Tree leaf life prediction method, equipment, medium and product

Abstract

The invention discloses a tree leaf life prediction method and device, a medium and a product, and relates to the field of ecology. The method comprises the following steps: firstly, obtaining the diameter at breast height and a plurality of sample branches of each sample tree in a plurality of sample trees; each sample branch comprises branchlets of all age groups from the shortest leaf age to the longest leaf age; for each sample tree, counting the number of existing leaves and the total number of historical growing leaves of each sample branch at different age groups, and calculating the survival rate of the leaves of each sample branch at different age groups according to the number of the existing leaves and the total number of the historical growing leaves; performing weighted calculation on the survival rates of the leaves of each sample branch at different age groups to obtain the leaf life of each sample branch; calculating the leaf life of each sample tree according to the leaf life of each sample branch; constructing a plurality of empirical models based on the diameter at breast height and the leaf life of each sample tree, and selecting the empirical model with the highest prediction precision as a leaf life prediction model; the leaf life prediction model is adopted to predict the leaf life of the tree, and the leaf life of the tree can be efficiently and accurately measured under the non-destructive condition.

Description

Technical Field

[0001] The present application relates to the technical field of ecology, and particularly to a method, device, medium and product for predicting the leaf lifespan of trees. Background Art

[0002] As the main site of plant photosynthesis, the lifespan of leaves affects the accumulation of photosynthetic products. Leaf lifespan is an important manifestation of the ecological strategy of species and is directly related to the productivity of the ecosystem. Tree species with long leaf lifespans can fix carbon for a long time, which is of great significance to the regional and even global carbon cycle. Therefore, it is crucial to efficiently and accurately measure the leaf lifespan of trees for understanding the process of forest succession and clarifying the dynamic changes in the carbon sequestration capacity of the ecosystem, and it can also provide key data support for coping with climate change. However, currently, the destructive sampling method is usually used to measure the leaf lifespan of evergreen tree species. Although this method is accurate in measurement, it is destructive, time-consuming and laborious. Especially for tall trees, it is difficult to obtain sample leaves, which greatly limits the popularity of the destructive sampling method. Therefore, there is an urgent need to propose a method suitable for efficiently and accurately measuring the leaf lifespan of trees of different plant sizes under non-destructive conditions. Summary of the Invention

[0003] The purpose of the present application is to provide a method, device, medium and product for predicting the leaf lifespan of trees to efficiently and accurately measure the leaf lifespan of trees under non-destructive conditions.

[0004] To achieve the above object, the present application provides the following solutions.

[0005] In a first aspect, the present application provides a method for predicting the leaf lifespan of trees, including:

[0006] Obtaining the diameter at breast height and multiple sample branches of each sample tree among multiple sample trees; each sample branch contains twigs of each age from the shortest leaf age to the longest leaf age;

[0007] For each sample tree, counting the number of existing leaves and the total number of historically grown leaves of each sample branch at different ages;

[0008] Calculating the leaf survival rate of each sample branch at different ages according to the number of existing leaves and the total number of historically grown leaves of each sample branch at different ages;

[0009] Performing weighted calculation on the leaf survival rates of each sample branch at different ages to obtain the leaf lifespan of each sample branch;

[0010] Calculating the leaf lifespan of each sample tree according to the leaf lifespan of each sample branch;

[0011] Constructing multiple empirical models based on the diameter at breast height and leaf lifespan of each sample tree, and selecting the empirical model with the highest prediction accuracy as the leaf lifespan prediction model;

[0012] The leaf life of trees is predicted using a leaf life prediction model.

[0013] Optionally, the multiple sample branches of each sample tree are all complete sample branches growing in the upper south direction of the canopy of the sample tree.

[0014] Optionally, for each sample tree, the number of existing leaves and the total number of historically grown leaves at different age stages of each sample branch are counted, specifically including:

[0015] For each sample tree, the number of existing leaves N of the i-th age stage on the m-th sample branch is counted m,i ;

[0016] The number of leaf scars H on the twigs of the i-th age stage on the m-th sample branch of the sample tree is counted m,i , and the formula N m,i-total = H m,i ×Y is used to calculate the total number of historically grown leaves N of the i-th age stage on the m-th sample branch m,i-total ; where Y is the number of leaves corresponding to each leaf scar.

[0017] Optionally, according to the number of existing leaves and the total number of historically grown leaves at different age stages of each sample branch, the leaf survival rate at different age stages of each sample branch is calculated, specifically including:

[0018] According to the number of existing leaves N of the i-th age stage on the m-th sample branch m,i and the total number of historically grown leaves N of the i-th age stage on the m-th sample branch m,i-total , the formula SR m,i = N m,i / N m,i-total is used to calculate the leaf survival rate SR of the i-th age stage on the m-th sample branch m,i .

[0019] Optionally, the leaf survival rates at different age stages of each sample branch are weighted and calculated to obtain the leaf life of each sample branch, specifically including:

[0020] The formula is used to weight and calculate the leaf survival rates at different age stages on the m-th sample branch to obtain the leaf life LP of the m-th sample branch m ; where SR m,i-1 is the leaf survival rate of the (i - 1)-th age stage on the m-th sample branch; I is the maximum leaf age.

[0021] Optionally, according to the leaf life of each sample branch, the leaf life of each sample tree is calculated, specifically including:

[0022] The formula is used to calculate the leaf life LS of each sample tree; where M is the number of sample branches selected for the sample tree.

[0023] Optionally, a variety of empirical models are constructed based on the diameter at breast height (DBH) and leaf lifespan of each sample tree, and the empirical model with the highest prediction accuracy is selected as the leaf lifespan prediction model. Specifically, it includes:

[0024] Taking the DBH of each sample tree among multiple sample trees as the independent variable and the leaf lifespan of each sample tree as the dependent variable, several empirical models such as linear fitting, quadratic fitting, logarithmic fitting, exponential fitting, locally weighted regression, logistic fitting, piecewise fitting, and power function are used for fitting, and the empirical model with the highest prediction accuracy is selected as the leaf lifespan prediction model.

[0025] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the tree leaf lifespan prediction method.

[0026] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the tree leaf lifespan prediction method.

[0027] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the tree leaf lifespan prediction method.

[0028] According to the specific embodiments provided by the present application, the following technical effects are disclosed.

[0029] A tree leaf lifespan prediction method, device, medium, and product provided by the present application only need to fit the leaf lifespan prediction model of the corresponding tree species during the training stage, and then the leaf lifespan prediction model can be directly used for prediction during the use stage. On the one hand, it improves the convenience, prediction efficiency, and prediction accuracy of tree leaf lifespan prediction. On the other hand, it avoids damage to the trees, is conducive to long-term ecological monitoring and protection, and provides a strong guarantee for the sustainable development of the forest ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 It is a schematic flowchart of a tree leaf lifespan prediction method of the present application;

[0032] Figure 2 It is a distribution diagram of the DBH of Pinus koraiensis sample trees in an embodiment of the present application;

[0033] Figure 3 Schematic diagram of branches / leaves of Korean pine at different ages in the embodiments of the present application;

[0034] Figure 4 Schematic diagram of the empirical model for fitting different DBHs and leaf lifespan of Korean pine in the embodiments of the present application. Detailed implementation manners

[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0036] The present application proposes a method, device, medium and product for predicting the leaf lifespan of trees, aiming to efficiently and accurately measure the leaf lifespan of trees with different plant sizes under non-destructive conditions.

[0037] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0038] In an exemplary embodiment, as Figure 1 shown, a method for predicting the leaf lifespan of trees is provided, including the following steps 1 to 7.

[0039] Step 1: Obtain the DBH and multiple sample branches of each sample tree among multiple sample trees; each sample branch contains twigs at each age from the shortest leaf age to the longest leaf age.

[0040] For the tree species to be studied, first, multiple sample trees of the tree species are selected through sampling, and the DBH (diameter at breast height) of each sample tree is recorded. Further, for each sample tree, M complete sample branches are randomly selected in the upper south direction of its canopy; M is a positive integer greater than or equal to 3. The upper south direction of the canopy usually refers to the position in the tree crown that receives the most sunlight, that is, the upper and southern parts of the tree crown; at different positions in the tree crown, there are significant differences in light intensity and photosynthesis efficiency, which are mainly affected by the solar azimuth and the tree crown structure.

[0041] According to the biological characteristics, the branch ages of different twigs on the sample branch can be determined. The topmost one is defined as the current year's branch with a branch age of 1 year. The branch age of each twig represents the leaf age of the leaves on that section of the twig. That is, the leaf age of the leaves supported by the topmost twig is also 1 year, and so on. Therefore, when selecting the sample branch, each sample branch should include twigs of all age groups from the shortest leaf age (the most leaves) to the longest leaf age (the fewest leaves approaching complete withering).

[0042] Step 2: For each sample tree, count the current number of existing leaves and the total number of historically grown leaves of different age groups on each sample branch.

[0043] On the one hand, for each sample branch of each sample tree, taking the m-th sample branch as an example, where m = 1, 2,..., M, count the current number of existing leaves of the i-th age group on the m-th sample branch, that is, the number of leaves of i years old on the m-th sample branch, denoted as N m,i 。

[0044] On the other hand, count the number of leaf scars H on the twigs of the i-th age group on the m-th sample branch of this sample tree m,i , and multiplying the number of leaf scars by the number of leaves corresponding to each leaf scar can obtain the total number of leaves supported by the twigs of this branch age (before withering), that is, the total number of leaves of i years old before withering. In this application, it is called the total number of historically grown leaves, denoted as N m,i-total , and the calculation formula is as follows:

[0045] N m,i-total =H m,i ×Y (1)

[0046] where N m,i-total is the total number of historically grown leaves of the i-th age group on the m-th sample branch; Y is the number of leaves corresponding to each leaf scar.

[0047] Step 3: Calculate the leaf survival rate of each sample branch for different age groups according to the current number of existing leaves and the total number of historically grown leaves of each sample branch.

[0048] Dividing the current number of existing leaves by the total number of historically grown leaves before withering can obtain the survival rate (survival ratio, SR) of the leaves of this age group. The calculation formula is as follows:

[0049] SR m,i =N m,i / N m,i-total (2)

[0050] where N m,i and N m,i-total are the current number of existing leaves and the total number of historically grown leaves of the i-th age group on the m-th sample branch respectively. SR m,iis the leaf survival rate of the i-th age on the m-th sample branch, that is, the survival rate of the leaves of i years old on the sample branch m.

[0051] Step 4: Perform weighted calculation on the leaf survival rates of different ages of each sample branch to obtain the leaf life of each sample branch.

[0052] By performing weighted calculation on the leaf survival rates of different ages of each sample branch, the leaf life (life span, LP) of the leaves on this sample branch can be obtained. The calculation formula is as follows:

[0053]

[0054] where SR m,i-1 is the leaf survival rate of the (i - 1)-th age on the m-th sample branch; I is the maximum leaf age, that is, the longest leaf age. LP m is the leaf life of the m-th sample branch.

[0055] Step 5: Calculate the leaf life of each sample tree according to the leaf life of each sample branch.

[0056] Taking the average of the leaf lives obtained from all M sample branches of each sample tree can obtain the leaf life of this sample tree. The specific formula is as follows:

[0057]

[0058] where M is the number of sample branches selected for each sample tree. LS is the leaf life of each sample tree.

[0059] Step 6: Construct multiple empirical models based on the DBH and leaf life of each sample tree, and select the empirical model with the highest prediction accuracy as the leaf life prediction model.

[0060] Taking the DBH of each sample tree among multiple sample trees as the independent variable and the leaf life of this sample tree as the dependent variable, construct a sample data set. Randomly select 75% of the total amount of data in the sample data set to form a training set, and the remaining 25% to form a validation set. For multiple groups of (DBH, leaf life) sample data in the training set, respectively use empirical models such as linear fitting, quadratic fitting, logarithmic fitting, exponential fitting, locally weighted regression, logistic fitting, piecewise fitting, and power function for fitting, and use the sample data in the validation set to verify the prediction accuracy (forecast accuracy, FC) of each empirical model. Select the empirical model with the highest prediction accuracy as the leaf life prediction model for actual prediction.

[0061] The calculation formula of the prediction accuracy FC% is as follows:

[0062]

[0063] In the formula is the leaf lifespan predicted based on the empirical model; y k is the measured leaf lifespan; K is the sample size.

[0064] Step 7: Use the leaf lifespan prediction model to predict the leaf lifespan of trees.

[0065] Package the trained leaf lifespan prediction model. When predicting the leaf lifespan of trees subsequently, only the diameter at breast height of the tree needs to be measured and input, and the predicted leaf lifespan value can be output. This method can quickly, efficiently, and accurately determine the leaf lifespan of trees, and avoid damaging the trees, which is beneficial to long-term ecological monitoring and protection, provides a strong guarantee for the sustainable development of forest ecosystems, and helps to better protect and manage forest resources on the earth.

[0066] Taking Korean pine as the research object, the specific implementation process of the method of this application is described in detail below, including the following steps S1 to S7.

[0067] S1: Obtain the diameter at breast height and multiple sample branches of each sample tree among multiple sample trees; each sample branch contains twigs of all age groups from the shortest leaf age to the longest leaf age.

[0068] In this embodiment, Korean pine is taken as the research object. Five sampling points run through the distribution area of broad-leaved Korean pine forest in Northeast China. From the south end to the north end, they are Jilin Changbai Mountain National Nature Reserve (42.38°N, 128.08°E), Heilongjiang Muling Northeast Yew National Nature Reserve (43.48°N, 130.24°E), Heilongjiang Liangshui National Nature Reserve (47°10′50″N, 128°53′20″E), Heilongjiang Fenglin National Nature Reserve (48.06°N, 129.12°E), and Heilongjiang Shengshan National Nature Reserve (49.30°N, 126.48°E). At the end of August in 2019 - 2020, at each sampling point, 10 - 20 sample trees were randomly selected, and the diameter at breast height ranged from 0 to 100 cm. Finally, a total of 172 sample trees were selected at 5 sampling points, and the diameter at breast height ranged from 0.3 to 100 cm. The corresponding diameter at breast height distribution map is as Figure 2 shown, with the horizontal coordinate being the diameter at breast height and the vertical coordinate being the number of sample trees.

[0069] For each sample tree, randomly select 3 complete sample branches in the upper south direction of the canopy. According to the biological characteristics, the branch ages of different twigs on the sample branch can be determined. As Figure 3 shown, the topmost twig is defined as the current-year branch, and its branch age is 1 year. The age of each twig represents the leaf age of the needles on this section of the twig. That is, the leaf age of the needles supported by the topmost twig is also 1 year, and so on. Therefore, when selecting the sample branch, each sample branch should contain twigs of all age groups from the shortest leaf age (the most needles) to the longest leaf age (the fewest needles close to complete withering). Figure 3In the illustrated embodiment, there are a total of five age groups including branches or leaves from 1-year-old to 5-year-old.

[0070] S2: For each sample tree, count the number of existing leaves and the total number of historically grown leaves at different age groups of each sample branch.

[0071] For each sample branch of each sample tree, first remove the needles of different ages and record the number of needles. Denote the number of i-year-old needles on sample branch m as N m,i . And record the number of leaf scars H on each young branch of each age m,i , as shown in Figure 3 . Since Korean pine has five needles in a bundle and one bundle corresponds to one leaf scar, therefore, referring to formula (1), multiply the number of leaf scars H m,i by the number of needles Y = 5 corresponding to each leaf scar, and the total number of needles (before shedding) supported by the sample branch m of this branch age can be obtained, that is, the total number of historically grown leaves N m,i-total .

[0072] S3: Calculate the leaf survival rate of each sample branch at different age groups according to the number of existing leaves and the total number of historically grown leaves of each sample branch at different age groups.

[0073] Divide the current number of existing needles N m,i by the total number of historically grown leaves N before shedding m,i-total , and the survival rate of the needles at this age group can be obtained. That is, the survival rate of needles at different needle age groups is obtained by the following formula (6):

[0074] SR m,i = N m,i / N m,i-total (6)

[0075] where SR m,i is the survival rate of i-year-old needles; N m,i is the number of i-year-old needles on sample branch m; N m,i-total is the total number of needles of i-year-old needles on sample branch m before shedding.

[0076] S4: Perform weighted calculation on the leaf survival rate of each sample branch at different age groups to obtain the leaf life of each sample branch.

[0077] By weighting according to the survival rate of needles of different ages, the leaf life of the needles on this sample branch m can be obtained, and the calculation formula is as follows:

[0078]

[0079] In the formula, SR m,i-1 is the survival rate of (i - 1)-year-old needles. In this embodiment, the maximum needle age I = 5.

[0080] S5: Calculate the leaf lifespan of each sample tree according to the leaf lifespan of each sample branch.

[0081] The leaf lifespan LS of the sample tree can be obtained by taking the average of the needle lifespans obtained from the 3 sample branches of each sample tree. The calculation formula is as follows:

[0082]

[0083] In this embodiment, the number of sample branches M selected for each sample tree is 3.

[0084] S6: Based on the diameter at breast height and leaf lifespan of each sample tree, construct multiple empirical models, and select the empirical model with the highest prediction accuracy as the leaf lifespan prediction model.

[0085] Then, randomly select 75% of the total data volume, that is, K = 129 sets of diameter at breast height and leaf lifespan data, to construct an empirical model for predicting the leaf lifespan of Korean pine with different diameter classes. The remaining 25% of the dataset (43 sets of diameter at breast height and leaf lifespan data) is used to verify the prediction accuracy FC% of the empirical model. The finally selected empirical model with the highest prediction accuracy is the linear fitting model, which is used as the leaf lifespan prediction model for actual prediction. As Figure 4 shown, the formula of the finally fitted leaf lifespan prediction model is:

[0086] y = -0.0226x + 3.8276 (9)

[0087] where x is the diameter at breast height of Korean pine and y is the leaf lifespan. R 2 is the correlation coefficient, and the higher R 2 , the better the correlation. P is the significance level, which needs to be less than 0.05. The statistical results of the prediction accuracy FC% of the leaf lifespan of Korean pine at different sampling points are shown in Table 1.

[0088] Table 1 Statistical results of the prediction accuracy FC% of the leaf lifespan of Korean pine at different sampling points

[0089] Location Maximum value Minimum value Average value Standard deviation Coefficient of variation Changbai Mountain 99.82 69.25 87.76 9.35 10.65 Muling 99.62 83.80 92.93 8.71 9.38 Liangshui 91.80 75.05 83.72 3.26 3.89 Fenglin 97.54 74.45 83.41 7.59 9.11 Shengshan 95.59 88.14 90.65 5.54 6.11

[0090] As shown in Table 1, in this embodiment, at 5 sampling points, the highest prediction accuracy appears in Changbai Mountain, reaching 99.82%, and the average prediction accuracy is 83.41% (Fenglin) - 92.93% (Muling). Based on the maximum and minimum values, average value, standard deviation, and coefficient of variation data of the prediction accuracy, it can be shown the stability of the prediction accuracy of the empirical model proposed in this application. It can be seen that the empirical model (leaf lifespan prediction model) proposed in this application can accurately predict the leaf lifespan of Korean pine in different regions and different diameter classes (DBH = 0.3 - 100 cm). This provides good technical support and reference value for quickly, efficiently, and accurately measuring the leaf lifespan of coniferous tree species with different diameter classes under non-destructive conditions.

[0091] S7: Use the leaf lifespan prediction model to predict the leaf lifespan of trees.

[0092] Compared with the method of directly measuring the leaf lifespan of trees by using the destructive sampling method, in this application, only the leaf lifespan prediction model corresponding to the tree species needs to be fitted well in the training stage, and then the leaf lifespan prediction model can be directly used for prediction in the usage stage. On the one hand, this technology improves the convenience, prediction efficiency and prediction accuracy of tree leaf lifespan prediction. On the other hand, it avoids damaging the trees, which is beneficial to long-term ecological monitoring and protection, and also provides a strong guarantee for the sustainable development of forest ecosystems, helping to better protect and manage the forest resources on the earth.

[0093] In an exemplary embodiment, this application also provides a computer device, which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, the described tree leaf lifespan prediction method is implemented.

[0094] In an exemplary embodiment, this application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the described tree leaf lifespan prediction method is implemented.

[0095] In an exemplary embodiment, this application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the described tree leaf lifespan prediction method is implemented.

[0096] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiment of the above method. Among them, any reference to a memory or other medium provided in the embodiments of the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0097] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0098] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0099] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for predicting the leaf lifespan of trees, characterized in that, Including: Obtaining the diameter at breast height (DBH) and multiple sample branches of each sample tree among multiple sample trees; each sample branch contains twigs of various age groups from the shortest leaf age to the longest leaf age; For each sample tree, counting the number of existing leaves and the total number of historically grown leaves of each sample branch at different age groups; Calculating the leaf survival rate of each sample branch at different age groups according to the number of existing leaves and the total number of historically grown leaves of each sample branch at different age groups; Performing weighted calculation on the leaf survival rates of each sample branch at different age groups to obtain the leaf lifespan of each sample branch; Calculating the leaf lifespan of each sample tree according to the leaf lifespan of each sample branch; Constructing multiple empirical models based on the DBH and leaf lifespan of each sample tree, and selecting the empirical model with the highest prediction accuracy as the leaf lifespan prediction model; Using the leaf lifespan prediction model to predict the leaf lifespan of trees.

2. The method for predicting the leaf lifespan of a tree according to claim 1, wherein The multiple sample branches of each sample tree are all complete sample branches growing in the upper south direction of the canopy of the corresponding sample tree.

3. The method for predicting the leaf lifespan of a tree according to claim 1, characterized in that For each sample tree, counting the number of existing leaves and the total number of historically grown leaves of each sample branch at different age groups specifically includes: For each sample tree, count the number of existing leaves N at the i-th age on the m-th sample branch m,i ; Count the number of leaf scars H on the small branches of the i-th age on the m-th sample branch of the sample tree m,i , and use the formula N m,i-total = H m,i ×Y to calculate the total number of historically grown leaves N on the m-th sample branch of the i-th age m,i-total ; where Y is the number of leaves corresponding to each leaf scar.

4. The method for predicting the leaf lifespan of a tree according to claim 3, wherein Calculating the leaf survival rate of each sample branch at different age groups according to the number of existing leaves and the total number of historically grown leaves of each sample branch at different age groups specifically includes: According to the number of existing leaves N at the i-th age on the m-th sample branch m,i and the total number of historically grown leaves N m,i-total , the formula SR m,i = N m,i / N m,i-total is used to calculate the leaf survival rate SR at the i-th age on the m-th sample branch m,i .

5. The method for predicting the leaf lifespan of a tree according to claim 4, wherein, Performing weighted calculation on the leaf survival rates of each sample branch at different age groups to obtain the leaf lifespan of each sample branch specifically includes: Using the formula weighted calculation is performed on the leaf survival rates of different age groups on the m-th sample branch to obtain the leaf life LP of the m-th sample branch m ; where SR m,i-1 is the leaf survival rate of the (i - 1)-th age group on the m-th sample branch; I is the maximum leaf age 6. The method for predicting the leaf lifespan of a tree according to claim 5, wherein, Calculating the leaf lifespan of each sample tree according to the leaf lifespan of each sample branch specifically includes: Use the formula to calculate the leaf lifespan LS of each sample tree; where M is the number of sample branches selected for this sample tree.

7. The method for predicting the leaf lifespan of a tree according to claim 6, wherein Constructing multiple empirical models based on the DBH and leaf lifespan of each sample tree, and selecting the empirical model with the highest prediction accuracy as the leaf lifespan prediction model specifically includes: Taking the DBH of each sample tree among multiple sample trees as the independent variable and the leaf lifespan of each sample tree as the dependent variable, and respectively using several empirical models such as linear fitting, quadratic fitting, logarithmic fitting, exponential fitting, locally weighted regression, logistic fitting, piecewise fitting, and power function for fitting, and selecting the empirical model with the highest prediction accuracy as the leaf lifespan prediction model.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the leaf lifespan of trees according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the leaf lifespan of trees according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the leaf lifespan of trees according to any one of claims 1 to 7.

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