CN116128350A - A method and device for assessing the biodiversity value of railway corridors - Google Patents

Abstract

The invention relates to a method and an evaluation device for assessing the biodiversity value of railway corridors. The method includes: inputting pre-collected habitat quality data and landscape pattern data of target railway corridors into a pre-built model, and outputting the The ecological value index of the railway corridor; based on the pre-constructed social value type and evaluation rules, calculate the social value index of the target railway corridor; analyze the ecological value index and the social value by the pre-constructed analytic hierarchy process The index is weighted, and the ecological value index and the social value index are superimposed and calculated according to the weight to determine the comprehensive evaluation index of biodiversity value of the target railway corridor. This method combines multiple models or tools such as geographic information software, landscape pattern model, ecosystem service and trade-off comprehensive evaluation model, and obtains comprehensive research evaluation indicators, which improves the accuracy and authenticity of the biodiversity value of railway corridors. High practicality.

Description

A method and device for evaluating the biodiversity value of railway corridors

Technical Field

The invention relates to the technical field of civil engineering (railway) and ecology, and in particular to an evaluation method and an evaluation device for the biodiversity value of a railway corridor.

Background Art

Biodiversity is the material basis for the survival and development of human society. It has important ecological service functions such as supply, regulation and support, and plays an irreplaceable role in maintaining ecological balance.

As a long-span linear structure, railways are often surrounded by unique ecosystem characteristics such as heterogeneity in temporal and spatial distribution, combination of points, lines and surfaces, and complex and diverse influencing ecological factors.

In the existing technology, the region is often used as the research object, and only the landscape pattern of local areas is selected for research, such as traditional surface areas such as nature reserves and national parks; or, based on the professional knowledge and experience of evaluation experts, a scoring method is used to assign scores from low to high to the six parts of landscape/ecosystem, biological community, population/species, main protected objects, biosafety, and relevant interest groups according to the gradient of the project's impact on biodiversity from small to large. On the basis of the special scoring, the weight value of the evaluation index of each special project is determined and the total biodiversity impact index of the project on the affected area is calculated. Due to the defects of subjectivity and arbitrariness in the expert scoring and evaluation after the on-site investigation, some landscape pattern index evaluations lack overall consideration, are poorly adapted to the railway area, have limited ecological significance, and have low index sensitivity.

In view of this, how to effectively evaluate the biodiversity value of railway corridors based on remote sensing images has become a technical problem that needs to be solved urgently.

Summary of the invention

1. Technical issues to be resolved

In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method and device for evaluating the biodiversity value of a railway corridor, which solves the technical problems of low sensitivity, incomplete evaluation and low authenticity of the ecological diversity value evaluation index in areas along the railway corridor.

(II) Technical solution

In the first aspect, in order to achieve the above-mentioned purpose, the present invention provides a method for evaluating the biodiversity value of railway corridors, and the main technical solutions adopted include:

S1. 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 layer after remote sensing interpretation based on the remote sensing image map of the target railway corridor, and the landscape pattern data is data including ground object raster data after remote sensing interpretation based on the remote sensing image map of the target railway corridor;

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

S3. Weights are assigned to the ecological value index and the social value index through a pre-constructed hierarchical analysis method, and the ecological value index and the social value index are superimposed and calculated based on the weights to determine the comprehensive evaluation index of the biodiversity value of the target railway corridor.

Optionally, it also includes:

S4. Based on the pre-constructed spatial analysis method, the hot spots and cold spots of biodiversity value of the target railway corridor are determined, and a visual comprehensive value map is generated for display.

Optionally, before S1, S0 is also included:

The remote sensing image of the target railway corridor is remotely interpreted by 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.

Optionally, the S0 specifically includes:

S01. Generate standard images through atmospheric correction, geometric correction and orthorectification for pre-acquired high-resolution remote sensing images;

S02, performing supervised classification on the standard image, and outputting a land use classification image and a ground object remote sensing classification image;

S03, dividing the land use classification image into areas by using a raster tool of geographic information software, collecting land use raster data of the target railway corridor, and extracting an ecological threat factor layer based on the land use raster data;

S04, performing ground object remote sensing interpretation on the ground object remote sensing classification image by using an interpretation tool of geographic information software to obtain ground object vector data, and determining ground object raster data based on the ground object vector data;

Among them, the map types of the features are divided into secondary categories.

Optionally, the S1 includes:

S11, inputting the land use raster data and ecological threat factor layer into a pre-constructed comprehensive assessment model of ecosystem services and trade-offs, combining a pre-defined ecological threat factor scale, a land use type sensitivity scale and a target railway corridor range layer, and calculating a habitat quality index;

S12, inputting the ground feature grid data into a pre-built landscape pattern model, and outputting a landscape pattern index;

S13, using the Moran index, performing a correlation analysis on the habitat quality index and the landscape pattern index to obtain a correlation between the habitat quality index and the landscape pattern index;

S14. Based on the correlation, the habitat quality index and landscape pattern index are weighted according to the hierarchical analysis method, and the ecological value index data are obtained by superimposing the weights.

Based on the comprehensive assessment model of ecosystem services and trade-offs and the landscape pattern model, the habitat quality and landscape pattern evolution of the railway corridor area can be evaluated.

Optionally, the 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 method and public evaluation method;

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 value method, market value method, loss cost method, opportunity cost method, conditional value method, travel cost method, estimation method, protection cost method, restoration cost method and/or shadow value method;

S22. Based on the pre-constructed ecosystem service social value model, a weighted kernel density analysis is performed on all the social value points through the 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, a kernel density value index layer standardized to 0-10 is obtained. Based on the kernel density value index layer, an intermediate value index of the social value type is calculated, and an intermediate value index graph is obtained.

S24, performing an average nearest neighbor analysis on the social value points according to the average nearest neighbor tool embedded in the ecosystem service social value model, and obtaining spatial clustering results through the ratio R value and standard deviation Z value obtained by the average nearest neighbor analysis;

S25. Performing spatial analysis on the target railway corridor according to the social survey data and spatial data layer of the target railway corridor, and generating a spatial heterogeneity distribution map of each social value type;

S26, performing a buffer zone analysis on the elevation elements within the target railway corridor to obtain the social value index of the elevation elements, wherein the elevation elements include contour line element extraction, slope extraction, and distance from the railway centerline;

S27. Utilize 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 status map, and process them with the maximum entropy model to output the final social value map of the target railway corridor and obtain the social value index of the target railway corridor.

Optionally, the calculation formula for the comprehensive evaluation index of biodiversity value in S3 is:

Wherein, V is the comprehensive evaluation value of the target railway corridor; n is the number of evaluation factors; W _l is the combined weight of the evaluation factors; T _l is the standardized score value of the lth evaluation factor;

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

Optionally, the S4 includes:

S41, inputting 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 grids of the ecological value map and the social value map are used as evaluation units, and the Z value and P value are obtained by statistical calculation, and P is less than 0.1, and the hot and cold spot spatial clustering of the target railway corridor is determined;

S43, generating and displaying a comprehensive value map of the target railway corridor based on the spatial clustering of the hot spots and cold spots;

The statistical calculation formula is:

Among them, _Gi * is Z, _qj is the attribute value of the jth evaluation unit, _gij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

Optionally, it also includes:

S5. Input the ecological value index and social value index into a 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 the social system;

The coupling degree model is:

Among them, 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 model is:

T = αf(a) + βf(b);

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 development level of the social and ecological system of the railway corridor.

In a second aspect, an embodiment of the present invention further provides a device for evaluating the biodiversity value of a railway corridor, comprising:

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 layer after remote sensing interpretation based on the remote sensing image map of the target railway corridor, and the landscape pattern data is data including ground object raster data after remote sensing interpretation based on the remote sensing image map of the target railway corridor;

A social value index processing unit, used to calculate the social value index of the target railway corridor based on 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 through a pre-constructed hierarchical analysis method, and to superimpose the ecological value index and the social value index according to the weights to determine the comprehensive evaluation index of the biodiversity value of the target railway corridor.

Optionally, it also includes: a visual display unit;

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

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

The first submodule is used to perform remote sensing interpretation on the remote sensing image of the target railway corridor through 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 layer; the landscape pattern data includes ground feature raster data;

Generate standard images through atmospheric correction, geometric correction and orthorectification for pre-acquired high-resolution remote sensing images;

Performing supervised classification on the standard image, and outputting a land use classification image and a ground object remote sensing classification image;

Using a raster tool of geographic information software to divide the land use classification image into ranges, collecting land use raster data of the target railway corridor, and extracting an ecological threat factor layer based on the land use raster data;

Performing ground object remote sensing interpretation on the ground object remote sensing classification image through an interpretation tool of geographic information software to obtain ground object vector data, and determining ground object raster data based on the ground object vector data;

Among them, the map types of the features are divided into secondary categories.

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

Inputting the ground feature grid data into a pre-built landscape pattern model, and outputting a landscape pattern index;

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;

Based on the correlation, the habitat quality index and landscape pattern index are weighted according to the hierarchical analysis method, and the ecological value index data are obtained by superimposing calculations.

A social value index processing unit, specifically used to determine the social value point of the target railway corridor through expert scoring method and public evaluation method based on pre-constructed social value types and evaluation rules;

For example, 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 value method, market value method, loss cost method, opportunity cost method, conditional value method, travel cost method, estimation method, protection cost method, restoration cost method and/or shadow value method;

Based on the pre-built ecosystem service social value model, a weighted kernel density analysis is performed on all the social value points through the 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;

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, an intermediate value index of the social value type is calculated, and an intermediate value index graph is obtained;

Performing an average nearest neighbor analysis on the social value points according to the average nearest neighbor tool embedded in the ecosystem service social value model, and obtaining spatial clustering results through the ratio R value and standard deviation Z value obtained by the average nearest neighbor analysis;

Based on the social survey data and spatial data layers of the railway corridor, a spatial analysis is performed on the target railway corridor to generate a spatial heterogeneity distribution map of each social value type;

Performing buffer zone analysis on elevation elements within the railway corridor to obtain the social value index of the elevation elements, wherein the elevation includes contour line element extraction, slope extraction, and distance from the railway centerline;

Utilizing 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 status map, after being processed by the maximum entropy model, the final social value map of the target railway corridor is output to obtain the social value index of the target railway corridor.

A visualization display 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;

Based on the spatial analysis function of geographic information software, the grids of the ecological value map and the social value map are used as evaluation units, and the Z value and P value are obtained by statistical calculation, and P < 0.1, to determine the spatial clustering of hot spots and cold spots of the target railway corridor;

Based on the hotspot and coldspot spatial clustering, a comprehensive value map of the target railway corridor is generated and displayed;

The statistical calculation formula is:

Among them, _Gi * is Z, _qj is the attribute value of the jth evaluation unit, _gij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

Furthermore, the device for evaluating the biodiversity value of a railway corridor also includes: a coupling coordination unit, which is used to determine the coupling degree and coupling coordination degree between the target railway corridor ecosystem and the social system.

Input the ecological value index and social value index into a 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 the social system;

The coupling degree model is:

Among them, 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 model is:

T = αf(a) + βf(b);

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 development level of the social and ecological system of the railway corridor.

(III) Beneficial effects

The present invention provides a method for evaluating the biodiversity value of a railway corridor. The method obtains an ecological value index based on a remote sensing image of a target railway corridor, and obtains a community value index through social value types and evaluation rules, thereby achieving a comprehensive evaluation of the biodiversity value of the target railway corridor. The evaluation process is very comprehensive and has true reliability, overcoming the defects of inaccurate and incomplete manual evaluation, low sensitivity, and high cost in the prior art.

In specific use, the two indicators of habitat quality index and landscape pattern index of the target railway corridor ecosystem and multiple indicator factors are obtained based on the comprehensive evaluation model of ecosystem services and trade-offs and the landscape pattern model, so as to realize a more refined evaluation of the ecological value of biodiversity in the railway corridor, and judge the social value of the target railway corridor based on the social value type and evaluation rules. It can make a more realistic evaluation of the social value of biodiversity in the railway corridor under the limitation of the scale of the railway corridor, thereby determining the comprehensive evaluation index of the biodiversity value of the target railway corridor, accurately and precisely evaluating the biodiversity value of the target railway corridor, which can be effectively applied to the belt structure of the railway corridor, and solves the problem that the evaluation indicators of the belt structure research are not comprehensive enough and have limited practicality.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for evaluating the biodiversity value of a railway corridor provided by an embodiment of the present invention;

FIG2 is an evaluation flow chart of an evaluation method based on the biodiversity value of railway corridors provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of details of an evaluation process in the embodiment of FIG. 2 .

DETAILED DESCRIPTION

In order to better explain the present invention and facilitate understanding, the present invention is described in detail below in conjunction with the accompanying drawings through specific embodiments. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a clearer and more thorough understanding of the present invention and to be able to fully convey the scope of the present invention to those skilled in the art.

Biodiversity is the material basis for the survival and development of human society. It has important ecological service functions such as supply, regulation, and support, and plays an irreplaceable role in maintaining ecological balance. Railways, as a long-span linear structure, are often surrounded by unique ecosystem characteristics such as heterogeneity in temporal and spatial distribution, combination of points, lines, and surfaces, and complex and diverse ecological factors. Therefore, in various activities related to railways, such as site selection and construction, the impact on the ecological environment must be considered to achieve coordinated development. Therefore, the present invention proposes a method for evaluating the biodiversity value of railway corridors.

As shown in FIG. 1 , FIG. 1 is a flow chart of a method for evaluating the biodiversity value of a railway corridor provided in an embodiment of the present invention. The execution subject of this embodiment can be any computing device. Specifically, the method for evaluating the biodiversity value of a railway corridor mainly includes:

S1. 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 in this embodiment may be data including land use raster data and ecological threat factor layers after remote sensing interpretation based on the remote sensing image map of the target railway corridor, and the landscape pattern data may be data including ground object raster data after remote sensing interpretation based on the remote sensing image map of the target railway corridor.

Usually, the width of a railway corridor can be 1.2 to 1.5 meters, and there are corridor areas along the railway. To better achieve the evaluation, this embodiment can usually evaluate the biodiversity value of a railway corridor of a specified length, and the railway corridor of the specified length to be analyzed is the target railway corridor.

In this embodiment, the remote sensing image can be obtained by existing methods.

S2. Calculate the social value index of the target railway corridor based on the pre-constructed social value type and evaluation rules.

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

The evaluation rules may include: conditional value method, market value method, loss cost method, opportunity cost method, conditional value method, travel cost method, estimation method, protection cost method, restoration cost method and/or shadow value method.

S3. Weights are assigned to the ecological value index and the social value index through a pre-constructed hierarchical analysis method, and the ecological value index and the social value index are superimposed and calculated based on the weights to determine the comprehensive evaluation index of the biodiversity value of the target railway corridor.

Specifically, in one embodiment, the calculation formula for the comprehensive evaluation index of biodiversity value in S3 is:

Among them, V is the comprehensive evaluation value of the target railway corridor; n is the number of evaluation factors; W _l is the combined weight of the evaluation factors; T _l is the standardized score value of the lth evaluation factor, and l is a natural number less than or equal to n.

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 results calculated in actual applications.

In practical applications, the calculated comprehensive evaluation index of biodiversity value can also be ranked to determine the comprehensive evaluation value according to actual needs.

For example, in one embodiment, according to actual needs, when the comprehensive evaluation value is in the first 30%, the comprehensive evaluation of the biodiversity value of the region is high, and when the comprehensive evaluation value is in the last 30%, the comprehensive evaluation of the biodiversity value of the region is low. In another embodiment, the percentage can be adjusted to 10%, 20%, etc., which should be divided according to actual needs and is not limited here.

In practical applications, in the method shown in FIG1 , step S0 may be implemented before step S1:

S0. Perform remote sensing interpretation on the remote sensing image of the target railway corridor through 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.

It should be noted here that the method shown in FIG. 1 may be integrated in a computing device, and the computing device may also be integrated with geographic information software ArcGis, and habitat quality data and landscape pattern data may be obtained with the help of ArcGis software.

For better understanding, the above step S0 may specifically include:

S01. Generate standard images through atmospheric correction, geometric correction and orthorectification for pre-acquired high-resolution remote sensing images;

S02. Perform supervised classification on the standard image, and output a land use classification image and a ground object remote sensing classification image.

S03. Divide the land use classification image into areas using a raster tool of geographic information software, collect land use raster data of the target railway corridor, and extract an ecological threat factor layer based on the land use raster data.

S04. Performing ground object remote sensing interpretation on the ground object remote sensing classification image through an interpretation tool of geographic information software to obtain ground object vector data, and determining ground object raster data based on the ground object vector data.

Among them, the map types of the features are divided into secondary categories.

Specifically, in practical applications, when implementing step S01, the accuracy can also be increased with the help of other reference images and ground verification points. For example, in one embodiment, after obtaining a high-resolution remote sensing image of the target railway corridor, a standard image is generated through atmospheric correction, geometric correction, and orthorectification, and a digital elevation model (DEM) with a resolution of not less than 30m and a high-precision reference image library, a high-precision control point library, and a field high-precision GPS point are used as auxiliary data. Remote sensing image processing software (ENVI, ERDAS) and other software are used for supervised classification, and the reviewed ground verification points are used for verification, and land use classification images and ground object remote sensing classification images are output.

In the above-mentioned assessment method of the biodiversity value of railway corridors, land use raster data and ground feature raster data are obtained through remote sensing interpretation of high-resolution remote sensing images, and then the pre-constructed model is used to output various indicator factors of ecological value and social value to obtain the ecological value index and social value index. Finally, weights are assigned through methods such as hierarchical analysis, and superposition calculations are performed based on the weights to obtain the comprehensive value index of ecological diversity of the target railway corridor, which can effectively conduct a comprehensive and authentic assessment of the ecological diversity value along the railway.

Specifically, in one embodiment, the S1 may be implemented as follows:

S11. Input the land use raster data and ecological threat factor layer into a pre-constructed integrated assessment model of ecosystem services and trade-offs (In VEST model), and calculate and obtain 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.

The calculation formulas involved may include:

Where _Qxj is the habitat quality of grid x in land type (land use type) j, dimensionless; _Dxj is the degree of disturbance of grid x in land type j, that is, the degree of habitat degradation, dimensionless; K is a half-saturation constant. In practical applications, the value of K can be half of the scale value of the grid unit size, dimensionless; _Hj is the habitat suitability of land type j, dimensionless, and z is a normalized constant.

Where R is the stress factor, dimensionless; y is the number of grids in the stress factor r grid layer; Y _r is the total number of grids occupied by the stress factor; w _r is the weight of the stress factor r, ranging from 0 to 1, dimensionless; _ry is the stress factor value of grid y (0 or 1), dimensionless; i _rxy is the interference degree of the stress factor r of grid y on the land type grid x, dimensionless; β _x is the accessibility level of grid x, ranging from 0 to 1, 1 means extremely easy to reach, dimensionless; S _jr is the sensitivity of land type j to the stress factor r, dimensionless, and the calculation formula of i _rxy is:

Where _dxy is the straight-line distance between grid x and grid y, in km; _drmax is the maximum impact distance of threat factor r, in km.

Specifically, the habitat quality index of each grid data of the target railway corridor is represented by 0-1. If the output habitat quality index is higher, it is judged that the biodiversity maintenance capacity of the grid is stronger.

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, the corresponding land use type sensitivity scale, the weights and important impact distances of threat sources, the habitat suitability of different land use types and their sensitivity to each threat source, etc. should be modified and adjusted in combination with the target railway grade and expert opinions.

S12, inputting the ground feature grid data into a pre-built landscape pattern model (Fragstats model), and outputting a landscape pattern index.

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

Dynamic indicator data of patch diversity, dynamic indicator data of landscape heterogeneity and dynamic indicator data of landscape fragmentation.

The plate diversity dynamic indicator data may include: quantity, density, area, fractal dimension, and shape index.

The landscape heterogeneity dynamic indicator data may include: diversity index, connectivity index, dominance index, aggregation index, and uniformity index.

The landscape fragmentation dynamic data may include: a patch number fragmentation index, a patch shape fragmentation index, and an internal habitat area fragmentation index.

The above selected indices are all preferred and representative items suitable for the belt structure, with the characteristics of sensitivity, comprehensiveness and science. Calculating the above multiple indices can increase the completeness, authenticity and precision of the landscape pattern of the target railway corridor. Of course, in addition to the above multiple indices, in actual applications, there may be some other indices that need to be calculated, and the indices should be adjusted according to actual needs.

In a specific embodiment, the landscape pattern index may be:

(1) Patch diversity dynamics

①Quantity

a. The calculation formula for the number of landscape patches NP is:

NP＝N； (6)

Where N is the total number of patches in the landscape, and its value range is: NP ≥ 1, with no upper limit.

b. The calculation formula for the number of type plaques NP _e is:

NP _e =N _e ; (7)

Where _Ne is the number of patches of a certain landscape type e.

② Density

a. The calculation formula of landscape patch density PD is:

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

b. The calculation formula of plaque density PD _e of type is:

Where PD _e refers to the number of patches per unit area of a certain type of landscape e, which can more directly reflect the degree of fragmentation of landscape components; A _e and _Ne are the area and number of patches of a certain type of landscape e, respectively.

c. The calculation formula of landscape boundary density ED is:

ED＝EA； (10)

In the formula, ED is 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 length of the boundary in the landscape (i.e., the sum of the perimeters of patches), and A is the total area of the landscape, usually expressed in km/ ^hm2 .

d. The calculation formula of type patch boundary density ED _e is:

Where ED _e (unit: km/hm ² ) is the boundary length of a landscape type e within a unit area, and P _ef is the perimeter of the patch.

③ Area

a. The formula for calculating the average area of type plaque is:

ef represents the fth patch in landscape type e.

b. The calculation formula of landscape similarity index is:

LS＝ _Ae /A； (13)

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 landscape as a whole, and in a relative sense also explains the contribution rate of the landscape type to the entire landscape.

④The shape index calculation formula is:

In the formula, S is the class spot shape index of the e-th landscape type, _Pe is the perimeter of the patch of the e-th landscape type, and _Ae is the area of the e-th landscape type. The class spot shape index value is generally greater than or equal to 1. The closer S is to 1, the closer the shape of the class spot is to a circle. The larger the value, the more complex or oblate the shape is, and the farther it deviates from a circle.

⑤The calculation formula of fractal dimension is:

D＝2Ln(P _e /4)/LnA _e ; (15)

In the formula, D is the fractal dimension; _Pe is the perimeter of a patch of a certain landscape type e; and _Ae is the area of a patch of a certain landscape type e. The theoretical range of the D value is 1.0 to 2.0, with 1.0 representing the simplest square patch and 2.0 representing the most complex patch of equal area. The same D value for different landscape elements indicates that they have a consistent landscape pattern.

(2) Dynamics of landscape heterogeneity

① The Shannon-Wiener diversity index calculation formula is:

Where H is the Shannon diversity index. The larger its value, the greater the diversity of the landscape. m is the total number of patch types in the landscape. _Pi is the proportion of the i-th type of patch to the total area of the landscape.

②The calculation formula of landscape dominance index is:

In the formula, H is the Shannon diversity index, H _max is its maximum value, and N is the total number of landscape element types of the research object. D is 0, indicating that the proportions of various landscape types are equal.

③The calculation formula of landscape uniformity index is:

In the formula, H is the Shannon diversity index, H _max is its maximum value, and N is the total number of landscape element types of the research object. When E approaches 1, the uniformity of landscape patch distribution is the greatest.

④The calculation formula of landscape concentration index is:

Where: C _max is the maximum value of the aggregation index [2ln(n)], n is the total number of patch types in the landscape, and P _uv is the probability that patch types u and v are adjacent. In a rasterized landscape, the general method for calculating P _uv is:

P _uv =P _u P _v/u ; (20)

Where: _Pu is the probability that a randomly selected grid cell belongs to patch type u (which can be estimated by the proportion of patch type u to the area of the entire landscape), and Pv _/u is the conditional probability that patch type v is adjacent to patch type u, that is:

P _{v / u} = m _uv / m _u ; (21)

Where: _muv is the number of cell edges adjacent to patches u and v in the landscape grid, and mu is the total number of edges of cells of patch type u.

⑤The calculation formula of landscape connectivity index is:

Where: P _ef is the perimeter of the fth patch in the eth type landscape, a _ef is the area of the fth patch in the eth type landscape, and A represents the total area of the ecosystem in the landscape.

(3) Landscape fragmentation dynamics,

①The calculation formula of forest patch fragmentation index FN is:

FN ₁ =(N _p -1)/N _c ; FN ₂ =MPS×(N _f -1)/N _c ; (23)

_Nc is the total number of grids in the grid of the landscape data matrix; _Np is the total number of patches of various types in the landscape; MPS is the average area of patches in the landscape; _Nf is the total number of forest patches in the landscape.

②The calculation formula of forest patch shape fragmentation index FS is:

FS ₁ ＝1-1/MSI; FS ₂ ＝1-1/ASI; (24)

MSI is the mean shape index of forest patches; ASI is the mean shape index of forest patches weighted by area.

③The calculation formula of the forest internal habitat area fragmentation index FI is:

FI ₁ ＝1-A _i /A; FI ₂ ＝1-A ₁ /A; (25)

_Ai is the total area of forest interior habitats; _A1 is the area of the largest forest patch; and A is the total landscape area.

The ecological value of biodiversity in railway corridors can be reflected through the calculation of the above multiple indicator factors.

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.

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

S131, constructing a spatial weight matrix;

The zoning statistics of habitat quality index and the calculation of landscape pattern index are based on 1km×1km grid as the basic spatial unit, grid number as the basic variable, and rook adjacency is selected to construct the spatial weight matrix to define the spatial relationship of different grids. In practical applications, this step can be performed with the help of GeoDA software. In practical applications, the size of the grid should be adjusted according to actual needs and is not a limitation here.

For example, it can be constructed as:

Where n represents the number of spatial units; w _ij represents the adjacency relationship between spatial units i and j. If the two have a common boundary, the value is 1, otherwise it is 0.

S132. Based on the Moran index, a global spatial correlation analysis of the habitat quality index and the landscape pattern index was conducted.

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

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

The corresponding global Moran index is calculated to characterize the spatial autocorrelation of the two variables. The formula is as follows:

为单元的观测平均值，；w_ij为单元i和j的空间邻接关系；S²表示观测值的方差。I的取值一般在[-1,1]之间，小于0表示在空间呈负相关，大于0表示在空间呈正相关；等于0表示不相关，随机分布。该值越趋近于0，表示两个变量间的全局相关关系越弱。Where: I is the Moran index; n is the number of spatial units, _xi and _xj are the observed values of units i and j respectively.

is the observed average value of the unit; w _ij is the spatial adjacency relationship between units i and j; S ² represents the variance of the observed value. The value of I is generally between [-1,1]. A value less than 0 indicates a negative correlation in space, a value greater than 0 indicates a positive correlation in space, and a value equal to 0 indicates no correlation and random distribution. The closer the value is to 0, the weaker the global correlation between the two variables.

S133. Based on the Moran index, local correlation analysis between habitat quality index and landscape pattern index was conducted.

In one embodiment, specifically:

Taking the average habitat quality index and landscape pattern index value of each grid as the data source, the spatial local autocorrelation degree between the habitat quality of the grid unit in the study area and the landscape pattern index of the adjacent grid unit was analyzed, and the local Moran index was calculated to characterize the correlation degree between the attribute value of a unit and the adjacent unit, and the LISA cluster distribution map was drawn based on the z test. The formula is as follows:

为单元的观测平均值。Where n is the number of spatial units, x _i and x _j represent the observations of spatial unit i and spatial unit j respectively, w _ij is the spatial adjacency relationship between spatial units i and j; S ² represents the variance of the observations,

is the observed mean value of the unit.

S14. Based on the correlation, the habitat quality index and landscape pattern index are weighted according to the hierarchical analysis method, and the ecological value index data are obtained by superimposing the weights.

Based on the InVEST model for comprehensive assessment of ecosystem services and trade-offs and the Fragstats model for landscape pattern index calculation, the grid analysis method can effectively evaluate the habitat quality and landscape pattern evolution in the railway corridor area.

Using the spatial autocorrelation theory, spatial correlation analysis and time series evolution characteristic analysis were conducted on the habitat quality index and the landscape pattern index to determine the spatial correlation law between the habitat quality and landscape pattern of the target railway corridor. Based on the hierarchical analysis method and the above correlation analysis between habitat quality and landscape pattern, weights were assigned to the habitat quality index and landscape pattern indicators, and the ecological value index data was obtained by superimposed calculation, which overcame the limitations of the traditional single evaluation indicator and the inconsistent research scales corresponding to different indicators, and achieved quantitative evaluation of the value of biodiversity.

In another embodiment, the S2 may include:

S21. Based on the pre-constructed social value types and evaluation rules, the social value points of the target railway corridor are determined through expert scoring method and public evaluation method.

The social value types may include: aesthetic value, economic value, life sustainability value, spiritual value and/or scientific research value and the like. In this embodiment, aesthetic value, economic value, life sustainability value, spiritual value and scientific research value are preferred.

The evaluation rules may include: conditional value method, market value method, loss cost method, opportunity cost method, conditional value method, travel cost method, estimation method, protection cost method, restoration cost method and/or shadow value method, etc. The social value of the target railway corridor is evaluated by using currency as a unit, which improves the authenticity of the social value evaluation.

Specifically, a 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:

Table 1

As shown in Table 1, through the social value types and evaluation rules, the social value of biodiversity is quantitatively represented through expert scoring method and public evaluation method, and the corresponding areas are marked on the map to determine the social value points.

S22. Based on the pre-built ecosystem service social value model, a weighted kernel density analysis is performed on all the social value points through the 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, a kernel density value index layer standardized to 0-10 is obtained. Based on the kernel density value index layer, an intermediate value index of the social value type is calculated, and an intermediate value index graph is obtained.

Specifically, in one embodiment, based on the grid divided in advance using the grid tool, the social value grid surface of each grid is input into the ecosystem service social value model, and the social value points are subjected to weighted kernel density analysis through the embedded kernel density analysis tool to obtain the overall spatial distribution density, kernel density surface and maximum weighted kernel density value, etc.

Furthermore, based on the maximum weighted kernel density value, the maximum grid value grid surface in the grid is determined, and the kernel density surface is divided by the maximum grid value grid surface to obtain a kernel density value index layer standardized to 0-10, and then these normalized surfaces are standardized to an exponential scale of 10 points to generate a constant grid of the maximum value in each value index grid. That is, the intermediate value index of the social value type is calculated, and the intermediate value index map is obtained.

S24, performing an average nearest neighbor analysis on the social value points according to the average nearest neighbor tool embedded in the ecosystem service social value model, and obtaining spatial clustering results through the ratio R value and standard deviation Z value obtained by the average nearest neighbor analysis;

S25. Based on the social survey data and spatial data layers of the target railway corridor, a spatial analysis is performed on the target railway corridor to generate a spatial heterogeneity distribution status map of each social value type.

The spatial data layers may be geographic spatial data layers such as land use coverage, elevation, slope, distance to water bodies, distance to protected areas, etc.

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

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

S27. Utilize 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 status map, after maximum entropy model processing, output the final social value map of the target railway corridor in ArcGIS to obtain the social value index of the target railway corridor.

The highest value index in each social value type is the maximum value index (valuc index x maximum, M-VI) of that value type. The result of M-VI shows the importance of various social value types. The larger the value, the higher the social value index and the greater the importance of the social value type.

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

All pixels in the target railway corridor study area are taken as the possible distribution space of social value, and pixels with known social value points are taken as sample points. Based on the environmental variables of the sample point pixels, the constraints are obtained; the probability distribution of maximum entropy (0-1) is estimated by machine learning method, indicating that the location is the maximum probability of social value distribution under given environmental conditions and known existence of social value conditions, and it is believed that the probability of social value distribution when entropy is maximum under this constraint condition 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 value corresponding to each social value type to generate the final index map (0-10) of each social value type.

In practical applications, when implementing step S27, a maximum entropy model evaluation may also be performed:

The model performance is evaluated by the area under the receiver operating characteristic curve (ROC curve) (AUC) embedded in the SolVES model, so that the social value index obtained is more accurate.

The closer the AUC value is to 1, the better the model evaluation effect is; the AUC value has lower accuracy when it is 0.5-0.7, has certain accuracy when it is 0.7-0.9, and has higher accuracy when it is above 0.9.

In practical applications, spatial correlation analysis between each social value type index and elevation elements can also be performed based on the social value index.

Furthermore, in order to make the social value of the target railway corridor more obvious and easy to read, in some embodiments, the social value map may provide a geographical and statistical display of each type of social value index.

The above-mentioned SolVES ecosystem service social value model is based on ArcGIS. The vectorization tool in ArcGIS software is used to align and vectorize the target railway corridor map, and the feature layers such as the boundary, water body, other types of wetlands of the target railway corridor, and the geographic spatial data layers such as land use coverage, elevation, slope, distance to water body, distance to protected area can be obtained. Euclidean distance is performed on water bodies, wetlands, nature reserves, etc. to obtain a series of environmental layers; social value points are vectorized to obtain social value point layers, etc.

In the prior art, social value is often obtained through interviews and questionnaires to obtain the attitudes and preferences of direct users towards ecosystem service products, and to measure the importance of each type of social value subjectively; or further adopt expert knowledge or tourists' willingness to pay survey value assessment methods, through questionnaires and based on the answers of interviewed experts or tourists to quantitatively determine the relevant value, without relying on monetary value. And biodiversity assessment based on expert knowledge or scientific knowledge tends to ignore the multiple values in biodiversity, including aesthetic value, economic value, life sustainability value, spiritual value and/or scientific research value, etc., and the social value finally obtained is subjective. In the method provided by the present invention, the knowledge system and academic attainments or practical expertise of different stakeholders can be integrated through pre-designed social value types and evaluation rules, a comprehensive knowledge system can be constructed, and the value of ecological diversity can be combined with expert knowledge or scientific knowledge, etc., to effectively evaluate the social value of the target area, thereby increasing the objectivity and comprehensiveness of the social value.

In some other embodiments, the assessment method may also include S4, determining the biodiversity value hotspots and cold spots of the target railway corridor based on a pre-constructed spatial analysis method, and generating a visual comprehensive value map for display.

Specifically, in one embodiment, the S4 is specifically implemented as follows:

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.

In practical applications, in order to make the final visualization map more intuitive, the natural breakpoint method of the spatial analysis function can be used in the 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.

S42. The spatial analysis function based on geographic information software, that is, based on the hot spot analysis tool (Getis-Ord Gi*), statistics are calculated, taking the grids of the ecological value map and the social value map as evaluation units, and statistically calculating to obtain the Z value and P value, P < 0.1, to determine the spatial clustering of hot spots and cold spots of the target railway corridor.

S43. Based on the spatial clustering of hot spots and cold spots, a comprehensive value map of the target railway corridor is generated and displayed.

The statistical calculation formula is:

Among them, _Gi * is Z, _qj is the attribute value of the jth evaluation unit, _gij is the spatial weight between the i-th and j-th evaluation units, and n is the total number of evaluation units.

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

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

Furthermore, Figure 2 is an evaluation flow chart of an evaluation method based on the biodiversity value of railway corridors provided in an embodiment of the present invention, and Figure 3 is a detailed diagram of part of the evaluation process in Figure 2. As shown in Figures 2 and 3, in some embodiments, the evaluation method may also include step S5, judging the coupling degree and coupling coordination degree between the target railway corridor ecosystem and the social system through pre-constructed coupling degree and model and coupling coordination degree model.

The coupling degree model is:

Among them, f(a) is the ecological value index of the railway corridor; f(b) is the social value index of the railway corridor;

The coupling degree C can be obtained by inputting the ecological value index and the social value index into the coupling degree model.

The coupling coordination model is:

T = αf(a) + βf(b);

The ecological value index and the social value index are weighted according to the hierarchical analysis method, 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, and 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 the pre-constructed formula to obtain the coupling coordination degree D.

In one embodiment, the social system and the ecological system of the railway corridor are considered to be equally important, and therefore, α=β=0.5.

In another embodiment, the coupling degree and coupling coordination degree are used to determine the coupling coordination type characteristics of the railway corridor biodiversity ecology and social system in this embodiment, as shown in Table 2:

Table 2

Based on the above social-ecological coupling analysis, the value of biodiversity can be evaluated from the levels of social activities and ecosystems, which increases the integrity and comprehensiveness of the value of biodiversity.

The evaluation method of railway corridor biodiversity provided by the above embodiments can judge the maintenance capacity of biodiversity in each region and the stability of biodiversity in the region by calculating the habitat quality index of the target railway corridor; the 18 indicator factors under the selected landscape pattern index are sensitive and not limited to the region, and can be effectively applied to belt structures such as railway corridors; the preferred 5 types of social value and multiple evaluation methods can flexibly and appropriately evaluate the social value of the target railway corridor in many aspects, increasing objectivity; further, the comprehensive value index of biodiversity is obtained by weighting and superimposing the calculation through the hierarchical analysis method, while considering the ecological value and social value, and can comprehensively and accurately evaluate the target railway corridor. Moreover, the method provided by the present invention effectively links the biodiversity evaluation at the scale of railway corridors with the social-ecological system framework at different time and space scales, thereby improving the comprehensiveness and analysis efficiency of biodiversity value evaluation.

Through ArcGis software, the map visualization of the target railway corridor can be realized, which can show the comprehensive value of the target railway corridor and the statistically significant spatial clustering of hot spots (high values) and cold spots (low values) in a more detailed and intuitive way. It can further intuitively see the spatial patterns between ecological systems and social systems, and take more reasonable measures when protecting related areas.

At the same time, the Moran index and coupling coordination type characteristics can effectively determine the spatial correlation of biodiversity in the railway corridor and the degree of coupling between the railway corridor ecosystem and the social system, quantitatively analyze the interaction intensity between the systems, and play a positive coordination role in each cycle of the railway corridor.

The present invention provides a railway corridor biodiversity assessment method that can integrate multiple types of biodiversity characteristics, systematically establish a comprehensive biodiversity evaluation method from multiple angles and achieve quantitative and visual expression, thereby establishing a systematic and effective biodiversity conservation strategy.

In addition, an embodiment of the present invention further provides a device for evaluating the biodiversity value of a railway corridor, which includes: an ecological value index processing unit, a social value index processing unit, and a comprehensive evaluation index processing unit;

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 layer after remote sensing interpretation based on the remote sensing image map of the target railway corridor, and the landscape pattern data is data including ground object raster data after remote sensing interpretation based on the remote sensing image map of the target railway corridor;

A social value index processing unit, used to calculate the social value index of the target railway corridor based on 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 through a pre-constructed hierarchical analysis method, and to superimpose the ecological value index and the social value index according to the weights to determine the comprehensive evaluation index of the biodiversity value of the target railway corridor.

The evaluation device for the biodiversity value of the railway corridor is integrated in the computing device. Based on the remote sensing image of the target railway corridor, the ecological value index is obtained, and the community value index is obtained through the social value type and evaluation rules, thereby realizing a comprehensive evaluation of the biodiversity value of the target railway corridor. The evaluation process is very comprehensive and has true reliability, overcoming the defects of the existing manual evaluation technology, which is imprecise, incomplete, low in sensitivity and high in cost.

In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

In the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may alter, modify, replace and modify the above embodiments within the scope of the present invention.

Claims (10)

1. A method of evaluating the biological diversity value of a railway corridor, comprising:

s1, inputting the habitat quality data and landscape pattern data of a target railway corridor into a pre-constructed model, and outputting an ecological value index of the railway corridor; the habitat quality data are data comprising land utilization grid data and ecological threat factor layers after remote sensing interpretation according to a remote sensing image map of a target railway corridor, and the landscape pattern data are data comprising land feature grid data after remote sensing interpretation according to a remote sensing image map of the target railway corridor;

s2, calculating a social value index of the target railway gallery based on a pre-constructed social value type and an evaluation rule;

and S3, weighting the ecological value index and the social value index through a pre-constructed analytic hierarchy process, and superposing and calculating the ecological value index and the social value index according to the weight to determine the comprehensive biological diversity value evaluation index of the target railway corridor.

2. The assessment method according to claim 1, further comprising:

s4, determining the biodiversity value hot spots and cold spots of the target railway corridor based on a pre-constructed space analysis method, and generating a visual comprehensive value map for display.

3. The method of evaluating according to claim 1, further comprising S0 before S1:

remote sensing interpretation is carried out on the remote sensing image map of the target railway corridor through geographic information software, and habitat quality data and landscape pattern data are obtained; the habitat quality data includes land utilization grid data and an ecological threat factor graph layer; the landscape layout data includes terrain raster data.

4. The evaluation method according to claim 3, wherein S0 specifically comprises:

s01, generating a standard image through atmospheric correction, geometric correction and orthographic correction aiming at a pre-acquired high-resolution remote sensing image;

s02, performing supervision classification on the standard images, and outputting land utilization classified images and land feature remote sensing classified images;

s03, dividing the range of the land utilization classified image through a grid tool of geographic information software, collecting land utilization grid data of the target railway corridor, and extracting an ecological threat factor layer based on the land utilization grid data;

S04, performing ground object remote sensing interpretation on the ground object remote sensing classified image through an interpretation tool of geographic information software to obtain ground object vector data, and determining ground object raster data based on the ground object vector data;

the pattern types of the ground object are divided according to the second class.

5. The evaluation method according to claim 1, wherein S1 comprises:

s11, inputting the land utilization grid data and the ecological threat factor layer into a pre-constructed comprehensive evaluation model of ecological system service and trade-off, and calculating to obtain a habitat quality index by combining a pre-defined ecological threat factor scale, a land utilization type sensitivity scale and a target railway corridor range layer;

s12, inputting the land feature grid data into a pre-constructed landscape pattern model, and outputting a landscape pattern index;

s13, carrying out association analysis on the habitat quality index and the landscape pattern index by using the Morand index to obtain a correlation relationship between the habitat quality index and the landscape pattern index;

and S14, weighting the habitat quality index and the landscape pattern index according to a hierarchical analysis method based on the correlation relationship, and superposing and calculating to obtain the ecological value index data.

6. The evaluation method according to claim 1, wherein S2 comprises:

s21, judging social value points of the target railway corridor through an expert evaluation method and a public evaluation method based on a pre-constructed social value type and an evaluation rule;

the social value types include: aesthetic, economic, life sustainable, mental, and/or scientific;

the evaluation rule includes: conditional value methods, market value methods, loss cost methods, opportunity cost methods, conditional value methods, travel fee methods, estimation methods, guard fee methods, recovery fee methods, and/or shadow value methods;

s22, carrying out weighted nuclear density analysis on all the social value points through an embedded nuclear density analysis tool based on a pre-constructed ecological system service social value model to obtain the total spatial distribution density, a nuclear density curved surface and a maximum weighted nuclear density value of the social value points;

s23, based on the nuclear density curved surface and the maximum weighted nuclear density value, acquiring a nuclear density value index layer standardized to 0-10, calculating an intermediate value index of the social value type based on the nuclear density value index layer, and acquiring an intermediate value index graph;

S24, carrying out average nearest neighbor analysis on the social value points according to an average nearest neighbor tool embedded in the ecological system service social value model, and obtaining a spatial clustering result through a ratio R value and a standard deviation Z value of the average nearest neighbor analysis;

s25, performing spatial analysis on the target railway corridor according to social investigation data and a spatial data layer of the target railway corridor to generate a spatial heterogeneity distribution situation diagram of each social value type;

s26, carrying out buffer area analysis on elevation elements in a target railway corridor range to obtain social value indexes of the elevation elements, wherein the elevation elements comprise contour line element extraction, gradient extraction and distance from a railway center line;

s27, utilizing a value drawing module, outputting a final social value map of the target railway corridor through maximum entropy model processing based on the social value points, the total spatial distribution density, the spatial clustering result, the intermediate value index graph, the elevation element social value index and the spatial heterogeneity distribution condition graph, and obtaining the social value index of the target railway corridor.

7. The evaluation method according to claim 1, wherein the comprehensive evaluation index of biodiversity value in S3 has a calculation formula:

Wherein V is the comprehensive evaluation value of the target railway corridor; n is the number of evaluation factors; w (W) _l To evaluate the causeCombining weights of the children; t (T) _l A normalized score value for the first evaluation factor;

the evaluation factors are an ecological value index and a social value index of the target railway corridor.

8. The assessment method according to claim 6, wherein S4 comprises:

s41, inputting 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 space analysis function of geographic information software, taking grids of the ecological value map and the social value map as evaluation units, carrying out statistical calculation to obtain Z values and P values, wherein P is less than 0.1, and judging hot spot and cold spot space clustering of the target railway corridor;

s43, generating and displaying a comprehensive value map of the target railway corridor based on the hot spot and cold spot spatial clustering;

the statistical calculation formula is as follows:

wherein G is _i * Is Z, q _j Is the attribute value of the j-th evaluation unit, g _ij Is the spatial weight between the i and j-th evaluation units, n is the total number of evaluation units.

9. The assessment method according to claim 1, further comprising:

S5, inputting the ecological value index and the social value index into a pre-constructed coupling degree model and a coupling coordination degree model, and judging the coupling degree and the coupling coordination degree of the target railway corridor ecological system and the social system;

the coupling degree model is as follows:

wherein f (a) -the railway corridor ecological value index; f (b) -a railway corridor social value index;

the coupling cooperative scheduling model is as follows:

T＝αf(a)+βf(b)；

wherein: f (a) -railway corridor ecological value index; f (b) -a railway corridor social value index;

the α=β=0.5; t is the comprehensive evaluation index of the development level of the railway corridor society and the ecological system.

10. An assessment device for the biological diversity value of a railway corridor as claimed in any one of claims 1-9, characterized by comprising:

the ecological value index processing unit is used for inputting the habitat quality data and the landscape pattern data of the target railway corridor into a pre-constructed model and outputting the ecological value index of the railway corridor; the habitat quality data are data comprising land utilization grid data and ecological threat factor layers after remote sensing interpretation according to a remote sensing image map of a target railway corridor, and the landscape pattern data are data comprising land feature grid data after remote sensing interpretation according to a remote sensing image map of the target railway corridor;

The social value index processing unit is used for calculating the social value index of the target railway corridor based on the pre-constructed social value type and the evaluation rule;

the comprehensive evaluation index processing unit is used for weighting the ecological value index and the social value index through a pre-constructed analytic hierarchy process, and determining the biological diversity value comprehensive evaluation index of the target railway corridor according to superposition calculation of the ecological value index and the social value index by the weight.

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