Method for estimating tree age based on average radial growth rate of outermost layer of tree trunk and diameter of tree trunk

Abstract

The invention discloses a method for estimating the age of a tree based on the latest N average radial annual growth rate of a trunk and the diameter of the trunk, and the method comprises the specific steps: 1, obtaining the radial growth rate of the trunk in the latest N years, N being a natural number and being the influence of a small climate change on the radial growth rate of the tree, and N being greater than or equal to 2; step 2, acquiring the diameter of the sampling part of the trunk; 3, the average radial growth rate of the latest N years is calculated according to the obtained diameter and the radial growth amount of the tree trunk in the latest N years, and the calculation formula is that the average radial growth rate of the latest N years = the radial growth amount of the latest N years / (N * diameter) * 100%; 4, constructing a tree age estimation mathematical model by taking the trunk diameter and the average radial growth rate of the latest N years as independent variables and taking the tree age as a dependent variable; and 5, measuring the average diameter growth rate and the diameter of the tree to be measured in the latest N years, and estimating the age of the tree to be measured by using the tree age estimation mathematical model. The tree age prediction method provided by the invention has no damage or slight damage to trees, is high in measurement precision, and has great economic value and social value.

Description

Technical Field

[0001] The invention relates to a method for estimating tree age based on an average radial growth rate of the outermost layer of a trunk and a trunk diameter, and belongs to the technical field of tree age determination. Background Art

[0002] Tree age is a fundamental indicator in forestry surveys, a crucial factor in forestry research and production, and a crucial basis for identifying ancient trees. Currently, the main methods for determining tree age include the trunk disc method, the growth cone method, the micro-drill resistance method, and mathematical models. The trunk disc method measures tree age by counting the annual rings on a trunk disc. While this method offers the highest accuracy, cutting the trunk disc damages forest resources, and collecting and processing the discs is time-consuming and labor-intensive. The growth cone method uses a growth cone to drill a core from a tree trunk and measures tree age by counting the annual rings in the core. While this method does not require felling, sampling the core still leaves a hole in the trunk, which negatively impacts tree growth and timber utilization. The micro-drill resistance method uses a motor to control the drill to penetrate the tree at a constant speed, and estimates tree age by the number of peaks in the resistance curve. This method has the advantages of fast measurement speed and little damage to trees. However, due to the vibration of the drill bit, the drill bit resistance signal contains a large amount of noise signals, resulting in the peaks in the resistance diagram not corresponding one-to-one with the tree rings. Therefore, this method is difficult to identify the tree rings and the identification accuracy is not high. The mathematical model method usually uses the relationship between the trunk diameter and the tree age to establish a mathematical model to estimate the tree age. This method has the advantages of being non-destructive and fast in measurement speed. However, due to the large differences in the radial growth rate of different trees, the age of trees with the same diameter varies greatly, so the accuracy of the mathematical modeling method in estimating tree age still needs to be improved. Therefore, how to accurately estimate tree age without damage is a difficult problem in the world's forestry.

[0003] Of the four commonly used tree age measurement methods, only the mathematical model method is non-destructive. If the accuracy of the mathematical model can be improved, this method will be widely used. However, due to the varying radial growth rates of different trees, the mathematical model method suffers from low estimation accuracy. This invention incorporates the radial growth rate of the trunk over the last N years into the tree age estimation model, significantly improving the accuracy of tree age estimation and possessing great application value. Summary of the Invention

[0004] Given that existing methods cannot achieve both high-precision and non-destructive tree age detection, the present invention introduces the average radial growth rate of the trunk in the last N years into the tree age estimation model, providing a method for estimating tree age based on the average radial growth rate of the trunk and the trunk diameter.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention relates to a method for estimating tree age based on average radial growth rate and trunk diameter of a tree trunk, comprising the following steps:

[0007] Step 1: Obtain the radial growth of the tree trunk in the last N years, where N is a natural number and N≥2;

[0008] Step 2, obtaining the diameter of the trunk sampling location;

[0009] Step 3: Calculate the average radial growth rate over the past N years based on the obtained diameter and the radial growth of the trunk over the past N years. The calculation formula is:

[0010] Average radial growth rate in the last N years = radial growth in the last N years (cm) / (N × diameter) × 100%, in %;

[0011] Step 4: Using trunk diameter and the average radial growth rate in the last N years as independent variables and tree age as the dependent variable, a mathematical model for tree age estimation is constructed;

[0012] Step 5: Use a tree age estimation mathematical model to estimate the age of the tree to be tested.

[0013] Preferably, in step 4, analyzing wood disc data of multiple tree species with fast, average, and slow radial growth rates is collected to establish a tree age estimation model; the steps include:

[0014] Step 1: Measure the width W of the outermost N growth rings of each disk in the four directions of east, south, west, and north. e 、W s 、W w 、W n ;

[0015] Step 2: Calculate the radial growth of each disk in the last N years. The calculation formula is:

[0016] Radial growth in the last N years = (W e +W s +W w +W n ) / 2;

[0017] Step 3, use a breast diameter ruler to measure the diameter of the disc;

[0018] Step 4: Calculate the radial growth rate in the last N years. The calculation formula is:

[0019] Average radial growth rate in the last N years = radial growth in the last N years (cm) / (N × diameter) × 100%, in %.

[0020] Preferably, the step 4 of constructing a tree age estimation mathematical model comprises the following steps:

[0021] Step 1: Use diameter as the independent variable and tree age as the dependent variable to draw a scatter plot between diameter and tree age;

[0022] Step 2: Based on the distribution of the diameter-tree age scatter plot, the linear model, exponential model, and logarithmic model are selected as candidate models for the single-factor mathematical model between diameter and tree age;

[0023] Step 3: Establish a linear model, exponential model, and logarithmic model between diameter and tree age, and select the model with the highest determination coefficient among these three models as the optimal single-factor model between diameter and tree age;

[0024] Step 4: Use the average radial growth rate in the last N years as the independent variable and tree age as the dependent variable to draw a scatter plot between the average radial growth rate and tree age;

[0025] Step 5, based on the distribution of the average radial growth rate-tree age scatter plot, the exponential model and the logarithmic model are selected as candidate models for the single-factor mathematical model between the average radial growth rate and tree age;

[0026] Step 6: Establish an exponential model and a logarithmic model between radial growth rate and tree age, and select the model with the highest determination coefficient among the two models as the optimal single factor model between average radial growth rate and tree age;

[0027] Step 7: construct a two-factor model between tree age and diameter and average radial growth rate based on the optimal single-factor model between diameter and tree age and the optimal single-factor model between average radial growth rate and tree age.

[0028] Preferably, in step 5, if there is trunk diameter data D0 of the tree to be tested N years ago, a non-destructive method is used to calculate the radial growth of the tree to be tested in the last N years. The specific steps are as follows:

[0029] Step 1, measure the current diameter D1 of the trunk;

[0030] Step 2: Calculate the radial growth of the wood to be tested. The calculation formula is: radial growth = D1-D0;

[0031] Step 3, calculate the average radial growth rate in the last N years;

[0032] Step 4: Estimate the tree age using the average radial growth rate and diameter in the last N years as independent variables.

[0033] Preferably, in step 5, if the trunk diameter data D0 of the tree to be tested N years ago is missing, a minimally invasive method is used to obtain the radial growth of the tree to be tested in the last N years. The specific steps are as follows:

[0034] Step 1: Use a sickle to scrape off the dead bark on the surface of the tree where the wood sample is to be tested;

[0035] Step 2, using a micro-core sampler to sample the outermost 2-3 cm long xylem of the trunk to obtain a micro-core;

[0036] Step 3, measuring the length L and the number N of the xylem with complete annual rings on the outermost layer of the microwood core;

[0037] Step 4, calculate the radial growth of the wood to be tested, the calculation formula is: radial growth = 2L;

[0038] Step 5, calculate the average radial growth rate in the last N years;

[0039] Step 6: Estimate the tree age using the average radial growth rate and diameter in the last N years as independent variables

[0040] The mathematical model for tree age estimation has the following characteristics:

[0041] The nonlinear relationship between diameter and tree age is manifested as follows: tree growth usually exhibits nonlinear characteristics, and nonlinear models have higher accuracy in simulating this growth trend. Therefore, using nonlinear regression models to describe tree growth patterns can better predict tree age.

[0042] The constructed dual-factor model better reflects the growth characteristics of trees. This is demonstrated by the fact that for different tree species in different regions, as well as for different age ranges of the same species, the dual-factor model provides higher prediction accuracy than the single-factor model. The model maintains strong prediction results, particularly for samples of older trees, validating its applicability across regions, species, and timescales.

[0043] The beneficial effects of the present invention are:

[0044] When a tree's growth environment remains unchanged, its radial growth rate follows a specific pattern. Therefore, the tree's average growth rate over recent years can be used to roughly determine its growth environment. This present invention incorporates the average radial growth rate of trees over recent years into the tree age estimation model to further improve the accuracy of the mathematical model. While existing methods often require drilling into the core of a growth cone to obtain growth ring information, which causes irreversible damage to the tree, this present invention uses a mathematical model to predict tree age. Simply by measuring the diameter at the measurement point and the average width of the outermost growth ring, the model can predict the tree's age.

[0045] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the method of the present invention.

[0047] Figure 2 Schematic diagram of the structure of a microgrowth cone.

[0048] Figure 3 Schematic diagram of the structure of the main body of the microgrowth cone.

[0049] Figure 4 Schematic diagram of the push rod structure of the microgrowth cone.

[0050] Figure 5 The width W of the two outermost growth rings on the disk is obtained e 、W s 、W w 、W n Schematic diagram of .

[0051] Figure 6 This is a scatter plot of diameter and tree age.

[0052] Figure 7 is a scatter plot of average radial growth rate versus tree age.

[0053] Figure 8 (a) and (b) are the fitting curves of the single-factor models with diameter and average radial growth rate as variables, respectively. DETAILED DESCRIPTION

[0054] The following is a diagram of the drawings in conjunction with the specification Figures 1-8 , the specific implementation methods of the present invention are further described in detail.

[0055] like Figure 1 As shown, the present invention provides a method for estimating tree age based on the average radial growth rate and trunk diameter of the trunk, comprising the following steps:

[0056] Step 1: Obtain the radial growth of the tree trunk in the last N years, where N is a natural number and N≥2;

[0057] Step 2, obtaining the diameter of the trunk sampling location;

[0058] Step 3: Calculate the average radial growth rate over the past N years based on the obtained diameter and the radial growth of the trunk over the past N years. The calculation formula is:

[0059] Average radial growth rate in the last N years = radial growth in the last N years (cm) / (N × diameter) × 100%, in %;

[0060] Step 4: Using trunk diameter and the average radial growth rate in the last N years as independent variables and tree age as the dependent variable, a mathematical model for tree age estimation is constructed;

[0061] Step 5: Use a tree age estimation mathematical model to estimate the age of the tree to be tested.

[0062] The number N in the present invention can be selected according to actual needs; in order to reduce the impact of climate change on the radial growth rate of trees, N ≥ 2. In one embodiment, taking the extraction of the average radial growth rate in the last two years as an example, the specific steps are as follows:

[0063] Step 1: Obtain the radial growth of the tree trunk in the last two years;

[0064] Step 2: Use a breast diameter ruler to measure the diameter of the disc;

[0065] Step 3: Calculate the radial growth rate in the last two years. The calculation formula is:

[0066] Average radial growth rate in the last two years = radial growth in the last two years (cm) / (2 × diameter (cm)) × 100%, in %;

[0067] Step 4: Using trunk diameter and the average radial growth rate in the last two years as independent variables and tree age as the dependent variable, a mathematical model for tree age estimation was constructed. The process of constructing the mathematical model is as follows:

[0068] Step 1: Modeling data collection and data preprocessing:

[0069] 1. Measure the width W of the two outermost growth rings of each disk in the four directions of east, south, west, and north. e 、W s 、W w 、W n， ,like Figure 5 As shown;

[0070] 2. Calculate the radial growth of each disk in the last two years using the following formula:

[0071] Radial growth in the last two years = (W e +W s +W w +W n ) / 2;

[0072] Here, analytic wood discs of seven tree species with fast, average, and slow radial growth rates in different climate zones were selected as modeling materials. The basic information of the analytic wood is shown in Table 1.

[0073] Table 1 Basic information of analytical wood

[0074]

[0075] At each tree height of 0.3m, 1.0m, 1.3m, 1.5m, and above 1.5m, cut a 5cm thick analytical wood disc every 1m, and measure the diameter of each disc. Polish the working surface of the disc until the annual ring line is clearly visible, measure the width of each annual ring and the age of each disc by using an annual ring analysis measuring instrument, and calculate the radial growth of each disc in the four directions in the last two years (W e 、W s 、W w 、W n ) data and calculated the radial growth of each disc over the past two years. Table 2 shows basic information on the number of discs collected for each tree species, including age range and diameter range.

[0076] Table 2 Basic information of disc

[0077]

[0078] There are 646 disc datasets in total. 2 / 3 of the parsed wooden discs, 430 data, are randomly selected as the modeling dataset, and the remaining 1 / 3 of the parsed wooden discs, 216 data, are used as the testing dataset.

[0079] 3. Calculate the radial growth rate in the last two years using the following formula:

[0080] Average radial growth rate in the last two years = radial growth in the last two years (cm) / (2 × diameter (cm)) × 100%;

[0081] Step 2, model construction and selection:

[0082] 1. Take diameter as the independent variable and tree age as the dependent variable, and draw a scatter plot between diameter and tree age, such as Figure 6 As shown;

[0083] 2. Based on the distribution of the diameter-age scatter plot, determine the candidate models for the diameter-age single-factor mathematical model. As can be seen from the figure, the linear model, exponential model, and logarithmic model are used as candidate models for the diameter-age single-factor mathematical model. The expressions of the candidate models are shown in Table 3.

[0084] Table 3 Diameter-age single factor mathematical model

[0085]

[0086] In the embodiment, y is the age of the tree, x1 is the diameter, and a and b are unknown parameters of the model.

[0087] 3. Establish linear model, exponential model and logarithmic model between diameter and tree age respectively, and select the model with the highest determination coefficient among these three models as the optimal single factor model between diameter and tree age. The fitting results of each model (specific expression and determination coefficient R 2 ) as shown in Table 4.

[0088] Table 4 Fitting results of the diameter-age single factor mathematical model

[0089]

[0090] The fitting curves of each model are as follows Figure 8 As shown in (a), the optimal diameter single-factor model is the logarithmic model, denoted as Model 1.

[0091] 4. Use the average radial growth rate in the last two years as the independent variable and tree age as the dependent variable to draw a scatter plot between the average radial growth rate and tree age, such as Figure 7 shown.

[0092] 5. According to the distribution of the scatter plot of average radial growth rate-tree age, determine the candidate models of the single-factor mathematical model of average radial growth rate-tree age; as can be seen from the figure, the exponential model and the logarithmic model are used as the candidate models of the single-factor mathematical model of average radial growth rate-tree age.

[0093] Table 5 Average radial growth rate-tree age single factor mathematical model

[0094]

[0095] In the embodiment, y is the age of the tree, x2 is the average radial growth rate of the diameter disk in the last two years, and a and b are unknown parameters of the model.

[0096] 6. Establish exponential and logarithmic models between radial growth rate and tree age, and select the coefficient of determination (R 2 ) was taken as the optimal single-factor model between the average radial growth rate and tree age; the fitting results of each model (specific expression and determination coefficient R 2 ) as shown in Table 6.

[0097] Table 6 Fitting results of the single-factor mathematical model of average radial growth rate-tree age

[0098]

[0099] The fitting curves of each model are as follows Figure 8 As shown in (b), the optimal single-factor model of average radial growth rate is the exponential model, denoted as Model 2.

[0100] 7. Using the optimal single-factor model between diameter and tree age and the optimal single-factor model between average radial growth rate and tree age, a two-factor model between tree age, diameter, and average radial growth rate was constructed. The expression of the two-factor model with diameter and the average radial growth rate in the last two years as predictors is shown in (2), denoted as Model 3, and its determination coefficient R 2 is: 0.76.

[0101] y=3.99*log(1.33*x1)+35*exp(-0.42*x2)#(2)

[0102] Where y is the tree age, x1 is the diameter, and x2 is the average radial growth rate in the last two years.

[0103] Step 3: Model accuracy test:

[0104] 1. Model testing accuracy: To evaluate the accuracy and stability of the constructed model using a test dataset of 216 trees of seven different tree species, multiple evaluation metrics were used, including root mean square error (RMSE), expression (3); mean absolute error (MAE), expression (4); and average estimated accuracy (ε), expression (5). Finally, a t-value test (expression (6)) was performed on the tree age prediction errors of the three models to analyze whether there were significant differences in the prediction errors of the three models.

[0105]

[0106] In the formula, y i represents the i-th actual value, represents the i-th predicted value, n represents the sample size, α1 and α2 are the means of sample 1 and sample 2 respectively, s1 and s2 are the variances of sample 1 and sample 2 respectively, and n1 and n2 are the sample sizes of sample 1 and sample 2 respectively.

[0107] The expressions of the above three models of the test data set are shown in Table 7:

[0108] Table 7 Test model

[0109]

[0110] The test parameter results of each model are shown in Table 8:

[0111] Table 8 Test results

[0112]

[0113] The two-factor model 3 was combined with the two optimal single-factor models for a t-test. The t-value test results are shown in Table 9:

[0114] Table 9 t value test results

[0115]

[0116] Step 4, model accuracy test results:

[0117] Because radial growth rates vary significantly among trees, estimating tree age using only the diameter at breast height (DBH) model results in significant errors. Table 8 shows that a single-factor model containing only the average radial growth rate outperforms a single-factor model containing only diameter. To improve the accuracy of the mathematical model, a two-factor model combining the average radial growth rate factor and the diameter factor, which influence tree radial growth, was constructed. The two-factor model fully reflects the gradual change and decay of tree growth rate with age. It has the highest estimation accuracy, exceeding that of the single-factor diameter model by 25.74 percentage points and the single-factor average radial growth rate model by 8.38 percentage points. The two-factor model also has the lowest root mean square error (RMSE), which is 0.77 points lower than the single-factor average radial growth rate model and 3.6 points lower than the single-factor diameter model. The two-factor model also has the lowest mean absolute error (MAE), which is 0.87 points lower than the single-factor average radial growth rate model and 3.42 points lower than the single-factor diameter model, respectively. In summary, the prediction accuracy of the two-factor model is much higher than that of the single-factor models. As can be seen from Table 9, when the significance level is 0.001, there are significant differences between the estimation results of the two-factor model and the prediction results of the two single-factor models.

[0118] Step 5: Measure the average diameter growth rate and diameter of the trees to be tested over the past two years, and use a tree age estimation mathematical model to estimate the age of the trees to be tested. If the trunk diameter data D0 of the trees to be tested is available two years ago, a non-destructive method is used to calculate the radial growth of the trees to be tested over the past two years. The specific steps are as follows:

[0119] 1. Measure the current diameter D1 of the tree trunk;

[0120] 2. Calculate the radial growth of the wood to be tested: the calculation formula is: radial growth = D1-D0;

[0121] 3. Calculate the average radial growth rate over the last N years;

[0122] 4. Estimate the tree age using the average radial growth rate and diameter in the last N years as independent variables.

[0123] If the trunk diameter data D0 of the tree to be tested 2 years ago is missing, the radial growth of the tree to be tested in the last 2 years can be obtained using the minimally invasive method. The specific steps are as follows:

[0124] 1. Use a sickle to scrape off the dead bark on the tree surface where the sample is to be taken;

[0125] 2. Use the micro-core sampler of the present invention to sample the outermost 2-3 cm long wood of the tree trunk at the sampling site. The specific method is: first, use your left hand to align the drill bit of the micro-core sampler with the sampling site, and then use your right hand to rotate the handle of the micro-core sampler clockwise to drill the micro-core sampler into the tree; when the micro-core sampler is drilled into the wood 2-3 cm deep, use both hands to shake the micro-core sampler left and right and up and down to disconnect the wood core in the micro-core sampler from the trunk; finally, rotate the micro-core sampler counterclockwise to withdraw the micro-core sampler from the trunk; align the push rod with the drill bit of the micro-core sampler, push the micro-core in the micro-core sampler into the sampling groove, and take out the micro-core from the sampling groove.

[0126] 3. Measure the length L and number of annual rings of the outermost layer of the microwood core with complete annual rings;

[0127] 4. Calculate the radial growth of the wood to be tested: the calculation formula is: radial growth = 2L;

[0128] 5. Calculate the average radial growth rate over the past two years;

[0129] 6. Estimate the tree age using the average radial growth rate and diameter in the last two years as independent variables.

[0130] In this embodiment, in order to measure the average width of the two outermost growth rings with minimal damage, the present invention designs a micro-coring sampler. The micro-coring sampler includes a sampler body 4 and a coring push rod. The sampler body 4 mainly includes a threaded drill bit 1, a coring groove 2, and a rotating handle 3. The coring push rod includes a push rod body and a push rod handle. The structure diagram is shown in FIG. Figure 2-4 As shown. The sampler body 4 is provided with a sampling cavity, one end of which extends through the sampling end face of the sampler, and the other end extends through the outer wall of the sampler body. The rear end of the sampler body is provided with an auxiliary rotating handle. The drill bit selected in this embodiment is a standard cutting bit with a length of 16 mm and a diameter of 2 mm.

[0131] The sampling cavity includes a sample hole and a sampling slot 2. The sample hole extends along the length of the sampler body from the end surface of the sampling end. The sampling slot 2 is located on one side of the sampler body, and the bottom of the sampling slot 2 is connected to the sample hole. The drill bit is a conical and hollow structure with a threaded structure on the outside of the drill bit.

[0132] The method of using the tree micro sampler is as follows:

[0133] 1. Determine the sampling location and remove the dead bark on the outside of the trunk. In order to obtain the new xylem of the trunk, do not damage the phloem when removing the dead bark;

[0134] 2. Place the threaded drill bit 1 directly against the tree trunk, hold the rotating handle 3, and rotate the micro sampler body 4 clockwise to allow the threaded drill bit 1 to drill into the tree trunk;

[0135] 3. After the threaded drill bit 1 has drilled 3-4 cm into the wood, shake the sampler body 4 left and right to separate the wood core from the trunk;

[0136] 4. Hold the rotating handle 3 and rotate the sampler body 4 counterclockwise to withdraw the micro sampler;

[0137] 5. Hold the push rod handle 5 of the thin push rod, push the push rod body 6 of the thin push rod into the sampling cavity of the sampler, and take out the wood core at the coring groove 2.

[0138] Due to complex spatial distribution and varying developmental stages, determining the age of trees in natural forests is difficult and can easily lead to damage. Therefore, indirect estimation is often used to determine age. The method provided by this invention can accurately, rapidly, and non-destructively measure the age of forest trees, providing a reliable basis for formulating forest management plans and maximizing forest productivity.

[0139] It should be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not preclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0140] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for estimating tree age based on average trunk radial growth rate and trunk diameter, characterized by: The steps include: Step 1: Obtain the radial growth of the tree trunk in the last N years, where N is a natural number and N≥2; Step 2, obtaining the diameter of the trunk sampling location; Step 3: Calculate the average radial growth rate over the past N years based on the obtained diameter and the radial growth of the trunk over the past N years. The calculation formula is: Average radial growth rate in the last N years = radial growth in the last N years / (N × diameter) × 100%; Step 4: Using trunk diameter and the average radial growth rate in the last N years as independent variables and tree age as the dependent variable, a mathematical model for tree age estimation is constructed; Step 5: Use a tree age estimation mathematical model to estimate the age of the tree to be tested.

2. The method for estimating tree age based on trunk average radial growth rate and trunk diameter according to claim 1, characterized in that: In step 4, analyzing wood disc data of multiple tree species with fast, average, and slow radial growth rates is collected to establish a tree age estimation model; the steps include: Step 1: Measure the width W of the outermost N growth rings of each disk in the four directions of east, south, west, and north. e 、W s 、W w 、W n ; Step 2: Calculate the radial growth of each disk in the last N years. The calculation formula is: Radial growth in the last N years = (W e +W s +W w +W n ) / 2; Step 3, measuring the diameter of the disc; Step 4: Calculate the radial growth rate in the last N years. The calculation formula is: Average radial growth rate in the last N years = radial growth in the last N years / (N×diameter)×100%.

3. The method for estimating tree age based on trunk average radial growth rate and trunk diameter according to claim 1, characterized in that: The step 4 of constructing a tree age estimation mathematical model comprises the following steps: Step 1: Use diameter as the independent variable and tree age as the dependent variable to draw a scatter plot between diameter and tree age; Step 2: Based on the distribution of the diameter-tree age scatter plot, the linear model, exponential model, and logarithmic model are selected as candidate models for the single-factor mathematical model between diameter and tree age; Step 3: Establish a linear model, exponential model, and logarithmic model between diameter and tree age, and select the model with the highest determination coefficient among these three models as the optimal single-factor model between diameter and tree age; Step 4: Use the average radial growth rate in the last N years as the independent variable and tree age as the dependent variable to draw a scatter plot between the average radial growth rate and tree age; Step 5, based on the distribution of the average radial growth rate-tree age scatter plot, the exponential model and the logarithmic model are selected as candidate models for the single-factor mathematical model between the average radial growth rate and tree age; Step 6: Establish an exponential model and a logarithmic model between radial growth rate and tree age, and select the model with the highest determination coefficient among the two models as the optimal single factor model between average radial growth rate and tree age; Step 7: construct a two-factor model between tree age and diameter and average radial growth rate based on the optimal single-factor model between diameter and tree age and the optimal single-factor model between average radial growth rate and tree age.

4. The method for estimating tree age based on trunk average radial growth rate and trunk diameter according to claim 1, characterized in that: In step 5, if there is trunk diameter data D0 of the tree to be tested N years ago, a non-destructive method is used to calculate the radial growth of the tree to be tested in the last N years. The specific steps are as follows: Step 1, measure the current diameter D1 of the trunk; Step 2: Calculate the radial growth of the wood to be tested. The calculation formula is: radial growth = D1-D0; Step 3, calculate the average radial growth rate in the last N years; Step 4: Estimate the tree age using the average radial growth rate and diameter in the last N years as independent variables.

5. The method for estimating tree age based on trunk average radial growth rate and trunk diameter according to claim 1, characterized in that: In step 5, if the trunk diameter data D0 of the tree to be tested N years ago is missing, a minimally invasive method is used to obtain the radial growth of the tree to be tested in the last N years. The specific steps are as follows: Step 1: Use a sickle to scrape off the dead bark on the surface of the tree where the wood sample is to be tested; Step 2, using a micro-core sampler to sample the outermost 2-3 cm long xylem of the trunk to obtain a micro-core; Step 3, measuring the length L and the number N of the xylem with complete annual rings on the outermost layer of the microwood core; Step 4, calculate the radial growth of the wood to be tested: the calculation formula is: radial growth = 2L; Step 5, calculate the average radial growth rate in the last N years; Step 6: Estimate the tree age using the average radial growth rate and diameter in the last N years as independent variables.

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