Railway corridor biodiversity value assessment method and assessment device

Abstract

The present application relates to a kind of railway corridor biodiversity value evaluation method and evaluation device, the method comprises: the habitat quality data of target railway corridor collected in advance and landscape pattern data are input to the model constructed in advance, the ecological value index of the railway corridor is output;Based on the social value type and evaluation rule constructed in advance, the social value index of the target railway corridor is calculated;The ecological value index and the social value index are weighted by the analytic hierarchy process constructed in advance, and the ecological value index and the social value index are superimposed according to the weight calculation, to determine the biodiversity value comprehensive evaluation index of the target railway corridor.This method is obtained by combining geographic information software, landscape pattern model, ecosystem service and the comprehensive evaluation model of trade-off multiple models or tools, and the research evaluation index is comprehensive, improves the accuracy and real of railway corridor biodiversity value, and the practicality is high.

Description

Technical Field

[0001] This invention relates to the fields of civil engineering (railway) and ecology, and in particular to a method and apparatus for assessing the biodiversity value of railway corridors. Background Technology

[0002] Biodiversity is the material foundation upon which human society depends for survival and development. It has important ecological service functions such as supply, regulation and support, and plays an irreplaceable role in maintaining ecological balance.

[0003] As a long-span linear structure, railways are often surrounded by unique ecosystems with heterogeneous spatial and temporal distribution, a combination of points, lines, and surfaces, and complex and diverse ecological factors.

[0004] Current technologies often focus on regional studies, selecting only specific areas for landscape pattern analysis, such as traditional isal areas like nature reserves and national parks. Alternatively, they utilize scoring methods based on the expertise and experience of evaluation experts. These methods assign scores to six components—landscape / ecosystem, biological community, population / species, major protected objects, biosecurity, and relevant stakeholders—according to their perceived impact on biodiversity. Based on these scores, the weights of each component's evaluation indicators are determined, and the overall biodiversity impact index of the project on the affected area is calculated. However, expert scoring based on on-site surveys suffers from significant subjectivity and arbitrariness. Furthermore, some landscape pattern indices lack holistic consideration, have limited applicability to areas along railway lines, limited ecological significance, and low sensitivity.

[0005] Therefore, how to effectively assess the biodiversity value of railway corridors based on remote sensing imagery has become an urgent technical problem to be solved. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method and device for assessing the biodiversity value of railway corridors, which solves the technical problems of low sensitivity, incomplete assessment and low accuracy of the biodiversity value assessment index of areas along railway corridors.

[0008] (II) Technical Solution

[0009] Firstly, in order to achieve the above objectives, the present invention provides a method for assessing the biodiversity value of railway corridors, the main technical solutions of which include:

[0010] S1. Input the habitat quality data and landscape pattern data of the target railway corridor into the pre-built model and output the ecological value index of the railway corridor. The habitat quality data is the data including land use raster data and ecological threat factor layer after remote sensing interpretation of the remote sensing image of the target railway corridor. The landscape pattern data is the data including land feature raster data after remote sensing interpretation of the remote sensing image of the target railway corridor.

[0011] S2. Calculate the social value index of the target railway corridor based on the pre-constructed social value types and evaluation rules;

[0012] S3. The ecological value index and the social value index are weighted by a pre-constructed hierarchical analysis method. The ecological value index and the social value index are superimposed and calculated according to the weights to determine the comprehensive evaluation index of biodiversity value of the target railway corridor.

[0013] Optionally, it also includes:

[0014] S4. Based on a pre-built spatial analysis method, identify the biodiversity value hotspots and coldspots of the target railway corridor, and generate a visualized comprehensive value map for display.

[0015] Optionally, before S1, S0 is also included:

[0016] Remote sensing interpretation of remote sensing images of the target railway corridor is performed using geographic information software to obtain habitat quality data and landscape pattern data; the habitat quality data includes land use raster data and ecological threat factor layers; the landscape pattern data includes land feature raster data.

[0017] Optionally, S0 specifically includes:

[0018] S01. For the pre-acquired high-resolution remote sensing image, generate a standard image through atmospheric correction, geometric correction and orthorectification;

[0019] S02. Perform supervised classification on the standard image and output land use classification image and land feature remote sensing classification image;

[0020] S03. Using the raster tool of geographic information software, the land use classification image is divided into ranges, land use raster data of the target railway corridor is collected, and an ecological threat factor layer is extracted based on the land use raster data.

[0021] S04. Perform remote sensing interpretation of the land cover classification image using the interpretation tool of geographic information software to obtain land cover vector data, and determine land cover raster data based on the land cover vector data;

[0022] The types of land features are classified into two levels.

[0023] Optionally, S1 includes:

[0024] S11. Input the land use raster data and ecological threat factor layer into the pre-constructed integrated assessment model of ecosystem services and trade-offs, and calculate the habitat quality index by combining the predefined ecological threat factor scale, land use type sensitivity scale and target railway corridor range layer.

[0025] S12. Input the land feature raster data into the pre-constructed landscape pattern model and output the landscape pattern index;

[0026] S13. Using the Moran index, a correlation analysis is performed on the habitat quality index and the landscape pattern index to obtain the correlation between the habitat quality index and the landscape pattern index.

[0027] S14. Based on the aforementioned correlation, the habitat quality index and landscape pattern index are weighted according to the analytic hierarchy process, and the ecological value index data is obtained by superimposing and calculating.

[0028] Based on the integrated assessment model and landscape pattern model of the ecosystem services and trade-offs, it is possible to assess the habitat quality and landscape pattern evolution in railway corridor areas.

[0029] Optionally, S2 includes:

[0030] S21. Based on the pre-constructed social value types and evaluation rules, the social value points of the target railway corridor are determined by expert scoring and public evaluation methods.

[0031] The types of social value include: aesthetic value, economic value, life sustainability value, spiritual value and / or scientific research value;

[0032] The evaluation rules include: conditional valuation method, market valuation method, loss cost method, opportunity cost method, conditional valuation method, travel cost method, estimation method, protection cost method, recovery cost method and / or shadow valuation method;

[0033] S22. Based on the pre-built ecosystem service social value model, a weighted kernel density analysis is performed on all the social value points using an embedded kernel density analysis tool to obtain the overall spatial distribution density, kernel density surface, and maximum weighted kernel density value of the social value points.

[0034] S23. Based on the kernel density surface and the maximum weighted kernel density value, obtain a kernel density value index layer standardized to 0-10. Based on the kernel density value index layer, calculate the median index of the social value type and obtain the median index map.

[0035] S24. Perform average nearest neighbor analysis on the social value points based on the average nearest neighbor tool embedded in the ecosystem service social value model, and obtain the spatial clustering results through the ratio R value and standard deviation Z value obtained from the average nearest neighbor analysis.

[0036] S25. Based on the social survey data and spatial data layer of the target railway corridor, perform spatial analysis on the target railway corridor and generate a spatial heterogeneity distribution map of each social value type.

[0037] S26. Perform buffer analysis on the elevation elements within the target railway corridor area to obtain the social value index of the elevation elements. The elevation elements include contour line element extraction, slope extraction, and distance from the railway centerline.

[0038] S27. Using the value mapping module, based on the above-mentioned social value points, overall spatial distribution density, spatial clustering results, median index map, elevation element social value index, and spatial heterogeneity distribution map, and after processing by the maximum entropy model, output the final social value map of the target railway corridor and obtain the social value index of the target railway corridor.

[0039] Optionally, the formula for calculating the comprehensive evaluation index of biodiversity value in S3 is:

[0040]

[0041] Where V is the comprehensive evaluation value of the target railway corridor; n is the number of evaluation factors; W l The combined weights of the evaluation factors; T l is the standardized score value of the l-th evaluation factor;

[0042] The evaluation factors are the ecological value index and social value index of the target railway corridor.

[0043] Optionally, S4 includes:

[0044] S41. Input the habitat quality index and the landscape pattern index into geographic information software to generate an ecological value map of the target railway corridor;

[0045] S42. Based on the spatial analysis function of geographic information software, the grid of the ecological value map and the social value map is used as the evaluation unit. The Z value and P value are statistically calculated and obtained. P < 0.1. The hot spots and cold spots of the target railway corridor are spatially clustered.

[0046] S43. Based on the spatial clustering of hot and cold spots, generate and display a comprehensive value map of the target railway corridor;

[0047] The statistical calculation formula is as follows:

[0048]

[0049] Among them, G i * represents Z, q j It is the attribute value of the j-th evaluation unit, g ij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

[0050] Optionally, it also includes:

[0051] S5. Input the ecological value index and social value index into the pre-constructed coupling degree model and coupling coordination degree model to determine the coupling degree and coupling coordination degree between the target railway corridor ecosystem and social system;

[0052] The coupling degree model is as follows:

[0053]

[0054] Wherein, f(a) is the ecological value index of the railway corridor; f(b) is the social value index of the railway corridor.

[0055] The coupling coordination degree model is as follows:

[0056] T = αf(a) + βf(b);

[0057]

[0058] Where: f(a) – ecological value index of railway corridor; f(b) – social value index of railway corridor;

[0059] α = β = 0.5; T is the comprehensive evaluation index of the social and ecological development level of the railway corridor.

[0060] Secondly, embodiments of the present invention also provide an apparatus for assessing the biodiversity value of railway corridors, comprising:

[0061] The ecological value index processing unit is used to input the habitat quality data and landscape pattern data of the target railway corridor into a pre-built model and output the ecological value index of the railway corridor. The habitat quality data is data including land use raster data and ecological threat factor layers after remote sensing interpretation of the remote sensing image of the target railway corridor. The landscape pattern data is data including land feature raster data after remote sensing interpretation of the remote sensing image of the target railway corridor.

[0062] The social value index processing unit is used to calculate the social value index of the target railway corridor based on the pre-constructed social value types and evaluation rules.

[0063] The comprehensive evaluation index processing unit is used to assign weights to the ecological value index and the social value index using a pre-constructed hierarchical analysis method, and to calculate the comprehensive evaluation index of the biodiversity value of the target railway corridor by superimposing the ecological value index and the social value index according to the weights.

[0064] Optionally, it may also include: a visualization display unit;

[0065] The visualization unit is used to determine the biodiversity value hotspots and cold spots of the target railway corridor based on a pre-built spatial analysis method, and generate a visualized comprehensive value map for display.

[0066] Optionally, the ecological value index processing unit specifically includes: a first submodule and a second submodule;

[0067] The first submodule is used to perform remote sensing interpretation of remote sensing images of the target railway corridor using geographic information software to obtain habitat quality data and landscape pattern data; the habitat quality data includes land use raster data and ecological threat factor layers; the landscape pattern data includes land cover raster data.

[0068] Standard images are generated from pre-acquired high-resolution remote sensing images through atmospheric correction, geometric correction, and orthorectification.

[0069] Supervised classification is performed on the standard images to output land use classification images and remote sensing classification images of land features;

[0070] The land use classification image is divided into ranges using the raster tool of geographic information software, land use raster data of the target railway corridor is collected, and an ecological threat factor layer is extracted based on the land use raster data.

[0071] The remote sensing interpretation of the land cover classification image is performed using the interpretation tool of geographic information software to obtain land cover vector data, and land cover raster data is determined based on the land cover vector data;

[0072] The types of land features are classified into two levels.

[0073] The second submodule is used to input the land use raster data and ecological threat factor layer into a pre-built integrated assessment model of ecosystem services and trade-offs, and calculate and obtain the habitat quality index by combining the predefined ecological threat factor scale, land use type sensitivity scale and target railway corridor range layer.

[0074] The land feature raster data is input into a pre-constructed landscape pattern model, and the landscape pattern index is output.

[0075] Using the Moran index, a correlation analysis was conducted on the habitat quality index and the landscape pattern index to obtain the correlation between the habitat quality index and the landscape pattern index;

[0076] Based on the aforementioned correlation, the habitat quality index and landscape pattern index are weighted according to the analytic hierarchy process, and the ecological value index data is obtained by superimposing and calculating.

[0077] The social value index processing unit is specifically used to determine the social value points of the target railway corridor based on pre-constructed social value types and evaluation rules, through expert scoring and public evaluation methods.

[0078] For example, the types of social value include: aesthetic value, economic value, life sustainability value, spiritual value and / or scientific research value;

[0079] The evaluation rules include: conditional valuation method, market valuation method, loss cost method, opportunity cost method, conditional valuation method, travel cost method, estimation method, protection cost method, recovery cost method and / or shadow valuation method;

[0080] Based on a pre-built social value model of ecosystem services, a weighted kernel density analysis is performed on all the social value points using an embedded kernel density analysis tool to obtain the overall spatial distribution density, kernel density surface, and maximum weighted kernel density value of the social value points.

[0081] Based on the kernel density surface and the maximum weighted kernel density value, a kernel density value index layer standardized to 0-10 is obtained. Based on the kernel density value index layer, the median index of the social value type is calculated, and a median index map is obtained.

[0082] The average nearest neighbor tool embedded in the ecosystem service social value model is used to perform average nearest neighbor analysis on the social value points, and the spatial clustering results are obtained by the ratio R value and standard deviation Z value obtained from the average nearest neighbor analysis.

[0083] Based on the social survey data and spatial data layers of the railway corridor, a spatial analysis of the target railway corridor is conducted to generate a spatial heterogeneity distribution map of each social value type.

[0084] A buffer analysis is performed on the elevation elements within the railway corridor to obtain the social value index of the elevation elements. The elevation includes contour element extraction, slope extraction, and distance from the railway centerline.

[0085] Using the value mapping module, based on the aforementioned social value points, overall spatial distribution density, spatial clustering results, median index map, elevation element social value index, and spatial heterogeneity distribution map, and after processing with the maximum entropy model, the final social value map of the target railway corridor is output, and the social value index of the target railway corridor is obtained.

[0086] The visualization unit is specifically used to input the habitat quality index and the landscape pattern index into geographic information software to generate an ecological value map of the target railway corridor.

[0087] Based on the spatial analysis function of geographic information software, the grid of the ecological value map and the social value map is used as the evaluation unit. The Z value and P value are obtained by statistical calculation. P < 0.1. The hot spots and cold spots of the target railway corridor are spatially clustered.

[0088] Based on the spatial clustering of hot and cold spots, a comprehensive value map of the target railway corridor is generated and displayed;

[0089] The statistical calculation formula is as follows:

[0090]

[0091] Among them, G i * represents Z, q j It is the attribute value of the j-th evaluation unit, g ij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

[0092] Furthermore, the assessment device for the biodiversity value of railway corridors also includes a coupling coordination unit, which is used to determine the coupling degree and coupling coordination degree between the target railway corridor's ecosystem and social system.

[0093] The ecological value index and social value index are input into the pre-constructed coupling degree model and coupling coordination degree model to determine the coupling degree and coupling coordination degree between the target railway corridor ecosystem and social system.

[0094] The coupling degree model is as follows:

[0095]

[0096] Wherein, f(a) is the ecological value index of the railway corridor; f(b) is the social value index of the railway corridor.

[0097] The coupling coordination degree model is as follows:

[0098] T = αf(a) + βf(b);

[0099]

[0100] Where: f(a) – ecological value index of railway corridor; f(b) – social value index of railway corridor;

[0101] α = β = 0.5; T is the comprehensive evaluation index of the social and ecological development level of the railway corridor.

[0102] (III) Beneficial Effects

[0103] This invention provides a method for assessing the biodiversity value of railway corridors. Based on remote sensing images of the target railway corridor, an ecological value index is obtained, and a community value index is obtained through social value types and evaluation rules. This enables a comprehensive assessment of the biodiversity value of the target railway corridor. The assessment process is very comprehensive and reliable, overcoming the shortcomings of existing technologies such as inaccurate and incomplete manual assessments, low sensitivity, and high cost.

[0104] In practical application, based on integrated assessment models of ecosystem services and trade-offs and landscape pattern models, two indicators—habitat quality index and landscape pattern index—and multiple sub-indicator factors of the target railway corridor ecosystem are obtained. This enables a more refined evaluation of the ecological value of biodiversity in railway corridors. Based on social value types and evaluation rules, the social value of the target railway corridor is determined. Under the constraint of railway corridor scale, a more realistic evaluation of the social value of biodiversity in railway corridors can be conducted. This determines the comprehensive evaluation index of biodiversity value of the target railway corridor, accurately and realistically assessing the biodiversity value of the target railway corridor. This approach can be effectively applied to the zonal structure of railway corridors, solving the problem that the evaluation indicators for zonal structure research are not comprehensive enough and have limited practicality. Attached Figure Description

[0105] Figure 1 A method and flowchart for assessing the biodiversity value of railway corridors provided in an embodiment of the present invention;

[0106] Figure 2 The flowchart illustrates the assessment method for evaluating the biodiversity value of railway corridors, as provided in an embodiment of the present invention.

[0107] Figure 3 for Figure 2 A detailed schematic diagram of the evaluation process in the first embodiment. Detailed Implementation

[0108] To better explain and facilitate understanding of the present invention, it will be described in detail below with reference to the accompanying drawings and specific embodiments. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a clearer and more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0109] Biodiversity is the material foundation upon which human society depends for survival and development, possessing vital ecological service functions such as supply, regulation, and support, and playing an irreplaceable role in maintaining ecological balance. Railways, as long-span linear structures, are often surrounded by unique ecosystems characterized by spatial and temporal heterogeneity, a combination of point, line, and surface features, and a complex diversity of influencing ecological factors. Therefore, all railway-related activities, such as site selection and construction, must consider their impact on the ecological environment to achieve coordinated development. Therefore, this invention proposes a method for assessing the biodiversity value of railway corridors.

[0110] like Figure 1 As shown, Figure 1 This is a flowchart of a method for assessing the biodiversity value of railway corridors according to an embodiment of the present invention. The executing entity in this embodiment can be any computing device. Specifically, the method for assessing the biodiversity value of railway corridors mainly includes:

[0111] S1. Input the habitat quality data and landscape pattern data of the target railway corridor into the pre-built model and output the ecological value index of the railway corridor.

[0112] In this embodiment, the habitat quality data can be data including land use raster data and ecological threat factor layers after remote sensing interpretation of the remote sensing image of the target railway corridor, and the landscape pattern data can be data including ground feature raster data after remote sensing interpretation of the remote sensing image of the target railway corridor.

[0113] Typically, railway corridors are 1.2 to 1.5 meters wide, and corridor areas exist along the railway line. To better facilitate the assessment, this embodiment typically assesses the biodiversity value of a specified length of railway corridor; in this case, the specified length of railway corridor to be analyzed is the target railway corridor.

[0114] In this embodiment, the remote sensing image can be obtained through existing methods.

[0115] S2. Calculate the social value index of the target railway corridor based on the pre-constructed social value types and evaluation rules.

[0116] For example, types of social value may include: aesthetic value, economic value, life sustainability value, spiritual value and / or scientific research value;

[0117] The evaluation rules may include: conditional valuation method, market valuation method, loss cost method, opportunity cost method, travel cost method, estimation method, protection cost method, recovery cost method and / or shadow valuation method.

[0118] S3. The ecological value index and the social value index are weighted by a pre-constructed hierarchical analysis method. The ecological value index and the social value index are superimposed and calculated according to the weights to determine the comprehensive evaluation index of biodiversity value of the target railway corridor.

[0119] Specifically, in one embodiment, the formula for calculating the comprehensive evaluation index of biodiversity value in S3 is as follows:

[0120]

[0121] Where V is the comprehensive evaluation value of the target railway corridor; n is the number of evaluation factors; W l The combined weights of the evaluation factors; T l Let l be the standardized score of the l-th evaluation factor, where l is a natural number and less than or equal to n.

[0122] In this embodiment, the evaluation factors are the ecological value index and social value index of the target railway corridor, and the specific number should be determined based on the calculation results in actual application.

[0123] In practical applications, the calculated comprehensive evaluation index of biodiversity value can be sorted and the comprehensive evaluation value can be judged according to actual needs.

[0124] For example, in one embodiment, based on actual needs, a region is considered to have a high overall biodiversity value when its comprehensive evaluation value is in the top 30%, and a region is considered to have a low overall biodiversity value when its comprehensive evaluation value is in the bottom 30%. In another embodiment, the percentage can be adjusted to 10%, 20%, etc., and the specific division should be based on actual needs, which is not a limitation here.

[0125] In practical applications, Figure 1 In the method shown, step S0 may be performed before step S1:

[0126] S0. Remote sensing interpretation of remote sensing images of the target railway corridor using geographic information software to obtain habitat quality data and landscape pattern data, etc.; the habitat quality data includes land use raster data and ecological threat factor layers, etc.; the landscape pattern data includes land feature raster data, etc.

[0127] It should be noted here that execution Figure 1 The method shown can be integrated into a computing device, which may also integrate ArcGIS software, to obtain habitat quality data and landscape pattern data.

[0128] To better understand, step S0 above can specifically include:

[0129] S01. For the pre-acquired high-resolution remote sensing image, generate a standard image through atmospheric correction, geometric correction and orthorectification;

[0130] S02. Perform supervised classification on the standard image and output land use classification image and land feature remote sensing classification image.

[0131] S03. The land use classification image is divided into ranges using the raster tool of geographic information software, land use raster data of the target railway corridor is collected, and ecological threat factor layers are extracted based on the land use raster data.

[0132] S04. Perform remote sensing interpretation of the land cover classification image using the interpretation tool of geographic information software to obtain land cover vector data, and determine land cover raster data based on the land cover vector data.

[0133] The types of land features are classified into two levels.

[0134] Specifically, in practical applications, when implementing step S01, the accuracy can be increased by using other reference images and ground verification points. For example, in one embodiment, after acquiring a high-resolution remote sensing image of the target railway corridor, a standard image is generated through atmospheric correction, geometric correction, and orthorectification. A digital elevation model (DEM) with a resolution of no less than 30m, a high-precision reference image library, a high-precision control point library, and high-precision GPS points in the field are used as auxiliary data. Supervised classification is performed using remote sensing image processing software (ENVI, ERDAS), and verification is performed using reviewed ground verification points. The result is the output of a land use classification image and a land feature remote sensing classification image.

[0135] The aforementioned method for assessing the biodiversity value of railway corridors involves acquiring land use raster data and feature raster data through remote sensing interpretation of high-resolution remote sensing images. A pre-built model is then used to output various indicator factors of ecological and social value, obtaining ecological and social value indices. Finally, weights are assigned using methods such as the analytic hierarchy process (AHP), and the weights are overlaid to obtain the comprehensive biodiversity value index of the target railway corridor. This method effectively provides a comprehensive and accurate assessment of the biodiversity value along railway lines.

[0136] Specifically, in one embodiment, S1 can be implemented as follows:

[0137] S11. Input the land use raster data and ecological threat factor layer into the pre-built integrated assessment model of ecosystem services and trade-offs (In VEST model), and calculate the habitat quality index by combining the predefined ecological threat factor scale, the corresponding land use type sensitivity scale and the target railway corridor range layer.

[0138] The calculation formulas involved may include:

[0139]

[0140] In the formula, Q xj D represents the habitat quality of raster x in land use type j, dimensionless; xj H represents the degree of disturbance experienced by raster x in land use type j, i.e., the degree of habitat degradation, and is dimensionless; K is a half-saturation constant, which in practical applications can be half the size of the raster cell, and is dimensionless; j Let z be the habitat suitability of land type j, which is dimensionless, and z be a normalized constant.

[0141]

[0142] In the formula, R is the stress factor, which is dimensionless; y is the number of raster cells in the stress factor r raster layer; Y r The total number of grid cells occupied by the stress factor; w r The weight of the stress factor r, taking values ​​from 0 to 1, is dimensionless; r y The stress factor value (0 or 1) for the raster y is dimensionless; i rxy β represents the degree of interference of the stress factor r of raster y on land cover raster x, which is dimensionless; x S represents the accessibility level of grid x, ranging from 0 to 1, where 1 indicates extremely easy access and is dimensionless; jr For land type j, the sensitivity to stress factor r is given; it is dimensionless. rxy The calculation formula is:

[0143]

[0144]

[0145] In the formula, d xy d represents the straight-line distance between grid x and grid y, in km. r max This represents the maximum influence distance of the threat factor r, in km.

[0146] Specifically, the habitat quality index of each grid data of the target railway corridor is represented by 0-1. The higher the output habitat quality index, the stronger the biodiversity maintenance capacity of the grid.

[0147] In practical applications, when calculating the habitat quality index based on the integrated assessment model of ecosystem services and trade-offs (INVEST model), the ecological threat factor scale and the corresponding land use type sensitivity scale, including the weight of threat sources and the distance of significant impact, the habitat suitability of different land use types and their sensitivity to each threat source, should be modified and adjusted in conjunction with the target railway grade and expert opinions.

[0148] S12. Input the land feature raster data into the pre-built landscape pattern model (Fragstats model) and output the landscape pattern index.

[0149] Specifically, in this embodiment, the landscape pattern index is preferably:

[0150] Dynamic data on patch diversity, landscape heterogeneity, and landscape fragmentation.

[0151] The dynamic indicators of plate diversity may include: quantity, density, area, fractal dimension, and shape index.

[0152] The dynamic indicators of landscape heterogeneity may include: diversity index, connectivity index, dominance index, clustering index, and evenness index.

[0153] The dynamic data on landscape fragmentation may include: patch number fragmentation index, patch shape fragmentation index, and internal habitat area fragmentation index.

[0154] The selected indices are preferred and representative options applicable to strip-shaped structures, possessing characteristics of sensitivity, comprehensiveness, and scientific rigor. Calculating these multiple indices can enhance the completeness, authenticity, and precision of the landscape pattern of the target railway corridor. Of course, in addition to the aforementioned indices, other indices may require calculation in practical applications, and adjustments should be made based on actual needs.

[0155] In one specific embodiment, the landscape pattern index may be:

[0156] (1) Patch diversity dynamics

[0157] ①Quantity

[0158] a. The formula for calculating the number of landscape patches (NP) is:

[0159] NP = N; (6)

[0160] In the formula, N is the total number of patches in the landscape, with a value range of NP≥1 and no upper limit.

[0161] b. Number of plaque types (NP) e The calculation formula is:

[0162] NP e =N e (7)

[0163] In the formula, N e The number of patches for a certain landscape type e.

[0164] ②Density

[0165] a. The formula for calculating the patch density (PD) of a landscape is:

[0166]

[0167] In the formula, PD refers to the number of patches per unit area in all patches of the landscape, and A is the total area of ​​the landscape. It reflects the degree of differentiation of the overall landscape patches; the larger the value, the greater the degree of landscape fragmentation and spatial heterogeneity. N is the total number of patches in the landscape.

[0168] b. Type of plaque density PD e The calculation formula is:

[0169]

[0170] In the formula, PD e The number of patches per unit area for a specific landscape type e is used to more directly reflect the degree of fragmentation of landscape components; A e and N e These represent the area and number of patches for a certain landscape type e, respectively.

[0171] c. The formula for calculating the landscape boundary density (ED) is:

[0172] ED = EA; (10)

[0173] In the formula, ED represents the length of the landscape boundary per unit area within the landscape, reflecting the degree of landscape fragmentation. Its size directly affects the edge effect and species composition. E represents the boundary length in the landscape (i.e., the sum of the perimeters of patches), and A is the total landscape area, usually expressed in km / hm². 2 To express.

[0174] d. Type of patch boundary density ED e The calculation formula is:

[0175]

[0176] In the formula, ED e(Unit: km / hm) 2 P is the boundary length of a certain landscape type e within a unit area. ef This represents the perimeter of the plaque.

[0177] ③ Area

[0178] a. The formula for calculating the average area of ​​type a plaque is:

[0179]

[0180] ef represents the f-th patch in landscape type e.

[0181] b. The formula for calculating the landscape similarity index is:

[0182] LS=A e / A; (13)

[0183] In the formula, LS refers to the similarity index of the landscape, which is the ratio of the area of ​​a certain type to the total area of ​​the landscape. It measures the degree of similarity between a single type and the overall landscape, and in a relative sense, it also indicates the contribution rate of the landscape type to the entire landscape.

[0184] ④ The formula for calculating the shape index is:

[0185]

[0186] In the formula, S is the patch shape index of the e-th landscape type, and P e It is the perimeter of the patch of landscape type e, A e This represents the area of ​​landscape type e. The shape index of the patch is generally greater than or equal to 1. The closer S is to 1, the closer the patch shape is to a circle; a larger value indicates a more complex or elongated shape, and a greater deviation from a circle.

[0187] ⑤ The formula for calculating fractal dimension is:

[0188] D = 2Ln(P) e / 4) / LnA e (15)

[0189] In the formula, D is the fractal dimension; P e A represents the perimeter of a patch of a certain landscape type e; e D represents the patch area of ​​a certain landscape type e. The theoretical range of the D value is 1.0 to 2.0, where 1.0 represents the simplest square patch and 2.0 represents the most complex patch in terms of perimeter for the same area. The fact that different landscape elements have the same D value indicates that they have a consistent landscape pattern.

[0190] (2) Landscape heterogeneity dynamics

[0191] ① The Shannon-Wiener diversity index is calculated using the following formula:

[0192]

[0193] In the formula, H is the Shannon diversity index; the larger the value, the greater the landscape diversity. m is the total number of patch types in the landscape, and P... i It is the proportion of the i-th type of patch to the total landscape area.

[0194] ②The formula for calculating the landscape dominance index is:

[0195]

[0196] In the formula, H is the Shannon diversity index, H max This is its maximum value, where N is the total number of landscape element types under study. A value of 0 for D indicates that the proportions of each landscape type are roughly equal.

[0197] ③ The formula for calculating the landscape evenness index is:

[0198]

[0199] In the formula, H is the Shannon diversity index, H max E represents its maximum value, where N is the total number of landscape element types under study. When E approaches 1, the uniformity of landscape patch distribution is maximized.

[0200] ④ The formula for calculating the landscape aggregation index is:

[0201]

[0202] In the formula: C max It is the maximum value of the aggregation index [2ln(n)], where n is the total number of patch types in the landscape, and P uv P represents the probability that patch types u and v are adjacent. In a rasterized landscape, P uv The general method for finding it is:

[0203] P uv =P u P v / u (20)

[0204] In the formula: P u P is the probability that a randomly selected raster cell belongs to patch type u (which can be estimated by the proportion of patch type u in the total landscape area), while P v / u Given patch type u, this represents the conditional probability of patch type v and its neighbors, i.e.:

[0205] P v / u =m uv / mu ; (twenty one)

[0206] Where: m uv is the number of adjacent cell edges between patches u and v in the landscape grid, and mu is the total number of edges of cells of patch type u.

[0207] ⑤ The formula for calculating the landscape connectivity index is:

[0208]

[0209] In the formula: P ef Let a be the perimeter of the f-th patch in the e-th landscape. ef Let A represent the area of ​​the f-th patch in the e-th landscape category, and let A represent the total area of ​​the ecosystem in the landscape.

[0210] (3) Landscape fragmentation dynamics

[0211] ①The formula for calculating the forest patch fragmentation index FN is:

[0212] FN1=(N p -1) / N c FN2 = MPS × (N f -1) / N c ; (twenty three)

[0213] N c N is the total number of cells in the grid of the landscape data matrix; p It is the total number of patches of all types in the landscape; MPS is the average area of ​​patches in the landscape; N f It represents the total number of forest patches in the landscape.

[0214] ②The formula for calculating the forest patch fragmentation index (FS) is:

[0215] FS1=1-1 / MSI; FS2=1-1 / ASI; (24)

[0216] MSI is the mean shape index of forest patches; ASI is the area-weighted mean shape index of forest patches.

[0217] ③ The formula for calculating the forest habitat fragmentation index FI is:

[0218] FI1 = 1 - A i / A; FI2 = 1 - A1 / A; (25)

[0219] A i A1 is the total area of ​​the forest's internal habitat; A1 is the area of ​​the largest forest patch; A is the total area of ​​the landscape.

[0220] The calculation of the above-mentioned multiple indicators can reflect the ecological value of biodiversity in railway corridors.

[0221] S13. Using the Moran index, perform a correlation analysis on the habitat quality index and the landscape pattern index to obtain the correlation between the habitat quality index and the landscape pattern index.

[0222] For example, in one embodiment, S13 is specifically implemented as follows:

[0223] S131. Construct the spatial weight matrix;

[0224] Both the habitat quality index zoning statistics and the landscape pattern index calculation use a 1km×1km grid as the basic spatial unit, with grid number as the basic variable. A spatial weight matrix is ​​constructed using rook adjacency to define the spatial relationships between different grids. In practical applications, this step can be performed using GeoDA software. The grid size should be adjusted according to actual needs in practical applications; it is not a limitation here.

[0225] For example, it can be constructed as follows:

[0226]

[0227] In the formula: n represents the number of spatial units; w ij This indicates the adjacency relationship between spatial units i and j. If they share a common boundary, the value is 1; otherwise, the value is 0.

[0228] S132. Based on the Moran index, conduct a global spatial correlation analysis between habitat quality index and landscape pattern index.

[0229] For example, in one embodiment, a 1km×1km grid is used as the basic unit. The average habitat quality index of each grid is calculated using the zoning statistics tool of geographic information software. Based on the average habitat quality index and the landscape pattern index, a scatter plot of the habitat quality index and each landscape pattern index of the target railway corridor is calculated and obtained.

[0230] Based on the Moran index, the global spatial correlation between the habitat quality index and various landscape pattern indices of the target railway corridor was investigated.

[0231] The corresponding global Moran index, which characterizes the spatial autocorrelation of the bivariate, is calculated using the following formula:

[0232]

[0233]

[0234]

[0235] In the formula: I is the Moran index; n is the number of spatial units, x i and x j The observations for cells i and j are respectively. The observed average value of the unit; w ij S represents the spatial adjacency relationship between units i and j; 2 I represents the variance of the observed values. The value of I generally ranges from -1 to 1. A value less than 0 indicates a negative spatial correlation, a value greater than 0 indicates a positive spatial correlation, and a value equal to 0 indicates no correlation and a random distribution. The closer this value is to 0, the weaker the global correlation between the two variables.

[0236] S133. Based on the Moran index, conduct a local correlation analysis between the habitat quality index and the landscape pattern index.

[0237] In one embodiment, specifically:

[0238] Using the average habitat quality index and landscape pattern index of each grid as data sources, the spatial local autocorrelation between the habitat quality of grid units and the landscape pattern index of neighboring grid units in the study area was analyzed. The local Moran index was calculated to characterize the correlation between the attribute values ​​of a unit and its neighboring units, and a LISA clustering distribution map was plotted based on the z-test. The formula is as follows:

[0239]

[0240] In the formula: n is the number of spatial units, x i and x j Let w represent the observations of spatial cell i and spatial cell j, respectively. ij S represents the spatial adjacency relationship between spatial units i and j; 2 Represents the variance of the observed values. This represents the average observed value for each unit.

[0241] S14. Based on the aforementioned correlation, the habitat quality index and landscape pattern index are weighted according to the analytic hierarchy process, and the ecological value index data is obtained by superimposing and calculating.

[0242] The InVEST model, which integrates ecosystem services and trade-offs, and the Fragstats model, which measures landscape patterns, can effectively assess habitat quality and landscape pattern evolution in railway corridor areas using grid analysis.

[0243] Using spatial autocorrelation theory, spatial correlation analysis and temporal evolution characteristic analysis are conducted on the habitat quality index and the landscape pattern index to determine the spatial correlation law between habitat quality and landscape pattern of the target railway corridor. Based on the analytic hierarchy process and the above correlation analysis between habitat quality and landscape pattern, weights are assigned to the habitat quality index and landscape pattern index, and the ecological value index data is obtained by superposition calculation. This overcomes the limitations of traditional assessment indicators being single and different indicators corresponding to different research scales, and realizes the quantitative assessment of biodiversity value.

[0244] In another embodiment, S2 may include:

[0245] S21. Based on the pre-constructed social value types and evaluation rules, the social value points of the target railway corridor are determined by expert scoring and public evaluation methods.

[0246] The types of social value may include multiple types such as aesthetic value, economic value, sustainable life value, spiritual value and / or scientific research value. In this embodiment, aesthetic value, economic value, sustainable life value, spiritual value and scientific research value are preferred.

[0247] The evaluation rules may include: contingent valuation method, market valuation method, loss cost method, opportunity cost method, travel cost method, estimation method, protection cost method, restoration cost method, and / or shadow valuation method, etc. Evaluating the social value of the target railway corridor in monetary terms improves the accuracy of the social value assessment.

[0248] Specifically, one feasible implementation of the social value types and evaluation rules in S21 is shown in Table 1 below. The social value of each social value type is quantified according to the evaluation method in Table 1:

[0249] Table 1

[0250]

[0251]

[0252] As shown in Table 1, the social value of biodiversity is quantitatively represented by the social value types and evaluation rules, and the corresponding areas are marked on the map to determine the social value points.

[0253] S22. Based on the pre-built ecosystem service social value model, a weighted kernel density analysis is performed on all the social value points using an embedded kernel density analysis tool to obtain the overall spatial distribution density, kernel density surface, and maximum weighted kernel density value of the social value points.

[0254] S23. Based on the kernel density surface and the maximum weighted kernel density value, obtain a kernel density value index layer standardized to 0-10. Based on the kernel density value index layer, calculate the median index of the social value type and obtain the median index map.

[0255] Specifically, in one embodiment, based on a grid pre-divided using a grid tool, the social value grid surface of each grid is input into the ecosystem service social value model. The social value points are then subjected to weighted kernel density analysis using an embedded kernel density analysis tool to obtain the overall spatial distribution density, kernel density surface, and maximum weighted kernel density value, etc.

[0256] Further, based on the maximum weighted kernel density value, the raster surface with the maximum raster value in the raster is determined. By dividing the kernel density surface by the raster surface with the maximum raster value, a kernel density value index layer normalized to 0-10 is obtained. Then, these normalized surfaces are normalized to an index scale of 10 points to generate a constant raster with the maximum value in each value index raster. That is, the median index of the social value type is calculated, and the median index map is obtained.

[0257] S24. Perform average nearest neighbor analysis on the social value points based on the average nearest neighbor tool embedded in the ecosystem service social value model, and obtain the spatial clustering results through the ratio R value and standard deviation Z value obtained from the average nearest neighbor analysis.

[0258] S25. Based on the social survey data and spatial data layers of the target railway corridor, perform spatial analysis on the target railway corridor to generate a spatial heterogeneity distribution map of each social value type.

[0259] The spatial data layer can be a geospatial data layer including land use cover, elevation, slope, distance from water bodies, and distance from protected areas.

[0260] In addition, the relationship between various social value types and geographical environmental conditions can be calculated using the maximum entropy (MaxEnt) statistical model output from the geographic information software ArcGIS.

[0261] S26. Perform buffer analysis on the elevation elements within the target railway corridor area to obtain the social value index of the elevation elements. The elevation elements include contour line extraction, slope extraction, and distance from the railway centerline.

[0262] S27. Using the value mapping module, based on the aforementioned social value points, overall spatial distribution density, spatial clustering results, median index map, elevation element social value index, and spatial heterogeneity distribution map, and after processing with the maximum entropy model, output the final social value map of the target railway corridor in ArcGIS and obtain the social value index of the target railway corridor.

[0263] The highest value index among all social value types is the maximum value index (M-VI) for that value type. The results of M-VI indicate the importance of various social value types; the higher the value, the higher the social value index, and the greater the importance of the social value type.

[0264] Specifically, the social value index of the target railway corridor obtained through the maximum entropy model mainly includes:

[0265] All pixels within the target railway corridor study area are considered as the potential distribution space of social value. Pixels with known social value points are used as sample points. Based on the environmental variables of the sample point pixels, constraints are derived. Machine learning methods are employed to estimate the probability distribution (0-1) of maximum entropy, representing the location with the highest probability of social value distribution given the known existence of environmental conditions and social value. It is assumed that the probability of social value distribution with maximum entropy under these constraints is closest to the actual distribution of social value. These logical layer indices (0-1) are multiplied by the maximum value of the intermediate index corresponding to each social value type to generate the final index map (0-10) for each social value type.

[0266] In practical applications, when implementing step S27, a maximum entropy model evaluation can also be performed:

[0267] The model performance is evaluated using the area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) embedded in the SolVES model, in order to make the obtained social value index more accurate.

[0268] The closer the AUC value is to 1, the better the model evaluation effect; the AUC value between 0.5 and 0.7 has low accuracy, the AUC value between 0.7 and 0.9 has some accuracy, and the AUC value above 0.9 has high accuracy.

[0269] In practical applications, spatial correlation analysis between various social value type indices and elevation elements can also be conducted based on the aforementioned social value index.

[0270] Furthermore, in order to make the social value of the target railway corridor more apparent and readable, in some embodiments, the social value map can provide a geographical and statistical display of each type of social value index.

[0271] The aforementioned SolVES ecosystem service social value model is based on ArcGIS. Using the vectorization tools in ArcGIS software, the target railway corridor map is registered and vectorized to obtain feature layers such as the boundary of the target railway corridor, water bodies, and other types of wetlands, as well as geospatial data layers such as land use cover, elevation, slope, distance from water bodies, and distance from protected areas. Euclidean distance is calculated for water bodies, wetlands, and nature reserves to obtain a series of environmental layers; social value points are vectorized to obtain social value point layers, and so on.

[0272] In existing technologies, the acquisition of social value often relies on interviews and questionnaires to obtain users' attitudes and preferences towards ecosystem service products, subjectively measuring the importance of various social value types. Alternatively, expert knowledge or tourists' willingness-to-pay surveys can be used to quantitatively determine relevant values ​​through questionnaires and the responses of experts or tourists, without relying on monetary value. However, biodiversity assessments based on expert or scientific knowledge easily overlook the diverse values ​​within biodiversity, including aesthetic, economic, sustainable, spiritual, and / or scientific values, resulting in a subjective assessment of the social value. The method provided by this invention integrates the knowledge systems, academic expertise, and practical experience of different stakeholders through pre-designed social value types and evaluation rules, constructing a comprehensive knowledge system that combines ecological diversity value with expert or scientific knowledge. This effectively evaluates the social value of the target area, increasing the objectivity and comprehensiveness of the social value assessment.

[0273] In some other embodiments, the assessment method may also include S4, determining the biodiversity value hotspots and coldspots of the target railway corridor based on a pre-built spatial analysis method, and generating a visualized comprehensive value map for display.

[0274] Specifically, in one embodiment, S4 is implemented as follows:

[0275] S41. Input the habitat quality index and the landscape pattern index into the geographic information software to generate an ecological value map of the target railway corridor.

[0276] In practical applications, to make the final visualized map more intuitive, the natural breakpoint method of spatial analysis function can be used in ArcGIS software environment to divide the habitat quality of the target railway corridor into multiple levels such as low, lower, medium, higher and high, and display them through color or other forms to reduce the difficulty of map reading and improve the speed of understanding.

[0277] S42. Based on the spatial analysis function of geographic information software, that is, based on the hot spot analysis tool (Getis-Ord Gi*), the grid of the ecological value map and the social value map is used as the evaluation unit to calculate and obtain the Z value and P value. P < 0.1, and determine the hot spot and cold spot spatial clusters of the target railway corridor.

[0278] S43. Based on the spatial clustering of hot and cold spots, generate and display a comprehensive value map of the target railway corridor.

[0279] The statistical calculation formula is as follows:

[0280]

[0281] Among them, G i * represents Z, q j It is the attribute value of the j-th evaluation unit, g ij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

[0282] Specifically, in this embodiment, if the Z value is > 1.65, the point is determined to be a hot spot (high value) spatial cluster; if the Z value is [-1.65, 1.65], the point is determined to be a warm point; and if the Z value is < -1.65, the point is determined to be a cold point (low value).

[0283] Of course, in practical applications, the Z value may be adjusted based on actual conditions, which is not a limitation here.

[0284] Furthermore, Figure 2 The following is a flowchart illustrating the assessment method for evaluating the biodiversity value of railway corridors according to an embodiment of the present invention. Figure 3 for Figure 2 Detailed diagrams of some evaluation processes, such as Figure 2 , Figure 3 As shown, in some embodiments, the evaluation method may further include step S5, determining the coupling degree and coupling coordination degree of the target railway corridor ecosystem and social system through a pre-constructed coupling degree and coupling coordination degree model.

[0285] The coupling degree model is as follows:

[0286]

[0287] Wherein, f(a) is the ecological value index of the railway corridor; f(b) is the social value index of the railway corridor.

[0288] By inputting the ecological value index and the social value index into the coupling degree model, the coupling degree C can be obtained.

[0289] The coupling coordination degree model is as follows:

[0290] T = αf(a) + βf(b);

[0291]

[0292] The ecological value index and the social value index are weighted according to the analytic hierarchy process, where α is the weight of the ecological value index and β is the weight of the social value index. The comprehensive evaluation index T of the social and ecological system development level of the railway corridor is calculated. The coupling degree C and the comprehensive evaluation index T of the social and ecological system development level of the railway corridor are input into a pre-constructed formula to obtain the coupling coordination degree D.

[0293] In one embodiment, the social system and the ecosystem of the railway corridor are considered equally important; therefore, α = β = 0.5.

[0294] In another embodiment, the coupling degree and coupling coordination degree were used to determine the coupling coordination type characteristics of the biodiversity ecological and social system of the railway corridor in this embodiment, as shown in Table 2:

[0295] Table 2

[0296]

[0297] Based on the above socio-ecological coupling analysis, the value of biodiversity can be assessed from the perspectives of social activities and ecosystems, thus increasing the integrity and comprehensiveness of biodiversity value.

[0298] The biodiversity assessment methods for railway corridors provided in the above embodiments can determine the biodiversity maintenance capacity and stability of each region by calculating the habitat quality index of the target railway corridor. The 18 index factors selected under the landscape pattern index are sensitive, not limited to a specific region, and can be effectively applied to linear structures such as railway corridors. The five preferred social value types and multiple evaluation methods enable flexible and comprehensive assessment of the social value of the target railway corridor, increasing objectivity. Furthermore, the comprehensive biodiversity value index is obtained by weighting and superimposing calculations using the analytic hierarchy process, considering both ecological and social values, enabling a comprehensive and accurate evaluation of the target railway corridor. Moreover, the method provided by this invention effectively links biodiversity assessment at the railway corridor scale with the socio-ecological system framework at different spatiotemporal scales, improving the comprehensiveness and analytical efficiency of biodiversity value assessment.

[0299] By using ArcGIS software to visualize target railway corridors on maps, the comprehensive value of the target railway corridors can be displayed in a more detailed and intuitive way, and the spatial clustering of hot spots (high values) and cold spots (low values) with statistical significance can be achieved. Furthermore, the spatial patterns between ecosystems and social systems can be seen more intuitively, enabling more reasonable measures to be taken when protecting related areas.

[0300] Meanwhile, by using the Moran index and coupling coordination type characteristics, we can effectively determine the spatial correlation of biodiversity in railway corridors and the degree of coupling between railway corridor ecosystems and social systems, quantitatively analyze the intensity of interaction between systems, and play a positive coordinating role in various cycles of railway corridors.

[0301] The present invention provides a method for assessing biodiversity in railway corridors, which can integrate multiple types of biodiversity characteristics, establish a comprehensive evaluation method for biodiversity from multiple perspectives, and achieve quantitative and visual expression, thereby establishing a systematic and effective biodiversity conservation strategy.

[0302] In addition, this invention also provides an assessment device for the biodiversity value of railway corridors, which includes: an ecological value index processing unit, a social value index processing unit, and a comprehensive evaluation index processing unit.

[0303] The ecological value index processing unit is used to input the habitat quality data and landscape pattern data of the target railway corridor into a pre-built model and output the ecological value index of the railway corridor. The habitat quality data is data including land use raster data and ecological threat factor layers after remote sensing interpretation of the remote sensing image of the target railway corridor. The landscape pattern data is data including land feature raster data after remote sensing interpretation of the remote sensing image of the target railway corridor.

[0304] The social value index processing unit is used to calculate the social value index of the target railway corridor based on the pre-constructed social value types and evaluation rules.

[0305] The comprehensive evaluation index processing unit is used to assign weights to the ecological value index and the social value index using a pre-constructed hierarchical analysis method, and to calculate the comprehensive evaluation index of the biodiversity value of the target railway corridor by superimposing the ecological value index and the social value index according to the weights.

[0306] The biodiversity value assessment device for railway corridors is integrated into the computing equipment. Based on remote sensing images of the target railway corridor, it obtains the ecological value index and the community value index through social value types and evaluation rules. This enables a comprehensive evaluation of the biodiversity value of the target railway corridor. The evaluation process is very comprehensive and reliable, overcoming the shortcomings of existing technologies such as imprecise and incomplete manual evaluation, low sensitivity, and high cost.

[0307] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0308] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0309] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for assessing the biodiversity value of railway corridors, characterized in that, include: S1. Input the habitat quality data and landscape pattern data of the target railway corridor into the pre-built model and output the ecological value index of the railway corridor. The habitat quality data is the data including land use raster data and ecological threat factor layer after remote sensing interpretation of the remote sensing image of the target railway corridor. The landscape pattern data is the data including land feature raster data after remote sensing interpretation of the remote sensing image of the target railway corridor. S1 includes: S11. Input the land use raster data and ecological threat factor layer into the pre-constructed integrated assessment model of ecosystem services and trade-offs, and calculate the habitat quality index by combining the predefined ecological threat factor scale, land use type sensitivity scale and target railway corridor range layer. S12. Input the land feature raster data into the pre-constructed landscape pattern model and output the landscape pattern index; The landscape pattern index includes dynamic index data of patch diversity, dynamic index data of landscape heterogeneity, and dynamic index data of landscape fragmentation. The dynamic index data of patch diversity includes number, density, area, fractal dimension, and shape index. The dynamic index data of landscape heterogeneity includes diversity index, connectivity index, dominance index, aggregation index, and evenness index. The dynamic index data of landscape fragmentation includes patch number fragmentation index, patch shape fragmentation index, and internal habitat area fragmentation index. S13. Using the Moran index, a correlation analysis is performed on the habitat quality index and the landscape pattern index to obtain the correlation between the habitat quality index and the landscape pattern index. S13 includes: S131, the zoning statistics of habitat quality index and the calculation of landscape pattern index all use a 1km×1km grid as the basic spatial unit, grid number as the basic variable, and select rook adjacency to construct a spatial weight matrix. S132. Based on the Moran index, conduct a global spatial correlation analysis between habitat quality index and landscape pattern index; S133. Based on the Moran index, conduct a local correlation analysis between habitat quality index and landscape pattern index; S14. Based on the aforementioned correlation, weights are assigned to the habitat quality index and landscape pattern index according to the analytic hierarchy process, and the ecological value index data is obtained by superimposing and calculating. S2. Calculate the social value index of the target railway corridor based on the pre-constructed social value types and evaluation rules; S3. The ecological value index and the social value index are weighted by a pre-constructed hierarchical analysis method. The ecological value index and the social value index are superimposed and calculated according to the weights to determine the comprehensive evaluation index of biodiversity value of the target railway corridor.

2. The evaluation method as described in claim 1, characterized in that, Also includes: S4. Based on a pre-built spatial analysis method, identify the biodiversity value hotspots and coldspots of the target railway corridor, and generate a visualized comprehensive value map for display.

3. The evaluation method as described in claim 1, characterized in that, Before S1, S0 is also included: Remote sensing interpretation of remote sensing images of the target railway corridor is performed using geographic information software to obtain habitat quality data and landscape pattern data; the habitat quality data includes land use raster data and ecological threat factor layers; the landscape pattern data includes land feature raster data.

4. The evaluation method as described in claim 3, characterized in that, The S0 specifically includes: S01. For the pre-acquired high-resolution remote sensing image, generate a standard image through atmospheric correction, geometric correction and orthorectification; S02. Perform supervised classification on the standard image and output land use classification image and land feature remote sensing classification image; S03. Using the raster tool of geographic information software, the land use classification image is divided into ranges, land use raster data of the target railway corridor is collected, and an ecological threat factor layer is extracted based on the land use raster data. S04. Perform remote sensing interpretation of the land cover classification image using the interpretation tool of geographic information software to obtain land cover vector data, and determine land cover raster data based on the land cover vector data; The types of land features are classified into two levels.

5. The evaluation method as described in claim 1, characterized in that, S2 includes: S21. Based on the pre-constructed social value types and evaluation rules, the social value points of the target railway corridor are determined by expert scoring and public evaluation methods. The types of social value include: aesthetic value, economic value, life sustainability value, spiritual value and / or scientific research value; The evaluation rules include: conditional valuation method, market valuation method, loss cost method, opportunity cost method, conditional valuation method, travel cost method, estimation method, protection cost method, recovery cost method and / or shadow valuation method; S22. Based on the pre-built ecosystem service social value model, a weighted kernel density analysis is performed on all the social value points using an embedded kernel density analysis tool to obtain the overall spatial distribution density, kernel density surface, and maximum weighted kernel density value of the social value points. S23. Based on the kernel density surface and the maximum weighted kernel density value, obtain a kernel density value index layer standardized to 0-10. Based on the kernel density value index layer, calculate the median index of the social value type and obtain the median index map. S24. Perform average nearest neighbor analysis on the social value points based on the average nearest neighbor tool embedded in the ecosystem service social value model, and obtain the spatial clustering results through the ratio R value and standard deviation Z value obtained from the average nearest neighbor analysis. S25. Based on the social survey data and spatial data layer of the target railway corridor, perform spatial analysis on the target railway corridor and generate a spatial heterogeneity distribution map of each social value type. S26. Perform buffer analysis on the elevation elements within the target railway corridor area to obtain the social value index of the elevation elements. The elevation elements include contour line element extraction, slope extraction, and distance from the railway centerline. S27. Using the value mapping module, based on the above-mentioned social value points, overall spatial distribution density, spatial clustering results, median index map, elevation element social value index, and spatial heterogeneity distribution map, and after processing by the maximum entropy model, output the final social value map of the target railway corridor and obtain the social value index of the target railway corridor.

6. The evaluation method as described in claim 1, characterized in that, The formula for calculating the comprehensive evaluation index of biodiversity value in S3 is as follows: ； Where V is the comprehensive evaluation value of the target railway corridor; n is the number of evaluation factors; W l The combined weights of the evaluation factors; T l For the first l Standardized scores of each evaluation factor; The evaluation factors are the ecological value index and social value index of the target railway corridor.

7. The evaluation method as described in claim 5, characterized in that, S4 includes: S41. Input the habitat quality index and the landscape pattern index into geographic information software to generate an ecological value map of the target railway corridor; S42. Based on the spatial analysis function of geographic information software, the grid of the ecological value map and the social value map is used as the evaluation unit. The Z value and P value are statistically calculated and obtained. P < 0.

1. The hot spots and cold spots of the target railway corridor are spatially clustered. S43. Based on the spatial clustering of hot and cold spots, generate and display a comprehensive value map of the target railway corridor; The statistical calculation formula is as follows: ； in, G i * For Z, q j It is the attribute value of the j-th evaluation unit, g ij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

8. The evaluation method as described in claim 1, characterized in that, Also includes: S5. Input the ecological value index and social value index into the pre-constructed coupling degree model and coupling coordination degree model to determine the coupling degree and coupling coordination degree between the target railway corridor ecosystem and social system; The coupling degree model is as follows: ； Wherein, f(a) is the ecological value index of the railway corridor; f(b) is the social value index of the railway corridor. The coupling coordination degree model is as follows: ； ； Where: f(a) – ecological value index of railway corridor; f(b) – social value index of railway corridor; The α=β =0.5; T is the comprehensive evaluation index of the social and ecological development level of the railway corridor.

9. An apparatus for assessing the biodiversity value of a railway corridor as described in any one of claims 1-8, characterized in that, include: The ecological value index processing unit is used to input the habitat quality data and landscape pattern data of the target railway corridor into a pre-built model and output the ecological value index of the railway corridor. The habitat quality data is data including land use raster data and ecological threat factor layers after remote sensing interpretation of the remote sensing image of the target railway corridor. The landscape pattern data is data including land feature raster data after remote sensing interpretation of the remote sensing image of the target railway corridor. The ecological value index processing unit is specifically used to input the land use raster data and ecological threat factor layer into a pre-constructed comprehensive assessment model of ecosystem services and trade-offs, and calculate the habitat quality index by combining a predefined ecological threat factor scale, a land use type sensitivity scale, and a target railway corridor range layer; input the land cover raster data into a pre-constructed landscape pattern model, and output a landscape pattern index, which includes dynamic indicators of patch diversity, landscape heterogeneity, and landscape fragmentation. The dynamic indicators of patch diversity include quantity, density, area, fractal dimension, and shape index; the dynamic indicators of landscape heterogeneity include diversity index, connectivity index, dominance index, aggregation index, and evenness index; and the dynamic indicators of landscape fragmentation include... The data includes patch fragmentation index, patch shape fragmentation index, and internal habitat area fragmentation index. Using the Moran index, a correlation analysis is performed on the habitat quality index and landscape pattern index to obtain their correlation. This correlation analysis includes using a 1km×1km grid as the basic spatial unit for both habitat quality index zoning statistics and landscape pattern index calculation, with grid number as the basic variable, selecting rook adjacency to construct a spatial weight matrix, performing global spatial correlation analysis and local correlation analysis based on the Moran index, and then assigning weights to the habitat quality index and landscape pattern index according to the analytic hierarchy process (AHP) to obtain the ecological value index data. The social value index processing unit is used to calculate the social value index of the target railway corridor based on the pre-constructed social value types and evaluation rules. The comprehensive evaluation index processing unit is used to assign weights to the ecological value index and the social value index using a pre-constructed hierarchical analysis method, and to calculate the comprehensive evaluation index of the biodiversity value of the target railway corridor by superimposing the ecological value index and the social value index according to the weights.

Images