A method for evaluating site selection and line selection of plateau lakeside ecological corridor and ecological restoration

Abstract

The application provides a highland lakeside ecological corridor and ecological restoration site selection and line selection evaluation method, including obtaining present natural resource data to construct a partition evaluation technical index system containing multiple system layer indexes, performing two-by-two importance comparison on the system layer indexes to generate a judgment matrix and calculate system layer weight values; obtaining ecological corridor line selection demand data to construct an ecological corridor line selection evaluation technical index system containing multiple index layer indexes, performing two-by-two importance comparison on the index layer indexes to generate a judgment matrix and calculate index layer weight values; performing partition evaluation according to the system layer weight values to obtain partition evaluation results, performing line selection evaluation on multiple to-be-selected corridors according to the index layer weight values to obtain line selection evaluation results, and finally generating a comprehensive evaluation scheme of highland lakeside ecological restoration. The application can realize scientific and quantitative evaluation of a highland lakeside ecological restoration region and accurate line selection of an ecological corridor, and improve the systematicness and operability of ecological restoration planning.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, and more specifically, to a method for evaluating the site selection and alignment of ecological corridors and ecological restoration along plateau lakeshores. Background Technology

[0002] As a transitional zone between terrestrial and lake ecosystems, plateau lakeshores possess unique ecological functions and significant ecosystem service value. Their ecological restoration is crucial for maintaining regional ecological balance, ensuring water quality safety, and enhancing biodiversity. Currently, plateau lakeshore ecological restoration projects typically rely on expert experience or single-indicator evaluation during the planning stage, lacking systematic quantitative assessment methods and making it difficult to scientifically identify areas with the highest restoration value. Simultaneously, ecological corridors, as a vital component of lakeshore ecological restoration, often lack the technical means to comprehensively consider multi-dimensional factors such as ecological landscape value, human activity disturbance, habitat diversity, cultural characteristics, and service functions, resulting in difficulties in ensuring the rationality of corridor layout and ecological benefits. While the Analytic Hierarchy Process (AHP), a multi-criteria decision-making method combining qualitative and quantitative approaches, has been widely applied in the field of ecological and environmental assessment, its systematic application in the zoning assessment and corridor selection for plateau lakeshore ecological restoration, the construction of a complete assessment indicator system, and the development of supporting quantitative assessment tools still lack mature technical solutions. Furthermore, existing technologies have shortcomings in determining indicator weights, verifying consistency, and spatially expressing assessment results, making it difficult to meet the practical needs of rapid, accurate, and operable plateau lakeshore ecological restoration planning.

[0003] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: there is a lack of a systematic zoning assessment index system for the characteristics of ecological restoration of plateau lake shores, making it impossible to scientifically identify priority restoration areas; the selection of ecological corridors lacks a multi-factor comprehensive quantitative assessment method, and the selection decision is highly subjective; the process of determining index weights lacks a consistency verification mechanism, and the reliability of the assessment results is insufficient; the assessment index system is not closely integrated with spatial analysis tools, making it difficult to support rapid decision-making in the planning stage. Summary of the Invention

[0004] This invention provides a method for evaluating the site selection and alignment of plateau lakeside ecological corridors and ecological restoration, including: Obtain current natural resource data for the plateau lakeside zone, and construct a zoning assessment technical indicator system based on the current natural resource data. The zoning assessment technical indicator system includes multiple system-level indicators. The importance of each system-level indicator is compared pairwise. Based on the comparison results, a judgment matrix from the system level to the target level is generated. The eigenvector normalization of the judgment matrix is ​​then performed to obtain the system-level weight value corresponding to each system-level indicator. Obtain the route selection requirements data for plateau lakeside ecological corridors, and construct an ecological corridor route selection evaluation technical indicator system based on the route selection requirements data. The ecological corridor route selection evaluation technical indicator system includes multiple indicator layers. The importance of each indicator in the indicator layer is compared pairwise. Based on the comparison results, a judgment matrix from the indicator layer to the target layer is generated. The feature vector normalization calculation is performed on the judgment matrix to obtain the indicator layer weight value corresponding to each indicator layer. The study area is partitioned and evaluated based on the system layer weight values ​​to obtain the partition evaluation results. Based on the weight values ​​of the index layer, the route selection evaluation is performed on multiple candidate corridors to obtain the route selection evaluation results; Based on the zoning assessment results and the route selection assessment results, a comprehensive assessment scheme for the ecological restoration of plateau lakeshores is generated.

[0005] The embodiments of the present invention have at least the following beneficial effects: 1. This invention constructs a system-level indicator system encompassing five dimensions: spatial visibility, ecological function, anti-interference intensity, cultural characteristics, and service functions. It also constructs an indicator-level indicator system encompassing six dimensions: ecological landscape component diversity, ecological landscape value, intensity of human activity disturbance, habitat diversity, cultural characteristics, and potential service functions. By using the analytic hierarchy process (AHP) to determine the weights of each indicator, it achieves a scientific and quantitative assessment of ecological restoration areas along plateau lakeshores. This solves the problems of existing technologies, such as the lack of a systematic zoning assessment indicator system and the inability to scientifically identify priority restoration areas, thereby improving the systematicness and pertinence of ecological restoration planning.

[0006] 2. This invention addresses the needs of ecological corridor route selection by establishing a specialized technical indicator system for route evaluation. It comprehensively considers multiple factors such as ecological landscape, human activities, habitat conditions, cultural elements, and service functions. By comparing the importance of each pair of corridors and calculating the judgment matrix, the weights of the indicators are determined. This enables comprehensive quantitative evaluation and ranking of multiple candidate corridors, solving the problems of existing technologies that lack multi-factor comprehensive quantitative evaluation methods and have strong subjectivity in route selection decisions. This improves the rationality of corridor layout and ecological benefits.

[0007] 3. This invention introduces a consistency verification mechanism in the process of constructing the judgment matrix and calculating the weights. By calculating the maximum eigenvalue, consistency index and consistency ratio, it ensures that the weight calculation results are retained only when the consistency ratio of the judgment matrix is ​​less than the threshold. For cases that fail the verification, pairwise comparisons are re-performed and adjusted. This solves the problem of the lack of a consistency verification mechanism and insufficient reliability of evaluation results in the existing technology for determining index weights, and ensures the scientificity and credibility of the evaluation results. Attached Figure Description

[0008] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating the evaluation method for site selection and alignment of plateau lakeside ecological corridors and ecological restoration, provided in an embodiment of the present invention. Detailed Implementation

[0009] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0010] The following is for reference. Figure 1 , Figure 1 This is a flowchart illustrating a method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration, provided in an embodiment of the present invention. Figure 1 As shown, a method for evaluating the site selection and alignment of plateau lakeside ecological corridors and ecological restoration includes: S1. Obtain current natural resource data of the plateau lakeside zone, and construct a zoning assessment technical indicator system based on the current natural resource data. The zoning assessment technical indicator system includes multiple system-level indicators. S2. Perform pairwise importance comparisons on the system layer indicators, generate a judgment matrix from the system layer to the target layer based on the comparison results, and perform eigenvector normalization calculation on the judgment matrix to obtain the system layer weight values ​​corresponding to each system layer indicator. S3. Obtain the route selection requirements data for the plateau lakeside ecological corridor, and construct an ecological corridor route selection evaluation technical indicator system based on the route selection requirements data. The ecological corridor route selection evaluation technical indicator system includes multiple indicator layers. S4. Perform pairwise importance comparisons on the indicators of the indicator layer, generate a judgment matrix from the indicator layer to the target layer based on the comparison results, and perform feature vector normalization calculation on the judgment matrix to obtain the indicator layer weight values ​​corresponding to each indicator layer. S5. Based on the system layer weight values, the study area is partitioned and evaluated to obtain the partition evaluation results; S6. Evaluate the route selection of multiple candidate corridors based on the weight values ​​of the index layer to obtain the route selection evaluation results. S7. Based on the zoning assessment results and the route selection assessment results, generate a comprehensive assessment scheme for the ecological restoration of plateau lakeshores.

[0011] In some embodiments, the system-level indicators include spatial visibility indicators, ecological function indicators, anti-interference strength indicators, cultural characteristic indicators, and service function indicators; the pairwise importance comparison of the system-level indicators specifically involves: using a 5-point importance scale to compare and assign values ​​to the spatial visibility indicators, ecological function indicators, anti-interference strength indicators, cultural characteristic indicators, and service function indicators pairwise; the generation of the judgment matrix from the system layer to the target layer specifically involves: constructing a 5-order matrix based on the assignment results, where the element aij in the matrix represents the importance scale value of the i-th indicator relative to the j-th indicator.

[0012] The system-level indicators consist of five dimensions: spatial visibility indicators, ecological function indicators, disturbance resistance indicators, cultural characteristic indicators, and service function indicators. These indicators reflect the ecological restoration value and quality of the plateau lakeside area from different perspectives. Spatial visibility indicators focus on the visual perception effect of the ecological landscape; ecological function indicators focus on the ecosystem's material cycle and biodiversity maintenance capacity; disturbance resistance indicators focus on the ecosystem's resistance to external disturbances; cultural characteristic indicators focus on the region's cultural heritage and landscape features; and service function indicators focus on the recreational and aesthetic services provided by the ecosystem to humans. By comparing the pairwise importance of these five indicators and quantifying them using a 5-point importance scale, the relative importance of each indicator in the ecological restoration value assessment of the plateau lakeside area can be systematically reflected. This allows for the construction of a judgment matrix from the system level to the target level, laying the foundation for subsequent weight calculations and comprehensive evaluation.

[0013] Specifically, the aforementioned 5-point importance scale is a scoring standard used to quantify the relative importance between indicators. 5 represents extreme importance, meaning one indicator has an absolute importance advantage over another; 4 represents strong importance, meaning the difference in importance is significant but not extreme; 3 represents significant importance, meaning the difference in importance is clearly perceptible; 2 represents slightly important, meaning the difference in importance is small; and 1 represents equally important, meaning the two indicators are of comparable importance. The reciprocal of the scale represents the opposite importance relationship. For example, if the importance scale of indicator A relative to indicator B is 3, then the importance scale of indicator B relative to indicator A is 1 / 3. Pairwise comparisons refer to comparing the importance of each of the 5 system-level indicators with the other 4 indicators one by one, for a total of 10 comparisons.

[0014] A 5th-order matrix is ​​a 5x5 square matrix composed of 5 system-level indicators. The elements in the matrix represent the importance scale value of the row indicator relative to the column indicator. Diagonal elements are 1, indicating a comparison between the indicator and itself. Off-diagonal elements are filled with corresponding scale values ​​based on pairwise comparisons, and satisfy reciprocity, meaning the element in the i-th row, j-th column is the reciprocal of the element in the j-th row, i-th column. Spatial visibility indicators specifically include five factor-level indicators: ecological landscape continuity, ecological landscape seasonal diversity, environmental harmony, psychological perception indicators, and visibility harmony. These reflect the spatial connectivity of the landscape, its temporal variation characteristics, its harmony with the surrounding environment, the viewer's psychological experience, and the balance between open and closed views, respectively. Ecological function indicators include four factor-level indicators: species abundance, vegetation cover, pollutant reduction rate, and habitat diversity. These reflect the richness of biological species, vegetation growth status, water purification capacity, and habitat type diversity, respectively. Disturbance resistance indicators include three factor-level indicators: human activity disturbance intensity, ecosystem disturbance resistance intensity, and topographic relief, reflecting the magnitude of anthropogenic stress, the stability of the natural system, and the complexity of the terrain, respectively. The cultural characteristics indicators include two factor-level indicators: cultural heritage preservation and landscape harmony, reflecting the degree of preservation of local cultural elements and the overall harmony of the landscape, respectively. The service function indicators include three factor-level indicators: aesthetic value, recreational value, and recommendation value, reflecting the aesthetic appreciation value of the landscape, the suitability for leisure activities, and tourists' willingness to recommend it, respectively.

[0015] In practice, pairwise importance comparisons are achieved through expert scoring. An evaluation panel composed of experts with experience in plateau lakeside ecological restoration, landscape ecology, and ecological planning is invited. Each expert independently compares the importance of five system-level indicators and completes a scoring sheet. The scoring sheet is in matrix form; experts only need to fill in the scale value of the upper or lower triangular portion, and the other half is automatically determined based on reciprocity. After collecting the scores from all experts, an arithmetic mean or geometric mean can be used to synthesize the results, yielding the final judgment matrix element values. When constructing the judgment matrix, it is necessary to ensure the matrix's positive reciprocity, i.e., for any two indicators i and j, the product of the element in the i-th row and j-th column and the element in the j-th row and i-th column of the matrix equals 1. If individual expert scores show significant deviations, outliers can be filtered out by calculating the inter-expert consistency coefficient before further synthesis. After the judgment matrix is ​​constructed, the largest eigenvalue and its corresponding eigenvector are calculated using the power method or the square root method. The eigenvectors are then normalized to obtain the weight values ​​of each system-level indicator. A larger weight value indicates greater importance of the indicator in the ecological restoration value assessment of plateau lakeshores. For example, according to actual calculation results, the weight of the ecological function indicator can reach 0.4107, the highest among the five system-level indicators, indicating that ecological function is a core consideration in the ecological restoration value assessment of plateau lakeshores.

[0016] In some embodiments, consistency checks are used to verify the logical consistency of expert scoring results, avoiding logical contradictions such as indicator A being more important than indicator B, indicator B being more important than indicator C, but indicator C being more important than indicator A. By calculating the maximum eigenvalue, consistency index, average random consistency index, and consistency ratio of the judgment matrix, the degree of consistency of the judgment matrix can be quantitatively evaluated. Only when the consistency ratio is less than 0.1 is the judgment matrix considered to have satisfactory consistency, and the calculated weight values ​​are reasonable and reliable. If the consistency ratio is greater than or equal to 0.1, it indicates that there is a significant logical contradiction in the expert scoring, requiring pairwise comparisons and adjustments to the values ​​until the consistency requirements are met. This mechanism effectively ensures the scientific rigor and reliability of the evaluation results, avoiding the arbitrariness of subjective judgment.

[0017] The largest eigenvalue is the largest eigenvalue obtained in the eigenvalue decomposition of the judgment matrix. It reflects the degree to which the judgment matrix deviates from perfect consistency. When the judgment matrix is ​​perfectly consistent, the largest eigenvalue equals the matrix order. The consistency index is a value calculated by the largest eigenvalue and the matrix order. It measures the degree of inconsistency of the judgment matrix. It is calculated by subtracting the matrix order from the largest eigenvalue and then dividing by the difference between the matrix order and 1. The larger this value, the more severe the inconsistency. The average random consistency index is a statistical value obtained through random simulation. It is used to eliminate the influence of the matrix order on consistency judgments. Different order judgment matrices correspond to different average random consistency index values; for example, a 5-order matrix corresponds to an average random consistency index of 1.12.

[0018] The consistency ratio is the ratio of the consistency index to the average random consistency index. This index eliminates the influence of matrix order, making the consistency of judgment matrices of different orders comparable. When the ratio is less than 0.1, the judgment matrix passes the consistency check, and the calculated weight values ​​of each system layer are retained. When the ratio is greater than or equal to 0.1, the judgment matrix fails the consistency check and needs to be re-compared pairwise and reassigned until the ratio is less than 0.1. When reassigning values, special attention should be paid to comparison results with large scale differences or close to extreme values, such as 5 or 1 / 5, as these positions are more prone to logical contradictions.

[0019] Preferably, in the actual calculation process, the maximum eigenvalue can be calculated using the power method iterative method. The specific steps are as follows: First, arbitrarily select an initial positive vector with the same dimension as the judgment matrix. By repeatedly multiplying the judgment matrix with this vector and performing normalization processing, until the change between two adjacent iterations is less than a preset precision threshold, such as 0.0001, the normalized vector obtained at this time is the eigenvector, and the corresponding eigenvalue is the maximum eigenvalue. In the calculation of the consistency index, the matrix order is 5, which is the number of system-level indicators. When obtaining the average random consistency index, it can be directly obtained by looking up a table. This table is an empirical value obtained by calculating and statistically analyzing 500 random judgment matrices. The average random consistency index of the 1st and 2nd order matrices is 0, the 3rd order is 0.58, the 4th order is 0.90, the 5th order is 1.12, the 6th order is 1.24, the 7th order is 1.32, the 8th order is 1.41, and the 9th order is 1.45. In calculating the consistency ratio, when the numerator is the consistency index and the denominator is the average random consistency index, if the result is less than 0.1, the consistency check is passed. In this case, the eigenvectors corresponding to the eigenvalues ​​are normalized by dividing each component by the sum of all components to obtain the weight value of each system-level index. If the result is greater than or equal to 0.1, the pairwise comparison step must be returned, and experts must re-examine the comparison items with significant contradictions in the judgment matrix, adjust the relevant scaling values, and recalculate until the consistency requirement is met. In practical applications, if the consistency requirement is still difficult to meet after multiple adjustments, consider increasing the number of experts or using a group decision-making method to synthesize the opinions of multiple experts to reduce the bias of individual judgments.

[0020] Specifically, the spatial visibility factor layer indicators are the lower-level indicators of the spatial visibility indicators, including five specific indicators: ecological landscape continuity, ecological landscape seasonal diversity, environmental harmony, psychological perception indicators, and visual harmony. Ecological landscape continuity reflects the degree of spatial connectivity of ecological landscape resources, which can be quantified using the landscape connectivity index or patch aggregation index; ecological landscape seasonal diversity reflects the richness of the landscape effect as vegetation changes with the seasons, which can be assessed by analyzing vegetation index changes in different seasons through remote sensing imagery; environmental harmony reflects the degree of harmony between the restored area and the surrounding natural environment, which can be evaluated through the landscape pattern index or visual compatibility analysis; psychological perception indicators reflect the psychological comfort and aesthetic experience of the viewer, which can be obtained through questionnaires or psychophysical methods; visual harmony reflects the balance between canopy closure and openness in the spatial field of vision, which can be quantified through visual field analysis or landscape visibility evaluation. The ecological function factor layer indicators are the lower-level indicators of the ecological function indicators, including four specific indicators: species abundance, vegetation cover, pollutant reduction rate, and habitat diversity. Species abundance reflects the richness of species in a region and can be obtained through field surveys or biodiversity databases; vegetation cover reflects the proportion of the land surface covered by vegetation and can be obtained through remote sensing image interpretation or field measurements; pollutant reduction rate reflects the ecosystem's ability to purify pollutants and can be obtained by calculating the ratio of the difference in pollutant concentration between the influent and effluent water bodies to the influent concentration using water quality monitoring data; habitat diversity reflects the diversity of habitat types and can be quantified through habitat type classification and landscape diversity index.

[0021] The anti-interference factor layer indicators are the lower-level indicators of the anti-interference intensity indicators, including three specific indicators: human activity disturbance intensity, ecosystem anti-interference intensity, and topographic relief. Human activity disturbance intensity reflects the pressure exerted on the ecosystem by human activities and can be quantified through land use intensity, population density, or the human activity footprint index. Ecosystem anti-interference intensity reflects the ecosystem's resistance and recovery capacity to external disturbances and can be assessed through the ecosystem stability index or resilience index. Topographic relief reflects the complexity of the terrain and can be obtained by calculating the elevation standard deviation or topographic relief index using a digital elevation model. The cultural characteristic factor layer indicators are the lower-level indicators of the cultural characteristic indicators, including two specific indicators: cultural heritage and landscape harmony. Cultural heritage reflects the degree to which local traditional cultural elements are reflected in ecological restoration and can be quantified through historical and cultural resource surveys and cultural landscape evaluations. Landscape harmony reflects the overall harmony and local characteristics of the landscape in the restored area and can be assessed through landscape evaluation or compliance with urban design guidelines. The service function factor layer indicators are the lower-level indicators of the service function indicators, including three specific indicators: aesthetic value, recreational value, and recommendation value.

[0022] Appreciation value reflects the aesthetic appeal of a landscape, which can be quantified using the beauty score evaluation method or visual quality assessment. Recreation value reflects the suitability of an area for leisure and recreational activities, which can be assessed through recreational resource evaluation or recreational opportunity spectrum analysis. Recommendation value reflects the strength of tourists' willingness to recommend the area to others, which can be obtained through tourist satisfaction surveys and online evaluation analysis. The construction method of the judgment matrix from the above five factor layers to the system layer is the same as that of the judgment matrix from the system layer to the target layer, both using a 5-point importance scale for pairwise comparisons, with matrix orders of 5, 4, 3, 2, and 3, respectively.

[0023] In some embodiments, the zoning assessment is performed by fusing multi-source remote sensing data with ground survey data to obtain quantitative data for each factor layer indicator.

[0024] Specifically, high-resolution remote sensing images of the study area across multiple time phases were acquired, the normalized vegetation index (NDI) was calculated, and vegetation cover was then retrieved. The moving window method was used to calculate the ecological landscape continuity index, with the size of the moving window consistent with the size of the evaluation unit to reflect the connectivity between landscape patches within each evaluation unit. The ecological landscape continuity index was calculated based on land use classification data, analyzing the adjacency relationships and distance thresholds between patches to output a continuous value between 0 and 1.

[0025] Digital elevation model data of the study area is obtained, and the standard deviation of elevation is calculated at the evaluation unit scale as a quantitative indicator of topographic relief.

[0026] A human activity disturbance intensity index is constructed by integrating land use classification data, road network vector data, and nighttime light remote sensing data. Specifically, the proportion of built-up land area, road density (i.e., the ratio of road length to the area of ​​the evaluation unit), and average nighttime light radiation are calculated for each evaluation unit. These three indicators are then weighted and summed according to preset weights to obtain the human activity disturbance intensity index. The weights can be determined through expert scoring or the entropy weight method.

[0027] Then, an on-site biodiversity survey was conducted to record the species types and quantities in each evaluation unit. Combined with species distribution models, such as the maximum entropy model, spatial predictions were made for unsurveyed areas. Kriging interpolation was used to generate a spatially continuous distribution raster of species abundance for the entire study area.

[0028] The quantitative data of the above-mentioned factor-level indicators (vegetation coverage, ecological landscape continuity index, topographic relief, human activity disturbance intensity index, species abundance, etc.) are subjected to range normalization processing. This involves subtracting the minimum value of each indicator from its original value in the study area, and then dividing by the difference between the maximum and minimum values, thus mapping the value range of all indicators uniformly to the 0-1 interval. Then, according to a pre-defined grading and scoring standard, the normalized continuous values ​​are converted into discrete grading scores. For example, the 0-1 interval can be divided into multiple levels, such as an equally spaced five-point system or a nine-point system, with each level corresponding to a score. A higher score indicates a greater contribution of that factor-level indicator to ecological restoration.

[0029] Preferably, when constructing the judgment matrix from the factor layer to the system layer, differentiated data acquisition and quantification methods are adopted for different types of indicators. For the ecological landscape continuity indicator, the patch connectivity index can be calculated based on landscape pattern analysis software. The input parameters include patch area, inter-patch distance threshold, and connection probability. The output result is a continuous value between 0 and 1, with the value closer to 1 indicating better continuity. For the vegetation cover indicator, the normalized vegetation index can be calculated based on remote sensing images. The input parameters are the reflectance values ​​of the near-infrared and infrared bands. The output result is a value between -1 and 1, with a value greater than 0.2 typically indicating vegetation cover. For the human activity disturbance intensity indicator, a comprehensive disturbance index can be constructed. The input parameters include the proportion of built-up land, road density, population density, and nighttime light index. The weights of each parameter are determined through principal component analysis or entropy weight method, and the weighted sum is used to obtain a comprehensive disturbance intensity score.

[0030] For subjective indicators such as psychological perception and aesthetic value, a questionnaire survey can be conducted using the semantic differential method or the Likert scale. Evaluators are invited to rate the psychological comfort and aesthetic value of the landscape. The input parameters are the number of valid samples returned from the questionnaire and the frequency distribution of each rating level. The output is a weighted average score. When constructing the judgment matrix from the cultural characteristic factor layer to the system layer, since the judgment matrices for the two indicators of cultural heritage and landscape harmony are of order 2, according to matrix theory, second-order matrices naturally possess complete consistency, and weights can be directly calculated without consistency verification. For the other four factor layers to the system layer judgment matrices, the same consistency verification process as that for the system layer to the target layer judgment matrices must be followed to ensure that the consistency ratio of each judgment matrix is ​​less than 0.1, thereby guaranteeing the logical consistency of the entire hierarchical structure model and the reliability of the evaluation results.

[0031] Specifically, eigenvector normalization calculation refers to standardizing the eigenvector corresponding to the largest eigenvalue of the judgment matrix, making the sum of all components equal to 1. The result is the weight value of each indicator. The spatial view factor layer weight values ​​refer to the weights of five indicators—ecological landscape continuity, ecological landscape seasonal diversity, environmental harmony, psychological perception index, and view harmony—relative to the spatial view index, expressed as 0.3138, 0.1799, 0.2874, 0.1224, and 0.0965 respectively, with a sum of 1. The ecological function factor layer weight values ​​refer to the weights of four indicators—species abundance, vegetation cover, pollutant reduction rate, and habitat diversity—relative to the ecological function index, expressed as 0.3996, 0.2086, 0.0776, and 0.3142 respectively, with a sum of 1. Among these, species abundance has the highest weight, indicating the core position of biodiversity in ecological function assessment. The weight values ​​of the anti-interference factor layer refer to the weights of three indicators—human activity interference intensity, ecosystem anti-interference intensity, and topographic relief—relative to the anti-interference intensity indicator. These are represented by 0.2969, 0.5396, and 0.1635, respectively, with a sum of 1. Among them, the weight of ecosystem anti-interference intensity exceeds 0.5, indicating that the inherent stability of the natural system is the dominant factor in anti-interference ability.

[0032] The weight values ​​for the cultural characteristics factor layer refer to the weights of the two indicators, cultural heritage and style harmony, relative to the cultural characteristics indicator, denoted by 0.75 and 0.25 respectively, with a sum of 1. Cultural heritage holds a dominant position, reflecting the importance of local cultural characteristics in cultural value assessment. The weight values ​​for the service function factor layer refer to the weights of the three indicators, aesthetic value, recreational value, and recommendation value, relative to the service function indicator, denoted by 0.1571, 0.5936, and 0.2493 respectively, with a sum of 1. The recreational value weight is close to 0.6, indicating that leisure and recreational function is the core of service function assessment. All the above weight values ​​are calculated using the same mathematical method: solving the eigenvalue problem of the judgment matrix, extracting the eigenvector corresponding to the largest eigenvalue, and then normalizing it.

[0033] Preferably, the normalization calculation of the feature vector can be simplified using the square root method. The specific steps are as follows: First, calculate the product of all elements in each row of the judgment matrix. Then, take the nth root of this product, where n is the matrix order, to obtain a preliminary weight vector. Finally, normalize this vector by dividing each component by the sum of the components to obtain the final weight value. Taking the judgment matrix of the spatial vision factor layer as an example, the input parameters are the 25 element values ​​of a 5th-order judgment matrix, including one 1 on the diagonal, 10 scale values ​​in the upper triangular part, and 10 scale values ​​in the lower triangular part determined by reciprocity. During the calculation process, for each row, calculate the product of 5 elements, take the 5th root of the product to obtain 5 square root values, sum the 5 square root values ​​to obtain the total, and divide each square root value by the total to obtain the normalized weight.

[0034] After obtaining the weight values ​​of each factor layer, the combined weight of the factor layer indicators relative to the overall goal can be further calculated. This combined weight is the product of the factor layer weight and the weight of its respective system layer. This combined weight reflects the comprehensive importance of each factor layer indicator in the entire value assessment system for ecological restoration of plateau lakeshores. For example, the combined weight of species abundance is 0.3996 multiplied by 0.4107, which equals 0.1641, indicating that the comprehensive contribution rate of species abundance in the entire assessment system is 16.41%, ranking first among all 17 factor layer indicators. Through the ranking analysis of combined weights, the key factors with the greatest impact on the ecological restoration value of plateau lakeshores can be identified, providing a basis for decision-making on priority areas and key improvement directions for ecological restoration projects. After the weight calculation is completed, it is recommended to present the weight results in tabular form, including three columns of data: system layer weight, factor layer weight, and combined weight, to facilitate subsequent assessment calculations and result interpretation.

[0035] Similar to the consistency check of the judgment matrices from the system layer to the target layer, the judgment matrices from the factor layer to the system layer also need to undergo consistency checks to ensure the logical rationality of the expert scoring results. Since the number of factor layer indicators varies under different system layers, the order of each judgment matrix also differs. It is necessary to calculate their maximum eigenvalue, consistency index, and consistency ratio separately, and then compare them using the corresponding average random consistency index obtained based on the matrix order. Specifically, the order of the judgment matrix from the cultural feature factor layer to the system layer is 2. According to matrix theory, a 2nd-order judgment matrix naturally possesses complete consistency and can be directly judged as passing without needing a consistency check. This mechanism ensures the reliability of the weight calculation from the factor layer to the system layer, providing quality assurance for establishing a complete hierarchical weight system.

[0036] Specifically, the spatial view factor layer to system layer judgment matrix is ​​a 5th-order matrix composed of five factor layer indicators: ecological landscape continuity, ecological landscape seasonal diversity, environmental harmony, psychological perception index, and view harmony. Its maximum eigenvalue is 5.37, the consistency index is 0.0925, the average random consistency index is 1.12, and the consistency ratio is 0.083, which is less than 0.1, thus the matrix passes the consistency check. The ecological function factor layer to system layer judgment matrix is ​​a 4th-order matrix composed of four factor layer indicators: species abundance, vegetation cover, pollutant reduction rate, and habitat diversity. Its maximum eigenvalue is 4.20, the consistency index is 0.0667, the average random consistency index is 0.90, and the consistency ratio is 0.0741, which is less than 0.1, thus the matrix passes the consistency check. The judgment matrix from the anti-interference factor layer to the system layer is a third-order matrix composed of three factor layer indicators: human activity interference intensity, ecosystem anti-interference intensity, and topographic relief. Its maximum eigenvalue is 3.01, the consistency index is 0.005, the average random consistency index is 0.58, and the consistency ratio is 0.0086, which is much less than 0.1. Therefore, the matrix is ​​judged to have passed the consistency check.

[0037] The judgment matrix from the cultural characteristic factor layer to the system layer is a second-order matrix composed of two factor layer indicators: cultural heritage and style harmony. According to matrix theory, a second-order matrix has complete consistency, and it can be directly judged as passing without calculating the consistency ratio. The judgment matrix from the service function factor layer to the system layer is a third-order matrix composed of three factor layer indicators: aesthetic value, recreational value, and recommendation value. Its maximum eigenvalue is 3.05, the consistency index is 0.025, the average random consistency index is 0.58, and the consistency ratio is 0.0431, which is less than 0.1, so the matrix is ​​judged to have passed the consistency check. Only when the consistency ratio of all the above judgment matrices is less than 0.1 is the overall judgment matrix from each factor layer to the system layer judged to have passed the consistency check and can be used for subsequent weight calculation and evaluation.

[0038] Taking the spatial vision factor layer judgment matrix as an example, the input parameters include the matrix order of 5 and the values ​​of 25 elements in the matrix. The calculation module automatically executes the following processing flow: First, it calls the eigenvalue calculation function to solve for the largest eigenvalue of the matrix. Then, it calculates the consistency index according to the formula, where the numerator is the difference between the largest eigenvalue and the matrix order, and the denominator is the matrix order minus 1. Next, it looks up the value 1.12 corresponding to the order of 5 according to the preset average random consistency index table. Finally, it calculates the consistency ratio by comparing the consistency index with the average random consistency index. The output results include five data items: the largest eigenvalue, the consistency index, the average random consistency index, the consistency ratio, and the verification conclusion.

[0039] If the output conclusion is "pass," the calculated weight values ​​of 0.3138, 0.1799, 0.2874, 0.1224, and 0.0965 are retained. If the output conclusion is "fail," the matrix is ​​marked as needing readjustment, and the system prompts experts to re-examine comparison items that may have logical contradictions, such as ecological landscape continuity and environmental harmony, and psychological perception indicators and visual harmony. For the cultural characteristic factor layer judgment matrix, after the system identifies its order as 2, it automatically skips the consistency verification step and directly outputs completely consistent judgment results and weight values ​​of 0.75 and 0.25. When batch processing five factor layer judgment matrices, the system generates a consistency verification summary table, clearly showing the verification status and key parameters of each matrix. Only when the verification status of all matrices in the summary table is "pass," is the subsequent evaluation calculation module activated; otherwise, the system is locked and a correction prompt is displayed, ensuring the logical rigor and reliability of the entire evaluation system.

[0040] In some embodiments, to address the specific needs of ecological corridor route selection in plateau lakeside ecological restoration, a six-dimensional indicator system was constructed. By comparing the importance of each indicator in each layer pairwise, a 5-point importance scale was used for quantification, constructing a 6th-order judgment matrix from the indicator layer to the target layer, providing a decision-making basis for the scientific selection of ecological corridor routes. Unlike the zoning assessment indicator system, which focuses on identifying restoration value, the route selection assessment indicator system places greater emphasis on evaluating the suitability of corridor spatial layout. It comprehensively considers multiple factors such as ecological landscape, human disturbance, habitat conditions, cultural characteristics, and service functions, enabling a systematic assessment of the ecological benefits and feasibility of candidate corridors and guiding the determination of the optimal corridor path.

[0041] Specifically, the ecological landscape diversity index is used to evaluate the richness of ecological landscape types along the candidate corridor, including the composition of different landscape components such as forests, grasslands, wetlands, and farmland. It can be quantified through landscape type classification and diversity index calculation. The input parameter is the area proportion of each landscape type within the corridor buffer zone, and the output is a diversity index value between 0 and 1; a higher value indicates richer landscape components. The ecological landscape value index is used to evaluate the overall ecological landscape quality along the candidate corridor, comprehensively considering factors such as landscape aesthetic value, ecological integrity, and naturalness. It can be assessed through expert scoring or landscape evaluation models. Input parameters include landscape visual quality score, ecosystem integrity score, and natural disturbance degree score; the output is a comprehensive landscape value score. The human activity disturbance intensity index is used to evaluate the degree of impact of human activities along the candidate corridor, including the impact of disturbance sources such as urban construction, agricultural development, and transportation facilities. It can be quantified through land use type analysis and disturbance index calculation. Input parameters are the distance attenuation weight and area weight of each disturbance source; the output is a comprehensive disturbance intensity score; a higher value indicates stronger disturbance and poorer suitability.

[0042] Habitat diversity indicators are used to evaluate the diversity and quality of habitat types along the candidate corridors, including the distribution and connectivity of different habitat types. This can be assessed through habitat classification and landscape pattern analysis. Input parameters include the richness of habitat types and the occurrence of rare habitat types along the corridor, with the output being a habitat diversity score. Cultural characteristic indicators are used to evaluate the exploitable cultural elements and their value along the candidate corridors, including historical and cultural relics, traditional villages, and ethnic customs. This can be quantified through cultural resource surveys and value assessments. Input parameters include the number, level, and distribution density of cultural resource points, with the output being a cultural value score. Potential service function indicators are used to evaluate the ecological service potential that the candidate corridors can provide after construction, including functions such as water conservation, climate regulation, and recreation. This can be predicted using a service value assessment model. Input parameters include corridor length, width, connectivity, and surrounding population distribution, with the output being a potential service value score. The judgment matrix of the above 6 indicator layers is constructed in the same way as that of the system layer indicators. A 5-point importance scale is used for pairwise comparison. The elements in the 6-order matrix represent the importance scale value of the row indicator relative to the column indicator. The diagonal elements are 1, and the off-diagonal elements satisfy reciprocity.

[0043] For the ecological landscape diversity index, object-oriented classification can be performed based on high-resolution remote sensing imagery to extract landscape type patches within the corridor buffer zone. The Shannon or Simpson diversity index can then be calculated, with the input parameter being the area proportion of each landscape type. The diversity index value is calculated using a formula; a higher value indicates higher landscape heterogeneity and stronger landscape connectivity within the ecological corridor. For the human activity disturbance intensity index, a comprehensive disturbance evaluation model based on multi-source data fusion can be constructed. Input parameters include construction land vector data, road network data, nighttime light remote sensing data, and population density raster data. Through buffer zone analysis and spatial overlay operations, a spatial distribution map of disturbance intensity is generated, and disturbance intensity values ​​are obtained by sampling along the corridor path. During the construction of the 6th-order judgment matrix, to address the specific needs of ecological corridor route selection, experts in ecological planning, landscape design, and geographic information systems can be organized to score the data. Emphasis should be placed on the synergistic relationship between ecological landscape diversity and habitat diversity, and the trade-off between human activity disturbance intensity and other indicators. After scoring, the geometric mean method is used to synthesize the opinions of multiple experts, reducing individual judgment bias, and constructing the final judgment matrix. The consistency verification process for the 6th-order matrix is ​​the same as that for the 5th-order matrix, but the average random consistency index is 1.24. If the consistency ratio is less than 0.1, the verification is passed. The weight values ​​of each index layer are 0.2192, 0.1684, 0.1423, 0.3257, 0.0811, and 0.0633, respectively. Among them, habitat diversity has the highest weight, indicating that habitat protection is the primary consideration for ecological corridor selection.

[0044] Similar to the consistency verification principle from the system layer to the target layer and from the factor layer to the system layer, the indicator layer judgment matrix has an order of 6, which differs from the previous 5th, 4th, 3rd, and 2nd order matrices. Therefore, an average random consistency index corresponding to order 6 is required for comparison. By calculating the maximum eigenvalue, consistency index, and consistency ratio of the 6th order matrix, the logical consistency of the expert scoring is verified. Only when the consistency ratio is less than 0.1 is the judgment matrix deemed to have passed the consistency verification, and the calculated weight values ​​of each indicator layer are retained; otherwise, pairwise comparisons need to be performed again and the values ​​adjusted until the consistency requirements are met. This mechanism ensures the reliability of the ecological corridor selection assessment indicator system and the rationality of the weight calculation results, providing a solid decision-making foundation for subsequent corridor selection assessments.

[0045] Specifically, the 6th-order matrix is ​​a square matrix composed of six indicator layers: ecological landscape component diversity, ecological landscape value, human activity disturbance intensity, habitat diversity, cultural characteristics, and potential service function. The 36 elements in the matrix represent the relative importance relationships between the indicators. The largest eigenvalue is the largest eigenvalue obtained in the eigenvalue decomposition of this 6th-order matrix, reflecting the overall consistency of the judgment matrix. When the matrix has complete consistency, the largest eigenvalue equals 6. The consistency index is a value calculated by subtracting 6 from the largest eigenvalue and then dividing by 5.

[0046] The average random consistency index is a statistical average calculated from a large number of randomly generated 6th-order judgment matrices. It is used to eliminate the influence of matrix order on consistency judgments; the average random consistency index for a 6th-order matrix is ​​1.24. The consistency ratio is the ratio of the consistency index to the average random consistency index. This index eliminates the influence of matrix order, making the consistency of judgment matrices of different orders comparable. When this ratio is less than 0.1, the judgment matrix passes the consistency check, and the calculated weight values ​​for each index layer are retained. When the ratio is greater than or equal to 0.1, the judgment matrix fails the consistency check and needs to be re-compared pairwise and reassigned until the ratio is less than 0.1. During reassignment, special attention should be paid to comparison items with large scaling differences or unclear logical relationships, such as the trade-off between human activity disturbance intensity and habitat diversity, or the relative importance between cultural characteristics and potential service functions.

[0047] In practical calculations, the maximum eigenvalue of a 6th-order judgment matrix can be calculated iteratively using the power method: First, arbitrarily choose a 6-dimensional initial positive vector, usually a vector where all components are 1. Then, repeatedly multiply the judgment matrix by this vector and normalize it by dividing each component by the sum of the six components, until the change between two adjacent iterations is less than a preset precision threshold of 0.0001. The normalized vector obtained at this point is the eigenvector, and the corresponding eigenvalue is the maximum eigenvalue. Taking a practical calculation as an example, the input parameters are the 36 elements of a 6th-order judgment matrix, including six 1s on the diagonal, 15 expert scoring scale values ​​in the upper triangular part, and 15 scale values ​​in the lower triangular part determined by reciprocity. After iterative calculation, the maximum eigenvalue is 6.54. In the calculation of the consistency index, the numerator is the difference between 6.54 and 6 (0.54), the denominator is 5, and the calculation result is 0.108. Looking up the average random consistency index table, the value corresponding to order 6 is 1.24. In the calculation of the consistency ratio, the numerator is the consistency index of 0.108, the denominator is 1.24, and the calculation result is 0.0871, which is less than 0.1. Therefore, the 6th order judgment matrix is ​​determined to pass the consistency check.

[0048] At this point, the eigenvectors corresponding to the eigenvalues ​​are normalized by dividing each component by the sum of the six components, resulting in weight values ​​of 0.2192, 0.1684, 0.1423, 0.3257, 0.0811, and 0.0633 for each indicator layer, with a sum of 1. Habitat diversity has the highest weight at 0.3257, indicating that protecting and connecting diverse habitats is the primary objective in ecological corridor route selection assessment. Ecological landscape component diversity has the second highest weight at 0.2192, indicating that landscape heterogeneity is also an important factor in route selection. Potential service function has the lowest weight at 0.0633, indicating that under the principle of ecological priority in route selection, the weight of service function is relatively low. If the calculated consistency ratio is greater than or equal to 0.1, the system automatically marks abnormal comparison items, prompting experts to re-examine judgments that may contain logical contradictions between ecological landscape value and the intensity of human activity disturbance, cultural characteristics and potential service functions, etc., and recalculate after adjusting the corresponding scaling values ​​until the consistency check is passed.

[0049] In some embodiments, the step of partitioning and evaluating the study area based on system layer weight values ​​specifically includes: dividing the study area into multiple evaluation units; acquiring measured data for each evaluation unit on each factor layer indicator; converting the measured data of each factor layer indicator into corresponding factor layer score values ​​according to a preset grading and scoring standard for each factor layer indicator; multiplying the spatial view factor layer score value of each evaluation unit by its corresponding spatial view factor layer weight value and summing the results to obtain the spatial view system layer score of that evaluation unit; multiplying the ecological function factor layer score value of each evaluation unit by its corresponding ecological function factor layer weight value and summing the results to obtain the ecological function system layer score of that evaluation unit; and multiplying the anti-interference factor layer score value of each evaluation unit by its corresponding anti-interference factor layer weight value and summing the results to obtain the evaluation score. The evaluation system calculates the following scores: The cultural characteristic factor score of each evaluation unit is calculated by multiplying its cultural characteristic factor score by its corresponding weight and summing the results. The service function score of each evaluation unit is calculated by multiplying its service function factor score by its corresponding weight and summing the results. The spatial vision system score, ecological function system score, anti-interference strength system score, cultural characteristic system score, and service function system score are multiplied by their respective system weights and summed to obtain the comprehensive restoration value score for each evaluation unit. Based on these comprehensive restoration value scores, the evaluation units are ranked, and those with comprehensive restoration value scores higher than a preset threshold are selected as priority restoration areas.

[0050] Evaluation units refer to the basic spatial units used for assessment within the study area. These units can be set as regular grid units or irregular administrative boundary units according to actual management needs. Grid unit sizes are typically 100m x 100m or 500m x 500m, while administrative boundary units can be village boundaries, township boundaries, or functional zone boundaries. The measured data for factor-level indicators refer to the specific values ​​of each evaluation unit on the 17 factor-level indicators obtained through field surveys, remote sensing monitoring, or model simulations. For example, ecological landscape continuity data can be obtained by calculating the patch connectivity index using landscape pattern analysis software; species abundance data can be obtained by recording the number of species through biodiversity field surveys; vegetation cover data can be obtained by interpreting remote sensing images to obtain the normalized vegetation index; human activity disturbance intensity data can be obtained by calculating the proportion of construction land using land use data; and topographic relief data can be obtained by calculating the elevation standard deviation using a digital elevation model.

[0051] The factor-level score refers to the result of converting measured data into a standardized score according to a preset grading and scoring standard. A 9-point scoring system is adopted, where 1 point represents extremely low restoration value, 3 points represent low value, 5 points represent medium value, 7 points represent good value, and 9 points represent excellent value. Each level corresponds to a specific range of measured data. For example, vegetation coverage of 0 to 16% corresponds to 1 point, 16% to 28% corresponds to 3 points, 28% to 39% corresponds to 5 points, 39% to 49% corresponds to 7 points, and greater than 49% corresponds to 9 points.

[0052] The spatial vision system layer score is the comprehensive score obtained by multiplying the evaluation unit's scores on the five spatial vision factor layer indicators by their corresponding factor layer weight values ​​of 0.3138, 0.1799, 0.2874, 0.1224, and 0.0965, respectively, and then summing the results. The ecological function system layer score is the comprehensive score obtained by multiplying the evaluation unit's scores on the four ecological function factor layer indicators by their corresponding factor layer weight values ​​of 0.3996, 0.2086, 0.0776, and 0.3142, respectively, and then summing the results. The anti-interference strength system layer score is the comprehensive score obtained by multiplying the evaluation unit's scores on the three anti-interference factor layer indicators by their corresponding factor layer weight values ​​of 0.2969, 0.5396, and 0.1635, respectively, and then summing the results.

[0053] The cultural characteristic system layer score is the comprehensive score obtained by multiplying the scores of the evaluation unit on the two cultural characteristic factor layer indicators by their corresponding factor layer weight values ​​of 0.75 and 0.25, respectively, and then summing the results. The service function system layer score is the comprehensive score obtained by multiplying the scores of the evaluation unit on the three service function factor layer indicators by their corresponding factor layer weight values ​​of 0.1571, 0.5936, and 0.2493, respectively, and then summing the results. The comprehensive restoration value score is the final score obtained by multiplying the above five system layer scores by their corresponding system layer weight values ​​of 0.2685, 0.4107, 0.1918, 0.0791, and 0.0499, respectively, and then summing the results. This score comprehensively reflects the ecological restoration value of the evaluation unit. The preset threshold is the scoring boundary used to select priority restoration areas. It can be set according to the overall score distribution of the study area, for example, taking the upper quartile or the top 20% quantile of the comprehensive restoration value scores of all evaluation units as the threshold.

[0054] In practice, the study area is first divided into 500m x 500m regular fishing net grids as evaluation units, generating spatial vector data containing unique identifiers. For the 17 factor-level indicators, spatial database layers are established, and spatial connectivity tools are used to link the indicator data to the evaluation units. For the ecological landscape continuity indicator, the input parameter is land use type raster data. The moving window method is used to calculate the landscape connectivity index within each evaluation unit, with the window size matching the evaluation unit size. The output is a continuity index value between 0 and 1. For indicators that are difficult to quantify directly, such as psychological perception indicators, a beauty evaluation method can be used. Representative sample points are selected to take landscape photographs, and evaluators are invited to score landscape preferences. A statistical relationship model between landscape elements and psychological perception is established. The input parameters are quantitative indicators of landscape elements such as green view rate, sky rate, and water body rate. The output is a predicted value for psychological perception scores.

[0055] In the weighted calculation process, map algebra tools are used to perform spatial weighted operations on raster or vector data. First, the measured data of each factor layer index are converted into a 9-point scoring raster. Then, each index is multiplied by its corresponding factor layer weight, and the results are summed to obtain the system layer score raster. Finally, the five system layer score rasters are multiplied by their respective system layer weights and accumulated to generate a spatial distribution map of the comprehensive restoration value score. The scoring rasters are reclassified according to a preset threshold, and evaluation units with scores higher than the threshold are extracted as priority restoration areas. The output is a spatial distribution map of these priority restoration areas. Simultaneously, an attribute table is generated containing the identifier codes of each evaluation unit, scores for 17 factor layers, scores for 5 system layers, the comprehensive restoration value score, and priority restoration indicators, facilitating subsequent planning, design, and engineering implementation.

[0056] In some embodiments, the step of partitioning the study area according to the system layer weight value to obtain the partitioning evaluation result further includes: For each evaluation unit, its score at each factor level of the spatial vision factor layer is multiplied by the corresponding weight value of the spatial vision factor layer, and then the products are summed to obtain the spatial vision system layer score for that evaluation unit. Similarly, the system layer scores for ecological function, anti-interference intensity, cultural characteristics, and service function are calculated respectively.

[0057] The scores of the five system layers are multiplied by their corresponding system layer weights, and then the five multipliers are summed to obtain the comprehensive restoration value score for each evaluation unit. This score comprehensively reflects the priority value of the evaluation unit in ecological restoration; the higher the score, the greater the restoration value.

[0058] An adaptive thresholding method based on score distribution was used to screen candidate repair units. Specifically, the distribution characteristics of the comprehensive repair value scores of all evaluation units were statistically analyzed, such as calculating the upper quartile and interquartile range of the scores. The threshold was set as the upper quartile plus a certain multiple of the interquartile range. Evaluation units with comprehensive repair value scores higher than this threshold were extracted as candidate repair units. The adaptive thresholding method avoids the subjectivity of manually setting a fixed threshold, allowing the screening results to be dynamically adjusted according to the data distribution.

[0059] Finally, spatial connectivity clustering analysis is performed on the candidate restoration units. Specifically, four-neighbor or eight-neighbor connectivity rules are used to merge spatially adjacent candidate restoration units into the same cluster patch. The area of ​​each cluster patch is calculated, and isolated sporadic patches with areas smaller than a preset area threshold are removed. This threshold can be determined based on the smallest mapping unit in the study area, such as a multiple of the evaluation unit area. The remaining cluster patches with areas larger than the threshold are output as priority restoration areas. The average comprehensive restoration value score, maximum score, and spatial boundary information of each priority restoration area are recorded for subsequent planning use.

[0060] Candidate ecological corridors refer to potential ecological corridor paths initially identified within the study area based on ecological security pattern analysis or minimum cumulative resistance models. There are usually multiple alternative routes. These corridors connect important ecological source areas or habitat patches and have different spatial orientations, lengths, and coverage areas. Spatial data refers to geographic information data describing the spatial characteristics and attributes of candidate corridors, including corridor vector line data, landscape type data within the buffer zone, human activity distribution data, habitat type data, cultural resource point data, and service function potential data.

[0061] In some embodiments, before evaluating the route selection of multiple candidate corridors based on the weight values ​​of the indicator layer, the process further includes generating candidate ecological corridors and establishing a corridor evaluation model. The specific implementation process is as follows: The first step is to identify ecological source areas. The priority restoration areas identified in the previous steps, along with previously identified important habitats such as key bird sanctuaries and fish spawning grounds, and the core areas of nature reserves, are collectively designated as ecological source areas. These source areas are core patches with high ecological value or requiring protection and connection.

[0062] The second step is to construct a comprehensive resistance surface. Four factors are selected as resistance factors: topographic relief, intensity of human activity disturbance, vegetation cover, and road network. Each factor is assigned a different resistance value based on its degree of obstruction to ecological flow; for example, areas with high vegetation cover have low resistance, while areas with high human activity disturbance have high resistance. The weight of each resistance factor is determined using the analytic hierarchy process (AHP), i.e., experts compare each factor pairwise to determine its relative importance to ecological flow, and the weight values ​​are calculated. Then, each resistance factor grid is multiplied by its weight and summed to generate a comprehensive resistance surface grid. The value of each grid cell represents the relative cost required for biological or ecological flow to pass through that location.

[0063] The third step involves identifying key corridor cores using a circuit theory model. Using ecological source areas as nodes (voltage sources) and a comprehensive resistance surface as the conductive medium (whose resistivity is proportional to its resistance value), the random walk of current within the medium is simulated. The current density value of each grid cell is calculated; areas with higher current density indicate a greater likelihood of ecological flow. All grids are sorted from highest to lowest current density, and the cumulative current density contribution is calculated. Grid cells with cumulative contributions reaching a certain proportion, such as the top 30%, are identified as key corridor cores. These grids constitute the core areas that the ecological corridor must traverse.

[0064] The fourth step is to generate candidate ecological corridors. Using the key corridor core as the framework, shortest path algorithms, such as Dijkstra's algorithm or A* algorithm, are employed to connect the various ecological source areas. The algorithm uses the comprehensive resistance surface as the cost surface, searching for the path with the minimum cumulative resistance between each pair of source areas, thus generating multiple candidate ecological corridors. Each corridor can have a minimum width set, for example, a width value determined based on actual management needs, forming corridor surface elements.

[0065] The fifth step is to establish an analysis buffer and calculate indicator data. For each candidate ecological corridor, an analysis buffer is established by extending a certain width to both sides of its centerline. The width can be determined according to the ecological function requirements of the corridor, for example, ranging from tens to hundreds of meters. Within this buffer, the spatial data of each indicator layer are calculated: the ecological landscape component diversity index, such as the Shannon diversity index, is calculated using landscape pattern index software; the ecological landscape value score is calculated using a beauty evaluation model, based on the results of a public preference survey of landscape photographs; the average disturbance intensity within the buffer is calculated by overlaying a human activity disturbance intensity raster; the habitat diversity index, such as habitat type richness, is calculated using a habitat type map; cultural characteristic scores are calculated based on the density and level of cultural resource points, such as cultural relics, historical sites, and traditional villages; and potential service function values, such as water conservation capacity and recreational potential, are estimated using an ecosystem service assessment model, such as the InVEST model.

[0066] The indicator-level score refers to the standardized score obtained by converting the spatial data of each candidate corridor into a score based on a preset grading standard. It also uses a 9-point scoring system, where 1 point represents extremely poor suitability, 3 points represent poor, 5 points represent moderate, 7 points represent good, and 9 points represent excellent. Each level corresponds to a specific spatial data range. For example, extremely low ecological landscape diversity index corresponds to 1 point, relatively low to 3 points, moderate to 5 points, relatively high to 7 points, and extremely high to 9 points; extremely high human activity disturbance intensity corresponds to 1 point, relatively high to 3 points, moderate to 5 points, relatively low to 7 points, and extremely low to 9 points. The ecological landscape diversity score is the weighted score obtained by multiplying the ecological landscape diversity index score of the candidate corridor by the corresponding indicator-level weight value of 0.2192.

[0067] The ecological landscape value score is a weighted score obtained by multiplying the ecological landscape value index score of the candidate corridor by the corresponding index layer weight value of 0.1684. The human activity disturbance intensity score is a weighted score obtained by multiplying the human activity disturbance intensity index score of the candidate corridor by the corresponding index layer weight value of 0.1423. Since human activity disturbance intensity is a negative indicator, a higher score indicates less disturbance and better suitability. The habitat diversity score is a weighted score obtained by multiplying the habitat diversity index score of the candidate corridor by the corresponding index layer weight value of 0.3257. The cultural characteristics score is a weighted score obtained by multiplying the cultural characteristics index score of the candidate corridor by the corresponding index layer weight value of 0.0811. The potential service function score is a weighted score obtained by multiplying the potential service function index score of the candidate corridor by the corresponding index layer weight value of 0.0633. The comprehensive route selection score is the final score obtained by summing the scores of the above 6 indicators. This score comprehensively reflects the suitability of the selected corridor route, and the higher the score, the better the corridor.

[0068] Preferably, in practice, the ecological corridor selection assessment can be spatialized and compared using a geographic information system (GIS) platform. First, multiple candidate ecological corridors are generated using a minimum cumulative resistance model or circuit theory model. Input parameters include ecological source area distribution data, resistance surface data, and corridor width parameters. The output is vector data of multiple potential corridor paths. For each candidate corridor, a buffer zone extending 500 meters on each side of the corridor is established as the analysis area. For the ecological landscape component diversity index, the input parameter is the landscape type classification data within the buffer zone. Landscape pattern analysis software is used to calculate the Shannon diversity index or the Simpson diversity index, and the output is the diversity index value, which is converted into a 9-point score according to the grading standard.

[0069] For the habitat diversity index, the input parameters are habitat type data within the buffer zone. Habitat type richness and the frequency of rare habitat occurrences are calculated, and the output is a habitat diversity score. For the human activity disturbance intensity index, the input parameters include construction land vector data, road network data, and nighttime light data. A multi-ring buffer analysis is used to calculate the weighted cumulative impact of each disturbance source, and the output is a comprehensive disturbance intensity value. When converting this to a score, it's important to note that this is a negative indicator; the lower the disturbance intensity, the higher the score. During the weighted calculation process, an attribute table calculation tool is used to multiply the scores of each indicator with their weights, generating six indicator score fields for each candidate corridor. These scores are then accumulated using a field calculator to generate a comprehensive route selection score field.

[0070] All candidate corridors are ranked in descending order based on their comprehensive route selection scores. The corridor with the highest score is determined as the optimal route selection scheme. A detailed scoring table for each corridor is also output, including scores for six indicator layers, scores for each of the six indicators, and the comprehensive route selection score. This allows decision-makers to understand the advantages and disadvantages of each alternative scheme. If the comprehensive route selection score of the optimal route selection scheme is significantly higher than other schemes, it is directly recommended for implementation. If multiple schemes have similar scores, further consideration of external factors such as engineering feasibility and construction costs is required for a comprehensive comparison.

[0071] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for evaluating the site selection and alignment of plateau lakeside ecological corridors and ecological restoration, characterized in that, Includes the following steps: Obtain current natural resource data for the plateau lakeside zone, and construct a zoning assessment technical indicator system based on the current natural resource data. The zoning assessment technical indicator system includes multiple system-level indicators. The importance of each system-level indicator is compared pairwise. Based on the comparison results, a judgment matrix from the system level to the target level is generated. The eigenvector normalization of the judgment matrix is ​​then performed to obtain the system-level weight value corresponding to each system-level indicator. Obtain the route selection requirements data for plateau lakeside ecological corridors, and construct an ecological corridor route selection evaluation technical indicator system based on the route selection requirements data. The ecological corridor route selection evaluation technical indicator system includes multiple indicator layers. The importance of each indicator in the indicator layer is compared pairwise. Based on the comparison results, a judgment matrix from the indicator layer to the target layer is generated. The feature vector normalization calculation is performed on the judgment matrix to obtain the indicator layer weight value corresponding to each indicator layer. The study area is partitioned and evaluated based on the system layer weight values ​​to obtain the partition evaluation results. Based on the weight values ​​of the index layer, the route selection evaluation is performed on multiple candidate corridors to obtain the route selection evaluation results; Based on the zoning assessment results and the route selection assessment results, a comprehensive assessment scheme for the ecological restoration of plateau lakeshores is generated.

2. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 1, characterized in that, The system-level indicators include spatial visibility indicators, ecological function indicators, anti-interference strength indicators, cultural characteristic indicators, and service function indicators. The pairwise importance comparison of the system-level indicators specifically involves using a 5-point importance scale to compare and assign values ​​to each of the spatial visibility indicators, ecological function indicators, anti-interference strength indicators, cultural characteristic indicators, and service function indicators. The generation of the judgment matrix from the system level to the target level specifically involves constructing a 5-order matrix based on the assignment results, where the element aij represents the importance scale value of the i-th indicator relative to the j-th indicator.

3. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 2, characterized in that, In the zoning assessment, quantitative data of indicators for each factor layer are obtained by fusing multi-source remote sensing data with ground survey data: vegetation cover is retrieved using multi-temporal remote sensing images and the ecological landscape continuity index is calculated; topographic relief is calculated using a digital elevation model; the intensity index of human activity disturbance is calculated based on land use, road, and nighttime light data; the spatial distribution of species abundance is obtained by combining a species distribution model with field surveys; and the quantitative data of indicators for each factor layer are normalized and converted into graded scoring values.

4. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 2, characterized in that, The spatial visibility index comprises multiple spatial visibility factor layer indicators, the ecological function index comprises multiple ecological function factor layer indicators, the anti-interference intensity index comprises multiple anti-interference factor layer indicators, the cultural characteristic index comprises multiple cultural characteristic factor layer indicators, and the service function index comprises multiple service function factor layer indicators. The method further includes the step of constructing a factor layer-to-system layer judgment matrix: performing pairwise importance comparisons on the spatial visibility factor layer indicators to generate a spatial visibility factor layer-to-system layer judgment matrix; performing pairwise importance comparisons on the ecological function factor layer indicators to generate an ecological function factor layer-to-system layer judgment matrix; performing pairwise importance comparisons on the anti-interference factor layer indicators to generate an anti-interference factor layer-to-system layer judgment matrix; performing pairwise importance comparisons on the cultural characteristic factor layer indicators to generate a cultural characteristic factor layer-to-system layer judgment matrix; and performing pairwise importance comparisons on the service function factor layer indicators to generate a service function factor layer-to-system layer judgment matrix.

5. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 4, characterized in that, It also includes the step of calculating the weight values ​​of the factor layer: performing eigenvector normalization calculation on the judgment matrix from the spatial vision factor layer to the system layer to obtain the weight values ​​of the spatial vision factor layer corresponding to each spatial vision factor layer index; The eigenvector normalization calculation is performed on the judgment matrix from the ecological function factor layer to the system layer to obtain the weight values ​​of the ecological function factor layer corresponding to each ecological function factor layer index; the eigenvector normalization calculation is performed on the judgment matrix from the anti-interference factor layer to the system layer to obtain the weight values ​​of the anti-interference factor layer corresponding to each anti-interference factor layer index; the eigenvector normalization calculation is performed on the judgment matrix from the cultural characteristic factor layer to the system layer to obtain the weight values ​​of the cultural characteristic factor layer corresponding to each cultural characteristic factor layer index. The eigenvector normalization calculation is performed on the judgment matrix from the service function factor layer to the system layer to obtain the service function factor layer weight values ​​corresponding to each service function factor layer index.

6. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 5, characterized in that, The step of partitioning the study area according to the system layer weight value to obtain the partitioning evaluation result further includes: weighted summing of the factor layer score value of each evaluation unit with the corresponding factor layer weight value to obtain the system layer score; weighted summing of the system layer score with the corresponding system layer weight value to obtain the comprehensive restoration value score of each evaluation unit; using an adaptive threshold method based on score distribution to select evaluation units with a comprehensive restoration value score significantly higher than the average level as candidate restoration units; performing spatial connectivity clustering on the candidate restoration units, removing isolated patches smaller than a preset area threshold, and outputting the remaining clustered areas as priority restoration areas.

7. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 1, characterized in that, The indicator layer indicators include ecological landscape component diversity indicators, ecological landscape value indicators, human activity disturbance intensity indicators, habitat diversity indicators, cultural characteristic indicators, and potential service function indicators. The pairwise importance comparison of the indicator layer indicators specifically involves using a 5-point importance scale to compare and assign values ​​to the ecological landscape component diversity indicators, ecological landscape value indicators, human activity disturbance intensity indicators, habitat diversity indicators, cultural characteristic indicators, and potential service function indicators. The generation of the judgment matrix from the indicator layer to the target layer specifically involves constructing a 6th-order matrix based on the assignment results.

8. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 7, characterized in that, Before evaluating the selection of multiple candidate corridors based on the weight values ​​of the indicator layers, the process includes generating candidate ecological corridors and establishing a corridor evaluation model: The priority restoration area and identified important habitats are considered as ecological source areas; a comprehensive resistance surface is constructed, whose resistance factors include topographic relief, intensity of human activity disturbance, vegetation cover, and road network, with the weights of each resistance factor determined using the analytic hierarchy process (AHP); a circuit theory model is used to simulate the flow of current on the comprehensive resistance surface, generating a current density map, and high current density areas are extracted as key corridor cores; using the key corridor cores as a framework, the shortest path algorithm is used to connect the various ecological source areas, generating multiple candidate ecological corridors; for each candidate ecological corridor, an analysis buffer is established by extending a certain width to both sides of its centerline, and spatial data of each indicator layer are calculated within the analysis buffer.

9. The evaluation method for site selection and alignment of plateau lakeside ecological corridors and ecological restoration according to claim 5 or 6, characterized in that, The specific steps of partitioning and evaluating the study area based on system-level weight values ​​include: dividing the study area into multiple evaluation units; acquiring measured data for each evaluation unit on each factor-level indicator; converting the measured data of each factor-level indicator into corresponding factor-level score values ​​according to a preset grading and scoring standard for each factor-level indicator; multiplying the spatial view factor-level score value of each evaluation unit by its corresponding spatial view factor-level weight value and summing the results to obtain the spatial view system-level score of that evaluation unit; multiplying the ecological function factor-level score value of each evaluation unit by its corresponding ecological function factor-level weight value and summing the results to obtain the ecological function system-level score of that evaluation unit; and multiplying the anti-interference factor-level score value of each evaluation unit by its corresponding anti-interference factor-level weight value and summing the results to obtain the anti-interference score of that evaluation unit. The system layer score is calculated as follows: The cultural characteristic factor layer score of each evaluation unit is multiplied by its corresponding cultural characteristic factor layer weight value and summed to obtain the cultural characteristic system layer score for that evaluation unit. The service function factor layer score of each evaluation unit is multiplied by its corresponding service function factor layer weight value and summed to obtain the service function system layer score for that evaluation unit. The spatial vision system layer score, ecological function system layer score, anti-interference intensity system layer score, cultural characteristic system layer score, and service function system layer score are multiplied by their respective system layer weight values ​​and summed to obtain the comprehensive restoration value score for each evaluation unit. Based on the comprehensive restoration value scores, the evaluation units are ranked, and those with comprehensive restoration value scores higher than a preset threshold are selected as priority restoration areas.

10. The method for site selection and alignment assessment of plateau lakeside ecological corridors and ecological restoration according to claim 7 or 8, characterized in that, The specific steps of evaluating multiple candidate corridors based on indicator layer weights include: acquiring spatial data for each candidate ecological corridor at each indicator layer; converting the spatial data of each indicator layer for each candidate ecological corridor into corresponding indicator layer scores according to a pre-defined grading and scoring standard for each indicator layer; multiplying the ecological landscape diversity index score of each candidate ecological corridor by its corresponding indicator layer weight to obtain the ecological landscape diversity score; multiplying the ecological landscape value index score of each candidate ecological corridor by its corresponding indicator layer weight to obtain the ecological landscape value score; multiplying the human activity disturbance intensity index score of each candidate ecological corridor by its corresponding indicator layer weight to obtain the human activity disturbance intensity score; and finally, evaluating each candidate ecological corridor... The habitat diversity index score is obtained by multiplying the score of each candidate ecological corridor by the corresponding index layer weight value. The cultural characteristic index score is then multiplied by the corresponding index layer weight value to obtain the cultural characteristic score. The potential service function index score is also multiplied by the corresponding index layer weight value to obtain the potential service function score. Finally, the ecological landscape component diversity score, ecological landscape value score, human activity disturbance intensity score, habitat diversity score, cultural characteristic score, and potential service function score of each candidate ecological corridor are summed to obtain the comprehensive route selection score. Based on the comprehensive route selection score, the candidate ecological corridors are ranked, and the candidate ecological corridor with the highest comprehensive route selection score is determined as the optimal route selection scheme.

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