CN105893737A - Construction method of geological model-based forest growing stock estimation model - Google Patents
Construction method of geological model-based forest growing stock estimation model Download PDFInfo
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- CN105893737A CN105893737A CN201610172414.1A CN201610172414A CN105893737A CN 105893737 A CN105893737 A CN 105893737A CN 201610172414 A CN201610172414 A CN 201610172414A CN 105893737 A CN105893737 A CN 105893737A
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Abstract
The invention relates to a construction method of a geological model-based forest growing stock estimation model. According to the construction method, a the geological model-based forest growing stock estimation model is constructed in allusion to the estimation problem of forest growing stock per unit area; more variable factors can be brought, and variable factors presenting nonlinear relationships with the growing stock can be introduced to construct the model, so that a new method and a new thought are provided for the estimation of the forest growing stock.
Description
One, technical field
A kind of method that the present invention relates to forest reserves estimation models, a kind of forest reserves based on geography model is estimated
Survey the construction method of model.
Two, technical background
Forest reserves is to evaluate Ecological Function and the fundamental of economic worth, be also forest survey Main Factors it
One.Stocking per unit area, indicates the effect of PRODUCTIVITY OF FOREST SOIL and Operation Measures to a certain extent.Standing forest unit are
The size of upper accumulation, in addition to being affected by artificial intensive farming, mainly by the density of crop, land occupation condition and Stand Age
Combined influence.At present, forest reserves is estimated the linear model homing method that main employing is traditional, due to forest unit plane
Relation between savings accumulated amount and each image factor is complicated, is not simple linear relationship, so its model accuracy is limited.This
Invention proposes a kind of forest reserves estimation models based on geography model, for the estimation of forest reserves provide new method and
New approaches.
Three, summary of the invention
In order to overcome deficiency and the limitation of current forest reserves prediction model, it is an object of the invention to provide a kind of based on ground
The method of the forest reserves estimation models of model.
The object of the present invention is achieved like this:
The size of standing forest stocking per unit area, in addition to being affected by artificial intensive farming, mainly by the density of crop, on the spot bar
Part and the combined influence of Stand Age.Thus build following geography model for predicting forest stocking per unit area.ai-environmental factors (landform, soil) coefficient；(i=0,1,2 ...)；δi-
On the spot (landform, soil) non-quantitation (qualitative) factor coefficient；bi-crop type (forest cover) Quantitative factor coefficient；εi-woods
Type (forest cover) non-quantitation (qualitative) factor coefficient；ci-the age of stand (t) factor coefficient；About xi/δi, x1-elevation, x2-
The gradient (tan δi)x3-thickness of soil (A+B) x4-chad rate；δ1-slope aspect (8), δ11-δ18Represent respectively east, south, west,
North, northeast, the southeast, northwest, southwest；δ2Position ,-slope (5) δ21-δ25Represent ridge, upper, middle and lower, paddy, δ respectively2-flat
Ground (1), δ4-(3) δ41-δ42Represent carse, medium and small respectively.About yi/εi, satellite RS:NDVI (N), b0,K is mixed friendship rate；UAV:n (density),(average crown diameter),ε1(4) ε11-ε14Generation respectively
Table filling, pin, wealthy, mixed
This invention has the advantage that
1. can be incorporated in the middle of model by more Variable Factors, through suitable screening, making the precision of raising model become can
Energy.
2. design for Forest Planning, afforest decision support and forest management and administration provides a kind of efficient technological means.
Four, detailed description of the invention:
In order to overcome deficiency and the limitation of current forest reserves prediction model, it is an object of the invention to provide a kind of based on
The method of the forest reserves estimation models of geography model.Its detailed description of the invention is as follows:
Selected survey region, according to institute's testing index, can be testing index (accumulation, the gradient, position, slope, slope on the spot
To, height above sea level, the density of crop etc.) or remote-sensing inversion index (Remote sensing parameters relevant with accumulation) determines Variable Factors,
Variable Factors includes three parts substantially, and vegetation measures the factor, environmental factors and the Stand Age factor, according to basic modelCarry out matching and the structure of unit are forest reserves model.
Claims (1)
1. a construction method for forest reserves estimation models based on geography model, is characterized by: standing forest stocking per unit area
Size, in addition to being affected by artificial intensive farming, mainly by the combined influence of the density of crop, land occupation condition and Stand Age.
Thus build following geography model for predicting forest stocking per unit area,
ai-environmental factors (landform, soil) coefficient (i=0,1,2 ...)；δi-
On the spot (landform, soil) non-quantitation (qualitative) factor coefficient；bi-crop type (forest cover) Quantitative factor coefficient；εi-woods
Type (forest cover) non-quantitation (qualitative) factor coefficient；ci-the age of stand (t) factor coefficient；About xi/δi, x1-elevation, x2-
The gradient (tan δi)x3-thickness of soil (A+B) x4-chad rate；δ1-slope aspect (8), δ11-δ13Represent respectively east, south, west,
North, northeast, the southeast, northwest, southwest；δ2Position ,-slope (5) δ21-δ25Represent ridge, upper, middle and lower, paddy, δ respectively3-flat
Ground (1), δ4-(3) δ41-δ43Represent carse, medium and small respectively；About yi/εi, satellite RS:NDVI (N),
K is mixed friendship rate；UAV:n (density),(average crown diameter),ε1(4) ε11-ε14Represent respectively filling, pin,
Wealthy, mixed.
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| CN201610172414.1A CN105893737A (en) | 2016-03-24 | 2016-03-24 | Construction method of geological model-based forest growing stock estimation model |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106815850A (en) * | 2017-01-22 | 2017-06-09 | 武汉地普三维科技有限公司 | The method that canopy density forest reserves very high is obtained based on laser radar technique |
| CN108733619A (en) * | 2018-05-17 | 2018-11-02 | 北京林业大学 | Global arbitrary forest bottom class growth prediction model quantitative estimation method |
| CN108764583A (en) * | 2018-06-06 | 2018-11-06 | 浙江农林大学 | The unbiased predictor method of forest reserves |
| CN108776850A (en) * | 2018-06-06 | 2018-11-09 | 浙江农林大学 | A kind of accurate predictor method of forest reserves |
| CN109190178A (en) * | 2018-08-07 | 2019-01-11 | 广西壮族自治区林业科学研究院 | A kind of China fir carbon density calculation method based on DEM terrain factor |
| CN110020961A (en) * | 2019-01-18 | 2019-07-16 | 北京林业大学 | Chinese main arbor species plot/bottom class's sub-index grinds the technical method built |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106815850A (en) * | 2017-01-22 | 2017-06-09 | 武汉地普三维科技有限公司 | The method that canopy density forest reserves very high is obtained based on laser radar technique |
| CN108733619A (en) * | 2018-05-17 | 2018-11-02 | 北京林业大学 | Global arbitrary forest bottom class growth prediction model quantitative estimation method |
| CN108764583A (en) * | 2018-06-06 | 2018-11-06 | 浙江农林大学 | The unbiased predictor method of forest reserves |
| CN108776850A (en) * | 2018-06-06 | 2018-11-09 | 浙江农林大学 | A kind of accurate predictor method of forest reserves |
| CN108776850B (en) * | 2018-06-06 | 2021-09-28 | 浙江农林大学 | Accurate estimation method for forest accumulation |
| CN108764583B (en) * | 2018-06-06 | 2021-09-28 | 浙江农林大学 | Unbiased prediction method for forest accumulation |
| CN109190178A (en) * | 2018-08-07 | 2019-01-11 | 广西壮族自治区林业科学研究院 | A kind of China fir carbon density calculation method based on DEM terrain factor |
| CN109190178B (en) * | 2018-08-07 | 2022-11-01 | 广西壮族自治区林业科学研究院 | Method for calculating carbon density of fir based on DEM (digital elevation model) terrain factor |
| CN110020961A (en) * | 2019-01-18 | 2019-07-16 | 北京林业大学 | Chinese main arbor species plot/bottom class's sub-index grinds the technical method built |
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