A method for identifying asymmetric urban ecological corridor width based on connectivity performance index

Abstract

The present application relates to a kind of asymmetric urban ecological corridor width identification method based on connection degree performance index;The method is based on the function connection degree current density simulated by circuit theory, and in the width search range set, it is generated single-side buffer zone gradually and the accumulated current performance and width ratio are calculated, the width-performance curve of continuous derivable is constructed by spline interpolation, the candidate width is identified from the performance peak point, elbow inflection point and marginal sensitivity point three angles three complementary mathematical indexes respectively, the single-side optimal width is determined by median, and the asymmetric ecological corridor is generated by merging after being calculated independently on left and right sides.The present application breaks through the symmetry assumption and experience threshold dependence of traditional corridor width identification, realizes the data internal characteristic driving of width determination, multi-index fusion robust determination and asymmetric space generation, and is suitable for urban ecological corridor planning, biodiversity protection network construction and green infrastructure space optimization.

Description

Technical Field

[0001] This invention belongs to the field of urban ecological assessment technology, specifically a method for identifying the width of urban ecological corridors based on connectivity performance index. Background Technology

[0002] Ecological corridors are crucial spatial carriers connecting fragmented habitat patches and maintaining the flow of matter and energy within ecosystems. In densely built-up urban environments, determining the appropriate width of ecological corridors is one of the core challenges in ecological planning: too narrow a width results in insufficient ecological function, failing to effectively support the migration and dispersal of target species; too wide a width leads to waste of land resources and creates a sharp conflict with urban construction land use. Finding the optimal balance between ecological performance and land costs is a pressing technical problem that needs to be addressed in urban ecological corridor planning.

[0003] Currently, the determination of ecological corridor width mainly relies on the following methods: The first type is the empirical threshold method. This method directly sets the corridor width based on the ecological characteristics of a specific target species or empirical values ​​from literature. However, this method ignores the heterogeneity of landscape resistance and ecological processes under different geographical environments, lacks spatial precision, and is difficult to adapt to the differentiated ecological conditions of different regions.

[0004] The second type is based on the current density grading method. This method simulates the diffusion current density of species on the landscape resistance surface according to circuit theory, using high current density areas as corridor spatial locations. This method determines corridor width by grading current density using the natural discontinuity method or the quantile method. However, the grading methods often rely on human selection and do not consider the marginal variation law of ecological corridor functional connectivity (current density), resulting in a high degree of subjectivity in threshold selection.

[0005] The third category is the performance-cost ratio inflection point method. Existing technologies include methods for determining the minimum ideal width of urban ecological corridors based on functional connectivity. These methods calculate the ratio of cumulative current density to area (connectivity performance-cost ratio) of buffer zones of different widths and use a piecewise linear regression model to extract inflection points as the minimum ideal width. However, this method still has the following shortcomings: First, using piecewise linear regression to extract a single inflection point makes it sensitive to data noise and lacks robustness. Second, it only identifies one inflection point, failing to fully utilize the complete mathematical structure information of the performance curve. Third, and more importantly, existing methods generally employ the symmetry assumption, i.e., using the same width on both sides of the centerline. This fails to reflect the asymmetrical characteristics of resistance distribution in real-world landscapes—the landscape pattern, land use, and resistance conditions on both sides of the corridor often differ significantly. The symmetry assumption leads to a systematic bias in the definition of the corridor's spatial range, making it difficult to meet the precise requirements of high-density urban ecological corridor construction.

[0006] Furthermore, existing corridor width identification methods based on current density grading often rely on subjective methods such as the natural breakpoint method or quantile method for selecting grading thresholds, lacking in-depth analysis of the mathematical structure of the functional connectivity performance curve. Single-index judgments are prone to misjudgment under data noise, resulting in insufficient statistical robustness in width identification.

[0007] In summary, there is an urgent need in this field for an ecological corridor width identification method that can overcome the symmetry assumption, be driven by the internal features of the data, and be robustly determined by the fusion of multiple indicators. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an asymmetric urban ecological corridor width identification method based on connectivity performance index. This invention automatically identifies inflection points through the mathematical structure of the performance curve, making width determination entirely driven by internal data features and eliminating the subjectivity of threshold selection; it fully utilizes the derivative information of the performance curve to capture the critical position where the performance growth pattern undergoes a qualitative change from the mathematical structure; it suppresses the risk of misjudgment by a single indicator under data noise through a multi-indicator fusion mechanism, improving the statistical robustness of width determination; and it breaks through the symmetry assumption, allowing the left and right sides of the corridor to independently determine the corridor width based on their respective landscape resistance patterns, thus realizing the generation of asymmetric corridor space.

[0009] To achieve the above-mentioned objectives, the technical solution provided in this application is as follows: An asymmetric method for identifying the width of urban ecological corridors based on a connectivity performance index, the method specifically includes the following steps: S1. Obtain ecological source area and comprehensive resistance surface data of the target area, simulate the corridor functional connectivity current density grid data based on circuit theory, and extract the center line of the ecological corridor. S2, Set the single-sided width search range and step size to form a width sequence. , where n is the total number of width gradients; S3, with current width W i Single-sided buffer zones are generated to the left and right along the centerline, respectively. The ratio of the current grid value to the width within the buffer zone is calculated as the corridor performance index B(w). i ): , Among them, buffer(w i ) indicates a width of w i The set of grid points covered by the single-sided buffer, where current(p) is the current value at grid point p; the performance index sequence B is calculated for the left and right sides respectively. L (w i ) and B R (w i ); S4. Spline interpolation is performed on the discrete width-performance sequences on the left and right sides respectively to obtain smooth and first-differentiable continuous curves. And calculate the first derivative FD and the chord L(w) i ): ,

[0010] Among them, FD(w i L(w) reflects the rate of change in the corridor performance index. i This reflects the ideal value under the condition that performance changes linearly with corridor width; S5 calculates candidate widths from three perspectives: peak performance point, elbow inflection point, and marginal sensitivity point, using three complementary mathematical indicators. The median is then used to comprehensively determine the optimal width on one side. , Among them, W peak The peak performance point, i.e. Maximum point, FD(w) i ) corresponds to a width of 0; W dist The elbow inflection point, i.e. With L(w i The width corresponding to the maximum absolute value of the difference; W marg For the marginal sensitive point, i.e., FD(w) i The width corresponding to the point with the largest absolute value; S6, Perform steps S3-S5 on the left and right sides respectively to obtain the optimal width W on the left side. opt,L And the optimal width W on the right opt,R ; S7, with Wo pt,L and W opt,R The left-side and right-side single-sided buffer zones are generated separately and merged into a polygon, i.e., the asymmetric ecological corridor range.

[0011] Furthermore, in S2, the width search range is 10m to 200m, and the step size is 5m.

[0012] Furthermore, the spline interpolation in S4 is cubic spline interpolation.

[0013] Furthermore, W in S5 peak W dist and W marg The specific identification method is as follows: W peak The first derivative FD(w) i The width value corresponding to 0 within the search range represents the maximum value of the corridor performance index. W dist for With L(w i The maximum absolute value of the difference corresponds to the width, representing the position where the deviation from the chord line is greatest; W marg The first derivative FD(w) i The width value corresponding to the absolute value reaching its maximum value represents the position where the width response is most sensitive.

[0014] Furthermore, the process preceding S1 includes steps for identifying ecological source areas and constructing integrated resistance surfaces: Based on morphological spatial pattern analysis, the ecological space of the target area is analyzed morphologically to identify and screen core areas with an area greater than a preset threshold as ecological source areas. Based on multiple indicators including normalized vegetation index, tree ratio, canopy height, land use type, distance from road, distance from water system, building ratio, and night light index, a comprehensive resistance surface is constructed using spatial principal component analysis.

[0015] Furthermore, the acquisition of the current density grid data specifically involves: identifying ecological corridors based on the minimum cumulative resistance model, and simulating the functional connectivity current density using paired modes of circuit theory.

[0016] Furthermore, the total width of the asymmetric ecological corridor is the sum of the optimal width on the left and the optimal width on the right: W total =W opt,L +W opt,R .

[0017] Based on the above technical solutions, the asymmetric urban ecological corridor width identification method based on connectivity performance index proposed in this application has achieved the following technical advantages through practical application: 1. Eliminate the subjectivity of threshold selection; This invention automatically identifies inflection points through the mathematical structure of the width performance curve, and the width determination is entirely driven by the internal features of the data, avoiding human bias caused by subjective threshold selection methods such as the natural breakpoint method and the quantile method.

[0018] 2. Make full use of the deep mathematical structure of the curve; This invention not only utilizes the first derivative (performance peak, marginal performance), but also the degree of deviation (elbow inflection point) to capture the critical position of qualitative change in performance growth pattern from a mathematical perspective, making fuller use of information.

[0019] 3. Multi-indicator fusion enhances robustness of judgment; This invention uses three complementary indicators—performance peak point, elbow inflection point, and marginal sensitivity point—to identify candidate widths and then synthesizes them using the median, effectively suppressing the risk of misjudgment under data noise due to a single indicator and improving the statistical robustness of width determination.

[0020] 4. Breaking through the symmetry assumption to achieve asymmetric identification: This invention enables the left and right sides of the corridor to independently determine the corridor width based on their respective landscape resistance patterns, which can accurately reflect the asymmetric characteristics of resistance distribution in the real landscape and overcome the systematic bias caused by the traditional symmetry assumption. Attached Figure Description

[0021] Figure 1 This is a line graph showing the average functional connectivity over 60m in an embodiment of the present invention.

[0022] Figure 2 This is a line graph showing the average functional connectivity over 100m in an embodiment of the present invention.

[0023] Figure 3 This is a line graph showing the average functional connectivity over 200m in an embodiment of the present invention.

[0024] Figure 4 This is a line graph showing the average connectivity of the present invention.

[0025] Figure 5 This is a schematic diagram comparing the average connectivity of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is described below with reference to specific examples shown in the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0027] Example 1: An asymmetric urban ecological corridor width identification method based on connectivity performance index, the method specifically includes the following steps: S1. Obtain ecological source area and comprehensive resistance surface data of the target area, simulate the corridor functional connectivity current density grid data based on circuit theory, and extract the center line of the ecological corridor. S2, Set the single-sided width search range and step size to form a width sequence. , where n is the total number of width gradients; S3, with current width W i Single-sided buffer zones are generated to the left and right along the centerline, respectively. The ratio of the current grid value to the width within the buffer zone is calculated as the corridor performance index B(w). i ): , Among them, buffer(w i ) indicates a width of w iThe set of grid points covered by the single-sided buffer, where current(p) is the current value at grid point p; the performance index sequence B is calculated for the left and right sides respectively. L (w i ) and B R (w i ); S4. Spline interpolation is performed on the discrete width-performance sequences on the left and right sides respectively to obtain smooth and first-differentiable continuous curves. And calculate the first derivative FD and the chord L(w) i ): ,

[0028] Among them, FD(w i L(w) reflects the rate of change in the corridor performance index. i This reflects the ideal value under the condition that performance changes linearly with corridor width; S5 calculates candidate widths from three perspectives: peak performance point, elbow inflection point, and marginal sensitivity point, using three complementary mathematical indicators. The median is then used to comprehensively determine the optimal width on one side. , Among them, W peak The peak performance point, i.e. Maximum point, FD(w) i ) corresponds to a width of 0; W dist The elbow inflection point, i.e. With L(w i The width corresponding to the maximum absolute value of the difference; W marg For the marginal sensitive point, i.e., FD(w) i The width corresponding to the point with the largest absolute value; S6, Perform steps S3-S5 on the left and right sides respectively to obtain the optimal width W on the left side. opt,L And the optimal width W on the right opt,R ; S7, with Wo pt,L and W opt,R The left-side and right-side single-sided buffer zones are generated separately and merged into a polygon, i.e., the asymmetric ecological corridor range.

[0029] Furthermore, in S2, the width search range is 10m to 200m, and the step size is 5m.

[0030] Furthermore, the spline interpolation in S4 is cubic spline interpolation.

[0031] Furthermore, W in S5 peak Wdist and W marg The specific identification method is as follows: W peak The first derivative FD(w) i The width value corresponding to a value of 0 within the search range represents the maximum value of the corridor performance index. W dist for With L(w i The maximum absolute value of the difference corresponds to the width, representing the position where the deviation from the chord line is greatest; W marg The first derivative FD(w) i The width value corresponding to the absolute value reaching its maximum value represents the position where the width response is most sensitive.

[0032] The steps preceding S1 also include ecological source area identification and comprehensive resistance surface construction: Based on morphological spatial pattern analysis, the ecological space of the target area is analyzed morphologically to identify and screen core areas with an area greater than a preset threshold as ecological source areas. Based on multiple indicators including normalized vegetation index, tree ratio, canopy height, land use type, distance from road, distance from water system, building ratio, and night light index, a comprehensive resistance surface is constructed using spatial principal component analysis.

[0033] The acquisition of the current density grid data specifically involves: identifying ecological corridors based on the minimum cumulative resistance model, and simulating the functional connectivity current density using paired modes of circuit theory.

[0034] The total width of the asymmetric ecological corridor is the sum of the optimal width on the left and the optimal width on the right: W total =W opt,L +W opt,R .

[0035] Example 2, I. Study Area and Data Source: This implementation method takes the central urban area of ​​a city as the research object; the study area is a typical area with high urbanization, complex landscape pattern, high degree of fragmentation of ecological land, and an urgent need for accurate identification of ecological corridors.

[0036] II. Identification of Ecological Source Areas Based on the Morphological Spatial Pattern Analysis (MSPA) method, morphological analysis was conducted on the ecological space of the study area to identify core areas, bridging areas, branches, isolated areas, edge areas, loops, and perforations. Core areas with an area greater than 5 hectares were extracted and screened, and patches with a distance of less than 150 meters from each other were merged. Finally, patches with an area greater than 20 hectares were selected as ecological source areas, resulting in the identification of a total of 30 ecological source areas.

[0037] III. Construction of Comprehensive Resistance Surface Eight indicators were selected: Normalized Difference Vegetation Index (NDVI), tree percentage, canopy height, land use type, distance from roads, distance from water systems, building percentage, and nighttime light intensity index. A comprehensive resistance surface was constructed using spatial principal component analysis. Each indicator reflects the magnitude of resistance to the diffusion of ecological flows in the landscape from different dimensions: NDVI, tree percentage, and canopy height reflect vegetation ecological quality; land use type reflects the ecological suitability of land cover; distance from roads and building percentage reflect the intensity of human activity disturbance; distance from water systems reflects hydrological conditions; and nighttime light intensity index reflects the intensity of nighttime human activity.

[0038] IV. Ecological Corridor Identification and Current Density Simulation Using a minimum cumulative resistance model, the maximum number of adjacent connections in each core area is limited to 4, and the truncation cost-weighted distance threshold is set to 200,000, identifying 66 ecological corridors. Based on circuit theory simulations of functional connectivity, the truncation cost-weighted distance is set to 10,000, and pairwise operation is employed.

[0039] V. Identification of Asymmetric Corridor Width According to the method steps of this invention, the following operations are performed on each ecological corridor: The single-sided width search range is set to 10m to 200m, with a step size of 5m. Single-sided buffer zones of different widths are generated on the left and right sides of each corridor, and the ratio of the sum of the current grid values ​​in each buffer zone to the width is calculated. Cubic spline interpolation is performed on the discrete width performance sequence to obtain a smooth continuous curve, and the first derivative and chord are calculated. The performance peak point (FD is 0), the elbow inflection point (the point of maximum deviation), and the marginal sensitivity point (|FD| maximum value) are identified respectively, and the median of these three values ​​is taken as the optimal width for that side.

[0040] VI. Recognition Results An asymmetric urban ecological corridor width identification method based on the functional connectivity width performance index was used to identify the optimal left and right widths for 66 ecological corridors. The identification results are as follows: Table 1 shows the optimal left-side width, optimal right-side width, and total width of the 66 ecological corridors identified. 1 37 58 95 2 54 19 73 3 38 22 60 4 19 19 38 5 18 19 37 6 97 55 152 7 20 18 38 8 52 21 73 9 19 51 70 10 131 55 186 11 23 23 46 12 34 16 50 13 18 54 72 14 54 55 109 15 43 43 86 16 19 18 37 17 18 18 36 18 18 18 36 19 12 51 63 20 49 83 132 21 54 19 73 22 19 19 38 23 53 18 71 24 19 17 36 25 18 17 35 26 20 23 43 27 18 20 38 28 51 18 69 29 18 18 36 30 19 46 65 31 53 19 72 32 18 18 36 33 58 36 94 34 19 18 37 35 18 18 36 36 59 57 116 37 55 17 72 38 49 41 90 39 21 46 67 40 56 17 73 41 18 17 35 42 51 17 68 43 20 20 40 44 50 22 72 45 21 21 42 46 18 18 36 47 45 54 99 48 19 18 37 49 21 51 72 50 19 19 38 51 52 19 71 52 18 18 36 53 19 71 90 54 21 21 42 55 19 18 37 56 50 20 70 57 20 18 38 58 18 18 36 59 77 44 121 60 18 18 36 61 49 48 97 62 65 19 84 63 43 43 86 64 42 114 156 65 22 21 43 66 54 18 72

[0041] The recognition results show that: (1) Significant asymmetry: There are obvious differences in the width between the left and right sides of many corridors, such as 54m on the left and 19m on the right of corridor No. 2; 131m on the left and 55m on the right of corridor No. 10; and 42m on the left and 114m on the right of corridor No. 64. This fully demonstrates that there is a significant asymmetry in the resistance conditions on both sides of the corridor in the real landscape. If the traditional symmetry assumption (equal width on both sides) is adopted, a systematic deviation will occur.

[0042] (2) Wide range of width distribution; the identified single-sided widths range from 12m to 131m, and the total widths range from 35m to 186m, reflecting that there are huge differences in the optimal width of ecological corridors under different locations and landscape patterns, and the empirical threshold method cannot accurately adapt to such differentiated needs.

[0043] (3) The data-driven characteristics are obvious; all width values ​​are automatically generated by the mathematical structure of the performance curve without introducing any artificial thresholds or empirical parameters, which reflects the advantage of this method being driven entirely by the internal characteristics of the data.

[0044] VII. Result Verification After identification, left and right single-sided buffer zones are generated based on the optimal widths on the left and right sides, respectively, and then merged into polygons, representing the asymmetric ecological corridor range. Verification shows that the identified corridor space effectively covers high current density areas (i.e., areas with a high probability of ecological flow passage), while avoiding the problems of being too wide or too narrow due to the symmetry assumption, thus validating the effectiveness and practicality of this method.

[0045] VIII. Several Alternative Implementation Methods This section lists several alternative implementation methods, all of which fall within the protection scope of this invention: 8.1 Alternatives to the width search range.

[0046] The search width can be adjusted according to the specific ecological conditions and planning needs of a city, and is not limited to 10m to 200m. For areas with high ecological sensitivity, it can be extended to 300m or 500m; for the core area of ​​an old city with extremely limited land, it can be narrowed to 5m to 150m.

[0047] 8.2 Alternative options for step size.

[0048] The step size can be adjusted according to accuracy requirements, and is not limited to 5m. For high-precision recognition requirements, a step size of 2m or 1m can be used; for rapid evaluation requirements, a step size of 10m can be used.

[0049] 8.3 Alternative choices for interpolation methods.

[0050] In addition to cubic spline interpolation, other interpolation methods that can guarantee second-order differentiability, such as B-spline interpolation and Akma interpolation, can also be used.

[0051] 8.4 Alternative options for multi-indicator fusion methods.

[0052] Besides median fusion, mean fusion and weighted average fusion can also be used. Median fusion has good robustness to outliers and is the preferred implementation method.

[0053] 8.5 Alternative options for screening thresholds for ecological source areas.

[0054] The area threshold for screening ecological source areas can be adjusted according to the size of the study area and the distribution characteristics of the ecological patches, and is not limited to 5 hectares and 20 hectares in this embodiment.

[0055] 8.6 Alternative options for resistance surface construction indicators.

[0056] The resistance surface construction indicators can be added, removed, or replaced based on data availability and regional ecological characteristics, and are not limited to the eight indicators in this implementation.

[0057] IX. Applicable Fields Preferred application areas for this method include, but are not limited to: urban ecological corridor planning and design, biodiversity conservation network construction, green infrastructure spatial optimization, ecological security pattern construction, key area identification for national land space ecological restoration, and any ecological space planning scenario that requires finding the optimal balance between ecological performance and land costs.

[0058] like Figures 1-5 As shown, comparing widths of 60m (30m on one side), 100m (50m on one side), and 200m (100m on one side), it was found that the corridor functional connectivity of the present invention is 71.2% better than the empirical threshold with a width of 60m, 95.5% better than the empirical threshold with a width of 100m, and 98.5% better than the empirical threshold with a width of 200m.

[0059] This case study uses cubic smoothed splines to construct a width-ecological performance continuous function, implemented using the R language smoothed spline function, with the smoothing parameter set to spar=0.5. Preprocessing rules for fitted nodes include: removing missing values ​​and duplicate width samples; sorting independent variables in ascending order of corridor width; using a default width of 30m when there are insufficient effective samples; limiting the optimization range to the sampling width interval; and not performing extrapolation.

[0060] Without performing a consistency check, this invention provides an optimal width candidate value for each of the three feature points from three independent dimensions. Taking the median as the final recommended value has the following statistical advantages: First, the median allows for extreme deviations in one of the three indicators without affecting the final result; second, the median always falls within the interval determined by the three candidate values, ensuring the boundedness of the recommended width; third, the median is equivalent to a majority consensus strategy, with at least two dimensions supporting a value near this width, avoiding the defect of the mean being pulled by extreme values ​​under a skewed distribution.

[0061] When the width recommendations given by the three indicators from different dimensions differ significantly, it indicates that the optimal width problem has an inherent multi-criteria nature. Taking the median is equivalent to minimizing the maximum deviation strategy: regardless of whether the decision-maker's true preference leans towards efficiency, structure, or sensitivity, the median guarantees that the deviation in the worst case does not exceed the minimum of the worst deviation of any single indicator, making it a robust multi-criteria compromise solution.

[0062] This case study uses Circuit Landscape version 4.07. When solving the circuit network, the landscape grid is transformed into a large-scale sparse linear equation system, with the unknowns being the voltages of each node. When the number of grid cells is large, directly solving this equation system (such as matrix inversion) is computationally infeasible; therefore, an iterative algorithm is needed to gradually approximate the true solution. The Choreski decomposition method is used as the direct solver for the linear system. This method is suitable for symmetric positive definite matrices and can guarantee the stability of the numerical solution under large-scale grid conditions. The convergence criterion for the iterative process is set as follows: monitoring the maximum change ΔV of the voltages of all nodes between two adjacent iterations; when ΔV is less than 1 × 10⁻⁶, the convergence criterion is met. -6 (i.e., convergence tolerance is 1×10) -6 When the iteration is considered to have converged and the voltage distribution has reached a steady state, the iteration is terminated and the current current density distribution is output. At the same time, in order to prevent the calculation from failing to converge due to abnormal resistance surface configuration or excessive grid size and running idle for a long time, the maximum number of iterations is set to 10,000. If the convergence condition is not met after exceeding this number, the iteration is forcibly terminated and the final iteration result is output as the approximate solution of current density.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for identifying the width of urban ecological corridors based on a connectivity performance index, characterized in that, The method specifically includes the following steps: S1. Obtain ecological source area and comprehensive resistance surface data of the target area, simulate the corridor functional connectivity current density grid data based on circuit theory, and extract the center line of the ecological corridor. S2, Set the single-sided width search range and step size to form a width sequence. , where n is the total number of width gradients; S3, with current width W i Single-sided buffer zones are generated to the left and right along the centerline, respectively. The ratio of the sum of the current grids within the buffer zone to the width is calculated as the corridor performance index B(w). i ): ， Among them, buffer(w i ) indicates a width of w i The set of grid points covered by the single-sided buffer, where current(p) is the current value at grid point p; the performance index sequence B is calculated for the left and right sides respectively. L (w i ) and B R (w i ); S4. Spline interpolation is performed on the discrete width-performance sequences on the left and right sides respectively to obtain smooth and first-differentiable continuous curves. And calculate the first derivative FD and the chord L(w) i ): ， ， Among them, FD(w i L(w) reflects the rate of change in the corridor performance index. i This reflects the ideal value under the condition that performance changes linearly with corridor width; S5 calculates candidate widths from three perspectives: peak performance point, elbow inflection point, and marginal sensitivity point, using three complementary mathematical indicators. The median is then used to comprehensively determine the optimal width on one side. ， Among them, W peak The peak performance point, i.e. Maximum point, FD(w) i ) corresponds to a width of 0; W dist The elbow inflection point, i.e. With L(w i The width corresponding to the maximum absolute value of the difference; W marg This is the point where marginal sensitivity is maximized, i.e., FD(w) i The width corresponding to the point with the largest absolute value; S6, Perform steps S3-S5 on the left and right sides respectively to obtain the optimal width W on the left side. opt,L And the optimal width W on the right opt,R ; S7, with Wo pt,L and W opt,R The left-side and right-side single-sided buffer zones are generated separately and merged into a polygon, i.e., the asymmetric ecological corridor range.

2. The method for identifying the width of an asymmetric urban ecological corridor based on a connectivity performance index according to claim 1, characterized in that, The width search range in S2 is 10m to 200m, with a step size of 5m.

3. The method for identifying the width of an asymmetric urban ecological corridor based on a connectivity performance index according to claim 1, characterized in that, The spline interpolation in S4 is cubic spline interpolation.

4. The method for identifying the width of an asymmetric urban ecological corridor based on a connectivity performance index according to claim 1, characterized in that, W in S5 peak W dist and W marg The specific identification method is as follows: W peak The first derivative FD(w) i The width value corresponding to a value of 0 within the search range represents the maximum value of the corridor performance index. W dist for With L(w i The maximum absolute value of the difference corresponds to the width, representing the position where the deviation from the chord line is greatest; W marg The first derivative FD(w) i The width value corresponding to the absolute value reaching its maximum value represents the position where the width response is most sensitive.

5. The method for identifying the width of an asymmetric urban ecological corridor based on a connectivity performance index according to claim 1, characterized in that, The steps preceding S1 also include ecological source area identification and comprehensive resistance surface construction: Based on morphological spatial pattern analysis, the ecological space of the target area is analyzed morphologically to identify and screen core areas with an area greater than a preset threshold as ecological source areas. Based on multiple indicators including normalized vegetation index, tree ratio, canopy height, land use type, distance from road, distance from water system, building ratio, and night light index, a comprehensive resistance surface is constructed using spatial principal component analysis.

6. The method for identifying the width of an asymmetric urban ecological corridor based on a connectivity performance index according to claim 1, characterized in that, The acquisition of the current density grid data specifically involves: identifying ecological corridors based on the minimum cumulative resistance model, and simulating the functional connectivity current density using paired modes of circuit theory.

7. The method for identifying the width of an asymmetric urban ecological corridor based on a connectivity performance index according to claim 1, characterized in that, The total width of the asymmetric ecological corridor is the sum of the optimal width on the left and the optimal width on the right: W total =W opt,L +W opt,R .

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