CN111368248A - Estimation method for biomass of Yunnan pine seedlings - Google Patents
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- 239000002028 Biomass Substances 0.000 title claims abstract description 122
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
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- 102000004190 Enzymes Human genes 0.000 description 1
- 108090000790 Enzymes Proteins 0.000 description 1
- 238000010220 Pearson correlation analysis Methods 0.000 description 1
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- 238000004177 carbon cycle Methods 0.000 description 1
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a method for estimating biomass of Yunnan pine seedlings, which comprises the following steps: (1) selecting the fresh weight of leaves of the nursery stock as independent variables, and taking root biomass (W roots), stem biomass (W stems), leaf biomass (W leaves), aboveground part biomass (W ground) and single plant biomass (W single plants) as dependent variables; (2) selecting a model, and constructing a biomass estimation model by selecting a common function, namely y-axb(ii) a To satisfy the alignment, the power function is linearized taking the logarithm: logY is log a + blogX; (3) establishing a biomass estimation model, and obtaining organs and places of roots, stems and leaves by utilizing the quantitative relation between the fresh weight of the leaves and the biomassUpper part and single plant biomass; (4) according to a decision coefficient R2The model accuracy is evaluated by the standard error SEE and the root mean square error RMSE of the estimated value, and the linear relationship between the estimated value and the measured value is compared with the difference between y and x. The method is an intuitive and accurate biomass estimation method, and facilitates the biomass estimation in the Yunnan pine seedling stage.
Description
Technical Field
The invention relates to the technical field of biomass estimation research, in particular to a Yunnan pine seedling biomass estimation method which is suitable for quick and accurate estimation of seedling biomass.
Background
The nursery stock is the material basis of afforestation, and the afforestation of planting seedlings is always the most important afforestation mode in China. Taking the forestry statistics of 1999 + 2008 as an example, 4504 million hectas are accumulated in the national planting and afforestation, and 3273 hundred million seedlings used for afforestation account for 84.9% of the total area of afforestation. The quality of the nursery stock is related to the overall level of forestry production, the nursery stock is guaranteed to have excellent quality, and the method is the key point for research of forestry production researchers. However, the seedling biomass is closely related to the quality of the seedlings and is extremely important for evaluating the quality of the seedlings, so that research on the seedling biomass is necessary.
The biomass is the most basic quantitative characteristic of the ecosystem and is an important index for quantitatively researching the carbon cycle of the ecosystem and the carbon reserve of vegetation. The estimation of the biomass is an important content of forest resource investigation and evaluation, and has important significance for understanding global climate change and improving the estimation precision of the carbon reserve of the land ecosystem. The existing biomass measurement methods mainly comprise an actual measurement method, a model method and a remote sensing method, and although the accuracy of a direct measurement method is relatively high, the harvesting workload is large, the cost is high, and the damage to local resources is serious. Compared with the biomass model method, the method is more economical and practical, and particularly can greatly reduce the workload in the large-scale forest biomass investigation. The relative growth model method is the most commonly applied one in the biomass model, and is an ideal model for standing wood biomass simulation, and the expression is y-axbSimply referred to as CAR model. The biomass is estimated by establishing the corresponding relation between the factors (ground diameter and seedling height) easy to measure and the biomass, and the method has high accuracy and small destructiveness.
The Yunnan pine is one of the main tree species in the southwest region of China, has wide distribution, is lucidity, quick in growth, drought-resistant and barren-resistant, is a pioneer tree species for afforestation of barren mountains in the southwest region and a main material tree species, and has higher economic value and ecological benefit. However, the biomass of Yunnan pine seedlings is mainly measured by a whole plant harvesting method at present, and although the measuring method is the most accurate, the method has the defects of time consumption, labor consumption, great destructiveness and the like, and is only suitable for biomass research with small range and small quantity. The corresponding relation between the biomass of the Yunnan pine seedling and the easily-detected factors is established, a more visual and accurate biomass estimation method is provided on the basis of reducing the workload and the destructiveness, and the biomass estimation in the Yunnan pine seedling stage is facilitated. The prior art is short in time consumption, labor consumption and great destructiveness, and is not enough in reducing the workload and reducing the destructiveness.
At present, an intuitive and accurate method for estimating the biomass of Yunnan pine seedlings is lacked.
Disclosure of Invention
The invention provides a visual and accurate estimation method for the biomass of Yunnan pine seedlings.
The technical scheme of the invention is as follows: the invention discloses a method for estimating biomass of Yunnan pine seedlings, which comprises the following steps:
(1) determining independent variables and dependent variables of the model, selecting the fresh weight of leaves of the nursery stock as the independent variables, and taking root biomass (W roots), stem biomass (W stems), leaf biomass (W leaves), aboveground part biomass (W overground) and single plant biomass (W single plants) as the dependent variables;
(2) selecting a model, and constructing a biomass estimation model by selecting a common function (power function equation), namely y is axb(ii) a To satisfy the alignment, the power function is linearized taking the logarithm: logY is log a + blogX;
(3) establishing a biomass estimation model, and obtaining the biomass of each organ, overground part and single plant of roots, stems and leaves by utilizing the quantitative relation between the fresh weight of the leaves and the biomass;
(4) evaluating the accuracy of the model based on the decision coefficient R2The model accuracy is evaluated by the standard error SEE and the root mean square error RMSE of the estimated value, and the linear relationship between the estimated value and the measured value is compared with the difference between y and x.
Further, in the step (4),
determining the coefficient (R)2):
In the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
Further, in the step (4),
standard error (SEE):
in the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
Further, in the step (4),
root Mean Square Error (RMSE):
in the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
Has the advantages that: the method is an intuitive and accurate biomass estimation method, and facilitates the biomass estimation in the Yunnan pine seedling stage.
Compared with the prior art, the invention has the following advantages: (1) according to the correlation between the fresh weight of the Yunnan pine seedling leaves and the biomass, the invention replaces the former method that more factors (ground diameter and seedling height) with lower precision are adopted by using the relative easily-measured factor fresh weight of the leaves. The method is simple and easy to implement, and the biological quantity estimation precision of the Yunnan pine seedlings is obviously improved.
(2) The biomass estimation in the Yunnan pine seedling stage is convenient. The research result can provide theoretical and technical reference for the biological quantity measurement of Yunnan pine seedlings and other seedlings. Is suitable for quick and accurate estimation of the biomass of the nursery stock.
Drawings
FIG. 1 is a graph showing the relationship between biomass and fresh leaf weight according to the present invention; wherein a, b, c, d and e represent biomass model maps of fresh weight of roots, stems, leaves, ground, single plants and leaves respectively.
FIG. 2 is a graph showing the linear relationship between the measured biomass value and the estimated value according to the present invention; and comparing with Y ═ X; wherein a, b, c, d and e represent the linear relationship between measured values and estimated values of root, stem, leaf, ground and individual plant biomass respectively.
Detailed Description
The invention is further illustrated by the following examples. It should be understood that these examples are illustrative and exemplary of the present invention, and are not intended to limit the scope of the present invention in any way.
The invention discloses a method for estimating biomass of Yunnan pine seedlings, which comprises the following steps:
(1) determining independent variables and dependent variables of the model, selecting the fresh weight of leaves of the nursery stock as the independent variables, and taking root biomass (W roots), stem biomass (W stems), leaf biomass (W leaves), aboveground part biomass (W overground) and single plant biomass (W single plants) as the dependent variables;
(2) selecting a model, and constructing a biomass estimation model by selecting a common function (power function equation), namely y is axb(ii) a To satisfy the alignment, the power function is linearized taking the logarithm: logY is log a + blogX;
(3) establishing a biomass estimation model, and obtaining the biomass of each organ, overground part and single plant of roots, stems and leaves by utilizing the quantitative relation between the fresh weight of the leaves and the biomass;
(4) evaluating the accuracy of the model based on the decision coefficient R2The model accuracy is evaluated by the standard error SEE and the root mean square error RMSE of the estimated value, and the linear relationship between the estimated value and the measured value is compared with the difference between y and x.
Determining the coefficient (R)2):
Standard error (SEE):
root Mean Square Error (RMSE):
in the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
Test example 1
Step 2, model independent variable inspectionAnd verifying the feasibility of selecting the fresh weight of the leaves as an independent variable. Selecting fresh weight of nursery stock leaves to be respectively matched with the biomass (W) of nursery stock rootsRoot of herbaceous plant) Stem biomass (W)Stem of a tree) Leaf biomass (W)Leaf of Chinese character) Aboveground biomass (W)On the ground) And biomass of the individual plant (W)Single plant) Pearson correlation analysis was performed and the results are shown in Table 1.
And 3, constructing a biomass estimation model. Using fresh leaf weight as independent variable, root, stem, leaf, ground and single plant biomass (W) as dependent variable, selecting common function power function equation (y ═ ax)b) Constructing a biomass estimation model, and taking logarithm of a power function to linearize in order to meet the homodyne: log y ═ log a + blogX, and the biomass equation constructed is shown in table 2, and the model diagram is shown in fig. 1.
And 4, checking the model precision. According to the coefficient of determination (R)2) The model accuracy was evaluated by the standard error of the estimated value (SEE) and the Root Mean Square Error (RMSE) (table 2).
And step 5, comparing the model measured value with the estimated value, drawing a regression equation scatter diagram (figure 2) between the biomass measured value and the estimated value, wherein the X axis represents the biomass estimated value, the Y axis represents the biomass measured value, a middle oblique line (a dotted line) represents a linear scatter regression relation between the biomass measured value and the estimated value, and comparing the regression equation between the measured value and the estimated value with the difference of Y & ltX & gt (figure 2).
As can be seen from fig. 2: the correlation between the estimated value and the measured value is good, the determination coefficient of a linear regression equation of the estimated value and the measured value is between 0.74 and 0.94, the slope a and the intercept b of the regression linear equation have no significant difference with the slope and the intercept of the x-line, which means that the linear regression equation between the predicted value and the measured value is basically the same with the y-x-line, thus the difference between the estimated value and the measured value is small, and the accuracy of the constructed biomass model meets the requirement. The correlation coefficient of the leaf freshness and biomass of Yunnan pine seedlings is shown in table 1:
TABLE 1
Note: indicates a 0.01 significant level.
The biological quantity estimation model and the precision test of the Yunnan pine seedling are shown in table 2:
TABLE 2
According to the method for estimating the biomass of the Yunnan pine seedlings, provided by the invention, the biomass estimation model is established according to the corresponding relation between the fresh weight of the leaves of the seedlings and the biomass, the biomass of the Yunnan pine seedlings can be quickly obtained, the measurement process is convenient, the accuracy is high, and the estimation precision of the biomass of the seedlings is improved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (4)
1. A method for estimating biomass of Yunnan pine seedlings is characterized by comprising the following steps:
(1) determining independent variables and dependent variables of the model, selecting the fresh weight of leaves of the nursery stock as the independent variables, and taking root biomass (W roots), stem biomass (W stems), leaf biomass (W leaves), aboveground part biomass (W overground) and single plant biomass (W single plants) as the dependent variables;
(2) selecting a model, and constructing a biomass estimation model by selecting a common function (power function equation), namely y is axb(ii) a To satisfy the alignment, the power function is linearized taking the logarithm: logY is log a + blogX;
(3) establishing a biomass estimation model, and obtaining the biomass of each organ, overground part and single plant of roots, stems and leaves by utilizing the quantitative relation between the fresh weight of the leaves and the biomass;
(4) evaluation of model accuracy based on the decision coefficientR2The model accuracy is evaluated by the standard error SEE and the root mean square error RMSE of the estimated value, and the linear relationship between the estimated value and the measured value is compared with the difference between y and x.
2. The method for estimating biomass of Yunnan pine seedlings according to claim 1, which is characterized in that: in the step (4), the step (c),
determining the coefficient (R)2):
In the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
3. The method for estimating biomass of Yunnan pine seedlings according to claim 1, which is characterized in that: in the step (4), the step (c),
standard error (SEE):
in the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
4. The method for estimating biomass of Yunnan pine seedlings according to claim 1, which is characterized in that: in the step (4), the step (c),
root Mean Square Error (RMSE):
in the formula: y isiTo be the measured value of the biomass,
in order to be a biomass estimation value,
n is the number of samples as the average value of the biomass measured values.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112651143A (en) * | 2021-01-26 | 2021-04-13 | 兰州交通大学 | Estimation method for biomass on haloxylon ammodendron ground |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109214591A (en) * | 2018-10-12 | 2019-01-15 | 北京林业大学 | A kind of xylophyta ground biomass prediction technique and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109214591A (en) * | 2018-10-12 | 2019-01-15 | 北京林业大学 | A kind of xylophyta ground biomass prediction technique and system |
Non-Patent Citations (4)
| Title |
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| 杨烁: "不同类型草地地上生物量的估测", 《中国优秀硕士学位论文全文数据库 农业科技》 * |
| 汪梦婷: "云南松苗木构件生物量的分配及其预估模型构", 《西部林业科学》 * |
| 王得贤: "用单叶鲜重速测青贮玉米地上生物量", 《草业科学》 * |
| 魏海燕等: "雷山常绿阔叶林甜储拷的单木生物量估测", 《贵州农业科学》 * |
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| CN112651143A (en) * | 2021-01-26 | 2021-04-13 | 兰州交通大学 | Estimation method for biomass on haloxylon ammodendron ground |
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