Ecological vulnerability evaluation method based on road domain landscape pattern optimization

Abstract

The invention discloses an ecological vulnerability evaluation method based on road domain landscape pattern optimization, and the method comprises the steps: obtaining expressway road domain environment data as training data, and obtaining ecological environment data; calculating an ecological environment quality index of the ecological environment data, and obtaining landscape structure features in the target area; integrating the landscape pattern index into an ecological sensitive factor, and constructing an ecological vulnerability evaluation model by adopting an SRP structure; performing spatial autocorrelation analysis on space-time evolution of the ecological vulnerability of the road domain according to the ecological vulnerability evaluation index, establishing an ecological vulnerability multi-scene simulation model, performing overlay analysis on an ecological vulnerability evaluation result and a land utilization type, obtaining a specific restoration strategy, tracking space-time evolution characteristics of the ecological vulnerability, and performing road region ecological vulnerability evaluation. Ecological vulnerability influence factors are adjusted based on different scenes, future possible ecological vulnerability risks are simulated, specific restoration strategies are made according to different partition standards, and effective configuration and use efficiency of resources are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of highway road landscape optimization, and in particular to an ecological vulnerability assessment method based on road landscape pattern optimization. Background Art

[0002] Highways and other transportation infrastructure not only alter land cover types but also influence hydrological cycles, plant and animal migration pathways, and other factors. Consequently, the road environment possesses unique ecological characteristics. As transportation networks expand, minimizing the impact on the surrounding ecosystem has become a pressing issue. This includes reducing the fragmentation of wildlife habitats, controlling soil erosion, and improving water quality. Current highway-based ecological vulnerability assessment methods still suffer from the following issues:

[0003] (1) Existing highway ecological vulnerability assessment methods focus on a specific aspect, such as vegetation loss or soil erosion, and ignore the potential impact of socioeconomic factors on the ecosystem, such as traffic volume growth and development intensity along the highway. They fail to fully consider the interactions and combined effects of multiple ecological factors.

[0004] (2) Existing ecological vulnerability assessment methods only provide static assessment results, ignoring the development and changes of the ecological environment over time and the long-term impact of human activities. In addition, high-quality environmental data are difficult to obtain, which limits the accuracy and reliability of the assessment model. Summary of the Invention

[0005] The purpose of the present invention is to provide an ecological vulnerability assessment method based on road landscape pattern optimization to solve the technical problems in the existing technology that the interactions between multiple ecological factors and their combined effects are not fully considered and the impact of changes in the ecological environment over time on the assessment results is ignored.

[0006] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0007] The present invention provides an ecological vulnerability assessment method based on road landscape pattern optimization, comprising the following steps:

[0008] Obtaining highway environment data as training data, dividing the training data into grids based on longitude and latitude, and resampling the grid data of different scales to obtain ecological environment data;

[0009] Calculating an ecological environment quality index for the ecological environment data, setting multiple landscape pattern indices based on the ecological environment quality index, and obtaining landscape structure characteristics within the target area;

[0010] The landscape pattern index is integrated into the ecological sensitivity factor, and the ecological vulnerability assessment model is constructed using the SRP structure to obtain the ecological vulnerability assessment index;

[0011] Based on the ecological vulnerability evaluation indicators, a spatial autocorrelation analysis was conducted on the spatiotemporal evolution of road-area ecological vulnerability, and an ecological vulnerability multi-scenario simulation model was established. The target area was zoned for protection by dividing the ecological space. The ecological vulnerability evaluation results and land use types were superimposed and analyzed to obtain specific restoration strategies.

[0012] As a preferred solution of the present invention, highway environment data is obtained as training data, the training data is divided into grids according to longitude and latitude, and the grid data of different scales are resampled to obtain ecological environment data, including:

[0013] The highway environment data is obtained as training data through satellite remote sensing images, geographic information system (GIS) database, field survey records and public ecological environment data platform.

[0014] Using GIS software to obtain the latitude and longitude information of the training data, and dividing the training data of the target area into grids according to the latitude and longitude information to form a series of mutually adjacent rectangular grid units;

[0015] The training data is processed by adopting a bilinear interpolation method, the training data in all grid units are unified into a data format, and the training data is represented in a matrix form with the grid as a unit.

[0016] As a preferred solution of the present invention, principal component analysis is performed on the training data in matrix form to obtain ecological environment data, including:

[0017] Establishing an initial variable matrix X for the training data in matrix form, and calculating a correlation coefficient matrix of the initial variable matrix X;

[0018] The eigenvalues ​​and eigenvectors of the correlation coefficient matrix are calculated to obtain an eigenvector matrix, and a linear transformation is performed on the eigenvector matrix to obtain ecological environment data.

[0019] As a preferred solution of the present invention, calculating the ecological environment quality index based on the ecological environment data includes:

[0020] Assign a response weight to each indicator based on the weights of vegetation coverage, soil quality, water resources and biodiversity in the ecological and environmental data;

[0021] Calculate the environmental quality index Q for each grid cell xj , whose expression is:

[0022]

[0023] Among them, Q xj represents the environmental index of grid x in land type j, H j represents the ecological suitability of land type j, D xj represents the disturbance level of grid x in land type j, K represents the normalization constant, R represents the number of stress factors, r represents any stress factor in the number of stress factors R, and Y r is the total number of units of the stress factor, y represents the stress factor Y r Any unit in ω r represents the weight, r y represents the number of stress factors on the grid cell, σ rxy Indicates the degree of influence of stress factors, β x represents the accessibility level of grid x, γ jr is the sensitivity of land type j to stress factors;

[0024] Comprehensive analysis of the environmental quality index Q of each grid cell xj , based on its performance in various ecological environment indicators, the environmental quality index Q of each grid cell is xj Scoring of individual items and comprehensive calculation of ecological environment quality index Q eqi , whose expression is:

[0025]

[0026] in, represents the weight of the i-th indicator, ε i represents the standardized score of the i-th indicator, m represents the number of land type classifications, and n represents the total number of ecological environment indicators.

[0027] As a preferred solution of the present invention, multiple landscape pattern indices are set according to the ecological environment quality index to obtain landscape structure characteristics in the target area, including:

[0028] Through the ecological environment quality index Q eqi Dividing the target area into different landscape types, performing rasterization processing on the landscape types in different areas, and generating a landscape type map;

[0029] The spatial analysis module in ArcGIS was used to calculate the pattern index of each landscape type in the landscape type map, and the Shannon diversity index was used to measure the proportion and uniformity of different landscape types;

[0030] The degree of landscape segmentation is assessed by the landscape fragmentation index, the landscape types within the grid are aggregated, and the aggregation index is used to reflect the degree of aggregation between similar landscape patches, thereby obtaining the landscape pattern index value within the target area.

[0031] The spatial distribution characteristics of the landscape pattern in the target area are analyzed according to the values ​​of the landscape pattern indexes, and the landscape pattern indexes of different landscape types are compared according to the time series to obtain the landscape pattern characteristics.

[0032] As a preferred solution of the present invention, the landscape pattern index is integrated into the ecological sensitivity factor, and an ecological vulnerability assessment model is constructed using the SRP structure to obtain ecological vulnerability assessment indicators, including:

[0033] Divide the target area into ecological functional zones according to the landscape type, and divide the ecological functional zones into three types: high-quality ecological functional zones, medium-quality ecological functional zones, and poor ecological functional zones;

[0034] Generate an ecological vulnerability cluster map of the target area using ArcGIS software based on the landscape pattern index;

[0035] Integrating ecological sensitivity factors into the ecological vulnerability cluster diagram, performing cross-detection analysis on the ecological sensitivity factors in pairs, and obtaining the impact weights of the interactive effects of the ecological sensitivity factors on ecological vulnerability;

[0036] According to the impact weight of the ecological vulnerability, an ecological vulnerability assessment model based on the SRP structure is constructed to obtain ecological vulnerability assessment indicators.

[0037] As a preferred solution of the present invention, a spatial autocorrelation analysis is performed on the spatiotemporal evolution of road-area ecological vulnerability based on the ecological vulnerability evaluation index, including:

[0038] Calculating the ecological vulnerability value of each grid cell using the ecological environment quality index based on the ecological vulnerability cluster map at different time points;

[0039] Performing a global spatial autocorrelation analysis on the ecological vulnerability values, and determining whether similar ecological vulnerabilities tend to cluster together in space on the ecological vulnerability cluster map;

[0040] Based on the local spatial correlation LISA, local areas with significant spatial clustering characteristics are identified and marked on the ecological vulnerability cluster map.

[0041] Compare the spatial autocorrelation results of different periods, analyze the spatiotemporal evolution characteristics of ecological vulnerability, and obtain real-time spatiotemporal evolution trends of ecological vulnerability.

[0042] As a preferred solution of the present invention, an ecological network is constructed based on the spatiotemporal evolution trend to identify ecological vulnerability scenarios, including:

[0043] Identifying key ecological vulnerability areas on the ecological vulnerability cluster map according to the spatiotemporal evolution trend, and using the key ecological vulnerability areas as ecological network nodes;

[0044] The ecological resistance analysis is performed on the ecological network nodes to calculate the ecological resistance coefficient at the ecological network nodes. The expression is:

[0045]

[0046] Among them, ρ i Nμ represents the ecological resistance coefficient of grid i after highway sensitivity correction. i represents the highway sensitivity index of grid i, Nθ j represents the average sensitization coefficient of land type j corresponding to grid i, and ρ represents the resistance coefficient of land type corresponding to grid i;

[0047] The ecological resistance coefficient is used to comprehensively analyze the landscape pattern index and extract the ecological corridor, which is expressed as follows:

[0048]

[0049] Among them, L MCR The ecological corridor value representing the minimum cumulative resistance, D tj represents the spatial distance of species on land type j from the source to landscape unit t, m represents the number of landscape units, n represents the number of land types, R t represents the ecological resistance coefficient of landscape unit t;

[0050] The ecological network nodes are dynamically adjusted according to the ecological corridor values, and the ecological resistance values ​​between different ecological network nodes are calculated to evaluate the species flow resistance between nodes, generate an ecological network, and identify ecological vulnerability scenarios.

[0051] As a preferred solution of the present invention, a multi-scenario simulation model of ecological vulnerability is established based on the ecological network, including:

[0052] Converting the ecological network into readable raster data, extracting the ecological resistance value and ecological corridor value of the ecological network node, combining the temporal and spatial evolution trend data of ecological vulnerability, and using them as input data for the ecological vulnerability multi-scenario simulation model;

[0053] Based on the ecological network, the stability of ecological resistance and ecological corridor values ​​in the multi-scenario simulation model of ecological vulnerability was evaluated, and the distribution and changes of ecological vulnerability under different scenarios were calculated by adjusting the ecological vulnerability influencing factors.

[0054] Run the multi-scenario simulation model of ecological vulnerability under different set scenarios to generate future ecological vulnerability distribution maps, obtain the ecological vulnerability index under each scenario and the ecological risk level of different regions.

[0055] As a preferred solution of the present invention, the target area is protected by dividing the ecological space, and the ecological vulnerability assessment results and land use types are superimposed and analyzed to obtain specific restoration strategies, including:

[0056] Develop zoning standards for target areas based on the ecological vulnerability index under each scenario and the ecological risk level of different regions, combined with land use types;

[0057] Use GIS software to perform spatial overlay analysis on the ecological vulnerability assessment results and land use type maps to identify the distribution of ecological vulnerability under different land types;

[0058] Based on the distribution of ecological vulnerability, restoration strategies will be formulated, long-term monitoring points will be established, ecological and environmental parameters will be collected regularly, and restoration progress will be tracked.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention obtains a comprehensive ecological and environmental status by integrating multi-source data, and uses PCA to perform dimensionality reduction processing on training data in matrix form, thereby reducing redundant information, improving data analysis efficiency, and enhancing the explanatory power of the assessment. It integrates multi-dimensional data such as landscape pattern index and ecological sensitivity factor to provide a more comprehensive ecological and environmental assessment. By generating an ecological vulnerability cluster map, the spatial autocorrelation results of different periods are compared and analyzed, the spatiotemporal evolution characteristics of ecological vulnerability are tracked, potential risks are discovered and responded to in a timely manner, and the ecological vulnerability influencing factors are adjusted based on different scenarios. Possible ecological vulnerability risks in the future are simulated, and specific restoration strategies are formulated according to different zoning standards to ensure the effective allocation and utilization efficiency of resources. Ecological and environmental parameters are regularly collected, protection and restoration strategies are dynamically adjusted, restoration progress is tracked, and the effectiveness of restoration measures is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0062] Figure 1 This is a flow chart of an ecological vulnerability assessment method based on road landscape pattern optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] like Figure 1 As shown, the present invention provides an ecological vulnerability assessment method based on road landscape pattern optimization, comprising the following steps:

[0065] Obtaining highway environment data as training data, dividing the training data into grids based on longitude and latitude, and resampling the grid data of different scales to obtain ecological environment data;

[0066] In this embodiment, by dividing the training data into grids based on longitude and latitude and resampling data of different scales, the accuracy and consistency of the data are ensured, providing high-quality basic data for subsequent analysis.

[0067] Calculating an ecological environment quality index for the ecological environment data, setting multiple landscape pattern indices based on the ecological environment quality index, and obtaining landscape structure characteristics within the target area;

[0068] In this embodiment, by calculating the ecological environment quality index based on ecological environment data and setting multiple landscape pattern indices to obtain the landscape structure characteristics within the target area, the health status of the ecosystem and its spatial distribution characteristics can be comprehensively evaluated, which helps to identify key protection areas and priority governance areas.

[0069] The landscape pattern index is integrated into the ecological sensitivity factor, and the ecological vulnerability assessment model is constructed using the SRP structure to obtain the ecological vulnerability assessment index;

[0070] In this embodiment, the ecological vulnerability assessment model is constructed using the sensitivity, resilience, and pressure SRP structure, which can comprehensively assess ecological vulnerability from the three dimensions of ecosystem sensitivity, resilience, and the pressure it is subjected to, thereby improving the reliability and practicality of the assessment results.

[0071] Based on the ecological vulnerability evaluation indicators, a spatial autocorrelation analysis was conducted on the spatiotemporal evolution of road-area ecological vulnerability, and an ecological vulnerability multi-scenario simulation model was established. The target area was zoned for protection by dividing the ecological space. The ecological vulnerability evaluation results and land use types were superimposed and analyzed to obtain specific restoration strategies.

[0072] In this embodiment, a multi-scenario simulation model of ecological fragility is established to predict future changes in ecological fragility based on different development scenarios, such as enhanced ecological protection and rapid urbanization, so as to formulate targeted zoning protection strategies and improve resource utilization efficiency and environmental protection effects.

[0073] In this embodiment, it can not only reflect the current status of ecological vulnerability, but also combine historical data and future scenario simulations to predict the temporal and spatial evolution trend of ecological vulnerability, providing a basis for long-term planning.

[0074] Obtain highway environment data as training data, divide the training data into grids based on longitude and latitude, resample the grid data of different scales, and obtain ecological environment data, including:

[0075] The highway environment data is obtained as training data through satellite remote sensing images, geographic information system (GIS) database, field survey records and public ecological environment data platform.

[0076] In this embodiment, by rasterizing and resampling training data from different sources such as satellite remote sensing images, GIS databases, and field survey records, the consistency of spatial resolution of all data can be ensured, which helps to eliminate analysis errors caused by scale differences in the original data. By integrating multi-source data, the ecological and environmental conditions of the target area can be fully reflected.

[0077] Using GIS software to obtain the latitude and longitude information of the training data, and dividing the training data of the target area into grids according to the latitude and longitude information to form a series of mutually adjacent rectangular grid units;

[0078] In this embodiment, grid division is performed based on longitude and latitude information, which ensures that each grid unit has a clear spatial position, providing a data basis for spatial analysis.

[0079] The training data is processed by adopting a bilinear interpolation method, the training data in all grid units are unified into a data format, and the training data is represented in a matrix form with the grid as a unit.

[0080] In this embodiment, bilinear interpolation is used to process training data, which can smoothly transition data of different resolutions while maintaining data continuity, making the numerical changes between adjacent grids more natural and reasonable, thereby improving the quality and reliability of the data.

[0081] In this embodiment, the training data is represented in a matrix form with grids as the unit, which facilitates rapid processing and analysis using computer algorithms. It is particularly suitable for operations on large-scale data sets and can significantly improve the speed and efficiency of data processing.

[0082] Performing principal component analysis on the training data in matrix form to obtain ecological environment data includes:

[0083] Establishing an initial variable matrix X for the training data in matrix form, and calculating a correlation coefficient matrix of the initial variable matrix X;

[0084] The eigenvalues ​​and eigenvectors of the correlation coefficient matrix are calculated to obtain an eigenvector matrix, and a linear transformation is performed on the eigenvector matrix to obtain ecological environment data.

[0085] In this embodiment, principal component analysis (PCA) can be used to convert multiple correlated variables into a set of linearly uncorrelated variables, which helps to remove redundant information in the original data, simplify complex data sets, and project high-dimensional data into a low-dimensional space, which can significantly reduce the amount of computation and storage requirements.

[0086] Calculating the ecological environment quality index based on the ecological environment data includes:

[0087] Assign a response weight to each indicator based on the weights of vegetation coverage, soil quality, water resources and biodiversity in the ecological and environmental data;

[0088] In this embodiment, by considering multiple key ecological factors and assigning corresponding weights to each indicator, the ecological environment quality of a region can be comprehensively evaluated, ensuring the scientificity and rationality of the evaluation results and more accurately reflecting the actual environmental conditions.

[0089] Calculate the environmental quality index Q for each grid cell xj , whose expression is:

[0090]

[0091] Among them, Q xj represents the environmental index of grid x in land type j, Hj represents the ecological suitability of land type j, D xj represents the disturbance level of grid x in land type j, K represents the normalization constant, R represents the number of stress factors, r represents any stress factor in the number of stress factors R, and Y r is the total number of units of the stress factor, y represents the stress factor Y r Any unit in ω r represents the weight, r y represents the number of stress factors on the grid cell, σ rxy Indicates the degree of influence of stress factors, β x represents the accessibility level of grid x, γ jr is the sensitivity of land type j to stress factors;

[0092] In this embodiment, the environmental quality index of each grid cell is calculated, making the evaluation process more quantitative and accurate, which helps to eliminate the deviation caused by subjective judgment and provide more objective results.

[0093] In this embodiment, various ecological environmental indicators are standardized to convert data of different dimensions to the same scale, which facilitates comparison and comprehensive analysis.

[0094] Comprehensive analysis of the environmental quality index Q of each grid cell xj , based on its performance in various ecological environment indicators, the environmental quality index Q of each grid cell is xj Scoring of individual items and comprehensive calculation of ecological environment quality index Q eqi , whose expression is:

[0095]

[0096] in, represents the weight of the i-th indicator, ε i represents the standardized score of the i-th indicator, m represents the number of land type classifications, and n represents the total number of ecological environment indicators.

[0097] According to the ecological environment quality index, multiple landscape pattern indices are set to obtain the landscape structure characteristics in the target area, including:

[0098] Through the ecological environment quality index Q eqi Dividing the target area into different landscape types, performing rasterization processing on the landscape types in different areas, and generating a landscape type map;

[0099] In this embodiment, the target area is divided into different landscape types through the ecological environment quality index, which can finely classify the area based on ecological conditions. This classification method is more scientific and reasonable than the traditional single-dimensional classification and helps to identify areas with similar ecological characteristics.

[0100] In this embodiment, the landscape types of different regions are rasterized to generate a high-resolution landscape type map, so that each grid unit has clear ecological attributes, providing accurate basic data support for subsequent spatial analysis.

[0101] The spatial analysis module in ArcGIS was used to calculate the pattern index of each landscape type in the landscape type map, and the Shannon diversity index was used to measure the proportion and uniformity of different landscape types;

[0102] The degree of landscape segmentation is assessed by the landscape fragmentation index, the landscape types within the grid are aggregated, and the aggregation index is used to reflect the degree of aggregation between similar landscape patches, thereby obtaining the landscape pattern index value within the target area.

[0103] In this example, multiple landscape pattern indices are used to comprehensively assess the landscape structural characteristics within the target area. This not only considers the diversity and uniformity of landscape types, but also focuses on the size, shape, and spatial distribution pattern of landscape patches, thereby providing a more comprehensive perspective for understanding landscape pattern.

[0104] The spatial distribution characteristics of the landscape pattern in the target area are analyzed according to the values ​​of the landscape pattern indexes, and the landscape pattern indexes of different landscape types are compared according to the time series to obtain the landscape pattern characteristics.

[0105] In this embodiment, analyzing the spatial distribution characteristics of the landscape pattern in the target area based on the values ​​of each landscape pattern index can help identify which areas have relatively intact or fragile ecosystems. By comparing and analyzing the landscape pattern indexes in different periods, the changing trends of the landscape pattern over time can be tracked, which helps to identify long-standing ecological problems and emerging risk points.

[0106] The landscape pattern index is integrated into the ecological sensitivity factor, and the ecological vulnerability assessment model is constructed using the SRP structure to obtain ecological vulnerability assessment indicators, including:

[0107] Divide the target area into ecological functional zones according to the landscape type, and divide the ecological functional zones into three types: high-quality ecological functional zones, medium-quality ecological functional zones, and poor ecological functional zones;

[0108] In this embodiment, ecological functional zones are divided according to landscape types and further subdivided into three types: high-quality, medium, and poor. This makes the assessment more accurate and helps identify key areas that require priority protection or restoration.

[0109] Generate an ecological vulnerability cluster map of the target area using ArcGIS software based on the landscape pattern index;

[0110] Integrating ecological sensitivity factors into the ecological vulnerability cluster diagram, performing cross-detection analysis on the ecological sensitivity factors in pairs, and obtaining the impact weights of the interactive effects of the ecological sensitivity factors on ecological vulnerability;

[0111] In this example, by combining the landscape pattern index and the ecological sensitivity factor, a comprehensive assessment of the ecological vulnerability of the target area can be conducted from multiple perspectives, taking into account not only the physical characteristics of the landscape itself, such as diversity and fragmentation, but also the impact of multiple factors such as environmental pressure and socioeconomic activities.

[0112] In this embodiment, by performing pairwise cross-detection analysis on ecologically sensitive factors, the interactions between the factors and their joint impact on ecological vulnerability can be revealed, thereby more accurately quantifying the contribution of each factor.

[0113] According to the impact weight of the ecological vulnerability, an ecological vulnerability assessment model based on the SRP structure is constructed to obtain ecological vulnerability assessment indicators.

[0114] In this embodiment, the SRP structure provides a systematic framework to construct an ecological vulnerability assessment model, which can not only assess the sensitivity of the ecosystem to external disturbances, but also measure its resilience and the pressure it is under, thereby comprehensively reflecting the vulnerability of the ecosystem.

[0115] Based on the ecological vulnerability evaluation indicators, a spatial autocorrelation analysis of the spatiotemporal evolution of road-area ecological vulnerability is conducted, including:

[0116] Calculating the ecological vulnerability value of each grid cell using the ecological environment quality index based on the ecological vulnerability cluster map at different time points;

[0117] Performing a global spatial autocorrelation analysis on the ecological vulnerability values, and determining whether similar ecological vulnerabilities tend to cluster together in space on the ecological vulnerability cluster map;

[0118] In this embodiment, by calculating the spatial autocorrelation statistics, it is possible to determine whether similar ecological vulnerabilities in the entire study area tend to cluster spatially, identify overall spatial patterns and trends, and obtain the distribution of ecological vulnerabilities. By regularly updating data and recalculating ecological vulnerability values ​​and their spatial autocorrelation statistics, the latest spatiotemporal evolution trends of ecological vulnerability can be obtained.

[0119] Based on the local spatial correlation LISA, local areas with significant spatial clustering characteristics are identified and marked on the ecological vulnerability cluster map.

[0120] Compare the spatial autocorrelation results of different periods, analyze the spatiotemporal evolution characteristics of ecological vulnerability, and obtain real-time spatiotemporal evolution trends of ecological vulnerability.

[0121] In this example, by comparing the spatial autocorrelation results at different time points, the changing trend of ecological vulnerability can be clearly explained. For example, if a region changes from having no obvious clustering to forming a clear high-vulnerability cluster area, this change may indicate a potential ecological crisis.

[0122] Based on the spatiotemporal evolution trends, an ecological network is constructed to identify ecological vulnerability scenarios, including:

[0123] Identifying key ecological vulnerability areas on the ecological vulnerability cluster map according to the spatiotemporal evolution trend, and using the key ecological vulnerability areas as ecological network nodes;

[0124] In this embodiment, by analyzing the spatiotemporal evolution trend on the ecological vulnerability cluster map, key ecological vulnerability areas are determined and used as nodes of the ecological network, so that key areas that require special attention and protection can be accurately identified.

[0125] The ecological resistance analysis is performed on the ecological network nodes to calculate the ecological resistance coefficient at the ecological network nodes. The expression is:

[0126]

[0127] Among them, ρ i Nμ represents the ecological resistance coefficient of grid i after highway sensitivity correction. i represents the highway sensitivity index of grid i, Nθ j represents the average sensitization coefficient of land type j corresponding to grid i, and ρ represents the resistance coefficient of land type corresponding to grid i;

[0128] In this embodiment, an ecological resistance analysis is performed on the ecological network nodes, and the ecological resistance coefficient at each node is calculated. Factors such as highway sensitivity correction, land type and its corresponding average sensitivity coefficient and resistance coefficient are taken into account. This comprehensive consideration helps to more accurately assess the difficulty of species movement between different regions and improve the effectiveness and connectivity of the ecological network.

[0129] The ecological resistance coefficient is used to comprehensively analyze the landscape pattern index and extract the ecological corridor, which is expressed as follows:

[0130]

[0131] Among them, L MCR The ecological corridor value representing the minimum cumulative resistance, D tj represents the spatial distance of species on land type j from the source to landscape unit t, m represents the number of landscape units, n represents the number of land types, R t represents the ecological resistance coefficient of landscape unit t;

[0132] In this embodiment, the ecological corridor value with the minimum cumulative resistance is calculated and combined with parameters such as the spatial distance of landscape units and the ecological resistance coefficient to extract the ecological corridor. This ensures that the selected ecological corridor can not only connect important ecological nodes, but also minimize the resistance encountered during species migration, which is conducive to the maintenance and restoration of biodiversity.

[0133] The ecological network nodes are dynamically adjusted according to the ecological corridor values, and the ecological resistance values ​​between different ecological network nodes are calculated to evaluate the species flow resistance between nodes, generate an ecological network, and identify ecological vulnerability scenarios.

[0134] In this embodiment, the ecological network nodes are dynamically adjusted according to the ecological corridor values, and the resistance to species flow is evaluated by calculating the ecological resistance values ​​between different nodes, thereby generating an optimal ecological network. This method allows the network structure to be continuously optimized as the environment changes or new data is input, thereby improving the network adaptability and response speed.

[0135] An ecological vulnerability multi-scenario simulation model is established based on the ecological network, including:

[0136] Converting the ecological network into readable raster data, extracting the ecological resistance value and ecological corridor value of the ecological network node, combining the temporal and spatial evolution trend data of ecological vulnerability, and using them as input data for the ecological vulnerability multi-scenario simulation model;

[0137] In this embodiment, the ecological network is converted into raster data, and the ecological resistance value and ecological corridor value of the node are extracted, ensuring the consistency and accuracy of all input data. The rasterization processing makes the data more suitable for spatial analysis and simulation, facilitating subsequent calculations.

[0138] Based on the ecological network, the stability of ecological resistance and ecological corridor values ​​in the multi-scenario simulation model of ecological vulnerability was evaluated, and the distribution and changes of ecological vulnerability under different scenarios were calculated by adjusting the ecological vulnerability influencing factors.

[0139] In this example, by evaluating the stability of ecological resistance values ​​and ecological corridor values ​​under different scenarios, we can better understand the actual functions and potential weaknesses of the ecological network and help identify which areas or pathways are critical to the connectivity and stability of the ecosystem.

[0140] Run the multi-scenario simulation model of ecological vulnerability under different set scenarios to generate future ecological vulnerability distribution maps, obtain the ecological vulnerability index under each scenario and the ecological risk level of different regions.

[0141] In this example, by adjusting the ecological vulnerability influencing factors according to different scenarios, such as land use change and climate change, a variety of possible future development paths can be simulated. This flexibility enables the model to adapt to different assumptions and improve the comprehensiveness and accuracy of the prediction.

[0142] By dividing the ecological space into zones for protection, the target area is then overlaid with the ecological vulnerability assessment results and land use types for analysis to obtain specific restoration strategies, including:

[0143] Develop zoning standards for target areas based on the ecological vulnerability index under each scenario and the ecological risk level of different regions, combined with land use types;

[0144] In this embodiment, based on the ecological vulnerability index under each scenario and the ecological risk level of different regions, combined with land use types, a scientific and reasonable zoning standard can be formulated, which can accurately identify key areas that require priority protection or emergency restoration, ensure the effective allocation of resources, and carry out refined management of target areas based on the zoning standards. Different protection measures are adopted for different zones, such as strictly protected areas, sustainable management areas, and ecological restoration areas, thereby improving the pertinence and effectiveness of protection measures.

[0145] Use GIS software to perform spatial overlay analysis on the ecological vulnerability assessment results and land use type maps to identify the distribution of ecological vulnerability under different land types;

[0146] In this embodiment, GIS software is used to perform spatial overlay analysis on the ecological vulnerability assessment results and the land use type map, which can comprehensively display the distribution of ecological vulnerability under different land types. This not only helps to reveal the spatial distribution patterns of ecological and environmental problems, but also can intuitively display the overlay results of ecological vulnerability and land use types, making it easier for decision makers to quickly understand complex data information.

[0147] Based on the distribution of ecological vulnerability, restoration strategies will be formulated, long-term monitoring points will be established, ecological and environmental parameters will be collected regularly, and restoration progress will be tracked.

[0148] In this embodiment, specific restoration strategies are formulated based on the distribution of ecological vulnerability. For example, ecological restoration projects are implemented in high-vulnerability areas, sustainable management measures are promoted in medium-vulnerability areas, and daily protection is strengthened in low-vulnerability areas. This ensures the targetedness and effectiveness of restoration measures. Moreover, the restoration strategy is not only based on the results of ecological vulnerability assessment, but also combines factors such as land use type, making the strategy more comprehensive and scientific.

[0149] The present invention obtains a comprehensive ecological and environmental status by integrating multi-source data, and uses PCA to perform dimensionality reduction processing on training data in matrix form, thereby reducing redundant information, improving data analysis efficiency, and enhancing the explanatory power of the assessment. It integrates multi-dimensional data such as landscape pattern index and ecological sensitivity factor to provide a more comprehensive ecological and environmental assessment. By generating an ecological vulnerability cluster map, the spatial autocorrelation results of different periods are compared and analyzed, the spatiotemporal evolution characteristics of ecological vulnerability are tracked, potential risks are discovered and responded to in a timely manner, and the ecological vulnerability influencing factors are adjusted based on different scenarios. Possible ecological vulnerability risks in the future are simulated, and specific restoration strategies are formulated according to different zoning standards to ensure the effective allocation and utilization efficiency of resources. Ecological and environmental parameters are regularly collected, protection and restoration strategies are dynamically adjusted, restoration progress is tracked, and the effectiveness of restoration measures is ensured.

[0150] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. An ecological vulnerability assessment method based on road landscape pattern optimization, characterized by: The following steps are involved: Obtaining highway environment data as training data, dividing the training data into grids based on longitude and latitude, and resampling the grid data of different scales to obtain ecological environment data; Calculating an ecological environment quality index for the ecological environment data, setting multiple landscape pattern indices based on the ecological environment quality index, and obtaining landscape structure characteristics within the target area; The landscape pattern index is integrated into the ecological sensitivity factor, and the ecological vulnerability assessment model is constructed using the SRP structure to obtain the ecological vulnerability assessment index; Based on the ecological vulnerability evaluation indicators, a spatial autocorrelation analysis was conducted on the spatiotemporal evolution of road-area ecological vulnerability, and an ecological vulnerability multi-scenario simulation model was established. The target area was zoned for protection by dividing the ecological space. The ecological vulnerability evaluation results and land use types were superimposed and analyzed to obtain specific restoration strategies.

2. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 1 is characterized in that: Obtain highway environment data as training data, divide the training data into grids based on longitude and latitude, resample the grid data of different scales, and obtain ecological environment data, including: The highway environment data is obtained as training data through satellite remote sensing images, geographic information system (GIS) database, field survey records and public ecological environment data platform. Using GIS software to obtain the latitude and longitude information of the training data, and dividing the training data of the target area into grids according to the latitude and longitude information to form a series of mutually adjacent rectangular grid units; The training data is processed by adopting a bilinear interpolation method, the training data in all grid units are unified into a data format, and the training data is represented in a matrix form with the grid as a unit.

3. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 2 is characterized in that: Performing principal component analysis on the training data in matrix form to obtain ecological environment data includes: Establishing an initial variable matrix X for the training data in matrix form, and calculating a correlation coefficient matrix of the initial variable matrix X; The eigenvalues ​​and eigenvectors of the correlation coefficient matrix are calculated to obtain an eigenvector matrix, and a linear transformation is performed on the eigenvector matrix to obtain ecological environment data.

4. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 3 is characterized in that: Calculating the ecological environment quality index based on the ecological environment data includes: Assign a response weight to each indicator based on the weights of vegetation coverage, soil quality, water resources and biodiversity in the ecological and environmental data; Calculate the environmental quality index Q for each grid cell xj , whose expression is: Among them, Q xj represents the environmental index of grid x in land type j, H j represents the ecological suitability of land type j, D xj represents the disturbance level of grid x in land type j, K represents the normalization constant, R represents the number of stress factors, r represents any stress factor in the number of stress factors R, and Y r is the total number of units of the stress factor, y represents the stress factor Y r Any unit in ω r represents the weight, r y represents the number of stress factors on the grid cell, σ rxy Indicates the degree of influence of stress factors, β x represents the accessibility level of grid x, γ jr is the sensitivity of land type j to stress factors; Comprehensive analysis of the environmental quality index Q of each grid cell xj , based on its performance in various ecological environment indicators, the environmental quality index Q of each grid cell is xj Scoring of individual items and comprehensive calculation of ecological environment quality index Q eqi , whose expression is: in, represents the weight of the i-th indicator, ε i represents the standardized score of the i-th indicator, m represents the number of land type classifications, and n represents the total number of ecological environment indicators.

5. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 3 is characterized in that: According to the ecological environment quality index, multiple landscape pattern indices are set to obtain the landscape structure characteristics in the target area, including: Through the ecological environment quality index Q eqi Dividing the target area into different landscape types, performing rasterization processing on the landscape types in different areas, and generating a landscape type map; The spatial analysis module in ArcGIS was used to calculate the pattern index of each landscape type in the landscape type map, and the Shannon diversity index was used to measure the proportion and uniformity of different landscape types; The degree of landscape segmentation is assessed by the landscape fragmentation index, the landscape types within the grid are aggregated, and the aggregation index is used to reflect the degree of aggregation between similar landscape patches, thereby obtaining the landscape pattern index value within the target area. The spatial distribution characteristics of the landscape pattern in the target area are analyzed according to the values ​​of the landscape pattern indexes, and the landscape pattern indexes of different landscape types are compared according to the time series to obtain the landscape pattern characteristics.

6. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 5 is characterized in that: The landscape pattern index is integrated into the ecological sensitivity factor, and the ecological vulnerability assessment model is constructed using the SRP structure to obtain ecological vulnerability assessment indicators, including: Divide the target area into ecological functional zones according to the landscape type, and divide the ecological functional zones into three types: high-quality ecological functional zones, medium-quality ecological functional zones, and poor ecological functional zones; Generate an ecological vulnerability cluster map of the target area using ArcGIS software based on the landscape pattern index; Integrating ecological sensitivity factors into the ecological vulnerability cluster diagram, performing cross-detection analysis on the ecological sensitivity factors in pairs, and obtaining the impact weights of the interactive effects of the ecological sensitivity factors on ecological vulnerability; According to the impact weight of the ecological vulnerability, an ecological vulnerability assessment model based on the SRP structure is constructed to obtain ecological vulnerability assessment indicators.

7. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 6 is characterized in that: Based on the ecological vulnerability evaluation indicators, a spatial autocorrelation analysis of the spatiotemporal evolution of road-area ecological vulnerability is conducted, including: Calculating the ecological vulnerability value of each grid cell using the ecological environment quality index based on the ecological vulnerability cluster map at different time points; Performing a global spatial autocorrelation analysis on the ecological vulnerability values, and determining whether similar ecological vulnerabilities tend to cluster together in space on the ecological vulnerability cluster map; Based on the local spatial correlation LISA, local areas with significant spatial clustering characteristics are identified and marked on the ecological vulnerability cluster map. Compare the spatial autocorrelation results of different periods, analyze the spatiotemporal evolution characteristics of ecological vulnerability, and obtain real-time spatiotemporal evolution trends of ecological vulnerability.

8. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 7 is characterized in that: Based on the spatiotemporal evolution trends, an ecological network is constructed to identify ecological vulnerability scenarios, including: Identifying key ecological vulnerability areas on the ecological vulnerability cluster map according to the spatiotemporal evolution trend, and using the key ecological vulnerability areas as ecological network nodes; The ecological resistance analysis is performed on the ecological network nodes to calculate the ecological resistance coefficient at the ecological network nodes. The expression is: Among them, ρ i Nμ represents the ecological resistance coefficient of grid i after highway sensitivity correction. i represents the highway sensitivity index of grid i, Nθ j represents the average sensitization coefficient of land type j corresponding to grid i, and ρ represents the resistance coefficient of land type corresponding to grid i; The ecological resistance coefficient is used to comprehensively analyze the landscape pattern index and extract the ecological corridor, which is expressed as follows: Among them, L MCR The ecological corridor value representing the minimum cumulative resistance, D tj represents the spatial distance of species on land type j from the source to landscape unit t, m represents the number of landscape units, n represents the number of land types, R t represents the ecological resistance coefficient of landscape unit t; The ecological network nodes are dynamically adjusted according to the ecological corridor values, and the ecological resistance values ​​between different ecological network nodes are calculated to evaluate the species flow resistance between nodes, generate an ecological network, and identify ecological vulnerability scenarios.

9. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 8 is characterized in that: An ecological vulnerability multi-scenario simulation model is established based on the ecological network, including: Converting the ecological network into readable raster data, extracting the ecological resistance value and ecological corridor value of the ecological network node, combining the temporal and spatial evolution trend data of ecological vulnerability, and using them as input data for the ecological vulnerability multi-scenario simulation model; Based on the ecological network, the stability of ecological resistance and ecological corridor values ​​in the multi-scenario simulation model of ecological vulnerability was evaluated, and the distribution and changes of ecological vulnerability under different scenarios were calculated by adjusting the ecological vulnerability influencing factors. Run the multi-scenario simulation model of ecological vulnerability under different set scenarios to generate future ecological vulnerability distribution maps, obtain the ecological vulnerability index under each scenario and the ecological risk level of different regions.

10. The ecological vulnerability assessment method based on road landscape pattern optimization according to claim 7 is characterized in that: By dividing the ecological space into zones for protection, the target area is then overlaid with the ecological vulnerability assessment results and land use types for analysis to obtain specific restoration strategies, including: Develop zoning standards for target areas based on the ecological vulnerability index under each scenario and the ecological risk level of different regions, combined with land use types; Use GIS software to perform spatial overlay analysis on the ecological vulnerability assessment results and land use type maps to identify the distribution of ecological vulnerability under different land types; Based on the distribution of ecological vulnerability, restoration strategies will be formulated, long-term monitoring points will be established, ecological and environmental parameters will be collected regularly, and restoration progress will be tracked.

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