Coastal wetland ecological restoration system and method based on mangroves

Abstract

The present invention relates to the technical field of ecological environment restoration, and discloses a mangrove-based coastal wetland ecological restoration system and method, including collecting multi-source monitoring data of coastal wetlands, conducting regional division, extracting key ecological indicators, and calculating an ecological degradation index; identifying important ecological regions, setting a regional importance index, calculating an accessibility index, and generating a wetland restoration priority ranking map; determining alternative mangrove planting areas according to the ranking map, carrying out habitat suitability evaluation, identifying suitable growth areas, constructing a mangrove planting optimization model, and obtaining the optimal planting density; formulating an implementation plan for mangrove wetland restoration, and specifying the protection and restoration tasks, time schedule, budget, technical route, and management and protection measures for each stage. By combining multi-source monitoring data with an optimization model, the present invention improves the scientificity and effectiveness of mangrove planting and promotes the sustainable development of coastal wetland ecosystems.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment restoration, and more specifically, to a mangrove-based coastal wetland ecological restoration system and method. Background Art

[0002] With the rapid development of social economy, coastal wetland ecosystems are facing multiple threats such as changes in land use and increased environmental pollution, which have led to a sharp decrease in wetland area, a decline in biodiversity, and degradation of ecosystem service functions. Scholars have conducted a lot of research on the problem of coastal wetland ecological degradation.

[0003] For example, the existing Chinese patent with the publication number CN114386816A discloses a national space ecological restoration key area identification system, which is based on the national space "element-pattern-process-service" cascade framework, and emphasizes the integrated measurement and evaluation of mountain, water, forest, farmland, lake and grass elements, layout of ecological security network, maintenance of ecological security, and thus realizes the identification of multi-scale and multi-level ecological restoration key areas. This system can be widely used in the formulation of national space ecological restoration plans and planning, especially for the analysis of regional social-ecological coupling relationship and feedback mechanism, ecological environment status, ecological function importance, vulnerability evaluation research, and identification of key areas of ecological restoration. It has strong reference significance and reference value.

[0004] For example, the existing Chinese patent with the publication number CN1808480A discloses an optimal evaluation method for ecosystem service functions. This method divides the ecosystem into different ecological subsystems, and establishes relevant constraints and optimal planning models related to these conditions based on reasonable protection requirements for the ecosystem and its ecological subsystems, and finds the maximum value of the service value of the entire ecosystem and the area and service value of the relevant ecological subsystem at this time as the standard for evaluating the service function of the entire ecosystem. Finally, the actual area and service value of the entire ecosystem and the relevant ecological subsystems calculated are compared with the area and service value of the standard ecosystem obtained by the optimal planning to achieve the optimal evaluation of the ecosystem service function. This evaluation method has the characteristics of being reasonable, simple, and easy to apply.

[0005] However, existing coastal wetland ecological restoration methods mainly focus on a single ecological factor in the assessment and restoration process, lack the fusion analysis of multi-source heterogeneous data, and rarely consider the impact of socioeconomic factors such as regional importance and accessibility on the priority of ecological restoration. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a coastal wetland ecological restoration system and method based on mangroves, integrates multi-source heterogeneous monitoring data, refines ecological degradation assessment indicators, innovatively introduces indicators such as regional importance index, accessibility index, and ecological degradation index, carries out wetland restoration priority sorting and planning, and proposes a "suitable place and suitable tree" restoration plan based on mangrove planting for key restoration areas, in order to provide a systematic solution for the ecological protection and restoration of coastal wetlands.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Mangrove-based coastal wetland ecological restoration methods include:

[0009] Step S1000, collecting multi-source monitoring data of coastal wetlands, dividing coastal wetlands into regions, extracting key ecological indicators of coastal wetlands based on the multi-source monitoring data, and calculating ecological degradation indexes based on the key ecological indicators;

[0010] Step S2000, identifying important ecological areas in coastal wetlands, setting regional importance indexes for different areas of coastal wetlands; obtaining road network data, and calculating accessibility indexes based on the road network data; calculating wetland restoration priority indexes for each area according to the accessibility index, regional importance index, and ecological degradation index; and generating a wetland restoration priority ranking map according to the wetland restoration priority indexes;

[0011] Step S3000, obtaining candidate mangrove planting areas according to the wetland restoration priority ranking map, conducting habitat suitability evaluation in the candidate planting areas, and identifying suitable mangrove growth areas; collecting growth characteristic data of mangrove tree species to be planted, constructing a mangrove planting optimization model for mangrove growth areas according to the growth characteristic data, and obtaining the optimal planting plan for different tree species in each mangrove growth area according to the mangrove planting optimization model;

[0012] Step S4000, based on the optimal planting plan, formulate an implementation plan for mangrove wetland restoration. The implementation plan includes formulating short-term, medium-term and long-term phased goals, and detailing the protection and restoration tasks, time schedules, financial budgets, technical routes and management measures for each stage.

[0013] Furthermore, the step S1000 includes:

[0014] Step S1100, collecting multi-source monitoring data of coastal wetlands, and establishing a spatiotemporal database based on the multi-source monitoring data; the multi-source monitoring data includes satellite remote sensing image data, drone aerial image data, and field survey data;

[0015] Step S1200, preprocessing the multi-source monitoring data, wherein the preprocessing includes geometric correction, radiation correction and image enhancement for satellite remote sensing image data and drone aerial image data, and outlier detection and elimination for field survey data;

[0016] Step S1300, dividing the coastal wetland into regions based on the pre-processed multi-source monitoring data, based on the natural geographical features, ecological function zones and administrative management zones;

[0017] Step S1400, constructing a coastal wetland key ecological indicator extraction model based on the pre-processed multi-source monitoring data, and extracting key ecological indicators of each area of ​​the coastal wetland, wherein the key ecological indicators include vegetation coverage, plant biomass, water quality parameters, and soil physical and chemical properties;

[0018] Step S1500, calculating the ecological degradation index according to key ecological indicators.

[0019] Furthermore, the step S1400 includes:

[0020] Step S1410, collecting measured data of key ecological indicators as label data for a key ecological indicator extraction model for coastal wetlands;

[0021] Step S1420, pairing the preprocessed multi-source monitoring data with the measured data to construct a training data set and a verification data set for the coastal wetland key ecological indicator extraction model; annotating the training data set;

[0022] Step S1430, designing an end-to-end ecological indicator extraction network based on the CNN network, inputting the preprocessed multi-source monitoring data into the CNN model, extracting the multi-scale and multi-level spatiotemporal characteristics of the coastal wetland ecology; adding a regression layer at the end of the CNN model, and outputting multiple key ecological indicators at the same time; using the labeled training data set to train the CNN model, optimizing the model parameters through the back propagation algorithm, and minimizing the error between the predicted value and the measured value;

[0023] Step S1440, using the verification data set to verify the CNN model, and tuning the model according to the verification result to obtain the final coastal wetland key ecological indicator extraction model;

[0024] Step S1450, applying the coastal wetland key ecological indicator extraction model to the spatiotemporal database of the entire coastal wetland to extract the key ecological indicators of each area.

[0025] Furthermore, the step S1500 includes:

[0026] Step S1510, comparing and scoring the importance of each key ecological indicator in pairs, and constructing a judgment matrix;

[0027] Step S1520, calculating the eigenvector of the judgment matrix and performing normalization processing to obtain the weight of each key ecological indicator;

[0028] Step S1530, performing a consistency check on the judgment matrix. If the consistency check passes, the weight is valid; if it fails, the judgment matrix needs to be adjusted to recalculate the weights of the key ecological indicators;

[0029] Step S1540, performing standardization processing on the key ecological indicators, and calculating the ecological degradation index according to the standardized key ecological indicators and the weights of the key ecological indicators;

[0030] The method for calculating the ecological degradation index according to the standardized key ecological indicators and the weights of each key ecological indicator includes:

[0031]

[0032] Among them, EDI is the ecological degradation index, is the standardized vegetation cover, is the standardized plant biomass, is the standardized water quality parameter, is the standardized soil physical and chemical properties, w V is the weight of vegetation coverage, w B is the weight of plant biomass, w Q is the weight of water quality parameters, w S is the weight of soil physical and chemical properties, α is the interaction parameter between vegetation coverage and water quality parameters, and β is the interaction parameter between plant biomass and soil physical and chemical properties.

[0033] Furthermore, the step S2000 includes:

[0034] Step S2100, identifying important ecological regions in the coastal wetland, and setting regional importance indexes for different regions of the coastal wetland according to regional importance;

[0035] Step S2200, obtaining road network data, and based on the road network data, calculating the cost distance from each area to the nearest town or traffic artery, mapping the cost distance to the range of 0-1, and obtaining the accessibility index of different areas;

[0036] Step S2300, calculating the wetland restoration priority index of each region according to the accessibility index, regional importance index and ecological degradation index of each region;

[0037] Step S2400, according to the restoration priority index, the lakeside wetland is divided into five restoration priorities, namely, extremely high priority, high priority, medium priority, low priority and extremely low priority; the five restoration priorities are represented by different colors to generate a wetland restoration priority sorting diagram; the higher the priority index, the higher the restoration priority, which is represented by red in the priority sorting diagram; the lower the priority index, the lower the restoration priority, which is represented by green in the priority sorting diagram.

[0038] Furthermore, the step S3000 includes:

[0039] Step S3100, according to the wetland restoration priority ranking diagram, the coastal wetland areas with very high priority and high priority for wetland restoration are selected as candidate mangrove planting areas;

[0040] Step S3200, conducting habitat suitability assessment in the candidate mangrove planting area to identify areas suitable for mangrove growth;

[0041] Step S3300, collecting growth characteristic data of the mangrove tree species to be planted, the growth characteristic data including an appropriate range of elevation, an appropriate range of salinity, an appropriate range of flooding frequency, and an appropriate range of soil texture;

[0042] Step S3400, constructing a mangrove planting optimization model for mangrove suitable growth areas based on the growth characteristic data of the mangrove tree species to be planted, and obtaining the optimal planting density of different tree species in each mangrove suitable growth area based on the mangrove planting optimization model;

[0043] Step S3500, predicting and comparing the ecosystem service value and cost of different planting plans based on the optimal planting density of different tree species in each mangrove suitable growth area, and selecting the planting plan with high ecosystem service value and low cost as the optimal planting plan.

[0044] Furthermore, the step S3200 includes:

[0045] Step S3210, dividing the mangrove candidate planting area into a plurality of grids, and collecting elevation data, salinity data, flooding frequency data, and soil texture data in each grid;

[0046] Step S3220, calculating the habitat suitability index of each grid according to the elevation data, salinity data, flooding frequency data and soil texture data;

[0047] Step S3230, identifying areas suitable for mangrove growth based on the habitat suitability index of each grid.

[0048] Furthermore, the step S3230 includes:

[0049] Step S3231, set the suitability threshold, determine whether the habitat suitability index of the grid is greater than the suitability threshold, if so, the grid is regarded as a high suitability grid, and proceed to step S3232, if not, determine whether the habitat suitability index of the next grid is greater than the suitability threshold, until all grids are determined;

[0050] Step S3232, defining grid adjacency rules, taking each high suitability grid as a starting point, searching for adjacent high suitability grids according to the adjacency rules, and marking them as the same connected area; repeating this process until all high suitability grids are assigned to a connected area;

[0051] Step S3233, calculating the number of grids contained in each connected region, and converting it into the actual area of ​​the connected region according to the grid size;

[0052] Step S3234, setting a minimum area threshold, extracting connected areas whose actual area is larger than the minimum area threshold, and marking them as areas suitable for mangrove growth; for connected areas whose actual area is less than or equal to the minimum area threshold, mark them as areas unsuitable for mangrove growth.

[0053] Furthermore, the step S3400 includes:

[0054] Step S3410, taking the planting density of each mangrove tree species in each mangrove suitable growth area as a decision variable;

[0055] Step S3420, constructing an objective function to maximize the ecosystem service value; the ecosystem service value is represented by the weighted sum of the planting density of each tree species in each mangrove suitable growth area, and the weight is the ecological value per unit area of ​​each tree species;

[0056] Step S3430, based on the growth characteristic data of the mangrove tree species to be planted, constraining conditions are constructed, the constraints including that the sum of the planting density in each mangrove suitable growth area is equal to the total planting density of the area; the planting density of each tree species in each mangrove suitable growth area does not exceed the maximum planting density of the tree species; the elevation, salinity, flooding frequency and soil texture of each tree species in each mangrove suitable growth area are all within the suitable range of the tree species; the total planting cost does not exceed the budget limit;

[0057] Step S3440, solving the mangrove planting optimization model to obtain the optimal planting density of different tree species in each mangrove suitable growth area.

[0058] The mangrove-based coastal wetland ecological restoration system is used to implement the above-mentioned mangrove-based coastal wetland ecological restoration method, including:

[0059] Data acquisition module: used to collect multi-source monitoring data of coastal wetlands, including satellite remote sensing image data, drone aerial image data and field survey data;

[0060] Data preprocessing module: used to preprocess multi-source monitoring data, including geometric correction, radiation correction and image enhancement for satellite remote sensing image data and drone aerial image data, and outlier detection and elimination for field survey data;

[0061] Regional division module: used to divide coastal wetlands into regions based on natural geographical features, ecological functional areas and administrative management areas according to pre-processed multi-source monitoring data;

[0062] Key ecological indicator extraction module: used to build a coastal wetland key ecological indicator extraction model based on pre-processed multi-source monitoring data, and extract key ecological indicators for each area of ​​the coastal wetland;

[0063] Ecological degradation index calculation module: used to calculate the ecological degradation index based on key ecological indicators;

[0064] Restoration priority ranking module: used to set regional importance indexes for different areas of coastal wetlands, calculate accessibility indexes and wetland restoration priority indexes for each area, and generate wetland restoration priority ranking maps;

[0065] Mangrove planting planning module: used to identify suitable mangrove growth areas, construct mangrove planting optimization models for suitable mangrove growth areas, and obtain the optimal planting density of different tree species in each mangrove growth area; based on the optimal planting density, predict and compare the ecosystem service value and cost of different planting plans, and select the optimal planting plan; based on the optimal planting plan, formulate an implementation plan for mangrove wetland restoration.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention establishes a spatiotemporal database through multi-source remote sensing monitoring data, and combines key ecological indicator extraction models, ecological degradation index calculations, and restoration priority rankings. It can scientifically evaluate the ecological status of coastal wetlands, determine the priority areas and optimal density of mangrove planting, and improve the pertinence and rationality of planting, thereby improving the survival rate and effect of planting.

[0068] Mangroves, as an important component of coastal wetland ecosystems, play important ecological functions such as carbon fixation, coastline protection, and providing habitats for other organisms. The present invention can effectively repair damaged wetlands, maintain biodiversity, and promote the sustainable development of the entire coastal wetland ecosystem through the scientific planting of mangroves.

[0069] The present invention not only optimizes the mangrove planting plan, but also formulates short-term, medium-term and long-term restoration implementation plans, details the protection and restoration tasks, time schedules, financial budgets, technical routes and management measures for each stage, provides specific guidance for actual operations, and helps to efficiently implement wetland restoration work.

[0070] Through scientific model analysis and optimization, the present invention can maximize the survival rate of planting, avoid the waste of resources and ecological risks caused by blind operations, and fundamentally reduce the overall cost and risk of wetland restoration.

[0071] Healthy mangrove wetlands can provide a variety of ecosystem services such as carbon sequestration, flood prevention and disaster reduction, and maintenance of biodiversity; the present invention helps to restore and enhance the ecological functions of coastal wetlands and create more ecological, economic and social benefits for human society. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0073] Figure 1 This is a flow chart of the mangrove-based coastal wetland ecological restoration method of the present invention;

[0074] Figure 2 This is a functional module diagram of the mangrove-based coastal wetland ecological restoration system in the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Example 1

[0077] See also Figure 1 As shown, this embodiment provides a method for ecological restoration of coastal wetlands based on mangroves, including:

[0078] Step S1000: Collect multi-source monitoring data of coastal wetlands, divide coastal wetlands into regions, extract key ecological indicators of coastal wetlands based on the multi-source monitoring data, and calculate ecological degradation index based on the key ecological indicators;

[0079] Furthermore, step S1000 includes:

[0080] Step S1100, collecting multi-source monitoring data of coastal wetlands, and establishing a spatiotemporal database based on the multi-source monitoring data; the multi-source monitoring data includes satellite remote sensing image data, drone aerial image data, and field survey data;

[0081] Specifically, collect high-resolution satellite remote sensing image data, such as WorldView, GeoEye, etc., to obtain multispectral, high-definition images of coastal wetlands. These images contain rich spectral and spatial information, which can reflect the characteristics of wetland vegetation, water bodies, terrain, etc. Obtain visible light and multispectral image data from drone aerial photography. Compared with satellite images, the advantages of drone aerial photography are higher spatial resolution and more flexible acquisition time, which can provide more detailed wetland information. Conduct field surveys and sampling to obtain ground-truth data, such as plant species, coverage, biomass, water quality parameters, soil physical and chemical properties, etc. Measured data can be used to verify and calibrate remote sensing interpretation results and improve data accuracy. Establish a spatiotemporal database for coastal wetlands to standardize the storage and management of multi-source and multi-temporal monitoring data. Use a combination of relational databases (such as PostgreSQL) and geographic information systems (GIS) to achieve efficient data storage, query and analysis. By integrating information from different data sources, the ecological status of wetlands can be comprehensively assessed.

[0082] Step S1200, preprocessing the multi-source monitoring data, wherein the preprocessing includes geometric correction, radiation correction and image enhancement for satellite remote sensing image data and drone aerial image data, and outlier detection and elimination for field survey data;

[0083] Specifically, the satellite remote sensing image data and the UAV aerial image data are geometrically corrected to eliminate the geometric distortion of the image and ensure the spatial registration of different data sources. Geometric correction is to correct the image to a unified spatial reference system to ensure the accurate registration of different data sources in space, providing a basis for subsequent data fusion and analysis. Common correction methods include polynomial correction and orthorectification. The satellite remote sensing image data and the UAV aerial image data are radiometrically corrected to convert the digital value (DN value) of the image into reflectivity or radiance value to eliminate the influence of atmospheric effects and sensor response differences. Radiometric correction establishes a connection between the grayscale value of the image and the actual reflection characteristics of the object to obtain the radiation with a clear physical meaning, which can be used to quantitatively invert surface parameters. Common methods include absolute radiometric correction and relative radiometric correction. Image enhancement techniques such as histogram matching and image fusion are used to improve the visual interpretation quality of satellite remote sensing image data and UAV aerial image data, improve the visual effect of the image, and highlight the target features of interest, such as the texture and color difference of vegetation and water bodies, which helps to more accurately interpret the vegetation type, water body boundary and other information of the wetland. Outlier detection and elimination are performed on the field survey data to ensure the accuracy and consistency of the data. Statistical methods, such as the 3σ principle and the Laida criterion, are used to identify and remove outliers. Outlier detection can remove erroneous records or abnormal deviations in the measured data, improve data quality, and ensure the reliability of subsequent analysis. Data preprocessing is an important step to improve data availability, eliminate errors and noise, and provide high-quality data support for wetland ecological degradation assessment.

[0084] Step S1300, dividing the coastal wetland into regions based on the pre-processed multi-source monitoring data, based on the natural geographical features, ecological function zones and administrative management zones;

[0085] Specifically, using remote sensing image data, wetlands are divided into different natural units according to natural elements such as topography, water system distribution, and vegetation type. This division method can better reflect the natural attributes of wetlands and lay the foundation for assessing habitat quality. Wetlands are divided into different functional zones based on the dominant ecological functions of wetlands, such as protected areas, buffer zones, agricultural and fishery production zones, etc. This division is conducive to the subsequent formulation of targeted ecological management and utilization measures. Considering the administrative divisions where wetlands are located is conducive to linking with local management policies and plans, and facilitating the implementation of subsequent protection and utilization plans. On the basis of the above three levels, the importance of different factors is weighed according to specific circumstances, and a comprehensive division is made. For example, if some areas have sensitive natural conditions, natural geographical features can be given priority; if some areas have frequent human interference activities, the division of ecological functional zones can be focused on.

[0086] Step S1400, constructing a coastal wetland key ecological indicator extraction model based on the pre-processed multi-source monitoring data, and extracting key ecological indicators of each area of ​​the coastal wetland, wherein the key ecological indicators include vegetation coverage, plant biomass, water quality parameters, and soil physical and chemical properties;

[0087] Further, step S1400 includes:

[0088] Step S1410, collecting measured data of key ecological indicators as label data for a key ecological indicator extraction model for coastal wetlands;

[0089] Step S1420, pairing the preprocessed multi-source monitoring data with the measured data to construct a training data set and a verification data set for the coastal wetland key ecological indicator extraction model; annotating the training data set;

[0090] Step S1430, designing an end-to-end ecological indicator extraction network based on a convolutional neural network (CNN), inputting the preprocessed multi-source monitoring data into the CNN model, and extracting the multi-scale and multi-level spatiotemporal characteristics of the coastal wetland ecology; adding a regression layer at the end of the CNN model, and outputting multiple key ecological indicators at the same time; using the labeled training data set to train the CNN model, optimizing the model parameters through the back propagation algorithm, and minimizing the error between the predicted value and the measured value;

[0091] Step S1440, using the verification data set to verify the CNN model, and tuning the model according to the verification result to obtain the final coastal wetland key ecological indicator extraction model;

[0092] Step S1450, applying the coastal wetland key ecological indicator extraction model to the spatiotemporal database of the entire coastal wetland to extract the key ecological indicators of each area;

[0093] Vegetation coverage refers to the proportion of the surface covered by vegetation in a certain area. It is an important indicator for measuring the health of an ecosystem and reflects the protective role of vegetation on the environment, such as preventing soil erosion and regulating climate. High vegetation coverage usually indicates that the ecosystem is relatively healthy and that vegetation can provide habitats, food and protection. Plant biomass refers to the total weight of plants per unit area. It is a direct reflection of plant growth and productivity and affects carbon and nutrient cycles. High plant biomass indicates that the ecosystem has strong productivity and carbon fixation capacity and is an important indicator of ecosystem stability. Water quality parameters refer to physical, chemical and biological indicators in water bodies, such as pH, dissolved oxygen, nitrogen and phosphorus content, etc., which reflect the degree of water pollution and the health of the ecosystem and are important conditions for the survival of aquatic organisms. Good water quality is the basis of a healthy ecosystem. Deterioration of water quality will lead to a decline in biodiversity and degradation of ecological functions. Soil physical and chemical properties refer to the physical structure (such as particle composition and porosity) and chemical composition (such as pH, organic matter content, and nutrient content) of the soil. The physical and chemical properties of soil determine the fertility of the soil and the plant growth environment, affecting the health and productivity of the ecosystem. Good soil physical and chemical properties are conducive to plant growth and ecosystem stability, while poor or polluted soil can lead to ecological degradation.

[0094] Step S1400 uses an end-to-end ecological indicator extraction model based on CNN, which can effectively extract key ecological indicators of coastal wetlands from multi-source monitoring data. Key ecological indicators include vegetation coverage, plant biomass, water quality parameters, and soil physical and chemical properties. This method uses machine learning algorithms to automatically extract relevant indicator values ​​from multi-source monitoring data, greatly improving work efficiency; it can be trained based on a large amount of labeled data to provide more objective and consistent results and improve data reliability; at the same time, it uses satellite remote sensing, drone aerial photography and field survey data to integrate information of different scales and perspectives, and can comprehensively and accurately describe the ecological status of wetlands; by applying to the entire spatiotemporal database, the model can efficiently extract ecological indicator values ​​for each region and each time point, realize dynamic and continuous monitoring of wetland ecology, and provide timely decision-making basis for formulating protection measures.

[0095] Step S1500, calculating the ecological degradation index according to the key ecological indicators;

[0096] Further, step S1500 includes:

[0097] Step S1510, comparing and scoring the importance of each key ecological indicator in pairs, and constructing a judgment matrix;

[0098] Step S1520, calculating the eigenvector of the judgment matrix and performing normalization processing to obtain the weight of each key ecological indicator;

[0099] Step S1530, performing a consistency check on the judgment matrix. If the consistency check passes, the weight is valid; if it fails, the judgment matrix needs to be adjusted to recalculate the weights of the key ecological indicators;

[0100] Step S1540, performing standardization processing on the key ecological indicators, and calculating the ecological degradation index according to the standardized key ecological indicators and the weights of the key ecological indicators;

[0101] According to the standardized key ecological indicators and the weights of each key ecological indicator, the methods for calculating the ecological degradation index include:

[0102]

[0103] in:

[0104] EDI: Ecological Degradation Index;

[0105] Normalized vegetation cover;

[0106] Normalized plant biomass;

[0107] Standardized water quality parameters;

[0108] Standardized soil physical and chemical properties;

[0109] w V : The weight of vegetation coverage is determined by those skilled in the art based on a large number of experiments;

[0110] w B : The weight of plant biomass is determined by those skilled in the art based on a large number of experiments;

[0111] w Q : The weights of water quality parameters are determined by those skilled in the art based on a large number of experiments;

[0112] w S : The weights of soil physical and chemical properties are determined by technicians in this field based on a large number of experiments and meet the requirements of w V +w B +w Q +w S =1;

[0113] α: interaction parameter between vegetation coverage and water quality parameters, used to adjust the mutual influence between vegetation coverage and water quality parameters;

[0114] β: interaction parameter between plant biomass and soil physical and chemical properties, used to adjust the mutual influence between plant biomass and soil physical and chemical properties;

[0115] Step S1500 eliminates the dimensional differences between different ecological indicators through standardization processing, so that each indicator can be comprehensively evaluated on the same scale. By weighted comprehensiveness of various indicators, the ecological degradation index can comprehensively reflect the ecological health status of coastal wetlands. Interaction parameters are introduced into the formula, and the mutual influence between different ecological indicators is taken into account, thereby improving the accuracy and scientificity of the evaluation.

[0116] Step S2000: Identify important ecological areas in coastal wetlands, and set regional importance indexes for different areas of coastal wetlands; obtain road network data, and calculate accessibility indexes based on the road network data; calculate wetland restoration priority indexes for each area according to the accessibility index, regional importance index, and ecological degradation index; generate a wetland restoration priority ranking map according to the wetland restoration priority index;

[0117] Furthermore, step S2000 includes:

[0118] Step S2100, identifying important ecological regions in the coastal wetland, and setting regional importance indexes for different regions of the coastal wetland according to regional importance;

[0119] For example, national and provincial nature reserves, internationally important wetlands, national wetland parks, etc., set the regional importance index to 5; identify important fishery waters, tourist attractions, shipping channels, etc., set the regional importance index to 3; the regional importance index of other areas is set to 1; national and provincial nature reserves, internationally important wetlands, national wetland parks and other areas have extremely high ecological value and protection significance, and are key areas for maintaining biodiversity and protecting rare species, so their regional importance index is set to the highest value of 5. Important fishery waters, tourist attractions, shipping channels and other areas have high ecological service functions and socio-economic value, and their ecological restoration is also conducive to supporting regional sustainable development, so their regional importance index is set to a medium value of 3. Although the ecological value of other areas is relatively low, their ecological restoration is still important for improving the overall ecological environment quality, so their regional importance index is set to the lowest value of 1.

[0120] Step S2100 identifies important ecological areas in coastal wetlands and sets regional importance indexes for different areas according to regional importance, so as to effectively distinguish the conservation value and ecological significance of different areas of wetlands. This lays the foundation for the subsequent formulation of zoning and grading restoration strategies, ensuring the priority allocation and precise management of resources. By reasonably setting the regional importance index, the role and value of different regions in ecological protection, economic development, etc. can be fully reflected, providing a basis for the scientific allocation of restoration resources and the formulation of differentiated protection measures, so that limited manpower and material resources can be concentrated on the key areas that need protection the most, improving the pertinence and efficiency of restoration work, and maximizing the comprehensive benefits of restoration work.

[0121] Step S2200, obtaining road network data, and based on the road network data, calculating the cost distance from each area to the nearest town or traffic artery, mapping the cost distance to the range of 0-1, and obtaining the accessibility index of different areas;

[0122] Specifically, road network data can be obtained through traffic geographic data open to government departments, online map service providers, remote sensing image interpretation or on-site field measurements. Based on the road network data, the cost distance from each area to the nearest town or traffic artery is calculated. The smaller the cost distance, the better the accessibility. Then the cost distance is mapped to the range of 0-1 to obtain the accessibility index. The higher the index, the more convenient the traffic and the higher the feasibility of restoration implementation. By calculating the cost distance from each area to the nearest town or traffic artery, the degree of traffic convenience and infrastructure conditions of the area can be reasonably evaluated. The higher the accessibility index, the better the traffic conditions in the area, the more favorable the external conditions are for wetland restoration work, and the stronger the operability and feasibility. The accessibility index directly reflects the difficulty of carrying out wetland restoration work in the future. For areas with poor accessibility, not only is the transportation cost of construction materials high and the efficiency of engineering development low, but the subsequent monitoring and maintenance work will also be greatly hindered, and the overall restoration effect may be greatly reduced. In contrast, areas with better accessibility are relatively time-saving and labor-saving, which can minimize engineering costs and improve restoration quality. The calculation process of the accessibility index in step S2200 is scientific and reasonable, based on actual road network data, and maps the cost distance to the interval of 0-1, which has good quantification and comparability. This provides convenience for combining the accessibility index with other indicators and comprehensively determining the repair priority.

[0123] Step S2300, calculating the wetland restoration priority index of each region according to the accessibility index, regional importance index and ecological degradation index of each region;

[0124] According to the accessibility index, regional importance index and ecological degradation index of each area, the method of calculating the wetland restoration priority index of each area includes:

[0125] The accessibility index, regional importance index and ecological degradation index of each region are standardized to obtain the standardized accessibility index A of each region. i , Regional Importance Index I i and ecological degradation index E i ; Then according to A i ,I i and E i Calculate wetland restoration priority index;

[0126] According to A i ,I i and E i The methodology for calculating the wetland restoration priority index includes:

[0127]

[0128] in:

[0129] Pi: wetland restoration priority index of the ith region;

[0130] wE: weight of ecological degradation index;

[0131] wI: weight of regional importance index;

[0132] wA: weight of accessibility index;

[0133] ε: adjustment parameter of ecological degradation index;

[0134] σ: adjustment parameter of regional importance index;

[0135] γ: adjustment parameter of accessibility index;

[0136] i: the i-th area of ​​coastal wetland;

[0137] Ei: standardized ecological degradation index of the ith region;

[0138] Ai: standardized accessibility index of the ith region;

[0139] Ii: standardized regional importance index of the ith region;

[0140] When determining the weights, experts in the fields of ecology and wetland management can be invited to give scores, and the weight values ​​can be determined by combining the opinions of all parties. E The higher the value, the more attention is paid to the degree of ecological degradation, and the priority is to restore the areas with the most serious ecological degradation; the higher the w I The value of w indicates that more attention is paid to the ecological value and importance of the region, and key ecological areas are restored first; higher w AA higher value means that more emphasis is placed on the accessibility of the area, giving priority to repairing areas with convenient transportation.

[0141] Including the ecological degradation index in the calculation can ensure that priority is given to the restoration of areas where the ecosystem is severely damaged and in urgent need of restoration. Including the regional importance index in the calculation can give priority to the protection and restoration of key areas with high ecological value and important significance. Including the accessibility index in the calculation can give priority to the restoration of areas with convenient transportation and low project implementation costs. Through weighted comprehensive calculation of the above three indexes, the wetland restoration priority index can comprehensively and scientifically evaluate the restoration needs of each area and reasonably sort the importance of restoration. This helps to reasonably allocate limited human and material resources, improve the pertinence and efficiency of restoration work, and enable resources to be truly prioritized in key areas that are most in need of protection, thereby maximizing the comprehensive benefits of restoration work.

[0142] Step S2400: According to the restoration priority index, the lakeside wetland is divided into five restoration priorities, namely, extremely high priority, high priority, medium priority, low priority and extremely low priority; the five restoration priorities are represented by different colors, and a wetland restoration priority sorting diagram is generated; the higher the priority index, the higher the restoration priority, which is represented by red in the priority sorting diagram; the lower the priority index, the lower the restoration priority, which is represented by green in the priority sorting diagram;

[0143] Specifically, the natural breakpoint method can be used to grade the restoration priority index, and the wetlands can be divided into five priorities (very high priority, high priority, medium priority, low priority and very low priority), which are represented by red, orange, yellow, light green and dark green. The generated wetland restoration priority ranking map intuitively reflects the importance of restoration and can provide an intuitive decision-making reference for wetland management departments. High-priority areas urgently need to be restored to control the degradation process and restore wetland functions; low-priority areas can be gradually restored in the future. At the same time, in the arrangement of restoration measures, we must also take a comprehensive approach. While giving priority to repairing the most severely damaged areas, we must also pay attention to the overall protection and restoration of the ecosystem.

[0144] Step S2400 generates a wetland restoration priority ranking map. This visual expression uses color gradients to clearly show which areas are in the most urgent need of restoration (red) and which areas can be restored later (green), which helps decision makers quickly grasp the priority of restoration. Based on the priority ranking map, wetland management departments can formulate restoration plans in stages and batches, reasonably arrange manpower and material resources, and improve work efficiency. Through the priority ranking map, the differences between different areas are clear at a glance, which helps to formulate differentiated restoration strategies according to actual conditions and promote the coordinated development of the entire coastal wetland.

[0145] Step S3000, obtaining candidate mangrove planting areas according to the wetland restoration priority ranking map, conducting habitat suitability evaluation in the candidate planting areas, and identifying suitable mangrove growth areas; collecting growth characteristic data of mangrove tree species to be planted, constructing a mangrove planting optimization model for mangrove growth areas according to the growth characteristic data, and obtaining the optimal planting density of different tree species in each mangrove growth area according to the mangrove planting optimization model;

[0146] Furthermore, step S3000 includes:

[0147] Step S3100, according to the wetland restoration priority ranking diagram, the coastal wetland areas with very high priority and high priority for wetland restoration are selected as candidate mangrove planting areas;

[0148] Specifically, referring to the wetland restoration priority map, extremely high priority and high priority areas were extracted. These areas have severe ecological degradation and great restoration potential, and are the key areas for priority restoration.

[0149] Step S3200, conducting habitat suitability assessment in the candidate mangrove planting area to identify areas suitable for mangrove growth;

[0150] Further, step S3200 includes:

[0151] Step S3210, dividing the mangrove candidate planting area into a plurality of grids, and collecting elevation data, salinity data, flooding frequency data, and soil texture data in each grid;

[0152] Specifically, the geographical boundaries of the candidate mangrove planting areas are determined through GPS, GIS software or topographic maps. According to the size of each area and the required accuracy, an appropriate grid size (such as 10m x 10m or 50m x50m) is selected. The candidate planting areas are divided into multiple grids using GIS software, each grid representing a small area. Elevation data represents the height of the ground within the regional grid. Elevation is crucial to the growth of mangroves because mangroves usually grow in the intertidal zone, that is, the area between the high tide line and the low tide line. The appropriate elevation range allows mangroves to be regularly flooded (providing nutrients and water) and exposed to the water surface (preventing long-term flooding from causing hypoxia). Too high or too low elevations will affect the healthy growth of mangroves. Elevation data is obtained through remote sensing images or topographic measuring instruments. Salinity data indicates the concentration of dissolved salts in water bodies. Mangroves adapt to a certain range of salinity, within which they can grow best. The appropriate salinity range helps mangroves to photosynthesize and maintain the osmotic pressure of cells. Too high salinity will cause mangroves to dehydrate, while too low salinity may be detrimental to the growth of mangroves. Use a portable salinometer to measure salinity in each grid, or extract it from existing salinity monitoring data to record the salinity value in each grid. Flooding frequency data indicates the frequency of a certain location being flooded in a specific period of time. Mangroves need regular flooding to obtain the required water and nutrients, and also need to be exposed to the air for gas exchange. The appropriate flooding frequency can help mangroves meet their water needs while avoiding the hypoxic environment caused by long-term flooding. Too high or too low a flooding frequency is not conducive to the healthy growth of mangroves. Record the flooding frequency in each grid through field observation, or use remote sensing image interpretation statistics. Soil texture data includes the physical and chemical properties of soil, such as soil particle size distribution, organic matter content, nutrient content, etc. Suitable soil texture provides nutrients and stable root support for mangrove growth. The organic matter content and nutrient content of the soil directly affect the growth rate and health of mangroves. Soil that is too sticky may lead to poor drainage, while soil that is too sandy may cause nutrient and water loss too quickly. Soil sampling is carried out in each grid to analyze soil organic matter content, particle distribution, etc., and the analysis results are assigned to the corresponding grid.

[0153] Step S3220, calculating the habitat suitability index of each grid according to the elevation data, salinity data, flooding frequency data and soil texture data;

[0154] Methods for calculating habitat suitability indices for each raster based on elevation data, salinity data, flooding frequency data, and soil texture data include:

[0155]

[0156] in:

[0157] S ij : Habitat suitability index of grid (i, j);

[0158] H ij : The elevation of grid (i, j);

[0159] H opt : The best elevation for mangrove growth;

[0160] G ij : salinity of grid (i,j);

[0161] G opt : The optimal salinity for mangrove growth;

[0162] F ij : Flooding frequency of grid (i,j);

[0163] F opt : The optimal flooding frequency for mangrove growth;

[0164] O ij : soil organic matter content of grid (i, j);

[0165] O opt : Optimal organic matter content for mangrove growth;

[0166] w1: the weight of elevation, determined by those skilled in the art based on a large number of experiments;

[0167] w2: weight of salinity, determined by those skilled in the art based on a large number of experiments;

[0168] w3: weight of flooding frequency, determined by technicians in this field based on a large number of experiments;

[0169] w4: weight of soil organic matter content, determined by technicians in this field based on a large number of experiments;

[0170] k1: adjustment coefficient of elevation, determined by technicians in this field based on a large number of experiments;

[0171] k2: salinity adjustment coefficient, determined by those skilled in the art based on a large number of experiments;

[0172] k3: adjustment coefficient of flooding frequency, determined by technicians in this field based on a large number of experiments;

[0173] k4: adjustment coefficient of soil organic matter content, determined by technicians in this field based on a large number of experiments;

[0174] Step S3220 can comprehensively evaluate the habitat suitability index of each grid of the coastal wetland by comprehensively analyzing the elevation data, salinity data, flooding frequency data and soil texture data. Specifically, the elevation data reflects the terrain undulation and flooding risk, the salinity data reflects the salt content of the soil and water body, the flooding frequency data reveals the hydrological dynamic characteristics of the wetland, and the soil texture data describes the physical properties of the soil. By integrating these multidimensional data to calculate the habitat suitability index, it is possible to accurately identify areas suitable for ecological restoration and provide a scientific basis for the formulation of restoration strategies. The calculation formula of the habitat suitability index quantifies the suitability of each grid habitat, so that the ecological restoration priority of different regions can be objectively evaluated. The weights of different factors reflect their relative importance in habitat suitability. The weights are determined by expert scoring or data analysis, making the evaluation results more scientific. The formula integrates the influence of multiple factors into a suitability index, avoids the one-sidedness of single factor evaluation, and provides a more comprehensive ecological assessment. Expressing habitat suitability in a quantitative way facilitates comparison and priority sorting between different regions and supports scientific decision-making.

[0175] Step S3230, identifying suitable mangrove growth areas according to the habitat suitability index of each grid;

[0176] Further, step S3230 includes:

[0177] Step S3231, set the suitability threshold, determine whether the habitat suitability index of the grid is greater than the suitability threshold, if so, the grid is regarded as a high suitability grid, and proceed to step S3232, if not, determine whether the habitat suitability index of the next grid is greater than the suitability threshold, until all grids are determined;

[0178] Step S3232, defining grid adjacency rules, taking each high suitability grid as a starting point, searching for adjacent high suitability grids according to the adjacency rules, and marking them as the same connected area; repeating this process until all high suitability grids are assigned to a connected area;

[0179] There are two common adjacency rules: 4-adjacency and 8-adjacency. 4-adjacency means that the grid is adjacent to other grids in the four directions of up, down, left, and right; 8-adjacency includes the diagonal direction in addition to the four directions of up, down, left, and right, that is, a grid is adjacent to the surrounding 8 grids.

[0180] Step S3233, calculating the number of grids contained in each connected region, and converting it into the actual area of ​​the connected region according to the grid size;

[0181] Step S3234, setting a minimum area threshold, extracting connected areas whose actual area is larger than the minimum area threshold, and marking them as areas suitable for mangrove growth; for connected areas whose actual area is less than or equal to the minimum area threshold, marking them as areas unsuitable for mangrove growth;

[0182] Step S3230 can quantitatively evaluate the suitability of mangrove growth by calculating the habitat suitability index of each grid, avoiding the arbitrariness of subjective judgment. Setting the suitability threshold and area threshold improves the scientificity and accuracy of the recognition results. Using raster data and adjacency rules, connected suitable areas can be automatically and efficiently identified, greatly saving manpower and material resources and improving work efficiency. Based on unified rules and thresholds, the recognition results obtained by different personnel have good consistency, avoiding the deviation caused by subjective differences, which is conducive to the objectivity and repeatability of the results. By searching for adjacent high-suitability grids and classifying them as the same connected area, the integrity and completeness of the identified suitable areas can be ensured, providing a basis for subsequent planning. The setting of the suitability threshold and the minimum area threshold can be adjusted according to actual needs, which improves the flexibility of the recognition results and adapts to different application scenarios.

[0183] Habitat suitability assessment takes into account multiple key factors that affect the growth of mangroves, and can comprehensively assess the suitability of a region. Through this assessment, planting sites can be selected scientifically and rationally to avoid the waste of resources and ecological risks caused by blind planting. A suitable habitat environment is a prerequisite for the healthy growth of mangroves. By identifying suitable areas, the best living environment can be provided for mangroves, fundamentally improving the survival rate and survival rate after planting, and ensuring that the invested planting resources will not be wasted. Mangroves grow vigorously in suitable areas, which can maximize their ecological functions such as embankment reinforcement, water purification, and habitat provision, and maximize ecological benefits. Compared with unreasonable planting areas, the ecological performance of restoration can be significantly enhanced. Planting mangroves in unsuitable areas will face serious threats to their survival, and they are prone to large-scale death and rot, which not only wastes resources, but may also cause new pollution to the ecological environment and endanger other organisms. By avoiding these areas, ecological risks can be effectively prevented.

[0184] Step S3300, collecting growth characteristic data of the mangrove tree species to be planted, the growth characteristic data including an appropriate range of elevation, an appropriate range of salinity, an appropriate range of flooding frequency, and an appropriate range of soil texture;

[0185] Specifically, different mangrove tree species have different adaptability to habitat conditions, so it is necessary to collect their growth characteristics data according to the characteristics of the tree species to be planted. These data are obtained by consulting literature, field surveys, expert consultation, etc. The suitable elevation range refers to the elevation range in which mangrove tree species can grow normally, which is usually closely related to the tidal water level. Knowing the suitable elevation range of tree species, you can choose a suitable planting location to ensure that mangroves can adapt to periodic flooding and exposure. The suitable salinity range refers to the range of water salinity that mangrove tree species can tolerate. Mangroves have a certain salt tolerance, but the degree of salt tolerance varies among different tree species. Knowing the suitable salinity range of tree species, you can choose suitable tree species according to the salinity conditions of the planting area. The suitable range of flooding frequency refers to the range of flooding frequency that mangrove tree species can adapt to. Mangroves need a certain frequency of flooding to obtain nutrients, but too high a flooding frequency may cause the trees to lack oxygen. Knowing the suitable range of flooding frequency of tree species, you can choose a suitable planting area to ensure that the tree species can obtain suitable flooding conditions. The suitable range of soil texture refers to the requirements of mangrove species for the physical and chemical properties of the soil. Different tree species have different adaptability to soil texture. Some tree species prefer sandy soil, while others are adapted to heavy clay soil. Knowing the suitable range of soil texture for a tree species can help you choose an area with suitable soil conditions for planting.

[0186] Collecting growth characteristic data of mangrove tree species to be planted can comprehensively describe the ecological niche requirements of each tree species, lay the foundation for formulating personalized planting strategies, avoid a one-size-fits-all approach, and maximize the specific needs of different species. Combined with the results of habitat suitability assessment, the most suitable tree species can be selected according to different habitat characteristics. The tree species are matched with the habitat, the planting is more targeted, and the survival rate and growth conditions will be more ideal. With the growth characteristic data of multiple tree species, the tree species combination and spatial pattern of mixed planting can be reasonably designed to make full use of habitat heterogeneity, achieve complementary coexistence, and improve the species diversity and landscape diversity of mangroves.

[0187] Step S3400, constructing a mangrove planting optimization model for mangrove suitable growth areas based on the growth characteristic data of the mangrove tree species to be planted, and obtaining the optimal planting density of different tree species in each mangrove suitable growth area based on the mangrove planting optimization model;

[0188] Further, step S3400 includes:

[0189] Step S3410, taking the planting density of each mangrove tree species in each mangrove suitable growth area as a decision variable;

[0190] Step S3420, constructing an objective function to maximize the ecosystem service value; the ecosystem service value is represented by the weighted sum of the planting density of each tree species in each mangrove suitable growth area, and the weight is the ecological value per unit area of ​​each tree species;

[0191] Step S3430, based on the growth characteristic data of the mangrove tree species to be planted, constraining conditions are constructed, the constraints including that the sum of the planting density in each mangrove suitable growth area is equal to the total planting density of the area; the planting density of each tree species in each mangrove suitable growth area does not exceed the maximum planting density of the tree species; the elevation, salinity, flooding frequency and soil texture of each tree species in each mangrove suitable growth area are all within the suitable range of the tree species; the total planting cost does not exceed the budget limit;

[0192] Step S3440, solving the mangrove planting optimization model to obtain the optimal planting density of different tree species in each mangrove suitable growth area;

[0193] Specifically, the mangrove planting optimization model is a mathematical programming model. Ecosystem services refer to the various beneficial services provided by mangroves, such as windbreak and sand fixation, water conservation, and habitat provision. The higher the ecosystem service, the greater the ecological benefits of mangroves in the area. The objective function is constructed in the form of weighted summation of the planting density of each tree species in each area, with the weight being the ecological value per unit area of ​​each tree species. That is, the planting density of each tree species in the area is multiplied by the ecological value coefficient per unit area of ​​the tree species, and the total ecosystem service value of the area is obtained after summing. By finding the maximum value, the planting plan that maximizes the ecological benefits can be determined. The ecological value per unit area refers to the ecological service value that can be provided by the ecosystem per unit area. Different tree species provide different services in the ecosystem, and the ecological value per unit area can comprehensively measure the contribution of each tree species. In the restoration of mangrove ecology, the weighted summation is performed according to the ecological value per unit area of ​​each tree species to optimize the planting strategy and maximize the ecological service value. Through the weighted summation method, tree species and areas with high ecological service value are identified, and they are planted and protected first to ensure efficient resource utilization. By consulting relevant literature and research reports and conducting field surveys, we can obtain the ecological service values ​​of different tree species per unit area. By using mathematical optimization algorithms (such as linear programming, nonlinear programming, etc.), with the objective function as the goal, we can solve the decision variables (i.e. the optimal planting density of each tree species) when the objective function reaches the maximum value while satisfying all constraints. This optimization method can fully consider various biological and non-biological constraints, and obtain the optimal planting density combination plan that maximizes ecological and economic values ​​while satisfying various conditions.

[0194] Step S3400, by constructing a mangrove planting optimization model, can comprehensively consider a variety of constraints and scientifically determine the optimal planting density of each tree species in different areas; taking the maximization of ecosystem service value as the objective function, it can ensure that the planting plan plays the greatest ecological benefit, such as carbon fixation and oxygen release, windbreak and embankment reinforcement, and habitat provision, under the premise of meeting various constraints, so as to maximize the ecological function of mangroves. The constraints take into account the maximum planting density of tree species, ensure the moderate diversity of species composition, avoid the ecological risks of a single tree species, and create conditions for the formation of a three-dimensional stratified complex community. The introduction of tree species growth characteristic constraints ensures that the selected tree species match the environmental conditions such as elevation, salinity, and flooding frequency of the region, which fundamentally improves the survival rate and growth status of the tree species; the total planting cost is included in the constraints, so that the optimized planting plan can not only meet the maximum ecological benefit, but also be controlled within an affordable budget, thereby improving the feasibility of the plan.

[0195] Step S3500, predicting and comparing the ecosystem service values ​​and costs of different planting plans based on the optimal planting densities of different tree species in each mangrove suitable growth area, and selecting a planting plan with high ecosystem service value and low cost as the optimal planting plan;

[0196] Specifically, each planting scheme corresponds to a different set of tree species combinations and planting density distributions. The ecosystem service value of different planting schemes can be estimated based on the optimal planting density of tree species in each mangrove suitable growth area obtained by solving the mangrove planting optimization model. At the same time, the total cost of different planting schemes can also be calculated based on the afforestation cost of each tree species. The ecosystem service value and cost are compared, and the scheme with high ecosystem service value and relatively low cost is selected as the optimal planting scheme. By conducting quantitative ecological value and cost evaluation of different schemes, the advantages and disadvantages of each scheme can be objectively compared, providing a basis for the final scheme selection and avoiding subjectivity and arbitrariness.

[0197] Step S4000, based on the optimal planting plan, formulate an implementation plan for mangrove wetland restoration. The implementation plan includes formulating short-term, medium-term and long-term phased goals, and detailing the protection and restoration tasks, time schedules, financial budgets, technical routes and management measures for each stage.

[0198] Specifically, the entire mangrove wetland restoration project is divided into three phases: short-term, medium-term, and long-term, and the target tasks and time schedule for each phase are formulated. The focus of the short-term phase is to control the trend of wetland degradation, implement mangrove planting in priority restoration areas, and carry out necessary ecological restoration projects, such as water system connectivity and alien species removal. In the medium-term phase, on the basis of short-term restoration, continue to promote mangrove planting, expand the planting area, and strengthen vegetation management to promote the healthy development of the ecosystem. The long-term phase is mainly to carry out continuous management and monitoring of mangrove wetlands, and carry out replanting in some areas with poor growth conditions to ensure the stability and sustainability of the ecosystem. When formulating the implementation plan, it is necessary to refine the protection and restoration tasks of each stage, clarify the time schedule, reasonably estimate the funds and resources required, and formulate detailed technical routes and management measures. The budget should take into account various expenses such as seedling procurement, labor employment, equipment purchase and long-term management, and strive for government special funds as much as possible. It can also explore diversified investment models involving social capital. The technical route should formulate rigorous operating procedures from seedling cultivation, afforestation construction, water system dredging to monitoring and evaluation to ensure the quality of the project. Management and protection measures should establish a long-term mechanism, clarify the main body of management and protection, strengthen daily management and maintenance, and promptly carry out vegetation cultivation, pest and disease control, etc. to ensure the healthy growth of mangroves. At the same time, scientific monitoring should be strengthened, monitoring plots and fixed observation points should be established, and key ecological indicators such as mangrove growth, hydrological characteristics, and soil physical and chemical properties should be tracked over the long term to provide data support for evaluating restoration effects and optimizing management decisions.

[0199] Step S4000 decomposes the repair work into three phases: short-term, medium-term, and long-term. The goals are hierarchical and orderly, and the tasks are reasonably connected, ensuring the systematic and continuous nature of the repair process and avoiding the interruption and disorder of the work. The specific tasks and technical routes of each phase are refined to make the work goals and implementation paths clearer and more operational, which is conducive to the precise and efficient connection of each link and improves the pertinence and effectiveness of the work.

[0200] Example 2

[0201] This embodiment provides a coastal wetland ecological restoration system based on mangroves on the basis of embodiment 1. Figure 2 As shown, including:

[0202] Data acquisition module: used to collect multi-source monitoring data of coastal wetlands, including satellite remote sensing image data, drone aerial image data and field survey data;

[0203] Data preprocessing module: used to preprocess multi-source monitoring data, including geometric correction, radiation correction and image enhancement for satellite remote sensing image data and drone aerial image data, and outlier detection and elimination for field survey data;

[0204] Regional division module: used to divide coastal wetlands into regions based on natural geographical features, ecological functional areas and administrative management areas according to pre-processed multi-source monitoring data;

[0205] Key ecological indicator extraction module: used to build a coastal wetland key ecological indicator extraction model based on pre-processed multi-source monitoring data, and extract key ecological indicators for each area of ​​the coastal wetland;

[0206] Ecological degradation index calculation module: used to calculate the ecological degradation index based on key ecological indicators;

[0207] Restoration priority ranking module: used to identify important ecological areas in coastal wetlands and set regional importance indexes for different areas of coastal wetlands; obtain road network data and calculate accessibility index based on road network data; calculate wetland restoration priority index for each area based on accessibility index, regional importance index and ecological degradation index; generate wetland restoration priority ranking map based on wetland restoration priority index;

[0208] Mangrove planting planning module: used to obtain candidate mangrove planting areas based on the wetland restoration priority ranking map, conduct habitat suitability evaluation in the candidate planting areas, and identify suitable mangrove growth areas; collect growth characteristic data of mangrove species to be planted, and construct a mangrove planting optimization model for mangrove growth areas based on the growth characteristic data; based on the mangrove planting optimization model, obtain the optimal planting density of different tree species in each mangrove growth area; based on the optimal planting density, predict and compare the ecosystem service value and cost of different planting plans, and select the optimal planting plan; based on the optimal planting plan, formulate an implementation plan for mangrove wetland restoration.

[0209] Example 3

[0210] This embodiment discloses an electronic device, including a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned mangrove-based coastal wetland ecological restoration method is implemented.

[0211] Since the electronic device introduced in this embodiment is an electronic device used to implement the coastal wetland ecological restoration method based on mangroves in the embodiment of this application, based on the coastal wetland ecological restoration method based on mangroves introduced in the embodiment of this application, the technical personnel of this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as the technical personnel of this field implement the electronic device used in the coastal wetland ecological restoration method based on mangroves in the embodiment of this application, it belongs to the scope of protection of this application.

[0212] Example 4

[0213] This embodiment discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the above-mentioned mangrove-based coastal wetland ecological restoration method is implemented.

[0214] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters, weights and thresholds in the formula are set by technicians in this field according to actual conditions.

[0215] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD) or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0216] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0217] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0218] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0219] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0220] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0221] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0222] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for ecological restoration of coastal wetlands based on mangroves, characterized in that: include: Step S1000, collecting multi-source monitoring data of coastal wetlands, dividing coastal wetlands into regions, extracting key ecological indicators of coastal wetlands based on the multi-source monitoring data, and calculating ecological degradation index according to the key ecological indicators; the key ecological indicators include vegetation coverage, plant biomass, water quality parameters and soil physical and chemical properties; Step S2000, identifying important ecological areas in the coastal wetland, and setting regional importance indexes for different areas of the coastal wetland; Obtain road network data, and calculate the accessibility index based on the road network data; calculate the wetland restoration priority index for each region based on the accessibility index, regional importance index, and ecological degradation index; Generate a wetland restoration priority ranking map based on the wetland restoration priority index; Step S3000, obtaining candidate mangrove planting areas according to the wetland restoration priority ranking map, conducting habitat suitability evaluation in the candidate planting areas, and identifying suitable mangrove growth areas; collecting growth characteristic data of mangrove tree species to be planted, constructing a mangrove planting optimization model for mangrove growth areas according to the growth characteristic data, and obtaining the optimal planting plan for different tree species in each mangrove growth area according to the mangrove planting optimization model; Step S4000: Based on the optimal planting plan, formulate an implementation plan for mangrove wetland restoration. The implementation plan includes formulating short-term, mid-term and long-term phased goals, and detailing the protection and restoration tasks, time schedule, funding budget, technical route and management measures for each stage; The calculation of the ecological degradation index based on key ecological indicators includes: Step S1510, comparing and scoring the importance of each key ecological indicator in pairs, and constructing a judgment matrix; Step S1520, calculating the eigenvector of the judgment matrix and performing normalization processing to obtain the weight of each key ecological indicator; Step S1530, performing a consistency check on the judgment matrix. If the consistency check passes, the weight is valid; if it fails, the judgment matrix needs to be adjusted to recalculate the weights of the key ecological indicators; Step S1540, performing standardization processing on the key ecological indicators, and calculating the ecological degradation index according to the standardized key ecological indicators and the weights of the key ecological indicators; The method for calculating the ecological degradation index according to the standardized key ecological indicators and the weights of each key ecological indicator includes: ； Among them, EDI is the ecological degradation index, is the standardized vegetation cover, is the standardized plant biomass, is the standardized water quality parameter, is the standardized soil physical and chemical properties, is the weight of vegetation coverage, is the weight of plant biomass, is the weight of water quality parameters, is the weight of soil physical and chemical properties, is the interaction parameter between vegetation coverage and water quality parameters, It is the interaction parameter between plant biomass and soil physical and chemical properties.

2. The method for ecological restoration of coastal wetlands based on mangroves according to claim 1, characterized in that: The step S1000 includes: Step S1100, collecting multi-source monitoring data of coastal wetlands, and establishing a spatiotemporal database based on the multi-source monitoring data; the multi-source monitoring data includes satellite remote sensing image data, drone aerial image data, and field survey data; Step S1200, preprocessing the multi-source monitoring data, wherein the preprocessing includes geometric correction, radiation correction and image enhancement for satellite remote sensing image data and drone aerial image data, and outlier detection and elimination for field survey data; Step S1300, dividing the coastal wetland into regions based on the pre-processed multi-source monitoring data, based on the natural geographical features, ecological function zones and administrative management zones; Step S1400, constructing a coastal wetland key ecological indicator extraction model based on the pre-processed multi-source monitoring data, and extracting key ecological indicators of each area of ​​the coastal wetland; Step S1500, calculating the ecological degradation index according to key ecological indicators.

3. The method for ecological restoration of coastal wetlands based on mangroves according to claim 2, characterized in that: The step S1400 includes: Step S1410, collecting measured data of key ecological indicators as label data for a key ecological indicator extraction model for coastal wetlands; Step S1420, pairing the preprocessed multi-source monitoring data with the measured data to construct a training data set and a verification data set for the coastal wetland key ecological indicator extraction model; annotating the training data set; Step S1430, designing an end-to-end ecological indicator extraction network based on the CNN network, inputting the preprocessed multi-source monitoring data into the CNN model, extracting the multi-scale and multi-level spatiotemporal characteristics of the coastal wetland ecology; adding a regression layer at the end of the CNN model, and outputting multiple key ecological indicators at the same time; using the labeled training data set to train the CNN model, optimizing the model parameters through the back propagation algorithm, and minimizing the error between the predicted value and the measured value; Step S1440, using the verification data set to verify the CNN model, and tuning the model according to the verification result to obtain the final coastal wetland key ecological indicator extraction model; Step S1450, applying the coastal wetland key ecological indicator extraction model to the spatiotemporal database of the entire coastal wetland to extract the key ecological indicators of each area.

4. The method for ecological restoration of coastal wetlands based on mangroves according to claim 1, characterized in that: The step S2000 includes: Step S2100, identifying important ecological regions in the coastal wetland, and setting regional importance indexes for different regions of the coastal wetland according to regional importance; Step S2200, obtaining road network data, and based on the road network data, calculating the cost distance from each area to the nearest town or traffic artery, mapping the cost distance to the range of 0-1, and obtaining the accessibility index of different areas; Step S2300, calculating the wetland restoration priority index of each region according to the accessibility index, regional importance index and ecological degradation index of each region; Step S2400, according to the restoration priority index, the lakeside wetland is divided into five restoration priorities, namely, extremely high priority, high priority, medium priority, low priority and extremely low priority; the five restoration priorities are represented by different colors to generate a wetland restoration priority sorting diagram; the higher the priority index, the higher the restoration priority, which is represented by red in the priority sorting diagram; the lower the priority index, the lower the restoration priority, which is represented by green in the priority sorting diagram.

5. The method for ecological restoration of coastal wetlands based on mangroves according to claim 1, characterized in that: The step S3000 includes: Step S3100, according to the wetland restoration priority ranking diagram, the coastal wetland areas with very high priority and high priority for wetland restoration are selected as candidate mangrove planting areas; Step S3200, conducting habitat suitability assessment in the candidate mangrove planting area to identify areas suitable for mangrove growth; Step S3300, collecting growth characteristic data of the mangrove tree species to be planted, the growth characteristic data including an appropriate range of elevation, an appropriate range of salinity, an appropriate range of flooding frequency, and an appropriate range of soil texture; Step S3400, constructing a mangrove planting optimization model for mangrove suitable growth areas based on the growth characteristic data of the mangrove tree species to be planted, and obtaining the optimal planting density of different tree species in each mangrove suitable growth area based on the mangrove planting optimization model; Step S3500, predicting and comparing the ecosystem service value and cost of different planting plans based on the optimal planting density of different tree species in each mangrove suitable growth area, and selecting the planting plan with high ecosystem service value and low cost as the optimal planting plan.

6. The method for ecological restoration of coastal wetlands based on mangroves according to claim 5, characterized in that: The step S3200 includes: Step S3210, dividing the mangrove candidate planting area into a plurality of grids, and collecting elevation data, salinity data, flooding frequency data, and soil texture data in each grid; Step S3220, calculating the habitat suitability index of each grid according to the elevation data, salinity data, flooding frequency data and soil texture data; Step S3230, identifying areas suitable for mangrove growth based on the habitat suitability index of each grid.

7. The method for ecological restoration of coastal wetlands based on mangroves according to claim 6, characterized in that: The step S3230 includes: Step S3231, set the suitability threshold, determine whether the habitat suitability index of the grid is greater than the suitability threshold, if so, the grid is regarded as a high suitability grid, and proceed to step S3232, if not, determine whether the habitat suitability index of the next grid is greater than the suitability threshold, until all grids are determined; Step S3232, defining grid adjacency rules, taking each high suitability grid as a starting point, searching for adjacent high suitability grids according to the adjacency rules, and marking them as the same connected area; repeating this process until all high suitability grids are assigned to a connected area; Step S3233, calculating the number of grids contained in each connected region, and converting it into the actual area of ​​the connected region according to the grid size; Step S3234, setting a minimum area threshold, extracting connected areas whose actual area is larger than the minimum area threshold, and marking them as areas suitable for mangrove growth; for connected areas whose actual area is less than or equal to the minimum area threshold, mark them as areas unsuitable for mangrove growth.

8. The method for ecological restoration of coastal wetlands based on mangroves according to claim 5, characterized in that: The step S3400 includes: Step S3410, taking the planting density of each mangrove tree species in each mangrove suitable growth area as a decision variable; Step S3420, constructing an objective function to maximize the ecosystem service value; the ecosystem service value is represented by the weighted sum of the planting density of each tree species in each mangrove suitable growth area, and the weight is the ecological value per unit area of ​​each tree species; Step S3430, based on the growth characteristic data of the mangrove tree species to be planted, constraining conditions are constructed, the constraints including that the sum of the planting density in each mangrove suitable growth area is equal to the total planting density of the area; the planting density of each tree species in each mangrove suitable growth area does not exceed the maximum planting density of the tree species; the elevation, salinity, flooding frequency and soil texture of each tree species in each mangrove suitable growth area are all within the suitable range of the tree species; the total planting cost does not exceed the budget limit; Step S3440, solving the mangrove planting optimization model to obtain the optimal planting density of different tree species in each mangrove suitable growth area.

9. A mangrove-based coastal wetland ecological restoration system, which is used to implement the mangrove-based coastal wetland ecological restoration method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module: used to collect multi-source monitoring data of coastal wetlands, including satellite remote sensing image data, drone aerial image data and field survey data; Data preprocessing module: used to preprocess multi-source monitoring data, including geometric correction, radiation correction and image enhancement for satellite remote sensing image data and drone aerial image data, and outlier detection and elimination for field survey data; Regional division module: used to divide coastal wetlands into regions based on natural geographical features, ecological functional areas and administrative management areas according to pre-processed multi-source monitoring data; Key ecological indicator extraction module: used to build a coastal wetland key ecological indicator extraction model based on pre-processed multi-source monitoring data, and extract key ecological indicators for each area of ​​the coastal wetland; Ecological degradation index calculation module: used to calculate the ecological degradation index based on key ecological indicators; Restoration priority ranking module: used to set regional importance indexes for different areas of coastal wetlands, calculate accessibility indexes and wetland restoration priority indexes for each area, and generate wetland restoration priority ranking maps; Mangrove planting planning module: used to identify suitable mangrove growth areas, construct mangrove planting optimization models for suitable mangrove growth areas, and obtain the optimal planting density of different tree species in each mangrove growth area; based on the optimal planting density, predict and compare the ecosystem service value and cost of different planting plans, and select the optimal planting plan; based on the optimal planting plan, formulate an implementation plan for mangrove wetland restoration.

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