Quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation

Abstract

The present invention discloses a quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation. First, the minimum monitoring grids are selected by combining remote sensing interpretation and spatial simulated annealing algorithm; then, a sample plot survey is carried out in the selected monitoring grids, and the biomass and growth rate per unit area of ​​submerged plants are obtained by using the efficacy analysis method with the minimum sampling frequency; then, the dominant species are selected and brought back to the laboratory to monitor the parameters required for the submerged plant model, and a submerged plant growth and decline-nitrogen and phosphorus absorption and release model is constructed to simulate the dynamic change process of biomass and net nitrogen and phosphorus content in plants; then, the submerged plant model is coupled to the river and lake hydrodynamic-water quality model to simulate the dynamic change process of nitrogen and phosphorus concentration in urban rivers and lakes where submerged plants grow. Finally, different submerged plant harvesting scenarios are set, the accessibility of nitrogen and phosphorus water quality targets in urban rivers and lakes is analyzed, and the optimal harvesting plan for submerged plants is determined. The present invention can realize quantitative, efficient and precise control of submerged plants in urban rivers and lakes.

Description

Technical Field

[0001] The present invention relates to the field of water ecological restoration, and in particular to a quantitative control method for submerged plants in urban rivers and lakes based on remote sensing interpretation-field investigation-model construction-multi-scenario simulation prediction-water quality target reachability assessment. Background Art

[0002] Urban rivers and lakes are important water resources for residents' production and life, and have both natural and social attributes. In recent years, the rapid urbanization process has changed the natural hydrological laws, geomorphological characteristics and biological characteristics of rivers, leading to a series of ecological and environmental problems such as black and smelly water, eutrophication of water bodies, and loss of water ecosystems. Submerged plants have important ecological value in maintaining and improving the biodiversity and stability of river and lake ecosystems and controlling eutrophication of rivers and lakes. In the process of urban retention river management, artificial aquatic grass technology is widely used because of its low investment, quick results, and no restrictions on water transparency, suspended impurities and pollution load. However, in some shallow water areas, due to the worsening degree of eutrophication, submerged plants gradually expand and their biomass is too large, causing some species to gradually evolve from companion species to absolute dominant species, and form a dense canopy near the water surface, hindering the flow of water bodies. In a certain period of time, they not only fail to play their role in absorbing nutrients and purifying water quality, but also become internal pollution sources of nitrogen and phosphorus, resulting in water quality degradation and black and smelly water bodies and other malignant water sensory states, affecting the landscape and leisure functions of rivers and lakes. Therefore, reasonable harvesting of submerged plants is not only conducive to plant recovery and growth, increasing biodiversity, and improving community stability, but also can transfer nutrients from plants, avoid the negative effects of excessive growth of submerged plants, and achieve the balance of nitrogen and phosphorus in rivers and lakes or reduce the pollution load of rivers and lakes. At present, there is insufficient understanding of the impact mechanism of the growth and decline of submerged plants on the water environment, and the ecological and environmental effects of submerged plant outbreaks are unclear. In addition, most studies are based on indoor simulation experiments and lack long-term field monitoring results of wild water bodies. At the same time, most submerged plants in urban rivers and lakes are not harvested or harvested too randomly. When the harvesting intensity is not enough, submerged plants are prone to broken branches and the whole plant floats. Excessive harvesting may lead to failure of submerged plant reconstruction.

[0003] Submerged plant models can simulate the dynamic changes in the nutrient content of submerged plants during the vigorous growth and decline periods, as well as the response of river and lake water quality under different submerged plant control schemes. Therefore, they play a supporting role in proposing optimal plans for submerged plant outbreak prevention and control, and in making scientific decisions on water environment governance, protection and management of urban rivers and lakes. At present, a variety of water environment models have been developed, from simple output coefficient models, statistical models to complex mechanism models (such as MIKE and EFDC), which do not consider the various factors that affect the complex nutrient removal process of submerged plants in river and lake ecosystems (such as nutrient concentration of incoming water, hydraulic retention time, temperature, plant species and coverage, etc.). In particular, in existing process-based models, temperature adjustment is unnecessary. However, when the temperature increases from 10℃ to 35℃, the plant growth rate will increase accordingly, and low temperature will inhibit the absorption of nutrients by plants. In addition, the biomass growth of plants will be limited by the availability of nitrogen and phosphorus. Therefore, considering the temperature changes and the nutrient utilization of nitrogen and phosphorus during the growth and death of plants, constructing a submerged plant growth and death-nitrogen and phosphorus absorption and release model is of great significance for the quantitative harvesting of submerged plants in urban rivers and lakes. Summary of the invention

[0004] Purpose of the invention: In view of the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a method for quantitative control of submerged plants in urban rivers and lakes based on plant growth and death-nitrogen and phosphorus absorption and release simulation, so as to more scientifically and effectively overcome the problems of strong randomness in the management of submerged plants in the process of ecological restoration of urban rivers and lakes and the need for a large amount of field sampling and indoor cultivation. By independently constructing a submerged plant growth and death-nitrogen and phosphorus absorption and release model, quantitative management of submerged plants in urban rivers and lakes can be achieved.

[0005] Technical solution: To achieve the above-mentioned invention object, the present invention adopts the following technical solution:

[0006] A quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation includes the following steps:

[0007] (1) Use multispectral remote sensing images to obtain the green light band with the reflectance spectrum characteristics of submerged plants, and analyze the area of ​​submerged plants and their spatial distribution characteristics; use the reflectance of the green light band as the main variable, and the distance from the shore and water depth as covariates. With the minimum variance of the mean regression kriging estimation as the criterion, select the minimum monitoring grids through the spatial simulated annealing algorithm;

[0008] (2) During different growth periods of submerged plants, sample plot surveys were conducted in the selected monitoring grids. The relationship between the sampling frequency and the biomass of submerged plants per unit area within the monitoring grids was established using the efficacy analysis method. The biomass and growth rate of submerged plants per unit area in different regions were obtained with the minimum sampling frequency.

[0009] (3) Select dominant species and bring them back to the laboratory to monitor the parameters required for constructing a submerged plant model, including morphological parameters and nitrogen and phosphorus absorption and release rates; construct a submerged plant growth and decline-nitrogen and phosphorus absorption and release model to simulate the dynamic changes in biomass and the net nitrogen and phosphorus content in plants;

[0010] (4) The submerged plant model is coupled to the river and lake hydrodynamics-water quality model to simulate the spatiotemporal variation of nitrogen and phosphorus concentrations in urban rivers and lakes covered by submerged plants. Based on the nitrogen and phosphorus water quality targets for rivers and lakes, the expected reduction in nitrogen and phosphorus fluxes by harvesting submerged plants is determined;

[0011] (5) Set up different scenarios with different harvesting intensities and harvesting times in different regions, simulate the nitrogen and phosphorus concentrations in rivers and lakes under different scenarios, analyze the accessibility of nitrogen and phosphorus water quality targets, and determine the optimal harvesting plan for submerged plants after comparison.

[0012] Preferably, the step (1) comprises the following steps:

[0013] (1-1) Using multispectral remote sensing images, extract the green light band, reclassify and assign the pixel values ​​sensitive to submerged plants to 1, and the pixel values ​​sensitive to non-submerged plants to 0; visually select multiple areas with high, medium and low distribution of submerged plants in the image, and generate corresponding vector layers; extract the reclassified green light band image with the vector layer as the boundary, resample, calculate the proportion of pixels with a pixel value of 1 in the entire sample, and analyze the area of ​​submerged plants and their spatial distribution characteristics;

[0014] (1-2) The reclassified green light band reflectance is used as the main variable, and the distance from the shore and water depth are used as covariates. The spatial simulated annealing algorithm is used to find the monitoring grid with the global minimum average estimation variance.

[0015] Preferably, the specific steps of the spatial simulated annealing algorithm are: discretizing the study area into a number of grids according to a preset sample size, randomly selecting a group of sample points from the original sample point set as the optimal solution, using the optimal solution to interpolate and predict the main variable and the covariate, and calculating the mean regression kriging estimate variance of the initial solution; randomly selecting a point in the residual set outside the initial solution to replace the point in the initial solution to generate a new solution, continuing to interpolate and predict the new solution, and calculating the corresponding mean regression kriging estimate variance; and selecting the least monitoring grids based on the criterion of minimizing the mean regression kriging estimate variance.

[0016] Preferably, the step (2) comprises the following steps:

[0017] The aboveground biomass of submerged plants is collected continuously on the water surface of a preset sample size. According to the power analysis method, when the sampling frequency reaches n times and the mean of the sum of the biomass per unit area of ​​the previous n times and the mean of all data of the monitoring grid are within the preset error range, the sampling frequency (minimum sampling frequency) is used to estimate the biomass of submerged plants; biomass surveys are carried out in the germination period, seedling period, growth period and maturity period of submerged plants, and the growth rate of submerged plants is calculated.

[0018] Preferably, in step (3), the dominant species are selected and brought back to the laboratory, the fresh plants are naturally air-dried, the root-to-stem ratio is weighed and calculated, the total nitrogen content of the plants is determined by the micro-Kjeldahl method, and the total phosphorus content of the plants is determined by the sulfuric acid-hydrogen peroxide digestion method.

[0019] Preferably, in step (3), a submerged plant growth and decline-nitrogen and phosphorus absorption and release model is independently constructed to simulate the dynamic changes in biomass and net nitrogen and phosphorus content in plants, wherein:

[0020] The plant growth equation of the submerged plant model is:

[0021] BSH T =BSH T-ΔT +((1-k STR ) SHOOTGrow f Upt f PLT -k SHOOTDec f SHOOTDecT )BSH T

[0022] BRO T =BRO T-ΔT +k STR k SHOOTGrow f Upt f PLT BSH T -k ROOTDec f ROOTDecT BRO T

[0023]

[0024]

[0025]

[0026]

[0027] In the formula, the superscript T represents the time, ΔT represents the time step, and BSH T and BRO T Respectively represent the stem and root biomass of submerged plants at time T; k STRrepresents the proportion of stems of submerged plants transferred to the root system; k SHOOTGrow represents the maximum growth rate of the stem of a submerged plant under optimal conditions; k SHOOTDec represents the nitrogen and phosphorus release rate from the stems of submerged plants; k ROOTDec represents the nitrogen and phosphorus release rate of the roots of submerged plants; f Upt represents the nitrogen or phosphorus concentration limitation of submerged plant uptake; f PLT represents the temperature limit for the growth of submerged plants; f SHOOTDecT and f ROOTDecT Represent the temperature limits of stem and root decay of submerged plants; NH T 、NO T and represent the concentrations of ammonium nitrogen, nitrate nitrogen and phosphate in rivers and lakes at time T; KH Upt represents the half-saturation constant of nitrogen or phosphorus absorption by submerged plants; θ PL represents the effect coefficient of temperature on the growth of submerged plants; θ SHOOTDec represents the coefficient of temperature on the rate of nitrogen or phosphorus release from the stems of submerged plants; θ ROOTDec Represents the coefficient of temperature on the nitrogen or phosphorus release rate of submerged plant roots; Represents the water temperature of rivers and lakes at time T; TAve represents the daily average water temperature of rivers and lakes; T PL1 and T PL2 Represents the suitable temperature range for the growth of submerged plants;

[0028] The nitrogen absorption-release equation of the submerged plant model is:

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, and They represent ammonium nitrogen and nitrate nitrogen absorbed by submerged plants, respectively; and represent the organic nitrogen and ammonium nitrogen released by submerged plants; k NHUpt and k NOUpt Respectively represent the ratio of ammonium nitrogen and nitrate nitrogen in the nitrogen absorbed by submerged plants; k ORGNDec and k NHDec Respectively represent the proportion of organic nitrogen and ammonium nitrogen in nitrogen released by submerged plants; k NTB represents the proportion of nitrogen content in biomass; h represents the depth of rivers and lakes;

[0034] The phosphorus absorption-release equation of the submerged plant model is:

[0035]

[0036]

[0037]

[0038] In the formula, represents phosphate taken up by submerged plants; and represent the organic phosphorus and phosphate released by submerged plants, respectively; k DIPUpt Represents the proportion of phosphate in phosphorus absorbed by submerged plants; k ORGPDec and k DIPDec Respectively represent the ratio of organic phosphorus and phosphate in phosphorus released by submerged plants; k PTB Represents the proportion of phosphorus content in biomass.

[0039] Preferably, the step (4) comprises the following steps:

[0040] Using meteorology as driving data and bottom topography as input data, the river and lake hydrodynamic-water quality model is used to simulate the river and lake water temperature and nitrogen and phosphorus concentration time series data. Then, the river and lake water temperature and nitrogen and phosphorus concentrations are used as the boundary conditions of the submerged plant model to simulate the spatiotemporal variation process of nitrogen and phosphorus concentrations in rivers and lakes under the participation of submerged plants in biochemical reactions, and determine the spatiotemporal intervals where nitrogen and phosphorus concentrations exceed the standard. According to the nitrogen and phosphorus water quality targets for rivers and lakes, the expected reduction in nitrogen and phosphorus fluxes by harvesting submerged plants is determined.

[0041] Preferably, in step (5), different submerged plant harvesting scenarios are designed, including different harvesting areas, different harvesting intensities and harvesting times, to simulate the impact of submerged plant harvesting on nitrogen and phosphorus concentrations in water bodies. According to nitrogen and phosphorus water quality target constraints, a suitable submerged plant harvesting plan is determined, and the harvesting time is recommended to be controlled before the absorption of nitrogen and phosphorus by submerged plants exceeds the release.

[0042] Based on the same inventive concept, the present invention also provides a quantitative management and control system for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation, including:

[0043] The remote sensing interpretation and grid selection module is used to obtain the green light band with the reflectance spectrum characteristics of submerged plants using multispectral remote sensing images, and analyze the area of ​​submerged plants and their spatial distribution characteristics; the reflectance of the green light band is used as the main variable, and the distance from the shore and the water depth are used as covariates. The minimum variance of the mean regression kriging estimation is used as the criterion, and the minimum monitoring grids are selected through the spatial simulated annealing algorithm;

[0044] The sampling and analysis module is used for different growth stages of submerged plants. Sample plot surveys are carried out in the selected monitoring grids. The efficacy analysis method is used to establish the relationship between the sampling frequency and the biomass of submerged plants per unit area in the monitoring grids, so as to obtain the biomass and growth rate of submerged plants per unit area in different areas with the minimum sampling frequency.

[0045] The submerged plant model construction module is used to select dominant species and bring them back to the laboratory to monitor the parameters required for constructing the submerged plant model, including morphological parameters and nitrogen and phosphorus absorption and release rates; to construct a submerged plant growth and decline-nitrogen and phosphorus absorption and release model to simulate the dynamic changes in submerged plant biomass and the net nitrogen and phosphorus content in the plant body;

[0046] And a scenario simulation module, which is used to couple the submerged plant model with the river and lake hydrodynamics-water quality model, simulate the spatiotemporal variation of nitrogen and phosphorus concentrations in urban rivers and lakes covered by submerged plants, and determine the expected reduction in nitrogen and phosphorus fluxes by harvesting submerged plants based on the nitrogen and phosphorus water quality targets of rivers and lakes; and set up scenarios of different regions, different harvesting intensities and harvesting times, simulate the nitrogen and phosphorus concentrations of rivers and lakes under different scenarios, analyze the accessibility of nitrogen and phosphorus water quality targets, and determine the best harvesting plan for submerged plants after comparison.

[0047] Beneficial effects: Compared with the prior art, the present invention has established a method for optimizing submerged plant sampling points based on a spatial simulated annealing algorithm and an efficacy analysis method; at the same time, a submerged plant growth and decline-nitrogen and phosphorus absorption and release model has been constructed to simulate the dynamic changes in nitrogen and phosphorus content in submerged plants during the growth and decline periods, and the response of nitrogen and phosphorus concentrations in rivers and lakes under different submerged plant control scenarios. The model fully considers the nutrient utilization of nitrogen and phosphorus during temperature changes and plant growth and decline, and is more in line with the growth laws of submerged plants in urban rivers and lakes and the mechanism of action of nitrogen and phosphorus. Compared with field surveys and indoor simulation experimental methods, the technical route proposed by the present invention can accurately predict the nitrogen and phosphorus concentrations in rivers and lakes under different submerged plant control measures based on the least sampling, and realize quantitative control of submerged plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic diagram of a flow chart of an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of the minimum number of sampling points and the minimum sampling frequency of submerged plants in an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the biomass and net nitrogen and phosphorus content changes simulated by the submerged plant model in the embodiment of the present invention;

[0051] Figure 4 Schematic diagram of the change process of nitrogen and phosphorus concentrations in urban rivers and lakes under different submerged plant harvesting scenarios in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The scheme of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0053] like Figure 1 As shown, the embodiment of the present invention discloses a quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation, which mainly includes the following steps:

[0054] (1) Use multispectral remote sensing images to obtain the green light band with the reflectance spectrum characteristics of submerged plants, analyze the area of ​​submerged plants and their spatial distribution characteristics; use the reflectance of the green light band as the main variable, and the distance from the shore and water depth as covariates, and use the minimum variance of the mean regression kriging estimation as the criterion to select the minimum monitoring grids through the spatial simulated annealing algorithm. This step specifically includes:

[0055] (1-1) This embodiment selects Gaoyou Lake in Gaoyou City as the research object. The total area of ​​Gaoyou Lake is 760.67 square kilometers (flat water area), and the submerged plant type is mainly water chestnut. Using multispectral remote sensing images, extract the green light band, reclassify the pixels sensitive to submerged plants (peaks with a reflectivity of 10% to 20% appearing near green light) and assign them a value of 1, and the remaining pixel values ​​are all assigned a value of 0; visually select multiple areas with high, medium and low distribution of submerged plants in the image, and generate corresponding vector layers; extract the reclassified green light band image with the vector layer as the boundary, and resample it to calculate the proportion of pixels with a pixel value of 1 in the entire sample. High coverage = 17501 / (17501+34695) = 33.53%, medium coverage = 8628 / (8628+31878) = 21.30%, low coverage = 2365 / (2365+37324) = 5.96%.

[0056] (1-2) The spatial simulated annealing algorithm is used to optimize the layout of the sampling points of submerged plants, with the reflectance of the green light band as the main variable, and the distance from the shore and the water depth as the covariates. The specific steps of the spatial simulated annealing algorithm are as follows: the study area is discretized into several grids according to the preset size, a group of sample points are randomly selected from the original sample point set as the optimal solution, the optimal solution is used to interpolate and predict the main variable and the covariate, and the mean regression kriging estimation variance of the initial solution is calculated; a point is randomly selected from the residual set outside the initial solution to replace the point in the initial solution to generate a new solution, and the new solution is further interpolated and predicted, and the corresponding mean regression kriging estimation variance is calculated; the minimum mean regression kriging estimation variance is used as the criterion to select the minimum monitoring grid. In this embodiment, the study area is discretized into 30 grids of 5km×5km. When the number of monitoring grids is 9, the mean regression kriging estimation variance on the potential sampling points can be minimized (0.08), which has good representativeness in geographic space.

[0057] (2) During the different growth stages of submerged plants, sample plot surveys were carried out in the selected monitoring grids. The efficacy analysis method was used to establish the relationship between sampling frequency and the biomass of submerged plants per unit area, so as to obtain the biomass and growth rate of submerged plants per unit area in different areas with the minimum sampling frequency.

[0058] In this step, sample plot surveys were carried out in the 9 selected monitoring grids, and the aboveground biomass of submerged plants was continuously collected on 0.2m×0.2m plots. The power analysis method was used, and under a 90% confidence interval, the effect value of 0.8 was used as the benchmark value. When the number of samplings reached n times and the mean of the sum of the biomass per unit area of ​​the first n times was close to the mean of all data in the monitoring grid, the sampling frequency (minimum sampling frequency) was used to estimate the biomass of submerged plants in the grid (Table 1). Field surveys were carried out during the germination period (October to November), seedling period (December to January), green period (February to April), maturity period (April to May), and decline period (June to August) of Water Chestnut, and the growth rate of submerged plants was calculated to be 0.035day -1 .

[0059] Table 1 Minimum sampling frequency and biomass per unit area of ​​different monitoring grids

[0060]

[0061] (3) Select dominant species and bring them back to the laboratory to monitor the parameters required for constructing a submerged plant model, including morphological parameters and nitrogen and phosphorus absorption and release rates; construct a submerged plant growth and decline-nitrogen and phosphorus absorption and release model to simulate the dynamic changes in submerged plant biomass and the net nitrogen and phosphorus content in the plant body. This step specifically includes:

[0062] The roots, stems and leaves of the collected submerged plants were collected and dried in a constant temperature oven at 60°C to a constant weight, and the root-to-stem ratio was calculated. The total nitrogen content of the plants was determined by the micro-Kjeldahl method, and the total phosphorus content of the plants was determined by the sulfuric acid-hydrogen peroxide digestion method.

[0063] The important parameters of the submerged plant model determined experimentally are shown in Table 2:

[0064] Table 2 Water environment effect parameters of submerged plant model

[0065]

[0066] (4) The submerged plant model is coupled to the river and lake hydrodynamics-water quality model to simulate the spatiotemporal variation of nitrogen and phosphorus concentrations in urban rivers and lakes covered by submerged plants. Based on the nitrogen and phosphorus water quality targets for rivers and lakes, the expected reduction in nitrogen and phosphorus fluxes by harvesting submerged plants is determined.

[0067] In this step, the weather (precipitation, temperature, etc.) is used as the driving data, and the river and lake bottom topography is used as the input data. The river and lake hydrodynamic-water quality model is used to simulate the time series data such as river and lake water temperature, nitrogen and phosphorus concentration, and then the river and lake water temperature and nitrogen and phosphorus concentration are used as the boundary conditions of the submerged plant model to simulate the spatiotemporal variation process of river and lake nitrogen and phosphorus concentration under the biochemical reaction of submerged plants, and determine the spatiotemporal interval of nitrogen and phosphorus concentration exceeding the standard; according to the river and lake nitrogen and phosphorus water quality targets, the nitrogen and phosphorus flux expected to be reduced by harvesting submerged plants is determined. In this example, the average total nitrogen concentration of Gaoyou Lake from June to August is 1.75 mg / L. If the total nitrogen target of 1.50 mg / L is to be maintained, the total nitrogen load in the southern lake area needs to be reduced by 107,670 kg; the average total phosphorus concentration of Gaoyou Lake from June to August is 0.15 mg / L. If the total phosphorus target of 0.08 mg / L is to be maintained, the total phosphorus load in the southern lake area needs to be reduced by 15,520 kg.

[0068] (5) Set up different scenarios with different harvesting intensities and harvesting times in different regions, simulate the nitrogen and phosphorus concentrations in rivers and lakes under different scenarios, analyze the accessibility of nitrogen and phosphorus water quality targets, and determine the optimal harvesting plan for submerged plants after comparison.

[0069] In this step, according to the simulation results of the submerged plant model, by harvesting submerged plants, the total nitrogen load can be reduced by up to 150,000 kg; in different harvesting scenarios, more than 75% can ensure that the total nitrogen concentration is stable and meets the standard; based on the absorption and release rate of total nitrogen by submerged plants, it is recommended to harvest in late April every year. The optimal biomass is controlled at 1.51×10 8 Within kg.

[0070] According to the simulation results of the submerged plant model, the total phosphorus load can be reduced by up to 40100kg by harvesting submerged plants; in different harvesting scenarios, more than 50% can ensure that the total phosphorus concentration is stable and meets the standard; based on the absorption and release rate of total phosphorus by submerged plants, it is recommended to harvest in late May every year. The optimal biomass is controlled at 2.07×10 8 Within kg.

[0071] Based on the same inventive concept, the present invention also provides a quantitative management and control system for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation, including: a remote sensing interpretation and grid selection module, which is used to use multispectral remote sensing images to obtain green light bands with submerged plant reflection spectral characteristics, and analyze the submerged plant area and its spatial distribution characteristics; taking the green light band reflectivity as the main variable, and the shore distance and water depth as covariates, and taking the minimum variance of the mean regression Kriging estimation as the criterion, the minimum monitoring grids are selected through the spatial simulated annealing algorithm; a sampling and analysis module, which is used for conducting sample plot surveys in the selected monitoring grids during different growth periods of submerged plants, and using the efficacy analysis method to establish the relationship between the sampling frequency in the monitoring grid and the submerged plant biomass per unit area, so as to obtain the submerged plants in different areas with the minimum sampling frequency. Biomass and growth rate per unit area of ​​aquatic plants; submerged plant model construction module, used to select dominant species and bring them back to the laboratory to monitor the parameters required for constructing the submerged plant model, including morphological parameters and nitrogen and phosphorus absorption and release rates; construct a submerged plant growth and decline-nitrogen and phosphorus absorption and release model to simulate the dynamic changes in biomass and net nitrogen and phosphorus content in plants; and a scenario simulation module, used to couple the submerged plant model with the river and lake hydrodynamic-water quality model, simulate the spatiotemporal changes in nitrogen and phosphorus concentrations in urban rivers and lakes covered by submerged plants, and determine the nitrogen and phosphorus fluxes expected to be reduced by harvesting submerged plants according to the nitrogen and phosphorus water quality targets of rivers and lakes; and set different regions, different harvesting intensities and harvesting time scenarios to simulate the nitrogen and phosphorus concentrations of rivers and lakes under different scenarios, analyze the accessibility of nitrogen and phosphorus water quality targets, and determine the best harvesting plan for submerged plants after comparison. For specific implementation details, please refer to the above method embodiment and will not be repeated.

[0072] Technologies not described in detail in the present invention are prior arts, such as the hydrodynamic-water quality model of rivers or lakes, the method for determining the nitrogen and phosphorus content of plants, etc., which will not be described in detail.

[0073] The above disclosure is only a preferred embodiment of the present invention, and cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation, characterized in that: The method comprises the following steps: (1) Use multispectral remote sensing images to obtain the green light band with the reflectance spectrum characteristics of submerged plants and analyze the area and spatial distribution characteristics of submerged plants; The reflectivity of the green light band is used as the main variable, the distance from the shore and the water depth are used as covariates, and the minimum variance of the mean regression kriging estimation is used as the criterion. The minimum monitoring grids are selected through the spatial simulated annealing algorithm; the specific steps of the spatial simulated annealing algorithm are: discretizing the study area into a number of grids according to the preset size, randomly selecting a group of sample points from the original sample point set as the optimal solution, using the optimal solution to interpolate and predict the main variable and the covariate, and calculating the mean regression kriging estimation variance of the initial solution; randomly selecting a point in the residual set outside the initial solution to replace the point in the initial solution to generate a new solution, continuing to interpolate and predict the new solution, and calculating the corresponding mean regression kriging estimation variance; Taking the minimum variance of mean regression kriging estimation as the criterion, the minimum number of monitoring grids is selected; (2) During different growth periods of submerged plants, sample plot surveys were conducted in the selected monitoring grids. The efficacy analysis method was used. When the mean of the sum of the biomass per unit area after the sampling frequency reached n times and the mean of all data in the monitoring grid were within the preset error range, the submerged plant biomass was estimated using the sampling frequency. The relationship between the sampling frequency and the biomass per unit area of ​​submerged plants in the monitoring grid was established, and the biomass per unit area and growth rate of submerged plants in different regions were obtained with the minimum sampling frequency. (3) Select dominant species and bring them back to the laboratory to monitor the parameters required for constructing a submerged plant model, including morphological parameters and nitrogen and phosphorus absorption and release rates; construct a submerged plant growth and decline-nitrogen and phosphorus absorption and release model to simulate the dynamic changes in biomass and net nitrogen and phosphorus content in plants; among which: The plant growth equation of the submerged plant model is: BSH T =BSH T-ΔT +((1-k STR )k SHOOTGrow f Upt f PLT -k SHOOTDec f SHOOTDecT )BSH T BRO T =BRO T-ΔT +k STR k SHOOTGrow f Upt f PLT BSH T -k ROOTDec f ROOTDecT BRO T In the formula, the superscript T represents the time, ΔT represents the time step, and BSH T and BRO T Respectively represent the stem and root biomass of submerged plants at time T; k STR represents the proportion of stems of submerged plants transferred to the root system; k SHOOTGrow represents the maximum growth rate of the stem of a submerged plant under optimal conditions; k SHOOTDec represents the nitrogen and phosphorus release rate from the stems of submerged plants; k ROOTDec represents the nitrogen and phosphorus release rate of submerged plant roots; f Upt represents the nitrogen or phosphorus concentration limitation of submerged plant uptake; f PLT represents the temperature limit for the growth of submerged plants; f SHOOTDecT and f ROOTDecT Represent the temperature limits of stem and root decay of submerged plants; NH T 、NO T and represent the concentrations of ammonium nitrogen, nitrate nitrogen and phosphate in rivers and lakes at time T; KH Upt represents the half-saturation constant of nitrogen or phosphorus absorption by submerged plants; θ PL represents the effect coefficient of temperature on the growth of submerged plants; θ SHOOTDec represents the coefficient of temperature on the rate of nitrogen or phosphorus release from the stems of submerged plants; θ ROOTDec Represents the coefficient of temperature on the nitrogen or phosphorus release rate of submerged plant roots; Represents the water temperature of rivers and lakes at time T; TAve represents the average daily water temperature of rivers and lakes; T PL1 and T PL2 Represents the suitable temperature range for the growth of submerged plants; The nitrogen absorption-release equation of the submerged plant model is: In the formula, and They represent ammonium nitrogen and nitrate nitrogen absorbed by submerged plants, respectively; and represent the organic nitrogen and ammonium nitrogen released by submerged plants; k NHUpt and k NOUpt Respectively represent the ratio of ammonium nitrogen and nitrate nitrogen in the nitrogen absorbed by submerged plants; k ORGNDec and k NHDec Respectively represent the proportion of organic nitrogen and ammonium nitrogen in nitrogen released by submerged plants; k NTB represents the proportion of nitrogen content in biomass; h represents the depth of rivers and lakes; The phosphorus absorption-release equation of the submerged plant model is: In the formula, represents phosphate taken up by submerged plants; and represent the organic phosphorus and phosphate released by submerged plants, respectively; k DIPUpt Represents the proportion of phosphate in phosphorus absorbed by submerged plants; k ORGPDec and k DIPDec Respectively represent the ratio of organic phosphorus and phosphate in phosphorus released by submerged plants; k PTB Represents the proportion of phosphorus content in biomass; (4) The submerged plant model is coupled to the river and lake hydrodynamics-water quality model to simulate the spatiotemporal variation of nitrogen and phosphorus concentrations in urban rivers and lakes covered by submerged plants. Based on the nitrogen and phosphorus water quality targets for rivers and lakes, the expected reduction in nitrogen and phosphorus fluxes by harvesting submerged plants is determined; (5) Set up different scenarios with different harvesting intensities and harvesting times in different regions, simulate the nitrogen and phosphorus concentrations in rivers and lakes under different scenarios, analyze the accessibility of nitrogen and phosphorus water quality targets, and determine the optimal harvesting plan for submerged plants after comparison.

2. The quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation according to claim 1 is characterized in that: The step (1) comprises the following steps: (1-1) Using multispectral remote sensing images, extract the green light band, reclassify and assign the pixel values ​​sensitive to submerged plants to 1, and the pixel values ​​sensitive to non-submerged plants to 0; visually select multiple areas with high, medium and low distribution of submerged plants in the image, and generate corresponding vector layers; extract the reclassified green light band image with the vector layer as the boundary, resample, calculate the proportion of pixels with a pixel value of 1 in the entire sample, and analyze the area of ​​submerged plants and their spatial distribution characteristics; (1-2) The reclassified green light band reflectance is used as the main variable, and the distance from the shore and water depth are used as covariates. The spatial simulated annealing algorithm is used to find the monitoring grid with the global minimum average estimation variance.

3. The quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation according to claim 1 is characterized in that: In the step (2), biomass surveys are conducted during the germination period, seedling period, growth period, and maturity period of the submerged macrophytes, and the growth rate of the submerged macrophytes is calculated.

4. The quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation according to claim 1 is characterized in that: In the step (3), the dominant species are selected and brought back to the laboratory, the fresh plants are naturally air-dried, the root-to-stem ratio is weighed and calculated, the total nitrogen content of the plants is determined by the micro-Kjeldahl method, and the total phosphorus content of the plants is determined by the sulfuric acid-hydrogen peroxide digestion method.

5. The quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation according to claim 1 is characterized in that: The step (4) comprises the following steps: Using meteorology as driving data and bottom topography as input data, the river and lake hydrodynamic-water quality model is used to simulate the river and lake water temperature and nitrogen and phosphorus concentration time series data. Then, the river and lake water temperature and nitrogen and phosphorus concentrations are used as the boundary conditions of the submerged plant model to simulate the spatiotemporal variation process of nitrogen and phosphorus concentrations in rivers and lakes under the participation of submerged plants in biochemical reactions, and determine the spatiotemporal intervals where nitrogen and phosphorus concentrations exceed the standard. According to the nitrogen and phosphorus water quality targets for rivers and lakes, the expected reduction in nitrogen and phosphorus fluxes by harvesting submerged plants is determined.

6. The quantitative control method for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation according to claim 1 is characterized in that: In the step (5), different submerged plant harvesting scenarios are designed, including different harvesting areas, different harvesting intensities and harvesting times, to simulate the impact of submerged plant harvesting on the nitrogen and phosphorus concentrations in the water body. The submerged plant harvesting plan is determined based on the nitrogen and phosphorus water quality target constraints. It is recommended that the harvesting time be controlled before the absorption of nitrogen and phosphorus by the submerged plants is greater than the release.

7. A quantitative control system for submerged plants in urban rivers and lakes based on plant growth and decline-nitrogen and phosphorus absorption and release simulation, characterized in that: include: Remote sensing interpretation and grid selection module, used to obtain the green light band with submerged plant reflectance spectrum characteristics using multispectral remote sensing images, and analyze the area of ​​submerged plants and their spatial distribution characteristics; The reflectivity of the green light band is used as the main variable, the distance from the shore and the water depth are used as covariates, and the minimum variance of the mean regression kriging estimation is used as the criterion. The minimum monitoring grids are selected through the spatial simulated annealing algorithm; the specific steps of the spatial simulated annealing algorithm are: discretizing the study area into a number of grids according to the preset size, randomly selecting a group of sample points from the original sample point set as the optimal solution, using the optimal solution to interpolate and predict the main variable and the covariate, and calculating the mean regression kriging estimation variance of the initial solution; randomly selecting a point in the residual set outside the initial solution to replace the point in the initial solution to generate a new solution, continuing to interpolate and predict the new solution, and calculating the corresponding mean regression kriging estimation variance; Taking the minimum variance of mean regression kriging estimation as the criterion, the minimum number of monitoring grids is selected; The sampling and analysis module is used for different growth periods of submerged plants. Sample plot surveys are carried out in the selected monitoring grids. The efficacy analysis method is used. When the mean of the sum of the biomass per unit area after the sampling times reaches n times and the mean of all data of the monitoring grid are within the preset error range, the sampling frequency is used to estimate the biomass of submerged plants; the relationship between the sampling frequency and the biomass per unit area of ​​submerged plants in the monitoring grid is established, and the biomass per unit area and growth rate of submerged plants in different regions are obtained with the minimum sampling frequency; The submerged plant model construction module is used to select dominant species and bring them back to the laboratory to monitor the parameters required for constructing the submerged plant model, including morphological parameters and nitrogen and phosphorus absorption and release rates; to construct a submerged plant growth and decline-nitrogen and phosphorus absorption and release model to simulate the dynamic changes in biomass and the net nitrogen and phosphorus content in plants; And the scenario simulation module is used to couple the submerged plant model with the river and lake hydrodynamic-water quality model, simulate the spatiotemporal variation of nitrogen and phosphorus concentrations in urban rivers and lakes covered by submerged plants, and determine the nitrogen and phosphorus fluxes expected to be reduced by harvesting submerged plants according to the nitrogen and phosphorus water quality targets of rivers and lakes; and set different regions, different harvesting intensities and harvesting times to simulate the nitrogen and phosphorus concentrations of rivers and lakes under different scenarios, analyze the accessibility of nitrogen and phosphorus water quality targets, and determine the best harvesting plan for submerged plants after comparison; In the constructed submerged plant growth and decline-nitrogen and phosphorus absorption and release model: The plant growth equation of the submerged plant model is: BSH T =BSH T-ΔT +((1-k STR )k SHOOTGrow f Upt f PLT -k SHOOTDec f SHOOTDecT )BSH T BRO T =BRO T-ΔT +k STR k SHOOTGrow f Upt f PLT BSH T -k ROOTDec f ROOTDecT BRO T In the formula, the superscript T represents the time, ΔT represents the time step, and BSH T and BRO T Respectively represent the stem and root biomass of submerged plants at time T; k STR represents the proportion of stems of submerged plants transferred to the root system; k SHOOTGrow represents the maximum growth rate of the stem of a submerged plant under optimal conditions; k SHOOTDec represents the nitrogen and phosphorus release rate from the stems of submerged plants; k ROOTDec represents the nitrogen and phosphorus release rate of submerged plant roots; f Upt represents the nitrogen or phosphorus concentration limitation of submerged plant uptake; f PLT represents the temperature limit for the growth of submerged plants; f SHOOTDecT and f ROOTDecT Represent the temperature limits of stem and root decay of submerged plants; NH T 、NO T and represent the concentrations of ammonium nitrogen, nitrate nitrogen and phosphate in rivers and lakes at time T; KH Upt represents the half-saturation constant of nitrogen or phosphorus absorption by submerged plants; θ PL represents the effect coefficient of temperature on the growth of submerged plants; θ SHOOTDec represents the coefficient of temperature on the rate of nitrogen or phosphorus release from the stems of submerged plants; θ ROOTDec Represents the coefficient of temperature on the nitrogen or phosphorus release rate of submerged plant roots; Represents the water temperature of rivers and lakes at time T; TAve represents the average daily water temperature of rivers and lakes; T PL1 and T PL2 Represents the suitable temperature range for the growth of submerged plants; The nitrogen absorption-release equation of the submerged plant model is: In the formula, and They represent ammonium nitrogen and nitrate nitrogen absorbed by submerged plants, respectively; and represent the organic nitrogen and ammonium nitrogen released by submerged plants; k NHUpt and k NOUpt Respectively represent the ratio of ammonium nitrogen and nitrate nitrogen in the nitrogen absorbed by submerged plants; k ORGNDec and k NHDec Respectively represent the proportion of organic nitrogen and ammonium nitrogen in nitrogen released by submerged plants; k NTB represents the proportion of nitrogen content in biomass; h represents the depth of rivers and lakes; The phosphorus absorption-release equation of the submerged plant model is: In the formula, represents phosphate taken up by submerged plants; and represent the organic phosphorus and phosphate released by submerged plants, respectively; k DIPUpt Represents the proportion of phosphate in phosphorus absorbed by submerged plants; k ORGPDec and k DIPDec Respectively represent the ratio of organic phosphorus and phosphate in phosphorus released by submerged plants; k PTB Represents the proportion of phosphorus content in biomass.

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