Method for estimating biomass on grass land

Abstract

The invention relates to the field of above-ground biomass estimation, and particularly provides a method for estimating grass-ground above-ground biomass, which comprises four steps of data acquisition, model construction, model selection and model checking.According to the method, through image processing and statistical analysis technologies, the problem that the environment is damaged by a traditional on-site sampling method is effectively solved, and the estimation accuracy of the grass-ground above-ground biomass is improved. A herbaceous sample plot is recorded through photographing, and herbaceous coverage information is extracted through image processing software; then constructing a relation model of the herbal coverage and the biomass; selecting an optimal model by comparing the fitting degrees of different models; and verifying the accuracy of the model 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 herbs. Background Art

[0002] Biomass is a key data for estimating carbon storage, and the method used to estimate biomass determines its estimation accuracy to some extent. At present, the evaluation of aboveground biomass of herbaceous plants mainly relies on on-site sampling, which is not only environmentally destructive but also time-consuming and labor-intensive. In addition, the acquisition of herbaceous cover data mostly relies on manual judgment, which results in relatively large errors. Therefore, there is an urgent need for an efficient method for estimating herbaceous biomass 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 herbs, 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 herbaceous plants, comprising the following steps:

[0005] (1) Data acquisition: Select an estimation area, take photos of herbs in the area, and use Image J software to process the images to obtain the coverage value of herbs in the area. Sample the herbs in the estimation area to obtain the actual value of herbaceous biomass.

[0006] (2) Model construction: Based on the coverage of herbs, a herbaceous cover and biomass model was constructed;

[0007] (3) Model selection: Determine the optimal model of herbaceous biomass model by comparing the model's fit index;

[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, the sample square frame is placed on the herbs, and the photo is taken from a bird's-eye view at the center position above the sample square frame to ensure the integrity of the sample square frame and the herbs in the frame.

[0010] Preferably, in step (1), the sample box is a square box, and based on the side length of the square box, a ruler is added to the ImageJ software and measurement options are set to measure the herbaceous area and coverage data.

[0011] Preferably, in step (1), during sampling, the herbs in the sample frame after photographing and recording are harvested by a harvesting method and bagged and taken back to determine the actual biomass.

[0012] Preferably, in step (2), the coverage (C) and biomass (W) model includes: Model 1 and Model 2, wherein:

[0013] Model 1: W=a+b×C;

[0014] Model 2: W = a × C b .

[0015] 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).

[0016] Preferably,

[0017] Adjusted R 2 The formula is:

[0018] The formula for SSE is:

[0019] The formula for RMSE is:

[0020] The formula of AIC is: AIC = 2k-2ln(L),

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

[0022] 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.

[0023] 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.

[0024] 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.

[0025] Compared with the prior art, the advantages and positive effects of the present invention are:

[0026] 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.

[0027] 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 herbaceous plants can be estimated. The optimization and verification of the model further ensure the reliability of the estimation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is the model diagram of model 1;

[0029] Figure 2 This is the model diagram of model 2;

[0030] Figure 3 Figure 2 shows the actual biomass of herbs and the biomass predicted by model 2. DETAILED DESCRIPTION

[0031] A method for estimating aboveground biomass of herbaceous plants, see Figures 1 to 3 , including the following steps:

[0032] (1) Data acquisition:

[0033] Place the sample frame on the herbs, and take a photo from the top center of the sample frame to ensure the integrity of the sample frame and the herbs in the frame. After taking the photo, record the type and number of the sampled plants, and conduct partial sampling of the plants in the estimated area to obtain the actual value of the biomass of a single plant;

[0034] Based on the scale of the dedicated ruler, add a ruler and set the measurement options in the Image J software to measure the coverage data;

[0035] (2) Model construction:

[0036] See also Figure 1 , 2 , set the herb cover as X, the herb biomass as Y, and perform model fitting in SPSS software to construct a herb cover (C) and biomass (W) model. The formula of model 1 is W = a + b × C, and the formula of model 2 is W = a × C b The final fitting model 1 is: W = 0.11 × C - 0.02; model 2 is: W = 0.13 × C 2.33 .

[0037] (3) Model selection:

[0038] 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);

[0039] Adjusted R 2 The formula is:

[0040] The formula for SSE is:

[0041] The formula for RMSE is:

[0042] The formula of AIC is: AIC = 2k-2ln(L),

[0043] The formula for BIC is: BIC=ln(n)k-2ln(L).

[0044] 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.

[0045] Adjusted 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. As shown in Table 1, the adjusted R 2 It is 0.92, which is higher than Model 1, and the SSE, RMSE, AIC and BIC values ​​of Model 2 are all smaller than those of Model 1, that is, Model 2 is the optimal model.

[0046] Table 1

[0047]

[0048] (4) Model verification:

[0049] See also Figure 3 , compare and analyze the measured data with the predicted data of model 2. When the predicted points are distributed on the 1:1 line, it indicates that the model prediction value is accurate.

[0050] This method effectively solves the damage to the environment caused by traditional on-site sampling methods through image processing and statistical analysis techniques. It uses photography to record herbaceous sample plots and extracts herbaceous cover information through image processing software. It then constructs a relationship model between herbaceous cover and biomass. It selects the optimal model by comparing the fit of different models. It verifies the accuracy of the model with measured data, which not only protects the ecological environment, but also greatly improves the efficiency of biomass estimation, providing strong technical support for carbon sink calculations.

[0051] 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 herbaceous plants, characterized in that: The following steps are involved: (1) Data acquisition: Select an estimation area, take photos of herbs in the area, and use Image J software to process the images to obtain the coverage value of herbs in the area. Sample the herbs in the estimation area to obtain the actual value of herbaceous biomass. (2) Model construction: Based on the coverage of herbs, a herbaceous cover and biomass model was constructed; (3) Model selection: Determine the optimal model of herbaceous biomass model by comparing the model's fit index; (4) Model verification: The optimal model is verified by comparing the measured data with the model prediction data.

2. The method for estimating aboveground biomass of herbaceous plants according to claim 1, characterized in that: In step (1), when taking photos, place the sample square frame on the herbs, and take photos from a bird's-eye view at the center of the sample square frame to ensure the integrity of the sample square frame and the herbs in the frame.

3. The method for estimating aboveground biomass of herbaceous plants according to claim 2, characterized in that: In step (1), the sample box is a square box. Based on the side length of the square box, a ruler is added to the Image J software and the measurement options are set to measure the herbaceous area and coverage data.

4. The method for estimating aboveground biomass of herbaceous plants according to claim 2, characterized in that: In step (1), during sampling, the herbs in the sample box after photographing and recording are harvested by the harvesting method and bagged and taken back to determine the actual biomass.

5. The method for estimating aboveground biomass of herbaceous plants according to claim 1, characterized in that: In step (2), the cover (C) and biomass (W) model includes: Model 1 and Model 2, wherein, Model 1: W = a + b × C; Model 2: W = a × C b .

6. The method for estimating aboveground biomass of herbaceous plants 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. The method for estimating aboveground biomass of herbaceous plants 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 herbaceous plants according to claim 6 or 7, 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. The method for estimating aboveground biomass of herbaceous plants 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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