Vegetation yield influence assessment method and system based on multi-factor stress

Abstract

The invention relates to the field of vegetation yield influence evaluation, and discloses a vegetation yield influence evaluation method and system based on multi-factor stress, and the method comprises the steps: obtaining leaf area index data in a vegetation growth cycle of a research region, and obtaining a monthly average leaf area index value corresponding to the research region; utilizing the prediction model and the monthly average leaf area index value to obtain a vegetation yield prediction value corresponding to the research area; obtaining monthly average values of various stress factors according to various stress factor data; and correcting the vegetation yield predicted value according to the monthly average value of each stress factor to realize evaluation of the vegetation yield. According to the method, the vegetation yield is accurately and efficiently evaluated by monitoring various stress factor data.

Description

Technical Field

[0001] The present invention relates to the field of vegetation yield assessment, and in particular to a method and system for assessing the impact of vegetation yield based on multi-factor stress. Background Art

[0002] Vegetation yield impact assessment is a quantitative analysis of the degree to which vegetation yield is affected by various factors. Through the assessment, we can understand how different factors affect vegetation growth and yield, thus providing a scientific basis for vegetation management and resource allocation.

[0003] By evaluating vegetation yield, we can better understand the growth status and yield potential of vegetation, so as to formulate reasonable agricultural management measures, protect and restore the ecological environment, and ensure the long-term sustainable development of agriculture. The results of vegetation yield evaluation can be used as the basis for formulating national or regional agricultural development plans, determining investment directions and related agricultural policies, and ensuring the effectiveness and pertinence of policies. The application of vegetation yield evaluation technology can promote the informatization and intelligent development of agricultural production, improve agricultural production efficiency and quality, and promote the process of agricultural modernization. However, the traditional vegetation yield impact assessment method lacks consideration of leaf area index (LAI), which is one of the most important variables for evaluating canopy structure and can accurately simulate the energy balance, gas exchange process and light distribution in the orchard canopy. Traditional assessment methods often ignore the importance of LAI, resulting in inaccurate assessment of vegetation growth and yield. In addition, traditional assessment methods do not fully consider stress factors, resulting in inaccurate final vegetation yield assessment.

[0004] Therefore, it is necessary to design a vegetation yield impact assessment method and system based on multi-factor stress to solve the problem of inaccurate assessment results caused by ignoring LAI and incomplete consideration of stress factors in traditional vegetation yield assessment methods. Summary of the invention

[0005] In view of this, the present invention proposes a vegetation yield impact assessment method and system based on multi-factor stress, aiming to solve the problem of inaccurate assessment results caused by ignoring LAI and incomplete consideration of stress factors in traditional vegetation yield assessment methods.

[0006] In one aspect, the present invention provides a method for evaluating the impact of vegetation yield based on multi-factor stress, comprising:

[0007] Acquire leaf area index data within the vegetation growth cycle of the study area, and preprocess the leaf area index data to obtain a monthly average leaf area index value corresponding to the study area;

[0008] Constructing a prediction model consisting of historical target vegetation yield, historical monthly average leaf area index values, model outcomes and constants, and inputting the monthly average leaf area index value of the study area into the prediction model to obtain the vegetation yield prediction value corresponding to the study area;

[0009] Obtain the diameter moisture data within the vegetation growth cycle of the study area to obtain the monthly average diameter moisture value corresponding to the study area; obtain the leaf temperature data within the vegetation growth cycle of the study area to obtain the monthly average leaf temperature value corresponding to the study area; obtain the soil pH value data within the vegetation growth cycle of the study area to obtain the monthly average soil pH value corresponding to the study area; obtain the biological abundance index data within the vegetation growth cycle of the study area to obtain the monthly average biological abundance index value corresponding to the study area;

[0010] According to the monthly average diameter water content, the monthly average leaf temperature, the monthly average soil pH value and the monthly average biological abundance index value, the vegetation yield prediction value is corrected, and the vegetation yield target value corresponding to the study area is obtained to achieve the evaluation of vegetation yield;

[0011] Furthermore, the vegetation yield prediction value is corrected according to the monthly average diameter water content value, the monthly average leaf temperature value, the monthly average soil pH value and the monthly average biological abundance index value, and the vegetation yield target value corresponding to the study area is obtained to achieve the evaluation of vegetation yield, which specifically includes:

[0012] Acquire a number of historical data corresponding to the study area, and filter out historical data corresponding to the monthly average diameter moisture value, the monthly average leaf temperature value, the monthly average soil pH value, and the monthly average biological abundance index value within a preset time interval from the several historical data;

[0013] Determine the influence degree of the monthly average diameter moisture value on the vegetation yield of the study area through the historical data corresponding to the monthly average diameter moisture value, and determine the influence weight coefficient of the monthly average diameter moisture value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average diameter moisture value;

[0014] Determine the influence degree of the monthly average leaf temperature value on the vegetation yield of the study area through the historical data corresponding to the monthly average leaf temperature value, and determine the influence weight coefficient of the monthly average leaf temperature value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average leaf temperature value;

[0015] Determine the influence degree of the monthly average soil pH value on the vegetation yield of the study area through the historical data corresponding to the monthly average soil pH value, and determine the influence weight coefficient of the monthly average soil pH value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average soil pH value;

[0016] Determine the influence degree of the monthly average biological abundance index value on the vegetation yield of the study area through the historical data corresponding to the monthly average biological abundance index value, and determine the influence weight coefficient of the monthly average biological abundance index value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average biological abundance index value;

[0017] According to the monthly average diameter moisture value and the influence weight coefficient corresponding to the monthly average diameter moisture value, the monthly average leaf temperature value and the influence weight coefficient corresponding to the monthly average leaf temperature value, the monthly average soil pH value and the influence weight coefficient corresponding to the monthly average soil pH value, and the monthly average biological abundance index value and the influence weight coefficient corresponding to the monthly average biological abundance index value, the vegetation yield prediction value is corrected to obtain the vegetation yield target value corresponding to the study area, thereby realizing the evaluation of vegetation yield.

[0018] Furthermore, the acquisition of leaf area index data within the vegetation growth cycle of the study area specifically includes:

[0019] Through LAI Smart, vegetation in several study areas is photographed at different zenith angles, and leaf area index data corresponding to the several study areas are obtained through image classification algorithms.

[0020] Furthermore, before constructing the prediction model consisting of the historical target vegetation yield, the historical monthly average leaf area index value, the model intercept and the constant, the method also includes:

[0021] Obtaining historical vegetation canopy photos corresponding to the study area, and based on the historical vegetation canopy photos, determining the historical target vegetation yield and historical monthly average leaf area index value corresponding to the study area;

[0022] According to the data relationship between the historical target vegetation yield and the historical monthly average leaf area index value, the model intercept and constant corresponding to the historical target vegetation yield obtained from the monthly average leaf area index value are determined.

[0023] Furthermore, the diameter body moisture data in the vegetation growth period of the study area is obtained to obtain the monthly average diameter body moisture value corresponding to the study area, specifically including:

[0024] Using satellite remote sensors, remote sensing inversion and detection of vegetation in several study areas are performed, and remote sensing satellite data corresponding to the several study areas are obtained;

[0025] Based on the remote sensing satellite data corresponding to the study area, determine the diameter water data of the study area during the vegetation growth period, and derive the diameter water bands in the diameter water data;

[0026] Convert the data formats of the plurality of framed images corresponding to the radial body moisture band into a specified data format, and convert the projection coordinate systems of the plurality of framed images corresponding to the radial body moisture band into a specified coordinate system;

[0027] Deleting invalid boundary values ​​in the diameter body moisture data, and splicing the multiple framed images in the specified coordinate system into a panoramic image;

[0028] The monthly average diameter-body moisture value corresponding to the study area is calculated according to the diameter-body moisture band corresponding to the panoramic image.

[0029] Furthermore, the obtaining of leaf temperature data within the vegetation growth period of the study area to obtain the monthly average leaf temperature value corresponding to the study area specifically includes:

[0030] Based on the remote sensing satellite data corresponding to the study area, determining the leaf temperature data of the study area within the vegetation growth cycle, and exporting the leaf temperature bands in the leaf temperature data;

[0031] Convert the data formats of the multiple framed images corresponding to the leaf temperature band into a specified data format, and convert the projection coordinate systems of the multiple framed images corresponding to the leaf temperature band into a specified coordinate system;

[0032] Deleting invalid boundary values ​​in the blade temperature data, and splicing the multiple framed images in the specified coordinate system into a panoramic image;

[0033] The monthly average leaf temperature value corresponding to the study area is calculated according to the leaf temperature band corresponding to the panoramic image.

[0034] Furthermore, the step of obtaining soil pH value data during the vegetation growth period of the study area to obtain the monthly average soil pH value corresponding to the study area specifically includes:

[0035] Based on the remote sensing satellite data corresponding to the study area, determine the soil pH value data of the study area within the vegetation growth cycle, and export the soil pH value bands in the soil pH value data;

[0036] Convert the data formats of the multiple framed images corresponding to the soil pH value band into a specified data format, and convert the projection coordinate systems of the multiple framed images corresponding to the soil pH value band into a specified coordinate system;

[0037] Deleting invalid boundary values ​​in the soil pH value data, and splicing the multiple framed images in the specified coordinate system into a panoramic image;

[0038] The monthly average soil pH value corresponding to the study area is calculated based on the soil pH value band corresponding to the panoramic image.

[0039] Furthermore, the obtaining of biological abundance index data within the vegetation growth cycle of the study area to obtain the monthly average biological abundance index value corresponding to the study area specifically includes:

[0040] Based on the remote sensing satellite data corresponding to the study area, determine the biological abundance index data of the study area within the vegetation growth cycle, and export the biological abundance index bands in the biological abundance index data;

[0041] The data formats of the multiple frame images corresponding to the biological abundance index band are converted into the specified data format, and the projection coordinate systems of the multiple frame images corresponding to the biological abundance index band are converted into the specified coordinate system;

[0042] Deleting invalid boundary values ​​in the biological abundance index data, and splicing the multiple framed images in the specified coordinate system into a panoramic image;

[0043] According to the biological abundance index band corresponding to the panoramic image, the monthly average biological abundance index value corresponding to the study area is calculated.

[0044] Compared with the prior art, the beneficial effects of the present invention are: vegetation yield is evaluated by LAI, there is a linear correlation between LAI and vegetation index, NDVI and LAI have a high correlation, and LAI can be quantitatively evaluated by calculating NDVI through remote sensing technology, so as to more accurately evaluate vegetation yield and production. This technical means is very important in practical applications, and can help farmers and scientists better manage farmland and forests, optimize crop yields and ecosystem health, and involve body moisture, leaf temperature, soil pH and biological abundance index as stress factors in the vegetation yield evaluation method and system, which greatly improves the accuracy of the evaluation results.

[0045] On the other hand, the present application also provides a vegetation yield impact assessment system based on multi-factor stress, comprising:

[0046] Preprocessing module: used to obtain leaf area index data within the vegetation growth cycle of the study area, and obtain the monthly average leaf area index value corresponding to the study area;

[0047] Vegetation yield prediction module: used to construct a prediction model, and use the prediction model and the monthly average leaf area index value to obtain the vegetation yield prediction value corresponding to the study area;

[0048] Stress factor value acquisition module: obtain the monthly average values ​​of various stress factors based on the body water data, leaf temperature data, soil pH data and biological abundance index data;

[0049] Vegetation yield assessment module: According to the monthly average diameter body moisture value, the monthly average leaf temperature value, the monthly average soil pH value and the monthly average biological abundance index value, the vegetation yield prediction value is corrected, and the vegetation yield target value corresponding to the study area is obtained to realize the assessment of vegetation yield.

[0050] It is understandable that the above-mentioned vegetation yield impact assessment method and system based on multi-factor stress have the same beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0052] Figure 1 A flow chart of a method for evaluating the impact of vegetation yield based on multi-factor stress provided by an embodiment of the present invention;

[0053] Figure 2 A functional block diagram of a vegetation yield impact assessment system based on multi-factor stress provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] Vegetation yield impact assessment is a remote sensing technology. By evaluating vegetation yield, we can better understand the growth status and yield potential of vegetation, so as to formulate reasonable agricultural management measures, protect and restore the ecological environment, and ensure the long-term sustainable development of agriculture. The results of vegetation yield assessment can be used as a basis for formulating national or regional agricultural development plans, determining investment directions and related agricultural policies, and ensuring the effectiveness and pertinence of policies. The application of vegetation yield assessment technology can promote the informatization and intelligent development of agricultural production, improve agricultural production efficiency and quality, and promote the process of agricultural modernization. However, traditional vegetation yield impact assessment methods lack consideration of leaf area index (LAI), which is one of the most important variables for assessing canopy structure and can accurately simulate the energy balance, gas exchange process and light distribution in the orchard canopy. Traditional assessment methods often ignore the importance of LAI, resulting in inaccurate assessment of vegetation growth and yield. In addition, traditional assessment methods do not fully consider stress factors, resulting in inaccurate final vegetation yield assessment. Therefore, it is necessary to design a vegetation yield impact assessment method and system based on multi-factor stress to solve the problem of inaccurate assessment results caused by ignoring LAI and incomplete consideration of stress factors in traditional vegetation yield assessment methods.

[0056] Reference Figure 1 In some embodiments of the present application, a method for evaluating the impact of vegetation yield based on multi-factor stress includes:

[0057] Step S100: obtaining leaf area index data within the vegetation growth cycle of the study area, and preprocessing the leaf area index data to obtain a monthly average leaf area index value corresponding to the study area.

[0058] Step S200: construct a prediction model consisting of historical target vegetation yield, historical monthly average leaf area index values, model outcomes and constants, and input the monthly average leaf area index value of the study area into the prediction model to obtain the vegetation yield prediction value corresponding to the study area.

[0059] Step S300: Obtain the diameter moisture data within the vegetation growth cycle of the study area, and obtain the monthly average diameter moisture value corresponding to the study area; obtain the leaf temperature data within the vegetation growth cycle of the study area, and obtain the monthly average leaf temperature value corresponding to the study area; obtain the soil pH value data within the vegetation growth cycle of the study area, and obtain the monthly average soil pH value corresponding to the study area; obtain the biological abundance index data within the vegetation growth cycle of the study area, and obtain the monthly average biological abundance index value corresponding to the study area.

[0060] Step S400: According to the monthly average diameter moisture value, the monthly average leaf temperature value, the monthly average soil pH value and the monthly average biological abundance index value, the vegetation yield prediction value is corrected, and the vegetation yield target value corresponding to the study area is obtained to realize the evaluation of vegetation yield.

[0061] Specifically, by preprocessing the leaf area index data to obtain the monthly average leaf area index value, the accuracy and reliability of the data can be improved. The accuracy and reliability of the data can be ensured by using geometric precision correction, radiation calibration, atmospheric correction, etc. These preprocessing steps can eliminate the errors in the measurement, so that the data can more truly reflect the actual situation. The monthly average leaf area index data can provide the average status of a time period, which is convenient for comparing and analyzing data from different regions and different time points. This is of great significance for evaluating the growth status of vegetation and environmental changes. The monthly average leaf area index data obtained through preprocessing can be used for long-term monitoring and research to observe the growth dynamics of vegetation and the impact of environmental changes on vegetation. This is of great significance for research in the fields of ecology and agricultural science. The monthly average leaf area index data can provide a scientific basis for decision-making in agricultural management, forest management, environmental protection, etc. By analyzing the changes in the leaf area index, we can better understand the health status of vegetation and formulate corresponding management measures.

[0062] Specifically, the prediction model predicts vegetation yield by analyzing historical data and characteristic attributes, using statistical methods and machine learning algorithms. The prediction model mainly relies on algorithms such as regression analysis and decision trees. By analyzing historical data, the key factors affecting yield are found and a prediction model is established. Regression analysis is a commonly used prediction method. It uses the regression model in statistics to associate yield with factors affecting yield and establish a prediction model. This method can consider the influence of multiple factors and improve the accuracy of the estimate; the decision tree algorithm analyzes historical data with clear results, finds features in the data, and uses this as a basis for predicting the newly generated data results. The decision tree is mainly composed of decision nodes, branches and leaf nodes. Each non-leaf node represents a test of a characteristic attribute, and each leaf node represents a result. This method can naturally process different types of characteristic data. In some embodiments of the present application, a prediction model consisting of historical target vegetation yield, historical monthly average leaf area index value, model outcome and constant is constructed using regression analysis, and the monthly average leaf area index value is used to obtain the vegetation yield prediction value corresponding to the study area.

[0063] Specifically, the vegetation yield prediction value is calculated through certain digital models and historical data, and it has certain assumptions and approximations. Using monthly average data for correction can better reflect the changes in the actual production process, thereby improving the accuracy and reliability of the prediction. In some embodiments of the present application, the vegetation yield prediction value is corrected using the monthly average diameter moisture value, the monthly average leaf temperature value, the monthly average soil pH value and the monthly average biological abundance index value, and the vegetation yield target value corresponding to the study area is obtained to achieve the evaluation of vegetation yield.

[0064] Specifically, according to the historical data of various stress factors, the weight coefficients of the influence of various stress factors on vegetation yield are determined, and then the vegetation yield prediction value is corrected to realize the evaluation of vegetation yield. By considering the degree of influence of different stress factors and assigning corresponding weight coefficients, the growth environment of vegetation can be more comprehensively evaluated, so as to obtain a prediction result that is more in line with the actual situation, construct the index weight, and make the prediction model more scientific and reliable. This method can not only improve the accuracy of prediction, but also provide a better basis for vegetation management and resource allocation. In some embodiments of the present application, historical data corresponding to the monthly average diameter water value, the monthly average leaf temperature value, the monthly average soil pH value and the monthly average biological abundance index value in a preset time interval are screened out from a number of historical data, and the degree of influence of various stress factors on vegetation yield is determined according to the historical data, and the weight coefficient of influence on vegetation yield is determined. The vegetation yield prediction value is corrected using the weight coefficient of influence of individual stress factors on vegetation yield, and the vegetation yield target value corresponding to the study area is obtained to realize the evaluation of vegetation yield.

[0065] Specifically, the leaf area index is a key indicator for measuring the growth and development of crops. Through the leaf area index, the changes in the leaf area of ​​crops can be obtained in real time, and then the growth stage, nutritional status and photosynthetic efficiency of crops can be inferred, and a scientific and reasonable assessment of the growth and development of crops can be made. Through long-term observation and recording of changes in the leaf area index of crops, the impact of climate change on crop growth can be deeply studied, crop yield fluctuations can be predicted, and a scientific basis can be provided for the improvement of crop germplasm resources and the breeding of new varieties. At the same time, the leaf area index is also an important parameter for evaluating vegetation restoration, forest carbon sink potential, and ecosystem health. In some embodiments of the present application, the leaf area index data within the vegetation growth cycle of the study area is obtained by shooting the vegetation in several study areas at different zenith angles through LAI Smart and using an image classification algorithm.

[0066] Specifically, the biological abundance index refers to the number of biological species and their distribution in a certain area, which is usually evaluated by calculating the diversity, richness and evenness of species in a specific area. Specific calculation methods include the number of species, species richness index, species evenness index, etc. These indicators can comprehensively reflect the health status and biodiversity of the ecosystem. The biological abundance index comprehensively considers the diversity, richness and distribution of species in the ecosystem, and can comprehensively reflect the health status of the ecosystem. By analyzing the changes in the biological abundance index, the degree to which vegetation yield is affected by environmental stress can be evaluated, thereby providing a scientific basis for ecological protection and restoration.

[0067] Specifically, the model intercept and constant of the vegetation yield prediction model are usually obtained by statistical analysis methods. The model intercept and constant are part of the model parameters, which are used to describe the relationship between vegetation yield and related variables. These parameters can be estimated from the data by regression analysis, time series analysis and other methods. Based on the vegetation canopy data, the target vegetation yield and the monthly average leaf area index value are obtained. The data are optimized by iteration and optimization algorithms, such as the least squares method, and the model intercept and constant of the prediction model are adjusted to improve the model's fit and prediction accuracy. In some embodiments of the present application, according to the data relationship between the historical target vegetation yield and the historical monthly average leaf area index value, the model intercept and constant corresponding to the historical target vegetation yield obtained by the monthly average leaf area index value are determined.

[0068] Specifically, the monthly average values ​​of various stress factors are obtained through remote sensing technology, the vegetation in several study areas is remotely sensed and detected through satellite remote sensors, and the remote sensing satellite data corresponding to several study areas are obtained, all stress factor data are obtained based on the remote sensing satellite data, the bands corresponding to the stress factors are exported, the data formats of multiple framed images corresponding to this band are converted into a specified data format, the projection coordinate systems of the multiple framed images are converted into a specified coordinate system, the invalid boundary values ​​in the data are deleted, the multiple framed images in the specified coordinate system are spliced ​​into a panoramic image, and the monthly average values ​​of all stress factors are calculated according to the bands corresponding to the panoramic image.

[0069] In the above-mentioned embodiment, the vegetation yield impact assessment method based on multi-factor stress can improve the accuracy and reliability of the data by preprocessing the leaf area index data to obtain the monthly average leaf area index value. By analyzing historical data and characteristic attributes, regression analysis and machine learning algorithms are used to predict vegetation yield, which can consider the influence of multiple factors and improve the accuracy of the estimate. The vegetation yield prediction value is corrected using the monthly average data, which can better reflect the changes in the actual production process, thereby improving the accuracy and reliability of the prediction. By taking the body moisture, leaf temperature, soil pH value and biological abundance index as stress factors, and by considering the degree of influence of different stress factors and assigning corresponding weight coefficients, the growth environment of the vegetation can be more comprehensively evaluated, thereby obtaining a prediction result that is more in line with the actual situation, constructing indicator weights, and making the prediction model more scientific and reliable.

[0070] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a vegetation yield impact assessment system based on multi-factor stress, including:

[0071] Preprocessing module: used to obtain the leaf area index data within the vegetation growth cycle of the study area and obtain the monthly average leaf area index value corresponding to the study area;

[0072] Vegetation yield prediction module: used to build a prediction model, and use the prediction model and the monthly average leaf area index value to obtain the vegetation yield prediction value corresponding to the study area;

[0073] Stress factor value acquisition module: obtain the monthly average values ​​of various stress factors based on the body water data, leaf temperature data, soil pH data and biological abundance index data;

[0074] Vegetation yield assessment module: According to the monthly average diameter moisture value, monthly average leaf temperature value, monthly average soil pH value and monthly average biological abundance index value, the vegetation yield prediction value is corrected, and the vegetation yield target value corresponding to the study area is obtained to realize the assessment of vegetation yield.

[0075] It is understandable that the above-mentioned vegetation yield impact assessment and system based on multi-factor stress have the same beneficial effects and will not be repeated here.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the impact of vegetation yield based on multi-factor stress, characterized in that: include: Acquire leaf area index data within the vegetation growth cycle of the study area, and preprocess the leaf area index data to obtain a monthly average leaf area index value corresponding to the study area; Constructing a prediction model consisting of historical target vegetation yield, historical monthly average leaf area index values, model outcomes and constants, and inputting the monthly average leaf area index value of the study area into the prediction model to obtain the vegetation yield prediction value corresponding to the study area; Obtain the diameter body moisture data during the vegetation growth cycle of the study area to obtain the monthly average diameter body moisture value corresponding to the study area; obtain the leaf temperature data during the vegetation growth cycle of the study area to obtain the monthly average leaf temperature value corresponding to the study area; Obtain soil pH value data during the vegetation growth cycle of the study area to obtain the monthly average soil pH value corresponding to the study area; obtain biological abundance index data during the vegetation growth cycle of the study area to obtain the monthly average biological abundance index value corresponding to the study area; According to the monthly average diameter water content, monthly average leaf temperature, monthly average soil pH value and monthly average biological abundance index value, the vegetation yield prediction value is corrected, and the vegetation yield target value corresponding to the study area is obtained to realize the evaluation of vegetation yield.

2. The method for evaluating the impact of vegetation yield on multi-factor stress according to claim 1, characterized in that: The vegetation yield prediction value is corrected according to the monthly average diameter water value, the monthly average leaf temperature value, the monthly average soil pH value and the monthly average biological abundance index value, and the vegetation yield target value corresponding to the study area is obtained to realize the evaluation of vegetation yield, which specifically includes: Acquire a number of historical data corresponding to the study area, and filter out historical data corresponding to the monthly average diameter moisture value, the monthly average leaf temperature value, the monthly average soil pH value, and the monthly average biological abundance index value within a preset time interval from the several historical data; Determine the influence degree of the monthly average diameter moisture value on the vegetation yield of the study area through the historical data corresponding to the monthly average diameter moisture value, and determine the influence weight coefficient of the monthly average diameter moisture value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average diameter moisture value; Determine the influence degree of the monthly average leaf temperature value on the vegetation yield of the study area through the historical data corresponding to the monthly average leaf temperature value, and determine the influence weight coefficient of the monthly average leaf temperature value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average leaf temperature value; Determine the influence degree of the monthly average soil pH value on the vegetation yield of the study area through the historical data corresponding to the monthly average soil pH value, and determine the influence weight coefficient of the monthly average soil pH value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average soil pH value; Determine the influence degree of the monthly average biological abundance index value on the vegetation yield of the study area through the historical data corresponding to the monthly average biological abundance index value, and determine the influence weight coefficient of the monthly average biological abundance index value on the vegetation yield of the study area according to the influence degree corresponding to the monthly average biological abundance index value; According to the monthly average diameter moisture value and the influence weight coefficient corresponding to the monthly average diameter moisture value, the monthly average leaf temperature value and the influence weight coefficient corresponding to the monthly average leaf temperature value, the monthly average soil pH value and the influence weight coefficient corresponding to the monthly average soil pH value, and the monthly average biological abundance index value and the influence weight coefficient corresponding to the monthly average biological abundance index value, the vegetation yield prediction value is corrected to obtain the vegetation yield target value corresponding to the study area, thereby realizing the evaluation of vegetation yield.

3. The method for evaluating the impact of vegetation yield on multi-factor stress according to claim 1, characterized in that: The obtaining of leaf area index data within the vegetation growth cycle of the study area specifically includes: Through LAI Smart, vegetation in several study areas is photographed at different zenith angles, and leaf area index data corresponding to the several study areas are obtained through image classification algorithms.

4. The method for evaluating the impact of vegetation yield on multi-factor stress according to claim 1, characterized in that: Before constructing the prediction model composed of the historical target vegetation yield, the historical monthly average leaf area index value, the model intercept and the constant, the method further includes: Obtaining historical vegetation canopy photos corresponding to the study area, and based on the historical vegetation canopy photos, determining the historical target vegetation yield and historical monthly average leaf area index value corresponding to the study area; According to the data relationship between the historical target vegetation yield and the historical monthly average leaf area index value, the model intercept and constant corresponding to the historical target vegetation yield obtained from the monthly average leaf area index value are determined.

5. The method for evaluating vegetation yield impact based on multi-factor stress according to claim 1, characterized in that: The step of obtaining the diameter water content data of the vegetation in the study area during the vegetation growth period to obtain the monthly average diameter water content value corresponding to the study area specifically includes: Using satellite remote sensors, remote sensing inversion and detection of vegetation in several study areas are performed, and remote sensing satellite data corresponding to the several study areas are obtained; Based on the remote sensing satellite data corresponding to the study area, determine the diameter water data of the study area during the vegetation growth period, and derive the diameter water bands in the diameter water data; Convert the data formats of the plurality of framed images corresponding to the radial body moisture band into a specified data format, and convert the projection coordinate systems of the plurality of framed images corresponding to the radial body moisture band into a specified coordinate system; Deleting invalid boundary values ​​in the diameter body moisture data, and splicing the multiple framed images in the specified coordinate system into a panoramic image; The monthly average diameter-body moisture value corresponding to the study area is calculated according to the diameter-body moisture band corresponding to the panoramic image.

6. The method for evaluating vegetation yield impact based on multi-factor stress according to claim 1, characterized in that: The step of obtaining the leaf temperature data within the vegetation growth period of the study area to obtain the monthly average leaf temperature value corresponding to the study area specifically includes: Based on the remote sensing satellite data corresponding to the study area, determining the leaf temperature data of the study area within the vegetation growth cycle, and exporting the leaf temperature bands in the leaf temperature data; Convert the data formats of the multiple framed images corresponding to the leaf temperature band into a specified data format, and convert the projection coordinate systems of the multiple framed images corresponding to the leaf temperature band into a specified coordinate system; Deleting invalid boundary values ​​in the blade temperature data, and splicing the multiple framed images in the specified coordinate system into a panoramic image; The monthly average leaf temperature value corresponding to the study area is calculated according to the leaf temperature band corresponding to the panoramic image.

7. The method for evaluating the impact of vegetation yield on multi-factor stress according to claim 1, characterized in that: The step of obtaining soil pH value data within the vegetation growth period of the study area to obtain the monthly average soil pH value corresponding to the study area specifically includes: Based on the remote sensing satellite data corresponding to the study area, determine the soil pH value data of the study area within the vegetation growth cycle, and export the soil pH value bands in the soil pH value data; Convert the data formats of the multiple framed images corresponding to the soil pH value band into a specified data format, and convert the projection coordinate systems of the multiple framed images corresponding to the soil pH value band into a specified coordinate system; Deleting invalid boundary values ​​in the soil pH value data, and splicing the multiple framed images in the specified coordinate system into a panoramic image; The monthly average soil pH value corresponding to the study area is calculated based on the soil pH value band corresponding to the panoramic image.

8. The method for evaluating the impact of vegetation yield based on multi-factor stress according to claim 1, characterized in that: The step of obtaining biological abundance index data within the vegetation growth cycle of the study area to obtain the monthly average biological abundance index value corresponding to the study area specifically includes: Based on the remote sensing satellite data corresponding to the study area, determine the biological abundance index data of the study area within the vegetation growth cycle, and export the biological abundance index bands in the biological abundance index data; The data formats of the multiple frame images corresponding to the biological abundance index band are converted into the specified data format, and the projection coordinate systems of the multiple frame images corresponding to the biological abundance index band are converted into the specified coordinate system; Deleting invalid boundary values ​​in the biological abundance index data, and splicing the multiple framed images in the specified coordinate system into a panoramic image; According to the biological abundance index band corresponding to the panoramic image, the monthly average biological abundance index value corresponding to the study area is calculated.

9. A vegetation yield impact assessment system based on multi-factor stress, characterized in that: include: A preprocessing module is used to obtain leaf area index data within the vegetation growth cycle of the study area and obtain the monthly average leaf area index value corresponding to the study area; A vegetation yield prediction module is used to construct a prediction model, and obtain the vegetation yield prediction value corresponding to the study area by using the prediction model and the monthly average leaf area index value; The stress factor value acquisition module obtains the monthly average values ​​of various stress factors based on the body water data, leaf temperature data, soil pH data and biological abundance index data; The vegetation yield assessment module corrects the vegetation yield prediction value according to the monthly average diameter water content, the monthly average leaf temperature, the monthly average soil pH value and the monthly average biological abundance index value, and obtains the vegetation yield target value corresponding to the study area, thereby realizing the assessment of vegetation yield.

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