CN105303299A - Method for determining forest growth model - Google Patents
Method for determining forest growth model Download PDFInfo
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- CN105303299A CN105303299A CN201510653762.6A CN201510653762A CN105303299A CN 105303299 A CN105303299 A CN 105303299A CN 201510653762 A CN201510653762 A CN 201510653762A CN 105303299 A CN105303299 A CN 105303299A
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Abstract
A method for determining a forest growth model is characterized by comprising the steps of dividing a standing forest into 5-7 kinds according to different site conditions, and determining counting sampling trees according to different tree species, namely the standing forest comprises i=1,2,...,m tree species; and each tree species comprises j=1,2,...,n counting sampling trees; acquiring data of more than 40 observation points, performing more than two years of observation on the counting sampling trees, thereby obtaining the diameter-at-breast-height dij, tree height Hij and stand density Nij (stem/km<2>) of the counting sampling trees; and known the mixed ratio K, average height H, average diameter-at-breast-height D and total density N of the standing forest, and establishing a mathematical model for the diameter-at-breast-height and tree height. Growth factor data are observed in the standing forest for more than three years. A growth model for the diameter-at-breast-height and tree height of the standing forest is established by means of a GM(1,2) model.
Description
One, technical field
The method carries out determining quantifier elimination to Forest Growth.
Two, technical background
Forest growth model base is basis using standing forest general characteristic index as model, sets up stand growth or the relation between harvest yield and the general characteristic factor, estimates the Growth and yield of whole standing forest.
The model that forest growth model base constructs is mathematical model, mathematical model is to the measuring and calculating of the count issue amount of carrying out in forestry field, and study the mutual relationship of its amount and the dynamic law of quantitative change, model is found application in Forest Growth, and has been divided new classification according to the feature of field of forestry.A good forest growth model base can estimate the development trend of standing forest under various specified conditions, can become the prototype of forest management model.
Three, summary of the invention
This invention proposes a kind of method determining forest growth model base, it is characterized in that: standing forest, when considering the land occupation condition of certain standing forest, is divided into 5 ~ 7 classes according to the difference of land occupation condition by (1), and determine attributed sampling wood according to the difference of seeds, namely this standing forest has i=1,2 ..., m seeds, each seeds have product 1,2 ..., n attributed sampling wood; Collect the data of more than 40 observation stations, and carry out the observation of continuous more than 2 years to attributed sampling wood, the diameter of a cross-section of a tree trunk 1.3 meters above the ground obtaining attributed sampling wood is d
ij, the height of tree is H
ij, the density of crop is N
ij(/km
2); The mixed double ratio of this standing forest known is K, mean height is H, mean DBH increment is D, gross density is N, sets up the mathematical model of the diameter of a cross-section of a tree trunk 1.3 meters above the ground and Tree height growth 1.:
In formula, Δ d
ijfor annual increment, the Δ H of the diameter of a cross-section of a tree trunk 1.3 meters above the ground
ijfor the annual increment of the height of tree; Again observed reading is substituted into mathematical model 1. in, solve the value of 8 equation coefficients a, b, c, d, e, f, g, h;
(2) if when not considering the land occupation condition of standing forest, adopt gray system Coordination Model GM (1,2), this model is the first-order linear dynamic model having 2 variablees; The growth factor data of Continuous Observation more than 3 years in standing forest, use this model to set up the growth model of the standing forest diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree.
The advantage of this method is: according to built model, utilizes the observed reading of 2 years or 3 years just can calculate the annual increment amount of the diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree.
Four, accompanying drawing illustrates:
Nothing
Five, embodiment:
1, certain standing forest is divided into 5 ~ 7 classes according to the difference of land occupation condition, and determines attributed sampling wood according to the difference of seeds;
2, attributed sampling wood is carried out to the observation of continuous more than 2 years, the diameter of a cross-section of a tree trunk 1.3 meters above the ground obtaining attributed sampling wood is d
ij, the height of tree is H
ij, the density of crop is N
ij(/km
2);
3, according to the mixed double ratio of standing forest be K, mean height is H, mean DBH increment is D, gross density is N, sets up the mathematical model of the diameter of a cross-section of a tree trunk 1.3 meters above the ground and Tree height growth;
4, adopt gray system Coordination Model GM (1,2), the growth factor data of Continuous Observation more than 3 years in standing forest, use this model to set up the growth model of the standing forest diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree.
Claims (1)
1. determine the method for forest growth model base for one kind, it is characterized in that: standing forest, when considering the land occupation condition of certain standing forest, is divided into 5 ~ 7 classes according to the difference of land occupation condition by (1), and determine attributed sampling wood according to the difference of seeds, namely this standing forest has i=1,2 ..., m seeds, each seeds have product 1,2 ..., n attributed sampling wood; Collect the data of more than 40 observation stations, and carry out the observation of continuous more than 2 years to attributed sampling wood, the diameter of a cross-section of a tree trunk 1.3 meters above the ground obtaining attributed sampling wood is d
ij, the height of tree is H
ij, the density of crop is N
ij(/km
2); The mixed double ratio of this standing forest known is K, mean height is H, mean DBH increment is D, gross density is N, sets up the mathematical model of the diameter of a cross-section of a tree trunk 1.3 meters above the ground and Tree height growth 1.:
In formula, Δ d
ijfor annual increment, the Δ H of the diameter of a cross-section of a tree trunk 1.3 meters above the ground
ijfor the annual increment of the height of tree; Again observed reading is substituted into mathematical model 1. in, try to achieve the value of equation coefficient a, b, c, d, e, f, g, h;
(2) if when not considering the land occupation condition of standing forest, adopt gray system Coordination Model GM (1,2), this model is the first-order linear dynamic model having 2 variablees; The growth factor data of Continuous Observation more than 3 years in standing forest, use this model to set up the growth model of the standing forest diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree.
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Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106022939A (en) * | 2016-06-27 | 2016-10-12 | 北京林业大学 | Evaluation and calculation method for five structural indexes of forest spatial distribution |
CN106294990A (en) * | 2016-08-08 | 2017-01-04 | 青岛智能产业技术研究院 | Tree breast-height diameter Forecasting Methodology |
CN107945171A (en) * | 2017-12-07 | 2018-04-20 | 北华大学 | Arboreal growth history observation procedure |
CN110853699A (en) * | 2019-10-30 | 2020-02-28 | 北京林业大学 | Method for establishing single-tree growth model under large-area condition |
CN111353628A (en) * | 2018-12-24 | 2020-06-30 | 北京林业大学 | Method for researching standard growth index of Chinese leading tree species |
CN112183802A (en) * | 2019-07-02 | 2021-01-05 | 北京林业大学 | Prediction and forecast method for relative growth of 28 arbor species in China |
CN112182829A (en) * | 2019-07-02 | 2021-01-05 | 北京林业大学 | Method for predicting and forecasting extreme growth of 30 arbor species in China |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
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CN101447050A (en) * | 2008-12-18 | 2009-06-03 | 北京林业大学 | Model system and prediction method for forest growth |
CN103678870A (en) * | 2013-09-24 | 2014-03-26 | 中国林业科学研究院资源信息研究所 | Growth and management interactive visualization simulation method for forest stand |
-
2015
- 2015-10-12 CN CN201510653762.6A patent/CN105303299B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101447050A (en) * | 2008-12-18 | 2009-06-03 | 北京林业大学 | Model system and prediction method for forest growth |
CN103678870A (en) * | 2013-09-24 | 2014-03-26 | 中国林业科学研究院资源信息研究所 | Growth and management interactive visualization simulation method for forest stand |
Non-Patent Citations (1)
Title |
---|
梁彭等: "基于GM(1,3)模型的树木生长量预测", 《中南林业科技大学学报》 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106022939A (en) * | 2016-06-27 | 2016-10-12 | 北京林业大学 | Evaluation and calculation method for five structural indexes of forest spatial distribution |
CN106294990A (en) * | 2016-08-08 | 2017-01-04 | 青岛智能产业技术研究院 | Tree breast-height diameter Forecasting Methodology |
CN106294990B (en) * | 2016-08-08 | 2019-06-25 | 青岛智能产业技术研究院 | Tree breast-height diameter prediction technique |
CN107945171A (en) * | 2017-12-07 | 2018-04-20 | 北华大学 | Arboreal growth history observation procedure |
CN111353628A (en) * | 2018-12-24 | 2020-06-30 | 北京林业大学 | Method for researching standard growth index of Chinese leading tree species |
CN112183802A (en) * | 2019-07-02 | 2021-01-05 | 北京林业大学 | Prediction and forecast method for relative growth of 28 arbor species in China |
CN112182829A (en) * | 2019-07-02 | 2021-01-05 | 北京林业大学 | Method for predicting and forecasting extreme growth of 30 arbor species in China |
CN110853699A (en) * | 2019-10-30 | 2020-02-28 | 北京林业大学 | Method for establishing single-tree growth model under large-area condition |
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