Method for estimating aboveground biomass of shrubs

Abstract

The invention relates to the field of overground biomass estimation, in particular to a method for estimating overground biomass of shrubs. According to the method, through image processing and statistical analysis technologies, the problem that the environment is damaged by a traditional field sampling method is effectively solved. The method comprises four steps of data acquisition, model construction, model selection and model inspection. According to the method, shrub forms are recorded through photographing, plant height and overground area data are extracted through image processing software, and then a biomass relation model is constructed according to the plant height and the overground area. By comparing the fitting degrees of different models, an optimal model is selected, and the accuracy of the model is verified by actually measured data. According to the method, the ecological environment is protected, the biomass estimation efficiency is greatly improved, and powerful technical support is provided for carbon sink calculation.

Description

Technical Field

[0001] The invention belongs to the field of aboveground biomass estimation, and in particular relates to a method for estimating aboveground biomass of shrubs. Background Art

[0002] Shrubs are an important component of the ecosystem, and their aboveground biomass is one of the key indicators for measuring ecosystem carbon storage. At present, the assessment of aboveground biomass of shrubs mainly relies on on-site sampling, which is not only environmentally destructive but also time-consuming and labor-intensive. In addition, traditional sampling methods are difficult to adapt to the needs of large-scale biomass surveys, limiting their widespread application in ecological restoration and environmental monitoring. Therefore, there is an urgent need for an efficient biomass estimation method that does not damage plants. Summary of the invention

[0003] In view of the above-mentioned technical problems, the present invention proposes a method for estimating the aboveground biomass of shrubs, which avoids the direct felling and destruction of plants in traditional methods, reduces interference with the ecological environment, is simple to operate, easy to implement, and can quickly and accurately obtain estimation results.

[0004] In order to achieve the above object, the present invention provides a method for estimating aboveground biomass of shrubs, comprising the following steps:

[0005] (1) Data acquisition: Select an estimation area, take photos of the shrubs in the area, and use Image J software to process the images to obtain the values ​​of the shrub height and aboveground area. Sample the plants in the estimation area to obtain the actual value of the biomass.

[0006] (2) Model construction: constructing an aboveground biomass model based on the plant height and aboveground area, the model is divided into: plant height and biomass model, aboveground area and biomass model, and plant height, aboveground area and biomass model;

[0007] (3) Model selection: Determine the optimal model of shrub biomass by comparing the model fit;

[0008] (4) Model verification: The optimal model is verified by comparing the measured data with the model prediction data.

[0009] Preferably, in step (1), when taking photos, a side shot is adopted, a fully light-shielding white background cloth is used, and a special ruler is set in the background cloth.

[0010] Preferably, in step (1), based on the scale of the dedicated ruler, a ruler is added in the Image J software and measurement options are set to measure the data of the plant height and aboveground area.

[0011] Preferably, in step (1), during sampling, the plant samples taken and recorded are harvested by a harvesting method and bagged for retrieval to determine the actual biomass.

[0012] Preferably, in step (2), the plant height (H), aboveground area (S), and plant height (H) and aboveground area (S) of the shrub are analyzed with the actual biomass data of the shrub respectively; the shrub biomass (W) model includes: model 1, model 2, model 3 and model 4, wherein:

[0013] Model 1: W=a+b×H;

[0014] Model 2: W=a+b×S;

[0015] Model 3: W=a+b×S×H;

[0016] Model 4: W = a×S+b×H+c.

[0017] Preferably, in step (3), the indicators used for model fit include: adjusted coefficient of determination (R 2 ), residual sum of squares (SSE), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC).

[0018] Preferably, after adjustment R 2 The formula is:

[0019] The formula for SSE is:

[0020] The formula for RMSE is:

[0021] The formula of AIC is: AIC = 2k-2ln(L),

[0022] The formula for BIC is: BIC=ln(n)k-2ln(L).

[0023] Among them, W i and is the measured value and predicted value of the i-th biomass, is the average of all measured biomass, N is the number of samples tested, p is the number of regression coefficients, k is the number of model parameters, and L is the likelihood function.

[0024] Preferably, in step (3), after adjustment, R 2 The higher the value, the smaller the SSE or RMSE value, and the smaller the AIC or BIC value, indicating that the model is the optimal model.

[0025] Preferably, in step (4), the measured data is compared and analyzed with the model prediction data. When the prediction points are distributed on the 1:1 line, it indicates that the model prediction value is accurate.

[0026] Compared with the prior art, the advantages and positive effects of the present invention are:

[0027] 1. The present invention avoids the direct felling and destruction of plants in traditional methods, reduces the interference with the ecological environment, and estimates biomass through image method. The operation is simple and easy to implement. Users only need to take pictures and process images without on-site sampling.

[0028] 2. Using the image method to estimate biomass can complete the collection and analysis of a large amount of data in a short period of time, improving the efficiency of biomass estimation. Through professional image processing and model construction, the aboveground biomass of shrubs can be estimated. The optimization and verification of the model further ensure the reliability of the estimation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of Model 2 of the present invention;

[0030] Figure 2 This is a schematic diagram of the actual biomass of shrubs in the present invention and the biomass predicted by model 2. DETAILED DESCRIPTION

[0031] A method for estimating aboveground biomass of shrubs, see Figures 1 to 2 , including the following steps:

[0032] (1) Data acquisition:

[0033] During the field survey, a white shade cloth was used as the background board for the shrubs, and a ruler was attached to the background board. When taking pictures, the side view should be taken at the center of the shrubs. The image was processed using Image J software. Based on the scale of the special ruler, a ruler was added to Image J software and the measurement options were set to measure the plant height and aboveground area data. Plants in the estimated area were sampled, and the plant samples recorded by photography were harvested and bagged to determine the actual biomass.

[0034] (2) Model construction: The aboveground biomass models of shrubs are divided into: plant height and biomass model, aboveground area and biomass model, and plant height, aboveground area and biomass model. The specific model formulas are: Model 1: W = a + b × H; Model 2: W = a + b × S; Model 3: W = a + b × S × H; Model 4: W = a × S + b × H + c.

[0035] The plant height (H), aboveground area (S), and plant height (H) and aboveground area (S) of shrubs were analyzed with the actual biomass data of shrubs, and the formulas were obtained for model 1: W = -0.034 + 0.140 × H; model 2: W = 0.036 + 2.149 × S; model 3: W = 0.124 + 0.551 × S × H; model 4: W = 1.172 × S + 0.066 × H - 0.0004.

[0036] (3) Model selection: The optimal model of shrub biomass model was determined by comparing the model fit. The indicators used for model fit included: adjusted coefficient of determination (R 2 ), residual sum of squares (SSE), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC).

[0037] Adjusted R 2 The formula is:

[0038] The formula for SSE is:

[0039] The formula for RMSE is:

[0040] The formula of AIC is: AIC = 2k-2ln(L),

[0041] The formula for BIC is: BIC=ln(n)k-2ln(L).

[0042] Among them, W i and is the measured value and predicted value of the i-th biomass, is the average of all measured biomass, N is the number of samples tested, p is the number of regression coefficients, k is the number of model parameters, and L is the likelihood function.

[0043] When evaluating the goodness of fit of the model, the adjusted R 2 The higher the value, the smaller the SSE or RMSE value, and the smaller the AIC or BIC value, the better the model is. As shown in Table 1, the adjusted R 2 The highest was 0.973, the lowest SSE value was 0.0013, and the lowest RMSE value was 0.0126. The model fit was relatively optimal. However, since Model 3 and Model 4 used two parameters, plant height (H) and aboveground area (S), the AIC and BIC values ​​would increase. Considering that the principle of simplicity should be followed when selecting models, models with fewer parameters should be selected as much as possible. The adjusted R of Model 2 2, SSE or RMSE values ​​are second only to model 4, but the AIC and BIC values ​​are the smallest. After comprehensive consideration, model 2 is selected as the optimal model, that is, W = 0.036 + 2.149 × S.

[0044] Table 1

[0045]

[0046] (4) Model verification: By comparing the measured biomass data with the model-predicted biomass data, when the predicted points are distributed on the 1:1 line, it indicates that the model prediction value is accurate.

[0047] The method uses photography to record the morphology of shrubs, and extracts the height and above-ground area data of shrubs through image processing software, and then constructs a biomass relationship model based on the height and above-ground area. By comparing the fit of different models, the optimal model is selected, and the accuracy of the model is verified by measured data. The present invention not only protects the ecological environment, but also greatly improves the efficiency of biomass estimation, providing strong technical support for carbon sink calculation.

[0048] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for estimating aboveground biomass of shrubs, characterized in that: The following steps are involved: (1) Data acquisition: Select an estimation area, take photos of the shrubs in the area, and use Image J software to process the images to obtain the values ​​of the shrub height and aboveground area. Sample the plants in the estimation area to obtain the actual value of the biomass. (2) Model construction: constructing an aboveground biomass model based on the plant height and aboveground area, the model is divided into: a plant height and biomass model, an aboveground area and biomass model, and a product of plant height and aboveground area and biomass model; (3) Model selection: Determine the optimal model of shrub biomass by comparing the model fit; (4) Model verification: The optimal model is verified by comparing the measured data with the model prediction data.

2. A method for estimating aboveground biomass of shrubs according to claim 1, characterized in that: In step (1), when taking photos, a side shot method is adopted, a fully light-shielding white background cloth is used, and a special ruler is set in the background cloth.

3. A method for estimating aboveground biomass of shrubs according to claim 1, characterized in that: In step (1), based on the scale of the dedicated ruler, a ruler is added in the Image J software and measurement options are set to measure the data of the plant height and aboveground area.

4. A method for estimating aboveground biomass of shrubs according to claim 1, characterized in that: In step (1), during sampling, the plant samples that have been photographed and recorded are harvested by a harvesting method and bagged and taken back to determine the actual biomass.

5. A method for estimating aboveground biomass of shrubs according to claim 1, characterized in that: In step (2), the plant height (H), aboveground area (S), and plant height (H) and aboveground area (S) of the shrub are analyzed with the actual biomass data of the shrub respectively; the shrub biomass (W) model includes: model 1, model 2, model 3 and model 4, wherein, Model 1: W=a+b×H; Model 2: W=a+b×S; Model 3: W=a+b×S×H; Model 4: W = a×S+b×H+c.

6. A method for estimating aboveground biomass of shrubs according to claim 1, characterized in that: In step (3), the indicators used for model fit include: adjusted coefficient of determination (R 2 ), residual sum of squares (SSE), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC).

7. A method for estimating aboveground biomass of shrubs according to claim 6, characterized in that: Adjusted R 2 The formula is: The formula for SSE is: The formula for RMSE is: The formula of AIC is: AIC = 2k-2ln(L), The formula of BIC is: BIC = ln(n)k-2ln(L); Among them, W i and is the measured value and predicted value of the i-th biomass, is the average of all measured biomass, N is the number of samples tested, p is the number of regression coefficients, k is the number of model parameters, and L is the likelihood function.

8. A method for estimating aboveground biomass of shrubs according to claim 6, characterized in that: In step (3), after adjustment, R 2 The higher the value, the smaller the SSE or RMSE value, and the smaller the AIC or BIC value, indicating that the model is the optimal model.

9. A method for estimating aboveground biomass of shrubs according to claim 1, characterized in that: In step (4), the measured data is compared with the model prediction data. When the prediction points are distributed on the 1:1 line, it indicates that the model prediction value is accurate.

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