An integrated optimization method for coastal wetland nature reserves combining ecosystem integrity, authenticity, and high-quality development layout

Abstract

The present application relates to the technical field of ecological environment protection, and particularly relates to a coastal wetland nature reserve integration optimization method fusing ecological system integrity, authenticity and high-quality development layout, comprising the following steps: S1: acquiring and generating an ecological system integrity and authenticity spatial difference distribution map; S2: acquiring and generating a human activity intensity distribution map; S3: performing spatial superposition analysis to identify a protection and development conflict region; S4: performing spatial comparison to identify an ecological protection vacancy region; S5: calculating an integration optimization coefficient of each region respectively; and S6: dividing into different priority integration optimization types. The present application, by fusing the spatial adjacency relationship of ecological system integrity, authenticity and high-quality development layout, constructs differentiated integration optimization, realizes accurate identification and priority division of key regions in the coastal wetland ecological system, and improves the scientificity and operability of the nature reserve system optimization.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental protection technology, and in particular to a method for integrating and optimizing coastal wetland nature reserves that combines the integrity and authenticity of the ecosystem with a high-quality development layout. Background Technology

[0002] The integration and optimization of nature reserves is a core measure for practicing ecological civilization construction and ensuring regional ecological security. Existing technologies in this field are mostly concentrated in inland ecosystems, forming an assessment paradigm dominated by ecological protection gap analysis. This method mainly guides the spatial layout optimization of nature reserves by identifying areas with high ecological value that have not been included in the protection system. However, when this paradigm is directly applied to special areas such as coastal wetland ecosystems that have both marine and terrestrial attributes, its limitations become increasingly apparent: the interaction of marine and terrestrial ecological processes, unique species types and environmental conditions, and differences in response mechanisms to human activities make the value assessment standards and gap identification methods based solely on terrestrial ecological experience unsuitable, resulting in insufficient scientific rigor and relevance of the assessment results.

[0003] A deeper technical challenge lies in the fact that existing assessment systems generally treat ecological protection and regional development as two parallel and independent analytical threads, lacking an effective mechanism for quantitative coordination and conflict identification in spatial terms. This leads to ecological protection recommendations derived from gap analysis often spatially overlapping and conflicting with local development needs and potential areas, causing the final layout optimization plan to remain on paper due to the inability to reconcile contradictions and thus failing to be effectively implemented. Coastal wetland ecosystems, as the ecological transition zone between terrestrial and marine ecosystems, are also areas where the two-way impact of human activities on both land and sea intersects. Therefore, there is an urgent need in this field for an integrated optimization method that can deeply integrate the integrity, authenticity, and high-quality development layout of ecosystems, and provide clear and quantitative decision-making basis for resolving spatial conflicts among these three elements, in order to overcome the core deficiency of the disconnect between analysis and optimization in existing technical processes. Summary of the Invention

[0004] Based on the above objectives, this invention provides a method for integrating and optimizing coastal wetland nature reserves by combining ecosystem integrity, authenticity, and high-quality development layout.

[0005] A method for integrating and optimizing coastal wetland nature reserves, which combines ecosystem integrity, authenticity, and high-quality development layout, includes the following steps:

[0006] S1: Obtain the distribution data of ecosystem types and species of the target coastal wetland ecosystem. Based on the importance of ecosystem types and the survival dependence of species, calculate and generate a spatial difference distribution map of ecosystem integrity and authenticity of the target coastal wetland ecosystem.

[0007] S2: Obtain data on the intensity of economic and social activities and infrastructure coverage of the target coastal wetland ecosystem. Based on the current level of economic and social activity intensity and the support level of infrastructure coverage, calculate and generate a distribution map of human activity intensity of the target coastal wetland ecosystem.

[0008] S3: Spatial overlay analysis of the distribution map of spatial differences in ecosystem integrity and authenticity with the distribution map of human activity intensity is performed to identify spatial units in which both ecosystem integrity and authenticity and human activity intensity are at a high level, and these units are defined as areas of conflict between conservation and development.

[0009] S4: Spatial comparison of the spatial difference distribution map of ecosystem integrity and authenticity with the existing nature reserve boundary data is carried out to identify areas with high ecosystem integrity and authenticity levels but not covered by existing nature reserves, and define them as ecological protection gap areas.

[0010] S5: For areas where protection and development conflict and areas with ecological protection gaps, calculate the integration optimization coefficient for each area based on its spatial adjacency with existing nature reserves.

[0011] Optionally, S1 specifically includes:

[0012] S11: When acquiring ecosystem type distribution data, call the ecological monitoring database deployed in the target coastal wetland ecosystem, read the spatial distribution range of typical coastal wetland ecosystem types such as salt marshes, seagrass beds and mangroves in the region, and generate an ecosystem type dataset through a unified spatial grid format;

[0013] S12: When acquiring species distribution data, the regional biological monitoring records are called up, and the habitat location data of indicator species, flagship species and important associated species inhabiting the ecosystem types are integrated in chronological order. Through spatial matching, the species distribution data and ecosystem type data are aligned on the same grid to form a species distribution dataset. The dataset is then compared with the species list of existing nature reserves to identify whether there are species and their habitats with high conservation value but not listed in the protection list, and to determine the ecosystem integrity of the nature reserves.

[0014] S13: Construct basic sensitivity values ​​based on the degree of impact of human activities on ecosystems, assign corresponding sensitivity levels to ecosystem types in each grid unit according to their ecological function contribution, and form an ecosystem type sensitivity matrix;

[0015] S14: Construct species sensitivity values ​​based on species survival dependence, quantify the degree of dependence of each species in its corresponding ecosystem type according to deterministic levels, and map them to the corresponding grid cells to form a species sensitivity matrix;

[0016] S15: The ecosystem type sensitivity matrix and the species sensitivity matrix are superimposed and calculated according to a preset weighting method to generate the comprehensive ecological sensitivity value of each grid unit. The values ​​are then divided into different sensitivity intervals according to the numerical level to form a distribution map of the ecosystem integrity and authenticity of the target coastal wetland ecosystem.

[0017] Optionally, S15 specifically includes:

[0018] S151: According to the preset weighting method, the ecosystem type sensitivity matrix and the species sensitivity matrix are assigned weight coefficients respectively, so that the contribution ratio of ecosystem type sensitivity value and species sensitivity value in each grid cell is kept consistent, forming two types of sensitivity matrices after weighting.

[0019] S152: Perform an overlay calculation for each grid cell, summing the weighted ecosystem type sensitivity value and the weighted species sensitivity value in a linear combination to obtain the comprehensive ecological sensitivity value for the corresponding grid cell. ;

[0020] S153: Sort the overall sensitivity values ​​of all grid cells numerically, and divide the overall sensitivity values ​​into high sensitivity zone, medium sensitivity zone and low sensitivity zone according to the preset sensitivity level threshold.

[0021] S154: Map the divided sensitivity level results to the corresponding grid cells to generate an ecological sensitivity level layer composed of different level labels, and export it as an ecosystem integrity and authenticity distribution map of the target coastal wetland ecosystem under a unified spatial benchmark.

[0022] Optionally, S2 specifically includes:

[0023] S21: When acquiring human activity intensity data, call the remote sensing interpretation results of the past three years and the regional land use dynamic monitoring data to extract the spatial distribution of industrial facilities, residential settlements, transportation corridors and aquaculture land. Construct a human activity intensity index layer based on construction density and activity frequency, and convert it into a human activity intensity dataset in a unified grid format.

[0024] S22: When acquiring infrastructure coverage data, call the latest vector layer in the spatial infrastructure database, which includes roads, water conservancy, power and communication facilities, to calculate the coverage rate and type completeness of infrastructure in each grid cell, and form an infrastructure coverage dataset;

[0025] S23: Based on the current level of human activity intensity, the intensity values ​​of each grid unit are normalized according to the set index standard to generate human activity impact factors; at the same time, based on the support level of infrastructure coverage, the infrastructure density and service integrity of each grid unit are weighted and scored to generate infrastructure support factors.

[0026] S24: Combine the human activity impact factor and infrastructure support factor of each grid cell according to the weighting coefficients to generate the development potential index of that grid cell. ;

[0027] S25: Based on the development potential value of each grid cell, the development potential index is... The area was designated as a high-potential zone, and its development potential index was adjusted accordingly. The area is divided into medium-potential zones, and the development potential index is used to classify them. The area is divided into low-potential zones, and a development potential distribution map covering the entire target coastal wetland ecosystem is ultimately generated.

[0028] Optionally, S3 specifically includes:

[0029] S31: Based on the ecological sensitivity distribution map generated by S1 and the development potential distribution map generated by S2, the two types of layers are rasterized under a unified spatial reference so that each grid cell has consistent coordinate accuracy and resolution in spatial location.

[0030] S32: After completing the rasterization process, read the ecological sensitivity level and development potential level of each grid cell in sequence, and filter out the grid cells with high sensitivity level and high development potential level to form the initial conflict cell set.

[0031] S33: Perform spatial continuity analysis on the initial set of conflict units, aggregate adjacent or spatially contacting high-sensitivity-high-potential grid units to form conflict zones with clear boundaries, and mark the conflict zones as spatially defined protection and development conflict areas.

[0032] Optionally, S4 specifically includes:

[0033] S41: Based on the ecological sensitivity distribution map generated by S1, extract all grid cells marked as high sensitivity levels and construct a set of high ecological sensitivity spatial cells;

[0034] S42: Call the existing nature reserve boundary data, and based on the vector layer in the national or local nature reserve database, standardize the layer under a unified projection coordinate system to ensure consistency with the spatial reference of the ecological sensitivity layer;

[0035] S43: Perform spatial overlay analysis on the set of high ecological sensitivity spatial units and the boundary layer of nature reserves to determine whether each high sensitivity grid unit falls inside the boundary of any nature reserve. If it is not completely contained, it is marked as an uncovered unit.

[0036] S44: Aggregate all uncovered cells, merge adjacent uncovered grid cells into a continuous region based on spatial proximity, and define the continuous region as an ecological protection gap area.

[0037] Optionally, S43 specifically includes:

[0038] S431: For each highly sensitive grid cell in the set of highly ecologically sensitive spatial cells. Extract its center point coordinates ;

[0039] S432: Iterate through each protected area boundary in the existing protected area boundary layer. The point-polygon containment relationship determination function is called to determine whether the center point of a grid cell falls within the boundary of any protected area. The determination condition is as follows:

[0040] ;

[0041] in, Indicates the first The center coordinates of each highly sensitive grid cell; For the first The boundary polygon of a nature reserve; A function that determines whether the center point is included, and returns a boolean value;

[0042] S433: For each grid cell If it is in all The judgment results in are all ,Right now If the grid is not contained within any protected area, it is determined that the grid is not covered and is marked as an uncovered cell; otherwise, it is marked as a covered cell.

[0043] Optionally, S5 specifically includes:

[0044] S51: Extract the protection and development conflict areas identified in S3 and the ecological protection gap areas identified in S4 into independent spatial object sets respectively;

[0045] S52: Call the boundary layer of nature reserves, calculate the spatial adjacency relationship between each conflict area or missing area and the nearest nature reserve, quantify the degree of adjacency based on the minimum boundary distance and boundary contact length, and form a set of adjacency factor indicators;

[0046] S53: For areas where protection and development conflict, extract the average ecological sensitivity level, the average development potential level, and the adjacency factor with nature reserves to establish a combination of indicators for measuring the intensity of conflict and the urgency of integration.

[0047] S54: For areas with ecological protection gaps, extract their average ecological sensitivity level, spatial area, and adjacency factor to construct a combination of indicators to measure the severity of protection deficiencies and potential for integration.

[0048] S55: Weighted scoring is performed on various combinations of indicators for areas with conflict between protection and development and areas with ecological protection gaps, and corresponding integrated optimization coefficients are generated.

[0049] Optionally, S55 specifically includes:

[0050] S551: For each area in conflict between protection and development, read its corresponding average ecological sensitivity level, average development potential level, and adjacency factor; and calculate the weighted average of the three indicators according to the preset weighting coefficients to generate the integrated optimization coefficient for the corresponding conflict area. ;

[0051] S552: For each ecological protection gap area, read its corresponding average ecological sensitivity level, spatial area, and adjacency factor, and calculate the weighted average of the three types of indicators to generate the integrated optimization coefficient for the gap area. ;

[0052] S555: Summarize the integrated optimization coefficients for all areas where protection and development conflict and areas lacking ecological protection.

[0053] Optionally, S6 specifically includes:

[0054] S61: Integrate and optimize the coefficients of conflict zones Integration and optimization coefficients with vacant areas Normalization is performed, and the values ​​are sorted according to their magnitude to form a standardized integrated optimized value sequence;

[0055] S62: For areas where protection and development conflict, based on their integration optimization coefficients Based on the interval in which it is located, it is divided into three types of integrated optimization; specifically, when When, it is divided into a high-priority coordination area; when When, it is divided into a medium-priority boot sector; when When this happens, it is classified as a low-priority observation area;

[0056] S63: For areas lacking ecological protection, based on their integration and optimization coefficients Based on the interval in which it is located, it is divided into three types of integrated optimization; specifically, when When, it is divided into a high-priority expansion area; when When, it is divided into a medium-priority reserved area; when When that happens, it is classified as a low-priority supplementary area.

[0057] The beneficial effects of this invention are:

[0058] This invention constructs a dual-dimensional indicator system that integrates ecological sensitivity and development potential, and sequentially completes the generation of ecological sensitivity distribution maps, development potential distribution maps, spatial overlay conflict identification, ecological protection gap extraction, and integrated optimization factor calculation. It effectively realizes the systematic identification and integrated optimization of high-value areas in coastal wetland ecosystems, provides a complete quantitative support path for the optimization of nature reserve systems, and solves the problems of single indicators and fragmented identification logic in existing methods. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic diagram of the method for integrating and optimizing nature reserves according to an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the calculation and integration optimization coefficient process according to an embodiment of the present invention. Detailed Implementation

[0062] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0063] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0064] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0065] like Figures 1-2 As shown, a method for integrating and optimizing coastal wetland nature reserves that combines ecosystem integrity, authenticity, and high-quality development layout includes the following steps:

[0066] S1: Obtain the distribution data of ecosystem types and species of the target coastal wetland ecosystem. Based on the importance of ecosystem types and the survival dependence of species, calculate and generate a spatial difference distribution map of ecosystem integrity and authenticity of the target coastal wetland ecosystem.

[0067] S2: Obtain data on the intensity of economic and social activities and infrastructure coverage of the target coastal wetland ecosystem. Based on the current level of economic and social activity intensity and the support level of infrastructure coverage, calculate and generate a distribution map of human activity intensity of the target coastal wetland ecosystem.

[0068] S3: Spatial overlay analysis of the distribution map of spatial differences in ecosystem integrity and authenticity with the distribution map of human activity intensity is performed to identify spatial units in which both ecosystem integrity and authenticity and human activity intensity are at a high level, and these units are defined as areas of conflict between conservation and development.

[0069] S4: Spatial comparison of the spatial difference distribution map of ecosystem integrity and authenticity with the existing nature reserve boundary data is carried out to identify areas with high ecosystem integrity and authenticity levels but not covered by existing nature reserves, and define them as ecological protection gap areas.

[0070] S5: For areas where protection and development conflict and areas with ecological protection gaps, calculate the integration optimization coefficient for each area based on its spatial adjacency with existing nature reserves;

[0071] S6: Based on the magnitude and spatial distribution of the integration optimization coefficients, ecological protection gap areas and areas with conflicts between protection and development are divided into integration optimization types with different priorities.

[0072] S1 specifically includes:

[0073] S11: When acquiring ecosystem type distribution data, call the ecological monitoring database deployed in the target coastal wetland ecosystem, read the spatial distribution range of typical coastal wetland ecosystem types such as salt marshes, seagrass beds and mangroves in the region, and generate an ecosystem type dataset through a unified spatial grid format;

[0074] S12: When acquiring species distribution data, the regional biological monitoring records are called up, and the habitat location data of indicator species, flagship species and important associated species inhabiting the ecosystem types are integrated in chronological order. Through spatial matching, the species distribution data and ecosystem type data are aligned on the same grid to form a species distribution dataset. The dataset is then compared with the species list of existing nature reserves to identify whether there are species and their habitats with high conservation value but not listed in the protection list, and to determine the ecosystem integrity of the nature reserves.

[0075] S13: Construct basic sensitivity values ​​based on the degree of impact of human activities on ecosystems, assign corresponding sensitivity levels to ecosystem types in each grid unit according to their ecological function contribution, and form an ecosystem type sensitivity matrix;

[0076] S14: Construct species sensitivity values ​​based on species survival dependence, quantify the degree of dependence of each species in its corresponding ecosystem type according to deterministic levels, and map them to the corresponding grid cells to form a species sensitivity matrix;

[0077] S15: The ecosystem type sensitivity matrix and the species sensitivity matrix are overlaid and calculated according to a preset weighting method to generate the comprehensive ecological sensitivity value of each grid unit. The values ​​are then divided into different sensitivity intervals according to their numerical levels to form a distribution map of the ecosystem integrity and authenticity of the target coastal wetland ecosystem. The above steps, through unified spatial matching, hierarchical quantification, and weighted integration of ecosystem type distribution data and species distribution data, can accurately express the response sensitivity of different ecosystem types and biological communities to environmental changes at the same spatial scale. This makes the generated ecological sensitivity distribution map clear in structure and well-defined in relation to environmental changes, facilitating subsequent steps of spatial overlay analysis and protection priority determination, and improving the scientific rigor and consistency of the overall assessment process.

[0078] S15 specifically includes:

[0079] S151: According to the preset weighting method, the ecosystem type sensitivity matrix and the species sensitivity matrix are assigned weight coefficients respectively, so that the contribution ratio of ecosystem type sensitivity value and species sensitivity value in each grid cell is kept consistent, forming two types of sensitivity matrices after weighting.

[0080] S152: Perform an overlay calculation for each grid cell, summing the weighted ecosystem type sensitivity value and the weighted species sensitivity value in a linear combination to obtain the comprehensive ecological sensitivity value for the corresponding grid cell. The formula is: ,in, For the first The overall ecological sensitivity value of each grid; For the first Ecosystem type sensitivity values ​​for each grid; For the first Species sensitivity values ​​for each grid; The weighting coefficients for ecosystem type sensitivity and species sensitivity satisfy... This ensures consistent computational dimensions and avoids conflicts in units of measurement.

[0081] S153: Sort the overall sensitivity values ​​of all grid cells numerically, and divide the overall sensitivity values ​​into high sensitivity zone, medium sensitivity zone and low sensitivity zone according to the preset sensitivity level threshold, so that the boundaries of each level zone maintain a defined numerical range.

[0082] The classification of levels includes:

[0083] When the comprehensive ecological sensitivity value At that time, it was determined to be a high-sensitivity area;

[0084] when At that time, it was determined to be a medium sensitivity area;

[0085] when At that time, it was determined to be a low-sensitivity area;

[0086] S154: Map the divided sensitivity level results to the corresponding grid cells to generate an ecological sensitivity level layer composed of different level labels, and export it as an ecosystem integrity and authenticity distribution map of the target coastal wetland ecosystem under a unified spatial benchmark. The above steps, by weighting, overlaying and classifying the sensitivity matrix, can make the contribution relationship between ecosystem type and species dependence in the comprehensive sensitivity results clear and quantifiable, and make the sensitivity results of each grid cell comparable and distinguishable. Thus, the generated ecological sensitivity distribution map has a unified scale, clear levels and deterministic evaluation basis, and enhances the reliability and scientificity of subsequent spatial identification and protection priority judgment.

[0087] S2 specifically includes:

[0088] S21: When acquiring human activity intensity data, call the remote sensing interpretation results of the past three years and the regional land use dynamic monitoring data to extract the spatial distribution of industrial facilities, residential settlements, transportation corridors and aquaculture land. Construct a human activity intensity index layer based on construction density and activity frequency, and convert it into a human activity intensity dataset in a unified grid format.

[0089] S22: When acquiring infrastructure coverage data, call the latest vector layer in the spatial infrastructure database, which includes roads, water conservancy, power and communication facilities, to calculate the coverage rate and type completeness of infrastructure in each grid cell, and form an infrastructure coverage dataset;

[0090] S23: Based on the current level of human activity intensity, the intensity values ​​of each grid unit are normalized according to the set index standard to generate human activity impact factors; at the same time, based on the support level of infrastructure coverage, the infrastructure density and service integrity of each grid unit are weighted and scored to generate infrastructure support factors.

[0091] The formula for calculating the human activity impact factor is as follows: ;in, For the first Human activity impact factors in each grid cell; For the first In the grid cell, the th The building density values ​​for land used for human activities, such as factory density and residential density, range from [0, 1]. For the first The normalized frequency value of human-like activities represents the activity level per unit of time. This refers to the total number of types of human activities included in the statistics, such as industry, transportation, animal husbandry, and residential activities.

[0092] The formula for calculating the infrastructure support factor is: ,in, For the first Infrastructure support factor for each grid cell; For the first In the grid, the th The coverage of infrastructure types (such as road coverage, hydropower facility density, etc.) ranges from [0, 1]. For the first The weighting coefficient of a type of infrastructure indicates the supporting role of that type of infrastructure in development; The total number of infrastructure types participating in the assessment, such as transportation, electricity, water conservancy, and communications;

[0093] S24: Combine the human activity impact factor and infrastructure support factor of each grid cell according to the weighting coefficients to generate the development potential index of that grid cell. The calculation formula is: ,in, For the first The development potential value of each grid cell; For the first Human activity impact factors in each grid cell; For the first Infrastructure support factor for each grid cell; , Let these represent the weighting coefficients for human activities and infrastructure, respectively, satisfying... To avoid dimensional conflicts;

[0094] S25: Based on the development potential value of each grid cell, the development potential index is... The area was designated as a high-potential zone, and its development potential index was adjusted accordingly. The area is divided into medium-potential zones, and the development potential index is used to classify them. The area is divided into low-potential zones, ultimately generating a development potential distribution map covering the entire target coastal wetland ecosystem. The above steps introduce standardized human activity impact factors and infrastructure support factors, and construct a development potential index model in a weighted manner. This enables a comprehensive quantification of the carrying capacity and adaptability of each region to future human development activities, making the development potential distribution map spatially differentiated, clearly tiered, and logically clear. This provides a quantifiable basis for subsequent spatial conflict identification and integration optimization.

[0095] S3 specifically includes:

[0096] S31: Based on the ecological sensitivity distribution map generated by S1 and the development potential distribution map generated by S2, the two types of layers are rasterized under a unified spatial reference so that each grid cell has consistent coordinate accuracy and resolution in spatial location.

[0097] S32: After completing the rasterization process, read the ecological sensitivity level and development potential level of each grid cell in sequence, and filter out the grid cells with high sensitivity level and high development potential level to form the initial conflict cell set.

[0098] S33: Perform spatial continuity analysis on the initial set of conflict units, aggregate adjacent or spatially contacting high-sensitivity-high-potential grid units to form conflict zones with clear boundaries, and mark these conflict zones as spatially defined areas of conflict between protection and development. Output the conflict zones as independent layers so that they can be spatially overlaid with data on ecological protection gaps and nature reserve boundaries in subsequent steps. By unifying coordinates, filtering levels, and spatially aggregating the distribution maps of ecological sensitivity and development potential, the above steps can accurately define high-sensitivity-high-potential areas, effectively identify spatial units with strong protection needs and high human use pressure, provide a clear spatial conflict range for subsequent integration and optimization, and improve the pertinence and scientific nature of regional protection planning.

[0099] S4 specifically includes:

[0100] S41: Based on the ecological sensitivity distribution map generated by S1, extract all grid cells marked as high sensitivity levels, construct a set of high ecological sensitivity spatial cells, and maintain their original spatial location and boundary information;

[0101] S42: Call the existing nature reserve boundary data, and based on the vector layer in the national or local nature reserve database, standardize the layer under a unified projection coordinate system to ensure consistency with the spatial reference of the ecological sensitivity layer;

[0102] S43: Perform spatial overlay analysis on the set of high ecological sensitivity spatial units and the boundary layer of nature reserves to determine whether each high sensitivity grid unit falls inside the boundary of any nature reserve. If it is not completely contained, it is marked as an uncovered unit.

[0103] S44: Aggregate all uncovered units, merge adjacent uncovered grid units into continuous regions based on spatial proximity, and define the continuous regions as ecological protection gap areas. Output as an independent layer for subsequent assessment and integration. The above steps, by spatially comparing high-sensitivity areas with existing nature reserve boundaries and identifying uncovered units, can accurately extract gap areas in the ecological protection network, fill the gaps in the existing protection system, and provide scientific support for improving the integrity and connectivity of the ecosystem.

[0104] S43 specifically includes:

[0105] S431: For each highly sensitive grid cell in the set of highly ecologically sensitive spatial cells. Extract its center point coordinates And obtain its corresponding spatial boundary rectangle or polygon representation;

[0106] S432: Iterate through each protected area boundary in the existing protected area boundary layer. The point-polygon containment relationship determination function is called to determine whether the center point of a grid cell falls within the boundary of any protected area. The determination condition is as follows:

[0107] ;

[0108] in, Indicates the first The center coordinates of each highly sensitive grid cell; For the first The boundary polygon of a nature reserve; A function that determines whether the center point is included, and returns a boolean value;

[0109] S433: For each grid cell If it is in all The judgment results in are all ,Right now If the grid is not included in any nature reserve, it is marked as an uncovered unit; otherwise, it is marked as a covered unit. The above steps determine spatial inclusion by taking the grid center point as the representative position and combining it with polygon boundary calculation to achieve rapid batch identification. This can efficiently and accurately determine the spatial coverage relationship between highly sensitive units and nature reserve boundaries, providing calculable and quantifiable spatial basis for the extraction of ecological protection gaps.

[0110] S5 specifically includes:

[0111] S51: Extract the protection and development conflict areas identified in S3 and the ecological protection gap areas identified in S4 into independent sets of spatial objects to ensure that their boundary data are complete and have topological continuity.

[0112] S52: Utilize the boundary layer of nature reserves, calculate the spatial adjacency relationship between each conflict area or gap area and the nearest nature reserve, quantify the degree of adjacency based on the minimum boundary distance and boundary contact length, and form a set of adjacency factor indicators; the adjacency factor is calculated as follows:

[0113] ,in, For the first Adjacency factor values ​​for conflict or vacant regions; This is the minimum distance from the boundary of the area to the boundary of the nearest nature reserve; The maximum permissible adjacency distance is set for normalization; The length of the boundary where the region directly contacts the nearest nature reserve; This is the total boundary length of the region; Let be the weighting coefficient, satisfying This is used to control the combined weight of minimum distance and contact ratio to prevent dimensional conflicts.

[0114] S53: For areas where protection and development conflict, extract the average ecological sensitivity level, the average development potential level, and the adjacency factor with nature reserves to establish a combination of indicators for measuring the intensity of conflict and the urgency of integration.

[0115] S54: For areas with ecological protection gaps, extract their average ecological sensitivity level, spatial area, and adjacency factor to construct a combination of indicators to measure the severity of protection deficiencies and potential for integration.

[0116] S55: Weighted scoring is performed on various indicator combinations for areas with conflict between protection and development and areas lacking ecological protection, generating corresponding integration optimization coefficients to reflect the urgency of prioritizing the integration of these areas into the current protection framework. The above steps introduce spatial adjacency as an evaluation factor and construct indicator combination systems applicable to different regional types, enabling the calculation of integration optimization coefficients to simultaneously reflect the three factors of ecological importance, development pressure, and protection accessibility, providing a comprehensive and differentiated quantitative basis for subsequent regional priority division.

[0117] S55 specifically includes:

[0118] S551: For each area in conflict between protection and development, read its corresponding average ecological sensitivity level, average development potential level, and adjacency factor; and calculate the weighted average of the three indicators according to the preset weighting coefficients to generate the integrated optimization coefficient for the corresponding conflict area. The calculation formula is as follows: ,in, For the first Integration optimization coefficient for areas of conflict between protection and development; This represents the average ecological sensitivity level of the region. This represents the average level of development potential for the region. For the corresponding weight coefficients, satisfying ;

[0119] S552: For each ecological protection gap area, read its corresponding average ecological sensitivity level, spatial area, and adjacency factor, and calculate the weighted average of the three types of indicators to generate the integrated optimization coefficient for the gap area. The calculation formula is as follows: ,in, For the first Integration and optimization coefficients for ecological protection gap areas; This represents the average ecological sensitivity level of the region. This is the standardized value of the spatial area of ​​the region; This is the adjacency factor value for this region; For the corresponding weight coefficients, satisfying ;

[0120] S555: Summarize the integration optimization coefficients of all areas with conflict between protection and development and areas with ecological protection gaps, and output them as the basic dataset for subsequent priority division; the above steps, by constructing differentiated index combinations for conflict areas and gap areas respectively, and calculating integration optimization coefficients based on weighted formulas, can realize the quantitative determination of regional integration value, so that subsequent spatial optimization strategies have operability and accuracy support.

[0121] S6 specifically includes:

[0122] S61: Integrate and optimize the coefficients of conflict zones Integration and optimization coefficients with vacant areas Normalization is performed, and the values ​​are sorted according to their magnitude to form a standardized integrated optimized value sequence;

[0123] S62: For areas where protection and development conflict, based on their integration optimization coefficients Based on the interval in which it is located, it is divided into three types of integrated optimization; specifically, when When a region is designated as a high-priority coordination zone, it indicates high conflict intensity and strong adjacency, requiring priority to develop a control plan that balances protection and development; when At that time, it was designated as a medium-priority guidance zone, indicating that it has development potential but heavy ecological constraints, and should be utilized under guidance; when At that time, it is classified as a low-priority observation zone, indicating that the urgency of integration is low, and it can be included in the medium- to long-term observation list;

[0124] S63: For areas lacking ecological protection, based on their integration and optimization coefficients Based on the interval in which it is located, it is divided into three types of integrated optimization; specifically, when When designated as a high-priority expansion zone, it indicates high ecological value and strong adjacency, making it suitable for priority inclusion in the nature reserve system; when When designated as a medium-priority reserve area, it indicates a certain ecological value and integration potential, and should be included in the ecological reserve control area; when When the priority classification results are defined as low-priority supplementary areas, it indicates that the current ecological sensitivity is limited and can be considered as future reserve space. The priority classification results are output as an integrated optimization type layer to support the subsequent optimization of the nature reserve system architecture and implementation path planning.

[0125] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for integrating and optimizing coastal wetland nature reserves, which combines ecosystem integrity, authenticity, and high-quality development layout, characterized in that: Includes the following steps: S1: Obtain the distribution data of ecosystem types and species of the target coastal wetland ecosystem. Based on the importance of ecosystem types and the survival dependence of species, calculate and generate a spatial difference distribution map of ecosystem integrity and authenticity of the target coastal wetland ecosystem. S2: Obtain data on the intensity of economic and social activities and infrastructure coverage of the target coastal wetland ecosystem. Based on the current level of economic and social activity intensity and the support level of infrastructure coverage, calculate and generate a distribution map of human activity intensity of the target coastal wetland ecosystem. S3: Spatial overlay analysis of the distribution map of spatial differences in ecosystem integrity and authenticity with the distribution map of human activity intensity is performed to identify spatial units in which both ecosystem integrity and authenticity and human activity intensity are at a high level, and these units are defined as areas of conflict between conservation and development. S4: Spatial comparison of the spatial difference distribution map of ecosystem integrity and authenticity with the existing nature reserve boundary data is carried out to identify areas with high ecosystem integrity and authenticity levels but not covered by existing nature reserves, and define them as ecological protection gap areas. S5: For areas where protection and development conflict and areas with ecological protection gaps, calculate the integration optimization coefficient for each area based on its spatial adjacency with existing nature reserves; S6: Based on the magnitude and spatial distribution of the integration optimization coefficients, ecological protection gap areas and areas with conflicts between protection and development are divided into integration optimization types with different priorities.

2. The method for integrating and optimizing coastal wetland nature reserves according to claim 1, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S1 specifically includes: S11: When acquiring ecosystem type distribution data, call the ecological monitoring database deployed in the target coastal wetland ecosystem, read the spatial distribution range of typical coastal wetland ecosystem types such as salt marshes, seagrass beds and mangroves in the region, and generate an ecosystem type dataset through a unified spatial grid format; S12: When acquiring species distribution data, the regional biological monitoring records are called up, and the habitat location data of indicator species, flagship species and important associated species inhabiting the ecosystem types are integrated in chronological order. Through spatial matching, the species distribution data and ecosystem type data are aligned on the same grid to form a species distribution dataset. The dataset is then compared with the species list of existing nature reserves to identify whether there are species and their habitats with high conservation value but not listed in the protection list, and to determine the ecosystem integrity of the nature reserves. S13: Construct basic sensitivity values ​​based on the degree of impact of human activities on ecosystems, assign corresponding sensitivity levels to ecosystem types in each grid unit according to their ecological function contribution, and form an ecosystem type sensitivity matrix; S14: Construct species sensitivity values ​​based on species survival dependence, quantify the degree of dependence of each species in its corresponding ecosystem type according to deterministic levels, and map them to the corresponding grid cells to form a species sensitivity matrix; S15: The ecosystem type sensitivity matrix and the species sensitivity matrix are superimposed and calculated according to a preset weighting method to generate the comprehensive ecological sensitivity value of each grid unit. The values ​​are then divided into different sensitivity intervals according to the numerical level to form a distribution map of the ecosystem integrity and authenticity of the target coastal wetland ecosystem.

3. The method for integrating and optimizing coastal wetland nature reserves according to claim 2, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S15 specifically includes: S151: According to the preset weighting method, the ecosystem type sensitivity matrix and the species sensitivity matrix are assigned weight coefficients respectively, so that the contribution ratio of ecosystem type sensitivity value and species sensitivity value in each grid cell is kept consistent, forming two types of sensitivity matrices after weighting. S152: Perform an overlay calculation for each grid cell, summing the weighted ecosystem type sensitivity value and the weighted species sensitivity value in a linear combination to obtain the comprehensive ecological sensitivity value for the corresponding grid cell. ; S153: Sort the overall sensitivity values ​​of all grid cells numerically, and divide the overall sensitivity values ​​into high sensitivity zone, medium sensitivity zone and low sensitivity zone according to the preset sensitivity level threshold. S154: Map the divided sensitivity level results to the corresponding grid cells to generate an ecological sensitivity level layer composed of different level labels, and export it as an ecosystem integrity and authenticity distribution map of the target coastal wetland ecosystem under a unified spatial benchmark.

4. The method for integrating and optimizing coastal wetland nature reserves according to claim 1, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S2 specifically includes: S21: When acquiring human activity intensity data, call the remote sensing interpretation results of the past three years and the regional land use dynamic monitoring data to extract the spatial distribution of industrial facilities, residential settlements, transportation corridors and aquaculture land. Construct a human activity intensity index layer based on construction density and activity frequency, and convert it into a human activity intensity dataset in a unified grid format. S22: When acquiring infrastructure coverage data, call the latest vector layer in the spatial infrastructure database, which includes roads, water conservancy, power and communication facilities, to calculate the coverage rate and type completeness of infrastructure in each grid cell, and form an infrastructure coverage dataset; S23: Based on the current level of human activity intensity, the intensity values ​​of each grid unit are normalized according to the set index standard to generate human activity impact factors; at the same time, based on the support level of infrastructure coverage, the infrastructure density and service integrity of each grid unit are weighted and scored to generate infrastructure support factors. S24: Combine the human activity impact factor and infrastructure support factor of each grid cell according to the weighting coefficients to generate the development potential index of that grid cell. ; S25: Based on the development potential value of each grid cell, the development potential index is... The area is designated as a high-potential zone, and the development potential index is used to classify it. The area is divided into medium-potential zones, and the development potential index is used to classify them. The area is divided into low-potential zones, and a development potential distribution map covering the entire target coastal wetland ecosystem is ultimately generated.

5. The method for integrating and optimizing coastal wetland nature reserves according to claim 1, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S3 specifically includes: S31: Based on the ecological sensitivity distribution map generated by S1 and the development potential distribution map generated by S2, the two types of layers are rasterized under a unified spatial reference so that each grid cell has consistent coordinate accuracy and resolution in spatial location. S32: After completing the rasterization process, read the ecological sensitivity level and development potential level of each grid cell in sequence, and filter out the grid cells with high sensitivity level and high development potential level to form the initial conflict cell set. S33: Perform spatial continuity analysis on the initial set of conflict units, aggregate adjacent or spatially contacting high-sensitivity-high-potential grid units to form conflict zones with clear boundaries, and mark the conflict zones as spatially defined protection and development conflict areas.

6. The method for integrating and optimizing coastal wetland nature reserves according to claim 1, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S4 specifically includes: S41: Based on the ecological sensitivity distribution map generated by S1, extract all grid cells marked as high sensitivity levels and construct a set of high ecological sensitivity spatial cells; S42: Call the existing nature reserve boundary data, and based on the vector layer in the national or local nature reserve database, standardize the layer under a unified projection coordinate system to ensure consistency with the spatial reference of the ecological sensitivity layer; S43: Perform spatial overlay analysis on the set of high ecological sensitivity spatial units and the boundary layer of nature reserves to determine whether each high sensitivity grid unit falls inside the boundary of any nature reserve. If it is not completely contained, it is marked as an uncovered unit. S44: Aggregate all uncovered cells, merge adjacent uncovered grid cells into a continuous region based on spatial proximity, and define the continuous region as an ecological protection gap area.

7. The method for integrating and optimizing coastal wetland nature reserves according to claim 6, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... Specifically, S43 includes: S431: For each highly sensitive grid cell in the set of highly ecologically sensitive spatial cells. Extract its center point coordinates ; S432: Iterate through each protected area boundary in the existing protected area boundary layer. The point-polygon containment relationship determination function is called to determine whether the center point of a grid cell falls within any protected area boundary. The determination condition is as follows: ； in, Indicates the first The center coordinates of each highly sensitive grid cell; For the first The boundary polygon of a nature reserve; A function that determines whether the center point is included, and returns a boolean value; S433: For each grid cell If it is in all The judgment results in are all ,Right now If the grid is not contained within any protected area, it is determined that the grid is not covered and is marked as an uncovered cell; otherwise, it is marked as a covered cell.

8. The method for integrating and optimizing coastal wetland nature reserves according to claim 1, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S5 specifically includes: S51: Extract the protection and development conflict areas identified in S3 and the ecological protection gap areas identified in S4 into independent spatial object sets respectively; S52: Call the boundary layer of nature reserves, calculate the spatial adjacency relationship between each conflict area or missing area and the nearest nature reserve, quantify the degree of adjacency based on the minimum boundary distance and boundary contact length, and form a set of adjacency factor indicators; S53: For areas where protection and development conflict, extract the average ecological sensitivity level, the average development potential level, and the adjacency factor with nature reserves to establish a combination of indicators for measuring the intensity of conflict and the urgency of integration. S54: For areas with ecological protection gaps, extract their average ecological sensitivity level, spatial area, and adjacency factor to construct a combination of indicators to measure the severity of protection deficiencies and potential for integration. S55: Weighted scoring is performed on various combinations of indicators for areas with conflict between protection and development and areas with ecological protection gaps, and corresponding integrated optimization coefficients are generated.

9. The method for integrating and optimizing coastal wetland nature reserves according to claim 8, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... Specifically, S55 includes: S551: For each area in conflict between protection and development, read its corresponding average ecological sensitivity level, average development potential level, and adjacency factor; and calculate the weighted average of the three indicators according to the preset weighting coefficients to generate the integrated optimization coefficient for the corresponding conflict area. ; S552: For each ecological protection gap area, read its corresponding average ecological sensitivity level, spatial area, and adjacency factor, and calculate the weighted average of the three types of indicators to generate the integrated optimization coefficient for the gap area. ; S555: Summarize the integrated optimization coefficients for all areas where protection and development conflict and areas lacking ecological protection.

10. The method for integrating and optimizing coastal wetland nature reserves according to claim 9, which combines ecosystem integrity, authenticity, and high-quality development layout, is characterized in that... S6 specifically includes: S61: Integrate and optimize the coefficients of conflict zones Integration and optimization coefficient with vacant areas Normalization is performed, and the values ​​are sorted according to their magnitude to form a standardized integrated optimized value sequence; S62: For areas where protection and development conflict, based on their integration optimization coefficients Based on the interval in which it is located, it is divided into three types of integrated optimization; specifically, when When, it is divided into a high-priority coordination area; when When, it is divided into a medium-priority boot sector; when When this happens, it is classified as a low-priority observation area; S63: For areas lacking ecological protection, based on their integration and optimization coefficients Based on the interval in which it is located, it is divided into three types of integrated optimization; specifically, when When, it is divided into a high-priority expansion area; when When, it is divided into a medium-priority reserved area; when When that happens, it is classified as a low-priority supplementary area.

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