Method and device for evaluating above-ground biomass of spartina alterniflora based on coverage and height

Abstract

The application provides a method and device for evaluating aboveground biomass of Spartina alterniflora based on coverage and height. The method provided by the application comprises the following steps: setting a first quadrat in a Spartina alterniflora growth area, collecting all plants in the first quadrat, measuring the height and dry weight of each plant, establishing a first allometric growth relationship model between the height and dry weight of each plant based on the height and dry weight of each plant, setting a second quadrat in the Spartina alterniflora growth area, recording the quadrat coverage, average height and plant number in the second quadrat, calculating the total dry weight of the quadrat and converting the total dry weight into aboveground biomass, establishing a second allometric growth relationship model between the coverage, height and aboveground biomass based on the quadrat coverage, average height and aboveground biomass, matching a corresponding target allometric growth relationship model based on the scale and data conditions of a region to be evaluated, inputting corresponding data of the region to be evaluated into the target allometric growth relationship model, and calculating the aboveground biomass of the region to be evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aboveground biomass evaluation of Spartina alterniflora, and particularly relates to a method and device for evaluating aboveground biomass of Spartina alterniflora based on coverage and height. BACKGROUND

[0002] As an important vegetation of coastal salt marsh, the aboveground biomass of Spartina alterniflora is a core index for measuring regional carbon storage. As a C4 plant with high photosynthetic efficiency, Spartina alterniflora is widely distributed in the east coast of China and has a significant carbon sequestration capacity. Precise evaluation of the aboveground biomass of Spartina alterniflora is the basis for quantifying its carbon sink capacity and supporting carbon cycle research, and has important reference value for ecological management and carbon sink accounting.

[0003] At present, the evaluation of the aboveground biomass of Spartina alterniflora mainly relies on allometric models. Internationally, methods based on plant height have been explored, and in China, power function fitting of the relationship between aboveground and underground biomass has been used, and techniques such as unmanned aerial vehicles and laser radars have been gradually introduced to obtain parameters such as plant height and coverage to assist in improving evaluation efficiency. Some studies also introduce environmental factors such as density and latitude to optimize the model. However, the existing methods have obvious limitations. First, the regional adaptability is insufficient. Foreign models cannot be directly applied to the growth characteristics of Spartina alterniflora in the east coast of China, especially in Zhoushan Islands, and the genetic differences between island and mainland vegetation make it difficult to transfer the existing mainland research results. Second, the precision and universality are difficult to balance. Single-factor models (such as using only plant height) ignore the influence of ecotypes and seasons, while complex models rely on high-cost remote sensing data, and there is a lack of low-cost standardized methods. Third, there is a research gap in island scenarios. There is no effective solution for the evaluation of the aboveground biomass of Spartina alterniflora in Zhoushan Islands and other islands.

[0004] Therefore, there is an urgent need for a method to evaluate the aboveground biomass of Spartina alterniflora that is adapted to island environments and balances precision and operability. SUMMARY

[0005] In view of this, the present application provides a method and device for evaluating the aboveground biomass of Spartina alterniflora based on coverage and height, to achieve the evaluation of the aboveground biomass of Spartina alterniflora that is adapted to island environments and balances precision and operability.

[0006] Specifically, the present application is implemented through the following technical solutions:

[0007] The first aspect of the present application provides a method for evaluating the aboveground biomass of Spartina alterniflora based on coverage and height, which comprises:

[0008] setting a first quadrat in a Spartina alterniflora growth area, collecting all plants in the first quadrat, and measuring the height and dry weight of individual plants;

[0009] Based on the height and dry weight of a single plant, a first allometric growth relationship model between the height and dry weight of a single plant is established.

[0010] A second quadrat was set up in the Spartina alterniflora growing area. The quadrat coverage, average height and number of plants in the second quadrat were recorded. The total dry weight of the quadrat was calculated and converted into aboveground biomass.

[0011] Based on quadrat coverage, average height, and aboveground biomass, a second allometric growth model was established to represent the relationship between coverage, height, and aboveground biomass.

[0012] Based on the scale and data conditions of the area to be estimated, a corresponding target allometric growth model is matched, and the corresponding data of the area to be estimated is input into the target allometric growth model to calculate the aboveground biomass of the area to be estimated.

[0013] A second aspect of this application provides a biomass assessment device for Spartina alterniflora based on coverage and height, the device comprising a data acquisition module, a setup module, and a calculation module;

[0014] The acquisition module is used to set up a first quadrat in the Spartina alterniflora growing area, collect all plants in the first quadrat, and measure the height and dry weight of a single plant.

[0015] The establishment module is used to establish a first allometric growth relationship model between single plant height and single plant dry weight based on single plant height and single plant dry weight.

[0016] The collection module is also used to set up a second quadrat in the Spartina alterniflora growing area, record the quadrat coverage, average height and number of plants in the second quadrat, calculate the total dry weight of the quadrat and convert it into aboveground biomass;

[0017] The establishment module is also used to establish a second allometric growth relationship model between coverage, height and aboveground biomass based on quadrat coverage, average height and aboveground biomass;

[0018] The calculation module is used to match the corresponding target allometric growth relationship model based on the scale and data conditions of the area to be estimated, input the corresponding data of the area to be estimated into the target allometric growth relationship model, and calculate the aboveground biomass of the area to be estimated.

[0019] The application provides a Spartina alterniflora aboveground biomass evaluation method and device based on coverage and height. In the first aspect, a first quadrat is arranged in a Spartina alterniflora growth area, single plant height and single plant dry weight data are collected, and a first allometric growth relationship model is established, so that single plant level allometric analysis is realized. Single plant height and dry weight are the most basic constituent elements of biomass. The first allometric growth relationship model can accurately quantify the relationship between the two (for example, by power function or polynomial fitting), capture individual differences in resource allocation during single plant growth (for example, different plants may have different dry weights at the same height due to genetic or microenvironment differences), provide a direct and personalized tool for single plant biomass estimation, avoid the masking of individual characteristics caused by the averaging of population data, and improve the accuracy of single plant scale biomass estimation. In the second aspect, a second quadrat is arranged to record the coverage, average height and plant number of the quadrat, and a second allometric growth relationship model is established by combining the total dry weight of the quadrat with the aboveground biomass converted from the total dry weight, and the coverage is introduced to realize population level allometric analysis. As an index reflecting the spatial occupation degree of the population, the coverage, together with the average height, can comprehensively describe the growth state of the population (for example, a population with high coverage may have inhibited individual height due to competition but higher overall biomass), solve the problem that the single plant model cannot reflect the influence of individual interaction and spatial distribution on biomass, and extend the model from a single individual to a population level, which is more in line with the actual situation of natural communities. In the third aspect, in specific application, a target allometric growth relationship model is matched based on the scale (for example, single plant distribution in a small area or population distribution in a large area) and data conditions (for example, only single plant height data or population coverage data) of a region to be estimated. This adaptive mechanism ensures that the model input is highly consistent with the actual characteristics of the region. The first allometric growth relationship model is used in a single plant region to fully utilize the individual specificity advantage, the second allometric growth relationship model is used in a population region to exert the population characteristic capturing ability, errors caused by forcibly adapting a single model to all scenarios are avoided, the above-mentioned “tailor-made” model selection makes the aboveground biomass calculation result more accurately reflect the actual situation, and finally, the whole chain optimization from single plant to population and from modeling to application is realized, and the scientificity and practicability of Spartina alterniflora aboveground biomass evaluation are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of a Spartina alterniflora aboveground biomass evaluation method based on coverage and height is provided for the first embodiment of the application.

[0021] Figure 2 A structural schematic diagram of a Spartina alterniflora aboveground biomass evaluation device based on coverage and height is provided for the second embodiment of the application. DETAILED DESCRIPTION

[0022] The exemplary embodiments will be described in detail herein with reference to several drawings. Descriptions of well-known functions and structures are omitted so as not to unnecessarily obscure the application. The examples described herein represent the best

[0023] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0024] It is to be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. It is to be understood that the term "and / or" as used herein encompasses all possible combinations of one or more of the associated listed items and can be abbreviated as "or". It is to be understood that the terms "including", "comprising", "consisting" and "consisting essentially of" when used herein, specify the presence of stated features, integers, steps, operations, elements, components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components that are not expressly mentioned.

[0025] The following specific embodiments are given to further illustrate the technical solutions of the present application.

[0026] Figure 1 A flow chart of the method for evaluating aboveground biomass of Spartina alterniflora based on coverage and height provided by Embodiment One of the present application is shown in FIG. 1. Please refer to FIG. 1. Figure 1 The method provided by the present embodiment can include:

[0027] S101, setting a first quadrat in a Spartina alterniflora growth area, collecting all plants in the first quadrat, and measuring the height and dry weight of individual plants.

[0028] Specifically, the Spartina alterniflora growth area refers to an ecological environment such as a coastal salt marsh or an intertidal zone where Spartina alterniflora plants are naturally or artificially distributed. These areas need to meet the following conditions: Spartina alterniflora forms a stable community, and can reflect the spatial heterogeneity of its growth state (such as differences in distribution of different densities and heights), providing a representative research carrier for subsequent quadrat setting and data collection. The height of an individual plant refers to the vertical distance from the base (the point of contact with the ground) to the top of the plant (including the inflorescence if there is one), which is a core morphological parameter reflecting the growth state of an individual plant. The dry weight of an individual plant refers to the remaining weight after drying and removing all water from a single Spartina alterniflora plant, which is a key indicator directly reflecting the biomass (carbon accumulation) of an individual plant.

[0029] It should be noted that the above-ground biomass to be evaluated by the present application is essentially the total dry weight of plants per unit area, which is associated with the individual-group quantification of single plant dry weight. Specifically, single plant dry weight refers to the weight of a single Spartina alterniflora plant after drying to constant weight (i.e. actual biomass after removing water), which is the basic unit of above-ground biomass. Above-ground biomass is obtained by converting single plant dry weight to total dry weight of the sample plot, and then dividing by the area of the sample plot. In short, above-ground biomass is the integrated result of single plant dry weight at the group scale and area dimension, and accurate measurement of single plant dry weight is the core prerequisite for ensuring the accuracy of above-ground biomass evaluation.

[0030] In a specific implementation, the first sample plot is set in the Spartina alterniflora growth area, and all plants in the first sample plot are collected by: dividing the Spartina alterniflora growth area based on coverage and height to obtain a plurality of first areas; randomly setting a plurality of identical first sample plots in each first area, and dynamically adjusting the spacing between different first sample plots according to the spatial distribution of Spartina alterniflora in the Spartina alterniflora growth area; recording the center coordinates of each first sample plot and the coverage of Spartina alterniflora in the first sample plot.

[0031] Specifically, according to the coverage (e.g. coverage ≥ 50% or < 50%) and height (e.g. height ≥ 110 cm or < 110 cm) of plants in the Spartina alterniflora growth area, the area is divided into a plurality of first areas (e.g. areas with high coverage and high height, areas with high coverage and low height, areas with low coverage and low height, areas with low coverage and low height); in each first area after division, a plurality of (e.g. 3 to 5) first sample plots (e.g. 1m×1m sample plots) of the same size are randomly arranged, and the spacing between sample plots is dynamically adjusted according to the spatial distribution density of Spartina alterniflora in the area. If the plant distribution is dense, the sample plot spacing can be appropriately reduced (e.g. 2-3m); if the distribution is sparse, the sample plot spacing can be increased (e.g. 5-10m), i.e. the sample plot spacing is negatively correlated with the spatial distribution density. Further, the center coordinates of each first sample plot are recorded using a positioning tool, and the coverage of Spartina alterniflora in each first sample plot is recorded by visual estimation or grid method.

[0032] Optionally, the measuring individual plant height and individual plant dry weight comprises: within each first quadrat grid, measuring individual Spartina alterniflora based on a scale measuring rod perpendicular to the ground to obtain a first height value, and synchronously recording the relative position and temporary identification of the plant in the first quadrat; putting all individual Spartina alterniflora in the first quadrat into sample bags with unique numbers according to the temporary identification; in a laboratory environment, retrieving the field measurement records of the corresponding plants according to the sample bag numbers, measuring the height of each plant and classifying to obtain a second height value; the accuracy of the second height value is higher than that of the first height value; comparing the first height value and the second height value of the same plant Spartina alterniflora to form a height measurement deviation statistics, and taking the second height value as the core parameter of the individual plant allometric relationship model, while retaining the first height value as reference data; placing the individual plant Spartina alterniflora after height measurement in an oven to dry to a constant weight, weighing the dried individual plant Spartina alterniflora to record the individual plant dry weight data.

[0033] In specific implementation, within each first quadrat grid, a scale measuring rod perpendicular to the ground is placed close to the individual plant Spartina alterniflora, the vertical height is read from the base to the top of the plant to obtain a first height value; at the same time, marks are made on the edge of the quadrat with a marker pen to record the relative position of the plant in the quadrat (such as "30 cm from the left side of the quadrat, 25 cm from the bottom"), and a temporary identification is assigned to the plant (such as "Quadrat 1-Plant 5"). The individual plant Spartina alterniflora in the first quadrat is taken out from the soil in order according to the temporary identification and placed into a sample bag with a unique number (such as "Quadrat 1-Bag 005") and the temporary identification is marked on the surface of the sample bag; at the same time, the corresponding relationship of "quadrat number-temporary identification-sample bag number" is registered in the record table. Further, after transporting the sample bag to the laboratory, the field measurement records (including the first height value and the relative position) of the corresponding plant are retrieved according to the sample bag number; the height of each plant is measured using a more accurate measuring tool (such as a vernier caliper) (the measurement method is consistent with that in the field, i.e. from the base to the top), a second height value is obtained; the plants are classified according to whether they have inflorescences and growth status (such as whether they are damaged), and the classification results and the second height value are recorded respectively. The first height value and the second height value of the same plant are recorded in a data table, the difference (second height value-first height value) and the deviation rate (deviation value / second height value x 100%) are calculated to form a height measurement deviation statistics result; it is determined that the second height value is the core parameter of the individual plant allometric relationship model, and the first height value is retained as reference data for subsequent cross-validation with laboratory data. The individual plant Spartina alterniflora after height measurement is placed in an oven in the order of the sample bag number, the drying temperature (such as 80°C) is set, and the drying is continued until the weight difference of two consecutive weighings (interval of 2 hours) is less than 0.01g (constant weight standard is reached); the dried plant is taken out, cooled to room temperature, weighed with an electronic balance, and the individual plant dry weight data of the corresponding sample bag number is recorded after reading the weight value.

[0034] S102, based on the single plant height and the single plant dry weight, a first allometric growth relationship model of the single plant height and the single plant dry weight is established.

[0035] Specifically, the first allometric growth relationship model refers to a correlation model established based on the single plant height (taking the second height value as a core parameter) and the single plant dry weight. Specifically, the first allometric growth relationship model is to collect all the single plant height data and the corresponding single plant dry weight data of the Spartina alterniflora as basic data, and to construct a mathematical relationship model between the two by statistical methods, so as to reflect the quantitative corresponding relationship between the single plant height and the single plant dry weight.

[0036] In a specific implementation, the first allometric growth relationship model of the single plant height and the single plant dry weight is established based on the single plant height and the single plant dry weight, including: dividing all plants into a flowered group and a non-flowered group according to whether they have inflorescences; for the two groups of plants, a power function model is obtained by fitting the single plant height and the single plant dry weight using a logarithmic conversion power function form; the power function model includes a flowered group growth relationship model and a non-flowered group growth relationship model; the power function model is verified based on a polynomial regression equation, and the verification results of the two groups of models are combined to determine the first allometric growth relationship model.

[0037] Optionally, the power function model is verified based on the polynomial regression equation, the verification results of the two groups of models are combined, and the first allometric growth relationship model is determined, including: for the flowered group and the non-flowered group, a polynomial model is established respectively; the verification indexes of the power function model and the polynomial model are calculated respectively; the verification indexes include a determination coefficient and an Akaike information criterion; if the determination coefficient of the power function model is higher than that of the polynomial model, and the Akaike information criterion of the power function model is lower than that of the polynomial model, the power function model is determined as the first allometric growth relationship model; if the determination coefficient of the polynomial model is higher than that of the power function model and the difference between the determination coefficients is greater than a preset threshold, the polynomial model is determined as the first allometric growth relationship model.

[0038] In a specific implementation, all the obtained individual Spartina alterniflora plants are classified, and it is determined by visual observation whether each plant has an inflorescence. The plants with the inflorescence are classified into the inflorescence group, and the plants without the inflorescence are classified into the non-inflorescence group. The height (second height value) and the dry weight of each plant in each group are recorded. For the inflorescence group, the height and the dry weight of all the plants in the group are logarithmically converted, and the non-inflorescence group is logarithmically converted in the same manner. For the converted data of the inflorescence group, a power function model, i.e., the growth relationship model of the inflorescence group, is fitted by linear regression using the least square method. The converted data of the non-inflorescence group is fitted in the same manner to obtain the growth relationship model of the non-inflorescence group. The growth relationship model of the inflorescence group and the growth relationship model of the non-inflorescence group can be expressed as follows:

[0039] ;

[0040] wherein the dry weight of the i th plant is denoted as W i, the height of the i th plant is denoted as H i, the intercept term (initial biomass on the logarithmic scale) is denoted as a, and the allometric growth index is denoted as b.

[0041] Further, a polynomial model is established based on the height and the dry weight of the individual plants in the inflorescence group. The non-inflorescence group is established in the same manner based on the data of the group to establish a polynomial model.

[0042] The polynomial model can be expressed as follows:

[0043] ;

[0044] wherein the dry weight of the individual Spartina alterniflora plant is denoted as W, the height of the individual Spartina alterniflora plant is denoted as H, and the constants are denoted as a, b, c, d, and e.

[0045] ​​​​​​​​​​​​​Further, for the inflorescence group, the determination coefficients and the Akaike information criteria of the power function model and the polynomial model of the group are calculated respectively. The calculation method of the determination coefficient can be referred to the description in the related art, which will not be described here. The inflorescence group is calculated by the same method as described above, and the determination coefficients and the Akaike information criteria of the power function model and the polynomial model of the group are calculated respectively. If the determination coefficient of the power function model of the inflorescence group is higher than the determination coefficient of the polynomial model, and the Akaike information criterion of the power function model is lower than the Akaike information criterion of the polynomial model, the power function model of the inflorescence group is determined as the corresponding sub-model of the group. If the determination coefficient of the polynomial model of the inflorescence group is higher than the determination coefficient of the power function model, and the difference between the two determination coefficients is greater than a preset threshold (such as 0.05), the polynomial model of the inflorescence group is determined as the corresponding sub-model of the group. The model of the inflorescence group is determined according to the same rule as described above, and the determination coefficients, the Akaike information criteria and the determination coefficient difference of the power function model and the polynomial model of the group are compared to determine the corresponding sub-model of the inflorescence group. The two groups of determined sub-models are combined to form a first allometric growth relationship model.

[0046] The method provided by the embodiment is grouped according to the presence or absence of inflorescences. There is a significant difference in biomass allocation between plants with inflorescences and plants without inflorescences. Plants with inflorescences allocate part of the photosynthetic products to flower organs, resulting in different dry weight compositions at the same height from plants without inflorescences. After grouping, the respective “height-dry weight” correlation patterns of the two groups of data (for example, the dry weight of plants with inflorescences at the same height may be higher) are not disturbed by the data of the other group, avoiding the averaging of model parameters when fitting mixed data, so that the two initial models (power function models of the inflorescence group and the inflorescence-free group) can more accurately correspond to the biomass characteristics of the respective growth stages. Second, the power function is fitted by using logarithmic conversion for the first time. The power function has natural adaptability to the nonlinear correlation between “morphological parameters (height) and biomass (dry weight)” in allometric growth research. The logarithmic conversion can convert the nonlinear relationship of the power function into linear regression. This conversion can simplify the parameter calculation process and make the fitting results more consistent with the general law of biomass growth with height (for example, slow growth at the beginning and accelerated growth at the end), ensuring the basic reliability of the initial model. Third, the second verification is based on the polynomial model. The polynomial model (the polynomial can capture more complex curve trends) has sensitive fitting ability for data details. Objective screening is achieved by calculating specific verification indicators. The coefficient of determination directly reflects the degree of explanation of the model for the actual data (the higher the value, the closer the predicted value of the model to the measured dry weight). The Akaike information criterion balances the simplicity of the model (avoiding excessive complexity) by the number of parameters (fewer parameters for the power function and more parameters for the polynomial). If the coefficient of determination of the power function is higher and the Akaike information criterion is lower, it means that the power function can accurately reflect the correlation while being simpler (fewer parameters and easier to apply), which is suitable as the final model. If the coefficient of determination of the polynomial is significantly higher (the difference exceeds the preset threshold), it means that there is a complex trend (for example, a sudden change in dry weight at a certain height interval) in the data that the power function cannot capture. At this time, the polynomial is selected to avoid missing the key correlation. This “group fitting + secondary verification” process not only makes the model fit the biological characteristics of different growth stages by grouping (solving the problem of data heterogeneity), but also selects the model by quantitative indicators rather than subjective judgment (avoiding human bias). The final first allometric growth relationship model can accurately correspond to the growth state of a single plant (with or without inflorescences) and balance the fitting accuracy and practicality, providing a reliable basic tool for subsequent aboveground biomass estimation.

[0047] Optionally, the method for calculating the Akaike information criterion comprises: determining the number of unknown parameters in the to-be-verified model; calculating a maximum likelihood function value based on the sample size and the sum of squared residuals of the to-be-verified model; taking the natural logarithm of the maximum likelihood function value, and then multiplying the logarithmic result by a preset value to obtain a first calculation value; and subtracting the first calculation value from the number of unknown parameters to obtain the Akaike information criterion.

[0048] Specifically, the Akaike Information Criterion (AIC) is a metric used to measure the goodness of fit of a statistical model, and its core function is to seek a balance between the model's fitting accuracy and complexity. In this application, the Akaike Information Criterion is used to evaluate and compare the performance of power function models and polynomial models.

[0049] In practice, the number of unknown parameters in the model to be verified is first determined (for example, a power function model has 2 parameters). and (While polynomial models have multiple parameters), the more parameters there are, the higher the model complexity. The maximum likelihood function value (L) is calculated based on the sample size (n) and the sum of squared residuals (RSS) of the model to be validated. Under the assumption of normal distribution, the maximum likelihood function value can be expressed as:

[0050] = (2π•RSS / n)^(-n / 2)•e^(-n / 2);

[0051] Among them, the The maximum likelihood function value is given; the RSS is the sum of squared residuals; and n is the sample size of the model to be validated.

[0052] Furthermore, for the maximum likelihood function value ( Take the natural logarithm (ln( Multiply this by 2 (the preset value) to get the first calculated value; subtract the first calculated value from the number of unknown parameters of the model to be verified, i.e.:

[0053] ;

[0054] Among them, the The Akaike Information Criteria; The number of unknown parameters; This represents the maximum likelihood function value.

[0055] S103. Set up a second quadrat in the Spartina alterniflora growing area, record the quadrat coverage, average height and number of plants in the second quadrat, calculate the total dry weight of the quadrat and convert it into aboveground biomass.

[0056] Specifically, the sample coverage refers to the proportion of the horizontal area covered by the aboveground part (such as stems, leaves, etc.) of the Spartina alterniflora plants in the second sample to the total area of the sample, which is used to reflect the spatial distribution density of the Spartina alterniflora in the sample. The average height refers to the average height calculated by measuring the height of all Spartina alterniflora plants in the second sample (i.e. the sum of the heights of all plants divided by the number of plants), which is used to reflect the overall height level of the Spartina alterniflora in the sample. The number of plants refers to the total number of individual Spartina alterniflora in the second sample, which directly reflects the number of individual Spartina alterniflora in the sample. The total dry weight of the sample refers to the total weight in the dry state obtained by drying (removing water) after collecting the aboveground part (such as stems, leaves, inflorescences, etc.) of all Spartina alterniflora plants in the second sample, which is a direct quantitative result of the aboveground biomass in the sample.

[0057] Further, the total dry weight of the sample after unit conversion (such as conversion to dry weight per unit area) is the aboveground biomass corresponding to the sample. Both are essentially different forms of the same index, and the total dry weight of the sample is the core basis data for calculating the aboveground biomass.

[0058] In specific implementation, the second sample is set in the growth area of Spartina alterniflora, and the sample coverage, average height and number of plants in the second sample are recorded, which includes: selecting multiple target areas in the growth area of Spartina alterniflora after the first mowing; setting sample plots for each target area; the number of sample plots in different target areas is different; randomly setting multiple second samples in each sample plot, and uniformly numbering all second samples; sampling in different months after setting is completed, and recording the coverage, average height and number of plants of Spartina alterniflora in each second sample at each sampling time point, and associating the corresponding sample number and sampling season; wherein, the sampling in the target sampling season needs to avoid the growth area mowed for the second time.

[0059] Specifically, in the growth area where the Spartina alterniflora completes the first mowing (such as 1-2 weeks after mowing, the plant enters the regeneration stage), multiple target areas are selected according to terrain differences (such as nearshore zone, mid-tidal zone, farshore zone) or coverage gradient (such as 30%-50%, 50%-70%, more than 70%). Ensure that each target area can reflect different growth environments. Set a sample plot for each target area, and the sample plot area is uniform (such as 10m x 10m), and the number of sample plots in different target areas is different (such as 5 sample plots in area A with high coverage, and 3 sample plots in areas B, C, and D with low coverage). In each sample plot, multiple second quadrats are arranged using random sampling method (such as 12 0.5m x 0.5m quadrats per sample plot), all second quadrats are uniformly numbered, the numbering rule includes target area, sample plot and quadrat number (such as "target area C - sample plot 2 - quadrat 04"), and the specific position of each quadrat is recorded (such as "2m x 3m from the northwest corner" in the sample plot). Determine the sampling month (such as May, July, and October, corresponding to spring, summer, and autumn respectively), and sample in each target month after setting is completed, and each sampling is carried out in the same time period (such as the middle of the month). At each sampling time point, the second quadrats are observed on site: for the coverage of the quadrat, the grid method is used (such as dividing the quadrat into multiple small grids, and counting the proportion of small grids covered by plants); for the average height, 10 plants in the quadrat are randomly selected for height measurement (if there are less than 10 plants, all plants are measured), the average value is calculated and recorded; for the number of plants, all surviving plants in the quadrat are counted one by one, and the total number is recorded; at the same time, the corresponding quadrat number (such as "target area 3 - sample plot 2 - quadrat 04") and sampling season (such as "May 2024 - spring") are recorded in the record table. Before sampling, confirm the mowing state of the area through on-site investigation, if a certain area has been mowed for the second time (such as marked with mowing marks and plant height less than 5cm), the sample plots in the area and within a range of 5m are excluded from this sampling.

[0060] S104, based on the coverage of the quadrat, the average height and the aboveground biomass, a second allometric relationship model of coverage, height and aboveground biomass is established.

[0061] Specifically, the second allometric relationship model is a mathematical relationship model established by statistical analysis, taking the coverage and average height of Spartina alterniflora quadrat as independent variables, and taking the aboveground biomass as dependent variable. The core of the second allometric relationship model is to determine the quantitative correlation between the three indexes based on the "quadrat coverage", "average height" data obtained in the second quadrat in the early stage, and the "aboveground biomass" data converted from the total dry weight of the quadrat, and finally form a formula or model that can directly estimate the aboveground biomass through coverage and average height.

[0062] In the implementation, the second allometric relationship model of coverage, height and aboveground biomass is established based on the quadrat coverage, average height and aboveground biomass, comprising: matching the collected quadrat coverage, average height and aboveground biomass data of the corresponding quadrat, and forming data sets respectively according to seasons; using a multiple regression form containing height-only effect, coverage-only effect and height-coverage interaction effect, a three-dimensional relationship model is constructed; the parameter fitting is performed on each seasonal data set, and the coefficients of each effect term in the three-dimensional relationship model are determined by the least square method to obtain the second allometric relationship model.

[0063] Specifically, the quadrat coverage and average height data of each second quadrat are matched one by one with the aboveground biomass data of the corresponding quadrat converted from the total dry weight of the quadrat, and the association is established in the data record table through the quadrat number (such as "A area-quadrat 1-quadrat 01") and the sampling season (such as May, July and October); the matched data are classified and arranged according to the sampling season to form May data set, July data set and October data set, each of which contains the coverage, average height and aboveground biomass data of all effective quadrats in the season. Further, a three-dimensional relationship model is constructed using a multiple regression form, and the specific form of the three-dimensional relationship model is set as:

[0064] ;

[0065] Among them, the total aboveground biomass of a quadrat is represented by W, the average height of a quadrat is represented by h, the coverage of a quadrat is represented by C, the intercept is represented by a, the regression coefficients are represented by b, c and d, the height-only effect term is represented by h, the coverage-only effect term is represented by C, and the height-coverage interaction effect term is represented by hC.

[0066] For each seasonal data set, the aboveground biomass in the data set is used as the dependent variable, and the average height, coverage and product of the average height and coverage are used as the independent variables. The specific values of the unknown parameters in the model of the season are determined by using a data processing tool to calculate by the least square method. The specific values calculated are substituted into the three-dimensional relationship model to obtain the second allometric relationship model of coverage, height and aboveground biomass corresponding to each seasonal data set.

[0067] ​​​​​​​​​​S105, matching a corresponding target allometric relationship model based on the scale of the region to be estimated and data conditions, inputting corresponding data of the region to be estimated into the target allometric relationship model, and calculating to obtain aboveground biomass of the region to be estimated.

[0068] Specifically, the target allometric relationship model refers to a specific model suitable for aboveground biomass estimation of the region to be estimated, which is filtered from the established allometric relationship models (including the first allometric relationship model and the second allometric relationship model) according to the scale (such as spatial range size, sampling accuracy requirement, etc.) and data conditions (such as the types of available basic data, such as coverage, height, etc.) of the region to be estimated. For example, if the sampling data of the region to be estimated contains coverage, average height and corresponds to a certain season, the target model can be the second allometric relationship model of the season (coverage-height-aboveground biomass relationship model).

[0069] In a specific implementation, the matching of the corresponding target allometric relationship model based on the scale of the region to be estimated and the data conditions comprises: determining a corresponding model filtering method based on the area of the region to be estimated and the number of Spartina alterniflora plants; when the model filtering method is single plant, determining a corresponding first target allometric relationship model based on whether the plant has an inflorescence; when the model filtering method is group, determining a corresponding second target allometric relationship model based on the sampling season; when the model filtering method is a mixture of single plant and group, respectively calculating the first target allometric relationship model and the second target allometric relationship model, determining a weight based on the area proportion of the single plant region and the group region, and performing weighted summation on the two types of target allometric relationship models based on the weight to obtain the target allometric relationship model.

[0070] Specifically, the actual area of the to-be-estimated region and the total number of Spartina alterniflora plants in the region are counted, and a corresponding model screening method is determined according to the numerical characteristics of the two values. If the area of the region is small and the number of plants is small (such as only a small amount of single plant biomass needs to be estimated), the model screening method is determined as "single plant"; if the area of the region is large and the number of plants is large (such as the biomass of the whole sample plot needs to be estimated), the model screening method is determined as "group"; if part of the region in the region is distributed by a small amount of single plant and part of the region is densely distributed by group, the model screening method is determined as "mixed single plant and group". When the model screening method is single plant, whether the target plant in the to-be-estimated region has an inflorescence is observed one by one. If the plant has an inflorescence, the growth relationship model of the inflorescence group in the first allometric growth relationship model is determined as the first target allometric growth relationship model; if the plant has no inflorescence, the growth relationship model of the inflorescence-free group in the first allometric growth relationship model is determined as the first target allometric growth relationship model. When the model screening method is group, the sampling season corresponding to the to-be-estimated region (such as May, July, October) is determined, and the three-dimensional relationship model corresponding to the season is selected from the second allometric growth relationship model as the second target allometric growth relationship model. When the model screening method is mixed single plant and group, the first target allometric growth relationship model is determined according to the single plant screening rule, and the second target allometric growth relationship model is determined according to the group screening rule. The area of the single plant distribution region and the area of the group distribution region in the to-be-estimated region are measured, and the proportions of the two in the total area of the to-be-estimated region (i.e. the single plant area proportion and the group area proportion) are calculated. The two proportions are used as the weights of the corresponding models. The estimated result of the first target allometric growth relationship model is multiplied by the single plant area proportion, and the estimated result of the second target allometric growth relationship model is multiplied by the group area proportion. The two products are added together, and the result is the final target allometric growth relationship model.

[0071] Further, the corresponding data (such as average height, quadrat coverage, plant number, etc.) of the to-be-estimated region are input into the selected target allometric growth relationship model, and the target allometric growth relationship model processes the input data to obtain the aboveground biomass of the to-be-estimated region.

[0072] The method provided by the embodiment, in the first aspect, realizes the allometric analysis of the single plant by collecting the height and dry weight of the single plant in the first sample plot, fitting by the power function of the logarithmic conversion after grouping according to the presence or absence of the inflorescence, and then performing secondary verification by the polynomial regression combined with the determination coefficient and the Akaike information criterion. The presence of the inflorescence changes the resource allocation of the plant, and the grouping fitting avoids the model distortion caused by the mixing of data at different growth stages (with / without inflorescence), the power function fitting adapts to the nonlinear correlation of the allometric growth, the logarithmic conversion simplifies the parameter calculation, and the polynomial verification filters the optimal model through the quantitative indicators (the determination coefficient reflects the explanatory power, and the Akaike information criterion balances the fitting degree and simplicity), and finally the first allometric growth relationship model can accurately capture the internal correlation between the height and the dry weight of the single plant, and the estimation accuracy of the single plant biomass is improved. In the second aspect, the multi-regression model containing the height, the coverage and the interaction effect between the height and the coverage is established by recording the coverage and the average height in the second sample plot and sampling by seasons, and the coverage is introduced to realize the allometric analysis at the population level by seasons. The coverage reflects the spatial density of the population, and in combination with the average height, the growth state of the population (for example, the population with high coverage may be suppressed in height but has high total biomass) can be fully described, the modeling by seasons adapts to the growth difference in different seasons, and the interaction effect term captures the synergistic effect of the height and the coverage, so that the second allometric growth relationship model can reflect the influence of the spatial distribution and the seasonal dynamics at the population level on the biomass, and the problem that the single plant model cannot reflect the population characteristics is solved. In the third aspect, in the application, the model screening method (single plant, population, and mixed) is determined based on the area and the number of plants of the region to be estimated, the first model is matched according to the presence or absence of the inflorescence in the single plant scene, the second model is matched according to the season in the population scene, and the weighted sum is calculated according to the area ratio in the mixed scene. This matching mechanism makes the model highly consistent with the actual characteristics of the region - the individual specificity of the first model is used in the single plant scene, the population characteristic capturing ability of the second model is used in the population scene, and the advantages of the two types of models are integrated by weighting in the mixed scene, thereby avoiding the error of a single model adapting to all scenes, and finally the aboveground biomass estimation result is more consistent with the actual situation, and the prediction accuracy is significantly improved.

[0073] Corresponding to the foregoing embodiment of the aboveground biomass evaluation method of Spartina alterniflora based on coverage and height, the application also provides an embodiment of an aboveground biomass evaluation device of Spartina alterniflora based on coverage and height.

[0074] Figure 2 The structural schematic diagram of the aboveground biomass evaluation device of Spartina alterniflora based on coverage and height provided by the second embodiment of the application is shown in FIG. 2. Figure 2 The device provided by the embodiment includes an acquisition module 210, an establishment module 220 and a calculation module 230.

[0075] The acquisition module 210 is configured to set a first sample plot in a Spartina alterniflora growth region, collect all plants in the first sample plot, and measure the height and dry weight of the single plant.

[0076] The establishing module 220 is configured to establish a first allometric growth relationship model of single plant height and single plant dry weight based on the single plant height and the single plant dry weight.

[0077] The collecting module 210 is further configured to set a second quadrat in a growth area of Spartina alterniflora, record quadrat coverage, average height and plant quantity in the second quadrat, calculate total dry weight of the quadrat and convert the total dry weight into aboveground biomass.

[0078] The establishing module 220 is further configured to establish a second allometric growth relationship model of coverage, height and aboveground biomass based on the quadrat coverage, the average height and the aboveground biomass.

[0079] The calculating module 230 is configured to match a corresponding target allometric growth relationship model based on a scale and data conditions of a region to be estimated, input corresponding data of the region to be estimated into the target allometric growth relationship model, and calculate aboveground biomass of the region to be estimated.

[0080] The device of the embodiment can be used to execute Figure 1 The steps of the method embodiment are similar to the specific implementation principles and implementation processes, and thus will not be described here.

[0081] The implementation processes of the functions and roles of the units in the above device are specifically described in the implementation processes of the corresponding steps in the above method, and thus will not be described here.

[0082] For the device embodiment, since it basically corresponds to the method embodiment, the related parts can be understood by referring to the part of the method embodiment. The device embodiment described above is only illustrative, and the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Some or all of the modules can be selected to achieve the purposes of the application scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0083] The above is only the preferred embodiment of the application, and is not used to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for evaluating above-ground biomass of Spartina alterniflora based on coverage and height, characterized in that, The method comprises: setting a first quadrat in a Spartina alterniflora growth area, collecting all plants in the first quadrat, measuring the height and dry weight of individual plants; based on the height and dry weight of individual plants, establishing a first allometric growth relationship model between the height and dry weight of individual plants; setting a second quadrat in the Spartina alterniflora growth area, recording the quadrat coverage, average height and plant number in the second quadrat, calculating the total dry weight of the quadrat and converting it into aboveground biomass; based on the quadrat coverage, average height and aboveground biomass, establishing a second allometric growth relationship model between the coverage, height and aboveground biomass; based on the scale and data conditions of the region to be estimated, matching the corresponding target allometric growth relationship model, inputting the corresponding data of the region to be estimated into the target allometric growth relationship model, and calculating the aboveground biomass of the region to be estimated; wherein, based on the area and the number of Spartina alterniflora plants in the region to be estimated, a corresponding model screening method is determined; when the model screening method is individual plant, based on whether the plant has an inflorescence, a corresponding first target allometric growth relationship model is determined; when the model screening method is group, based on the sampling season, a corresponding second target allometric growth relationship model is determined; when the model screening method is a mixture of individual plants and groups, the first target allometric growth relationship model and the second target allometric growth relationship model are calculated respectively, the weight is determined based on the area proportion of individual plant area and group area, and the two types of target allometric growth relationship models are weighted and summed based on the weight to obtain the target allometric growth relationship model.

2. The method of claim 1, wherein, The method comprises: all plants are divided into an inflorescence group and a non-inflorescence group according to whether they have inflorescences; for the two groups of plants, a power function model is obtained by fitting the height and dry weight of individual plants using a logarithmic conversion power function form; the power function model comprises an inflorescence group growth relationship model and a non-inflorescence group growth relationship model; based on a polynomial regression equation, the power function model is verified, and the verification results of the two groups of models are combined to determine the first allometric growth relationship model.

3. The method of claim 2, wherein, The method comprises: for the inflorescence group and the non-inflorescence group, a polynomial model is established respectively; verification indexes of the power function model and the polynomial model are calculated respectively; the verification indexes comprise a determination coefficient and an AIC; if the determination coefficient of the power function model is higher than that of the polynomial model, and the AIC of the power function model is lower than that of the polynomial model, the power function model is determined as the first allometric growth relationship model; if the determination coefficient of the polynomial model is higher than that of the power function model and the difference between the determination coefficients is greater than a preset threshold, the polynomial model is determined as the first allometric growth relationship model.

4. The method of claim 1, wherein, The method comprises: Match the collected coverage, average height and aboveground biomass data of the corresponding sample plot to form a data set by season; A three-dimensional relationship model is constructed by using a multiple regression form containing height-only effect, coverage-only effect and height-coverage interaction effect; The coefficients of each effect term in the three-dimensional relationship model are determined by least squares method through parameter fitting of each seasonal data set, and a second allometric growth relationship model is obtained.

5. The method of claim 1, wherein, The first sample plot is set in the Spartina alterniflora growth area, and all plants in the first sample plot are collected, including: The Spartina alterniflora growth area is divided based on coverage and height to obtain a plurality of first regions; A plurality of identical first sample plots are randomly set in each first region, and the spacing between different first sample plots is dynamically adjusted according to the spatial distribution of Spartina alterniflora in the Spartina alterniflora growth area; The center coordinates of each first sample plot and the coverage of Spartina alterniflora in the first sample plot are recorded.

6. The method of claim 1, wherein, The single plant height and single plant dry weight are measured, including: In each first sample plot grid, the single Spartina alterniflora plant is measured based on a scale measuring rod perpendicular to the ground to obtain a first height value, and the relative position and temporary identification of the plant in the first sample plot are recorded synchronously; All single Spartina alterniflora plants in the first sample plot are loaded into sample bags with unique numbers according to the temporary identification; In a laboratory environment, the field measurement records of the corresponding plants are retrieved according to the sample bag number, the height of each plant is measured and classified to obtain a second height value; the accuracy of the second height value is higher than that of the first height value; The first height value and the second height value of the same Spartina alterniflora plant are compared to form a height measurement deviation statistics, and the second height value is used as the core parameter of the single plant scale modeling, while the first height value is used as the reference data; The single Spartina alterniflora plants with measured height are placed in an oven for drying to constant weight, and the single Spartina alterniflora plants after drying are weighed to record the single plant dry weight data.

7. The method of claim 1, wherein, The second sample plot is set in the Spartina alterniflora growth area, and the sample plot coverage, average height and plant number in the second sample plot are recorded, including: After the first mowing of Spartina alterniflora, a plurality of target regions are selected in the growth area; A sample plot is set for each target region; the number of sample plots in different target regions is different; A plurality of second sample plots are randomly set in each sample plot, and all second sample plots are numbered uniformly; Quarterly sampling is carried out in different months after the setting is completed, and at each sampling time point, the coverage, average height and plant number of Spartina alterniflora in each second sample plot are recorded, and the corresponding sample plot number and sampling season are associated; wherein, the sampling in the target sampling season needs to avoid the growth area mowed for the second time.

8. The method of claim 3, wherein, The calculation method of the Akaike information criterion includes: Determine the number of unknown parameters in the to-be-verified model; Based on the sample size and residual sum of squares of the to-be-verified model, the maximum likelihood function value is calculated; Take the natural logarithm of the maximum likelihood function value, and then multiply the logarithmic result by a preset value to obtain a first calculation value; Subtract the first calculation value from the number of unknown parameters to obtain the Akaike information criterion.

9. A device for evaluating above-ground biomass of Spartina alterniflora based on coverage and height, characterized in that, The device includes an acquisition module, an establishment module and a calculation module; The collection module is configured to set a first quadrat in a Spartina alterniflora growing area, collect all plants in the first quadrat, and measure the height and dry weight of each plant; The establishment module is configured to establish a first allometric growth relationship model between the height and dry weight of each plant based on the height and dry weight of each plant; The collection module is further configured to set a second quadrat in the Spartina alterniflora growing area, record the quadrat coverage, average height and plant number in the second quadrat, calculate the total dry weight of the quadrat and convert the total dry weight into aboveground biomass; The establishment module is further configured to establish a second allometric growth relationship model between the coverage, height and aboveground biomass based on the quadrat coverage, average height and aboveground biomass; The calculation module is configured to match a corresponding target allometric growth relationship model based on the scale and data conditions of a region to be estimated, input corresponding data of the region to be estimated into the target allometric growth relationship model, and calculate the aboveground biomass of the region to be estimated. The model screening method is determined based on the area and the number of Spartina alterniflora plants in the region to be estimated; when the model screening method is single plant, a first target allometric growth relationship model is determined based on whether the plant has an inflorescence; when the model screening method is group, a second target allometric growth relationship model is determined based on the sampling season; when the model screening method is a mixture of single plants and groups, the first target allometric growth relationship model and the second target allometric growth relationship model are calculated respectively, a weight is determined based on the area proportion of the single plant region and the group region, and the two types of target allometric growth relationship models are weighted and summed based on the weight to obtain a target allometric growth relationship model.

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