Forest biomass and carbon library metering technology based on sample plot survey information

Abstract

The invention discloses a forest biomass and carbon library metering technology based on sample plot survey information, the technical scheme comprises a data acquisition system, a data processing and conversion module and a data website module, the data acquisition system is a basic part, and the data processing and conversion module is a core part. And the data website module provides data products and visual display for the user. Each tree is managed through a two-dimensional code, automatic quality control is performed based on historical survey data, forest vegetation biomass and carbon reserve situations of a quadrat and a sample plot are calculated by associating information of each tree of the quadrat and the sample plot, and a forest vegetation carbon sequestration rate and a carbon sequestration function are obtained by comparing previous survey data. According to the system, the investigation efficiency can be greatly improved, the forest vegetation carbon source function can be evaluated in real time, historical data can be used for automatic quality control of each re-investigation, investigation data can be uploaded in real time, and the working efficiency can be improved by more than 20 times through the application of the system.

Description

Technical Field

[0001] The invention belongs to the field of information technology, and specifically relates to a forest biomass and carbon pool measurement technology based on sample plot survey information. Background Art

[0002] Forests have the advantages of huge biomass, productivity and high carbon content. They are the largest carbon storage reservoirs in terrestrial ecosystems. The carbon storage of forest ecosystems mainly includes vegetation carbon storage and soil carbon storage. Accurate estimation of forest ecosystem carbon storage is a major issue that has attracted widespread attention in the field of global climate change. Based on the information of permanent sample plot surveys, the vegetation carbon storage and soil carbon storage of forest ecosystems can be calculated, which can accurately assess the current status of forest ecosystem carbon storage and its carbon sink function at the sample plot scale. Through scale expansion, the carbon sink function and potential of forest ecosystems in large-scale regions can be assessed, providing a basis for the formulation of the "dual carbon" path and the realization of the goal. It reflects China's solemn commitment to mitigating global climate change and has important practical and political significance.

[0003] The usual surveys of biomass and productivity on the market are usually conducted once a year or once every five years. The mainstream survey in the industry is conducted once every five years. Generally, when a forest disaster occurs, such as a typhoon or insect plague, a temporary survey will be launched. The current survey plan is a group survey, with three people in a group. The division of labor is one person identifying the tree species, one person measuring the tree height and breast diameter, and one person responsible for recording. The survey data of each survey group are manually merged into a set of data. Based on the biomass equation derived from the previous felling of standard trees, the biomass of each part of the individual is calculated separately, and the total is the individual biomass of the sample tree. The individual biomass is combined to obtain the forest vegetation biomass of the sample plot; the calculated results of the vegetation biomass of the sample plot are converted into the carbon storage of the forest vegetation of the sample plot. According to the survey results at different times, combined with the basic data such as the amount of forest vegetation litter, plant death, fine root production, and animal and insect consumption during the two surveys, the net increase in biomass or NPP within the time interval between the two surveys is obtained, and the size of the carbon sink function of forest vegetation at the sample plot scale can be further obtained;

[0004] However, the existing productivity and biomass survey solutions still have the following defects: they are still mainly manual, with large survey errors; the survey requires a lot of manpower and material resources, is time-consuming and inefficient; the data processing process is complex and the calculation efficiency is low. In this regard, we propose a forest biomass and carbon pool measurement technology based on sample plot survey information. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a forest biomass and carbon pool measurement technology based on sample plot survey information, which solves the problems of large errors and low efficiency.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a forest biomass and carbon pool measurement technology based on sample plot survey information, the technical solution includes: a data acquisition system, a data processing and conversion module and a data website module, the data acquisition system is the basic part, the data processing and conversion module is the core part, and the data website module provides users with data products and visual displays.

[0007] Preferably, the data acquisition system includes: a manual observation mobile terminal and a data batch storage module. The monitoring personnel directly scan the corresponding QR code of each tree through the manual observation mobile terminal to submit the collected data. The data batch storage module can provide a batch import function for each tree survey data.

[0008] Preferably, the data processing and conversion module includes a sample plot data processing module, a biomass and calculation module, a biomass carbon storage conversion system module, an estimation model and data configuration.

[0009] Prior to this, the sample data processing uses webgis technology to carry out arbitrary multi-deformation planning on the tile base map, and the biomass and calculation modules are constructed through nonlinear simultaneous equations in the form of:

[0010] W1=W2+W3+W4

[0011] W2=W=a*(D^2H)^bW3=W=a*(D^2H)^bW4=W=a*(D^2H)^bW1 is the total biomass of the sample tree, W2 is the trunk biomass, W3 is the crown biomass, W4 is the root biomass, H is the tree height, D is the breast diameter, and a and b are constant parameters.

[0012] Preferably, the estimation model calculates biomass and carbon storage by updating the prediction of the growth of tree diameter at breast height and tree height, and based on the updated prediction of diameter at breast height and height.

[0013] Prior to this, the data website module refines the location of each tree on the satellite map, and conducts real-time updates of the dynamic data of each tree through linkage visualization, and analyzes the carbon sink fluctuations over the past period of time in real time, showing the carbon sequestration in the region this year, this month and today, and displays the survey records and trend analysis of each tree, litter survey, soil organic carbon stock survey, soil respiration, and soil inorganic carbon stock survey.

[0014] Preferably, the biomass carbon stock conversion system module uses variance analysis to analyze the changing characteristics of different carbon stocks. The variance analysis is used to test the significance of the difference between two or more sample means.

[0015] Preferably, the calculation formula of the variance analysis method is: S = ∑(x1-x2)∧2, where s is the total sum of squares, x1 is the sample data, and x2 is the sample mean.

[0016] Preferably, data configuration is to perform structured parsing of the analyzed data and convert the data into a chart shape as required.

[0017] The conversion of plant biomass into carbon storage is usually calculated based on the proportion of carbon in the dry weight of organic matter in the plant. The conversion rate varies with tree species composition, age and population structure, but the difference is not large, generally ranging from 0.45 to 0.5. Since it is difficult to obtain the conversion rate of various vegetation types, the commonly used conversion rate internationally is 0.50[3,4]. Here, a carbon conversion coefficient of 0.5 is used to calculate carbon storage, and the result is: Carbon storage = biomass × 0.5.

[0018] By establishing information files for each tree and referring to historical data for automatic quality control, the efficiency of the survey is improved and biomass, carbon storage, carbon sink and carbon volume can be calculated in real time.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention manages each tree through QR codes and associates all individuals in the sample plot, thereby realizing real-time evaluation of the carbon source function of forest vegetation. Historical data can also be used for automatic quality control of each re-survey, and the survey data can be uploaded in real time. The application of this system will increase work efficiency by more than 20 times.

[0021] The present invention establishes information files for each tree, manages each tree through a QR code, performs automatic quality control based on historical survey data, calculates and displays biomass and carbon reserves in real time by scanning the QR code information of each tree, calculates the current status of forest vegetation biomass and carbon reserves in the sample plots and sample plots by associating the information of each tree in the sample plots and sample plots, and obtains the forest vegetation carbon fixation rate and carbon sink function by comparing the data from previous surveys, which can greatly improve the survey efficiency.

[0022] The present invention creatively realizes the refined positioning of each tree on the satellite map, and dynamically updates the visualized data of each tree in real time. At the same time, it analyzes the carbon sink fluctuation curve over the past period in real time, displays the carbon fixation in the region this year, this month and today, and displays the records and trend analysis of each tree survey, litter survey, soil organic carbon stock survey, soil respiration, and soil inorganic carbon stock survey, so as to realize a visualized display of biomass and carbon reserves. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a system framework diagram of the present invention;

[0024] Figure 2 Manually observe the mobile terminal page map of the present invention;

[0025] Figure 3 It is the data diagram of the biomass model of the present invention;

[0026] Figure 4 It is the biomass carbon storage conversion coefficient diagram of each tree species of the present invention;

[0027] Figure 5 This is a diagram of the updated prediction model for deep forest vegetation carbon storage in the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The present invention provides a technical solution: a forest biomass and carbon pool measurement technology based on sample plot survey information. The technical solution includes: a data acquisition system, a data processing and conversion module and a data website module. The data acquisition system is the basic part, the data processing and conversion module is the core part, and the data website module provides users with data products and visual displays.

[0030] An information file is established for each tree, and each tree is managed through a QR code. The massive biomass equations in the database are entered in advance, and the most suitable biomass equation is matched for each tree based on information such as climate zone, altitude, plant life form, and plant species. By scanning the QR code information of each tree, species name, coordinates, tree height, breast height diameter, crown width, etc., individual biomass is automatically calculated and displayed. By associating all individuals in the sample plot, the calculation and display of the biomass of forest vegetation in the sample plot and even the entire sample plot is realized. The current carbon storage status of forest vegetation in the sample plot and even the entire sample plot can be obtained through the biomass-carbon conversion relationship. Based on survey information and historical data at different times, the dynamic changes of forest vegetation biomass and carbon storage can be obtained, thereby realizing real-time evaluation of the carbon source function of forest vegetation. Historical data can also be used for automatic quality control of each re-survey, and survey data can be uploaded in real time. The application of this system will increase work efficiency by more than 20 times.

[0031] Furthermore, the data acquisition system includes: a manual observation mobile terminal and a data batch storage module. The monitoring personnel directly scan the corresponding QR code of each tree through the manual observation mobile terminal to submit the collected data. The data batch storage module can provide a batch import function for each tree survey data.

[0032] Furthermore, the data processing and conversion module includes a sample plot data processing module, a biomass and calculation module, a biomass carbon storage conversion system module, an estimation model and data configuration.

[0033] Furthermore, for new or existing plots, the plot data processing uses webgis technology to perform arbitrary multi-deformation planning on the tile base map, and the biomass and calculation modules are constructed through nonlinear simultaneous equations in the form of:

[0034] W1=W2+W3+W4

[0035] W2=W=a*(D^2H)^bW3=W=a*(D^2H)^bW4=W=a*(D^2H)^bW1 is the total biomass of the sample tree, W2 is the trunk biomass, W3 is the crown biomass, W4 is the root biomass, H is the tree height, D is the breast diameter, and a and b are constant parameters;

[0036] The biomass model of the tree layer and shrub layer of the forest plant community. Each tree participating in the survey has been assigned a corresponding biomass model when the file was created. The biomass model is related to the climate zone, life form, species name, etc.

[0037] Furthermore, the estimation model updates the prediction of the tree diameter at breast height and tree height growth, and calculates the biomass and carbon storage based on the updated predicted diameter at breast height and height, such as Figure 5 shown.

[0038] Furthermore, the data website module refines the location of each tree on the satellite map, and conducts real-time visual updates of the data of each tree, and analyzes the carbon sink fluctuations over the past period of time in real time, showing the carbon sequestration in the region this year, this month and today, and displays the survey records and trend analysis of each tree, litter survey, soil organic carbon stock survey, soil respiration, and soil inorganic carbon stock survey.

[0039] Furthermore, the biomass carbon storage conversion system module uses variance analysis to analyze the changing characteristics of different carbon storages. The variance analysis method is used to test the significance of the difference between two or more sample means.

[0040] Carbon storage is obtained by multiplying biomass by carbon content coefficient. In current carbon storage research at home and abroad, this coefficient is usually taken as 0.45 or 0.5. Through a large number of experiments, it is found that different tree species groups use different carbon content coefficients to estimate carbon storage coefficients, such as Figure 4 As shown;

[0041] Furthermore, the calculation formula of the variance analysis method is: S = ∑(x1-x2)∧2, where s is the total sum of squares, x1 is the sample data, and x2 is the sample mean.

[0042] ANOVA is a statistical method used to compare whether there are significant differences in the mean values ​​between two or more groups. It decomposes the total variance into the difference caused by the difference within the group and the difference caused by the difference between the groups to determine whether the difference between the groups is significant.

[0043] The analysis of variance method mainly includes the following steps:

[0044] Determine the research subject and design: Identify the factors to be studied and design an experiment or survey to collect data. This usually requires one independent variable and one or more dependent variables.

[0045] Construct hypothesis: propose null hypothesis and alternative hypothesis, and determine the significance level;

[0046] Calculate the total sum of squares of deviations: sum the sum of squares of the differences between the sample data of each group and the overall mean;

[0047] Calculate the sum of squares of deviations within the group: Calculate the sum of squares of the differences between the sample data within each group and the mean of each group;

[0048] Calculate the sum of squares of the deviations between groups: Calculate the sum of the sum of squares of the differences between the mean of each group and the overall mean and the product of the number of samples in each group;

[0049] Calculate the mean square: divide the sum of squares of the within-group deviations by the degrees of freedom to obtain the within-group mean square, and divide the sum of squares of the between-group deviations by the degrees of freedom to obtain the between-group mean square;

[0050] Calculate the F value: Divide the mean square between groups by the mean square within groups to get the F value;

[0051] Conduct hypothesis testing: compare the calculated F value with the critical F value to determine whether the difference is significant;

[0052] Perform post hoc analysis: If the ANOVA results show differences between groups, you can perform a post hoc analysis to determine which specific groups are different.

[0053] Furthermore, data configuration is to perform structured analysis on the analyzed data and convert the data into the shape of a chart as needed.

[0054] The conversion of plant biomass into carbon storage is usually calculated based on the proportion of carbon in the dry weight of organic matter in the plant. The conversion rate varies with tree species composition, age and population structure, but the difference is not large, generally ranging from 0.45 to 0.5. Since it is difficult to obtain the conversion rate of various vegetation types, the commonly used conversion rate internationally is 0.50[3,4]. Here, a carbon conversion coefficient of 0.5 is used to calculate carbon storage, and the result is: Carbon storage = biomass × 0.5.

[0055] (1) By establishing an information file for each tree, managing each tree through a QR code, and updating the information of each tree through scanning, the solution can refer to historical data for automatic quality control, improve survey efficiency, and calculate biomass, carbon storage, carbon sink, and carbon sink in real time.

[0056] (2) The tree carbon storage update prediction model can use existing historical data to correct data errors by comparing the predicted data with the test data. By comparing the predicted results with the final measurement results, the measurement results can be manually corrected to improve the availability of the measurement results.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0058] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A forest biomass and carbon pool measurement technology based on plot survey information, characterized in that: The technical solution includes: data acquisition system, data processing and conversion module and data website module. The data acquisition system is the basic part, the data processing and conversion module is the core part, and the data website module provides users with data products and visual displays.

2. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 1 is characterized in that: The data acquisition system includes: manual observation mobile terminal and data batch storage module. Monitoring personnel directly scan the corresponding QR code of each tree through the manual observation mobile terminal to submit the collected data. The data batch storage module can provide batch import function of each tree survey data.

3. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 1 is characterized in that: The data processing and conversion module includes a sample plot data processing module, a biomass and calculation module, a biomass carbon storage conversion system module, an estimation model and data configuration.

4. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 3 is characterized by: The sample data processing uses webgis technology to carry out arbitrary multi-deformation planning on the tile base map. The biomass and calculation modules are constructed through nonlinear simultaneous equations in the form of: W1=W2+W3+W4 W2=W=a*(D^2H)^bW3=W=a*(D^2H)^bW4=W=a*(D^2H)^bW1 is the total biomass of the sample tree, W2 is the trunk biomass, W3 is the crown biomass, W4 is the root biomass, H is the tree height, D is the breast diameter, and a and b are constant parameters.

5. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 3 is characterized by: The estimation model updates the prediction of the growth of tree diameter at breast height and tree height, and calculates biomass and carbon storage based on the updated predicted diameter at breast height and height.

6. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 1 is characterized by: The data website module refines the location of each tree on the satellite map, and links and visualizes the real-time update of each tree's data. It also analyzes the carbon sink fluctuations over the past period of time in real time, and displays the carbon fixation in the region this year, this month and today. It also displays the survey records and trend analysis of each tree, litter survey, soil organic carbon stock survey, soil respiration, and soil inorganic carbon stock survey.

7. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 3 is characterized by: The biomass carbon storage conversion system module uses variance analysis to analyze the changing characteristics of different carbon stocks. Variance analysis is used to test the significance of the mean difference between two or more samples.

8. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 7 is characterized in that: The calculation formula of variance analysis method is: S = ∑(x1-x2)∧2, where s is the total sum of squares, x1 is the sample data, and x2 is the sample mean.

9. The forest biomass and carbon pool measurement technology based on sample plot survey information according to claim 3 is characterized by: Data configuration is to perform structured analysis on the analyzed data and convert the data into the shape of a chart as needed.

Images