Construction method of moso bamboo breast-diameter age joint distribution dynamic model

Abstract

The invention discloses a construction method of a moso bamboo breast-diameter age joint distribution dynamic model. The construction method comprises the following steps: step 1, collecting moso bamboo continuous checking sample plot data in a research area; 2, summarizing the continuous checking data of the moso bamboo sample plot at each stage in the research area into moso bamboo breast-diameter age two-dimensional statistical data; 3, calculating edge distribution values of the diameter at breast height and the age of the moso bamboo; 4, fitting the two-dimensional statistical data of thebreast-diameter age of the moso bamboos in each stage in the step 2 to obtain a parameter vector of each binary Copula density function of each stage of data; 5, obtaining the optimal binary Copula density function of the DBH age of the moso bamboo continuous checking data in each stage; step 6, establishing a linear regression relationship between the binary Copula density function parameter andthe period number; and step 7, obtaining a moso bamboo breast-diameter age joint distribution dynamic model. The invention has the advantages that the regional scale moso bamboo biomass can be accurately predicted, the dynamic change of the moso bamboo breast-diameter age can be reflected, and a theoretical basis is provided for carbon transaction and scientific operation of a moso bamboo forest.

Description

technical field [0001] The invention relates to a method for constructing a dynamic model for estimating regional forest biomass, in particular to a method for constructing a dynamic model for the combined distribution of diameter at breast height and age of moso bamboo. Background technique [0002] Moso bamboo is an important forest type in southern my country, and it is also the most important and typical type of bamboo forest resources. my country is one of the countries with the richest bamboo forest resources. According to the results of the 8th National Forest Resources Continuous Inventory, the area of ​​bamboo forests in my country is 600 million hm 2 , is the main producer of moso bamboo, with a forest area of ​​4.43 million hm 2 , its area accounts for about 70% of the national bamboo forest area. [0003] Phyllostachys moso has high economic benefits, strong carbon sequestration function, sustainable use of one-time afforestation, and selective felling without d...

AI Technical Summary

Problems solved by technology

[0006] 1) Since the modeling data is basically a certain period of tree-measurement factor data, the relationship between the parameters of the joint probability distribution function and time t cannot be established. Therefore, most of the reported binary probability distribution models of tree-measurement factor are static models, which cannot predict The future joint distribution (density) value of the tree factor;

[0007] 2) The existing binary probability distribution function has many parameters, such as the binary Sbb function has 9 parameters, and the binary Beta function with low estimation accuracy has two forms of 3 parameters and 5 parameters (Liu Enbin, 2010), binary Weibull function The distribution function has 7 parameters, etc., so establishing the functional relationship between the distribution function parameters and time t requires many periods of data, which is difficult to achieve;

[0008] 3) The marginal distribution (density) value of the dendrometer factor is easier to obtain, and its joint distribution (density) value provides more comprehensive information than the marginal distribution (density) value, and has higher practical value. The estimation accuracy of tree quantity, each diameter class of trees, the number of trees of each age class and the growth amount are higher, but the marginal distribution (density) value of the tree measurement factor is known, and the joint distribution (density) cannot be obtained by using the commonly used binary distribution (density) function value;

[0009] 4) It is necessary to measure the joint distribution (density) value of the tree factor when fitting the parameters of the commonly used binary distribution function, but it is difficult to obtain the joint distribution (density) value of the tree factor. joint distribution, but from the existing data, it is possible to obtain the number of moso bamboos of each diameter class and age class in each plot, but it is impossible to obtain the diameter and age data of each moso bamboo in each plot, that is Only the marginal distribution value of DBH and age of moso bamboo can be obtained, but the joint distribution value of DBH and age of moso bamboo can not be obtained. Another example is to study the joint distribution of masson pine tree height at a regional scale. Two sets of masson pine samples with high precision, one set of samples can only get the distribution value of masson pine diameter at breast height, and the other set of samples can get the distribution value of its tree height, that is, the joint distribution value of masson pine tree height at breast height cannot be obtained, the above two cases , since the joint distribution value of the two tree-measurement factors cannot be obtained, the common binary distribution function cannot be used to construct the joint distribution model of the tree-measurement factor

[0010] The binary joint distribution dynamic model of arborescence factors can not only accurately predict the biomass and growth of future forests in a certain area, but also reflect the dynamic change trend of arborescence factors in this area over time, so as to provide scientific information for regional forest management and planning. Theoretical basis, but to make the dynamic model of the joint distribution of tree measurement factors constructed has theoretical and practical significance, at least the data of one business cycle of the tree species is needed, and many tree species have to obtain the data of one business cycle of the tree species because their growth and business cycle are too long It is very difficult. In addition, there are many parameters of the commonly used binary distribution function, so the construction of the dynamic model of the binary joint distribution of the tree measurement factor is difficult to achieve.

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