CN-120893617-B - Forest vegetation carbon sink potential prediction system and method based on forest growth rule

CN120893617BCN 120893617 BCN120893617 BCN 120893617BCN-120893617-B

Abstract

A forest vegetation carbon sink potential prediction system and method based on a forest growth rule belong to the technical field of carbon sink potential prediction. The method aims to solve the problem of dynamic response to forest carbon sink potential and long-term environmental climate change. The method comprises the steps of collecting data, establishing a model construction, verifying a data set and a data set for model calculation and carbon sink potential evaluation, wherein the data set for model calculation and carbon sink potential evaluation comprises forest basic data and forest environment data, conducting data processing, establishing a forest vegetation biomass model for predicting biomass growth of forest vegetation, evaluating a fitting result by adopting Root Mean Square Error (RMSE) and a decision coefficient (R 2 ) to obtain a verified forest vegetation biomass model, conducting forest vegetation biomass growth prediction by utilizing the verified forest vegetation biomass model, and conducting forest vegetation carbon sink potential prediction by utilizing the obtained forest vegetation biomass growth prediction result to obtain a forest vegetation carbon sink potential prediction result.

Inventors

HE NIANPENG
HAN XUEZHENG
ZHOU ZIXUAN
LI JIE
GAO WEIFENG

Assignees

东北林业大学

Dates

Publication Date: 20260508
Application Date: 20250728

Claims (6)

1. The forest vegetation carbon sink potential prediction method based on the forest growth rule is characterized by comprising the following steps of: S1, collecting data, establishing a model construction and verification data set and a data set for model calculation and carbon sink potential evaluation, wherein the data set for model calculation and carbon sink potential evaluation comprises forest basic data and forest environment data; S2, carrying out data processing on the data in the model construction and verification data set and the data set for model calculation and carbon sink potential evaluation obtained in the step S1 to obtain a processed model construction and verification data set and a data set for model calculation and carbon sink potential evaluation; The specific implementation method of the step S2 comprises the following steps: s2.1, data cleaning is carried out on the data collected in the step S1, and abnormal value data and incomplete data are removed; s2.2, constructing an integrated model, and verifying that the data in the data set comprise forest maximum biomass density, forest growth rate, forest biomass and forest age, and environmental data; Constructing a maximum biomass density model B m of forest vegetation in a given environment, wherein the expression is as follows: ; Wherein f (a, b, tr) is a forest land production potential index, a, b are respectively normalized gradient and slope direction, tr is a position index, emt is a determination parameter of environmental temperature for the maximum biomass of the forest, and emp is a determination parameter of environmental humidity for the maximum biomass of the forest; f (a, b, tr), emt and emp are trained using the collected data, expressed as: ; ; ; Constructing a vegetation growth rate model inr 0 under no stress, wherein the expression is as follows: ; Wherein r is a mature forest growth rate parameter, a corresponding position grade index table is formed by each forest stand, and assignment is carried out according to the chest diameter growth difference degree after forest maturation under different position grades, f pre (map) is an influence function of environmental humidity on the forest growth rate, and the acquired data are adopted for training, and B t is the forest vegetation biomass density at the moment t; ; Wherein map is total annual precipitation; then training and verifying a model parameter B m 、inr 0 model by using the collected maximum biomass density of the forest, the forest growth rate data and the annual average air temperature and annual total precipitation data of the forest sample; S3, constructing a forest vegetation biomass model for predicting biomass growth of forest vegetation; S4, verifying the forest vegetation biomass model constructed in the step S3 by using the model construction and verification data set obtained in the step S1, and evaluating a fitting result by adopting a Root Mean Square Error (RMSE) and a decision coefficient R 2 to obtain a verified forest vegetation biomass model; S5, predicting forest vegetation biomass growth by using the verified forest vegetation biomass model obtained in the step S4, and predicting forest vegetation carbon sink potential by using the obtained forest vegetation biomass growth prediction result to obtain a forest vegetation carbon sink potential prediction result.
2. The forest vegetation carbon sink potential prediction method based on the forest growth law of claim 1, wherein the specific implementation method of the step S1 comprises the following steps: S1.1, establishing a model construction and verification data set, wherein the model construction and verification data set comprises forest succession data of a forest biomass-forest age-coordinate matching relation, and actual measurement data of a forest maximum biomass density and a forest growth rate with geographic coordinate positioning are collected; s1.2, establishing a data set for model calculation and carbon sink potential evaluation, wherein the data set comprises forest basic data and forest environment data; forest basic data comprise forest stand composition, dominant wood, standard wood breast diameter, standard wood tree height, forest stand density, carbon content, forest age and absolute geographic coordinates; Forest environment data include slope, slope direction, canopy density, earth index, annual average air temperature under different climatic conditions, annual total precipitation data.
3. The forest vegetation carbon sink potential prediction method based on the forest growth law according to claim 2, wherein the specific implementation method of the step S3 comprises the following steps: S3.1, constructing a Logistic growth equation based on optimization of a forest growth rule to describe continuous growth of vegetation biomass along with the age of the forest, wherein the expression is as follows: ; Wherein, the forest age T (T) represents a forest age function, in the model calculation process, the forest age T (T) is a variable with a 1:1 growth rule along with the time T, B T(t) ' is the forest vegetation biomass growth rate of the forest age T (T), the unit is Mg/ha/yr, and n is a forest growth limiting factor; S3.2, calculating a forest growth limiting factor, wherein the expression is as follows: ; Wherein inr 0 is the vegetation growth rate under no stress, and B t/m is the theoretical growth potential of forest vegetation under a given environment at the time t, and the unit is; ; wherein DeltaB is the difference between the maximum biomass and forest biomass at the time t of the forest age, and the unit is Mg/ha; ; wherein B t is the forest vegetation biomass density at the time t, and the unit is Mg/ha; S3.3, resolving based on the formulas of the steps S3.1 and S3.2 to obtain forest vegetation biomass density B T(t) simulating different forest ages T (T) in the future, wherein the expression is as follows: ; Wherein B t0 is the forest vegetation biomass density at the initial time t 0 , and the unit is Mg/ha.
4. The forest vegetation carbon sink potential prediction method based on the forest growth law of claim 3, wherein the step S4 is used for carrying out fitting evaluation on the theoretical applicability of a forest vegetation biomass model and the prediction result of the forest vegetation biomass, sharing the sensitivity of the forest vegetation biomass model, adopting RMSE and R 2 to evaluate the fitting result, and the expression is as follows: ; ; Where N is the number of samples, B i is the true forest biomass density of the samples, B t(i) is the biomass density predicted by the forest vegetation biomass model, and meanB i is the average of the forest biomass densities of all samples.
5. The method for predicting forest vegetation carbon sink potential based on a forest growth rule according to claim 4, wherein the carbon content in step S5 is used for converting forest biomass into forest vegetation carbon reserve density, the verified forest vegetation biomass model obtained in step S4 is used for predicting forest vegetation biomass growth, the obtained forest vegetation biomass growth prediction result is used for predicting forest vegetation carbon sink potential, applicable parameters of the forest vegetation biomass model in the real calculation process of the model are obtained, annual average temperature and annual precipitation data in future climate situations are selected for predicting forest carbon sink potential, future selected target prediction time is set, calculation of forest vegetation carbon sink potential is performed on the basis of the change of carbon reserve, and a calculation formula is as follows: ; ; Wherein C sequestration is the carbon sink potential of the forest in the predicted scenario for the future time year, the unit is Mg/ha/yr, k is the carbon content of the forest vegetation, the unit is the biomass density of the forest vegetation at the time t, B t is the biomass density of the forest vegetation at the initial time t 0 , B t0 is the biomass density of the forest vegetation at the initial time t 0 , the unit is Mg/ha, the time is the predicted time length of the forest in the future, and the unit is yr.
6. A forest vegetation carbon sink potential prediction system based on a forest growth law, comprising a processor, a memory and a computer program stored in the memory and operable on the processor, the computer program when run implementing the steps of a forest vegetation carbon sink potential prediction method based on a forest growth law as claimed in any one of claims 1 to 5.

Description

Forest vegetation carbon sink potential prediction system and method based on forest growth rule Technical Field The invention belongs to the technical field of carbon sink potential prediction, and particularly relates to a forest vegetation carbon sink potential prediction system and method based on a forest growth rule. Background The forest age is an important productivity description index in the long-term prediction research of forest carbon sink, the prior art is difficult to couple the forest age and the dynamic process of the forest carbon sink by using a scientific mathematical method, the prediction result lacks the true inference of forest physiological characteristics, and the interpretation and the application of the simulation result in the long-time scale forest carbon sink prediction problem are not strong. The availability of initial input data may further limit the application of methodologies in the technical flow of the actual evaluation process. In addition, the current forest carbon reserve measurement and potential prediction is generally based on an empirical equation of sample plot research, and in the process of serving the green development of a region, because the space-time scale is pushed up, the actual parameters used by the model and the considered empirical relationship have great variation, the model loses the robustness and generalization capability of the methodology, and the traditional S-shaped growth curve lacks the later growth control capability, so that the carbon sink capability of the mature forest cannot be accurately quantified. Thus losing the credibility of methodology in the multi-scenario prediction of forest carbon sink potential. Disclosure of Invention The invention aims to solve the problem of generating dynamic response to forest carbon sink potential and long-term environmental climate change, and provides a forest vegetation carbon sink potential prediction system and method based on a forest growth rule. In order to achieve the above purpose, the present invention is realized by the following technical scheme: A forest vegetation carbon sink potential prediction method based on a forest growth rule comprises the following steps: S1, collecting data, establishing a model construction and verification data set and a data set for model calculation and carbon sink potential evaluation, wherein the data set for model calculation and carbon sink potential evaluation comprises forest basic data and forest environment data; S2, carrying out data processing on the data in the model construction and verification data set and the data set for model calculation and carbon sink potential evaluation obtained in the step S1 to obtain a processed model construction and verification data set and a data set for model calculation and carbon sink potential evaluation; S3, constructing a forest vegetation biomass model for predicting biomass growth of forest vegetation; S4, verifying the forest vegetation biomass model constructed in the step S3 by using the model construction and verification data set obtained in the step S1, and evaluating a fitting result by adopting a Root Mean Square Error (RMSE) and a decision coefficient R 2 to obtain a verified forest vegetation biomass model; S5, predicting forest vegetation biomass growth by using the verified forest vegetation biomass model obtained in the step S4, and predicting forest vegetation carbon sink potential by using the obtained forest vegetation biomass growth prediction result to obtain a forest vegetation carbon sink potential prediction result. Further, the specific implementation method of the step S1 includes the following steps: S1.1, establishing a model construction and verification data set, wherein the model construction and verification data set comprises forest succession data of a forest biomass-forest age-coordinate matching relation, and actual measurement data of a forest maximum biomass density and a forest growth rate with geographic coordinate positioning are collected; s1.2, establishing a data set for model calculation and carbon sink potential evaluation, wherein the data set comprises forest basic data and forest environment data; forest basic data comprise forest stand composition, dominant wood, standard wood breast diameter, standard wood tree height, forest stand density, carbon content, forest age and absolute geographic coordinates; Forest environment data include slope, slope direction, canopy density, earth index, annual average air temperature under different climatic conditions, annual total precipitation data. Further, the specific implementation method of the step S2 includes the following steps: s2.1, data cleaning is carried out on the data collected in the step S1, and abnormal value data and incomplete data are removed; s2.2, constructing an integrated model, and verifying that the data in the data set comprise forest maximum biomass density, forest growth rate, forest bioma