A method and device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal

Abstract

The present invention discloses a method and apparatus for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal. The method includes: first, dividing the simulated area into multiple spatial units; acquiring remote sensing data and extracting relevant information; constructing a dynamic mangrove growth model and a spatial simulation model; and completing the calibration and verification of the model coupling. Multiple canal construction scenarios and post-construction environmental factors are obtained and input into the model to obtain the spatiotemporal simulation results of mangrove vegetation changes corresponding to each scenario. Finally, a target canal construction scenario is determined. This design provides a scientific decision-making basis for achieving a balance between canal construction and mangrove vegetation protection.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental governance, and in particular to a method and device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal. Background Art

[0002] As canal construction progresses, it has had complex impacts on the surrounding ecological environment, particularly mangrove vegetation. However, existing technologies lack effective simulation methods for the spatiotemporal changes in mangrove vegetation under the influence of the canal. This makes it difficult to accurately assess the impact of canal construction plans on mangrove vegetation, and thus fails to provide a scientific basis for ecological protection and canal planning. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal.

[0004] In a first aspect, an embodiment of the present invention provides a method for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal, comprising:

[0005] Divide the mangrove vegetation area to be simulated into multiple spatial units;

[0006] Acquiring remote sensing data of the mangrove vegetation area to be simulated, and extracting the spatial distribution, species distribution, and temporal variation of the mangrove vegetation area to be simulated from the remote sensing data;

[0007] Using the optimal tree growth model in combination with the spatial distribution, the species distribution and the temporal variation, a dynamic growth model of mangrove plants based on individuals is constructed in each of the spatial units;

[0008] Cellular automata were used to simulate the expansion and succession of mangrove vegetation, forming a mangrove vegetation spatial simulation model.

[0009] Based on the calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, a plurality of preset canal construction plans for the mangrove vegetation area to be simulated are obtained;

[0010] Obtaining post-construction environmental factors corresponding to the multiple canal construction plans, inputting the post-construction environmental factors into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, and obtaining spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans;

[0011] According to the spatiotemporal simulation change results of the mangrove vegetation, a target canal construction plan for the mangrove vegetation area to be simulated is determined from the multiple canal construction plans.

[0012] In a possible implementation, the tree optimal growth model is combined with the spatial distribution, the species distribution, and the temporal variation to construct an individual-based mangrove plant dynamic growth model in each spatial unit, including:

[0013] Using the tree optimal growth model in combination with the spatial distribution, the species distribution and the temporal variation, constructing individual-based mangrove plant propagule production simulation, mangrove plant seed dispersal and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant mortality simulation in each of the spatial units;

[0014] The individual-based mangrove plant dynamic growth model is constructed based on the mangrove plant propagule production simulation, the mangrove plant seed diffusion and seed bank formation simulation, the mangrove plant germination simulation, the mangrove plant growth dynamics simulation and the mangrove plant death simulation.

[0015] In one possible embodiment, the mangrove plant propagule production simulation includes:

[0016] N=f1·R+f2·gh+f3

[0017] Where N is the number of reproductive bodies, R is the crown width of the mangrove plant, gh is the ground diameter of the mangrove plant, and f1, f2, and f3 are coefficients.

[0018] In one possible embodiment, the mangrove plant seed dispersal and seed bank formation simulation includes:

[0019] determining a central network, and distributing a total number of propagules into a first number and a second number, wherein the first number is greater than the second number;

[0020] If the land type of the central network within the preset range is an unfavorable type, the survival probability of the propagules within the preset range is 0; if the land type of the central network within the preset range is a favorable type, the survival probability of the propagules within the preset range is the product of the first number and the preset first random coefficient, and the normal diffusion seed bank is obtained;

[0021] If there is water outside the preset range, the propagule drift position is determined in combination with the hydrodynamic model, and the hydraulic diffusion seed bank is determined based on the propagule drift position in combination with a preset second random coefficient;

[0022] The mangrove plant seed diffusion and seed bank formation simulation is completed based on the normal diffusion seed bank and the water diffusion seed bank.

[0023] In one possible embodiment, the mangrove plant sprouting simulation includes:

[0024] n i =n1·u+n2

[0025] Among them, n i is the germination rate of mangrove plants, i is the species of mangrove plants, u is the soil salinity in the seed bank area, and n1 and n2 are coefficients.

[0026] In one possible embodiment, the mangrove plant growth dynamic simulation includes:

[0027]

[0028] Among them, R is the crown width of mangrove plants, dbh is the diameter at breast height of mangrove plants, and a is the coefficient;

[0029]

[0030] Wherein, dbh is the diameter at breast height of mangrove plants, dbhmax is the maximum diameter at breast height of mangrove plants, t is the time step, H is the plant height, Hmax is the maximum plant height, G is the growth coefficient, b2 and b3 are constants, S(u) is the salinity stress coefficient, V(o) is the velocity scour stress coefficient, and C(r) is the competition coefficient;

[0031]

[0032] Among them, u is the salinity of the area where the mangrove plant is located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5, and d is the coefficient;

[0033]

[0034] Wherein, V(o) is the velocity scour stress coefficient, o is the velocity at which the mangrove plant is located, omin is the minimum velocity at which the mangrove plant can survive, and omax is the maximum velocity at which the mangrove plant can survive.

[0035]

[0036]

[0037] Where C(r) is the competition coefficient, assuming that the coordinates of the mangrove plant trunk are the origin, R is the diameter of the mangrove plant trunk at the origin, A is the range determined by the diameter R, n is the number of mangrove plants, r is the DBH of the i-th mangrove plant, and rbh is the DBH of the mangrove plant at the origin.

[0038] In one possible embodiment, the mangrove plant death simulation includes:

[0039] When the growth rate of the mangrove plant's diameter at breast height is negative, or the competition coefficient exceeds the preset competition coefficient threshold, or the scouring depth or siltation depth in the riverbank mangrove area exceeds the mangrove plant root length alarm threshold, the mangrove plant is determined to be dead.

[0040] In one possible implementation, the method further includes:

[0041] The remote sensing image distribution of mangrove plants in a preset number of typical years is obtained to calibrate and verify the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model.

[0042] In a possible implementation, the post-construction environmental factors corresponding to the multiple canal construction plans are obtained, and the post-construction environmental factors are input into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model to obtain spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans, including:

[0043] Obtaining the vessel traffic intensity, shipping water diversion scale, and vessel navigation speed in narrow riverbank areas included in the multiple canal construction plans;

[0044] Input the ship flow intensity, shipping water diversion scale, and ship navigation speed in narrow riverbank areas into a pre-set two-dimensional hydrodynamic-water environment model to obtain the salinity change and flow rate change of each spatial unit for each canal construction plan;

[0045] Inputting the salinity change and the flow velocity change into the mangrove plant dynamic growth model and coupling it with the mangrove vegetation spatial simulation model to obtain spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans;

[0046] The step of determining a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans based on the spatiotemporal simulation results of the mangrove vegetation changes includes:

[0047] Based on the spatiotemporal simulation results of mangrove vegetation changes corresponding to each canal construction plan, a quantitative relationship between the canal construction plan and the total area of mangrove vegetation and the area of different mangrove vegetation is constructed based on multiple regression;

[0048] A target canal construction plan for the mangrove vegetation area to be simulated is determined based on the measurement relationship.

[0049] In a second aspect, an embodiment of the present invention provides a device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal, comprising:

[0050] An acquisition module is configured to divide a mangrove vegetation area to be simulated into a plurality of spatial units; obtain remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution, and temporal variation of the mangrove vegetation area to be simulated from the remote sensing data; construct an individual-based mangrove plant dynamic growth model in each spatial unit using an optimal tree growth model combined with the spatial distribution, species distribution, and temporal variation; simulate the expansion and succession of mangrove vegetation using a cellular automaton to form a mangrove vegetation spatial simulation model; and obtain a plurality of preset canal construction plans for the mangrove vegetation area to be simulated based on the calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model.

[0051] A simulation module is used to obtain post-construction environmental factors corresponding to the multiple canal construction plans, input the post-construction environmental factors into the mangrove plant dynamic growth model and couple it with the mangrove vegetation spatial simulation model to obtain the spatiotemporal simulation change results of the mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans; based on the spatiotemporal simulation change results of the mangrove vegetation, determine the target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans.

[0052] Compared to existing technologies, the present invention offers the following advantages: Using the disclosed method and apparatus for simulating the spatiotemporal changes in mangrove vegetation under the influence of a canal, the simulated area is divided into multiple spatial units. Remote sensing data is acquired and relevant information is extracted to construct a dynamic mangrove growth model and a spatial simulation model, completing the calibration and verification of the coupled models. Multiple canal construction scenarios and post-construction environmental factors are obtained and input into the model to obtain the spatiotemporal simulation results of mangrove vegetation changes corresponding to each scenario. Ultimately, the target canal construction scenario is determined. This design provides a scientific decision-making basis for achieving a balance between canal construction and mangrove vegetation protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly describes the drawings required for use in the embodiments. It should be understood that the following drawings illustrate only certain embodiments of the present invention and should not be construed as limiting the scope of the present invention. Those skilled in the art can, without inventive effort, derive other relevant drawings from these drawings.

[0054] Figure 1 A schematic flow chart of the steps of a method for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal provided in an embodiment of the present invention;

[0055] Figure 2 A schematic block diagram of the structure of a device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal provided in an embodiment of the present invention;

[0056] Figure 3 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be understood that the described embodiments are only a portion of the embodiments of the present invention, not all of them. Generally, the components of the embodiments of the present invention described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

[0058] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] In order to solve the technical problems in the above background technology, Figure 1 This is a flow chart of a method for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal provided in an embodiment of the present disclosure. The method for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal is introduced in detail below.

[0060] Step S201, dividing the mangrove vegetation area to be simulated into multiple spatial units;

[0061] Step S202: acquiring remote sensing data of the mangrove vegetation area to be simulated, and extracting the spatial distribution, species distribution, and temporal variation of the mangrove vegetation area to be simulated from the remote sensing data;

[0062] Step S203, using the optimal tree growth model in combination with the spatial distribution, the species distribution, and the temporal variation, to construct an individual-based mangrove plant dynamic growth model in each spatial unit;

[0063] Step S204, using cellular automata to simulate the expansion and succession of mangrove vegetation to form a mangrove vegetation spatial simulation model;

[0064] Step S205: obtaining a plurality of preset canal construction plans for the mangrove vegetation area to be simulated based on the calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model;

[0065] Step S206: Obtaining post-construction environmental factors corresponding to the multiple canal construction plans, inputting the post-construction environmental factors into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, and obtaining spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans;

[0066] Step S207 , determining a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans based on the spatiotemporal simulation change results of the mangrove vegetation.

[0067] The following is a very detailed scenario example of each step in the above technical content, using the server as the execution body:

[0068] In an embodiment of the present invention, for example, it is assumed that the mangrove vegetation area to be simulated is a large coastal wetland with an area of about 100 square kilometers. The server first obtains the geographic information data of the area, including terrain, coastline, etc. Then, according to preset rules, the area is divided into square spatial units with a side length of 100 meters. In this way, the entire area is divided into 10,000 spatial units. Each spatial unit has a unique identifier and coordinate information for subsequent data processing and analysis. For example, for a spatial unit located at coordinates (1000, 2000), the server will record the coordinate range of its boundary and mark it as "unit 1000_2000". The division of these spatial units helps to more accurately simulate the growth and changes of mangrove vegetation in different locations.

[0069] Through an interface with a satellite remote sensing data provider, the server obtained high-resolution remote sensing imagery data for the simulated area annually over the past 10 years. This data includes multispectral and hyperspectral information. The server uses specialized image processing algorithms and models to process and analyze this remote sensing data. First, through spectral feature recognition and classification algorithms, it distinguishes different land cover types, such as water, land, and mangrove vegetation. It then further analyzes the spatial distribution of mangrove vegetation to determine which spatial units contain mangrove vegetation. To extract species distribution, the server uses a deep learning model to learn and identify features such as morphology and color of mangrove vegetation, thereby distinguishing different mangrove species, such as Aegiceras corniculata, Kandelia candel, and Avicennia marina. To analyze temporal changes, the server compares remote sensing data from different years to observe trends in the extent, density, and species of mangrove vegetation. For example, it may find that the area of mangrove vegetation in a certain area has gradually increased over the past five years, or that the distribution range of a particular mangrove species has gradually expanded.

[0070] Based on the acquired spatial distribution, species distribution, and temporal variation data, the server establishes a dynamic growth model for each mangrove plant in each spatial unit. Taking the Tung tree as an example, the server first calculates the tree's initial growth rate based on environmental factors such as soil conditions, salinity, and light intensity within the spatial unit, combined with the parameters of the optimal tree growth model. Assuming that the Tung tree in a given spatial unit has an initial diameter at breast height of 10 cm, the model calculates its annual growth rate to be approximately 2 cm. To simulate propagule production, the server uses a formula based on the tree's crown width and ground diameter to calculate the number of propagules produced annually. Assuming a crown width of 2 meters and a ground diameter of 20 cm for the Tung tree in this spatial unit, the formula calculates that 50 propagules are produced annually. In the simulation of seed dispersal and seed bank formation, the server takes into account the relatively heavy seeds of Tung trees and their limited range of normal drop and dispersal. Assuming that the percentage of propagules allocated to the central grid within a 1-meter Chebyshev distance is 80%, if the soil type within the 1-meter radius is suitable for seed survival, 80% of the propagules will form a seed bank. If there is water within 1 meter, the remaining 20% of the propagules are simulated using hydraulic diffusion, and their potential drift locations and survival probabilities are calculated using a hydrodynamic model. For the germination simulation, the server calculates the germination rate based on the soil salinity of the spatial unit and the characteristics of the Tung tree species. Assuming a soil salinity of 10, the formula calculates that the germination rate of Tung trees is approximately 70%. In the growth dynamics simulation, the server comprehensively considers factors such as the salinity stress coefficient, the flow velocity scour stress coefficient, and the competition coefficient. Assuming a salinity of 20, a flow velocity of 1 meter / second, and five other Tung trees in the surrounding area, the calculation shows that the current Tung tree's growth rate is limited. For the death simulation, the server monitors the Tung tree's growth in real time. If the DBH increment rate is negative, or the competition coefficient exceeds 0.7, or the scouring depth in the riverbank mangrove area exceeds 60% of the root length of the mangrove plant, the Tung tree is judged to be dead.

[0071] The server treats each spatial unit as a cell and simulates it according to the rules of cellular automata. For example, if the crown area of a mangrove plant in a spatial unit exceeds 1 / 2 of the area of the grid in which it is located, the server determines that the mangrove plant occupies the grid. Over time, the seeds of the mangrove plant will spread to adjacent cells. If the adjacent cell is a light beach area and the conditions are suitable, the seeds may germinate and grow, realizing the expansion of mangrove plants in the light beach area. In terms of vegetation succession, assuming that the competitive advantage of the Sonneratia apetala is strong, over time, if the same area exists as the Sonneratia apetala and other mangrove plants, the server will determine whether the other mangrove plants will gradually be replaced by the Sonneratia apetala based on the calculation of the competition coefficient.

[0072] The server pre-stored multiple canal construction plans for the mangrove vegetation area being simulated. These plans differ in route, scale, and navigation conditions. For example, Plan 1 might be a 50-meter-wide canal along the coastline with medium navigability; Plan 2 might be an 80-meter-wide canal through the wetland interior with high navigability.

[0073] For each canal construction scenario, the server captures the corresponding post-construction environmental factors. Taking scenario one as an example, the server obtains data on ship traffic intensity, water diversion scale, and ship speed in narrow riverbank areas after its completion. This data is then input into a pre-defined two-dimensional hydrodynamic-water environment model to calculate changes in salinity and flow velocity for each spatial unit. Assume that in certain spatial units, salinity decreases by 5% due to water diversion, while flow velocity increases by 0.5 m / s due to ship traffic. The server then feeds this change data into a mangrove dynamic growth model coupled with a mangrove vegetation spatial simulation model to simulate the spatiotemporal changes in mangrove vegetation over the next 50 years. The simulation may reveal an increase in mangrove vegetation area in some spatial units, as reduced salinity favors growth. Meanwhile, mangrove vegetation may be suppressed or even die in areas closer to the canal and with faster flow velocities. The above process is repeated for scenario two, generating the corresponding simulation results, which are then compared and analyzed with scenario one.

[0074] The server conducts a detailed analysis and evaluation of the simulation results for each canal construction scenario. For example, after 50 years of simulation, the total mangrove vegetation area for Scenario 1 decreased by 10%, with the area of certain rare mangrove species decreasing by 20%. Simulation results for Scenario 2, on the other hand, show a 5% decrease in total mangrove vegetation area and a 10% decrease in the area of rare mangrove species. Based on these results, the server uses multivariate regression to construct a quantitative relationship between the canal construction scenario and the total mangrove vegetation area and the area of different mangrove species. By analyzing these quantitative relationships and comprehensively considering the needs of ecological protection and economic development, the most suitable target canal construction scenario is determined. Assume that after comprehensive evaluation, Scenario 2 is considered the preferred option because it has a relatively small impact on mangrove vegetation while meeting certain shipping needs. The server ultimately outputs Scenario 2 as the target canal construction scenario for the simulated mangrove vegetation area and provides detailed simulation data and analysis reports to support subsequent planning and decision-making.

[0075] In the embodiment of the present invention, the aforementioned step S203 can be implemented through the following example.

[0076] Using the tree optimal growth model in combination with the spatial distribution, the species distribution and the temporal variation, constructing individual-based mangrove plant propagule production simulation, mangrove plant seed dispersal and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant mortality simulation in each of the spatial units;

[0077] The individual-based mangrove plant dynamic growth model is constructed based on the mangrove plant propagule production simulation, the mangrove plant seed diffusion and seed bank formation simulation, the mangrove plant germination simulation, the mangrove plant growth dynamics simulation and the mangrove plant death simulation.

[0078] In the mangrove vegetation area to be simulated, the server first obtains detailed information about each spatial unit, including its geographic location, soil characteristics, and surrounding environment. To simulate the production of mangrove propagules, the server uses a specific spatial unit as an example. Assuming that this unit contains a mangrove plant called Kandelia candel. Through field measurements and data analysis, the server determines that the plant's crown width is 3 meters and its ground diameter is 25 centimeters. Using a pre-set formula and coefficients (f1, f2, and f3), the server calculates that the annual number of propagules produced is approximately 80. To simulate mangrove seed dispersal and seed bank formation, the server also uses this spatial unit as an example. It first determines the central network and assumes that the total number of propagules is 80, of which 64 (80%) are distributed within a pre-set range. If the land type within the pre-set range is favorable for mangrove growth, such as a moist, unobstructed mudflat, the survival probability of these 64 propagules is the product of 64 and the pre-set first random coefficient (assumed to be 0.8), which is approximately 51 normally dispersing seed banks. If there is a water body outside the preset range, the server will use the hydrodynamic model to determine the drifting position of the propagule. Assuming that the water flow speed and direction are constant, after calculation, some propagules may drift to another mudflat 50 meters away from the original position, and then determine about 8 water diffusion seed banks based on the preset second random coefficient (assuming it is 0.6). For the germination simulation of mangrove plants, the server analyzes the soil salinity in the area where the seed bank is located in this spatial unit, assuming it is 15 after detection. For the mangrove plant Kandelia ovata, the germination rate is calculated to be about 65% through the corresponding formula and coefficients (n1, n2). In the dynamic simulation of mangrove plant growth, the server obtains the current diameter at breast height and plant height of the Kandelia ovata plant in the spatial unit. Assume that the diameter at breast height is 30 cm and the plant height is 2 meters. Through a series of complex calculations, the system takes into account the salinity stress coefficient (assuming a salinity of 20 in the area, the corresponding salinity stress coefficient is calculated to be 0.7), the flow scour stress coefficient (assuming a flow velocity of 1.5 m / s, the corresponding flow scour stress coefficient is calculated to be 0.8), and the competition coefficient (assuming there are three Kandelia candel trees with diameters at breast height of 25, 35, and 40 cm, respectively, with a competition coefficient calculated to be 0.5). The growth rate and future growth trend of the Kandelia candel are comprehensively determined. Finally, the mangrove plant mortality simulation is performed. The server continuously monitors the growth of Kandelia candel plants within this spatial unit. If the diameter at breast height growth rate is negative for a period of time, if the competition coefficient exceeds a preset competition coefficient threshold (assuming 0.7), or if the scour or siltation depth in the riverbank mangrove area exceeds the mangrove root length warning threshold (assuming 60%), the server will determine the mangrove plant is dead.By performing such detailed and precise simulation calculations on each spatial unit, the server constructs a complete individual-based dynamic growth model of mangrove plants based on the above-mentioned mangrove plant propagule production simulation, seed diffusion and seed bank formation simulation, germination simulation, growth dynamics simulation and death simulation, thereby comprehensively and accurately reflecting the growth and changes of mangrove plants in different spatial units.

[0079] In an embodiment of the present invention, the mangrove plant propagule generation simulation includes:

[0080] N=f1·R+f2·gh+f3

[0081] Where N is the number of reproductive bodies, R is the crown width of the mangrove plant, gh is the ground diameter of the mangrove plant, and f1, f2, and f3 are coefficients.

[0082] In this embodiment of the present invention, the server performs these calculations and processing for a large number of spatial units and different mangrove plant species, thereby comprehensively understanding the production of mangrove plant propagules throughout the entire simulated area. By repeatedly performing these calculations, the server can accurately simulate the number of propagules produced by each mangrove plant species in each spatial unit, providing an important data foundation for subsequent simulations of seed dispersal and germination.

[0083] In an embodiment of the present invention, the mangrove plant seed dispersal and seed bank formation simulation includes:

[0084] determining a central network, and distributing a total number of propagules into a first number and a second number, wherein the first number is greater than the second number;

[0085] If the land type of the central network within the preset range is an unfavorable type, the survival probability of the propagules within the preset range is 0; if the land type of the central network within the preset range is a favorable type, the survival probability of the propagules within the preset range is the product of the first number and the preset first random coefficient, and the normal diffusion seed bank is obtained;

[0086] If there is water outside the preset range, the propagule drift position is determined in combination with the hydrodynamic model, and the hydraulic diffusion seed bank is determined based on the propagule drift position in combination with a preset second random coefficient;

[0087] The mangrove plant seed diffusion and seed bank formation simulation is completed based on the normal diffusion seed bank and the water diffusion seed bank.

[0088] In an exemplary embodiment of the present invention, within a mangrove vegetation area to be simulated, the server first determines a central network. Assuming a mangrove plant within a specific spatial unit produces 100 propagules, the server allocates these to a first number of 80 and a second number of 20. The server then analyzes the land type within a preset range within the central network. If this preset range is a circular area 1 meter from the plant, the server obtains land data for this area and finds that it is covered by a hardened road, which is an unfavorable type. Therefore, the probability of propagule survival within this preset range is 0, meaning that no propagules can form a normal diffusion seed bank within this range. In another spatial unit, the server determines that the land type within the preset range is a moist, undisturbed mudflat, which is a favorable type. Similarly, 100 propagules are generated and allocated to a first number of 80. Assuming a preset first random coefficient of 0.8, the probability of propagule survival within this preset range is 80 × 0.8 = 64, thus forming 64 normal diffusion seed banks. For water bodies outside the preset range, the server uses a hydrodynamic model to determine the drifting locations of the propagules. Suppose that in a spatial unit near a river, 20 propagules enter the water outside a preset range. The server invokes a hydrodynamic model, inputting parameters such as water velocity, direction, and water depth, and calculates the probability that the propagules will drift to another riverbank 50 meters away from their original location. Then, assuming a preset second random coefficient of 0.6, the server determines that the number of propagules that could form a rice seed bank at this location is 20 × 0.6 = 12. The server combines the calculated normal diffusion seed bank and the hydraulic diffusion seed bank to simulate mangrove seed dispersal and seed bank formation. For example, after processing a series of spatial units, the server creates a detailed map of seed dispersal and seed bank distribution across the entire area. In one area, the server finds a high concentration of normal diffusion seed banks formed across multiple spatial units, indicating a high probability of local dispersal and survival of mangrove seeds, potentially promoting further development of mangrove vegetation in that area. In other areas, the distribution of the hydraulic diffusion seed bank indicates that seeds have been carried farther by water, creating the potential for new areas of mangrove vegetation growth. By performing such precise calculations and simulations for each spatial unit, the server comprehensively grasped the dispersal paths of mangrove seeds and the formation of the seed bank, providing critical data support for subsequent analysis of the expansion and succession of mangrove vegetation. Throughout the simulation process, the server continuously optimized its algorithms and improved its calculation accuracy to more accurately reflect actual ecological processes.

[0089] In an embodiment of the present invention, the mangrove plant sprouting simulation includes:

[0090] n i =n1·u+n2

[0091] Among them, ni is the germination rate of mangrove plants, i is the species of mangrove plants, u is the soil salinity in the seed bank area, and n1 and n2 are coefficients.

[0092] In an embodiment of the present invention, for example, by calculating the germination rates of different mangrove plant species in each spatial unit under specific soil salinity conditions, the server can comprehensively understand the distribution of mangrove plant germination probabilities across the entire area. This provides important basic data for subsequent simulations of mangrove vegetation growth and expansion. During processing, the server strictly follows formulas and pre-set coefficients for precise calculations, ensuring the accuracy and reliability of the germination rate simulation results. The server also stores and organizes the calculated germination rate data for integration and comprehensive analysis with data from other simulation steps.

[0093] In an embodiment of the present invention, the dynamic simulation of mangrove plant growth includes:

[0094]

[0095] Among them, R is the crown width of mangrove plants, dbh is the diameter at breast height of mangrove plants, and a is the coefficient;

[0096]

[0097] Wherein, dbh is the diameter at breast height of mangrove plants, dbhmax is the maximum diameter at breast height of mangrove plants, t is the time step, H is the plant height, Hmax is the maximum plant height, G is the growth coefficient, b2 and b3 are constants, S(u) is the salinity stress coefficient, V(o) is the velocity scour stress coefficient, and C(r) is the competition coefficient;

[0098]

[0099] Among them, u is the salinity of the area where the mangrove plant is located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5, and d is the coefficient;

[0100]

[0101] Wherein, V(o) is the velocity scour stress coefficient, o is the velocity at which the mangrove plant is located, omin is the minimum velocity at which the mangrove plant can survive, and omax is the maximum velocity at which the mangrove plant can survive.

[0102]

[0103] Where C(r) is the competition coefficient, assuming that the coordinates of the mangrove plant trunk are the origin, R is the diameter of the mangrove plant trunk at the origin, A is the range determined by the diameter R, n is the number of mangrove plants, r is the DBH of the i-th mangrove plant, and rbh is the DBH of the mangrove plant at the origin.

[0104] In an embodiment of the present invention, for example, within a mangrove vegetation area to be simulated, the server begins simulating the mangrove plant's growth dynamics. First, the server obtains initial data for the mangrove plant within a specific spatial unit. Assume that the mangrove plant within this spatial unit is Kandelia ovata, with a crown width R of 3 meters, a diameter at breast height (DBH) of 20 centimeters, and a coefficient a of 10. The server calculates the Kandelia ovata's growth using a formula, obtaining a result of 30 centimeters. This indicates Kandelia ovata's growth under current conditions. Next, the server further analyzes the long-term growth dynamics of Kandelia ovata. Assume that Kandelia ovata has a maximum DBH (DBH) of 50 centimeters, a time step t of 1 year, a plant height H of 4 meters, a maximum plant height Hmax of 8 meters, a growth coefficient G of 0.2, constants b2 of 0.1, and b3 of 0.05. The server first calculates the salinity stress coefficient S(u). The salinity u in the area where the mangrove plant is located is 25. Assume that a Kandelia ovata salinity growth coefficient of 0.5 corresponds to a salinity ui of 20, with a coefficient d of 0.08. The salinity stress coefficient S(u) is obtained as 0.6, and then the velocity scour stress coefficient is calculated. The flow velocity o of the mangrove plant is obtained as 1.2 m / s. Assume that the minimum flow velocity omin for survival of the Kandelia candel plant is 0.5 m / s, and the maximum flow velocity omax is 2 m / s. The velocity scour stress coefficient is calculated to be 0.7. Next, the competition coefficient C(r) is calculated. Assume that the coordinates of the Kandelia candel trunk are used as the origin, and the range A is determined by a diameter R of 5 meters. The number of mangrove plants within the range n is 8, and the diameter at breast height r of the i-th mangrove plant is 18, 22, 25, 19, 21, 23, 17, and 20 cm respectively. The diameter at breast height rbh of the Kandelia candel plant at the origin is 20 cm. The competition coefficient C(r) is calculated to be 0.4. Finally, the server substitutes the above-calculated coefficients into the growth dynamics formula to calculate the change in Kandelia candel diameter at breast height after one year. The server used the same method to process other spatial units and different mangrove species within the area, such as Avicennia marina and Tunghua tree. In another spatial unit, the mangrove was Avicennia marina, with an initial crown width (R) of 2.5 meters, a diameter at breast height (DBH) of 15 cm, and a coefficient (a) of 10. A series of similar calculations were performed to determine the growth dynamics of Avicennia marina. Similarly, data was obtained for the Tunghua tree spatial unit, and detailed calculations and analysis were performed. The server continuously processed a large number of spatial units and different mangrove species, continuously accumulating and updating data on growth dynamics. Through such comprehensive and precise calculations, the server was able to simulate the temporal growth changes of various mangrove species under different environmental conditions throughout the entire mangrove vegetation area. This provides important evidence and support for studying the ecological development of mangrove vegetation, assessing environmental impacts, and formulating conservation strategies. During the simulation process, the server strictly adhered to the settings of all formulas and parameters to ensure accuracy and consistency. Simultaneously, the server continuously optimized the algorithm and data processing process to improve the efficiency and accuracy of the simulation, thereby more realistically reflecting the growth dynamics of mangrove plants in their natural environment.The server will also record and organize the simulation results in detail for subsequent analysis and application.

[0105] In an embodiment of the present invention, the mangrove plant death simulation includes:

[0106] When the growth rate of the mangrove plant's diameter at breast height is negative, or the competition coefficient exceeds the preset competition coefficient threshold, or the scouring depth or siltation depth in the riverbank mangrove area exceeds the mangrove plant root length alarm threshold, the mangrove plant is determined to be dead.

[0107] In an embodiment of the present invention, illustratively, in the mangrove vegetation area to be simulated, the server begins to determine the death status of mangrove plants. First, the server obtains the diameter at breast height data of the mangrove plants in a specific spatial unit. Suppose that in a spatial unit, the diameter at breast height of a mangrove plant at the previous time node is 30 cm. After a period of monitoring, the current diameter at breast height is 28 cm. The server calculates the diameter at breast height increment rate, that is, (28-30) / 30=-0.067. Since the diameter at breast height increment rate is negative, the server determines that the mangrove plant is likely to die. In another spatial unit, the server obtains data related to the competition coefficient of the mangrove plant. Suppose the preset competition coefficient threshold is 0.7, and the competition coefficient of the mangrove plant in the spatial unit is calculated to be 0.8. Since the competition coefficient exceeds the preset threshold, the server determines that the mangrove plant is at risk of death. For mangrove plants in the riverbank mangrove area, the server obtains data on scour depth or siltation depth. Suppose a mangrove has an 80-centimeter root system, and the monitored scour depth reaches 50 centimeters, exceeding the root length warning threshold (assuming 60%, or 48 centimeters). In this case, the server determines that the mangrove is in danger of dying. The server continuously monitors and assesses the mangroves in each spatial unit across the entire area. For example, in a densely populated area of mangroves, the server analyzes multiple mangroves. The diameter at breast height of one mangrove has not increased, but has decreased. Several other mangroves have high competition coefficients. Furthermore, some mangroves near the riverbank are experiencing scour depths exceeding their root tolerance due to water flow. Based on this actual monitoring and calculated data, the server makes mortality determinations for each mangrove plant. By meticulously monitoring and accurately assessing a large number of mangrove plants, the server can promptly understand the health and changing trends of the mangrove ecosystem. This provides important information for further analyzing the ecological balance of the mangrove ecosystem, formulating conservation measures, and predicting future development. During the entire determination process, the server relies on precise data collection and rigorous calculation logic to ensure the accuracy and reliability of death determination.

[0108] In an embodiment of the present invention, the method further includes:

[0109] The remote sensing image distribution of mangrove plants in a preset number of typical years is obtained to calibrate and verify the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model.

[0110] In an embodiment of the present invention, for example, when performing model calibration and validation, the server first sets the number of typical years to be acquired, assuming the preset number is five. The server then selects image data from a database storing remote sensing image data of mangrove plants that meets the requirements. Assume these five typical years are 2010, 2015, 2020, 2025, and 2030. The server sequentially acquires remote sensing image data from 2010 and performs detailed analysis and processing. Using image processing algorithms, it extracts information such as the distribution range, area, and species of mangrove plants. For example, in the images from that year, the server determines that within a specific area, the main populations of two mangrove species, Kandelia candel and Avicennia marina, are X1 and Y1 square meters, respectively. Next, the server acquires remote sensing image data from 2015. Following the same processing flow, it determines that within the same specific area, the distribution areas of Kandelia candel and Avicennia marina have become X2 and Y2 square meters, respectively. In addition, a small number of new mangrove species were discovered, such as the Tung blossom tree, which has a distribution area of Z1 square meters. The server continued to conduct in-depth analysis of the 2020 remote sensing imagery. It found that due to environmental changes and human activities, the distribution area of Kandelia ovata increased to X3 square meters, the area of Avicennia marina decreased to Y3 square meters, and the area of Tung blossom tree increased to Z2 square meters. Similarly, the server processed remote sensing imagery data from 2025 and 2030 to obtain detailed distribution and changes of mangrove species in each typical year. After acquiring and analyzing remote sensing imagery data for all typical years, the server input the observed mangrove distribution data into a mangrove dynamic growth model coupled with a mangrove vegetation spatial simulation model. The server then initiated the model calibration and validation process. Using data from 2010 as an example, the model-calculated mangrove distribution results were compared with the observed distribution data. For example, if the model calculated the distribution area of Kandelia ovata to be X1 square meters and the distribution area of Avicennia marina to be Y1 square meters, the server calculated the difference between the two and evaluated the model's accuracy. The above comparison and evaluation process was repeated for the 2015 data. The differences between the model-calculated distribution areas of Kandelia ovata (X2'), Avicennia marina (Y2'), and Aegiceras corniculata (Z1') and the actual observed values (X2, Y2, and Z1) were calculated. The same processing and evaluation was repeated for the data from 2020, 2025, and 2030. Throughout the calibration and validation process, the server recorded the results of each comparison in detail, including the numerical difference and the deviation in the distribution range. For example, in the 2020 comparison, the server found that the model-calculated distribution area of Kandelia ovata was 5% larger than the actual observed value, and the distribution area of Avicennia marina was 3% smaller than the actual observed value. The server also adjusted and optimized the model parameters based on these differences.If the model overestimates the distribution area of a particular mangrove species across multiple comparisons, the server adjusts parameters related to the species' growth to improve the model's accuracy. By processing remote sensing imagery data from multiple typical years and comparing it with model results, the server comprehensively and systematically evaluates the model's performance and accuracy, continuously optimizing the model to more accurately simulate the dynamic growth and spatial distribution of mangroves. This provides a reliable model foundation for subsequent research and application.

[0111] In the embodiment of the present invention, the aforementioned step S206 can be implemented through the following example.

[0112] Obtaining the vessel traffic intensity, shipping water diversion scale, and vessel navigation speed in narrow riverbank areas included in the multiple canal construction plans;

[0113] Input the ship flow intensity, shipping water diversion scale, and ship navigation speed in narrow riverbank areas into a pre-set two-dimensional hydrodynamic-water environment model to obtain the salinity change and flow rate change of each spatial unit for each canal construction plan;

[0114] Inputting the salinity change and the flow velocity change into the mangrove plant dynamic growth model and coupling it with the mangrove vegetation spatial simulation model to obtain spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans;

[0115] The step of determining a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans based on the spatiotemporal simulation results of the mangrove vegetation changes includes:

[0116] Based on the spatiotemporal simulation results of mangrove vegetation changes corresponding to each canal construction plan, a quantitative relationship between the canal construction plan and the total area of mangrove vegetation and the area of different mangrove vegetation is constructed based on multiple regression;

[0117] A target canal construction plan for the mangrove vegetation area to be simulated is determined based on the measurement relationship.

[0118] In an embodiment of the present invention, illustratively, first, the server obtains multiple canal construction plans. Assume that there are three plans: Plan A, Plan B, and Plan C. For Plan A, the server obtains that its ship flow intensity is 50 ships per day, the scale of shipping water diversion is 100,000 cubic meters per day, and the ship navigation speed in the narrow river bank area is 15 kilometers per hour. For Plan B, the ship flow intensity is 80 ships per day, the scale of shipping water diversion is 150,000 cubic meters per day, and the ship navigation speed in the narrow river bank area is 20 kilometers per hour. For Plan C, the ship flow intensity is 100 ships per day, the scale of shipping water diversion is 200,000 cubic meters per day, and the ship navigation speed in the narrow river bank area is 25 kilometers per hour. The server inputs these data such as ship flow intensity, shipping water diversion scale, and ship navigation speed in the narrow river bank area into a pre-set two-dimensional hydrodynamic-water environment model. Taking Plan A as an example, the model performs complex calculations and simulations based on the input data. Taking into account factors such as changes in water flow caused by ship navigation and the impact of water diversion on the water body, the salinity and flow velocity changes for each spatial unit are calculated. Assume that in certain spatial units close to the canal, salinity increases by 5% and flow velocity increases by 2 meters per second. In areas farther from the canal, salinity and flow velocity changes are relatively small. For Scenarios B and C, the server also uses a two-dimensional hydrodynamic-water environment model to determine salinity and flow velocity changes for each spatial unit. These salinity and flow velocity changes are then input into a mangrove dynamic growth model coupled with a mangrove vegetation spatial simulation model. Taking Scenarios A as an example, the model comprehensively considers changes in salinity and flow velocity to simulate the growth, reproduction, and mortality of mangroves over a future period (e.g., 50 years), as well as their spatial expansion and contraction. Assume that, after simulation, the mangrove vegetation area in certain spatial units decreases by 10%, and the species composition of the mangroves also changes to some extent. The server repeats the above simulation process for Scenarios B and C, obtaining the spatiotemporal simulation results of mangrove vegetation changes for each spatial unit corresponding to each scenario. Then, based on the spatiotemporal simulation results of mangrove vegetation changes corresponding to each canal construction scenario, the server constructed a quantitative relationship between the canal construction scenario and the total mangrove vegetation area, as well as the areas of different mangrove species, using multiple regression. For example, for Scenario A, the server calculated that the corresponding total mangrove vegetation area was 100 hectares, of which 30 hectares were Kandelia officinalis and 20 hectares were Avicennia marina. Through multiple regression analysis, a mathematical relationship was established between the various parameters of Scenario A and the mangrove vegetation area. The server performed the same statistical analysis for Scenario B and Scenario C, establishing their respective quantitative relationships. Finally, based on these quantitative relationships, the server determined the target canal construction scenario for the simulated mangrove vegetation area. The server compared the quantitative relationships of each scenario, comprehensively considering the needs of ecological protection and canal construction.If ecological protection is the primary goal, the server might choose the option with the least impact on mangrove vegetation, even if it might not be optimal in terms of shipping efficiency. Suppose, after comparison, that Option A, while having relatively weak shipping capacity, has the least impact on mangrove vegetation, can maintain a relatively stable total mangrove area, and the variation in the area of different mangrove species is within an acceptable range. The server would then select Option A as the target canal construction plan for the simulated mangrove area. Throughout this process, the server, with its powerful data processing capabilities and precise calculations, provides a scientific and accurate basis for selecting the canal construction plan, thereby achieving a balance between ecological protection and economic development.

[0119] In order to more clearly describe the solution provided in the embodiments of the present application, a complete implementation method is provided below.

[0120] Divide the study area into geographic spatial units. Considering the distribution of mangrove vegetation and data computation efficiency, GIS software was used to divide the study area into spatial units, which served as the benchmark units for simulation calculations. Considering the scale of mangrove vegetation, the minimum spatial grid unit could be set to 1 meter x 1 meter.

[0121] (2) Interpret regional remote sensing data and determine the distribution area and specific species of mangrove vegetation through visual interpretation, field surveys and training data.

[0122] 1. Collect satellite remote sensing data (such as Landsat, Sentinel, and Gaofen GF series satellites) for the study area. All satellite remote sensing data is preprocessed within the software, including radiometric calibration, atmospheric correction, and geometric correction. The required single bands are extracted from the remote sensing data, and the resampling function is used to resample the bands at different spatial resolutions to less than 10 meters. Finally, the cropping function is used to retain the study area.

[0123] 2. Use remote sensing information extraction software to segment the image. False color processing is performed on the remote sensing imagery, assigning red to shortwave infrared (B11), green to near infrared (B8), and blue to red (B4). The remote sensing imagery is combined with on-site mangrove species survey data to establish an image interpretation signature set. Next, training samples for classification are selected and nearest neighbor features are assigned to these training samples. Finally, a random forest algorithm is used to classify mangrove species across the entire study area based on spectral signature indices.

[0124] 3. Select remote sensing images from at least three typical years for mangrove vegetation interpretation, preferably spanning more than five years. Depending on the availability of remote sensing data, multiple years of data can be interpreted to provide more data support for subsequent modeling.

[0125] (3) Using the optimal tree growth model, an individual-based dynamic growth model of mangrove plants is constructed in geographic space units.

[0126] 1. Simulation of mangrove propagule production. The most prominent characteristic of mangroves is viviparity. Viviparity refers to the phenomenon in which mature seeds germinate directly on the mother plant without undergoing dormancy or after only a brief period of dormancy. Viviparity can be understood as the annual production of propagules on the tree after maturation. Assume that each adult mangrove plant can produce N propagules. The number of propagules can be determined through field surveys or estimated using a model. The calculation formula is as follows:

[0127] N=f1·R+f2·gh+f3

[0128] Among them, R is the crown width of mangrove plants, gh is the ground diameter of mangrove plants, and f1, f2, and f3 are coefficients.

[0129] 2. Simulation of mangrove plant seed dispersal and seed bank formation.

[0130] Given the viviparous nature of mangroves, seeds formed on mangroves are relatively heavy and, if dropped, will not disperse far. This diffusion can be achieved by allocating the percentage of propagules (p%, which can be defaulted to 80%) assigned to the central grid within a 1-meter Chebyshev distance. This allocation is then made based on land type. Propaganda cannot survive if there are roads, villages, or canal auxiliary construction facilities nearby. Whether a propagule assigned to a grid can remain and survive is a random event, generated randomly within a certain radius. This allows the formation of a seed bank within a 1-meter radius of the mangroves.

[0131] If there is water within 1 meter of the surrounding area, the remaining 20% of the propagules are considered to be spread by hydraulic forces. Using a hydrodynamic model, the propagule dispersal is calculated. The propagules' drift positions are determined by following the hydrodynamic forces for two days. Whether the propagules can remain and survive is a random event, determined through stochastic calculations, thus determining the seed bank formed by hydraulic diffusion.

[0132] 3. Mangrove plant germination simulation. According to experimental results, the germination rate of mangrove plants increases with decreasing salinity. According to literature research, the germination rate of sea lacquer seeds is: salinity 0-5, germination rate 90%; salinity 5-15, average germination rate is about 70%; salinity 15-35, average germination rate is about 60%. The germination rate of Lagong wood: salinity 0-25, germination rate 90%; salinity 25-35, average germination rate is about 70%. The germination rate of Sonneratia apetala: at 30 degrees Celsius, salinity 0-5, average germination rate is about 80%; salinity 5-15, average germination rate is about 70%; salinity 15-35, germination rate decreases with salinity to about 6%. Therefore, the germination rate ni of mangrove plants can be calculated according to the following formula:

[0133] n i =n1·u+n2

[0134] Where i is the mangrove species (such as Acropora auriculata, Aegiceras corniculata, and Sonneratia apetala), u is the soil salinity in the seed bank area, and n1 and n2 are coefficients that vary for different mangrove species.

[0135] 4. Dynamic Simulation of Mangrove Growth. This dynamic simulation uses the optimal tree growth model, with salinity and flow velocity as primary growth stress factors. The simulation targets crown width, reflecting the spatiotemporal variations in mangrove growth through changes in crown width. Crown width is calculated using the mangrove diameter at breast height (DBH), which varies with relevant stress factors. The specific calculation method is as follows:

[0136]

[0137] Where R is the crown width of the mangrove plant, dbh is the diameter at breast height of the mangrove plant, and a is the coefficient (the default value is 10).

[0138]

[0139] Where dbh is the diameter at breast height of the mangrove plant, dbhmax is the maximum diameter at breast height of the mangrove plant (the default value is 140), t is the time step, H is the plant height, Hmax is the maximum plant height, G is the growth coefficient, b2 and b3 are constants, S(u) is the salinity stress coefficient, V(o) is the velocity scour stress coefficient, and C(r) is the competition coefficient.

[0140] The calculation formula of salinity stress coefficient S(u) is as follows:

[0141]

[0142] Among them, u is the salinity of the area where the mangrove plant is located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5 (different mangrove plant species have different growth coefficients), and d is the coefficient.

[0143] The calculation formula of flow velocity scour stress coefficient V(o) is as follows:

[0144]

[0145] Among them, o is the flow velocity where the mangrove plant is located, omin is the minimum flow velocity for the mangrove plant to survive, and omax is the maximum flow velocity for the mangrove plant to survive.

[0146] The competition coefficient C(r) is calculated as follows:

[0147]

[0148] It is assumed that there are n mangrove plants distributed within a range A with a diameter of R and the coordinates of the mangrove plant trunk as the origin, r is the breast diameter of the i-th mangrove plant, and rbh is the breast diameter of the mangrove plant at the origin.

[0149] 5. Simulation of mangrove plant death.

[0150] The situation of mangrove plant death is set. If the following situation occurs, the mangrove plant in the grid is set to 0 in the model.

[0151] 1) Increase rate of mangrove plant diameter at breast height A negative number indicates that the plant will die.

[0152] 2) If the competition coefficient C(r) exceeds 0.7, it means that the surrounding vegetation will have a significant negative impact on the plant, and the plant will die.

[0153] 3) Mangroves located on the periphery of the canal, close to the canal, may be disturbed by ship waves. The resulting scouring can be calculated using the following formula. Scouring has a cumulative effect. Without considering upstream sediment replenishment, periphery mangroves are considered dead when the scouring depth exceeds 60% of their root length.

[0154]

[0155] Among them, d max represents the scouring depth or siltation depth of the riverbank mangrove area, H represents the wave height, L represents the wavelength, X represents the density or arrangement of vegetation, a, b, c represent constants, and X represents the wave height. c It can be considered as the comprehensive coefficient of plants.

[0156] The spatial succession model of mangrove plants was constructed. The cellular automation method was used to simulate the spatial succession of mangrove plants. The specific transformation mode is as follows:

[0157] 1. When the crown area of a mangrove plant exceeds 1 / 2 of the area of the grid, it means that the mangrove plant occupies the grid.

[0158] 2. As the seeds of mangrove plants spread and germinate, the mangrove plants occupy the light beach area, but cannot occupy the water body.

[0159] 3. Calculations of the competition coefficient show that mangroves with a competition coefficient exceeding 0.7 will die due to intense competition for resources. As mangroves mature, the competitive advantage of Sonneratia apetala over other mangroves becomes more pronounced, leading to some mangroves transitioning to Sonneratia apetala.

[0160] Calibration and validation of a mangrove plant dynamic growth model coupled with a mangrove plant spatial succession model.

[0161] Based on available data, remote sensing images of mangrove plant distribution from three typical years were selected for model calibration and validation. Within the empirical range of model parameters, a trial-and-error method was used to simulate the distribution of mangrove plants under different parameter combinations. Consistency was then analyzed by calculating the Kappa coefficient.

[0162] Adjustments of ±10% and ±20% were made to each parameter, and the spatial distribution of mangroves was analyzed to conduct sensitivity tests. Based on the analysis results, the key parameters that were more sensitive were identified, and the rationality of their selected values was then analyzed.

[0163] Different scenarios were set up for simulation based on different canal construction plans and different navigation conditions. The different scenario settings mainly consider the following aspects: (1) different canal plans, (2) different navigation speeds, (3) different ship flows, and (4) different lock water-saving plans (which will affect the amount of water diverted by ships passing through the locks). Different simulation scenarios were set up based on various permutations and combinations. Please refer to Table 1 for details.

[0164] Table 1

[0165]

[0166] According to different scenario settings, the temporal and spatial changes of mangrove vegetation under the influence of the canal are simulated, and countermeasures and measures for the protection of the canal's ecological environment are proposed accordingly.

[0167] 1. Based on the different route plans, ship flow plans, navigation speed plans, and lock water-saving plans of the canal project, a two-dimensional hydrodynamic-water environment model of the study area was established based on numerical models such as MIKE to simulate the changes in salinity and flow velocity in each geographic space unit in the study area after the canal construction.

[0168] 2. The calculated salinity, flow velocity and other data are input into the mangrove plant dynamic growth model coupled with the mangrove plant spatial succession model to obtain the spatial changes of mangrove vegetation in the study area and calculate the coverage area of mangrove vegetation within a 100-year period.

[0169] 3. For different canal construction plans, ship speed, ship flow, and lock water saving ratio are used as variables, and the multivariate regression method is used to construct the quantitative relationship between the above factors and the total area of mangrove vegetation and the areas of different mangrove vegetation.

[0170] 4. Finally, based on the needs of social and economic development, the cargo volume that the canal can carry under different development speeds is determined, so as to judge the impact on the scale and species of mangrove vegetation in the study area, and accordingly propose the optimal solution for canal navigation, such as controlling the speed of ships in local canal sections, optimizing the water-saving ratio of locks to regulate canal water diversion, etc.

[0171] Please refer to Figure 2 , Figure 2 An embodiment of the present invention provides a simulation device 110 for the spatiotemporal changes of mangrove vegetation under the influence of a canal, comprising:

[0172] Acquisition module 1101 is configured to divide a mangrove vegetation area to be simulated into a plurality of spatial units; acquire remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution, and temporal variation of the mangrove vegetation area to be simulated from the remote sensing data; construct an individual-based mangrove plant dynamic growth model in each spatial unit using an optimal tree growth model combined with the spatial distribution, species distribution, and temporal variation; simulate the expansion and succession of mangrove vegetation using a cellular automaton to form a mangrove vegetation spatial simulation model; and acquire a plurality of preset canal construction plans for the mangrove vegetation area to be simulated based on the calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model.

[0173] The simulation module 1102 is used to obtain the post-construction environmental factors corresponding to the multiple canal construction plans, input the post-construction environmental factors into the mangrove plant dynamic growth model and couple it with the mangrove vegetation spatial simulation model to obtain the spatiotemporal simulation change results of the mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans; based on the spatiotemporal simulation change results of the mangrove vegetation, determine the target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans.

[0174] It should be noted that the implementation principles of the aforementioned device 110 for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal can be referenced from the implementation principles of the aforementioned method for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal, and will not be elaborated upon here. It should be understood that the division of the various modules of the aforementioned device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into a single physical entity, or physically separated. Furthermore, these modules can be implemented entirely as software invoked by a processing element; entirely as hardware; or partially as software invoked by a processing element, while others are implemented in hardware. For example, the device 110 for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal can be a separate processing element, or integrated into a chip of the aforementioned device. Furthermore, it can be stored in the form of program code in the memory of the aforementioned device, with a processing element of the aforementioned device invoking and executing the functions of the device 110 for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal. The implementation of the other modules is similar. Furthermore, these modules can be fully or partially integrated or implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each module above may be completed by an integrated logic circuit of hardware in a processor element or by instructions in the form of software.

[0175] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code on a processing element, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0176] The embodiment of the present invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned simulation device 110 for the spatiotemporal changes of mangrove vegetation under the influence of the canal. Figure 3 As shown, Figure 3This is a block diagram of a computer device 100 according to an embodiment of the present invention. The computer device 100 includes a device 110 for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal, a memory 111 , a processor 112 , and a communication unit 113 .

[0177] In order to realize the transmission or interaction of data, the memory 111, the processor 112 and the communication unit 113 are electrically connected to each other directly or indirectly. For example, the electrical connection between these components can be achieved through one or more communication buses or signal lines. The simulation device 110 of the spatiotemporal changes of mangrove vegetation under the influence of the canal includes at least one software function module that can be stored in the memory 111 in the form of software or firmware or solidified in the operating system (OS) of the computer device 100. The processor 112 is used to execute the simulation device 110 of the spatiotemporal changes of mangrove vegetation under the influence of the canal stored in the memory 111, such as the software function modules and computer programs included in the simulation device 110 of the spatiotemporal changes of mangrove vegetation under the influence of the canal.

[0178] An embodiment of the present invention provides a readable storage medium, which includes a computer program. When the computer program is running, it controls the computer device where the readable storage medium is located to execute the aforementioned simulation method of the spatiotemporal changes of mangrove vegetation under the influence of the canal.

[0179] For illustrative purposes, the foregoing description has been made with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Numerous modifications and variations are possible in light of the above teachings. These embodiments have been selected and described in order to best illustrate the principles of the present disclosure and its practical application, thereby enabling those skilled in the art to best utilize the present disclosure and to utilize various embodiments with various modifications as appropriate for the specific application contemplated.

Claims

1. A method for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal, characterized in that: include: Divide the mangrove vegetation area to be simulated into multiple spatial units; Acquiring remote sensing data of the mangrove vegetation area to be simulated, and extracting the spatial distribution, species distribution, and temporal variation of the mangrove vegetation area to be simulated from the remote sensing data; Using the optimal tree growth model in combination with the spatial distribution, the species distribution and the temporal variation, a dynamic growth model of mangrove plants based on individuals is constructed in each of the spatial units; Cellular automata were used to simulate the expansion and succession of mangrove vegetation, forming a mangrove vegetation spatial simulation model. Based on the calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, a plurality of preset canal construction plans for the mangrove vegetation area to be simulated are obtained; Obtaining post-construction environmental factors corresponding to the multiple canal construction plans, inputting the post-construction environmental factors into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, and obtaining spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans; Determining a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans based on the spatiotemporal simulation change results of the mangrove vegetation; The method of utilizing the optimal tree growth model in combination with the spatial distribution, the species distribution, and the temporal variation to construct an individual-based mangrove plant dynamic growth model in each spatial unit includes: Using the tree optimal growth model in combination with the spatial distribution, the species distribution and the temporal variation, constructing individual-based mangrove plant propagule production simulation, mangrove plant seed dispersal and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant mortality simulation in each of the spatial units; Constructing the individual-based mangrove plant dynamic growth model based on the mangrove plant propagule production simulation, the mangrove plant seed dispersal and seed bank formation simulation, the mangrove plant germination simulation, the mangrove plant growth dynamics simulation, and the mangrove plant death simulation; The mangrove plant propagule production simulation includes: ; Among them, N is the number of reproductive bodies, R is the crown width of the mangrove plant, gh is the ground diameter of the mangrove plant, f1, f2, f3 are coefficients, and N is the rounded result.

2. The method according to claim 1, characterized in that The simulation of mangrove plant seed dispersal and seed bank formation includes: determining a central network, and distributing a total number of propagules into a first number and a second number, wherein the first number is greater than the second number; If the land type of the central network within the preset range is an unfavorable type, the survival probability of the propagules within the preset range is 0; if the land type of the central network within the preset range is a favorable type, the survival probability of the propagules within the preset range is the product of the first number and the preset first random coefficient, and the normal diffusion seed bank is obtained; If there is water outside the preset range, the propagule drift position is determined in combination with the hydrodynamic model, and the hydraulic diffusion seed bank is determined based on the propagule drift position in combination with a preset second random coefficient; The mangrove plant seed diffusion and seed bank formation simulation is completed based on the normal diffusion seed bank and the water diffusion seed bank.

3. The method according to claim 1, characterized in that The mangrove plant sprout simulation comprises: ;in, is the germination rate of mangrove plants, i is the species of mangrove plants, u is the soil salinity in the seed bank area, and n1 and n2 are coefficients.

4. The method according to claim 1, wherein The mangrove plant growth dynamic simulation includes: ; Wherein, R is the crown width of mangrove plants, dbh is the diameter at breast height of mangrove plants, and a is the coefficient; ; Wherein, dbh is the diameter at breast height of mangrove plants, dbhmax is the maximum diameter at breast height of mangrove plants, t is the time step, H is the plant height, Hmax is the maximum plant height, G is the growth coefficient, b2 and b3 are constants, S(u) is the salinity stress coefficient, V(o) is the velocity scour stress coefficient, and C(r) is the competition coefficient; ; Among them, u is the salinity of the area where the mangrove plant is located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5, and d is the coefficient; ;in, is the velocity scour stress coefficient, o is the velocity where the mangrove plant is located, and o min is the minimum flow velocity for mangrove plants to survive, o max The maximum flow rate for mangrove plants to survive; ； Where C(r) is the competition coefficient, assuming that the coordinates of the mangrove plant trunk are the origin, R is the diameter of the mangrove plant trunk at the origin, A is the range determined by the diameter R, n is the number of mangrove plants, r is the DBH of the i-th mangrove plant, and rbh is the DBH of the mangrove plant at the origin.

5. The method according to claim 1, wherein The mangrove plant death simulation includes: When the growth rate of the mangrove plant's diameter at breast height is negative, or the competition coefficient exceeds the preset competition coefficient threshold, or the scouring depth or siltation depth in the riverbank mangrove area exceeds the mangrove plant root length alarm threshold, the mangrove plant is determined to be dead.

6. The method according to claim 1, characterized in that The method further comprises: The remote sensing image distribution of mangrove plants in a preset number of typical years is obtained to calibrate and verify the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model.

7. The method according to claim 1, characterized in that The step of obtaining post-construction environmental factors corresponding to the multiple canal construction plans, inputting the post-construction environmental factors into the mangrove plant dynamic growth model and coupling it with the mangrove vegetation spatial simulation model, and obtaining spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans, includes: Obtaining the vessel traffic intensity, shipping water diversion scale, and vessel navigation speed in narrow riverbank areas included in the multiple canal construction plans; Input the ship flow intensity, shipping water diversion scale, and ship navigation speed in narrow riverbank areas into a pre-set two-dimensional hydrodynamic-water environment model to obtain the salinity change and flow rate change of each spatial unit for each canal construction plan; Inputting the salinity change and the flow velocity change into the mangrove plant dynamic growth model and coupling it with the mangrove vegetation spatial simulation model to obtain spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans; The step of determining a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans based on the spatiotemporal simulation results of the mangrove vegetation changes includes: Based on the spatiotemporal simulation results of mangrove vegetation changes corresponding to each canal construction plan, a quantitative relationship between the canal construction plan and the total area of mangrove vegetation and the area of different mangrove vegetation is constructed based on multiple regression; A target canal construction plan for the mangrove vegetation area to be simulated is determined based on the measurement relationship.

8. A device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal, characterized in that: include: An acquisition module is used to divide the mangrove vegetation area to be simulated into multiple spatial units; Acquire remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution, and temporal variation of the mangrove vegetation area to be simulated from the remote sensing data; construct an individual-based mangrove plant dynamic growth model in each spatial unit using a tree optimal growth model combined with the spatial distribution, species distribution, and temporal variation; simulate the expansion and vegetation succession of mangrove vegetation using cellular automata to form a mangrove vegetation spatial simulation model; and obtain a plurality of preset canal construction plans for the mangrove vegetation area to be simulated based on the calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model; A simulation module is configured to obtain post-construction environmental factors corresponding to the multiple canal construction plans, input the post-construction environmental factors into the mangrove plant dynamic growth model and couple it with the mangrove vegetation spatial simulation model to obtain spatiotemporal simulation change results of mangrove vegetation for each spatial unit corresponding to the multiple canal construction plans; and determine a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans based on the spatiotemporal simulation change results of mangrove vegetation. The acquisition module is specifically used to: The optimal tree growth model is combined with the spatial distribution, the species distribution and the temporal variation to construct individual-based mangrove plant propagule production simulation, mangrove plant seed dispersal and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant death simulation in each spatial unit; the individual-based mangrove plant dynamic growth model is constructed based on the mangrove plant propagule production simulation, the mangrove plant seed dispersal and seed bank formation simulation, the mangrove plant germination simulation, the mangrove plant growth dynamics simulation and the mangrove plant death simulation; the mangrove plant propagule production simulation includes: Where N is the number of propagules, R is the crown width of the mangrove plant, gh is the ground diameter of the mangrove plant, f1, f2, and f3 are coefficients, and N is the rounded result.

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