Method for measuring diameter at breast height of cunninghamia lanceolata by using laser

Abstract

The invention provides a method for measuring the diameter at breast height of cunninghamia lanceolata by using laser radar data of an unmanned aerial vehicle, belongs to the field of data measurement, and develops a single tree diameter at breast height (DBH) model suitable for large-scale application for a cunninghamia lanceolata forest by using ground measurement data of trees and corresponding aviation laser radar data. The contribution of stand density and tree features to improvement of the DBH model is evaluated. An optimal combination of virtual variables and stochastic effects is selected using the Akeke Information Criterion (AIC) and error metrics. The prediction performance of the model is evaluated by using an independent data set. The forest stand competition degree and the crown breadth index are added into the DBH model, so that the model fitting statistical magnitude can be improved. The model containing virtual variables to distinguish growth stages is superior to a basic model, and the model introducing a random effect to consider the level difference between the region and the sample plot further improves the prediction precision. When the method is applied to airborne laser radar data, the method can be suitable for researching individual tree characteristics of other species on a large spatial scale.

Description

Technical Field

[0001] The invention relates to the field of data measurement, and in particular to a method for measuring the breast diameter of a Chinese fir tree by using unmanned aerial vehicle laser radar data. Background Art

[0002] In recent years, due to the impact of climate change, forests have once again attracted the attention of scholars due to their carbon sink function and role in regulating climate. DBH is crucial to forest management and is a basic variable for estimating key forest indicators such as tree stock, biomass and carbon storage.

[0003] Traditional methods for measuring DBH include using simple manual tools such as calipers, which are accurate but inefficient and gradually fail to meet the needs of current forest resource assessment. With the development of remote sensing technology, new survey tools such as satellite remote sensing, visible light cameras and lidar have solved the shortage of manpower and time in manual surveys due to their high efficiency and repeatability. Satellite remote sensing is equipped with multiple sensors and can conduct long-term and large-scale fixed-point monitoring, which has significant advantages in analyzing forest changes and species distribution. Visible light cameras are good at image acquisition and species recognition, and can further obtain forest information through image recognition. Lidar is better at capturing spatial structural information and has obvious advantages in understanding forest structure and estimating forest indicators. Scholars have applied lidar to estimate stand indicators, and its application in predicting single tree indicators has also made some progress. However, due to the working mechanism of lidar, although handheld or backpack lidar can improve the efficiency of DBH surveys, it requires a relatively clean understory environment or is limited to small-scale surveys. In addition, airborne lidar cannot directly measure DBH, which limits its full potential of large-scale and high-efficiency lidar, indicating that large-scale DBH measurement methods still need further exploration.

[0004] According to different forest management needs, DBH can be divided into stand-level DBH and single-tree DBH. The DBH at the stand level focuses on understanding the forest from a macroscopic perspective in order to formulate long-term forest management strategies; understanding the DBH of individual trees is conducive to more detailed forest management, thereby further improving precise forest management; in addition, accurately obtaining the DBH of individual trees is a method to obtain the DBH at the stand level, so this study focuses on further exploring the DBH of individual trees. Modeling is one of the most commonly used methods to estimate DBH, and DBH model variables are usually growth factors such as tree height and crown width. Many scholars have established DBH models for different regions using tree height and other growth factors, and have achieved good model fitting results. However, factors such as tree age, regional differences, and competition can also significantly affect the growth of DBH. The DBH growth rate of trees of different ages varies greatly, and incorporating tree age into the DBH model can obtain more reliable estimation results. Therefore, DBH models developed for specific growth stages are not applicable to other stages, and such differences are also observed between different tree species. In addition, the DBH of the same tree species under different growth environments will also be different. Models that cannot explain such differences face great limitations in application. In addition, the intensity of competition within the forest stand is another key variable that affects DBH growth. Generally speaking, when forest competition is fierce, the growth strategy of plants will prioritize height growth, while when there is less competition, the growth strategy of plants will tend to radial growth, resulting in large differences in diameter under the same conditions and tree age. However, no DBH model has been found that takes all of the above factors into account at the same time. Summary of the invention

[0005] The purpose of this patent is to solve the problems mentioned in the above background technology and provide a method for measuring the breast diameter of Chinese fir using drone lidar data.

[0006] The technical solution of the present invention provides: a method for measuring the breast diameter of Chinese fir using unmanned aerial vehicle laser radar data, comprising the following steps:

[0007] S101: Data Selection

[0008] A representative Chinese fir plantation was selected, and the basic conditions of the plot were recorded. The tree height, DBH, crown base height and crown width of each tree with DBH>5 cm in the plot were measured in four vertical directions. The exact location of each tree in the plot was also recorded. Data obtained by two methods, UAV LiDAR and ground measurement, were statistically analyzed. The tree height was obtained by the maximum height of each tree after segmentation, as well as other indicators such as crown width, crown area and crown volume.

[0009] Wherein, DBH: outer diameter of the trunk bark at 1.3 m height; LH: tree height measured by lidar; LCD: crown width measured by lidar; S: number of live trees per hectare: S: number of live trees per hectare;

[0010] S102: Estimation Methods

[0011] Basic models: Tree height is highly correlated with DBH, and five candidate basic models describing the curvilinear relationship between DBH and tree height were selected, including linear model, Weibull model, logistic model, Wykoff model, and Gompertz model. In addition, DBH is one of the most commonly used variables in tree height estimation models, so the equations of several versatile tree height-DBH models were also inverted as candidate models for estimating DBH. Finally, 10 basic models were identified.

[0012] S103: Variable Selection

[0013] Crown width, canopy area, canopy volume and stand density were selected as auxiliary variables of the model to strengthen the explanation of DBH changes and improve the interpretability and fitting performance of the model; Pearson correlation analysis was performed on the relationship between these variables and DBH; after the basic model was established, other variables were gradually introduced through reparameterization or other statistical methods to optimize the evaluation indicators of the model (Formulas 13-16);

[0014] S104: Dummy variable modeling

[0015] The growth pattern of trees will be different at different growth stages. In order to accurately describe the functional relationship between DBH and other variables at different growth stages of Metasequoia and minimize the modeling workload, the influence of growth stage on the relationship between DBH and height was considered; the growth stage of trees was used as a dummy variable and a DBH model was established; the model expression is as follows

[0016] DBH=f((S i ,a i )g(H L ,C,c i ))+ε(11)

[0017] Among them: a i and C i is the model parameter, i=1,2,3,4,5; Si is a dummy variable, which can be 0 or 1; g(H L , C, c i ) indicates that H L and C are variables, c i The DBH model with parameters H L and C are the independent variables in the model, and ε represents the error term;

[0018] The established dummy variable model not only includes tree individual variables and competition variables, but also considers the impact on DBH changes; the best form of the model and its fitting performance are determined and selected based on the model evaluation index (Formula 13-16);

[0019] S103: Mixed Effects Models

[0020] Multiple plots in the same region will lead to different growth of tree DBH due to slight comprehensive differences, so it is necessary to consider the impact of regional differences on DBH, as well as the impact of differences between plots in the same region on DBH; therefore, a two-level mixed effects modeling method was adopted; the expression of the two-level nonlinear mixed effects model is as follows:

[0021]

[0022] Among them, DBH ijk represents the DBH of the kth tree in the jth plot in the ith region; f ijk It is a DBH model with two-level random effects; it is based on HL ijk and C ijk is the model with variables and parameters to be estimated; i and vij are random effect vectors, representing the levels of the region and the plot within the region, respectively; represents the random error of the DBH of the kth tree in the jth plot within the ith region, refers to the corresponding random effect variance-covariance matrix, and refers to the random error variance; the random effect assumption and the error term assumption are independent of each other and each follows a normal distribution;

[0023] S103: Model Evaluation

[0024] The model evaluation indicators are AIC Akaike information criterion, R2 determination coefficient, RMSE root mean square error and TRE total relative error;

[0025] AIC=-2lnl+2p(13)

[0026]

[0027] Where: l is the maximum likelihood value of the model; n is the number of observations; p is the number of parameters in the model; D i is the observed value of the i-th breast height diameter; is the predicted value of the i-th breast height diameter; It is the average diameter at breast height.

[0028] As a preference, the basic DBH-height models evaluated among the 10 base models are:

[0029] Equation of the linear model: D = a + bH;

[0030] The equation of the allometric model: D = aH b ;

[0031] Equation of exponential function: D = ae bH ;

[0032] Equation of logarithmic function: D = alnH + b;

[0033] Equation of the allometric model with intercept: D = a + bH c ;

[0034] D: diameter at breast height; H: tree height; a, b and c are parameters to be estimated.

[0035] As a preferred method, the inverse function model of the tree height-chest height model among the 10 basic models is:

[0036] Power model equation: D = e bln((H-1.3) / a) , corresponding HD model: H = 1.3 + a * D b ;

[0037] The equation of the Weibull model is: The corresponding HD model: H = a(1-e -bDc );

[0038] Equation of Richards model: The corresponding HD model: H = a(1-e -bD ) c ;

[0039] The equation of the Wykoff model is: Corresponding HD model:

[0040] Equations for the Exponential model: Corresponding HD model:

[0041] D represents the diameter at breast height; H represents the tree height; a, b and c are model parameters.

[0042] Beneficial effects of the present invention:

[0043] Abies is a unique tree species native to southern China with a strong ability to produce high-quality timber and regulate the surrounding ecosystem. Rapid and accurate acquisition of DBH of individual trees in fir forests is helpful to improve forest management. This study took Abies as the research object and established a remote sensing inversion model for DBH of individual trees in pure plantations based on ground survey data of 130 plots and airborne LiDAR data, taking into account growth stage, stand competition and regional differences. This study aims to: (1) establish a two-level nonlinear mixed effect model with plots and plots as random effects to improve the accuracy of DBH estimation of individual fir trees; (2) evaluate the effects of growth stage and stand competition on DBH estimation of fir plantations; and (3) propose a large-scale DBH estimation method based on airborne LiDAR data. This provides technical support for forest resource surveys and accurate improvement of forest quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present disclosure, the following briefly introduces the drawings required to be used in some embodiments of the present disclosure. Obviously, the drawings described below are only drawings of some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can also be obtained based on these drawings. In addition, the drawings described below can be regarded as schematic diagrams, and are not limitations on the actual size of the product involved in the embodiments of the present disclosure, the actual process of the method, the actual timing of the signal, etc.

[0045] Figure 1 The heat map shows the correlation between breast height diameter and various variables.

[0046] Figure 2 It is the ratio of the measured DBH to the DBH predicted by the four DBH height models (M5, M17, M18 and M19). DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0049] The present invention is described in further detail below by way of examples.

[0050] Example 1

[0051] The method for measuring the breast diameter of Chinese fir using UAV laser radar data includes the following steps:

[0052] S101: Data Collection

[0053] The study area is located in five forest areas of Guangdong Province, China (20°09′~25°31′N, 109°45′~117°20′E). The study area is mainly located in the northwest of Guangdong Province, and the landforms are mainly mountains and low hills. The slope of the area is between 10° and 40°, with higher terrain in the north and lower terrain in the south. The precipitation in the area is concentrated from April to September, with an average annual rainfall of 1,777 mm. The highest recorded annual average rainfall can reach 2,321 mm. The average annual temperature in the area is 21.8℃, and it is warm all year round with abundant rainfall. The soil in the study area is mainly red soil and yellow soil. The area is an important part of China's mid-subtropical zone and one of the main distribution areas of Abies.

[0054] In March 2024, representative fir plantations were selected and 130 plots were established, each with an area of ​​666.67 square meters, with a total of 26,768 fir trees. The basic conditions of the plots were recorded, and the tree height, DBH, crown base height, and crown width were measured in four vertical directions for each tree in the plot (DBH>5 cm). The exact location of each tree in the plot was also recorded.

[0055] In May of the following year, data for all plots were collected using an airborne lidar. The equipment used to collect the data was an AS-1300HL lidar system equipped with a RigelVUX-1LR laser scanner. The system operated at a wavelength of 1550nm, a pulse duration of 3.5ns, and a laser beam divergence of 0.5mrad. The pulse repetition rate was set to 50kHz, the maximum scanning angle was 30°, and the scanning frequency was 49Hz. A grid pattern flight path was used, with a lateral overlap of 50% for the point cloud. The average flight speed was 10m / s, and the average point cloud density was 110 points per square meter.

[0056] The independent validation method is one of the most commonly used methods to test the generalization ability and fitting effect of the model. 70% of the data is used as modeling data and 30% as an independent validation data set. The tree height is derived from the maximum height of each tree after segmentation and other indicators such as crown width, crown area and crown volume. A detailed summary of the data is shown in Table 1.

[0057] Table 1 Summary of data statistics obtained by two methods (UAV LiDAR and ground measurement)

[0058]

[0059] DBH: outer bark diameter of the trunk at 1.3 m height (cm); LH: tree height measured by lidar (m); LCD: crown width measured by lidar (m); S: number of living trees per hectare (hectares): S: number of living trees per hectare (hectares).

[0060] S102: Estimation Methods

[0061] Basic Model

[0062] Tree height is highly correlated with DBH, and obtaining accurate tree height data is one of the main advantages of airborne lidar. In order to accurately reflect the relationship between DBH and lidar-derived height, five candidate basic models describing the curvilinear relationship between DBH and tree height were selected, such as linear model, Weibull model, Logistic model, Wykoff model and Gompertz model. In addition, DBH is one of the most commonly used variables in tree height estimation models, so the equations of several versatile tree height-DBH models were also inverted as candidate models for estimating DBH. Finally, 10 basic models were determined. Common model forms are shown in Table 2, and the inverse function of the tree height-chest height model is shown in Table 3.

[0063] Table 2 Basic DBH-height models evaluated

[0064]

[0065] D: diameter at breast height; H: tree height; a, b and c are parameters to be estimated.

[0066] Table 3 Inverse functions of common tree height-diameter models

[0067]

[0068]

[0069] D represents the diameter at breast height; H represents the tree height; a, b and c are model parameters.

[0070] S103: Variable Selection

[0071] Crown width, canopy area, canopy volume and stand density were selected as auxiliary variables of the model to strengthen the explanation of DBH changes and improve the interpretability and fitting performance of the model. Pearson correlation analysis was performed on the relationship between these variables and DBH. After the basic model (Tables 2 and 3) was established, other variables were gradually introduced through reparameterization or other statistical methods to optimize the evaluation indicators of the model (Equations 13-16).

[0072] S104: Dummy variable modeling

[0073] The growth pattern of trees will be different at different growth stages, which can be observed in some tree species. In order to accurately describe the functional relationship between DBH and other variables at different growth stages of Metasequoia and minimize the modeling workload, the effect of growth stage on the relationship between DBH and height was considered. The DBH model was established by taking the tree growth stage as a dummy variable. The model expression is as follows:

[0074] DBH=f((S i ,a i )g(H L ,C,c i ))+ε(11)

[0075] Among them: a i and C i is the model parameter, i=1,2,3,4,5; Si is a dummy variable, which can be 0 or 1; g(H L , C, c i ) indicates that H L and C are variables, c i The DBH model with parameters H L and C are the independent variables in the model, and ε represents the error term.

[0076] The established dummy variable model not only includes tree individual variables and competition variables, but also considers the impact on DBH changes. The best form of the model and its fitting performance are determined and selected based on the model evaluation index (Formulas 13-16).

[0077] S105: Mixed Effects Models

[0078] Under normal circumstances, areas with better site conditions tend to promote larger DBH of trees of the same age. However, comprehensive factors such as climatic conditions in different regions will continue to enhance the impact on DBH growth. Similarly, multiple plots in the same region will also lead to different DBH growth of trees due to subtle comprehensive differences. Therefore, it is necessary to consider the impact of regional differences on DBH, as well as the impact of differences between plots in the same region on DBH. Therefore, a two-level mixed effects modeling method was adopted. The expression of the two-level nonlinear mixed effects model is as follows:

[0079]

[0080] Among them, DBH ijk represents the DBH of the kth tree in the jth plot in the ith region; f ijk It is a DBH model with two-level random effects; it is based on HL ijk and C ijk is the model with variables and parameters to be estimated; iand vij are random effect vectors, representing the levels of the region and the sample plots within the region, respectively; represents the random error of the DBH of the kth tree in the jth sample plot in the ith region, refers to the corresponding random effect variance-covariance matrix, and refers to the random error variance; the random effect assumption and the error term assumption are independent of each other and each obeys a normal distribution.

[0081] S106: Model Evaluation

[0082] The model evaluation indicators are AIC (Akaike's information criterion), R2 (coefficient of determination), RMSE (root mean square error) and TRE (total relative error).

[0083] AIC=-2lnl+2p(13)

[0084]

[0085] Where: l is the maximum likelihood value of the model; n is the number of observations; p is the number of parameters in the model; D i is the observed value of the i-th breast height diameter; is the predicted value of the i-th breast height diameter; It is the average diameter at breast height.

[0086] result

[0087] Selected variables and their correlation with DBH

[0088] Pearson correlation analysis showed that the tree height measured by lidar had the highest fitting accuracy with DBH measured on the ground. Stand density and crown width were correlated with DBH, and both variables were biologically meaningful. In contrast, crown area and crown volume were weakly correlated with DBH. In order to improve the versatility of the model and prevent overfitting, it was finally decided to include tree height, crown width (representing the growth proportion in the stand), and stand density (representing the competition in the stand) as covariates in the DBH model. The correlation heat map between DBH and various variables is shown in Figure 2. Figure 1 shown. Figure 1 Figure 2 is a heat map showing the correlation between diameter at breast height and various variables; where DBH is the ground truth diameter at breast height (DBH), LH, LCD, LCA, and LCV are the tree height and crown width derived from LiDAR data, and S is the stand density.

[0089] Generalized OLS Model

[0090] As shown in Table 4. Model M5 has the smallest R2, RMSE, and AIC, indicating that the most appropriate relationship between DBH and tree height of a single tree is the intercept plus power function, so it is selected as the optimal model. Subsequently, the variables will be added to M5 to form a generalized OLS model to cope with the impact of multiple variables on DBH estimation.

[0091] Table 4. Basic model evaluation indicators

[0092]

[0093] These variables were added gradually by stepwise reparameterization and continuous product of power functions. Ten reconstructed model forms were compared and it was found that the continuous product of power functions had the best fit. The extended version of the basic model, the generalized OLS model (M17), outperformed the best basic model (M5) (Table 5). The expression of this model is as follows

[0094] DBH=2.520+3.842LH 0.894 LCD 0.062 S -0.152 (17)

[0095] Table 5. Fit statistics for M5 and M17

[0096]

[0097] Dummy variable model

[0098] Dummy variables are used to represent different age groups. In order to reduce the complex problems caused by including too many variables, the research focuses on using dummy variables for only one parameter. Through one-by-one testing and analysis, the model with dummy variables for parameter b has the highest fit and the lowest AIC value. The model specifications are as follows:

[0099] DBH=1.592+(3.901G 1 +3.946G 2 +4.052G 3 +4.092G 4 +4.177G 5 )LH 0.8567 LCD 0.0539 S -0.1356 (18)

[0100] Among them G 1 , G 2 , G 3 , G 4 and G 5 are dummy variables, representing the different age classes of Abies chinensis: young forest, middle-aged forest, near-mature forest, mature forest and over-mature forest.

[0101] Mixed effects models

[0102] Based on M18, a two-level random effect model of region and plot was established. Various forms of models were tried and evaluated according to the best model indicators. The greater the impact of random effects on parameters, the better the evaluation indicators of the model. Finally, when the regional random effect was applied to all parameters and the plot random effect was applied to parameter a, all indicators of the model were optimal. The evaluation indicators of the model are shown in Table 6. Therefore, the two-level mixed effect model for DBH expression was finally determined to be

[0103] DBH ijk =4.462+u ik1 +v ijk +(0.1906G 1 +0.2008G 2 +0.2035G 3 +0.2138G 4 +0.2307G 5 +u ik2 )

[0104]

[0105] Among them, DBH ijk Refers to the DBH of the kth tree in the jth plot in the ith region. 1 ~G 5 Dummy variables representing the five growth stages of Abies from sapling to upper tree. LH, LCD and S represent the height of a single tree in the plot corresponding to DBH, crown width and stand density, respectively.

[0106] Table 6 Evaluation indicators of the Chinese fir diameter model

[0107]

[0108] According to Table 6, adding relevant variables improved the fit of the DBH model and reduced the error index. Then, by gradually adding growth stages, regions, and plots, the model was gradually optimized and reached the optimum. The determination coefficient of the final model was 16.04% higher than that of the basic model. Figure 2 The predicted and observed values ​​from the base model to the two-level random effects dummy variable model are shown:

[0109] Figure 2 The ratios of the measured DBH to the DBH predicted by the four DBH height models (M5, M17, M18, and M19) are shown in the prediction graphs ①, ②, ③, and ④ of M5, M6, M7, and M8, respectively, and the reference line is the diagonal line of y=x.

[0110] in conclusion

[0111] A DBH model was established using airborne lidar data to estimate the DBH of individual trees in large-scale fir plantations. Compared with the DBH inversion model that only considers local areas, the newly constructed model takes into account regional heterogeneity and tree age, and is more suitable for processing data obtained by drones to obtain large-scale DBH survey data of individual trees. The determination coefficient of this model can reach 0.7025, which can promote the application of UVAS in single tree feature extraction. In short, this study provides a method for extracting DBH of individual trees by drones in large-scale forestry surveys, and can also be applied to the extraction of other large-scale individual tree indicators, providing a method for quickly completing large-scale forestry surveys.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0113] The above describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claim involved.

[0114] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for measuring the breast diameter of Chinese fir using unmanned aerial vehicle laser radar data, characterized in that: The following steps are involved: S101: Data Selection A representative Chinese fir plantation was selected, and the basic conditions of the plot were recorded. The tree height, DBH, crown base height and crown width of each tree with DBH>5 cm in the plot were measured in four vertical directions. The exact location of each tree in the plot was also recorded. Data obtained by two methods, UAV LiDAR and ground measurement, were statistically analyzed. The tree height was obtained by the maximum height of each tree after segmentation, as well as other indicators such as crown width, crown area and crown volume. Wherein, DBH: outer diameter of the trunk bark at 1.3 m height; LH: tree height measured by lidar; LCD: crown width measured by lidar; S: number of live trees per hectare: S: number of live trees per hectare; S102: Estimation Methods Basic models: Tree height is highly correlated with DBH, and five candidate basic models describing the curvilinear relationship between DBH and tree height were selected, including linear model, Weibull model, logistic model, Wykoff model, and Gompertz model. In addition, DBH is one of the most commonly used variables in tree height estimation models, so the equations of several versatile tree height-DBH models were also inverted as candidate models for estimating DBH. Finally, 10 basic models were identified. S103: Variable Selection Crown width, canopy area, canopy volume and stand density were selected as auxiliary variables of the model to strengthen the explanation of DBH changes and improve the interpretability and fitting performance of the model; Pearson correlation analysis was performed on the relationship between these variables and DBH; after the basic model was established, other variables were gradually introduced through reparameterization or other statistical methods to optimize the evaluation indicators of the model (Formulas 13-16); S104: Dummy variable modeling The growth pattern of trees will be different at different growth stages. In order to accurately describe the functional relationship between DBH and other variables at different growth stages of Metasequoia and minimize the modeling workload, the influence of growth stage on the relationship between DBH and height was considered; the growth stage of trees was used as a dummy variable and a DBH model was established; the model expression is as follows DBH=f((S i ,a i )g(H L ,C,c i ))+ε(11) Among them: a i and C i is the model parameter, i=1,2,3,4,5; Si is a dummy variable, which can be 0 or 1; g(H L , C, c i ) indicates that H L and C are variables, c i The DBH model with parameters H L and C are the independent variables in the model, and ε represents the error term; The established dummy variable model not only includes tree individual variables and competition variables, but also considers the impact on DBH changes; the best form of the model and its fitting performance are determined and selected based on the model evaluation index (Formula 13-16); S103: Mixed Effects Models Multiple plots in the same region will lead to different growth of tree DBH due to slight comprehensive differences, so it is necessary to consider the impact of regional differences on DBH, as well as the impact of differences between plots in the same region on DBH; therefore, a two-level mixed effects modeling method was adopted; the expression of the two-level nonlinear mixed effects model is as follows: Among them, DBH ijk represents the DBH of the kth tree in the jth plot in the ith region; f ijk It is a DBH model with two-level random effects; it is based on HL ijk and C ijk is the model with variables and parameters to be estimated; i and vij are random effect vectors, representing the levels of the region and the plot within the region, respectively; represents the random error of the DBH of the kth tree in the jth plot within the ith region, refers to the corresponding random effect variance-covariance matrix, and refers to the random error variance; the random effect assumption and the error term assumption are independent of each other and each follows a normal distribution; S103: Model Evaluation The model evaluation indicators are AIC, Akaike information criterion, R 2 Coefficient of determination, RMSE root mean square error and TRE total relative error; AIC=-2lnl+2p(13) Where: l is the maximum likelihood value of the model; n is the number of observations; p is the number of parameters in the model; D i is the observed value of the i-th breast height diameter; is the predicted value of the i-th breast height diameter; It is the average diameter at breast height.

2. The method according to claim 1, characterized in that: The basic DBH-height models evaluated out of 10 base models are: Equation of the linear model: D = a + bH; The equation of the allometric model: D = aH b ; Equation of exponential function: D = ae bH ; Equation of logarithmic function: D = alnH + b; Equation of the allometric model with intercept: D = a + bH c ; D: diameter at breast height; H: tree height; a, b and c are parameters to be estimated.

3. The method according to claim 1, characterized in that: The inverse function model of the tree height-chest height model among the 10 basic models is: Power model equation: D = e bln((H-1.3) / a) , corresponding HD model: H = 1.3 + a * D b ; The equation of the Weibull model is: Corresponding HD model: Equation of Richards model: The corresponding HD model: H = a(1-e -bD ) c ; The equation of the Wykoff model is: Corresponding HD model: Equation of Exponential model: Corresponding HD model: D represents the diameter at breast height; H represents the tree height; a, b and c are model parameters.

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