A method and system for constructing an ecological security pattern for karst areas

Abstract

The application discloses a kind of karst region-oriented ecological security pattern construction method and system, it is related to ecological security pattern construction technical field.Acquire the basic geographic data of target region, remote sensing data and underground river network data, process to obtain grid data set, and then obtain ecological source place distribution layer, according to underground river network data, analysis obtains underground river correction factor layer, based on grid data set, obtain surface ecological resistance surface, and with underground river correction factor layer is fused, generates coupling resistance surface layer, based on coupling resistance surface layer, combined with ecological source place distribution layer extraction obtains ecological corridor distribution layer, according to ecological corridor distribution layer, identify ecological node and ecological barrier point, analysis obtains ecological node distribution layer and ecological barrier point distribution layer, and then superimposed form karst region-oriented ecological security pattern, improve the integrity and accuracy of ecological security pattern construction.

Description

Technical Field

[0001] This invention relates to the field of ecological security pattern construction technology, specifically to a method and system for constructing ecological security patterns in karst regions. Background Technology

[0002] Karst regions, as one of the world's most important ecosystems, possess unique geographical and ecological characteristics. Particularly in southwestern China, karst landforms are complex, and underground karst systems are widely distributed. Unlike typical terrestrial ecosystems, constructing an ecological security pattern in karst regions requires consideration not only of the surface ecological environment but also the complexity of the underground ecosystem. In this context, the construction of an ecological security pattern must analyze not only surface ecological sources, ecological resistance surfaces, and ecological corridors but also fully consider the impact of underground river systems on ecological flows. This is especially true in areas with underground rivers, where their role and influence on ecological flows cannot be ignored.

[0003] Existing methods for constructing ecological security patterns typically assume that ecological flow migration occurs only at the surface. This approach neglects the impact of underground river systems on ecological flow, particularly in karst regions where underground rivers, as important ecological habitats and species migration corridors, are often excluded. In this method, ecological resistance surfaces are constructed entirely based on surface information, and the resulting ecological corridors are limited to surface paths, failing to accurately reflect the influence of underground rivers. Therefore, existing methods for constructing ecological security patterns cannot truly and effectively reflect the full picture of ecological flows in karst regions. This leads to a bias in the selection of ecological corridors towards surface paths, ignoring underground rivers as potential migration channels, thus affecting the accuracy and effectiveness of ecological security patterns. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for constructing an ecological security pattern for karst regions, which can effectively solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solution: a method for constructing an ecological security pattern for karst regions, comprising acquiring basic geographic data, remote sensing data and underground river network data of the target area, processing them to obtain a raster dataset of the target area, and then obtaining an ecological source distribution layer of the target area.

[0006] Based on the underground river network data of the target area, the underground river correction factor layer of the target area is obtained. Based on the raster dataset, the surface ecological resistance surface of the target area is obtained and fused with the underground river correction factor layer to generate the coupled resistance surface layer of the target area.

[0007] Based on the coupled resistance surface layer, the ecological corridor distribution layer of the target area is extracted by combining the ecological source area distribution layer. Based on the ecological corridor distribution layer, the ecological nodes and ecological obstacle points of the target area are identified, and the ecological node distribution layer and ecological obstacle point distribution layer of the target area are obtained by analysis.

[0008] Based on the ecological source area distribution layer and the coupling resistance surface layer of the target area, the ecological security zoning layer of the target area is obtained, and then superimposed to form an ecological security pattern facing the karst region.

[0009] Furthermore, the method for obtaining the ecological source area distribution layer through the analysis is as follows: Landscape connectivity analysis and ecological sensitivity assessment analysis were performed on the raster dataset of the target area to obtain the ecological source areas of the target area.

[0010] Based on the ecological source areas of the target area, an ecological source area distribution layer of the target area is obtained.

[0011] Furthermore, the method for obtaining the underground river correction factor layer for the target area through the analysis is as follows: Based on the underground river network data of the target area, the underground river connectivity index of each underground river section in the target area is obtained through comprehensive analysis.

[0012] Based on the underground river connectivity index of each underground river section in the target area, an underground river correction factor is generated for each grid cell in the target area.

[0013] Based on the underground river correction factors of each grid in the target area, an underground river correction factor layer is generated.

[0014] Furthermore, the method for generating the coupling resistance surface layer of the target region is as follows: Based on the raster dataset, nighttime light data and desertification data of the target area are extracted and corrected, and the surface ecological resistance value of each raster in the target area is obtained through analysis.

[0015] Based on the surface ecological resistance values ​​of each grid cell in the target area, the surface ecological resistance surface of the target area is obtained.

[0016] The surface ecological resistance surface of the target area is fused with the underground river correction factor layer. The coupling resistance value of each grid in the target area is obtained by using the minimum value fusion rule analysis, resulting in a binary coupling resistance surface layer of the target area.

[0017] Based on the underground river network data, the subterranean river sections of the target area are identified. Virtual connecting corridors are set between the start and end points of the subterranean river sections. The coupling resistance value of the area covered by the virtual connecting corridor is set to the preset minimum resistance value. The binary coupling resistance surface layer is then updated to obtain the coupling resistance surface layer of the target area.

[0018] Furthermore, the method for extracting the ecological corridor distribution layer of the target area by combining the ecological source area distribution layer is as follows: Based on the coupled resistance surface layer, taking the ecological source area distribution layer as the source point, the first ecological corridor set of the target area is extracted using the minimum cost path method, and the second ecological corridor set of the target area is extracted using the maximum current density method of circuit theory.

[0019] The first and second ecological corridor sets of the target area are overlaid and merged to obtain the ecological corridor distribution layer of the target area.

[0020] Furthermore, the method for obtaining the ecological node distribution layer and ecological barrier point distribution layer of the target area through the analysis is as follows: Based on the ecological corridor distribution layer and the ecological source area distribution layer, ecological nodes and ecological obstacle points in the target area are identified.

[0021] Based on the ecological nodes and ecological obstacle points in the target area, the distribution layers of ecological nodes and ecological obstacle points in the target area are obtained through analysis.

[0022] Furthermore, the method for obtaining the ecological security zoning layer of the target area through the analysis is as follows: Based on the raster dataset, the probability of farmland expansion and the probability of construction land expansion of each raster in the target area are analyzed, and then the comprehensive expansion probability of each raster in the target area is obtained through comprehensive analysis.

[0023] Based on the comprehensive expansion probability of each grid in the target area, the ecological sources in the ecological source distribution layer are classified, and the ecological corridors in the ecological corridor distribution layer are classified, thus obtaining the classified ecological network of the target area.

[0024] Based on the ecological source distribution layer and the coupling resistance surface layer of the target area, the minimum cumulative resistance value of ecological expansion of each grid in the target area is obtained.

[0025] Based on the minimum cumulative resistance value of ecological expansion of each grid in the target area, the ecological security zoning layer of the target area is obtained.

[0026] Furthermore, the method for superimposing to form an ecological security pattern for karst regions is as follows: An ecological security pattern for karst regions is formed by overlaying ecological node distribution layers, ecological obstacle point distribution layers, ecological network and ecological security zoning layers in the target area.

[0027] A second aspect of this invention provides a system for constructing an ecological security pattern for karst regions, comprising: The data analysis module is used to acquire basic geographic data, remote sensing data, and underground river network data of the target area, process them to obtain a raster dataset of the target area, and then obtain an ecological source distribution layer of the target area.

[0028] The layer fusion module is used to analyze the underground river network data of the target area to obtain the underground river correction factor layer of the target area, obtain the surface ecological resistance surface of the target area based on the raster dataset, and fuse it with the underground river correction factor layer to generate the coupled resistance surface layer of the target area.

[0029] The ecological corridor extraction module is used to extract the ecological corridor distribution layer of the target area based on the coupled resistance surface layer and the ecological source area distribution layer. Based on the ecological corridor distribution layer, the module identifies the ecological nodes and ecological obstacle points in the target area and analyzes them to obtain the ecological node distribution layer and ecological obstacle point distribution layer of the target area.

[0030] The ecological security pattern analysis module is used to analyze the ecological security zoning layer of the target area based on the ecological source area distribution layer and the coupling resistance surface layer of the target area, and then overlay them to form an ecological security pattern for karst regions.

[0031] The present invention has the following beneficial effects: This invention comprehensively utilizes basic geographic data, remote sensing data, and underground river network data, combined with spatial analysis techniques, to accurately identify and analyze key elements such as ecological source areas, ecological corridors, and ecological barrier points. By introducing the fusion of an underground river correction factor layer and a coupled resistance surface layer, this invention considers the unique impact of the underground river system on ecological flow migration, improving the accuracy and comprehensiveness of ecological security pattern construction. It can better reflect the complex interaction between groundwater and surface ecology in karst regions, thus providing more scientific and accurate data support for ecological protection and restoration.

[0032] This invention uses comprehensive extended probability analysis combined with deep learning technology to predict the expansion probability of arable land and construction land, effectively quantifying the potential for land use change. It can identify high-risk areas and prioritize their protection, breaking through the limitations of traditional single-factor analysis. It can dynamically assess the possibility of land transformation and provide more reliable support for forward-looking planning of ecological security patterns, especially suitable for ecosystem planning in complex terrains such as karst areas.

[0033] This invention proposes an analysis method for the minimum resistance value of ecological expansion by constructing an ecological security zoning layer, based on ecological source areas and coupled resistance surfaces, and combined with the minimum cumulative resistance method. This method not only improves the accuracy of ecological security zoning but also enhances the ability to predict future ecological flow paths, making the planning of ecological protection, restoration, and development measures more scientific and rational. Through the optimized ecological security zoning layer, the core ecological protection area, buffer zone, and restoration area can be clearly delineated, thereby effectively improving the sustainability and recovery capacity of the ecosystem and providing a solution for ecological security management in karst areas. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0035] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figure 1 As shown, the first aspect of the present invention provides a technical solution: a method for constructing an ecological security pattern for karst regions, including acquiring basic geographic data, remote sensing data and underground river network data of the target area, processing them to obtain a raster dataset of the target area, and then obtaining an ecological source distribution layer of the target area.

[0038] The basic geographic data of the target area includes, but is not limited to, topographic data, geomorphological data, soil type, vegetation type, and land use type; the remote sensing data of the target area includes, but is not limited to, land cover, vegetation cover, average temperature, average humidity, and vegetation index; the underground river network data of the target area includes the annual runoff, average width, and traversable distance of underground rivers in various sections of the target area. Basic geographic data of the target area is acquired through the Differential Global Positioning System (GPS), remote sensing data is acquired through satellite sensors, and underground river network data is acquired through dynamic automated monitoring equipment. Real-time acquisition from different data sources provides comprehensive and accurate geospatial information support, laying the foundation for subsequent ecological security pattern analysis.

[0039] The method for processing the raster dataset of the target region is as follows: Spatial registration, resampling, and rasterization are performed on the basic geographic data, remote sensing data, and underground river network data of the target area to obtain a raster dataset of the target area. Spatial registration unifies the coordinate systems of the various data sources, ensuring accurate alignment of data from different sources. Resampling ensures consistent spatial resolution across all data. Rasterization is then applied to all data to adapt it for further spatial analysis, resulting in a raster dataset of the target area. This raster dataset includes the basic geographic data, remote sensing data, and underground river network data corresponding to each raster in the target area. The spatial registration, resampling, and rasterization process ensures spatial consistency between different data sources, guaranteeing the accuracy of subsequent analysis.

[0040] Specifically, the method for analyzing and obtaining the distribution layer of ecological source areas is as follows: Landscape connectivity analysis and ecological sensitivity assessment were performed on the raster dataset of the target area to identify the ecological source areas. Ecological source areas are regions with important functions in the ecosystem, providing habitats, breeding grounds, or food sources for organisms. Ecological source areas are the core areas of the ecosystem, contributing to ecological restoration and the maintenance of ecosystem functions.

[0041] Landscape connectivity analysis and ecological sensitivity assessment were performed on raster datasets of the target area using software such as ArcGIS, Fragstats, and Circuitscape to identify the ecological source areas of the target area. These software programs can effectively process raster data through spatial analysis tools, perform connectivity analysis, and identify ecological source areas, providing basic data support for the subsequent construction of ecological security patterns.

[0042] Based on the ecological source areas of the target region, an ecological source area distribution layer is obtained. This layer characterizes the distribution of ecological source areas within the target region. The ecological source area distribution layer provides a precise spatial reference for subsequent construction of the ecological security pattern, ensuring that core areas can be focused when formulating ecological protection measures.

[0043] Based on the underground river network data of the target area, the underground river correction factor layer of the target area is obtained. Based on the raster dataset, the surface ecological resistance surface of the target area is obtained and fused with the underground river correction factor layer to generate the coupled resistance surface layer of the target area.

[0044] Specifically, the method for analyzing and obtaining the underground river correction factor layer for the target area is as follows: Based on the underground river network data of the target area, the underground river connectivity index of each underground river section in the target area is obtained through comprehensive analysis. The underground river connectivity index is used to quantify the underground river's ability to support ecological flow.

[0045] In this embodiment, the underground river connectivity index of each underground river section in the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the underground river connectivity index of the i-th underground river segment in the target area. This represents the annual runoff of the i-th underground river segment in the target area. This indicates the maximum annual runoff of the underground river in the target area. This represents the weighting coefficient corresponding to the preset annual runoff of the underground river. This represents the average width of the i-th underground river segment in the target area. This represents the maximum average width of the underground river in the target area. This represents the weighting coefficient corresponding to the preset maximum average width of the underground river. This represents the traversable distance of the i-th underground river segment within the target area. This indicates the maximum traversable distance of the underground river within the target area. This represents the weighting coefficient corresponding to the preset passable distance of the underground river, i=1, 2, 3, ..., m, where i represents the number of the underground river segment in the target area, and m represents the total number of underground river segments in the target area.

[0046] It should be added that, in this embodiment, the weight coefficients corresponding to the annual runoff of the underground river, the maximum average width of the underground river, and the traversable distance of the underground river are obtained from the security pattern construction database.

[0047] It should be explained that the weighting coefficients corresponding to the annual runoff of underground rivers, the maximum average width of underground rivers, and the traversable distance of underground rivers are used to adjust the importance of the annual runoff of underground rivers, the average width of underground rivers, and the traversable distance of underground rivers in various underground river sections of the target area in the process of analyzing and obtaining the underground river connectivity index. In the security pattern construction database, there is a pre-set mapping relationship between the annual runoff of underground rivers, the average width of underground rivers, and the traversable distance of underground rivers and their corresponding weighting coefficients. By matching the underground river network data of the target area with the pre-set mapping relationship, the weighting coefficients corresponding to the annual runoff of underground rivers, the maximum average width of underground rivers, and the traversable distance of underground rivers can be obtained.

[0048] In this embodiment, the annual runoff, average width, and traversable distance of underground rivers in different sections of the target area are correlated and do not exist independently. For example, the annual runoff of underground rivers directly affects the sustainability of water flow and the stability of ecological flow; the average width of underground rivers affects the distribution of flow velocity and flow rate; and the traversable distance characterizes the connectivity of the underground river network and affects the water flow's ability to support ecological flow. A comprehensive analysis yields the underground river connectivity index, which can more comprehensively quantify the ecological support capacity of underground rivers, help to more accurately assess the contribution of the underground river system to ecological flow, and make ecological flow and species migration more unimpeded within underground river sections, thus promoting the restoration of the overall ecosystem.

[0049] Based on the underground river connectivity index of each underground river section in the target area, an underground river correction factor is generated for each grid in the target area. The underground river correction factor is used to adjust the surface resistance value, indicating that the existence of underground rivers can reduce the equivalent resistance to ecological flow migration.

[0050] The method for generating the underground river correction factor for each grid cell in the target area is as follows: Land use types are extracted from each grid cell in the target area, and ecological resistance values ​​are obtained for each grid cell. These ecological resistance values ​​quantify the difficulty of ecological flows migrating within the target area. Specifically, ecological resistance values ​​reflect the degree of obstruction to ecological flows (such as plant and animal movement, gene flow, water flow, and species migration) under different land use types. High resistance values ​​indicate significant obstruction to ecological flows in the area, while low resistance values ​​indicate that the area is relatively easy to support the migration of ecological flows.

[0051] The method for matching the ecological resistance values ​​of each grid cell in the target area is as follows: The ecological resistance values ​​corresponding to each land use type stored in the database are matched with the land use type and the security pattern. The ecological resistance values ​​corresponding to each land use type are counted and recorded as the ecological resistance values ​​of each grid in the target area.

[0052] Based on the underground river connectivity index of each underground river section in the target area, and combined with the ecological resistance value of each grid in the target area, an underground river correction factor for each grid in the target area is generated.

[0053] In this embodiment, the underground river correction factor for each grid cell in the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the underground river correction factor for the u-th grid cell in the target area. This represents the minimum resistance value of the target area. This represents the underground river connectivity index corresponding to the i-th underground river segment of the u-th grid in the target area. This represents the ecological resistance value of the u-th grid in the target area, where u = 1, 2, 3, ..., n, u represents the grid number in the target area, and n represents the total number of grids in the target area.

[0054] It should be added that if the u-th grid cell of the target area is located directly above the corresponding i-th underground river segment, then Take the corresponding underground river connectivity index, otherwise Setting it to 0 ensures that only areas directly affected by the underground river section are included in the calculation of the correction factor, thereby enhancing the spatial accuracy of the correction.

[0055] Based on the underground river correction factors of each grid in the target area, an underground river correction factor layer is generated. The underground river correction factor layer is used to characterize the influence of the underground river system and the corrected ecological resistance. The underground river correction factor layer can correct the original surface ecological resistance surface, reflect the unique ecological impact of the underground river system in the karst region, and ensure that the construction of the ecological security pattern can comprehensively and accurately consider the factors of the underground river system.

[0056] Specifically, the method for generating the coupling resistance surface layer of the target region is as follows: Based on the raster dataset, nighttime light data and desertification data of the target area are extracted and corrected through remote sensing survey and monitoring system. The surface ecological resistance value of each raster in the target area is obtained by analysis. The ecological resistance value is corrected by nighttime light data and desertification data so that the resistance surface is more in line with the actual ecological barrier characteristics of the karst area.

[0057] It should be noted that a remote sensing survey and monitoring system refers to a device used to collect relevant data such as nighttime light intensity and desertification level of a target area in real time through satellite remote sensing, drones or ground sensors, and transmit the collected data to a data processing unit.

[0058] Based on the raster dataset, the ecological resistance values ​​of each raster in the target area are extracted.

[0059] The nighttime light values ​​of each grid in the target area are extracted from the nighttime light data of the target area. The average nighttime light value of the target area is obtained by averaging the values. The ratio of the nighttime light value of each grid in the target area to the average nighttime light value is recorded as the nighttime light correction coefficient of each grid in the target area. The nighttime light correction coefficient is used to quantitatively characterize the degree of urbanization or the intensity of human disturbance in the target area and reflect the hindering effect of nighttime light on ecological migration.

[0060] Extract the desertification degree classification value of each grid in the target area from the desertification degree data of the target area, and take the average value to obtain the average desertification degree classification value of the target area. The ratio of the desertification degree classification value of each grid in the target area to the average desertification degree classification value is recorded as the desertification degree correction coefficient of each grid in the target area. The desertification degree correction coefficient is used to quantitatively characterize the degree of desertification of the land and reflect the obstructive effect of desertification area on ecological migration.

[0061] Based on the ecological resistance values, nighttime light correction coefficients, and rocky desertification correction coefficients of each grid in the target area, a comprehensive analysis is conducted to obtain the surface ecological resistance values ​​of each grid in the target area. The surface ecological resistance values ​​are used to quantitatively characterize the ecological resistance encountered by each grid in the target area during the ecological flow migration process, reflecting the degree of influence of the surface environment on ecological flow. This helps to more clearly identify key areas where ecological flow is obstructed, thus providing a basis for further optimization of ecological flow.

[0062] In this embodiment, the surface ecological resistance value of each grid cell in the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the surface ecological resistance value of the u-th grid cell in the target area. This represents the ecological resistance value of the u-th grid cell in the target area. This represents the nighttime light correction factor for the u-th grid cell in the target area. This represents the desertification correction coefficient for the u-th grid cell in the target area.

[0063] Based on the surface ecological resistance values ​​of each grid in the target area, the surface ecological resistance surface of the target area is obtained. The surface ecological resistance surface is used to quantitatively characterize the difficulty of ecological flow migration in different grids in the target area. By reflecting the obstructive effect of different surface conditions on ecological flow, it can provide accurate spatial analysis data for the subsequent construction of ecological security patterns.

[0064] By fusing the surface ecological resistance surface and the underground river correction factor layer of the target area, and using the minimum value fusion rule to analyze and obtain the coupling resistance value of each grid in the target area, a binary coupling resistance surface layer of the target area is obtained. The binary coupling resistance surface layer integrates the influence of the surface ecological resistance surface and the underground river correction factor layer, which can more accurately reflect the actual resistance of ecological flow migration in the target area and effectively improve the accuracy of ecological security assessment.

[0065] The method for obtaining the coupling resistance value of each grid cell in the target region using the minimum value fusion rule analysis is as follows: The surface ecological resistance value of each raster is extracted from the surface ecological resistance surface layer of the target area, and the underground river correction factor value of each raster is extracted from the underground river correction factor layer of the target area. For each raster, based on the surface ecological resistance value and the underground river correction factor, a minimum value fusion rule is applied. That is, for each raster, the minimum value in both the surface ecological resistance surface layer and the underground river correction factor layer is selected as the coupling resistance value of the corresponding raster. This minimum value fusion rule ensures that factors with less impact on ecological flow migration are given priority, further improving the accuracy of ecological security assessment.

[0066] Based on underground river network data, groundwater monitoring equipment is used to identify the subterranean river sections in the target area. Virtual connecting corridors are set between the start and end points of the subterranean river sections. The coupling resistance value of the area covered by the virtual connecting corridors is set to a preset minimum resistance value. The binary coupling resistance surface layer is updated to obtain the coupling resistance surface layer of the target area. The coupling resistance surface layer is used to characterize the combined impact of surface and underground ecological resistance. Through the coupling resistance surface layer, the actual resistance to ecological flow migration in the target area can be accurately assessed. Especially in the subterranean river sections, the migration of ecological flow will be smoother, thus providing data support for the construction of an ecological security pattern. The subterranean river section refers to the area where the underground river is temporarily at or near the surface.

[0067] Based on the coupled resistance surface layer, the ecological corridor distribution layer of the target area is extracted by combining the ecological source area distribution layer. Based on the ecological corridor distribution layer, the ecological nodes and ecological obstacle points of the target area are identified, and the ecological node distribution layer and ecological obstacle point distribution layer of the target area are obtained by analysis.

[0068] Specifically, the method for extracting the ecological corridor distribution layer of the target area by combining the ecological source area distribution layer is as follows: Based on the coupling resistance surface layer, and taking the ecological source area distribution layer as the source point, the first ecological corridor set of the target area is extracted using the minimum cost path method, and the second ecological corridor set of the target area is extracted using the maximum current density method of circuit theory. By combining the minimum cost path method and the maximum current density method of circuit theory, the spatial distribution of ecological corridors can be analyzed from different dimensions. It considers both the coupling resistance of each grid and the density distribution of current in low resistance paths, and can provide more comprehensive and accurate ecological corridor identification.

[0069] The method for extracting the first set of ecological corridors in the target area using the minimum cost path method is as follows: A grid network is established by selecting grid cells from the ecological source distribution layer of the target area as source points. The coupling resistance value of each grid cell represents the resistance of that cell. The shortest path from each source point to other grid cells is calculated using the minimum cost path method. The cost of the path is determined by the coupling resistance value of the grid cell. The path starts from the source point and extends along the minimum cost path (i.e., the minimum coupling resistance value) until it reaches the boundary of the target area. The output is the first set of ecological corridors for the target area, containing all paths from the source points to the boundary of the target area. By optimizing the paths from the source points to the target area, the optimal ecological corridor paths can be clearly identified, ensuring the lowest resistance during ecological flow and thus optimizing the selection of ecological flow channels.

[0070] The method for extracting the set of second ecological corridors in the target area using the maximum current density method of circuit theory is as follows: Using circuit theory models, each grid in the target area is considered a node in a circuit network, with the coupling resistance of each grid as its resistance, and the connections between grids as current flow paths. Based on the maximum current density method, the current paths from each source point to each grid in the target area are analyzed. In this method, current travels faster through low-resistance areas, forming current density paths, which are potential ecological corridors. Based on the current density distribution, a second set of ecological corridors is extracted. These corridors represent more efficient ecological flow channels, better connecting ecological source areas with other important ecological regions. The current density method not only identifies the shortest paths but also considers the flow efficiency of the paths, identifying channels that better meet the needs of ecological flow and ensuring effective connections between species and ecological flows.

[0071] The first and second ecological corridor sets of the target area are superimposed and merged to obtain the ecological corridor distribution layer of the target area. The ecological corridor distribution layer is used to characterize the spatial distribution of ecological corridors. Ecological corridors are channels connecting different ecological sources and species habitats, ensuring gene exchange and migration between different regions.

[0072] Specifically, the method for analyzing and obtaining the ecological node distribution layer and ecological barrier point distribution layer of the target area is as follows: Using spatial analysis software such as ArcGIS, ecological nodes and ecological barrier points in the target area are identified based on the ecological corridor distribution layer and the ecological source area distribution layer.

[0073] It should be noted that ecological nodes in the target area are key components of the ecological network, possessing high biodiversity and ecological value. Ecological nodes are crucial for the stability of the regional ecosystem and the habitat of species. Identifying ecological nodes helps to determine priority protection areas. Ecological barriers in the target area are obstacles to species migration and ecological flow. Ecological barriers may be caused by human activities or natural obstacles (such as urbanization, transportation infrastructure, etc.). Analyzing these barriers helps to take measures to reduce the impact on ecological flow.

[0074] Based on the ecological nodes and ecological obstacle points in the target area, the distribution layers of ecological nodes and ecological obstacle points in the target area are obtained through analysis.

[0075] It should be added that the ecological node distribution layer is used to characterize the distribution of ecological nodes, and the ecological obstacle point distribution layer is used to characterize the distribution of ecological obstacle points.

[0076] Based on the distribution layer of ecological source areas and the coupled resistance surface layer of the target area, an ecological security zoning layer of the target area is obtained through analysis. This layer is then superimposed to form an ecological security pattern for karst regions. This helps to accurately delineate ecological security zones based on the distribution of ecological source areas and actual ecological flow resistance, thereby providing an effective basis for ecological protection and land use decisions.

[0077] Specifically, the method for analyzing and obtaining the ecological security zoning layer of the target area is as follows: Based on a raster dataset, the probability of cultivated land expansion and the probability of construction land expansion for each raster in the target area are analyzed. Then, a comprehensive expansion probability for each raster in the target area is obtained through integrated analysis. This comprehensive expansion probability is used to quantify the potential for land use change in the target area, reflecting the likelihood and intensity of cultivated land and construction land expansion in different regions during future development. The introduction of the comprehensive expansion probability provides a quantitative standard for the spatial distribution changes of cultivated land and construction land, thus making the delineation of ecological security zones more aligned with the dynamic needs of future development and ecological protection.

[0078] The raster dataset of the target area, including the basic geographic data, remote sensing data and underground river network data corresponding to each raster in the target area, is input into the trained farmland and construction land expansion probability analysis model to obtain the farmland expansion probability and construction land expansion probability of each raster in the target area.

[0079] Data preprocessing is performed on the basic geographic data, remote sensing data, and underground river network data corresponding to each grid in the target area to ensure consistent spatial resolution across different data sources and to transform the data into input features recognizable by the model. The input features of each grid are mapped one-to-one with the specific geographical location of the target area, forming a multi-dimensional feature vector that serves as the model's input data. Basic geographic data provides fundamental spatial features influencing land expansion, such as topographic relief, soil quality, and land use type. Remote sensing data reveals dynamic changes in land cover and vegetation cover, playing a crucial predictive role in land expansion trends. Underground river network data provides important geographical context for understanding the supporting capacity of groundwater flow and the role of underground river systems. The probability of arable land expansion reflects the likelihood of land being converted into arable land at a specific grid location within the target area, influenced by factors such as geographical conditions, climate change, and policy adjustments. The probability of construction land expansion characterizes the likelihood of land in a specific grid being converted into construction land, taking into account factors such as urbanization and infrastructure development.

[0080] The specific training method for the probability analysis model of farmland expansion for construction land is as follows: A training dataset for analyzing the probability of farmland expansion into construction land is constructed. The training dataset includes basic geographic data, remote sensing data, and underground river network data corresponding to each grid in the target area. Combined with historical land use data, the probability of farmland expansion and the probability of construction land expansion in each grid are labeled. By monitoring historical land use changes, land change data and corresponding expansion categories (farmland or construction land) of the target area are collected for each time period to form the original training dataset.

[0081] During training, deep learning models, such as convolutional neural networks or multilayer perceptrons, are used to learn from the input data. The input of the model is a multidimensional feature vector containing basic geographic data, remote sensing data, and underground river network data corresponding to the target area grid. The output is the predicted value of the probability of farmland expansion and the probability of construction land expansion corresponding to the input data. The predicted value reflects the possibility of the corresponding grid land being converted into farmland and construction land.

[0082] The training dataset is divided into a training set and a validation set according to a preset ratio. The training set is used for supervised training of the probability analysis model for the expansion of cultivated land and construction land. The training process calculates the prediction results through forward propagation and measures the error of the prediction results by the cross-entropy loss function between the model and the true label, the expansion probability of cultivated land or construction land. The model parameters are iteratively updated using the backpropagation algorithm and an optimizer, such as the Adam optimizer, until the loss function is minimized. To prevent overfitting, the validation set is used for performance evaluation, and the model is adjusted based on the recognition accuracy and recall on the validation set.

[0083] After training, the trained model is evaluated using a validation set. Evaluation metrics include recognition accuracy, prediction accuracy of expansion probability, and land conversion prediction capability for each grid cell. When the model meets the preset evaluation criteria on the validation set, and the prediction errors of arable land and construction land expansion probabilities are controlled within the engineering allowable range, the arable land and construction land expansion probability analysis model is considered successfully trained, resulting in a well-trained arable land and construction land expansion probability analysis model. This model can accurately predict the arable land expansion probability and construction land expansion probability of the target area, providing fundamental data support for the subsequent construction of ecological security patterns.

[0084] In this embodiment, the overall expansion probability of each grid cell in the target area can be obtained through the following analysis method, with the specific analysis conditions as follows: ; In the formula, This represents the overall expansion probability of the u-th grid cell in the target region. This represents the probability of farmland expansion in the u-th grid cell of the target region. This represents the influencing factor corresponding to the preset probability of arable land expansion. Let represent the probability of land expansion for the u-th grid cell in the target area. This represents the influencing factor corresponding to the preset probability of expansion of construction land.

[0085] It should be added that, in this embodiment, the influencing factors corresponding to the preset probability of farmland expansion and the influencing factors corresponding to the probability of construction land expansion are obtained from the security pattern construction database.

[0086] It should be explained that the influencing factors corresponding to the probability of arable land expansion and the probability of construction land expansion are used to adjust the importance of the probabilities of arable land expansion and construction land expansion in the process of analyzing and obtaining the comprehensive expansion probability. In the security pattern construction database, there is a pre-defined mapping relationship between the probabilities of arable land expansion and construction land expansion and their corresponding influencing factors. By matching the probabilities of arable land expansion and construction land expansion with the pre-defined mapping relationship, the influencing factors corresponding to the probabilities of arable land expansion and construction land expansion can be obtained.

[0087] Based on the comprehensive expansion probability of each grid in the target area, the ecological sources in the ecological source distribution layer are classified, and the ecological corridors in the ecological corridor distribution layer are classified, thus obtaining the classified ecological network of the target area.

[0088] The method for obtaining the ecological network of the target area after classification is as follows: The average comprehensive expansion probability of all grid cells within each ecological source area in the ecological source area distribution layer is calculated. If this average is less than a preset first-level source area classification threshold, the ecological source area is marked as a Level 1 ecological source area; if the average is greater than or equal to the preset first-level threshold and less than a preset second-level threshold, it is marked as a Level 2 ecological source area; and if the average is greater than or equal to the preset second-level threshold, it is marked as a Level 3 ecological source area. By classifying different ecological source areas, important ecological source areas can be spatially highlighted, providing key areas for priority protection within the target region for ecological protection and restoration.

[0089] Based on the comprehensive expansion probability of each grid cell in the target area, each ecological corridor in the ecological corridor distribution layer is classified: For each ecological corridor, the comprehensive expansion probability of all grid cells traversed by the corridor is extracted, and a weighted average is calculated using the current density value corresponding to the corridor as the weight. When the weighted average is less than a preset first corridor classification threshold, the ecological corridor is marked as a first-level ecological corridor; when the weighted average is greater than or equal to the preset first corridor classification threshold and less than a preset second corridor classification threshold, the ecological corridor is marked as a second-level ecological corridor; when the weighted average is greater than or equal to the preset second corridor classification threshold, the ecological corridor is marked as a third-level ecological corridor. The classified ecological source areas and classified ecological corridors are merged to obtain a classified ecological network. This ensures that the functions of ecological corridors are effectively distinguished, and important ecological corridors can receive higher protection priority, further guaranteeing the migration of species and the connection of ecological flows.

[0090] Based on the ecological source distribution layer and the coupling resistance surface layer of the target area, the minimum cumulative resistance value of ecological expansion of each grid in the target area is obtained.

[0091] The method for obtaining the minimum cumulative resistance value for ecological expansion of each grid in the target area is as follows: Using each ecological source area in the target region's ecological source area distribution layer as an expansion source point, and the coupling resistance value of each grid in the target region's coupling resistance surface layer as the unit cost of that grid, the cumulative resistance value from each grid in the target region to the nearest ecological source area is calculated. This cumulative resistance value is the sum of the coupling resistance values ​​of all grids along a path from that grid to the ecological source area. The value with the smallest sum among all possible paths is selected as the minimum cumulative resistance value for ecological expansion of that grid. When a grid cannot be connected to any ecological source area through any path, the minimum cumulative resistance value for ecological expansion of that grid is set to a preset infinity value. The minimum cumulative resistance value reflects the minimum resistance encountered by that grid during ecological expansion. This method can quantify the difficulty of ecological expansion for each grid in the target region, thereby identifying the most promising expansion paths of the ecosystem and providing quantitative guidance for ecological protection and restoration.

[0092] Based on the minimum cumulative resistance values ​​of ecological expansion for each grid cell in the target area, an ecological security zoning layer for the target area is obtained. By comprehensively analyzing the minimum cumulative resistance values ​​of the grid cells, the resistance encountered by each area during ecological expansion can be accurately reflected, thereby helping to delineate areas with different security levels and providing strong support for ecological protection, restoration, or development.

[0093] It should be added that the ecological security zoning layer is used to characterize the security level of ecological areas. Based on the ecological source area distribution layer and the coupled resistance surface layer, the ecological security zoning layer calculates the minimum cumulative resistance value of each grid cell using a comprehensive spread probability, thus dividing the area into different ecological security zones. This helps to identify areas suitable for ecological protection, restoration, or development.

[0094] The minimum cumulative resistance values ​​for ecological expansion of each grid cell in the target area are arranged in ascending order. Using the natural breakpoint method, all grid cells are divided into four safety levels: Level 1, Level 2, Level 3, and Level 4. Level 1 corresponds to the set of grid cells with the lowest minimum cumulative resistance values ​​for ecological expansion, and Level 4 corresponds to the set of grid cells with the highest minimum cumulative resistance values. Level 1 and Level 2 are merged and marked as the core ecological protection zone, Level 3 is marked as the ecological buffer zone, and Level 4 is marked as the ecological restoration zone. The partitioning results of all grid cells constitute the ecological safety zoning layer of the target area.

[0095] Specifically, the method for superimposing and forming an ecological security pattern for karst regions is as follows: Based on the ecological node distribution layer, ecological barrier point distribution layer, ecological network, and ecological security zoning layer of the target area, an ecological security pattern for karst regions is formed by overlaying these layers. By integrating various key ecological elements, a comprehensive ecological security assessment layer can be provided, further optimizing ecological protection strategies and planning for karst regions and enhancing the region's ecological restoration capacity.

[0096] It should be added that the ecological security pattern for karst regions is used to comprehensively characterize the ecological security status of the entire target area. The ecological security pattern layer can display the spatial relationships and overall pattern of elements such as ecological nodes, ecological corridors, ecological barriers, and ecological security zones in karst regions. It reflects the overall health status of the regional ecosystem and helps identify areas requiring key protection and restoration, thereby enhancing the ecological sustainability and recovery capacity of the corresponding regions.

[0097] A second aspect of this invention provides a system for constructing an ecological security pattern for karst regions, comprising: The data analysis module is used to acquire basic geographic data, remote sensing data, and underground river network data of the target area, process them to obtain a raster dataset of the target area, and then obtain an ecological source distribution layer of the target area.

[0098] The layer fusion module is used to analyze the underground river network data of the target area to obtain the underground river correction factor layer of the target area, obtain the surface ecological resistance surface of the target area based on the raster dataset, and fuse it with the underground river correction factor layer to generate the coupled resistance surface layer of the target area.

[0099] The ecological corridor extraction module is used to extract the ecological corridor distribution layer of the target area based on the coupled resistance surface layer and the ecological source area distribution layer. Based on the ecological corridor distribution layer, the module identifies the ecological nodes and ecological obstacle points in the target area and analyzes them to obtain the ecological node distribution layer and ecological obstacle point distribution layer of the target area.

[0100] The ecological security pattern analysis module is used to analyze the ecological security zoning layer of the target area based on the ecological source area distribution layer and the coupling resistance surface layer of the target area, and then overlay them to form an ecological security pattern for karst regions.

[0101] It should be noted that a method and system for constructing an ecological security pattern for karst regions also includes a security pattern construction database, which stores the weight coefficients corresponding to the annual runoff of underground rivers, the weight coefficients corresponding to the maximum average width of underground rivers, the weight coefficients corresponding to the navigable distance of underground rivers, the ecological resistance values ​​corresponding to each land use type, the influencing factors corresponding to the probability of cultivated land expansion, the influencing factors corresponding to the probability of construction land expansion, the first source area classification threshold, the second source area classification threshold, the first corridor classification threshold, and the second corridor classification threshold, all obtained through analysis of historical data.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0103] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for constructing an ecological security pattern in karst regions, characterized in that: include: Acquire basic geographic data, remote sensing data, and underground river network data of the target area, process them to obtain a raster dataset of the target area, and then obtain an ecological source distribution layer of the target area; Based on the underground river network data of the target area, the underground river correction factor layer of the target area is obtained. Based on the raster dataset, the surface ecological resistance surface of the target area is obtained and fused with the underground river correction factor layer to generate the coupled resistance surface layer of the target area. The method for obtaining the underground river correction factor layer for the target area in the analysis is as follows: Based on the underground river network data of the target area, the underground river connectivity index of each underground river section in the target area is obtained through comprehensive analysis; Based on the underground river connectivity index of each underground river section in the target area, generate the underground river correction factor for each grid in the target area. Based on the underground river correction factors of each grid in the target area, an underground river correction factor layer is generated; The method for generating the coupling resistance surface layer of the target region is as follows: Based on the raster dataset, nighttime light data and rocky desertification data of the target area are extracted and corrected, and the surface ecological resistance value of each raster in the target area is obtained through analysis. Based on the surface ecological resistance values ​​of each grid cell in the target area, the surface ecological resistance surface of the target area is obtained; The surface ecological resistance surface of the target area is fused with the underground river correction factor layer. The coupling resistance value of each grid in the target area is obtained by using the minimum value fusion rule analysis, resulting in a binary coupling resistance surface layer of the target area. Based on the underground river network data, the subterranean river section of the target area is identified. A virtual connecting corridor is set between the start and end points of the subterranean river section. The coupling resistance value of the area covered by the virtual connecting corridor is set to the preset minimum resistance value. The binary coupling resistance surface layer is then updated to obtain the coupling resistance surface layer of the target area. Based on the coupled resistance surface layer, the ecological corridor distribution layer of the target area is extracted by combining the ecological source area distribution layer. Based on the ecological corridor distribution layer, the ecological nodes and ecological barrier points of the target area are identified, and the ecological node distribution layer and ecological barrier point distribution layer of the target area are obtained by analysis. Based on the ecological source area distribution layer and the coupling resistance surface layer of the target area, the ecological security zoning layer of the target area is obtained, and then superimposed to form an ecological security pattern facing the karst region.

2. The method for constructing an ecological security pattern for karst regions according to claim 1, characterized in that: The method used to obtain the ecological source area distribution layer in the analysis is as follows: Landscape connectivity analysis and ecological sensitivity assessment analysis were performed on the raster dataset of the target area to obtain the ecological source areas of the target area; Based on the ecological source areas of the target area, an ecological source area distribution layer of the target area is obtained.

3. The method for constructing an ecological security pattern for karst regions according to claim 2, characterized in that: The method for extracting the ecological corridor distribution layer of the target area by combining the ecological source area distribution layer is as follows: Based on the coupled resistance surface layer, taking the ecological source area distribution layer as the source point, the first ecological corridor set of the target area is extracted using the minimum cost path method, and the second ecological corridor set of the target area is extracted using the maximum current density method of circuit theory. The first and second ecological corridor sets of the target area are overlaid and merged to obtain the ecological corridor distribution layer of the target area.

4. The method for constructing an ecological security pattern for karst regions according to claim 3, characterized in that: The method for obtaining the ecological node distribution layer and ecological barrier point distribution layer of the target area is as follows: Based on the ecological corridor distribution layer and the ecological source area distribution layer, ecological nodes and ecological barrier points in the target area are identified. Based on the ecological nodes and ecological obstacle points in the target area, the distribution layers of ecological nodes and ecological obstacle points in the target area are obtained through analysis.

5. The method for constructing an ecological security pattern for karst regions according to claim 1, characterized in that: The method for obtaining the ecological security zoning layer of the target area through the analysis is as follows: Based on the raster dataset, the probability of farmland expansion and the probability of construction land expansion of each raster in the target area are analyzed, and then the comprehensive expansion probability of each raster in the target area is obtained through comprehensive analysis. Based on the comprehensive expansion probability of each grid in the target area, the ecological sources in the ecological source distribution layer are classified, and the ecological corridors in the ecological corridor distribution layer are classified to obtain the classified ecological network of the target area. Based on the ecological source distribution layer and the coupled resistance surface layer of the target area, the minimum cumulative resistance value of ecological expansion for each grid in the target area is obtained. Based on the minimum cumulative resistance value of ecological expansion of each grid in the target area, the ecological security zoning layer of the target area is obtained.

6. The method for constructing an ecological security pattern for karst regions according to claim 1, characterized in that: The method for superimposing to form an ecological security pattern for karst regions is as follows: An ecological security pattern for karst regions is formed by overlaying ecological node distribution layers, ecological obstacle point distribution layers, ecological network and ecological security zoning layers in the target area.

7. A system for constructing an ecological security pattern for karst regions, used to implement the method for constructing an ecological security pattern for karst regions as described in any one of claims 1-6, characterized in that, include: The data analysis module is used to acquire basic geographic data, remote sensing data and underground river network data of the target area, process them to obtain a raster dataset of the target area, and then obtain an ecological source distribution layer of the target area. The layer fusion module is used to analyze the underground river network data of the target area to obtain the underground river correction factor layer of the target area, obtain the surface ecological resistance surface of the target area based on the raster dataset, and fuse it with the underground river correction factor layer to generate the coupled resistance surface layer of the target area. The ecological corridor extraction module is used to extract the ecological corridor distribution layer of the target area based on the coupled resistance surface layer and the ecological source area distribution layer. Based on the ecological corridor distribution layer, the module identifies the ecological nodes and ecological obstacle points in the target area and analyzes to obtain the ecological node distribution layer and ecological obstacle point distribution layer of the target area. The ecological security pattern analysis module is used to analyze the ecological security zoning layer of the target area based on the ecological source area distribution layer and the coupling resistance surface layer of the target area, and then overlay them to form an ecological security pattern for karst regions.

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