Simulation method and device for mangrove vegetation temporal and spatial variation under canal influence

Abstract

The invention discloses a simulation method and device for mangrove vegetation spatio-temporal change under the influence of a canal. The method comprises the steps that firstly, a to-be-simulated area is divided into a plurality of space units, remote sensing data is obtained, related information is extracted, a mangrove plant dynamic growth model and a space simulation model are constructed, and calibration and verification of model coupling are completed. A plurality of canal construction schemes and environmental factors after construction are obtained, the model is input to obtain mangrove vegetation space-time simulation change results corresponding to the schemes, and finally a target canal construction scheme is determined, so that a scientific decision basis is provided for realizing balance of 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 the canal construction progresses, it has a complex impact on the surrounding ecological environment, especially mangrove vegetation. However, the existing technology lacks an effective simulation method for the spatiotemporal changes of mangrove vegetation under the influence of the canal, making it difficult to accurately assess the impact of the canal construction plan on mangrove vegetation and unable 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 temporal and spatial 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] Acquire remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution and time variation of the mangrove vegetation area to be simulated from the remote sensing data;

[0007] Using the optimal tree growth model combined 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 are used to simulate the expansion and succession of mangrove vegetation to form a mangrove vegetation spatial simulation model;

[0009] Based on the completion of 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 schemes, 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 in each spatial unit corresponding to the multiple canal construction schemes;

[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, construct individual-based mangrove plant propagule production simulation, mangrove plant seed diffusion and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant death 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 a possible embodiment, the mangrove plant propagule production simulation includes:

[0016] N=f1·R+f2·gh+f3

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

[0018] In a possible implementation, 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 according to the propagule drift position combined 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 a possible implementation, the mangrove plant sprout 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 area where the seed bank is located, and n1 and n2 are coefficients.

[0026] In a possible implementation, 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] Among them, dbh is the mangrove plant diameter at breast height, dbhmax is the maximum diameter at breast height of the mangrove plant, 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 plants are located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5, and d is the coefficient;

[0033]

[0034] Among them, V(o) is the velocity scour stress coefficient, o is the velocity of the mangrove plant, omin is the minimum velocity for the survival of the mangrove plant, and omax is the maximum velocity for the survival of the mangrove plant.

[0035]

[0036]

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

[0038] In a possible implementation, the mangrove plant death simulation includes:

[0039] When the breast diameter increment rate of a mangrove plant is negative, or the competition coefficient exceeds a 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 a 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 schemes 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 the spatiotemporal simulation change results of mangrove vegetation in each spatial unit corresponding to the multiple canal construction schemes, including:

[0043] Obtaining the ship flow intensity, shipping water diversion scale, and ship navigation speed in the narrow river bank area included in the multiple canal construction plans;

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

[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, obtaining the spatiotemporal simulation change results of the mangrove vegetation in each spatial unit corresponding to the multiple canal construction plans;

[0046] The step of determining a target canal construction scheme for the mangrove vegetation area to be simulated from the plurality of canal construction schemes according to the spatiotemporal simulation change result of the mangrove vegetation includes:

[0047] According to the spatiotemporal simulation results of mangrove vegetation changes corresponding to each of the canal construction plans, the 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 used to divide the mangrove vegetation area to be simulated into multiple spatial units; obtain remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution and time change of the mangrove vegetation area to be simulated from the remote sensing data; use the tree optimal growth model to combine the spatial distribution, the species distribution and the time change to construct an individual-based mangrove plant dynamic growth model in each of the spatial units; use cellular automata to simulate the expansion and vegetation succession of mangrove vegetation to form a mangrove vegetation spatial simulation model; based on the completion of calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, obtain multiple preset canal construction plans for the mangrove vegetation area to be simulated;

[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 in each spatial unit corresponding to the multiple canal construction plans; according to 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 with the prior art, the beneficial effects provided by the present invention include: using a method and device for simulating the spatiotemporal changes of mangrove vegetation under the influence of a canal disclosed in the present invention, by dividing the area to be simulated into multiple spatial units, acquiring remote sensing data and extracting relevant information, constructing a dynamic growth model of mangrove plants and a spatial simulation model, and completing the calibration and verification of model coupling. Obtain multiple canal construction plans and environmental factors after construction, input the model to obtain the spatiotemporal simulation change results of mangrove vegetation corresponding to each plan, and finally determine the target canal construction plan. Such a design provides a scientific decision-making basis for achieving a balance between canal construction and mangrove vegetation protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative work.

[0054] Figure 1 A schematic diagram 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] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0058] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In order to solve the technical problems in the aforementioned 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 a plurality of 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 time 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 of the spatial units;

[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, 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;

[0065] Step S206, obtaining post-construction environmental factors corresponding to the multiple canal construction schemes, 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 in each spatial unit corresponding to the multiple canal construction schemes;

[0066] Step S207, determining a target canal construction plan for the mangrove vegetation area to be simulated from the multiple canal construction plans according to 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, with the server as the execution subject:

[0068] In an embodiment of the present invention, illustratively, 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 topography, coastline, etc. Then, according to preset rules, the area is divided into square space units with a side length of 100 meters. In this way, the entire area is divided into 10,000 space units. Each space unit has a unique identifier and coordinate information for subsequent data processing and analysis. For example, for a space 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 space units helps to more accurately simulate the growth and changes of mangrove vegetation in different locations.

[0069] The server obtains high-resolution remote sensing image data of the simulated area every year in the past 10 years through the interface with the satellite remote sensing data provider. These data include multispectral and hyperspectral information. The server uses special image processing algorithms and models to process and analyze these remote sensing data. First, different land cover types, such as water, land, and mangrove vegetation, are distinguished through the recognition and classification algorithms of spectral features. Then, the spatial distribution of mangrove vegetation is further analyzed to determine which spatial units have mangrove vegetation. For the extraction of species distribution, the server uses a deep learning model to learn and identify the morphology, color and other characteristics of mangrove vegetation, so as to distinguish different mangrove plant species, such as Paulownia, Kandelia candel, and Avicennia marina. In the analysis of temporal changes, the server compares remote sensing data from different years to observe the changing trends of the range, density and species of mangrove vegetation. For example, it is found that the area of ​​mangrove vegetation in a certain area has gradually increased in the past five years, or the distribution range of a certain mangrove plant has gradually expanded.

[0070] The server establishes a dynamic growth model for each mangrove plant individual in each spatial unit based on the acquired spatial distribution, species distribution and time change data. Taking the Paulownia tree as an example, the server first calculates the initial growth rate of the Paulownia tree based on the soil conditions, salinity, light and other environmental factors of the spatial unit, combined with the parameters of the optimal growth model of the tree. Assuming that in a certain spatial unit, the initial breast diameter of the Paulownia tree is 10 cm, according to the model calculation, its annual growth rate is about 2 cm. For the simulation of the generation of propagules, the server uses the corresponding formula to calculate the number of propagules produced each year based on the crown width and ground diameter of the Paulownia tree. Assuming that the crown width of the Paulownia tree in the spatial unit is 2 meters and the ground diameter is 20 cm, the formula calculates that 50 propagules are produced each year. In the simulation of seed diffusion and seed bank formation, the server takes into account that the seeds of the Paulownia tree are heavy and the normal drop diffusion range is small. Assuming that the percentage of propagules assigned to the outer grid ring with a Chebyshev distance of 1 meter from the central grid is 80%, if the land type within 1 meter of the surrounding area is suitable for seed survival, 80% of the propagules will form a seed bank; if there is a water body 1 meter away from the surrounding area, the remaining 20% ​​of the propagules will be simulated according to hydraulic diffusion, and their possible drifting positions and survival probabilities will be calculated in combination with the hydrodynamic model. For germination simulation, the server calculates the germination rate based on the soil salinity of the spatial unit and the species characteristics of the tung tree. Assuming that the soil salinity is 10, the germination rate of the tung tree is about 70% calculated by the formula. In the growth dynamic simulation, the server comprehensively considers factors such as salinity stress coefficient, flow velocity scour stress coefficient, and competition coefficient. Assuming that the salinity of the spatial unit is 20, the flow velocity is 1 meter / second, and there are 5 other tung trees around, it is calculated that the growth rate of the current tung tree is subject to certain restrictions. For death simulation, the server monitors the growth status of the tung tree 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 regards 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 where it is located, the server determines that the mangrove plant occupies the grid. Over time, the seeds of mangrove plants 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 Sonneratia apetala is strong, over time, if a certain area coexists with Sonneratia apetala and other mangrove plants, the server will determine whether other mangrove plants will gradually be replaced by Sonneratia apetala based on the calculation of the competition coefficient.

[0072] The server pre-stores multiple canal construction plans for the mangrove vegetation area to be simulated. These plans differ in terms of route, scale, navigation conditions, etc. For example, Plan 1 may be a canal along the coastline, with a width of 50 meters and medium navigation capacity; Plan 2 may be a canal passing through the wetland, with a width of 80 meters and strong navigation capacity.

[0073] For each canal construction plan, the server will obtain the corresponding post-construction environmental factors. Taking Plan 1 as an example, the server obtains data such as ship flow intensity, shipping water diversion scale, and ship navigation speed in narrow riverbank areas after the plan is completed. Then, these data are input into the pre-set two-dimensional hydrodynamic-water environment model to calculate the salinity changes and flow rate changes of each spatial unit. Assume that in some spatial units, the salinity is reduced by 5% due to shipping water diversion, and the flow rate is increased by 0.5 m / s due to ship navigation. The server inputs these change data into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model to simulate the spatiotemporal changes of mangrove vegetation in the next 50 years. After simulation, it may be found that the area of ​​mangrove vegetation in some spatial units has increased because the reduction in salinity is conducive to growth; while in areas close to the canal with faster flow rates, mangrove vegetation may be inhibited or even die. For Plan 2, the above process is repeated to obtain the corresponding simulation results, which are compared and analyzed with Plan 1.

[0074] The server conducts detailed analysis and evaluation of the simulation results of each canal construction scheme. For example, for Scheme 1, after 50 years of simulation, the total area of ​​mangrove vegetation decreased by 10%, of which the area of ​​some precious mangrove plant species decreased by 20%; while the simulation results of Scheme 2 showed that the total area of ​​mangrove vegetation decreased by 5%, and the area of ​​precious mangrove plant species decreased by 10%. Based on these results, the server constructs the quantitative relationship between the canal construction scheme and the total area of ​​mangrove vegetation and the area of ​​different mangrove vegetation based on multivariate regression. By analyzing these quantitative relationships, the most suitable target canal construction scheme is determined by comprehensively considering the needs of ecological protection and economic development. It is assumed that after comprehensive evaluation, Scheme 2 is considered to be a better choice because it has a relatively small impact on mangrove vegetation and can meet certain shipping needs. The server finally outputs Scheme 2 as the target canal construction scheme for the mangrove vegetation area to be simulated, 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 execution.

[0076] Using the tree optimal growth model in combination with the spatial distribution, the species distribution and the temporal variation, construct individual-based mangrove plant propagule production simulation, mangrove plant seed diffusion and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant death 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 of each spatial unit, including its geographical location, soil characteristics, surrounding environment, etc. For the simulation of the generation of mangrove plant propagules, the server takes a specific spatial unit as an example. Assuming that the mangrove plant, Kandelia candel, grows in this unit, through field measurements and data analysis, the server knows that the crown width of the plant is 3 meters and the ground diameter is 25 centimeters. Using the pre-set formula and coefficients (f1, f2, f3), it is calculated that the number of propagules produced each year is about 80. In the simulation of mangrove plant seed diffusion and seed bank formation, the server also takes this spatial unit as an example. It first determines the central network, assuming that the total number of propagules is 80, of which 64 (80%) are distributed within the preset range. If the land type within the preset range is a favorable type suitable for the growth of mangrove plants, such as a moist and obstacle-free tidal flat, then the survival probability of these 64 propagules is 64 times the product of the preset first random coefficient (assumed to be 0.8), that is, about 51 normal diffusion seed banks. If there is water outside the preset range, the server determines the drifting position of the propagule in combination with the hydrodynamic model. Assuming that the water flow speed and direction are constant, after calculation, some propagules may drift to another tidal flat 50 meters away from the original position, and then determine about 8 hydraulic 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 that it is 15 after detection. For the mangrove plant Kandelia candel, 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 breast diameter and plant height of Kandelia candel plants in this spatial unit. Assume that the breast diameter is 30 cm and the plant height is 2 meters. Through a series of complex calculations, taking into account the salinity stress coefficient (assuming that the salinity in the area is 20, the corresponding salinity stress coefficient is calculated to be 0.7), the velocity scour stress coefficient (assuming that the velocity is 1.5 m / s, the corresponding velocity scour stress coefficient is calculated to be 0.8) and the competition coefficient (assuming that there are 3 Kandelia candels with a breast diameter of 25, 35, and 40 cm distributed around, and the competition coefficient is calculated to be 0.5), the growth rate of Kandelia candel and its growth trend in the future are comprehensively obtained. The last is the mangrove plant death simulation. The server continuously monitors the growth status of Kandelia candel plants in this spatial unit. If the breast diameter increment rate is negative for a period of time, or the competition coefficient exceeds the preset competition coefficient threshold (assuming it is 0.7), or the scour depth or siltation depth in the riverbank mangrove area exceeds the mangrove plant root length alarm threshold (assuming it is 60%), the server determines that the mangrove plant is dead.The server performs such detailed and precise simulation calculations on each spatial unit, and builds 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] Among them, N is the number of reproductive bodies, R is the crown width of mangrove plants, gh is the ground diameter of mangrove plants, and f1, f2, and f3 are coefficients.

[0082] In the embodiment of the present invention, the server performs such calculations and processing on a large number of spatial units and different types of mangrove plants, so as to comprehensively understand the production of mangrove plant propagules in the entire simulated area. By repeating these calculations, the server can accurately simulate the number of propagules produced by each mangrove plant in each spatial unit, providing an important data basis for subsequent simulation processes such as 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 according to the propagule drift position combined 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 embodiment of the present invention, exemplarily, in the mangrove vegetation area to be simulated, the server first determines the central network. Assuming that a mangrove plant produces 100 propagules in a specific spatial unit, the server allocates them into a first quantity of 80 and a second quantity of 20. The server then analyzes the land type of the central network within a preset range. If this preset range is a circular area 1 meter away from the plant, the server obtains the land data of the area and finds that it is covered by a hardened road, which belongs to an unfavorable type. Therefore, the survival probability of the propagules within this preset range is 0, that is, no propagules can form a normal diffusion seed bank within this range. In another spatial unit, the land type of the preset range determined by the server is a moist and undisturbed tidal flat, which belongs to a favorable type. 100 propagules are also generated and allocated into a first quantity of 80. Assuming that the preset first random coefficient is 0.8, the survival probability of the propagules within this preset range is 80×0.8=64, thereby forming 64 normal diffusion seed banks. For the case where there is water outside the preset range, the server combines the hydrodynamic model to determine the drifting position of the propagules. Suppose that in a spatial unit near a river, 20 propagules enter the water body outside the preset range. The server calls the hydrodynamic model, inputs parameters such as water flow speed, direction, and water depth, and calculates that the propagules may drift to another river bank 50 meters away from the original position. Then, assuming that the preset second random coefficient is 0.6, the server determines that the number of propagules that can form a rice diffusion seed bank here is 20×0.6=12. The server combines the normal diffusion seed bank and the water diffusion seed bank obtained by the above calculations to complete the simulation of mangrove plant seed diffusion and seed bank formation. For example, after a series of spatial unit processing is completed, the server draws a detailed map of seed diffusion and seed bank distribution in the entire area. In a certain area, the server found that the normal diffusion seed bank formed by multiple spatial units is relatively concentrated, indicating that the mangrove plant seeds here have a higher chance of local diffusion and survival, which may promote the further development of mangrove vegetation in the area. In other areas, the distribution of the water diffusion seed bank shows that the seeds are spread to farther places with the water flow, providing the possibility for new mangrove vegetation growth areas. By performing such precise calculations and simulations for each spatial unit, the server has a comprehensive understanding of the diffusion path of mangrove plant seeds and the formation of the seed bank, providing key data support for subsequent analysis of the expansion and succession of mangrove vegetation. Throughout the simulation process, the server continuously optimizes the algorithm and improves the calculation accuracy to more accurately reflect the actual ecological process.

[0089] In an embodiment of the present invention, the mangrove plant sprout 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 area where the seed bank is located, and n1 and n2 are coefficients.

[0092] In an embodiment of the present invention, illustratively, by calculating the germination rate of different mangrove plant species in each spatial unit under specific soil salinity conditions, the server can fully understand the possible distribution of mangrove plant germination in the entire area. This provides important basic data for subsequent simulation of the growth and expansion of mangrove vegetation. During the processing, the server strictly follows the formula and pre-set coefficients for accurate calculations to ensure the accuracy and reliability of the germination rate simulation results. At the same time, the server stores and organizes the calculated germination rate data for integration and comprehensive analysis with data from other simulation links.

[0093] In an embodiment of the present invention, the mangrove plant growth dynamic simulation 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] Among them, dbh is the mangrove plant diameter at breast height, dbhmax is the maximum diameter at breast height of the mangrove plant, 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 plants are located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5, and d is the coefficient;

[0100]

[0101] Among them, V(o) is the velocity scour stress coefficient, o is the velocity of the mangrove plant, omin is the minimum velocity for the survival of the mangrove plant, and omax is the maximum velocity for the survival of the mangrove plant.

[0102]

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

[0104] In an embodiment of the present invention, exemplarily, in the mangrove vegetation area to be simulated, the server starts to simulate the growth dynamics of mangrove plants. First, the server obtains the initial data of mangrove plants in a specific spatial unit. Assume that the mangrove plant in the spatial unit is Kandelia ovata, whose crown width R is 3 meters, the breast diameter dbh is 20 centimeters, and the coefficient a is 10. The server calculates the growth of Kandelia ovata according to the formula and obtains a result of 30 centimeters. This indicates the growth change of Kandelia ovata under the current conditions. Then, the server further analyzes the long-term growth dynamics of Kandelia ovata. Assume that the maximum breast diameter dbhmax of Kandelia ovata is 50 centimeters, the time step t is 1 year, the plant height H is 4 meters, the maximum plant height Hmax is 8 meters, the growth coefficient G is 0.2, the constant b2 is 0.1, and b3 is 0.05. The server first calculates the salinity stress coefficient S (u). The salinity u of the area where the mangrove plant is located is obtained as 25. Assuming that the salinity growth coefficient of Kandelia ovata is 0.5, the corresponding salinity ui is 20, and the coefficient d is 0.08. The salinity stress coefficient S(u) is obtained to be 0.6, and then the velocity scour stress coefficient is calculated. The velocity o of the mangrove plant is obtained to be 1.2 m / s. It is assumed that the minimum velocity omin for the survival of the Kandelia candel plant is 0.5 m / s, and the maximum 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 taken as the origin, and the range A is determined by the diameter R of 5 meters. The number of mangrove plants n in the range is 8, and the breast diameter r of the i-th mangrove plant is 18, 22, 25, 19, 21, 23, 17, and 20 cm respectively. The breast diameter rbh of the Kandelia candel at the origin is 20 cm. The competition coefficient C(r) is calculated to be 0.4. Finally, the server substitutes the various coefficients calculated above into the growth dynamics formula for calculation to obtain the change in the breast diameter of Kandelia candel after one year. The server processes other spatial units and different types of mangrove plants in the area, such as Avicennia marina and Paulownia afforestation, in the same way. In another spatial unit, the mangrove plant is Avicennia marina, with an initial crown width R of 2.5 meters, a breast diameter dbh of 15 centimeters, and a coefficient a of 10. After a series of calculations similar to the above, the growth dynamics of Avicennia marina are obtained. For the spatial unit of Paulownia afforestation, the relevant data are also obtained and detailed calculations and analyses are performed. The server continuously processes a large number of spatial units and different types of mangrove plants, and continuously accumulates and updates data on growth dynamics. Through such comprehensive and accurate calculations, the server can simulate the growth changes of various mangrove plants over time under different environmental conditions in the entire mangrove vegetation area. This provides an important basis and support for studying the ecological development of mangrove vegetation, assessing environmental impacts, and formulating protection strategies. During the simulation process, the server strictly follows the settings of various formulas and parameters to ensure the accuracy and consistency of the calculations. At the same time, the algorithm and data processing process are continuously optimized to improve the efficiency and accuracy of the simulation to more realistically reflect the growth dynamics of mangrove plants in the 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 breast diameter increment rate of a mangrove plant is negative, or the competition coefficient exceeds a 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, exemplarily, in the mangrove vegetation area to be simulated, the server begins to determine the death of mangrove plants. First, the server obtains the diameter at breast height data of mangrove plants in a specific spatial unit. Assume 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 may die. In another spatial unit, the server obtains data related to the competition coefficient of the mangrove plant. Assume that the preset competition coefficient threshold is 0.7, and the calculation result of the competition coefficient of the mangrove plant in the spatial unit is 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. Assume that the root length of a mangrove plant is 80 cm, and the monitored scouring depth reaches 50 cm, which exceeds the root length alarm threshold (assuming 60%, i.e. 48 cm). In this case, the server determines that the mangrove plant is in danger of death. The server continuously monitors and determines the mangrove plants in each spatial unit of the entire area. For example, in an area with dense mangrove plants, the server analyzes multiple mangrove plants. The diameter at breast height of one mangrove plant has not increased, but has decreased; the competition coefficient between several other mangrove plants is generally high; at the same time, due to the scouring of water flow, the scouring depth of some mangrove plants near the river bank exceeds the range that the root system can withstand. The server makes death judgments one by one based on these actual monitoring and calculated data. Through the careful monitoring and accurate judgment of a large number of mangrove plants, the server can timely understand the health status and changing trends of mangrove vegetation. This provides an important basis for further analyzing the ecological balance of mangrove vegetation, formulating protection 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, illustratively, in the task of executing model calibration and verification, the server first sets the number of typical years to be obtained, assuming that the preset number is 5. Then, the server selects the image data of 5 typical years that meet the requirements from the database storing the remote sensing image data of mangrove plants. Assume that these 5 typical years are 2010, 2015, 2020, 2025 and 2030. The server sequentially obtains the remote sensing image data of 2010 and performs detailed analysis and processing on it. Through the image processing algorithm, the distribution range, area and species of mangrove plants are extracted. For example, in the image of this year, the server determines that in a certain specific area, two mangrove plants, Kandelia candel and Avicennia marina, are mainly distributed, and their distribution areas are X1 and Y1 square meters respectively. Next, the server obtains the remote sensing image data of 2015. After the same processing flow, it is determined that in 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 plant species were discovered, such as Tung Blossom Tree, which has a distribution area of ​​Z1 square meters. For the remote sensing images of 2020, the server continued to conduct in-depth analysis. It was 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 processes the remote sensing image data of 2025 and 2030 to obtain the detailed distribution and changes of mangrove plants in each typical year. After obtaining and analyzing the remote sensing image data of all typical years, the server inputs these actually observed mangrove plant distribution data into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model. Then, the server starts the calibration and verification procedure of the model. Taking the data of 2010 as an example, the mangrove plant distribution results calculated by the model are compared with the actually observed distribution data. For example, the distribution area of ​​Kandelia ovata calculated by the model is X1' square meters, and the distribution area of ​​Avicennia marina is Y1' square meters. The server calculates the difference between the two and evaluates the accuracy of the model. Repeat the above comparison and evaluation process for the 2015 data. Calculate the differences between the distribution areas of Kandelia ovata X2', Avicennia marina Y2', and Australis truncatula Z1' calculated by the model and the actual observed values ​​X2, Y2, and Z1. Perform the same processing and evaluation on the data for 2020, 2025, and 2030 in turn. Throughout the calibration and verification process, the server will record the results of each comparison in detail, including the numerical value of the difference, the deviation of the distribution range, etc. For example, in the comparison in 2020, the server found that the distribution area of ​​Kandelia ovata calculated by the model 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 will also adjust and optimize the parameters of the model based on these differences.If the model is found to have overestimated the distribution area of ​​a certain mangrove plant species in multiple comparisons, the server will adjust the parameters related to the growth of that species to improve the accuracy of the model. By processing remote sensing image data from multiple typical years and comparing them with the model results, the server can comprehensively and systematically evaluate the performance and accuracy of the model, and continuously optimize the model so that it can more accurately simulate the dynamic growth and spatial distribution of mangrove plants. This provides a reliable model foundation for subsequent research and applications.

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

[0112] Obtaining the ship flow intensity, shipping water diversion scale, and ship navigation speed in the narrow river bank area included in the multiple canal construction plans;

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

[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, obtaining the spatiotemporal simulation change results of the mangrove vegetation in each spatial unit corresponding to the multiple canal construction plans;

[0115] The step of determining a target canal construction scheme for the mangrove vegetation area to be simulated from the plurality of canal construction schemes according to the spatiotemporal simulation change result of the mangrove vegetation includes:

[0116] According to the spatiotemporal simulation results of mangrove vegetation changes corresponding to each of the canal construction plans, the 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 sailing speed in the narrow riverbank 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 sailing speed in the narrow riverbank 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 sailing speed in the narrow riverbank area is 25 kilometers per hour. The server inputs these data such as ship flow intensity, shipping water diversion scale, and ship sailing speed in the narrow riverbank 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 the change in water flow caused by ship navigation and the impact of shipping water diversion on the water body, the salinity change and flow rate change of each spatial unit are calculated. Assume that in some spatial units close to the canal, the salinity increases by 5% and the flow rate increases by 2 meters per second; while in areas farther away from the canal, the salinity and flow rate changes are relatively small. For Scheme B and Scheme C, the server also obtains the salinity change and flow rate change in each spatial unit through the two-dimensional hydrodynamic-water environment model. Next, the server inputs these salinity changes and flow rate changes into the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model. Taking Scheme A as an example, the model comprehensively considers the changes in salinity and flow rate, simulates the growth, reproduction, death and other processes of mangrove plants in the future (such as 50 years), and the expansion and contraction of mangrove vegetation in space. Assume that after simulation, the mangrove vegetation area in some spatial units has decreased by 10%, and the species composition of mangrove plants has also changed to a certain extent. For Scheme B and Scheme C, the server repeats the above simulation process to obtain the spatiotemporal simulation change results of mangrove vegetation in each spatial unit corresponding to each scheme. Then, based on the spatiotemporal simulation results of mangrove vegetation changes corresponding to each canal construction plan, the server constructs the quantitative relationship between the canal construction plan and the total area of ​​mangrove vegetation and the area of ​​different mangrove vegetation based on multiple regression. For example, for Plan A, the server calculates that the corresponding total area of ​​mangrove vegetation is 100 hectares, of which the area of ​​Kandelia ovata is 30 hectares, the area of ​​Avicennia marina is 20 hectares, etc. Through multiple regression analysis, the mathematical relationship between the various parameters of Plan A and the area of ​​mangrove vegetation is established. For Plan B and Plan C, the server also performs such statistics and analysis to establish their respective quantitative relationships. Finally, the server determines the target canal construction plan for the mangrove vegetation area to be simulated based on these quantitative relationships. The server compares the quantitative relationships of each plan and comprehensively considers the needs of ecological protection and canal construction.If ecological protection is the primary goal, the server may choose the option with the least impact on mangrove vegetation, even if it may not be optimal in terms of shipping efficiency. Assume that after comparison, although Option A has relatively weak shipping capacity, it has the least impact on mangrove vegetation, can maintain the relative stability of the total area of ​​mangrove vegetation, and the area changes of different mangrove vegetation types are also within an acceptable range. The server will determine Option A as the target canal construction plan for the mangrove vegetation area to be simulated. Throughout the process, the server provides a scientific and accurate basis for the selection of canal construction plans with its powerful data processing capabilities and precise calculations, so as to achieve a balance between ecological protection and economic development.

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

[0120] Divide geographic space units. Considering the distribution of mangrove vegetation and data calculation efficiency, GIS software is used to divide the study area into spatial units as the benchmark units for simulation calculations. Considering the growth scale of mangrove plants, the minimum spatial grid unit can be set to 1 meter × 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 of the study area (such as Landsat series satellites, Sentinel series satellites, Gaofen GF series satellites, etc.). All satellite remote sensing data are preprocessed in the software, including radiation calibration, atmospheric correction and geometric correction. For remote sensing data, the required single bands can be extracted, and the band data of different spatial resolutions can be resampled to less than 10 meters using the Resampling function. Finally, the study area is retained using the cropping function.

[0123] 2. Use remote sensing information extraction software to segment the image. Perform false color processing on the remote sensing image, that is, assign red to short-wave infrared (B11), green to near infrared (B8), and blue to red (B4). Combine the remote sensing image with the on-site mangrove species survey data to establish an image interpretation sign set. Then, select the training samples required for classification and configure the nearest neighbor features for the training samples. Finally, use the random forest algorithm to classify the mangrove species in the entire study area based on the spectral feature index.

[0124] 3. Select at least three typical years of remote sensing images for mangrove vegetation interpretation, and the time span of typical years should be more than 5 years. According to the availability of remote sensing data, data from multiple years 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 the production of mangrove plant propagules. The most prominent feature of mangrove plants is the viviparous phenomenon. Viviparous refers to the phenomenon that seeds germinate directly on the mother plant after maturity without dormancy or only a short dormancy. Regarding viviparous, it can be understood that mangrove plants will form propagules on the tree every year after maturity. Assume that each adult mangrove plant can form N propagules. The number of propagules can be obtained through field surveys or through model estimation. 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 seed dispersal and seed bank formation.

[0130] Considering the viviparous phenomenon of mangrove plants, the seeds formed on the mangroves are heavier and will not spread far after falling normally. This kind of diffusion can be used as follows: the percentage of propagules (p, %) of the central grid allocated to the outer grid ring with a Chebyshev distance of 1m can be defaulted to 80%. Then allocate according to the land type. If there are roads, villages, canal auxiliary construction facilities, etc. around, the propagules cannot survive. Whether the propagules allocated to the grid can stay and survive is a random event, which is randomly generated within a certain radius. Based on this, a seed bank within 1m around the mangrove can be formed.

[0131] If there is water 1m away, the remaining 20% ​​of the propagules will be considered as hydraulic diffusion. The hydrodynamic model is used to calculate the propagule diffusion. The propagule drift position is determined by drifting with the hydrodynamic force for 2 days. Whether the propagule can stay and survive is a random event, which is determined by random calculation, so as to judge the seed bank formed by hydraulic diffusion.

[0132] 3. Mangrove plant germination simulation. According to the experimental results, the germination rate of mangrove plants increases with the decrease of salinity. According to literature research, the germination rate of sea lacquer seeds: 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] Among them, i is the mangrove species (such as Aconitum carmichaelii, Paulownia affine, and Sonneratia apetala), u is the soil salinity in the seed bank area, and n1 and n2 are coefficients, which are different for different mangrove species.

[0135] 4. Dynamic simulation of mangrove plant growth. The dynamic growth simulation of mangrove plants adopts the optimal growth model of trees, and the growth stress factors mainly consider salinity and flow velocity erosion. The object of mangrove plant growth simulation is the crown width, and the changes in the crown width reflect the temporal and spatial changes in the growth of mangrove plants. The crown width is calculated by the breast diameter of the mangrove plant. The changes in the breast diameter are related to the relevant stress factors. The specific calculation method is as follows:

[0136]

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

[0138]

[0139] Among them, dbh is the breast diameter of mangrove plants, dbhmax is the maximum breast diameter of mangrove plants (the reference 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 flow 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 plants are 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 survival of the mangrove plant, and omax is the maximum flow velocity for the survival of the mangrove plant.

[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 death of mangrove plants is set. If the following situation occurs, the mangrove plants in this grid are 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) When 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 near the canal on the periphery may be disturbed by the waves generated by ships, and the scouring caused by them can be calculated by the following formula. Scouring will have a certain cumulative effect. Without considering the upstream sand supply, when the scouring depth exceeds 60% of the root length of the mangrove plants, the periphery mangrove plants are assumed to be dead.

[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, L represents the wave length, ... c It can be considered as the comprehensive coefficient of plants.

[0156] The spatial succession model of mangrove plants was constructed. The cellular automaton method was used to simulate the spatial succession of mangrove plants. The specific conversion 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. With the spread and germination of mangrove seeds, mangrove plants can occupy the light beach area, but cannot occupy the water body.

[0159] 3. Through the calculation of the competition coefficient, mangrove plants with a competition coefficient exceeding 0.7 will die due to fierce competition for resources. As mangrove plants mature, the competitive advantage of Sonneratia apetala over other mangrove plants will become more obvious, resulting in some mangrove plants succeeding to Sonneratia apetala.

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

[0161] According to the data, the remote sensing images of mangrove plants in three typical years were selected for model calibration and verification. According to the empirical range of model parameters, the trial and error method was used to simulate the distribution of mangrove plants under different parameter combinations, and then the consistency analysis was performed by calculating the Kappa coefficient.

[0162] Adjust each parameter by ±10% and ±20%, analyze the spatial distribution of mangrove plants, and conduct sensitivity tests. Based on the analysis results, identify which parameters are more sensitive key parameters, and then conduct a rationality analysis on their selected values.

[0163] Different scenarios are set for simulation according to 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 ship lock water-saving plans (which will affect the amount of water diversion generated by ships passing through the locks). Different simulation scenarios are set according to 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. According to the different route plans, ship flow plans, navigation speed plans, lock water-saving plans, etc. 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 rate of 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 time limit of 100 years.

[0169] 3. According to different canal construction plans, ship speed, ship flow, lock water saving ratio, etc. are taken 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, according to the needs of social and economic development, the cargo volume that the canal will 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 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 A simulation device 110 for the spatiotemporal changes of mangrove vegetation under the influence of a canal provided in an embodiment of the present invention includes:

[0172] The acquisition module 1101 is used to divide the mangrove vegetation area to be simulated into multiple spatial units; obtain remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution and time change of the mangrove vegetation area to be simulated from the remote sensing data; use the tree optimal growth model to combine the spatial distribution, the species distribution and the time change to construct an individual-based mangrove plant dynamic growth model in each of the spatial units; use cellular automata to simulate the expansion and vegetation succession of mangrove vegetation to form a mangrove vegetation spatial simulation model; based on the completion of calibration and verification of the mangrove plant dynamic growth model coupled with the mangrove vegetation spatial simulation model, obtain a plurality of preset canal construction plans for the mangrove vegetation area to be simulated;

[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; according to 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 principle of the simulation device 110 for the spatiotemporal change of mangrove vegetation under the influence of the canal can refer to the implementation principle of the simulation method for the spatiotemporal change of mangrove vegetation under the influence of the canal, which will not be repeated here. It should be understood that the division of the modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the simulation device 110 for the spatiotemporal change of mangrove vegetation under the influence of the canal can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and a processing element of the above device can call and execute the function of the simulation device 110 for the spatiotemporal change of mangrove vegetation under the influence of the canal. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in a processor element or an instruction in software form.

[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 (ASIC), or one or more microprocessors (digital signal processors, DSP), or one or more field programmable gate arrays (FPGA), etc. For another example, when a module above is implemented in the form of a processing element scheduling program code, 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 simulation device 110 for the spatiotemporal changes of mangrove vegetation under the influence of the canal. Figure 3 As shown, Figure 3The computer device 100 provided in the embodiment of the present invention is a structural block diagram. The computer device 100 includes a simulation device 110 for the spatiotemporal changes of mangrove vegetation under the influence of a canal, a memory 111, a processor 112 and a communication unit 113.

[0177] To achieve data transmission or interaction, 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 elements can be achieved through one or more communication buses or signal lines. The simulation device 110 for 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 for 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 for 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 is 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 form disclosed. Numerous modifications and variations are possible in accordance with the above teachings. These embodiments are selected and described in order to best illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can best utilize the present disclosure and utilize various embodiments with different modifications to suit the intended specific application.

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; Acquire remote sensing data of the mangrove vegetation area to be simulated, and extract the spatial distribution, species distribution and time variation of the mangrove vegetation area to be simulated from the remote sensing data; Using the optimal tree growth model combined 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 are used to simulate the expansion and succession of mangrove vegetation to form a mangrove vegetation spatial simulation model; Based on the completion of 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 schemes, 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 in each spatial unit corresponding to the multiple canal construction schemes; 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.

2. The method according to claim 1, characterized in that 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, construct individual-based mangrove plant propagule production simulation, mangrove plant seed diffusion and seed bank formation simulation, mangrove plant germination simulation, mangrove plant growth dynamics simulation and mangrove plant death simulation in each of the spatial units; 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.

3. The method according to claim 2, characterized in that The mangrove plant propagule production simulation comprises: N=f1·R+f2·gh+f3 Among them, N is the number of reproductive bodies, R is the crown width of mangrove plants, gh is the ground diameter of mangrove plants, and f1, f2, and f3 are coefficients.

4. 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 according to the propagule drift position combined 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.

5. The method according to claim 1, characterized in that The mangrove plant sprout simulation comprises: n i =n1·u+n2 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 area where the seed bank is located, and n1 and n2 are coefficients.

6. The method according to claim 1, characterized in that The mangrove plant growth dynamic simulation includes: 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; Among them, dbh is the mangrove plant diameter at breast height, dbhmax is the maximum diameter at breast height of the mangrove plant, 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 plants are located, ui is the salinity corresponding to the mangrove plant salinity growth coefficient of 0.5, and d is the coefficient; Among them, V(o) is the velocity scour stress coefficient, o is the velocity of the mangrove plant, omin is the minimum velocity for the survival of the mangrove plant, and omax is the maximum velocity for the survival of the mangrove plant. Wherein, C(r) is the competition coefficient, assuming that the coordinates of the mangrove plant trunk are taken as the origin, R is the diameter of the mangrove plant trunk as the origin, A is the range determined by the diameter R, n is the number of mangrove plants, r is the breast diameter of the i-th mangrove plant, and rbh is the breast diameter of the mangrove plant at the origin.

7. The method according to claim 1, characterized in that The mangrove plant death simulation includes: When the breast diameter increment rate of a mangrove plant is negative, or the competition coefficient exceeds a 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.

8. 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.

9. The method according to claim 1, characterized in that: The step of obtaining the post-construction environmental factors corresponding to the multiple canal construction schemes, 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 the spatiotemporal simulation change results of the mangrove vegetation in each spatial unit corresponding to the multiple canal construction schemes, includes: Obtaining the ship flow intensity, shipping water diversion scale, and ship navigation speed in the narrow river bank area included in the multiple canal construction plans; Input the ship flow intensity, shipping water diversion scale, and ship navigation speed in the narrow riverbank area into a pre-set two-dimensional hydrodynamic-water environment model to obtain the salinity change and flow rate change of each canal construction plan in each spatial unit; 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, obtaining the spatiotemporal simulation change results of the mangrove vegetation in each spatial unit corresponding to the multiple canal construction plans; The step of determining a target canal construction scheme for the mangrove vegetation area to be simulated from the plurality of canal construction schemes according to the spatiotemporal simulation change result of the mangrove vegetation includes: According to the spatiotemporal simulation results of mangrove vegetation changes corresponding to each of the canal construction plans, the 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.

10. A simulation device for the temporal and spatial 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 a mangrove plant dynamic growth model based on individuals in each of the spatial units by using the optimal tree growth model in combination with the spatial distribution, the species distribution and the temporal variation; use cellular automata to simulate the expansion and vegetation succession of mangrove vegetation to form a mangrove vegetation spatial simulation model; 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 used to obtain the post-construction environmental factors corresponding to the multiple canal construction schemes, input the post-construction environmental factors into the mangrove plant dynamic growth model and couple it to the mangrove vegetation spatial simulation model, so as to obtain the spatiotemporal simulation change results of the mangrove vegetation in each spatial unit corresponding to the multiple canal construction schemes; 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.

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