Method for optimizing configuration of mangrove species in Tuifang forest

Abstract

The present application relates to the technical field of forest breeding and seedling raising, and discloses a method for optimizing the configuration of mangrove species in a reforested pond. The method comprises the following steps: obtaining a high-precision digital elevation model and tidal hydrological data; simulating the submergence duration, frequency and flow rate of each grid unit in the whole domain by means of a two-dimensional shallow water dynamics model; combining a database of physiological tolerances of mangrove species to construct a multi-factor ecological suitability evaluation model, and calculating the suitability index of each grid to the candidate tree species; and based on the index, establishing a nonlinear density regulation model to generate an optimal tree species and density configuration map with spatial heterogeneity. The present application realizes the precision and intelligence of mangrove restoration, and significantly improves the survival rate and the stability of the ecological system.

Description

Technical Field

[0001] This invention belongs to the field of forest tree breeding and seedling technology, specifically involving a method for optimizing the configuration of mangrove tree species in fishponds converted from ponds to forests. Background Technology

[0002] Mangroves serve as crucial ecological barriers in tropical and subtropical coastal zones, playing an irreplaceable role in wind and wave protection, carbon sequestration, and biodiversity conservation. In recent years, with the comprehensive advancement of the "returning fishponds to forests" ecological restoration project, a large number of abandoned aquaculture ponds have been incorporated into mangrove reconstruction efforts. However, mangrove seedlings are highly sensitive to tidal hydrological conditions, and their survival rate is directly constrained by the duration and frequency of submersion determined by the periodic changes in tidal levels.

[0003] Current planting practices generally adopt a static configuration mode with fixed plant and row spacing, which ignores the dynamic differences in flooding experienced by different elevation areas in the intertidal zone during the daily tidal cycle. This leads to root hypoxia in the low tide zone due to long-term flooding, while the high tide zone may experience water stress due to prolonged exposure.

[0004] The species configuration of mangroves in the context of converting abandoned fishponds back to forests is particularly complex. Due to the uneven micro-topography and poor drainage that often result from the artificial modification of abandoned fishpond terrain, local areas experience delayed tidal response or prolonged water accumulation, further exacerbating hydrological heterogeneity.

[0005] Traditional methods rely on empirical density settings, which cannot dynamically adjust the planting layout according to daily tidal phases (such as spring tides and neap tides) and real-time water levels. This results in high-density planting creating "suffocation traps" in low-lying areas, significantly increasing the early mortality rate of seedlings and severely restricting the effectiveness of ecological restoration.

[0006] While some studies have attempted to incorporate remote sensing or hydrological models to assist in site selection, these approaches remain at the level of static suitability assessment and lack a density control mechanism coupled with real-time tidal dynamics. They have neither established a quantitative relationship between tide level, inundation duration, and root oxygen demand threshold, nor have they embedded dynamic response logic into the planting implementation phase. Summary of the Invention

[0007] To solve the above-mentioned technical problems, this invention provides a method for optimizing the configuration of mangrove tree species in reforestation projects, the core technical concept of which is:

[0008] First, through high-precision three-dimensional topographic mapping and long-term in-situ hydrological monitoring, a refined digital elevation model and tidal dynamic boundary conditions for the area to be restored are constructed.

[0009] Secondly, using a two-dimensional shallow water dynamics model, a grid-by-grid numerical simulation of the tidal inundation process is performed on the digital elevation model to generate a spatial distribution matrix of key hydrological stress factors such as inundation duration, inundation frequency, and water flow velocity in the entire domain at high resolution.

[0010] Furthermore, by combining the pre-set candidate mangrove tree species physiological tolerance database, which quantifies the tolerance thresholds and growth response functions of different tree species to environmental factors such as flooding duration and salinity, a multi-factor coupled ecological suitability evaluation model is established to calculate the suitability index of each grid unit for each candidate tree species.

[0011] Finally, based on the suitability index, a nonlinear inverse mapping relationship between planting density and comprehensive hydrological stress is established. That is, the planting density is automatically reduced in areas with high hydrological stress and increased accordingly in areas with low stress, thereby generating a high-precision planting configuration instruction map with spatial heterogeneity that integrates the optimal tree species selection and the optimal planting density.

[0012] This invention provides a method for optimizing the configuration of tree species in mangrove forests converted from fishponds to forests, which includes the following steps:

[0013] Acquire basic environmental data for the area to be restored, including a digital elevation model of the area, regional tidal data, sedimentary physicochemical properties data, and physiological and ecological parameters of candidate mangrove species.

[0014] Based on the digital elevation model and regional tidal data, a two-dimensional shallow water dynamics model is constructed and run to numerically simulate the tidal inundation process of the area to be restored within at least one complete synodic month cycle. This allows for the calculation and generation of a set of hydrodynamic characteristic parameters for each preset spatial grid cell of the area to be restored. The set of hydrodynamic characteristic parameters includes the average inundation duration, the maximum continuous inundation duration, the inundation frequency, and the average tidal flow velocity.

[0015] A multi-factor ecological suitability evaluation model was established, using the set of hydrodynamic characteristic parameters and the sedimentary physicochemical properties data as model inputs. Based on the tolerance threshold and response curve defined in the physiological and ecological parameters of the candidate mangrove species, the ecological suitability index of each spatial grid cell for each candidate mangrove species was calculated.

[0016] A quantitative control model for planting density and ecological suitability index is constructed. The model takes the ecological suitability index as input and calculates an optimal planting density value for each spatial grid cell through a preset density control function.

[0017] By integrating the ecological suitability index calculation results with the optimal planting density value, a unique recommended tree species and corresponding planting density are determined for each spatial grid unit of the area to be restored, ultimately generating an integrated mangrove planting spatial configuration scheme.

[0018] As one embodiment of the present invention, obtaining the digital elevation model of the area to be repaired specifically includes:

[0019] A drone equipped with a lidar system or a multi-view stereo photogrammetry camera is used to perform a full-coverage, high-overlapping flight scan or image acquisition of the area to be repaired; a digital elevation model with a spatial resolution of not less than 0.1 meters is generated by filtering point cloud data, denoising, classifying ground points and using terrain interpolation algorithms, or by using motion reconstruction structure and multi-view stereo matching algorithms.

[0020] As one embodiment of the present invention, the acquisition of regional tidal pattern data specifically includes:

[0021] Self-contained pressure tide gauges are deployed in the tidal inlet or representative water area of ​​the area to be repaired, and continuous synchronous observations are conducted for no less than 30 natural days to obtain raw pressure data. After pressure compensation, salinity density correction and filtering, tidal time series data accurate to the centimeter level are generated. This data serves as the open boundary driving condition for the two-dimensional shallow water dynamic model.

[0022] As one embodiment of the present invention, the sedimentary physicochemical property data includes sediment particle size distribution, organic matter content, pore water salinity and pH; the physiological and ecological parameters of the candidate mangrove species are structured and stored in a database, which defines for each candidate species the maximum flood tolerance threshold, salinity tolerance upper and lower limits, optimal growth salinity range, root adaptability coefficient to different sediment types, and the functional relationship between photosynthetic efficiency and flood duration.

[0023] As one embodiment of the present invention, the operation of the two-dimensional shallow water dynamic model specifically involves discretizing and solving the Saint-Venant equations using the finite volume method or the finite element method. The digital elevation model of the area to be repaired is used as the fixed bottom boundary of the model, and the tidal time series data is used as the water level boundary condition of the model. Through numerical integration, an inundation depth time series matrix covering the entire area with a time step of no more than 300 seconds is output. Furthermore, through statistical analysis of the inundation depth time series matrix, the cumulative inundation duration, the longest single continuous inundation duration, the frequency of inundation events, and the average tidal velocity calculated based on the Manning formula are calculated for each spatial grid cell throughout the entire simulation period.

[0024] As one embodiment of the present invention, the calculation process of the multi-factor ecological suitability evaluation model is as follows:

[0025] First, the parameters and sedimentary physicochemical properties data in the set of hydrodynamic characteristic parameters are normalized and converted into dimensionless stress factor values.

[0026] Secondly, based on the response curve of each candidate species defined in the physiological and ecological parameter database, the normalized stress factor values ​​are converted into corresponding single-factor growth stress indices.

[0027] Finally, using a weighted summation method, multiple single-factor growth stress indices are merged into a comprehensive ecological suitability index, the calculation formula of which is as follows:

[0028] ;

[0029] in, Representing the The species in the first The comprehensive ecological suitability index of each grid unit, For the first The weighting coefficients of environmental factors, i.e., stress factors. For the first The first grid cell of the first grid cell Normalized values ​​of environmental factors For the first The stress response function corresponds to each environmental factor. The weighting coefficients are determined using the analytic hierarchy process (AHP) combined with an expert scoring system.

[0030] As one embodiment of the present invention, the quantitative control model of planting density and ecological suitability index is specifically constructed using an S-shaped logistic growth function or a variant thereof, and its mathematical expression is as follows:

[0031] ;

[0032] in, For the first The optimal planting density is calculated from each grid cell. and These represent the maximum carrying capacity density of the species in ideal habitats and the minimum survival density in stressed habitats, respectively. For the first The highest ecological suitability index among all candidate species corresponding to each grid cell. The slope factor of the density change curve represents the sensitivity of density to changes in adaptability. The inflection point of the curve represents the critical transition value of the adaptability index. , , , All of these are species-specific parameters, pre-stored in a physiological and ecological parameter database.

[0033] As one embodiment of the present invention, the step of integrating and generating an integrated mangrove planting space configuration scheme specifically includes:

[0034] For each spatial grid cell in the area to be restored, its ecological suitability index for all candidate mangrove species is first compared, and the species with the highest index value is selected as the only recommended tree species for planting in that grid cell.

[0035] Then, the highest ecological suitability index value is substituted into the quantitative control model to calculate the optimal planting density corresponding to the grid unit;

[0036] Finally, the geographic coordinates, recommended tree species identifiers, and optimal planting density values ​​of all grid cells are integrated to generate a planting configuration map in vector or raster data format that can be read by the geographic information system. This map directly guides on-site construction.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] 1. By introducing high-precision topographic data and hydrodynamic simulation, this invention achieves a refined and quantitative spatial characterization of the inundation stress in the area of ​​fishpond conversion to forest, fundamentally overcoming the huge errors and uncertainties brought about by the reliance on macroscopic tidal zone division in traditional methods.

[0039] 2. This invention creates a multi-factor evaluation system that combines physical environment simulation with plant physiological and ecological knowledge, so that the selection of tree species is no longer based on rough judgment based on experience, but on scientific decision-making based on the specific environmental stress index of each plot, which significantly improves the level of site-appropriate tree configuration.

[0040] 3. This invention has uniquely established a nonlinear dynamic control model between planting density and quantitative ecological suitability index, realizing spatially variable and on-demand allocation of planting density. In areas with severe flooding stress, seedling survival is ensured by reducing density, while in suitable areas, community canopy closure is accelerated by increasing density. This maximizes resource utilization and ecological benefits throughout the restoration area, significantly improving the survival rate, growth rate, and long-term ecosystem stability of mangrove restoration projects.

[0041] 4. The digital, high-precision planting configuration map generated by this invention provides a clear and operable scientific basis for construction, and promotes the transformation of mangrove ecological restoration projects from traditional experience-based models to a new paradigm of vectorization, precision and intelligence. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall technical solution architecture of the method for optimizing the configuration of mangrove tree species in the process of returning fishponds to forests proposed in this invention;

[0043] Figure 2 This is a schematic diagram of the core principle framework of tree species-density synergistic optimization based on the coupling of hydrodynamic simulation and ecological suitability index in this invention.

[0044] Figure 3 This is a logical flowchart of the high-precision environmental data acquisition and digital elevation model construction in this invention;

[0045] Figure 4 This is a flowchart illustrating the logical process framework for numerical simulation of tidal inundation and generation of hydrological stress factors driven by a two-dimensional shallow water dynamics model in this invention.

[0046] Figure 5 This is a logical flowchart of the construction of the multi-factor ecological suitability evaluation model and the calculation of the suitability index in this invention.

[0047] Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the terminal construction end and the cloud decision-making system in this invention. Detailed Implementation

[0048] Please refer to Figures 1 to 6 This invention provides a method for optimizing the allocation of tree species in mangrove forests converted from fishponds to forests. Its core lies in the deep integration of high-precision environmental perception, hydrodynamic numerical simulation, and ecological suitability modeling to achieve precise, dynamic, and differentiated allocation of tree species selection and planting density for each spatial grid unit in the mangrove restoration area. This method completely abandons the traditional empirical approach that relies on macroscopic tidal zone division and fixed density schemes, instead constructing a quantitative decision-making system based on the coupling of physical process-driven and plant physiological responses.

[0049] The method first performs step S1: acquiring basic environmental data of the area to be restored. This basic environmental data includes a digital elevation model of the area, regional tidal data, sedimentary physicochemical properties data, and physiological and ecological parameters of candidate mangrove species. In this step, the digital elevation model is acquired by using a UAV equipped with a lidar system to perform a full-coverage flight scan of the area to be restored. The flight altitude is set at 80 meters, with a forward overlap of no less than 80% and a lateral overlap of no less than 70%, to ensure the integrity and redundancy of the point cloud data.

[0050] After denoising and filtering, the raw point cloud data was classified using a progressive triangulation algorithm to remove non-ground points such as vegetation, water surfaces, and man-made structures. Finally, a regular grid digital elevation model with a spatial resolution of 0.1 meters was generated using Kriging interpolation. This model accurately depicts the centimeter-level elevation differences in the area to be restored, formed by historical aquaculture pond embankments, ditches, and natural micro-topography, providing key substrate boundary conditions for subsequent hydrological simulations.

[0051] Regional tidal data were acquired by deploying a self-contained pressure-tide gauge at the main tidal inlet of the area to be restored. The device was set to sample once per minute, with a continuous observation period of 35 calendar days, covering at least one complete spring-neap tide cycle. The raw pressure data was corrected for atmospheric pressure and seawater density based on on-site measured salinity values. High-frequency noise was then eliminated using a 5-point moving average filter, ultimately generating tidal time series data with a time resolution of 1 minute and an accuracy better than 2 centimeters.

[0052] This sequence serves as the open boundary driving condition for the two-dimensional shallow water dynamic model, ensuring a faithful reproduction of the simulation results on real tidal dynamic processes. Sedimentary physicochemical properties data were obtained by collecting sediment samples from the surface layer at depths of 0 to 20 cm within the remediation area using a quincunx pattern of at least 50 sampling points. The samples underwent particle size distribution analysis, organic matter content determination, salinity calculations based on pore water conductivity, and pH testing in the laboratory. All data were spatially correlated according to the geographic coordinates of the sampling points, forming a sedimentary property raster layer with the same resolution as the digital elevation model.

[0053] The physiological and ecological parameters of candidate mangrove species were pre-structured and stored in a dedicated database. This database defined for each candidate species the maximum flood tolerance threshold, salinity tolerance upper and lower limits, optimal salinity range for growth, root adaptability coefficients to sandy, silty, and muddy sediments, and the functional relationship curve of photosynthetic efficiency with flood duration. These parameters form the biological basis for subsequent ecological suitability assessments.

[0054] After completing step S1, step S2 is executed: based on the digital elevation model and regional tidal data, a two-dimensional shallow water dynamic model is constructed and run to numerically simulate the tidal inundation process of the area to be repaired within at least one complete synodic month cycle, thereby calculating and generating the corresponding set of hydrodynamic characteristic parameters for each preset spatial grid cell of the area to be repaired. The set of hydrodynamic characteristic parameters includes the average inundation duration, the maximum continuous inundation duration, the inundation frequency, and the average tidal flow velocity.

[0055] In the specific implementation of this step, the two-dimensional shallow water dynamic model uses the finite volume method to discretize and solve the Saint-Venant equations. The model's computational domain completely covers the area to be restored and its necessary surrounding buffer zones. The grid size is consistent with the digital elevation model, both being 0.1 meters by 0.1 meters. The model's substrate friction resistance is calculated using the Manning formula, with the Manning roughness coefficient assigned according to sediment type: 0.025 for silty regions and 0.035 for sandy regions. The model's time step is automatically adjusted according to CFL stability conditions, with a maximum of 300 seconds. The upstream and lateral boundaries of the model are set as closed boundaries, while the downstream open boundary is driven by the aforementioned processed tidal time series data.

[0056] During model initialization, the water depth of the entire computational domain was set to 0. The simulation started at low tide during spring tide and ran continuously for 35 calendar days. The model output was a four-dimensional data cube, containing the water depth and current velocity vector information of each grid cell at each time step. Based on this, post-processing analysis was performed on the water depth time series data of each grid cell: when the water depth was greater than 0.05 meters, the grid cell was determined to be in a submerged state.

[0057] The average inundation duration is obtained by accumulating the duration of all inundation states throughout the entire simulation period. The longest consecutive inundation duration is determined by iterating through all consecutive inundation events. The total number of inundation events is then counted to obtain the inundation frequency. Simultaneously, the arithmetic mean of the velocity vector magnitude at each time step is calculated for the entire simulation period to obtain the average tidal velocity. Finally, a set of hydrodynamic characteristic parameters with four dimensions is generated for each spatial grid cell in the area to be restored.

[0058] The following step, S3, is then executed: A multi-factor ecological suitability evaluation model is established. The set of hydrodynamic characteristic parameters and the sedimentary physicochemical property data are used as model inputs. Based on the tolerance thresholds and response curves defined in the physiological and ecological parameters of the candidate mangrove species, the ecological suitability index of each spatial grid cell for each candidate mangrove species is calculated. The detailed implementation process of this step is as follows: First, all environmental factors input into the model are normalized.

[0059] For stress factors such as average inundation duration, maximum continuous inundation duration, average tidal velocity, sediment salinity, and pH, linear or nonlinear functions are used to map them to a range of 0 to 1, where 0 represents the most unfavorable stress state and 1 represents the most suitable state. For example, for the average inundation duration P, if the maximum inundation tolerance threshold for a certain species is... The normalized value is .

[0060] For promoting factors such as organic matter content, the opposite mapping logic is used. Secondly, based on the single-factor response curves pre-set for each candidate species in the physiological and ecological parameter database... The normalized environmental factor values ​​are converted into corresponding single-factor growth stress indices. These response curves can be piecewise linear functions, Gaussian functions, or sigmoid functions, accurately describing the nonlinear relationship between species growth potential and environmental factors. Finally, multiple single-factor growth stress indices are merged into a comprehensive ecological suitability index using a weighted summation method. Its calculation follows the formula below:

[0061] ;

[0062] in, Representing the The species in the first The comprehensive ecological suitability index of each grid unit, For the first The weighting coefficients of environmental factors, i.e., stress factors. For the first The first grid cell of the first grid cell Normalized values ​​of environmental factors For the first Stress response function corresponding to each environmental factor. Weighting coefficients. By employing the analytic hierarchy process (AHP) combined with a domain expert scoring system, the importance of different environmental factors on species survival is reasonably reflected. The model calculates a unique, quantified suitability index for each grid cell and for each candidate species; a higher index value indicates greater survival and growth potential for the species at that location.

[0063] Next, step S4 is executed: A quantitative control model of planting density and ecological suitability index is constructed. This model takes the ecological suitability index as input and calculates the optimal planting density value for each spatial grid cell using a preset density control function. The core of this step is to establish a non-linear, species-specific density control mechanism. In specific implementation, a variant of the sigmoid logistic growth function is used to construct this model, and its mathematical expression is:

[0064] ;

[0065] in, For the first The optimal planting density is calculated from each grid cell. and These are the maximum carrying capacity density of the species in an ideal habitat and the minimum survival density in a stressed habitat, respectively. These two parameters are directly derived from the physiological and ecological parameter database. For the first The highest ecological suitability index among all candidate species corresponding to each grid cell, i.e. the maximum value calculated in step S3. The slope factor of the density change curve represents the sensitivity of planting density to changes in the suitability index. The inflection point of the curve represents the critical value at which the adaptability index transitions from low density to high density. and Similarly, these species-specific parameters are pre-stored in a database. The function's design ensures that planting density is strictly limited in areas with extremely low ecological suitability indices (such as long-term submerged deep water areas). Nearby, to ensure the survival of seedlings under limited resources; while in ideal areas with an ecological suitability index close to one, the planting density can be increased to [missing information]. This accelerates the restoration of community canopy closure and ecosystem functions; in the intermediate transition zone, density increases smoothly and non-linearly with the suitability index, achieving optimal resource allocation.

[0066] Finally, step S5 is executed: The ecological suitability index calculation results are integrated with the optimal planting density value to determine a unique recommended tree species and corresponding planting density for each spatial grid unit of the area to be restored, ultimately generating an integrated mangrove planting spatial configuration scheme. The specific operation process of this step is as follows:

[0067] Traverse every spatial grid cell in the region to be repaired. For the current grid cell... Retrieve the ecological suitability index of all candidate species calculated in step S3. Compare all the values ​​in the set and select the maximum value. Corresponding species This is the only recommended tree species for planting in this grid cell. Subsequently, Substituting the values ​​into the quantitative control model constructed in step S4, the optimal planting density corresponding to this grid cell is calculated. . Grid cells Geographic coordinates, recommended tree species identifier t and optimal planting density The three are bound together to form a complete configuration record.

[0068] Repeat this process until all grid cells are covered. Finally, integrate all configuration records to generate a standard GIS raster data file, where each cell's attribute fields contain the tree species code and density value. This file can be directly imported into handheld construction navigation terminals or drone-based seeding systems, providing field workers with centimeter-level precision planting instructions, clearly indicating where to plant which tree species and the specific spacing between plants and rows, thus seamlessly translating complex scientific decisions into executable engineering practices.

[0069] Throughout the entire process, the data flow and processing logic are highly structured. The raw environmental data obtained in step S1 is transformed into a hydrological stress field through physical process simulation in step S2, then into a biological suitability field through ecological modeling in step S3, subsequently mapped into an engineering density field in step S4, and finally integrated into a unified and operable spatial configuration instruction map in step S5. This series of interconnected steps forms a complete closed loop from "environmental perception" to "physical simulation," then to "ecological assessment," and finally to "engineering decision-making." This method replaces subjective experience-based judgment with a rigorous quantitative model and replaces extensive homogenization schemes with explicit spatial configuration, fundamentally solving the problem of high seedling mortality caused by the mismatch between tidal inundation duration and planting density, providing a solid technical guarantee for the successful implementation of the reforestation project.

Claims

1. A method for optimizing the allocation of mangrove tree species in reforestation projects, characterized in that: include: Acquire basic environmental data for the area to be restored, including digital elevation model of the area, regional tidal pattern data, sedimentary physicochemical properties data, and physiological and ecological parameters of candidate mangrove species; Based on the digital elevation model and regional tidal data, a two-dimensional shallow water dynamics model is constructed and run to numerically simulate the tidal inundation process of the area to be restored within at least one complete synodic month cycle. This allows for the calculation and generation of a set of hydrodynamic characteristic parameters for each preset spatial grid cell of the area to be restored. The set of hydrodynamic characteristic parameters includes the average inundation duration, the maximum continuous inundation duration, the inundation frequency, and the average tidal flow velocity. A multi-factor ecological suitability evaluation model was established, with the set of hydrodynamic characteristic parameters and the sedimentary physicochemical properties data as model inputs. Based on the tolerance threshold and response curve defined in the physiological and ecological parameters of the candidate mangrove species, the ecological suitability index of each spatial grid cell for each candidate mangrove species was calculated. A quantitative control model for planting density and ecological suitability index is constructed. The model takes the ecological suitability index as input and calculates the optimal planting density value for each spatial grid cell through a preset density control function. By integrating the ecological suitability index calculation results with the optimal planting density value, a unique recommended tree species and corresponding planting density are determined for each spatial grid unit of the area to be restored, and an integrated mangrove planting spatial configuration scheme is finally generated. The two-dimensional shallow water dynamics model includes: The Saint-Venant equations are solved by discretization using the finite volume method or the finite element method. The digital elevation model of the area to be repaired is used as the fixed bottom boundary of the model, and the tidal time series data is used as the water level boundary condition of the model. The inundation depth time series matrix is ​​output through numerical integration, covering the entire region and with a time step of no more than 300 seconds. By statistically analyzing the inundation depth time series matrix, the average inundation duration, longest single continuous inundation duration, frequency of inundation events, and average tidal flow velocity of each spatial grid cell during the entire simulation period were calculated. The calculation process of the multi-factor ecological suitability evaluation model includes: The parameters and sedimentary physicochemical properties data in the set of hydrodynamic characteristic parameters are normalized and converted into dimensionless stress factor values. Based on the response curve of each candidate species defined in the physiological and ecological parameter database, the normalized stress factor values ​​are converted into corresponding single-factor growth stress indices. Multiple single-factor growth stress indices are integrated into a comprehensive ecological suitability index using a weighted summation method. The calculation formula is as follows: ； in, Representing the The species in the first The comprehensive ecological suitability index of each grid unit, For the first The weighting coefficients of environmental factors, i.e., stress factors. For the first The first grid cell of the first grid cell Each stress factor value, For the first The stress response function corresponding to each stress factor; the weighting coefficients are determined by combining the analytic hierarchy process with an expert scoring system; The quantitative regulation model of planting density and ecological suitability index is specifically constructed using the S-shaped logistic growth function or its variants, and its mathematical expression is as follows: ； in, For the first The optimal planting density is calculated from each grid cell. and These represent the maximum carrying capacity density of the species in ideal habitats and the minimum survival density in stressed habitats, respectively. For the first The highest ecological suitability index among all candidate species corresponding to each grid cell. The slope factor of the density change curve. The inflection point of the curve; , , , All of these are species-specific parameters, pre-stored in a physiological and ecological parameter database.

2. The method for optimizing the allocation of mangrove tree species for reforestation as described in claim 1, characterized in that, The process of obtaining the digital elevation model of the area to be repaired includes: A drone equipped with a lidar system or a multi-view stereo photogrammetry camera is used to perform a full-coverage, high-overlap aerial scan or image acquisition of the area to be repaired. By using point cloud data filtering, denoising, ground point classification, and terrain interpolation algorithms, or by using motion reconstruction structures and multi-view stereo matching algorithms, a digital elevation model with a spatial resolution of no less than 0.1 meters can be generated.

3. The method for optimizing the allocation of mangrove tree species for reforestation as described in claim 2, characterized in that, Obtain regional tidal pattern data, including: Self-contained pressure gauges were deployed in the tidal inlet or representative water area of ​​the area to be repaired, and continuous synchronous observations were conducted for no less than 30 calendar days to obtain raw pressure data. After pressure compensation, salinity density correction, and filtering, tidal level time series data accurate to the centimeter level are generated. This data serves as the open boundary driving condition for the two-dimensional shallow water dynamic model.

4. The method for optimizing the allocation of mangrove tree species for reforestation as described in claim 3, characterized in that, The sedimentary physicochemical properties data include sediment particle size distribution, organic matter content, pore water salinity and pH; the physiological and ecological parameters of the candidate mangrove species are structured and stored in the database, which defines for each candidate species the maximum flood tolerance threshold, salinity tolerance upper and lower limits, optimal growth salinity range, root adaptability coefficient to different sediment types, and the functional relationship between photosynthetic efficiency and flood duration.

5. The method for optimizing the allocation of mangrove tree species for reforestation as described in claim 4, characterized in that, In the normalization process, the average flooding duration, maximum continuous flooding duration, average tidal velocity, sediment salinity, and pH are mapped to the interval between 0 and 1 using linear or nonlinear functions, where zero represents the most unfavorable stress state and 1 represents the most suitable state; for organic matter content, the opposite mapping logic is used.

6. The method for optimizing the allocation of mangrove tree species for reforestation as described in claim 5, characterized in that, The steps for generating the integrated mangrove planting space configuration scheme include: For each spatial grid cell in the area to be restored, its ecological suitability index for all candidate mangrove species is first compared, and the species with the highest index value is selected as the only recommended tree species for planting in that grid cell. Then, the highest ecological suitability index value is substituted into the quantitative control model to calculate the optimal planting density corresponding to the grid unit; Finally, the geographic coordinates, recommended tree species identifiers, and optimal planting density values ​​of all grid cells are integrated to generate a planting configuration map in vector or raster data format that can be read by a geographic information system.

7. The method for optimizing the allocation of mangrove tree species for reforestation as described in claim 6, characterized in that, The grid size of the two-dimensional shallow water dynamics model is consistent with that of the digital elevation model, both being 0.1 meters by 0.1 meters; the time step of the model is automatically adjusted according to the CFL stability condition, with a maximum of no more than 300 seconds; the upstream and lateral boundaries of the model are set as closed boundaries, while the downstream open boundary is driven by the tidal time series data.

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