Ecological restoration area-oriented salt marsh habitat suitability evaluation method

Abstract

The application discloses a salt marsh habitat suitability evaluation method for ecological restoration area identification, and belongs to the technical field of coastal wetland ecological restoration and space suitability evaluation. The application is based on multi-source spatial data, comprehensively considers factors such as topography, hydrology, seed source diffusion and ecological disturbance, and forms the habitat suitability evaluation method for the salt marsh ecological restoration area identification through candidate tidal flat unit screening, index data construction, habitat suitability index system establishment, index weighting, grading, habitat suitability index calculation, single limiting factor control and suitability partitioning, so that quantitative evaluation and spatial grading of potential restoration areas are realized, and technical basis is provided for salt marsh ecological restoration space screening, optimized layout and ecological restoration engineering implementation.

Description

Technical Field

[0001] This invention belongs to the technical field of coastal wetland ecological restoration and spatial suitability assessment, specifically involving a method for assessing the suitability of saline marsh habitats for ecological restoration zone identification. Background Technology

[0002] In the actual identification of salt marsh ecological restoration areas, relying solely on current vegetation distribution, tidal level, or a few environmental indicators is insufficient to fully reflect the combined impacts of tidal flat hydrodynamic stability, tidal channel connectivity, and potential seed sources on vegetation restoration. For example, while some areas may have suitable tidal levels, strong wave erosion or prolonged erosion of the beach surface could still hinder vegetation establishment and sustainable expansion. Similarly, while some areas may have generally suitable natural conditions, a lack of nearby seed sources or competition from aquaculture, construction activities, and Spartina alterniflora could reduce the feasibility of ecological restoration. Therefore, single-factor evaluation or simple multi-indicator weighted summation is insufficient to fully identify such spatial constraints.

[0003] On the other hand, existing comprehensive evaluation methods typically allow for mutual compensation between different indicators. When a key condition affecting salt marsh restoration reaches a clearly unsuitable state, its adverse effects may still be offset by other high-scoring indicators, thus allowing spatial units with strong limiting factors to obtain higher comprehensive suitability scores. When such results are used for ecological restoration spatial screening, they may cause discrepancies between the evaluation results and actual vegetation planting conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing the suitability of saline marsh habitats for ecological restoration zone identification, in order to solve the problems of insufficient comprehensiveness of evaluation factors, easy obscuring of key ecological constraints by comprehensive scoring, and insufficient connection between evaluation results and ecological restoration space identification in existing saline marsh habitat suitability assessments.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for assessing the suitability of saline marsh habitats for ecological restoration zone identification includes the following steps: S1: Identify candidate tidal flat units; S2: Construct an index dataset for candidate tidal flat units; S3: Construct an index system for the suitability of saline marsh habitats; S4: Determine the indicator weights; construct the judgment matrices for the target layer, criterion layer, and indicator layer using the analytic hierarchy process (AHP), calculate the maximum eigenvalue and corresponding eigenvector of each judgment matrix, and normalize them to obtain the weights. Then calculate the consistency index CI and consistency ratio CR to perform a consistency test; when CR < 0.10, the judgment matrix passes the consistency test, and the obtained weights are valid; when CR ≥ 0.10, readjust the judgment matrix and repeat the calculation until CR < 0.10 is satisfied. S5: Establish a five-level evaluation standard; divide each indicator into five levels: high suitability, relatively high suitability, medium suitability, relatively low suitability, and unsuitability, and assign values ​​of 5, 4, 3, 2, and 1 respectively; S6: Calculate the habitat suitability index; calculate the comprehensive suitability index (HIS) of the salt marsh habitat based on the comprehensive weight of each evaluation indicator and the suitability assignment in S5. S7: Single-item constraint factor control; assign a constraint coefficient L=0 to the spatial range where the single-item index score is 1, and assign a constraint coefficient L=1 to the remaining spatial ranges; according to HSI * =HSI×L (L is the restriction coefficient) is used to adjust the overall suitability index of the salt marsh habitat. That is, the overall index HSI calculated in S6 is multiplied by the restriction coefficient 0 or 1 to obtain the final suitability index HSI. * ; S8: Suitability Zoning Output; The final score is divided into four levels: most suitable, relatively suitable, relatively unsuitable, and unsuitable, with the final suitability index HSI being the most suitable. * Satisfying 4≤HSI * ≤5、3≤HSI * <4、2≤HSI * <3 and 0≤HSI * When the value is less than 2, it is divided into four levels: most suitable, relatively suitable, relatively unsuitable, and unsuitable.

[0007] Furthermore, in S1: based on the shoreline, intertidal zone range, and Landsat and Sentinel-2 remote sensing images of the study area, the inundation frequency method is used to identify tidal flat spaces, and candidate evaluation units are obtained by combining spatial control and land use information.

[0008] Furthermore, in S2: the data on digital elevation, tide level, seabed sediment, tidal flat time series, salinity, waves, tidal channels, existing salt marshes, construction land, aquaculture ponds and Spartina alterniflora are subjected to projection unification, spatial registration, cropping, resampling and rasterization.

[0009] Furthermore, in S3: the indicator system includes four criterion layers: topographic and geomorphological conditions, hydrological conditions, diffusion basis, and disturbance conditions; the indicator layers include slope, substrate type, tidal flat stability, relative tidal level elevation, distance from tidal channel, surface salinity, wave exposure, distance from existing salt marsh, distance from construction land, distance from aquaculture pond, and Spartina alterniflora invasion degree.

[0010] Furthermore, the specific evaluation index system for the suitability of saline marsh habitat in S3 is shown in the table below:

[0011] Table 1. Evaluation Index System for Suitability of Salt Marsh Habitat .

[0012] Furthermore, S4 includes: S4-1: Establish a judgment matrix based on the relative importance of each evaluation factor to the suitability of the salt marsh habitat: criterion layer judgment matrix, topographic and geomorphological condition index judgment matrix, and hydrological condition index judgment matrix; under the diffusion basis condition, only the distance from the existing salt marsh is set as an index, so its local weight is 1, and its comprehensive weight is equal to the diffusion basis criterion layer weight. S4-2: Calculate the largest eigenvalue of the judgment matrix The corresponding eigenvectors are used, and the normalized eigenvectors are used as the local weights of each evaluation factor; the comprehensive weight of the indicator layer is obtained by multiplying the weight of its criterion layer by the corresponding local weight. S4-3: Calculate the consistency index CI and consistency ratio CR to perform a consistency test on the judgment matrix. When CR < 0.10, the judgment matrix is ​​considered to have acceptable consistency, and the weighting results are valid.

[0013] Furthermore, in S4-1, there is only one indicator under the diffusion-based condition, so its local weight is 1, and its comprehensive weight is equal to the weight of the corresponding criterion layer.

[0014] Furthermore, in S4-1: The criterion layer judgment matrix is: ; The topographic and geomorphological condition index judgment matrix is ​​as follows: ; The hydrological condition index judgment matrix is ​​as follows: ; The interference condition index judgment matrix is ​​as follows: .

[0015] Furthermore, in S4-2, the mathematical expression of the consistency index is as follows: ; ; In the formula, n is the order of the judgment matrix. To determine the largest eigenvalue of the judgment matrix, CR is the consistency ratio of the judgment matrix, CI is the consistency index of the judgment matrix, and RI is the average random consistency index of the judgment matrix; the values ​​of RI are shown in Table 2.

[0016] Table 2. Random Consistency Index (RI) Values ​​for Judging Matrix ; Note: RI is the standard average random consistency index in Saaty's analytic hierarchy process.

[0017] Furthermore, in S4-2: (1) In the criterion layer judgment matrix, the following is calculated: =4.0042, CI=0.0014, CR=0.0015<0.10, the criterion layer judgment matrix passes the consistency test; The corresponding eigenvectors, after normalization, yield the criterion layer weights, which are 0.3936, 0.3936, 0.0753, and 0.1375, respectively. (2) In the topographic and geomorphic condition index judgment matrix, the following is calculated: =3.0092, CI=0.0046, CR=0.0079<0.10, the judgment matrix passes the consistency test; The corresponding feature vectors are normalized to obtain the local weights of each index. The local weights are multiplied by the weights of the topographic and geomorphological condition criteria layer to obtain the comprehensive weights of the indexes. (3) The hydrological condition index judgment matrix is ​​calculated to obtain: =4.0310, CI=0.0103, CR=0.0115<0.10, the judgment matrix passes the consistency test; The corresponding eigenvectors are normalized to obtain the local weights of each indicator. The local weights are multiplied by the hydrological condition criterion layer weights to obtain the comprehensive weights of the indicators. (4) In the interference condition index judgment matrix, the following is calculated: =3.0000, CI=0, CR=0<0.10, the judgment matrix passes the consistency test; The corresponding eigenvectors are normalized to obtain the local weights of each indicator. The local weights are multiplied by the weights of the interference condition criterion layer to obtain the comprehensive weights of the indicators.

[0018] Furthermore, in step S4, the weights of the salt marsh habitat suitability evaluation indicators are obtained, as shown in Table 3:

[0019] Table 3 Weights of indicators for suitability of salt marsh habitat .

[0020] Furthermore, the specific evaluation criteria in S5 are shown in Table 4:

[0021] Table 4 Evaluation Criteria for Suitability Indicators of Salt Marsh Habitat .

[0022] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention integrates multi-source spatial data such as topography, hydrology, seed source dispersal and ecological disturbance to spatially quantify and comprehensively characterize the key habitat factors that affect the establishment, growth, expansion and stable maintenance of salt marsh vegetation, thereby improving the completeness and spatial refinement of salt marsh habitat suitability assessment. (2) This invention determines the weight of evaluation indicators by means of the analytic hierarchy process and converts different types of ecological and environmental factors into a unified suitability score by means of the hierarchical evaluation standard, so as to realize the comprehensive evaluation of multidimensional habitat factors and improve the comparability of evaluation results between different spatial units. (3) By setting single-item constraint factor control rules, this invention constrains spatial units that do not meet the key ecological conditions for the establishment and stable maintenance of salt marsh vegetation, avoids other highly suitable indicators from compensating for key constraint factors during the simple weighted superposition process, and improves the rationality of the ecological restoration area identification results. (4) This invention combines candidate tidal flat screening, habitat suitability comprehensive evaluation, limiting factor constraints and spatial zoning to achieve quantitative identification and classification of potential ecological restoration areas of salt marshes, which can provide technical support for spatial screening of salt marsh ecological restoration, optimization of restoration layout and implementation of related ecological restoration projects. Attached Figure Description

[0023] Figure 1 This is a flowchart of the salt marsh habitat suitability evaluation method of the present invention.

[0024] Figure 2 This is a zoning result map of the suitability evaluation of salt marsh habitat in an embodiment of the present invention; where a is Hangzhou Bay, b is Xiangshan Bay, c is Taizhou Bay, and d is Yueqing Bay. Detailed Implementation

[0025] The present invention will be further illustrated below with reference to the candidate tidal flat areas along the coast of Zhejiang Province. It should be noted that the following embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0026] Example 1: A method for assessing the suitability of saline marsh habitats for ecological restoration zone identification, such as Figure 1 As shown, it includes the following steps: (1) Determine candidate tidal flat units Based on the shoreline, intertidal zone range, and Landsat and Sentinel-2 remote sensing images of the study area, the inundation frequency method was used to identify tidal flat spaces, and candidate evaluation units for salt marsh ecological restoration were selected by combining spatial control and land use information.

[0027] (2) Principles for constructing an evaluation index system for the suitability of saline marsh habitats Salt marsh habitats are mainly controlled by factors such as elevation-flooding gradient, salinity conditions, tidal action, hydrodynamic intensity, and geomorphological stability. Salt marshes are relatively less restricted by regional low temperatures, but they respond more directly to surface elevation, tidal flooding regimes, wave action, and sedimentary stability.

[0028] Based on the natural environmental characteristics of tidal flats, the distribution patterns of salt marsh ecosystems, and data acquisition conditions, this invention follows the following principles when constructing an evaluation index system for the suitability of salt marsh habitats: A. Principle of Ecological Relevance The selection of indicators should reflect the key ecological processes of growth, establishment, expansion and maintenance of salt marsh vegetation, and prioritize the inclusion of factors closely related to topography, hydrology and salinity, tidal channel action, hydrodynamic environment and human disturbance, with a focus on elevation-flood gradient, tidal channel action, wave exposure and tidal flat stability.

[0029] B. Principle of Regional Applicability Considering the spatial differences in tides, hydrodynamics, and sedimentary environments among different bays, estuaries, and open coastal sections along the coast of Zhejiang Province, the indicator system not only reflects the general adaptability of salt marshes but also highlights the regional hydrogeomorphological characteristics, focusing on regional sensitive factors such as tidal flooding regimes, wave exposure, tidal channel connectivity, and tidal flat stability.

[0030] C. Data Availability Principle Considering the feasibility of provincial-scale evaluation, priority should be given to selecting indicators that can be extracted or calculated from publicly available remote sensing images, digital elevation data, reanalysis data, and existing vector data, avoiding over-reliance on field soil physicochemical parameters that are difficult to obtain comprehensively, so as to ensure the feasibility of the evaluation method at the regional scale.

[0031] D. The principle of representativeness and simplicity While ensuring that key ecological processes are reflected, we should avoid indicator duplication and information redundancy, control the number of indicators, and make the evaluation system not only able to comprehensively reflect the suitable conditions for salt marshland, but also facilitate subsequent weighting, standardization and comprehensive evaluation.

[0032] E. Principles of Quantification and Comparability All indicators should be expressed quantitatively as much as possible and converted into raster data with a unified spatial benchmark and resolution to facilitate hierarchical assignment, weighting, and spatial comparison. Evaluation methods appropriate to their ecological response relationships should be used for positive, negative, and interval indicators.

[0033] (3) Construction of a suitability index system for saline marsh habitat Salt marsh vegetation is mainly distributed in the mid-to-high intertidal zone of tidal flats. Its habitat formation and maintenance are closely related to elevation-flood gradient, salinity conditions, tidal channel action, wave exposure, sediment supply, and geomorphic stability. Meanwhile, existing salt marsh patches can provide a basis for seed dispersal, while development, aquaculture activities, and the invasion of Spartina alterniflora may create spatial encroachment and ecological competition pressures. Based on the above ecological processes and the environmental characteristics of the tidal flats along the Zhejiang coast, this embodiment constructs a salt marsh habitat suitability evaluation index system from four aspects: topographic conditions, hydrological conditions, dispersal basis, and disturbance conditions, as shown in Table 1.

[0034] Table 1. Evaluation Index System for Suitability of Salt Marsh Habitat

[0035] (4) Determine the weight of the indicators

[0036] The analytic hierarchy process (AHP) is used to construct judgment matrices for the criterion and indicator layers. The largest eigenvalue and its corresponding eigenvector are calculated and normalized to obtain the weights. The judgment matrices are then tested for consistency using the consistency index (CI) and the consistency ratio (CR). When CR < 0.10, the judgment matrix is ​​considered to have acceptable consistency, and the obtained weights are considered valid.

[0037] The mathematical representation of the consistency index is: ; ; In the formula, n is the order of the judgment matrix. To determine the largest eigenvalue of the matrix, CI is the consistency index, CR is the consistency ratio, and RI is the average random consistency index, as shown in Table 2.

[0038] Table 2. Random Consistency Index (RI) Values ​​for Judging Matrix ; Note: RI is the standard average random consistency index in Saaty's analytic hierarchy process.

[0039] The criterion-level judgment matrix is ​​shown in the table below: Table 5 Criterion Layer Judgment Matrix ; The calculation yielded: =4.0042, CI=0.0014, CR=0.0015<0.10, the criterion layer judgment matrix passes the consistency test, and the normalized criterion layer weights are 0.3936, 0.3936, 0.0753 and 0.1375 respectively.

[0040] The topographic and geomorphological condition index judgment matrix is ​​shown in the table below: Table 6. Topographic and Geomorphological Condition Indicator Judgment Matrix ; The calculation yielded: =3.0092, CI=0.0046, CR=0.0079<0.10, the judgment matrix passes the consistency test.

[0041] The hydrological condition index judgment matrix is ​​shown in the table below: Table 7. Hydrological Condition Indicator Judgment Matrix ; The calculation yielded: =4.0310, CI=0.0103, CR=0.0115<0.10, the judgment matrix passes the consistency test.

[0042] Under the diffusion-based conditions, only the distance from the existing salt marsh is set as an indicator, with a local weight of 1.

[0043] The interference condition index judgment matrix is ​​shown in the table below: Table 8 Interference Condition Indicator Judgment Matrix ; The calculation yielded: =3.0000, CI=0, CR=0<0.10, the judgment matrix passes the consistency test.

[0044] The final weights of the salt marsh habitat suitability evaluation indicators are shown in Table 3:

[0045] Table 3 Weights of indicators for suitability of salt marsh habitat ;

[0046] (5) Establish a five-level evaluation standard, as shown in the table below.

[0047] Table 4 Evaluation Criteria for Suitability Indicators of Salt Marsh Habitat

[0048] (6) Preprocessing of indicator data

[0049] 1) Slope

[0050] The slope tool in ArcGIS's spatial analysis module was used to perform terrain analysis on digital elevation data, calculating the slope value for each pixel. After calculation, the slope results were cropped, resampled, and range standardized to form a slope raster layer consistent with the spatial benchmark of other evaluation indicators.

[0051] 2) Stability of tidal flats

[0052] Based on the binarized tidal flat raster results for nine time periods (1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2025), a pixel-by-pixel overlay analysis method was used to construct a tidal flat stability index. First, the tidal flat raster data for the nine periods was summed pixel-by-pixel to count the number of times each pixel was identified as a tidal flat during the study period. Second, the ratio of this number to the total number of time periods was used to characterize tidal flat stability; the calculation formula is as follows: ; In the formula, Let i be the tidal flat stability index for the i-th pixel. The number of times a pixel is identified as a mudflat in each time phase is N, which is the total number of time phases. In this embodiment, N=9. After the calculation is completed, the results are cropped, resampled, and range unified to form a mudflat stability distribution raster layer that is consistent with the spatial benchmark of other evaluation indicators.

[0053] 3) Substrate type

[0054] The sediment type data were obtained from the sediment type distribution data of the surveyed sea area in the "Marine Atlas of China's Coastal Waters - Marine Sediment". The original sediment data were vectorized or rasterized, and the projection and spatial resolution were unified. The sediment types were reclassified into categories such as clayey silt, silt, sandy silt, clayey sand and others, and assigned corresponding suitability levels to form a sediment type distribution layer.

[0055] 4) Relative tide level

[0056] Digital elevation data covering the coastal tidal flats of Zhejiang Province was acquired and processed through stitching, projection transformation, outlier checking, and study area cropping. Tidal zones were divided based on the coastal bay geomorphology and tidal differentiation characteristics of Zhejiang, and representative tide gauge stations were selected for different shorelines to obtain mean low tide (MLW) and mean high tide (MHW). After unifying the elevation datum, the relative tidal elevation was calculated using the following formula: ; In the formula, RTE is the relative tide level, Z is the surface elevation, MLW is the mean low tide level, and MHW is the mean high tide level. The calculation results are uniformly resampled to the target spatial resolution to form a relative tide level raster layer.

[0057] 5) Surface salinity

[0058] Surface salinity data were obtained from marine environmental data provided by the Copernicus Marine Service. After reading the latitude, longitude, time, and surface salinity variables of the study area, spatial interpolation, resampling, and masking of the study area were performed on the salinity data. Spatially registration was then performed with other evaluation indicators to form a salinity distribution layer.

[0059] 6) Distance from tidal channel

[0060] Tidal channel data were primarily obtained through visual interpretation of Sentinel-2 remote sensing images during low tide, combined with Modified Normalized Difference Water Index (MNDWI) for auxiliary identification. Based on a comprehensive interpretation of the original image, false-color image, and water index layers, tidal channel line vectors were extracted and manually corrected using a high-resolution base map. Subsequently, Euclidean distance analysis was used to calculate the distance from each candidate evaluation unit to the nearest tidal channel, forming a raster layer representing the distance to the tidal channel.

[0061] 7) Wave exposure

[0062] Wave exposure is used to characterize the degree to which an evaluation unit is affected by open-sea wave disturbance and hydrodynamic erosion. Higher wave exposure may increase the risk of tidal flat erosion, sediment resuspension, and vegetation lodging, which is detrimental to the establishment and stable maintenance of salt marsh vegetation. In this embodiment, significant wave height is used as a quantitative proxy index for wave exposure. The significant wave height data is cropped, resampled, and normalized. A higher value indicates a higher degree of wave exposure. The normalization calculation uses the following formula: ; In the formula, x and y represent the values ​​before and after the transformation, respectively, and MaxValue and MinValue represent the maximum and minimum values ​​of the sample, respectively. The results are then cropped to the study area and uniformly resampled to a 30 m resolution to form a wave exposure raster layer.

[0063] 8) Distance from construction land

[0064] First, based on existing land use data, the types of construction land in the study area are extracted to form a construction land vector map layer. Then, using the construction land vector boundary as the distance source, the Euclidean distance tool in ArcGIS is used to calculate the straight-line distance from each pixel in the study area to the nearest construction land boundary, generating a raster layer showing the distance to construction land. To ensure consistency with other evaluation factors, the output raster is uniformly resampled to a 30 m spatial resolution and cropped to the study area, generating a raster layer showing the distribution of distance to construction land.

[0065] 9) Distance from the aquaculture pond

[0066] Based on data from aquaculture ponds in Zhejiang Province, the spatial range of aquaculture ponds was extracted. The Euclidean distance analysis method was used to calculate the distance from the candidate evaluation unit to the nearest aquaculture pond boundary, forming a raster layer of distance from the aquaculture pond.

[0067] 10) Invasiveness of Spartina alterniflora

[0068] The distribution of Spartina alterniflora was extracted based on coastal wetland vegetation classification data. Spartina alterniflora pixels were assigned a value of 1, and other pixels were assigned a value of 0. A moving window statistical method was used to calculate the Spartina alterniflora coverage ratio within a local neighborhood, which was used as the Spartina alterniflora invasion index. The calculation formula is as follows: ; In the formula, SI is the invasion index of Spartina alterniflora. To evaluate the area of ​​Spartina alterniflora within the unit, To evaluate the total area of ​​the cells, a raster layer showing the invasiveness distribution of Spartina alterniflora was calculated.

[0069] 11) Distance from existing salt marshes

[0070] Existing salt marsh distribution data is mainly extracted through remote sensing image classification and then corrected by combining sample points and existing results. Euclidean distance analysis is used to calculate the distance from candidate evaluation units to the nearest existing salt marsh patch, forming a raster layer representing the distance to the existing salt marsh.

[0071] (7) Calculate the habitat suitability index and implement restrictive factor control.

[0072] The comprehensive suitability index of saline marsh habitat is calculated based on the comprehensive weight of each evaluation indicator and the five-level suitability assignment: ; Among them, HSI is the comprehensive suitability index for salt marsh habitats. The comprehensive weight of the i-th evaluation index is... Assign a suitability value to the i-th evaluation index after five levels of evaluation.

[0073] Based on the comprehensive evaluation, individual limiting factors are set for control. Spatial units with a single indicator score of 1 or that trigger a preset strong limiting condition are assigned a limiting coefficient L=0, while other spatial units are assigned L=1. The final habitat suitability index HSI is obtained by multiplying the comprehensive suitability index HSI by the limiting coefficient L. * The final habitat suitability index (HSI) * Satisfying 4≤HSI * ≤5、3≤HSI * <4、2≤HSI * <3 and 0≤HSI * When the value is less than 2, it is divided into four levels: most suitable, relatively suitable, relatively unsuitable, and unsuitable.

[0074] (8) Suitability outcome

[0075] The above methods were used to evaluate candidate tidal flats along the coast of Zhejiang Province, and spatial zoning results of salt marsh habitat suitability were obtained, such as... Figure 2 As shown in the figure, the evaluation results indicate significant spatial differences in the suitability of salt marsh habitats in different bays. The candidate tidal flats on both the north and south banks of Hangzhou Bay are relatively continuous, with some sections forming relatively continuous most suitable areas. Sanmen Bay and Yueqing Bay show significant spatial differentiation in suitability, with some tidal flats forming concentrated most and relatively suitable patches. Xiangshan Bay's candidate tidal flats are generally dominated by relatively suitable areas, with most suitable areas being relatively scattered. In Taizhou Bay and Wenzhou Bay, suitable areas are mainly distributed along the coastline in a strip or patchy pattern, with relatively weak spatial continuity. Overall, the suitability for salt marsh restoration exhibits significant differences between bays and spatial heterogeneity within bays, reflecting the combined effects of topography, hydrological conditions, hydrodynamic environment, seed dispersal, and external disturbances. These evaluation results can identify tidal flat spatial units with high salt marsh restoration potential, providing a basis for the selection of salt marsh ecological restoration areas, optimization of spatial layout, and implementation of related ecological restoration projects.

[0076] This invention comprehensively quantifies factors such as tidal flat stability, relative tidal level elevation, tidal channel connectivity, salinity, wave exposure, seed dispersal conditions, and anthropogenic and biological disturbances, and sets constraints on key conditions that significantly limit salt marsh restoration. Utilizing multi-source geospatial data, this invention spatially quantifies and comprehensively analyzes tidal flat topography, hydrological conditions, hydrodynamic environment, vegetation dispersal foundation, and external disturbances to identify tidal flat areas with potential for salt marsh vegetation restoration, providing a technical basis for determining the scope of salt marsh ecological restoration, configuring restoration space, and formulating engineering schemes.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention. Any modifications, substitutions, combinations, or simplifications made under the guidance of the spirit and basic principles of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for assessing the suitability of saline marsh habitats for ecological restoration zone identification, characterized in that, Includes the following steps: S1: Identify candidate tidal flat units; S2: Construct an index dataset for candidate tidal flat units; S3: Construct an index system for the suitability of saline marsh habitats; S4: Determine the weights of the indicators; The analytic hierarchy process (AHP) is used to construct judgment matrices for the target layer, criterion layer, and indicator layer. The maximum eigenvalue and corresponding eigenvector of each judgment matrix are calculated and normalized to obtain the weights. Then, the consistency index CI and consistency ratio CR are calculated to perform consistency checks. When CR < 0.10, the judgment matrix passes the consistency check and the obtained weights are valid. When CR ≥ 0.10, the judgment matrix is ​​readjusted and the calculation is repeated until CR < 0.10 is satisfied. S5: Establish a five-level evaluation standard; divide each indicator into five levels: high suitability, relatively high suitability, medium suitability, relatively low suitability, and unsuitability, and assign values ​​of 5, 4, 3, 2, and 1 respectively; S6: Calculate the habitat suitability index; calculate the comprehensive suitability index (HIS) of the salt marsh habitat based on the comprehensive weight of each evaluation indicator and the suitability assignment in S5. S7: Single-item constraint factor control; assign a constraint coefficient L=0 to the spatial range where the single-item index score is 1, and assign a constraint coefficient L=1 to the remaining spatial ranges; according to HSI * =HSI×L is used to adjust the overall suitability index of the salt marsh habitat by limiting factors, where L is the limiting factor. That is, the overall index HSI calculated in S6 is multiplied by the limiting factor 0 or 1 to obtain the final suitability index HSI. * ; S8: Suitability Zoning Output; The final score is divided into four levels: most suitable, relatively suitable, relatively unsuitable, and unsuitable, with the final suitability index HSI being the most suitable. * Satisfying 4≤HSI * ≤5、3≤HSI * <4、2≤HSI * <3 and 0≤HSI * When the value is less than 2, it is divided into four levels: most suitable, relatively suitable, relatively unsuitable, and unsuitable.

2. The method for evaluating the suitability of saline marsh habitat as described in claim 1, characterized in that, In S1: Based on the shoreline, intertidal zone range, and Landsat and Sentinel-2 remote sensing images of the study area, the inundation frequency method is used to identify tidal flat spaces, and candidate evaluation units are obtained by combining spatial control and land use information.

3. The method for evaluating the suitability of saline marsh habitat as described in claim 1, characterized in that, In S2: the data on digital elevation, tide level, seabed sediment, tidal flat time series, salinity, waves, tidal channels, existing salt marshes, construction land, aquaculture ponds and Spartina alterniflora are subjected to projection unification, spatial registration, cropping, resampling and rasterization.

4. The method for evaluating the suitability of saline marsh habitat as described in claim 1, characterized in that, In the S3: the indicator system includes four criteria layers: topographic conditions, hydrological conditions, diffusion basis and disturbance conditions; the indicator layers include slope, bottom type, tidal flat stability, relative tidal level elevation, distance from tidal channel, surface salinity, wave exposure, distance from existing salt marsh, distance from construction land, distance from aquaculture pond and Spartina alterniflora invasion degree.

5. The method for evaluating the suitability of saline marsh habitat as described in claim 1, characterized in that, S4 includes: S4-1: Establish a judgment matrix based on the relative importance of each evaluation factor to the suitability of the salt marsh habitat: criterion layer judgment matrix, topographic and geomorphological condition index judgment matrix, and hydrological condition index judgment matrix; under the diffusion basis condition, only the distance from the existing salt marsh is set as an index, so its local weight is 1, and its comprehensive weight is equal to the diffusion basis criterion layer weight. S4-2: Calculate the largest eigenvalue of the judgment matrix The corresponding eigenvectors are used, and the normalized eigenvectors are used as the local weights of each evaluation factor; the comprehensive weight of the indicator layer is obtained by multiplying the weight of its criterion layer by the corresponding local weight. S4-3: Calculate the consistency index CI and consistency ratio CR, and perform a consistency test on the judgment matrix; when CR < 0.10, the judgment matrix is ​​considered to have acceptable consistency and the weighting results are valid.

6. The method for evaluating the suitability of saline marsh habitat as described in claim 5, characterized in that, In S4-1: The criterion layer judgment matrix is: ； The topographic and geomorphological condition index judgment matrix is ​​as follows: ； The hydrological condition index judgment matrix is ​​as follows: ； The interference condition index judgment matrix is ​​as follows: 。 7. The method for evaluating the suitability of saline marsh habitat as described in claim 5, characterized in that, In S4-2, the mathematical expression of the consistency index is as follows: ； ； In the formula, n is the order of the judgment matrix. Let be the largest eigenvalue of the judgment matrix, CR be the consistency ratio of the judgment matrix, CI be the consistency index of the judgment matrix, and RI be the average random consistency index of the judgment matrix.

8. The method for evaluating the suitability of saline marsh habitat as described in claim 5, characterized in that, In S4-2: (1) In the criterion layer judgment matrix, the following is calculated: =4.0042, CI=0.0014, CR=0.0015<0.10, the criterion layer judgment matrix passes the consistency test; The corresponding eigenvectors, after normalization, yield the criterion layer weights, which are 0.3936, 0.3936, 0.0753, and 0.1375, respectively. (2) In the topographic and geomorphic condition index judgment matrix, the following is calculated: =3.0092, CI=0.0046, CR=0.0079<0.10, the judgment matrix passes the consistency test; The corresponding feature vectors are normalized to obtain the local weights of each index. The local weights are multiplied by the weights of the topographic and geomorphological condition criteria layer to obtain the comprehensive weights of the indexes. (3) The hydrological condition index judgment matrix is ​​calculated to obtain: =4.0310, CI=0.0103, CR=0.0115<0.10, the judgment matrix passes the consistency test; The corresponding eigenvectors are normalized to obtain the local weights of each indicator. The local weights are multiplied by the hydrological condition criterion layer weights to obtain the comprehensive weights of the indicators. (4) In the interference condition index judgment matrix, the following is calculated: =3.0000, CI=0, CR=0<0.10, the judgment matrix passes the consistency test; The corresponding eigenvectors are normalized to obtain the local weights of each indicator. The local weights are multiplied by the weights of the interference condition criterion layer to obtain the comprehensive weights of the indicators.

9. The method for evaluating the suitability of saline marsh habitat as described in claim 1, characterized in that, In S4, the criterion layer weights for topographic conditions, hydrological conditions, diffusion basis, and disturbance conditions are 0.3936, 0.3936, 0.0753, and 0.1375, respectively; the comprehensive weights for slope, substrate type, tidal flat stability, relative tidal level elevation, distance from tidal channel, surface salinity, wave exposure, distance from existing salt marsh, distance from construction land, distance from aquaculture pond, and Spartina alterniflora invasion degree are 0.0643, 0.1169, 0.2124, 0.1839, 0.0630, 0.0376, 0.1091, 0.0753, 0.0275, 0.0275, and 0.0825, respectively.

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