis: 2020 was selected as the research base year. The data source was a fair and authoritative website. Principal component analysis was used to screen variables. The analysis steps are as follows: a. Collect p-dimensional random vectors for the original indicator data and construct the sample matrix N: N=(X1,X2,...,X p ) T (1) X i =(x i1 ,x i2 ,...,x ip ) T ,i=1,2,...,n>p (2) In the formula: X1, X2..., X P Represents the characteristic factor index, T represents the transpose of the matrix, and Xi represents the sample value; b. Normalize the sample matrix N to obtain the standardized matrix Z: In the formula: x ij Indicates that there are n samples, each sample has j values, represents the average value of j values, s j represents the variance of each column; in, c. Calculate the correlation coefficient matrix for matrix Z: In the formula, R represents the correlation coefficient matrix describing the linear correlation between variables, r ij Represents the element in the i-th row and j-th column of the matrix R, which represents the correlation coefficient between the i-th variable and the j-th variable, p, z T , z represents the standardized data matrix (n×p), with the mean of each column being 0 and the standard deviation being 1; Where: d. According to |R-λI p |=0 characteristic equation, eigenvalues and eigenvectors of the solver correlation matrix R; e. Calculate the cumulative contribution rate: In the formula, E j It represents the cumulative variance contribution rate of the first m principal components, that is, the proportion of original data information retained by the first m principal components, λ j It represents the eigenvalue of the jth principal component, reflecting the variance of the principal component; And E j ≥0.8 to determine the m value, so that the information utilization rate is more than 70%. j , j = 1, 2, ..., m, solve equation R b =λ jb The unit eigenvector In the formula, R b Indicates the load of variable b on a principal component or factor, reflecting the strength of the correlation between variable b and the principal component / factor, λ jb represents the eigenvalue (or eigenvector component) of the jth principal component / factor, which represents the contribution weight of the principal component / factor to the variable b; f. Convert the standardized indicator variables into principal components: In the formula, Z i T The standardized feature vector of the ii-th sample, b j o , represents the jth basis vector or projection direction; U1 is called the first principal component, U2 is called the second principal component, ..., U p It is called the pth principal component; g. According to factor loading b ij Screening index, b ij is the factor loading of the jth principal component on the i-th indicator, according to the principal component F j Factor loading on the i-th indicator |b ij |Screening indicator,|b ij The larger the value of |, the more significant the impact of the i-th indicator on the results of the influencing factor evaluation system. ij The smaller | is, the less obvious the impact of the i-th indicator on the evaluation system results is. ij |Big indicator.

2. The method for constructing an ecological function assessment model for the Three Gorges Reservoir drawdown zone according to claim 1 is characterized by: The data are obtained from the geospatial data cloud, MOD17A3HGF products, Google Earth Engine inversion, World Soil Database, Google Earth or manual visual interpretation.

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