Method for evaluating over-ground net primary productivity of mountain willow shrub

Abstract

The invention provides a mountain willow shrub overground net primary productivity assessment method, which comprises the following steps: selecting M mountain willow shrubs as samples, and collecting 9 parameters for each mountain willow shrub; the nine parameters are estimated through a linear function, an exponential function, a power function and a logarithmic function; and determining an optimal estimation function according to the correlation coefficient R2, the root mean square error RMSE, the average deviation Bias and the mean absolute error MAE. The method for evaluating the aboveground net primary productivity of the mountain willow shrub has the characteristics of high precision, low cost and the like, and can be widely applied to the field of productivity evaluation related to high and cold shrubs.

Description

Technical Field

[0001] The invention relates to a productivity assessment technology, and in particular to a method for assessing the above-ground net primary productivity of Salix montana shrubs. Background Art

[0002] Qilian Mountain is located in the mid-latitude northern temperate zone, deep inland and far away from the ocean. Due to its unique local climatic conditions, it has formed a special alpine ecosystem, which is of great strategic significance in maintaining the climate of the northwest plateau, soil and water conservation, and ensuring agricultural production and development in the Hexi oasis. Salix alpiniae is a perennial deciduous upright shrub of the Salix genus of the Salicaceae family. It is an excellent ecological tree species of the alpine shrubs in the eastern section of Qilian Mountain and one of the endemic tree species in my country. It is mainly distributed in high mountain ridges, slopes, and river valleys at an altitude of 3,000 to 3,500 meters. It has the characteristics of wide distribution, high productivity, and strong germination ability. Salix alpiniae not only has the function of protecting grass and cultivating grass, but also can effectively control soil erosion, conserve water sources, and cultivate soil fertility. The shrub with Salix alpiniae as the dominant species is the main vegetation type of the alpine shrub meadow in the eastern Qilian Mountains. It is not only an important pasture for local herdsmen, but also an important part of maintaining the ecological stability of the region. It plays an important role in improving the regional ecological environment, increasing species biodiversity, and maintaining community succession.

[0003] The above-ground net primary productivity (ANPP) of shrubland is an important indicator for measuring vegetation productivity and is also the basis for evaluating the function of ecosystems in arid and semi-arid regions. At present, most shrubland ANPP estimation methods use environmental and ecological factors such as climate and soil nutrients to establish assessment models. However, alpine shrub meadows have large spatial heterogeneity and frequent rainfall during the plant growing season. The ANPP assessment models established using environmental and ecological factors such as climate and soil nutrients have many limitations and low assessment accuracy. At the same time, there has been no report on the quantitative assessment of the ANPP of Salix matsudana shrubland.

[0004] It can be seen that in the prior art, the method for evaluating the aboveground net primary productivity of shrubs is only a qualitative evaluation, and the evaluation accuracy is relatively low; further, there is no quantitative evaluation method in the prior art. Summary of the invention

[0005] In view of this, the main purpose of the present invention is to provide a method for evaluating the aboveground net primary productivity of Salix montana shrubs with relatively high accuracy and capable of quantitative analysis.

[0006] In order to achieve the above object, the technical solution proposed by the present invention is:

[0007] A method for evaluating the aboveground net primary productivity of Salix montana shrubs comprises the following steps:

[0008] Step 1: Select M Salix montana shrubs as samples, and collect 9 parameters for each Salix montana shrub, which are: x N1 =H N 、x N2 =CA N 、x N3 =V N , x N5 =n N 、x N6 = lx, x N7 =d N 、x N8 =v N 、x N9 = l N ×d N ; Among them, H N represents the height of the Nth Salix altissima bush, CA N represents the crown area of ​​the Nth Salix alba shrub, V N represents the volume of the Nth Salix alba bush, represents the mean crown width of the Nth Salix alba shrub, n N represents the number of new branches of the Nth Salix altissima bush, l N represents the length of the new branches of the Nth Salix altissima bush, v N represents the volume of new branches of the Nth Salix altissima bush, d N represents the diameter of the new branches of the Nth Salix alba shrub; M and N are both natural numbers, and N = 1, 2, …, M.

[0009] Step 2: Use linear functions Exponential function Power function Logarithmic function Estimate the 9 parameters in step 1 respectively; where a, b, c, g, j, k are all parameters; i is a natural number, and i = 1, 2, ..., 9.

[0010] Step 3: For the 9 parameters in step 1, use the linear function Exponential function Power function Logarithmic function Calculate the corresponding correlation coefficient R 2 , root mean square error RMSE, average deviation Bias, mean absolute error MAE, as follows:

[0011]

[0012] Among them, y Ni represents the measured value of the ith parameter of the Nth sample; represents the mean of the measured values ​​of the i-th parameter of M strains; Represents the estimated value of the i-th parameter of the N-th sample.

[0013] Step 4: According to the correlation coefficient R 2 The value of is large and the value of the root mean square error RMSE, the value of the average deviation Bias or the value of the absolute average error MAE is small. From the linear function Exponential function Power function Logarithmic function The optimal estimation function is determined in .

[0014] In summary, in the method for evaluating the aboveground net primary productivity of Salix matsudana shrubs described in the present invention, four overall morphological parameters of the shrubs and five new branch parameters are selected as independent variables, and four models, namely, linear function, exponential function, power function, and logarithmic function, are used to evaluate the aboveground net primary productivity. Then, according to the correlation coefficient R of each parameter under the four models, 2 , root mean square error RMSE, average bias Bias or absolute average error MAE, and comprehensively evaluate the optimal model as the above-ground net primary productivity evaluation model of Salix fasciata shrubs. It can be seen that since the 9 parameters selected by the above-ground net primary productivity evaluation method of Salix fasciata shrubs described in the present invention are relatively comprehensive, and 4 function models are used for estimation and analysis at the same time, its evaluation accuracy is relatively high; moreover, the cost of using the above-ground net primary productivity evaluation method of Salix fasciata shrubs described in the present invention for evaluation is relatively low. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention provides a schematic diagram of the overall process of a method for evaluating the aboveground net primary productivity of Salix montana shrubs. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Figure 1 The overall process diagram of the method for evaluating the aboveground net primary productivity of Salix matsudana shrubs described in the present invention is shown in FIG. Figure 1 As shown, the method for evaluating the aboveground net primary productivity of Salix montana shrubs of the present invention comprises the following steps:

[0018] Step 1: Select M Salix montana shrubs as samples, and collect 9 parameters for each Salix montana shrub, which are: x N1 =H N 、x N2 =CA N 、x N3 =VN , x N5 =n N 、x N6 = l N 、x N7 =d N 、x N8 =v N 、x N9 = l N ×d N ; Among them, H N represents the height of the Nth Salix altissima bush, CA N represents the crown area of ​​the Nth Salix alba shrub, V N represents the volume of the Nth Salix alba bush, represents the mean crown width of the Nth Salix alba shrub, n N represents the number of new branches of the Nth Salix altissima bush, l N represents the length of the new branches of the Nth Salix altissima bush, v N represents the volume of new branches of the Nth Salix altissima bush, d N represents the diameter of the new branches of the Nth Salix alba shrub; M and N are both natural numbers, and N = 1, 2, …, M.

[0019] In the present invention, the average crown width of the Nth Salix altissima bush is for: Among them, D N1 represents the north-south crown width of the Nth Salix alba shrub, D N2 Represents the east-west crown width of the Nth mountain willow shrub.

[0020] In the present invention, the crown area of ​​the Nth Salix altissima bush is Wherein, π is the circumference of a circle. In fact, the crown width of the mountain willow shrub is irregular, so the crown width area CA in the present invention is N It is the crown area of ​​irregular crown.

[0021] In the present invention, the volume of the Nth Salix altissima bush is

[0022] In the present invention, the volume of new branches of the Nth Salix altissima bush is

[0023] Step 2: Use linear functions Exponential function Power function Logarithmic function Estimate the 9 parameters in step 1 respectively; where a, b, c, g, j, k are all parameters; i is a natural number, and i = 1, 2, ..., 9.

[0024] Step 3: For the 9 parameters in step 1, use the linear function Exponential function Power function Logarithmic function Calculate the corresponding correlation coefficient R 2 , root mean square error RMSE, average deviation Bias, mean absolute error MAE, as follows:

[0025]

[0026]

[0027] Among them, y Ni represents the measured value of the ith parameter of the Nth sample; represents the mean of the measured values ​​of the i-th parameter of M strains; Represents the estimated value of the i-th parameter of the N-th sample.

[0028] Step 4: According to the correlation coefficient R 2 The value of is large and the value of the root mean square error RMSE, the value of the average deviation Bias or the value of the absolute average error MAE is small. From the linear function Exponential function Power function Logarithmic function The optimal estimation function is determined in .

[0029] In summary, in the method for evaluating the aboveground net primary productivity of Salix matsudana shrubs described in the present invention, four overall morphological parameters of the shrubs and five new branch parameters were selected as independent variables, and four models, namely, linear function, exponential function, power function, and logarithmic function, were used to evaluate the aboveground net primary productivity. Then, the correlation coefficient R of each parameter under the four models was calculated. 2 , root mean square error RMSE, average bias Bias or absolute average error MAE, and comprehensively evaluate the optimal model as the above-ground net primary productivity evaluation model of Salix fasciata shrubs. It can be seen that since the 9 parameters selected by the above-ground net primary productivity evaluation method of Salix fasciata shrubs described in the present invention are relatively comprehensive, and 4 function models are used for estimation and analysis at the same time, its evaluation accuracy is relatively high; moreover, the cost of using the above-ground net primary productivity evaluation method of Salix fasciata shrubs described in the present invention for evaluation is relatively low.

[0030] Example

[0031] This embodiment selects the alpine meadow willow shrubs in the East Qilian Mountains as the research object, specifically: the alpine meadow willow shrubs in Zhuaxixiulong Town, Tianzhu Tibetan Autonomous County, Gansu Province are taken as the research object; 30 willow shrubs are randomly selected, and the four overall morphological parameters of the shrubs, including crown area, shrub volume, shrub height, and the product of the crown mean and shrub height, and the five new branch parameters, including the number of new branches, the length of new branches, the volume of new branches, the diameter of new branches, and the product of the length of new branches and the diameter of new branches, are selected as independent variables. The above-mentioned linear function, exponential function, power function, and logarithmic function are used to estimate the above-mentioned four overall morphological parameters of the shrubs, and the specific data are shown in Table 1.

[0032] Table 1 Evaluation values ​​of overall morphological parameters of Salix montana shrubs

[0033]

[0034] In Table 1, ** indicates extremely significant correlation, and * indicates significant correlation. According to the data in Table 1, the crown area CA of the Nth Salix altissima bush is N Among the four established models of linear function, exponential function, power function and logarithmic function, the four overall morphological parameters of the shrubs were significantly correlated, and the exponential function model had the largest correlation coefficient R2 and the smallest mean absolute error MAE, so the exponential function model performed best. N The corresponding correlation coefficients R2 of the four established models of linear function, exponential function, power function and logarithmic function were 0.87, 0.80, 0.87 and 0.81, and the corresponding mean absolute errors MAE were 19.00%, 25.17%, 17.40% and 23.20%, respectively. The comprehensive performance was second. N The corresponding correlation coefficients R2 of the four established models of linear function, exponential function, power function and logarithmic function were 0.63, 0.53, 0.59 and 0.62 respectively, and the highest mean absolute error MAE was 39.56%, which was the worst overall performance. N The exponential function model established Best.

[0035] The above-mentioned linear function, exponential function, power function and logarithmic function were used to estimate the above-mentioned five new branch parameters. The specific data are shown in Table 2.

[0036] Table 2 ANPP statistical model of new shoot parameters

[0037]

[0038] In Table 2, ** indicates extremely significant correlation, and * indicates significant correlation. According to the data in Table 2, the number of new branches n of the Nth Salix montaneensis bush is N The correlation coefficient R of the established power function model and linear function model 2 The power function model has the lowest root mean square error RMSE, average bias Bias and mean absolute error MAE, which are 14.79%, -0.74% and 12.30% respectively. The power function model has the best overall performance. N ×d N In the four models of linear function, exponential function, power function and logarithmic function established for the cause, the correlation coefficient R 2 The average absolute errors (MAE) were 27.74%, 30.81%, 28.56% and 33.85% respectively, and the comprehensive performance was second. N Among the four models of linear function, exponential function, power function and logarithmic function established, the correlation coefficients R 2 The minimum, and were 0.51, 0.59, 0.59 and 0.57 respectively, the root mean square error RMSE corresponding to the four models were 42.58, 38.88, 39.02, 44.29 respectively, the mean absolute error MAE corresponding to the four models were 28.7%, 25.1%, 24.77%, 30.1% respectively, and the root mean square error RMSE and mean absolute error MAE corresponding to the four models were relatively high. In short, by comparing the correlation coefficient R after the five new branch parameters were estimated using the above linear function, exponential function, power function, and logarithmic function 2 , root mean square error RMSE, average bias Bias and mean absolute error MAE, the optimal estimation model constructed with the parameters of the new branches of the mountain sedge shrub was selected as the number of new branches n of the Nth mountain sedge shrub N The power function model established:

[0039] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the aboveground net primary productivity of Salix montana shrubs, characterized in that: The evaluation method comprises the following steps: Step 1: Select M Salix montana shrubs as samples, and collect 9 parameters for each Salix montana shrub, which are: x N1 =H N 、x N2 =CA N 、x N3 =V N , x N5 =n N 、x N6 = l N 、x N7 =d N 、x N8 =v N 、x N9 = l N ×d N ; Among them, H N represents the height of the Nth Salix altissima bush, CA N represents the crown area of ​​the Nth Salix alba shrub, V N represents the volume of the Nth Salix alba bush, represents the mean crown width of the Nth Salix alba shrub, n N represents the number of new branches of the Nth Salix altissima bush, l N represents the length of the new branches of the Nth Salix altissima bush, v N represents the volume of new branches of the Nth Salix altissima bush, d N represents the diameter of the new branches of the Nth Salix matsudana bush; M and N are both natural numbers, and N = 1, 2, ..., M; Step 2: Use linear functions Exponential function Power function Logarithmic function Estimate the 9 parameters in step 1 respectively; where a, b, c, g, j, k are all parameters; i is a natural number, and i = 1, 2, ..., 9; Step 3: For the 9 parameters in step 1, use the linear function Exponential function Power function Logarithmic function Calculate the corresponding correlation coefficient R 2 , root mean square error RMSE, average deviation Bias, mean absolute error MAE, as follows: Among them, y Ni represents the measured value of the ith parameter of the Nth sample; represents the mean of the measured values ​​of the i-th parameter of M strains; represents the estimated value of the i-th parameter of the N-th sample; Step 4: According to the correlation coefficient R 2 The value of is large and the value of the root mean square error RMSE, the value of the average deviation Bias or the value of the absolute average error MAE is small. From the linear function Exponential function Power function Logarithmic function The optimal estimation function is determined in .

2. The method for evaluating aboveground net primary productivity of Salix montana shrubs according to claim 1, characterized in that: The average crown width of the Nth Salix altissima shrub for: Among them, D N1 represents the north-south crown width of the Nth Salix alba shrub, D N2 Represents the east-west crown width of the Nth mountain willow shrub.

3. The method for evaluating the aboveground net primary productivity of Salix matsudana shrubs according to claim 2, characterized in that: The crown area of ​​the Nth Salix alba shrub Among them, π is the ratio of a circle to its circumference.

4. The method for evaluating aboveground net primary productivity of Salix montana shrubs according to claim 3, characterized in that: The volume of the Nth Salix altissima bush 5. The method for evaluating aboveground net primary productivity of Salix montana shrubs according to claim 4, characterized in that: The volume of new branches of the Nth Salix altissima bush

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