A forest vegetation carbon sink potential prediction system and method based on forest growth rules

Abstract

A forest vegetation carbon sink potential prediction system and method based on forest growth law belong to the technical field of carbon sink potential prediction. In order to solve the problem of dynamic response of forest carbon sink potential to long-term environmental and climate change. The present application comprises collecting data, establishing model construction, verifying data set and data set for model calculation and carbon sink potential evaluation, the data set for model calculation and carbon sink potential evaluation includes forest basic data, forest environment data; data processing; constructing forest vegetation biomass model for predicting the biomass growth of forest vegetation; adopting root mean square error RMSE and determination coefficient R 2 Evaluate the fitting result to obtain the verified forest vegetation biomass model; use the verified forest vegetation biomass model to predict the growth of forest vegetation biomass, and obtain the forest vegetation biomass growth prediction result for forest vegetation carbon sink potential prediction, and obtain the forest vegetation carbon sink potential prediction result.

Description

Technical Field

[0001] This invention belongs to the field of carbon sequestration potential prediction technology, specifically relating to a system and method for predicting forest vegetation carbon sequestration potential based on the growth patterns of trees. Background Technology

[0002] Stand age is a crucial productivity indicator in long-term forest carbon sink prediction research. Existing technologies struggle to couple stand age with the dynamic processes of forest carbon sink using scientific mathematical methods, resulting in predictions lacking accurate inferences of forest physiological characteristics. Simulation results also suffer from weak interpretability and applicability in long-term forest carbon sink prediction. Furthermore, the availability of initial input data further limits methodological application in the actual assessment process. Moreover, current forest carbon storage measurement and potential prediction are typically based on empirical equations from plot studies. In serving regional green development, the actual parameters used in the models and the empirical relationships considered vary significantly due to the shift in temporal and spatial scales. This causes the models to lose their methodological robustness and generalization ability. Additionally, traditional S-shaped growth curves lack the ability to control later growth stages, failing to accurately quantify the carbon sink capacity of mature forests. Therefore, the methodological credibility is compromised in multi-scenario prediction of forest carbon sink potential. Summary of the Invention

[0003] The problem this invention aims to solve is to dynamically respond to the carbon sequestration potential of forests and long-term environmental and climate change, and proposes a system and method for predicting the carbon sequestration potential of forest vegetation based on the growth patterns of trees.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for predicting forest vegetation carbon sequestration potential based on tree growth patterns includes the following steps:

[0006] S1. Collect data, establish model building, validation datasets, and datasets for model calculation and carbon sink potential assessment, wherein the datasets for model calculation and carbon sink potential assessment include basic forest data and forest environmental data;

[0007] S2. Process the data in the model building, validation dataset and the dataset used for model calculation and carbon sink potential assessment obtained in step S1 to obtain the processed model building, validation dataset and dataset used for model calculation and carbon sink potential assessment.

[0008] S3. Construct a forest vegetation biomass model to predict forest vegetation biomass growth;

[0009] S4. Validate the forest vegetation biomass model constructed in step S3 using the model construction and validation dataset obtained in step S1, using the root mean square error (RMSE) and coefficient of determination (R²). 2 The fitting results were evaluated to obtain the validated forest vegetation biomass model.

[0010] S5. Using the validated forest vegetation biomass model obtained in step S4, predict the growth of forest vegetation biomass. Use the forest vegetation biomass growth prediction results to predict the carbon sequestration potential of forest vegetation.

[0011] Furthermore, the specific implementation method of step S1 includes the following steps:

[0012] S1.1. Establish model construction and validate datasets, including collecting forest succession data with forest biomass-storage-coordinate matching relationship, and collecting measured data of forest maximum biomass density and forest growth rate with geographic coordinate location;

[0013] S1.2. Establish a dataset for model calculation and carbon sink potential assessment, including basic forest data and forest environmental data;

[0014] Forest basic data includes stand composition, dominant trees, standard tree diameter at breast height (DBH), standard tree height, stand density, carbon content, stand age, and absolute geographic coordinates;

[0015] Forest environmental data includes slope, aspect, canopy closure, land level index, annual average temperature under different climate scenarios, and annual total precipitation data.

[0016] Furthermore, the specific implementation method of step S2 includes the following steps:

[0017] S2.1. Perform data cleaning on the data collected in step S1 to remove outlier data and incomplete data;

[0018] S2.2. Integrate the data in the model construction and validation dataset, including maximum forest biomass density, forest growth rate, forest biomass, forest age, and environmental data;

[0019] Constructing a model B for the maximum biomass density of forest vegetation under a given environment m The expression is:

[0020]

[0021] Where f(a,b,tr) is the forest land production potential index, a and b are the normalized slope and aspect, respectively, tr is the land level index, emt is the environmental temperature determining the maximum biomass of the forest, and emp is the environmental humidity determining the maximum biomass of the forest.

[0022] f(a,b,tr), emt, and emp are obtained through training on the collected data, and their expressions are:

[0023]

[0024]

[0025] ;

[0026] Construct a vegetation growth rate model inr0 under no-stress conditions, with the expression:

[0027]

[0028] Where r is the growth rate parameter of mature forest, which comes from the position level index table corresponding to each forest stand composition and is assigned a value according to the degree of difference in diameter at breast height (DBH) growth after forest maturity at different position levels; f pre (map) is a function representing the effect of environmental humidity on forest growth rate, obtained by training with collected data; B t Let be the forest vegetation biomass density at time t;

[0029]

[0030] Where map represents the total annual precipitation;

[0031] Then, the collected data on maximum forest biomass density, forest growth rate, and annual average temperature and total annual precipitation of forest samples were used to formulate model parameters B. m Training and validation of the inr0 model.

[0032] Furthermore, the specific implementation method of step S3 includes the following steps:

[0033] S3.1. Construct a Logistic growth equation based on optimized tree growth patterns to characterize the continuous increase of vegetation biomass with forest age. The expression is:

[0034]

[0035] Wherein, forest age T(t) represents the forest age function. In the model calculation process, forest age T(t) is a variable that increases in a 1:1 ratio with time t. T(t) ′ represents the forest vegetation biomass growth rate at stand age T(t), in units of Mg / ha / yr; n represents the limiting factor for forest growth.

[0036] S3.2. Calculate the tree growth limiting factor, the expression is:

[0037]

[0038] Wherein, inr0 represents the vegetation growth rate under no stress, in units of %; B t / m The theoretical growth potential of forest vegetation at age t under given conditions is expressed in percentage (%).

[0039] ;

[0040] Wherein, ΔB is the difference between the maximum biomass and the forest biomass at age t, in units of Mg / ha;

[0041]

[0042] Among them, B t Let t be the forest vegetation biomass density at time t, in Mg / ha;

[0043] S3.3. Based on the formulas in steps S3.1 and S3.2, calculate the simulated future forest vegetation biomass density B for different forest ages T(t). T(t) The expression is:

[0044]

[0045] Among them, B t0 The forest vegetation biomass density at the initial time t0 is expressed in Mg / ha.

[0046] Furthermore, step S4 evaluates the theoretical applicability of the forest vegetation biomass model and the prediction results of forest vegetation biomass, and shares the sensitivity of the forest vegetation biomass model; RMSE and R-squared are used. 2 The expression for evaluating the fitting results is:

[0047]

[0048]

[0049] Where N is the number of samples, B i B is the true forest biomass density of the sample. t(i) It is the biomass density predicted by the forest vegetation biomass model, meanB i It is the mean of the forest biomass density of all samples.

[0050] Furthermore, step S5 uses the carbon content to convert forest biomass into forest vegetation carbon storage density. The validated forest vegetation biomass model obtained in step S4 is used to predict forest vegetation biomass growth. The resulting forest vegetation biomass growth predictions are then used to predict forest vegetation carbon sequestration potential. Applicable parameters of the forest vegetation biomass model are obtained during the actual calculation process. Annual mean temperature and annual precipitation data under future climate scenarios are selected to predict forest carbon sequestration potential. The selected target prediction time is set, and the forest vegetation carbon sequestration potential is calculated based on changes in carbon storage. The calculation formula is as follows:

[0051]

[0052]

[0053] Among them, C sequestration This represents the carbon sequestration potential of forests over future time years under the predicted scenario, expressed in Mg / ha / yr; k is the carbon content of forest vegetation, expressed in %; B t This is the forest vegetation biomass density at time t, expressed in Mg / ha. t0 t0 is the forest vegetation biomass density at the initial time t0, in units of Mg / ha, and time is the predicted time length of the forest in the future, in units of yr.

[0054] A forest vegetation carbon sequestration potential prediction system based on tree growth patterns includes a processor, a memory, and a computer program stored in the memory and run on the processor. When the computer program runs, it implements the steps of the forest vegetation carbon sequestration potential prediction method based on tree growth patterns as described above.

[0055] The beneficial effects of this invention are:

[0056] This invention describes a method for predicting forest vegetation carbon sequestration potential based on tree growth patterns. Many technical methods for assessing and predicting forest vegetation carbon sequestration potential are based on theoretical models of forest ecosystem processes. This method employs mathematical equations and physical / physiological ecological principles to describe the mechanisms of key physiological and ecological processes. The overall theoretical framework is coupled with a mathematical model, enabling the model to dynamically respond to long-term environmental and climate changes.

[0057] The present invention discloses a method for predicting forest vegetation carbon sink potential based on the growth patterns of trees. Based on multi-source measured data of China's forest ecosystem, an improved Logistic growth equation is constructed to establish a forest carbon sink prediction model applicable to multiple scales (plot-region-country), which significantly improves the scientific nature of forest carbon sink potential assessment and the guiding significance of the assessment results.

[0058] This invention presents a method for predicting forest vegetation carbon sequestration potential based on tree growth patterns. By optimizing the traditional S-shaped growth curve, it overcomes the limitation of insufficient prediction of carbon sequestration capacity in mature forests, providing a more ecologically sound theoretical framework for assessing forest carbon sequestration potential. The model can adapt to the spatiotemporal heterogeneity of forest ecosystems, predicting forest carbon sequestration dynamics under different climate scenarios (such as temperature and precipitation changes) and at multiple spatiotemporal scales, thereby enhancing the reliability and applicability of the prediction results.

[0059] This invention presents a method for predicting forest vegetation carbon sink potential based on tree growth patterns. This method lowers the technical threshold for assessing forest carbon sink potential and improves its operability in carbon sink economic value accounting. Furthermore, by integrating long-term forestry survey data, the model can provide a scientific basis for policy formulation regarding carbon trading and ecological compensation, and support decisions on sustainable forest management. This research not only expands the theoretical methods for forest carbon sink prediction but also provides new technical support for forest carbon management in the context of global climate change. Attached Figure Description

[0060] Figure 1 This is a flowchart of a method for predicting forest vegetation carbon sink potential based on tree growth patterns, as described in this invention.

[0061] Figure 2 The figures are Logistic fitting curves of biomass growth for several typical tree species of the present invention, wherein (a) is the Logistic fitting curve of biomass growth for natural Korean pine forest, (b) is the Logistic fitting curve of biomass growth for natural birch forest, (c) is the Logistic fitting curve of biomass growth for natural Masson pine forest, (d) is the Logistic fitting curve of biomass growth for birch plantation, (e) is the Logistic fitting curve of biomass growth for poplar plantation, and (f) is the Logistic fitting curve of biomass growth for larch plantation.

[0062] Figure 3 The results are the verification results of the model and key parameters of this invention, where (a) is the verification result of the model using real values ​​in y1 natural forest and y2 plantation forest, (b) is the overall verification result of the model, (c) is the fitting result of the model's key parameter - forest growth rate without stress, and (d) is the calculation verification result of the model's key parameter - maximum forest biomass. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0064] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0065] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 - Appendix Figure 3 Detailed explanation is as follows:

[0066] Example 1:

[0067] A method for predicting forest vegetation carbon sequestration potential based on tree growth patterns includes the following steps:

[0068] S1. Collect data, establish model building, validation datasets, and datasets for model calculation and carbon sink potential assessment, wherein the datasets for model calculation and carbon sink potential assessment include basic forest data and forest environmental data;

[0069] Furthermore, the specific implementation method of step S1 includes the following steps:

[0070] S1.1. Establish model construction and validate datasets, including collecting forest succession data with forest biomass-storage-coordinate matching relationship, and collecting measured data of forest maximum biomass density and forest growth rate with geographic coordinate location;

[0071] S1.2. Establish a dataset for model calculation and carbon sink potential assessment, including basic forest data and forest environmental data;

[0072] Forest basic data includes stand composition, dominant trees, standard tree diameter at breast height (DBH), standard tree height, stand density, carbon content, stand age, and absolute geographic coordinates;

[0073] Forest environmental data includes slope, aspect, canopy closure, land level index, annual average temperature under different climate scenarios, and annual total precipitation data.

[0074] Furthermore, the basic forest data of the study area used for model calculations and carbon sequestration potential assessment, starting from the forest baseline scenario, predicts future forest vegetation carbon sequestration based on forest growth theory, improving the accuracy of the prediction results. Environmental factors in the forest environmental data of the study area used for model calculations have a strong regulatory effect on forest succession processes; combined with future climate data, it is possible to predict forest carbon sequestration potential under real-world human emission scenarios of climate change.

[0075] S2. Process the data in the model building, validation dataset and the dataset used for model calculation and carbon sink potential assessment obtained in step S1 to obtain the processed model building, validation dataset and dataset used for model calculation and carbon sink potential assessment.

[0076] Furthermore, the specific implementation method of step S2 includes the following steps:

[0077] S2.1. Perform data cleaning on the data collected in step S1 to remove outlier data and incomplete data;

[0078] S2.2. Integrate the data in the model construction and validation dataset, including maximum forest biomass density, forest growth rate, forest biomass, forest age, and environmental data;

[0079] Constructing a model B for the maximum biomass density of forest vegetation under a given environment m The expression is:

[0080]

[0081] Where f(a,b,tr) is the forest land production potential index, a and b are the normalized slope and aspect, respectively, tr is the land level index, emt is the environmental temperature determining the maximum biomass of the forest, and emp is the environmental humidity determining the maximum biomass of the forest.

[0082] Furthermore, maximum forest biomass density is the dependent variable, while annual average temperature and total annual precipitation are independent variables; forest growth rate is the dependent variable, while total annual precipitation is the independent variable. Then, the collected forest biomass, stand age, and environmental data are compared with the obtained B... m , inr0, are used together to train the Logistic growth equation, with forest biomass and forest age being the dependent and independent variables, respectively.

[0083] f(a,b,tr), emt, and emp are obtained through training on the collected data, and their expressions are:

[0084]

[0085]

[0086] ;

[0087] Construct a vegetation growth rate model inr0 under no-stress conditions, with the expression:

[0088]

[0089] Where r is the growth rate parameter of mature forest, which comes from the position level index table corresponding to each forest stand composition and is assigned a value according to the degree of difference in diameter at breast height (DBH) growth after forest maturity at different position levels; f pre (map) is a function representing the effect of environmental humidity on forest growth rate, obtained by training with collected data; B t Let be the forest vegetation biomass density at time t;

[0090]

[0091] Where map represents the total annual precipitation;

[0092] Then, the collected data on maximum forest biomass density, forest growth rate, and annual average temperature and total annual precipitation of forest samples were used to formulate model parameters B. m Training and validation of the inr0 model.

[0093] Furthermore, stand composition and dominant trees are important background information for forest biomass calculation, which can be used to select and reference allometric growth equations to facilitate accurate calculation of forest biomass density B. t (t / ha). By substituting the obtained standard tree diameter at breast height (DBH) and tree height information into the allometric growth equation confirmed by the stand composition, the tree biomass (t) of the standard trees can be calculated. Multiplying the tree biomass of the standard trees by the stand density yields the forest vegetation biomass density B at the current time (t0). t0 (t / ha). Slope, aspect, canopy closure, and position level indices can be developed into a forest land productivity potential index f(a,b,tr) based on the position level index table established according to forest stand composition. This provides specific growth information for forest vegetation growth simulation, ensuring consistent model stability during the scaling-up process of forest vegetation carbon sink prediction research. Absolute geographic coordinates can be obtained from future climate scenario data provided by the government to obtain the current climate data (annual average temperature, annual total precipitation) faced by the forest in the future. These coordinates, along with the stand age, are used as model parameters to calculate the forest carbon sink potential. Finally, the forest vegetation biomass density B... t0The initial parameters for forest vegetation research, including forest age (t / ha), forest age (T (yr), annual mean temperature (mat (°C), annual total precipitation (map (mm)), and forest land productivity index f(a,b,tr), are input into the forest vegetation biomass model and combined with the built-in parameters obtained from model training (key parameter: B). m (inr0), complete the prediction of forest vegetation biomass B t (t / ha).

[0094] S3. Construct a forest vegetation biomass model to predict forest vegetation biomass growth;

[0095] Furthermore, the specific implementation method of step S3 includes the following steps:

[0096] S3.1. Construct a Logistic growth equation based on optimized tree growth patterns to characterize the continuous increase of vegetation biomass with forest age. The expression is:

[0097]

[0098] Wherein, forest age T(t) represents the forest age function. In the model calculation process, forest age T(t) is a variable that increases in a 1:1 ratio with time t. T(t) ′ represents the forest vegetation biomass growth rate at stand age T(t), in units of Mg / ha / yr; n represents the limiting factor for forest growth.

[0099] S3.2. Calculate the tree growth limiting factor, the expression is:

[0100]

[0101] Wherein, inr0 represents the vegetation growth rate under no stress, in units of %; B t / m The theoretical growth potential of forest vegetation at age t under given conditions is expressed in percentage (%).

[0102] ;

[0103] Wherein, ΔB is the difference between the maximum biomass and the forest biomass at age t, in units of Mg / ha;

[0104]

[0105] B t0 t0 represents the forest vegetation biomass density, expressed in Mg / ha.

[0106] S3.3. Based on the formulas in steps S3.1 and S3.2, calculate the simulated future forest vegetation biomass density B for different forest ages T(t). T(t)The expression is:

[0107]

[0108] Among them, B t0 The forest vegetation biomass density at the initial time t0 is expressed in Mg / ha.

[0109] Furthermore, a forest vegetation carbon sink prediction method is constructed using the classic forest growth law-succession theory to build a forest vegetation carbon cycle mechanism. An improved Logistic growth equation is used to characterize the dynamic changes in forest productivity (i.e., photosynthesis) with forest age during forest succession. The coupling relationship between forest age and forest carbon sink capacity over time is established. A position index is used to define the baseline conditions for forest vegetation growth. The carbon sink process of forest vegetation over a long time scale is reconstructed through actual continuous forest growth. Forest succession data can be used for model training and performance verification after model construction. Based on the theory of vegetation succession in forest ecosystems, it is believed that as forest vegetation grows, its biomass will gradually increase and eventually reach a saturation state, at which point the forest has entered the mature stage. However, mature forests do not stop increasing their biomass. The traditional Logistic growth equation suffers from a weakening effect in the later stages of the curve and cannot quantify the growth of mature forests. Therefore, a Logistic growth equation optimized based on forest growth laws is used to characterize the continuous increase of vegetation biomass with forest age.

[0110] S4. Validate the forest vegetation biomass model constructed in step S3 using the model construction and validation dataset obtained in step S1, using the root mean square error (RMSE) and coefficient of determination (R²). 2 The fitting results were evaluated to obtain the validated forest vegetation biomass model.

[0111] Furthermore, step S4 evaluates the theoretical applicability of the forest vegetation biomass model and the prediction results of forest vegetation biomass, and shares the sensitivity of the forest vegetation biomass model; RMSE and R-squared are used. 2 The expression for evaluating the fitting results is:

[0112]

[0113]

[0114] Where N is the number of samples, B i B is the true forest biomass density of the sample. t(i) It is the biomass density predicted by the forest vegetation biomass model, meanB i It is the mean of the forest biomass density of all samples.

[0115] Furthermore, the applicability of the model equations was evaluated using successional growth data of several typical forest trees. The goodness-of-fact assessment of the model's evaluation results was performed using successional series data. Measured databases of forest maximum biomass density and forest growth rate were used to evaluate inr0 and B. m The results were validated. The results showed that the fitting results of the Logistic equation on the relationship between vegetation biomass and stand age during forest succession indicated that ( Figure 2 ), 97% of nonlinear regression models R 2 The RMSR exceeds 0.8, and 70% of the fitted results have an RMSR below 10. Figure 3 Model validation results show that using measured forest vegetation biomass data from forest vegetation succession data collected from typical forest ecosystems in China to validate the model results yields R0. 2 =0.68, the fit is close to a 1:1 line, and the model has good fit performance between plantations and natural forests. The model performs well in the key parameter (B). m Simulations of (inr0) also demonstrate that the model maintains a sound theoretical foundation and predictive ability (inr0 simulation R). 2 =0.52, B m Simulate R 2 =0.58). This demonstrates that the modeling method can effectively integrate forest growth dynamics data and reliably predict forest carbon sink potential based on carbon cycle processes. The research results have important practical guiding value for forest management.

[0116] S5. Using the validated forest vegetation biomass model obtained in step S4, predict the growth of forest vegetation biomass. Use the forest vegetation biomass growth prediction results to predict the carbon sequestration potential of forest vegetation.

[0117] Furthermore, step S5 uses the carbon content to convert forest biomass into forest vegetation carbon storage density. The validated forest vegetation biomass model obtained in step S4 is used to predict forest vegetation biomass growth. The resulting forest vegetation biomass growth predictions are then used to predict forest vegetation carbon sequestration potential. Applicable parameters for the forest vegetation biomass model under the predicted scenario are obtained. Annual mean temperature and annual precipitation data under future climate scenarios are selected to predict forest carbon sequestration potential. The selected target prediction time is set, and the forest vegetation carbon sequestration potential is calculated based on changes in carbon storage. The calculation formula is as follows:

[0118]

[0119]

[0120] Among them, C sequestrationThis represents the carbon sequestration potential of forests over future time years under the predicted scenario, expressed in Mg / ha / yr; k is the carbon content of forest vegetation, expressed in %; B t This is the forest vegetation biomass density at time t, expressed in Mg / ha. t0 t0 is the forest vegetation biomass density at the initial time t0, in units of Mg / ha, and time is the predicted time length of the forest in the future, in units of yr.

[0121] Furthermore, the carbon content (%) is used to convert forest biomass (t / ha) into forest vegetation carbon storage density (tC / ha). Similarly, the forest vegetation carbon storage density (tC / ha) at time t under future climate scenarios can be obtained through the forest vegetation biomass model. The change in forest vegetation carbon storage density is the forest vegetation carbon sink potential C. sequestration .

[0122] Example 2:

[0123] A forest vegetation carbon sequestration potential prediction system based on tree growth patterns includes a processor, a memory, and a computer program stored in the memory and run on the processor. When the computer program runs, it implements the steps of a forest vegetation carbon sequestration potential prediction method based on tree growth patterns as described in Example 1.

[0124] This system uses measured data to help the model simulate the real growth process of trees and uses a continuous succession process to advance the simulation of forest vegetation carbon sinks.

[0125] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0126] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for predicting forest vegetation carbon sequestration potential based on tree growth patterns, characterized in that, Includes the following steps: S1. Collect data, establish model building, validation datasets, and datasets for model calculation and carbon sink potential assessment, wherein the datasets for model calculation and carbon sink potential assessment include basic forest data and forest environmental data; S2. Process the data in the model building, validation dataset and the dataset used for model calculation and carbon sink potential assessment obtained in step S1 to obtain the processed model building, validation dataset and dataset used for model calculation and carbon sink potential assessment. The specific implementation method of step S2 includes the following steps: S2.

1. Perform data cleaning on the data collected in step S1 to remove outlier data and incomplete data; S2.

2. Integrate the data in the model construction and validation dataset, including maximum forest biomass density, forest growth rate, forest biomass, forest age, and environmental data; Constructing a model B for the maximum biomass density of forest vegetation under a given environment m The expression is: ； Where f(a,b,tr) is the forest land production potential index, a and b are the normalized slope and aspect, respectively, tr is the land level index, emt is the environmental temperature determining the maximum biomass of the forest, and emp is the environmental humidity determining the maximum biomass of the forest. f(a,b,tr), emt, and emp are obtained through training on the collected data, and their expressions are: ； ； ； Construct a vegetation growth rate model inr0 under no-stress conditions, with the expression: ； Where r is the growth rate parameter of mature forest, which comes from the position level index table corresponding to each forest stand composition and is assigned a value according to the degree of difference in diameter at breast height (DBH) growth after forest maturity at different position levels; f pre (map) is a function representing the effect of environmental humidity on forest growth rate, obtained by training with collected data; B t Let be the forest vegetation biomass density at time t; ； Where map represents the total annual precipitation; Then, the collected data on maximum forest biomass density, forest growth rate, and annual average temperature and total annual precipitation of forest samples were used to formulate model parameters B. m Training and validation of the inr0 model; S3. Construct a forest vegetation biomass model to predict forest vegetation biomass growth; S4. Validate the forest vegetation biomass model constructed in step S3 using the model construction and validation dataset obtained in step S1, using the root mean square error (RMSE) and coefficient of determination (R²). 2 The fitting results were evaluated to obtain the validated forest vegetation biomass model. S5. Using the validated forest vegetation biomass model obtained in step S4, predict the growth of forest vegetation biomass. Use the forest vegetation biomass growth prediction results to predict the carbon sequestration potential of forest vegetation.

2. The method for predicting forest vegetation carbon sequestration potential based on tree growth patterns according to claim 1, characterized in that, The specific implementation method of step S1 includes the following steps: S1.

1. Establish model construction and validate datasets, including collecting forest succession data with forest biomass-storage-coordinate matching relationship, and collecting measured data of forest maximum biomass density and forest growth rate with geographic coordinate location; S1.

2. Establish a dataset for model calculation and carbon sink potential assessment, including basic forest data and forest environmental data; Forest basic data includes stand composition, dominant trees, standard tree diameter at breast height (DBH), standard tree height, stand density, carbon content, stand age, and absolute geographic coordinates; Forest environmental data includes slope, aspect, canopy closure, land level index, annual average temperature under different climate scenarios, and annual total precipitation data.

3. The method for predicting forest vegetation carbon sequestration potential based on tree growth patterns according to claim 2, characterized in that, The specific implementation method of step S3 includes the following steps: S3.

1. Construct a Logistic growth equation based on optimized tree growth patterns to characterize the continuous increase of vegetation biomass with forest age. The expression is: ； Wherein, forest age T(t) represents the forest age function. In the model calculation process, forest age T(t) is a variable that increases in a 1:1 ratio with time t. T(t) ′ represents the forest vegetation biomass growth rate at stand age T(t), in units of Mg / ha / yr; n represents the limiting factor for forest growth. S3.

2. Calculate the tree growth limiting factor, the expression is: ； Wherein, inr0 represents the vegetation growth rate under no stress, in units of %; B t / m The theoretical growth potential of forest vegetation at age t under given conditions is expressed in percentage (%). ； Wherein, ΔB is the difference between the maximum biomass and the forest biomass at age t, in units of Mg / ha; ； Among them, B t Let t be the forest vegetation biomass density at time t, in Mg / ha; S3.

3. Based on the formulas in steps S3.1 and S3.2, calculate the simulated future forest vegetation biomass density B for different forest ages T(t). T(t) The expression is: ； Among them, B t0 The forest vegetation biomass density at the initial time t0 is expressed in Mg / ha.

4. The method for predicting forest vegetation carbon sequestration potential based on tree growth patterns according to claim 3, characterized in that, Step S4 evaluates the theoretical applicability of the forest vegetation biomass model and the fitting of the predicted forest vegetation biomass results, and shares the sensitivity of the forest vegetation biomass model; RMSE and R-squared are used. 2 The expression for evaluating the fitting results is: ； ； Where N is the number of samples, B i B is the true forest biomass density of the sample. t(i) It is the biomass density predicted by the forest vegetation biomass model, meanB i It is the mean of the forest biomass density of all samples.

5. The method for predicting forest vegetation carbon sequestration potential based on tree growth patterns according to claim 4, characterized in that, Step S5 uses carbon content to convert forest biomass into forest vegetation carbon storage density. The validated forest vegetation biomass model obtained in step S4 is used to predict forest vegetation biomass growth. The predicted forest vegetation biomass growth results are then used to predict forest vegetation carbon sequestration potential. Applicable parameters of the forest vegetation biomass model are obtained during the actual calculation process. Annual mean temperature and annual precipitation data under future climate scenarios are selected to predict forest carbon sequestration potential. The selected target prediction time is set, and the forest vegetation carbon sequestration potential is calculated based on changes in carbon storage. The calculation formula is as follows: ； ； Among them, C sequestration This represents the carbon sequestration potential of forests over future time years under the predicted scenario, expressed in Mg / ha / yr; k is the carbon content of forest vegetation, expressed in %; B t This is the forest vegetation biomass density at time t, expressed in Mg / ha. t0 t0 is the forest vegetation biomass density at the initial time t0, in units of Mg / ha, and time is the predicted time length of the forest in the future, in units of yr.

6. A forest vegetation carbon sequestration potential prediction system based on tree growth patterns, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the steps of a method for predicting forest vegetation carbon sink potential based on the growth patterns of trees as described in any one of claims 1-5.

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