A forest vegetation carbon storage annual monitoring and early warning method and system

Abstract

The present application relates to the technical field of forest resource investigation and monitoring, and discloses a forest vegetation carbon storage annual monitoring and early warning method and system.The method comprises the following steps: obtaining an encryption interval parameter by encrypting the interval of sample plots in an administrative region according to the annual monitoring accuracy standard of forest vegetation carbon storage; obtaining a minimum unit sample plot layout scheme by spatially configuring the parameter; classifying the forest vegetation coverage area levels of the minimum administrative unit to form a hierarchical structure, combining the layout scheme and the hierarchical structure, accurately measuring vegetation parameters, and obtaining vegetation data; obtaining annual carbon storage according to the vegetation data by summarizing the carbon storage of the maximum and minimum units; and generating a forest vegetation carbon storage early warning report by making a risk assessment on the annual carbon storage according to a preset threshold value.The present application can improve the accuracy of forest carbon storage monitoring by ensuring that the vegetation data is accurate and representative, and can accurately obtain annual carbon storage to ensure the results, and can improve the monitoring and early warning efficiency and provide a decision basis for carbon resource management.

Description

Technical Field

[0001] This invention relates to the field of forest resource survey and monitoring technology, and in particular to a method and system for annual monitoring and early warning of forest vegetation carbon storage. Background Technology

[0002] In the field of forest vegetation carbon storage monitoring, current monitoring methods have significant limitations. Traditional methods often fail to differentiate their design based on the accuracy requirements of different administrative levels. The layout of sample plots is often based on a single administrative unit, without fully considering the monitoring needs of lower-level units. This results in the number and spatial distribution of sample plots in lower-level units failing to meet accuracy standards, making it impossible to accurately obtain forest vegetation carbon storage data at each level, thus affecting the completeness and reliability of carbon storage statistics. Furthermore, existing methods lack a systematic hierarchical aggregation mechanism in the data processing stage, making it difficult to effectively connect carbon storage data from different administrative units, which greatly reduces the accuracy and practicality of annual carbon storage calculation results.

[0003] The current forest resource monitoring system has shortcomings in terms of sample plot optimization and stratified sampling. Traditional sample plot densification methods are not scientifically designed in conjunction with forest vegetation cover characteristics, and the determination of densification spacing lacks deep coupling with historical data and topographic features, resulting in low sample plot deployment efficiency and insufficient representativeness. In the process of stratified sampling, there is a lack of unified and accurate standards for the hierarchical classification of forest vegetation cover area, and the proportion of vegetation types and spatial distribution patterns are not fully considered. The rationality of the stratification structure is poor, which not only increases the difficulty of subsequent parameter measurement, but may also affect the carbon storage calculation results due to stratification errors, and cannot provide reliable data support for the annual monitoring and early warning of forest vegetation carbon storage. Summary of the Invention

[0004] This invention provides a method and system for annual monitoring and early warning of carbon storage in forest vegetation, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for annual monitoring and early warning of forest vegetation carbon storage, comprising:

[0006] S1. Based on the annual monitoring accuracy standard for forest vegetation carbon storage, the densification spacing of the sample plots is determined using administrative regions as the basic unit, and the densification spacing parameters of the sample plots are obtained.

[0007] S2. Spatial configuration of the encryption spacing parameters to obtain the layout scheme of the smallest unit in the basic unit;

[0008] S3. The forest vegetation coverage area of ​​the smallest administrative unit in the smallest unit is hierarchically classified to obtain the hierarchical structure of the forest vegetation coverage area.

[0009] S4. Based on the layout scheme and the layered structure, accurately measure the forest vegetation parameters in the sample plot to obtain the vegetation data of the sample plot;

[0010] S5. Based on the vegetation data, the forest vegetation carbon storage of the largest unit and the smallest unit in the basic unit is summarized step by step to obtain the annual carbon storage of the largest unit and the smallest unit.

[0011] S6. Based on a preset threshold, perform a risk assessment on the annual carbon storage to obtain an early warning report on the carbon storage of the forest vegetation.

[0012] In a preferred embodiment, the step of determining the densification spacing of sample plots based on the annual monitoring accuracy standard for forest vegetation carbon storage, using administrative regions as the basic unit, and obtaining the densification spacing parameters of the sample plots, includes:

[0013] By performing feature analysis on historical data of forest vegetation carbon storage in the administrative region, the distribution characteristics of the forest vegetation carbon storage are obtained.

[0014] Based on the annual monitoring accuracy standard for forest vegetation carbon storage, the forest vegetation areas with administrative regions as the basic unit are benchmarked and verified to obtain the sampling accuracy of the administrative regions.

[0015] Based on the distribution characteristics and the sampling accuracy, the layout topology of the sample plots of the basic unit is optimized to obtain the densification spacing of the sample plots;

[0016] Based on the annual monitoring accuracy standard for forest vegetation carbon storage, the densification spacing is deduced and verified to obtain the densification spacing parameters for the sample plots.

[0017] In a preferred embodiment, the step of spatially configuring the encryption spacing parameters to obtain the layout scheme of the smallest unit in the basic unit includes:

[0018] The grid of the sample plot is obtained by performing grid analysis on the encryption spacing parameters.

[0019] The deployment grid is spatially overlaid with the geographical boundary of the smallest unit in the basic unit to obtain the initial deployment points of the smallest unit;

[0020] Spatial adaptation adjustments are made to the initial deployment points to obtain the optimized deployment points of the smallest unit;

[0021] The optimized layout points are integrated and designed to obtain the layout scheme of the smallest unit.

[0022] In a preferred embodiment, the step of performing mesh analysis on the densification spacing parameters to obtain the layout mesh of the sample plot includes:

[0023] The basic dimensions of the basic unit are obtained by calibrating the encryption spacing parameters.

[0024] Based on the basic dimensions, the basic unit is discretized to obtain the initial mesh of the basic unit;

[0025] The topographic features of the sample plot are mapped onto the initial grid to obtain the layout grid of the sample plot.

[0026] In a preferred embodiment, the step of hierarchically classifying the forest vegetation cover area of ​​the smallest administrative unit within the smallest unit to obtain the hierarchical structure of the forest vegetation cover area includes:

[0027] The forest vegetation coverage area of ​​the smallest administrative unit and the total area of ​​the smallest administrative unit are normalized to obtain the forest vegetation coverage index of the forest vegetation coverage area and the total area of ​​the smallest administrative unit.

[0028] Based on the forest vegetation coverage index, the smallest administrative unit is hierarchically defined to obtain the primary level of the smallest administrative unit;

[0029] The primary hierarchy is structurally merged to obtain the hierarchical structure of the forest vegetation cover area.

[0030] In a preferred embodiment, the precise measurement of forest vegetation parameters in the sample plot based on the layout scheme and the layered structure to obtain vegetation data for the sample plot includes:

[0031] Based on the layout scheme, the location information of the sample plots is subjected to scheme shaping to obtain the spatial distribution of the sample plots;

[0032] Based on the spatial distribution, the forest vegetation parameters of the sample plots are extracted collaboratively to obtain the initial forest vegetation parameters of the sample plots.

[0033] Based on the hierarchical structure, the initial parameters of the forest vegetation are hierarchically calibrated to obtain the hierarchical parameters of the forest vegetation.

[0034] The stratification parameters are verified and filtered to obtain the effective forest vegetation parameters of the sample plots;

[0035] The effective parameters of the forest vegetation are fused from multiple sources to obtain the vegetation data of the sample plot.

[0036] In a preferred embodiment, the step of summarizing the forest vegetation carbon storage of the largest and smallest units in the basic unit based on the vegetation data to obtain the annual carbon storage of the largest and smallest units includes:

[0037] The vegetation data is transformed to obtain the forest vegetation carbon storage of the sample plot;

[0038] Based on the aforementioned stratified structure, the forest vegetation carbon storage of the sample plot is aggregated within each stratum to obtain the average stratified carbon storage of the sample plot.

[0039] The parameters of the layered structure are analyzed and deduced to obtain the layer area and layer adjustment coefficient of the layered structure;

[0040] The forest vegetation carbon storage of the smallest unit is obtained by calculating the layer area, layer adjustment coefficient, and average layer carbon storage. The formula for calculating the forest vegetation carbon storage of the smallest unit is as follows:

[0041] ;

[0042] in, This represents the carbon storage of the forest vegetation in the smallest unit. Indicates the first The average carbon storage of the layer described above. Indicates the first The area of ​​the layer described above, Indicates the first The layer adjustment coefficient described in the layer description. Indicates the number of layers in a hierarchical structure;

[0043] The forest vegetation carbon storage of the smallest unit is coupled in multiple dimensions to obtain the forest vegetation carbon storage of the largest unit in the basic unit.

[0044] The forest vegetation carbon storage of the largest unit and the forest vegetation carbon storage of the smallest unit are fused at multiple scales to obtain the annual carbon storage of the largest unit and the smallest unit.

[0045] In a preferred embodiment, the step of multi-dimensionally coupling the forest vegetation carbon storage of the smallest unit to obtain the forest vegetation carbon storage of the largest unit in the basic unit includes:

[0046] The forest vegetation carbon storage of the smallest unit is correlated and weighted to obtain the area weight coefficient of the smallest unit.

[0047] Based on the area weighting coefficient, the forest vegetation carbon storage of the smallest unit is scaled and corrected to obtain the standardized carbon storage of the smallest unit.

[0048] By coupling the standardized carbon storage in multiple dimensions, the forest vegetation carbon storage of the largest unit is obtained, wherein the calculation formula for the forest vegetation carbon storage of the largest unit is:

[0049] ;

[0050] in, This represents the forest vegetation carbon storage of the largest unit. Indicates the first The carbon storage of forest vegetation in the smallest unit. Indicates the first The area weighting coefficient of the smallest unit, This indicates the number of the smallest units in the basic unit. This represents the sum of the area weight coefficients of the smallest unit.

[0051] In a preferred embodiment, the step of assessing the annual carbon storage based on a preset threshold to obtain an early warning report on the forest vegetation carbon storage includes:

[0052] Dynamic features are extracted from the annual carbon storage and the historical carbon storage trends of the sample plots to obtain the changing characteristics of the forest vegetation carbon storage.

[0053] The abnormal change characteristics of the forest vegetation carbon storage are obtained by comparing and verifying the change characteristics with the preset threshold.

[0054] A comprehensive analysis of the abnormal change characteristics yields an early warning report on the carbon storage of the forest vegetation.

[0055] To address the above problems, the present invention also provides an annual monitoring and early warning system for forest vegetation carbon storage, the system comprising:

[0056] The parameter encryption module is used to determine the encryption spacing of the sample plots based on the annual monitoring accuracy standard for forest vegetation carbon storage, with administrative regions as the basic unit, and to obtain the encryption spacing parameters of the sample plots.

[0057] The scheme deployment module is used to spatially configure the encryption spacing parameters to obtain the deployment scheme of the smallest unit in the basic unit;

[0058] The structural hierarchical module is used to hierarchically classify the forest vegetation coverage area of ​​the smallest administrative unit in the smallest unit to obtain the hierarchical structure of the forest vegetation coverage area.

[0059] The data survey module is used to accurately measure the forest vegetation parameters in the sample plot based on the layout scheme and the layered structure, and obtain the vegetation data of the sample plot.

[0060] The data aggregation module is used to aggregate the forest vegetation carbon storage of the largest unit and the smallest unit in the basic unit according to the vegetation data, so as to obtain the annual carbon storage of the largest unit and the smallest unit.

[0061] The risk warning module is used to assess the risk of the annual carbon storage based on a preset threshold and obtain a warning report on the carbon storage of the forest vegetation.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. This technology uses administrative regions as the basic unit and combines the annual monitoring accuracy standards for forest vegetation carbon storage to determine the spacing parameters for sample plot densification. Through spatial configuration, it forms the smallest unit layout scheme. At the same time, it classifies the forest vegetation coverage area hierarchically to construct a layered structure. Based on the layout scheme and layered structure, it conducts accurate measurement of sample plot vegetation parameters, which can effectively ensure the accuracy and representativeness of vegetation data, provide high-quality data support for subsequent carbon storage calculation, significantly improve the accuracy of forest vegetation carbon storage monitoring, and make the carbon storage data at each administrative level more in line with the actual situation.

[0064] 2. This technology achieves accurate acquisition of the annual carbon storage of the largest and smallest units within the basic unit through hierarchical aggregation. It ensures the scientific validity of the carbon storage results through calculation using specific formulas. At the same time, it generates early warning reports by conducting risk assessment of the annual carbon storage based on preset thresholds. The entire process forms a complete and efficient workflow from sample plot deployment and data collection to carbon storage calculation and early warning, which greatly improves the efficiency of annual monitoring and early warning of forest vegetation carbon storage. It provides timely and reliable decision-making basis for forest carbon resource management and helps to carry out more scientific dynamic management and control of forest carbon storage. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating an annual monitoring and early warning method for forest vegetation carbon storage according to an embodiment of the present invention.

[0066] Figure 2 This is a functional module diagram of an annual monitoring and early warning system for forest vegetation carbon storage provided in an embodiment of the present invention;

[0067] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0068] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0069] This application provides a method for annual monitoring and early warning of forest vegetation carbon storage. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for annual monitoring and early warning of forest vegetation carbon storage can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0070] Reference Figure 1 The diagram shown is a flowchart illustrating an annual monitoring and early warning method for forest vegetation carbon storage according to an embodiment of the present invention. In this embodiment, the annual monitoring and early warning method for forest vegetation carbon storage includes:

[0071] S1. Based on the annual monitoring accuracy standard for forest vegetation carbon storage, the densification spacing of the sample plots is determined using administrative regions as the basic unit, and the densification spacing parameters of the sample plots are obtained.

[0072] In this embodiment of the invention, the step of determining the densification spacing of sample plots based on the annual monitoring accuracy standard for forest vegetation carbon storage, using administrative regions as the basic unit, and obtaining the densification spacing parameters of the sample plots, includes:

[0073] By performing feature analysis on historical data of forest vegetation carbon storage in the administrative region, the distribution characteristics of the forest vegetation carbon storage are obtained.

[0074] Based on the annual monitoring accuracy standard for forest vegetation carbon storage, the forest vegetation areas with administrative regions as the basic unit are benchmarked and verified to obtain the sampling accuracy of the administrative regions.

[0075] Based on the distribution characteristics and the sampling accuracy, the layout topology of the sample plots of the basic unit is optimized to obtain the densification spacing of the sample plots;

[0076] Based on the annual monitoring accuracy standard for forest vegetation carbon storage, the densification spacing is deduced and verified to obtain the densification spacing parameters for the sample plots.

[0077] Specifically, when performing feature analysis on historical data of forest vegetation carbon storage in an administrative region, the following steps are taken: First, collect forest vegetation carbon storage data for at least the past five years for that administrative region. This data must cover different years, different administrative sub-units (e.g., cities and counties), and different vegetation types (e.g., arbor forests, bamboo forests, shrub forests), along with the corresponding carbon storage values. Simultaneously, collect climate data for each year, such as annual precipitation and average annual temperature; topographic data, such as altitude, slope, and aspect; and human activity data, such as logging volume and afforestation area. Then, classify and organize the collected carbon storage data according to administrative sub-units and vegetation types, and statistically analyze each... The study analyzes the maximum, minimum, average, and annual changes in carbon storage for different vegetation types within administrative sub-units in each year. It examines the trends in carbon storage for different vegetation types over the years to determine whether there are obvious patterns of growth, decline, or stable fluctuations. By combining climate and human activity data, the study analyzes the correlation between these external factors and changes in carbon storage. For example, it examines whether the carbon storage of arbor forests in an administrative sub-unit increases after the afforestation area increases in a certain year. Ultimately, the study clarifies the spatial and temporal distribution patterns of forest vegetation carbon storage within the administrative region, thus obtaining the distribution characteristics of forest vegetation carbon storage.

[0078] Furthermore, based on the annual monitoring accuracy standards for forest vegetation carbon storage, when benchmarking and verifying forest vegetation areas with administrative regions as the basic unit, the first step is to clarify the annual monitoring accuracy standards for forest vegetation carbon storage stipulated by the state or industry. For example, the error of the annual carbon storage monitoring results for a certain administrative region should be controlled within ±5%. The administrative region is then divided into multiple monitoring blocks of equal area, with each block serving as a potential sampling unit. Based on the previously obtained distribution characteristics of forest vegetation carbon storage, a certain number of sampling units are selected in areas with different carbon storage levels to ensure that the sampling units can cover high, medium, and low carbon storage areas, and that the number of sampling units in each area is proportional to the area of ​​that area within the administrative region. The sampling proportions are matched; a field survey is conducted on each selected sampling unit, and the actual carbon storage of each sampling unit is obtained using standard biomass measurement methods; the average carbon storage of all sampling units is calculated and used as the sampling estimate of the forest vegetation carbon storage of the administrative region. At the same time, the error between the sampling estimate and the actual carbon storage obtained by the comprehensive survey of the administrative region is calculated. If the error is within the allowable range of the annual monitoring accuracy standard, the sampling accuracy of the administrative region under the current sampling method is determined to meet the requirements. If the error exceeds the allowable range, the number and distribution of sampling units are adjusted, and the sampling survey and error calculation are carried out again until the error meets the accuracy standard, and the sampling accuracy of the administrative region is finally obtained.

[0079] Furthermore, based on distribution characteristics and sampling accuracy, when performing topology optimization for the sample plots of the basic unit, the regions with large spatial variability in carbon storage and regions with small spatial variability in carbon storage within the administrative area are identified according to the previously obtained forest vegetation carbon storage distribution characteristics. For regions with large spatial variability in carbon storage, due to their uneven carbon storage distribution, to ensure that the sampling accurately reflects the carbon storage situation in the region, the spacing between sample plots needs to be reduced and the sample plot density increased, for example, one sample plot per 100 hectares in this region. For regions with small spatial variability in carbon storage, due to their relatively uniform carbon storage distribution, the spacing between sample plots can be appropriately increased and the sample plot density reduced. The sampling density is set at, for example, one sample plot per 200 hectares. Based on the established sampling accuracy for each administrative region, the initially set sample plot spacing is verified. If the error between the estimated carbon storage value obtained through sampling calculation and the actual value meets the sampling accuracy requirements after setting the sample plots at the current spacing, then this spacing is preliminarily determined as a candidate for denser spacing. If the error does not meet the requirements, the spacing is adjusted according to the error situation. If the error is too large, it indicates that the sample plot density is insufficient, and the spacing needs to be reduced. If the error is much smaller than the accuracy requirements, the spacing can be appropriately increased to reduce monitoring costs. After multiple adjustments and verifications, the topology optimization of the basic unit sample plot layout is finally completed, and the denser spacing of the sample plots is obtained.

[0080] Furthermore, based on the annual monitoring accuracy standard for forest vegetation carbon storage, when extrapolating and verifying the densification spacing, sample plots are simulated and laid out within the administrative region according to the obtained sample plot densification spacing, ensuring that the sample plots are evenly distributed and cover all administrative sub-units and vegetation type areas. A portion of the simulated sample plots are randomly selected as verification samples. Using the same biomass measurement method as the previous field survey, the carbon storage of each verification sample is calculated. Then, based on the carbon storage of the verification samples, an estimated value of the carbon storage for the administrative region is calculated. This estimated value is compared with the actual carbon storage for the administrative region, and the error between the two is calculated. If the error is within the range of forest vegetation carbon... If the annual monitoring accuracy standard for reserves is within the allowable range, it means that the current densification spacing can meet the monitoring accuracy requirements and can be determined as the densification spacing parameter for the sample plots. If the error exceeds the allowable range, the cause of the error should be analyzed. If the error is too large due to insufficient sample plot coverage in some areas, the densification spacing in those areas needs to be adjusted to reduce the spacing and increase the number of sample plots. If the monitoring cost is too high due to excessive sample plot density and the error is far below the accuracy requirements, the densification spacing in some areas can be appropriately increased. After adjustment, the simulation layout, sample selection, carbon reserve estimation and error calculation should be carried out again until the error meets the accuracy standard, and finally the densification spacing parameter for the sample plots is obtained.

[0081] In summary, using administrative regions as the basic unit and conducting benchmarking and verification in conjunction with the annual monitoring accuracy standards for forest vegetation carbon storage can directly determine whether the sampling accuracy of administrative regions meets the standards. This avoids the blind deployment of sample plots caused by traditional methods deviating from accuracy standards, ensuring that the subsequent densification spacing design revolves around accuracy requirements. This lays the foundation for monitoring results to meet the standards. Traditional methods often fail to differentiate their design for accuracy requirements, and sample plot deployment often lacks specificity. However, this method, through benchmarking and verification, makes the sampling accuracy clear and controllable, ensuring from the source that the monitoring direction does not deviate from the accuracy target.

[0082] In summary, by analyzing historical data on carbon storage in forest vegetation within administrative regions to obtain distribution characteristics, the spatial variation patterns of carbon storage can be clarified. Furthermore, by combining sampling accuracy with topological optimization of the layout of basic unit plots, it is possible to selectively densify plots in areas of high variation and reasonably reduce them in areas of low variation. This design breaks through the limitations of traditional plot densification that does not take into account vegetation cover characteristics, making the plot distribution more closely match the actual carbon storage distribution, solving the problem of insufficient representativeness of traditional plots, reducing monitoring errors caused by uneven plot distribution, and enabling plots to more accurately reflect the carbon storage situation in different regions.

[0083] In summary, the densification spacing was simulated and verified based on the annual monitoring accuracy standards for forest vegetation carbon storage. Through simulation deployment and error calculation, spacing schemes that do not meet the accuracy requirements can be eliminated. The final determined densification spacing parameters will not increase unnecessary monitoring costs due to excessively dense spacing, nor will they affect monitoring accuracy due to excessively sparse spacing. It can balance monitoring accuracy and efficiency, provide scientific and reliable parameter support for subsequent sample plot deployment, ensure the accuracy of subsequent carbon storage monitoring data, and avoid data deviations in subsequent monitoring stages due to unreasonable parameters.

[0084] S2. Spatial configuration of the encryption spacing parameters to obtain the layout scheme of the smallest unit in the basic unit;

[0085] In this embodiment of the invention, the step of spatially configuring the encryption spacing parameters to obtain the layout scheme of the smallest unit in the basic unit includes:

[0086] The grid of the sample plot is obtained by performing grid analysis on the encryption spacing parameters.

[0087] The deployment grid is spatially overlaid with the geographical boundary of the smallest unit in the basic unit to obtain the initial deployment points of the smallest unit;

[0088] Spatial adaptation adjustments are made to the initial deployment points to obtain the optimized deployment points of the smallest unit;

[0089] The optimized layout points are integrated and designed to obtain the layout scheme of the smallest unit.

[0090] In this embodiment of the invention, the step of performing mesh analysis on the encryption spacing parameters to obtain the layout mesh of the sample plot includes:

[0091] The basic dimensions of the basic unit are obtained by calibrating the encryption spacing parameters.

[0092] Based on the basic dimensions, the basic unit is discretized to obtain the initial mesh of the basic unit;

[0093] The topographic features of the sample plot are mapped onto the initial grid to obtain the layout grid of the sample plot.

[0094] Specifically, when performing grid analysis on the densification spacing parameters, first determine the actual distance scale corresponding to the densification spacing parameters. For example, if the densification spacing parameter is 2 kilometers, it means that the planned distance between sample plots is 2 kilometers. Using the geographic coordinate system of the administrative region as a reference, determine the starting coordinate point of the grid. Usually, the coordinates of the southwest corner of the administrative region boundary are selected as the starting point. Starting from the starting point, draw equidistant parallel lines in the horizontal and vertical directions according to the distance corresponding to the densification spacing parameters. The distance between the horizontal parallel lines and the distance between the vertical parallel lines are equal to the actual distance corresponding to the densification spacing parameters. The square or rectangular grid formed by the intersection of these parallel lines is the layout grid of the sample plots. The vertex position of each grid is the potential location of the sample plot in the preliminary planning.

[0095] Furthermore, when spatially overlaying the layout grid with the geographic boundary of the smallest unit in the basic unit, first obtain the detailed geographic boundary data of the smallest unit in the basic unit, such as a county-level administrative region. This data needs to contain the precise coordinate information of the boundary of the smallest unit. This boundary data can be imported through geographic information system software to generate boundary vector graphics. The previously obtained sample plot layout grid is also imported into the same geographic information system software to ensure that both use the same geographic coordinate system. The position of the layout grid is adjusted in the software so that the grid completely covers the geographic range of the smallest unit. Then, the spatial overlay operation is performed. At this time, the vertices in the layout grid located inside the geographic boundary of the smallest unit are the initial layout points of the smallest unit. For grid vertices located outside the geographic boundary of the smallest unit, they are excluded from the initial layout points.

[0096] Furthermore, when spatially adapting the initial layout points, the topographic and land use data within the smallest unit are first collected. The geographical location of each initial layout point is then checked. If a preliminary layout point is located in aquatic areas such as rivers or lakes where sample plots cannot be set up, or in densely built-up urban areas or within 50 meters of the centerline of a road, making it unsuitable for sample plot placement, the initial layout point is moved to the nearest suitable area. The moving distance is controlled to be no more than 1 / 4 of the distance corresponding to the densification spacing parameter to ensure the uniformity of sample plot distribution. If the initial layout points in a certain area are too dense, such as the distance between two initial layout points being less than 1 / 2 of the distance corresponding to the densification spacing parameter, one of the initial layout points that is farther from other suitable areas is deleted. If the initial layout points in a certain area are too sparse, such as the distance between two adjacent initial layout points being greater than 1.5 times the distance corresponding to the densification spacing parameter, a new layout point is added to the blank space in that area. The location of the added point must be selected in suitable woodland for sample plot placement, ultimately obtaining the optimized layout points for the smallest unit.

[0097] Furthermore, when integrating the optimized layout points, all optimized layout points are first numbered. The numbering rule can be arranged sequentially from west to east and from north to south, with each optimized layout point corresponding to a unique number. Detailed information is recorded for each optimized layout point, including its precise geographic coordinates, vegetation type, surrounding terrain features, and distance from major surrounding features. This information is then organized into a table, which must include fields such as "plot number," "geographic longitude," "geographic latitude," "vegetation type," "terrain features," and "distance from surrounding features." Simultaneously, a map of the smallest unit is drawn in the geographic information system software, marking all optimized layout points on the map and noting the corresponding plot number next to each marked point, forming a visualized plot distribution map. The tabular plot information and the visualized plot distribution map are integrated together to form the smallest unit layout scheme. This scheme must clearly present the specific location, relevant attributes, and overall distribution of each plot for use during subsequent field layout.

[0098] Specifically, when scaling the density spacing parameter to obtain the basic size of the basic unit, first clarify the actual spatial distance represented by the value of the density spacing parameter. For example, if the density spacing parameter is 3 kilometers, it means that the planned interval distance between sample plots is 3 kilometers. The basic unit is based on the administrative region. Combining the latitude and longitude span of the administrative region, the actual distance corresponding to the density spacing parameter is converted into the length in the geographic coordinate scale. For example, the actual distance corresponding to 1 degree of longitude and 1 degree of latitude is calculated based on the latitude of the administrative region. Then, the 3 kilometers of the density spacing parameter is converted into the corresponding latitude and longitude difference. The converted latitude and longitude difference is used as the benchmark for the grid side length. At the same time, the overall geographic range of the basic unit is taken into account. If the east-west latitude and longitude span of the prefecture-level city is large, the grid side length is divided in the east-west direction according to the latitude and longitude difference corresponding to the density spacing parameter. The same applies to the north-south direction. This ensures that the divided grid can completely cover the entire prefecture-level city and that the grid size is uniform. Finally, the grid side length used for laying sample plots in the basic unit is determined, which is the basic size of the basic unit.

[0099] Furthermore, based on the basic dimensions, the basic unit is discretized to obtain the initial grid of the basic unit. Using the geographical boundary of the basic unit as a reference, the boundary coordinate range of the prefecture-level city is determined in the geographic information system, finding the latitude and longitude coordinates corresponding to the westernmost, easternmost, northernmost, and southernmost points of the boundary. Starting from the intersection of the westernmost and southernmost coordinates, straight lines parallel to the north-south direction are drawn sequentially in the east-west direction according to the determined basic dimensions. The latitude and longitude difference between adjacent straight lines is equal to the latitude and longitude difference corresponding to the basic dimensions. Simultaneously, straight lines parallel to the east-west direction are drawn sequentially in the north-south direction, and the latitude and longitude difference between adjacent straight lines is also equal to the latitude and longitude difference corresponding to the basic dimensions. These mutually perpendicular straight lines intersect within the geographical range of the basic unit, dividing the entire geographical area of ​​the prefecture-level city into multiple rectangular grids of the same size. The side length of each rectangular grid is equal to the basic dimensions of the basic unit. These rectangular grids together constitute the initial grid of the basic unit.

[0100] Furthermore, when mapping the topographic features of the sample plots to the initial grid to obtain the layout grid for the sample plots, detailed topographic data within the basic units are first collected, including the distribution range and corresponding latitude and longitude coordinates of different topographic types such as mountains, hills, plains, valleys, lakes, and swamps. In the geographic information system, the collected topographic data is overlaid on the generated initial grid layer in the form of layers, and the topographic type corresponding to each initial grid is analyzed one by one. For topographic areas within the initial grid that are entirely lakes, swamps, or other areas where sample plots cannot be laid, the grid is marked as an "unusable grid." For large areas within the initial grid... Some areas are suitable terrain areas for setting up sample plots, such as mountains, hills, and plains. These grids are marked as "usable grids". For cases where the area ratio of suitable and unsuitable terrain areas within the initial grid is similar, the terrain details within the grid are further analyzed in detail. If the area of ​​a continuous suitable area for setting up sample plots reaches more than 50% of the grid area, it is still marked as a "usable grid"; otherwise, it is marked as an "unusable grid". Finally, after removing all "unusable grids", the remaining "usable grids" together form the sample plot layout grid, and the vertex of each "usable grid" is the potential layout location of the sample plot.

[0101] In summary, in the grid design stage of sample plot layout, the basic size is first determined based on the densification spacing parameters, and the basic unit is discretized to generate a uniform initial grid covering its entire area. Then, the initial grid is mapped to the layout grid in combination with the terrain data. By removing grids located in unsuitable terrain areas, it is ensured that the grid unit conforms to the actual geographical conditions, avoiding the unreasonable layout problem caused by ignoring the influence of terrain in traditional methods.

[0102] In summary, in the selection and optimization of sample plot layout points, the layout grid is first overlaid with the smallest unit administrative boundary to quickly locate the initial layout points within the boundary; then, based on land use and topographic data, the initial layout points are spatially adapted and adjusted, points that are removed or moved to unsuitable areas are eliminated, and the density between points is balanced so that the final layout points not only comply with administrative boundary constraints but also adapt to the actual environment, thereby improving the feasibility and rationality of sample plot layout.

[0103] In summary, during the layout scheme integration phase, the optimized layout points are uniformly numbered, their geographical and vegetation attributes are recorded, a visual distribution map is drawn, and integrated into a complete scheme containing tables and maps. This scheme clearly presents the location, attributes, and overall layout of each sample plot, providing clear guidance for on-site layout, effectively solving the problems of fragmented information and low operability in traditional schemes, and improving the execution efficiency of sample plot layout.

[0104] S3. The forest vegetation coverage area of ​​the smallest administrative unit in the smallest unit is hierarchically classified to obtain the hierarchical structure of the forest vegetation coverage area.

[0105] In this embodiment of the invention, the step of hierarchically classifying the forest vegetation cover area of ​​the smallest administrative unit within the smallest unit to obtain the hierarchical structure of the forest vegetation cover area includes:

[0106] The forest vegetation coverage area of ​​the smallest administrative unit and the total area of ​​the smallest administrative unit are normalized to obtain the forest vegetation coverage index of the forest vegetation coverage area and the total area of ​​the smallest administrative unit.

[0107] Based on the forest vegetation coverage index, the smallest administrative unit is hierarchically defined to obtain the primary level of the smallest administrative unit;

[0108] The primary hierarchy is structurally merged to obtain the hierarchical structure of the forest vegetation cover area.

[0109] Specifically, when normalizing the forest vegetation cover area of ​​the smallest administrative unit within the smallest unit to obtain the forest vegetation cover index, the smallest unit is first defined as a county-level administrative region, and the smallest administrative unit is an administrative village. The forest vegetation cover area of ​​the administrative village is obtained through remote sensing image interpretation technology. Specifically, high-resolution remote sensing imagery, combined with interpretation markers calibrated through field surveys, is used to distinguish pixels of forest vegetation types such as arbor forests and bamboo forests, and the actual area corresponding to these pixels is calculated. This refers to the forest vegetation coverage area of ​​the administrative village. Simultaneously, the total land area of ​​the administrative village is obtained from the administrative division database to ensure that the scope of the area statistics is completely consistent with the scope of the forest vegetation coverage area statistics. Then, the forest vegetation coverage area of ​​the administrative village is divided by its total land area; the resulting ratio is the forest vegetation coverage index. This index reflects the proportion of forest vegetation coverage within the administrative village. For example, if an administrative village has a forest vegetation coverage area of ​​200 hectares and a total land area of ​​500 hectares, its forest vegetation coverage index is 200 ÷ 500 = 0.4.

[0110] Furthermore, based on the forest vegetation cover index, the smallest administrative unit is hierarchically defined. To obtain the primary level of the smallest administrative unit, a hierarchical division standard is first set. Referring to the conventional classification logic in the field of forest resource surveys and actual monitoring needs, the forest vegetation cover index is divided into three intervals: index greater than or equal to 0.5, index greater than 0 and less than 0.5, and index equal to 0. Then, the forest vegetation cover index of each administrative village is compared with the above division standard. If the forest vegetation cover index of an administrative village is ≥0.5, the administrative village is defined as "high coverage level"; if the index is between 0 and 0.5, it is defined as "medium coverage level"; if the index = 0, it is defined as "no coverage level". Each administrative village corresponds to a unique level, and all administrative villages and their corresponding levels together constitute the primary level of the smallest administrative unit.

[0111] Furthermore, when structuring the primary levels to form a hierarchical structure based on forest vegetation cover area, the number of administrative villages and land area of ​​each primary level within the smallest unit are statistically analyzed to determine their spatial distribution. Spatially adjacent administrative villages belonging to the same primary level are merged: if their total land area exceeds 50 square kilometers and their forest vegetation type is consistent, they are merged into a single "hierarchical block"; otherwise, the original administrative village level is retained. Special distribution scenarios are also addressed: medium-coverage administrative villages completely surrounded by high-coverage levels should be merged into surrounding high-coverage blocks; while high-coverage administrative villages with an area less than 10 square kilometers and surrounded by no-coverage levels are assigned to no-coverage areas. All merged blocks and unmerged administrative villages are integrated to form a hierarchical structure with high, medium, and no-coverage level blocks / administrative villages as the core categories.

[0112] In summary, normalizing the forest vegetation cover area of ​​the smallest administrative unit within the smallest unit to the total area, and calculating the ratio between the two to obtain the forest vegetation cover index, can transform the vegetation cover of the smallest administrative units of different sizes into a unified quantitative indicator. This standardization process eliminates the interference of differences in the area of ​​administrative units on vegetation cover assessment, making the vegetation cover levels of different administrative units directly comparable, avoiding misjudgments of coverage caused by different area bases in traditional assessments, and providing an objective and unified data foundation for subsequent hierarchical division.

[0113] In summary, defining the smallest administrative unit based on the forest vegetation cover index allows for the clear identification of the vegetation cover level for each administrative unit according to the index range, forming a primary level. This process replaces the traditional subjective classification method with quantitative standards, reducing human judgment errors and ensuring that administrative units within the same level have similar vegetation cover characteristics. The differences between different levels are clear, providing an accurate classification basis for the stratified processing in subsequent carbon storage calculations, and making the stratification more consistent with the actual vegetation distribution patterns.

[0114] In summary, when merging the primary level structure, by combining the spatial distribution of administrative units with the consistency of vegetation types, adjacent and similar primary level administrative units are merged into hierarchical blocks, and reasonable adjustments are made to the hierarchical structure of scattered or special areas. This merging operation solves the problem of scattered administrative units in the primary level, which is not conducive to subsequent carbon storage aggregation and calculation, and forms a hierarchical structure with greater spatial continuity and management practicality. At the same time, it ensures that the hierarchical structure can reflect the differences in vegetation cover and adapt to the management needs of administrative regions, laying a clear structural framework for subsequent sample plot parameter measurement and carbon storage aggregation at each level.

[0115] S4. Based on the layout scheme and the layered structure, accurately measure the forest vegetation parameters in the sample plot to obtain the vegetation data of the sample plot;

[0116] In this embodiment of the invention, the step of accurately measuring the forest vegetation parameters in the sample plot based on the layout scheme and the layered structure to obtain the vegetation data of the sample plot includes:

[0117] Based on the layout scheme, the location information of the sample plots is subjected to scheme shaping to obtain the spatial distribution of the sample plots;

[0118] Based on the spatial distribution, the forest vegetation parameters of the sample plots are extracted collaboratively to obtain the initial forest vegetation parameters of the sample plots.

[0119] Based on the hierarchical structure, the initial parameters of the forest vegetation are hierarchically calibrated to obtain the hierarchical parameters of the forest vegetation.

[0120] The stratification parameters are verified and filtered to obtain the effective forest vegetation parameters of the sample plots;

[0121] The effective parameters of the forest vegetation are fused from multiple sources to obtain the vegetation data of the sample plot.

[0122] Specifically, based on the layout scheme, when shaping the location information of the sample plots to obtain the spatial distribution of forest vegetation in the sample plots, the precise geographic coordinates, sample plot numbers, and corresponding administrative affiliations of all sample plots are first extracted from the layout scheme. Using geographic information system tools, the geographic coordinates of each sample plot are marked on an electronic map, with the sample plot number and administrative affiliation information linked during the marking process. Simultaneously, referring to the design density of the sample plots in the layout scheme, such as the number of sample plots per square kilometer determined based on the densification spacing, the spatial distribution of the sample plots on the electronic map is checked to ensure it meets design requirements. For example, whether it evenly covers areas of different vegetation types and whether it avoids areas unsuitable for sample plot placement, such as rivers and building sites. The marked sample plot locations are then visualized, using different colors or symbols to distinguish sample plots in areas with different administrative affiliations or different preset vegetation types. Finally, a graphical result clearly presenting the spatial arrangement, density, and coverage of all sample plots is formed, thus obtaining the spatial distribution of forest vegetation in the sample plots.

[0123] Furthermore, based on the spatial distribution, the forest vegetation parameters of the sample plots are collaboratively extracted to obtain the initial forest vegetation parameters of the sample plots. Then, the actual location of each sample plot is determined according to its spatial distribution, and surveyors are organized to go to the site with measuring tools. Upon arrival at the sample plot, the boundary of the sample plot is first determined, and marker stakes are set around the perimeter of the sample plot. Subsequently, all trees within the sample plot are surveyed individually. A diameter at breast height (DBH) measuring rod is used to measure the DBH of each tree at 1.3 meters above the ground, accurate to 0.1 centimeters. A height measuring instrument is used to measure the height of each tree. The data is accurate to 0.1 meters; the species and growth status of each tree are recorded; for bamboo forests within the sample plot, the number of bamboo trees, average diameter at breast height (DBH), and average height are counted; for shrubs and herbaceous vegetation, their dominant species and coverage are recorded; all survey data are organized according to the sample plot number and a standardized forest vegetation parameter survey record form is filled out. The record form includes information such as the sample plot number, number of trees, DBH / height / species / growth status of each tree, bamboo forest parameters, and shrub and herbaceous parameters. These organized original survey data are the initial forest vegetation parameters of the sample plot.

[0124] Furthermore, based on the aforementioned hierarchical structure, the initial parameters of the forest vegetation are hierarchically labeled to obtain the hierarchical parameters of the forest vegetation. First, the criteria for dividing each level in the hierarchical structure are clarified. For example, using administrative villages as units, the "Arbor Forest" level, "Bamboo Forest" level, and "Other Types" level are divided according to the proportion of arbor forest and bamboo forest coverage, along with the corresponding administrative area scope of each level. For instance, which administrative villages are included in a certain "Arbor Forest" level, and which are included in a certain "Bamboo Forest" level. The administrative affiliation information of each sample plot is extracted from the initial parameters of the forest vegetation. Based on the hierarchical structure, the specific level to which the sample plot belongs is determined. For example, if a sample plot is located in administrative village A, administrative village A is classified as "Arbor Forest" because the proportion of arbor forest area is ≥50%. If a sample plot belongs to a layer, then the layer it belongs to is the "arbor forest" layer. In the survey record table of initial parameters of forest vegetation, a new field "belonging to layer" is added. The layer information corresponding to each sample plot is filled into this field to realize the association between initial parameters and layer structure. At the same time, the initial parameters are classified and summarized according to the layer. For example, the initial parameters of all sample plots belonging to the "arbor forest" layer are classified into one category, those belonging to the "bamboo forest" layer are classified into another category, and those belonging to the "other types" layer are classified into a third category. The initial parameters within each layer are preliminarily statistically analyzed, such as the average diameter at breast height, average tree height, and total number of trees of the sample plots in the "arbor forest" layer. These parameters with clear layer identification and classified and summarized according to the layer are the layer parameters of forest vegetation.

[0125] Furthermore, when verifying and screening the stratification parameters to obtain the effective forest vegetation parameters of the sample plots, a verification standard for the stratification parameters is first established. This standard refers to industry standards for forest resource surveys and the monitoring accuracy requirements of this invention. For example, the error in diameter at breast height (DBH) measurement must be ≤0.2 cm, the error in tree height measurement must be ≤0.3 m, tree species identification must be accurate, and the coverage record must be consistent with the actual situation on site. At the same time, it is stipulated that the missing tree survey data in a single sample plot shall not exceed 5% of the total number of trees in that sample plot. The stratification parameters of each sample plot are checked one by one according to the verification standard. If the measurement error of one tree in the DBH measurement value of a certain sample plot exceeds 0.2 cm, the tree must be re-verified. The original measurement records are reviewed. If there are clerical errors in the original records, they are corrected. If there are indeed errors in the field measurements, the survey personnel are arranged to re-measure the sample plots. If the proportion of missing tree survey data in a certain sample plot reaches 8%, exceeding the verification standard, a comprehensive survey of the sample plot is required to supplement the data. Invalid data found during the verification process, such as incorrect tree species identification or serious discrepancies between the recorded coverage and the actual situation, are directly discarded. Data that meets the verification standard after correction is retained. All stratification parameters that have passed the verification are reorganized according to the sample plot number to form a complete parameter table. The table contains only parameter information that meets the accuracy requirements. These parameters are the effective forest vegetation parameters of the sample plots.

[0126] Furthermore, when fusing multi-source data to obtain the vegetation data of the sample plot for the effective parameters of the forest vegetation, firstly, multi-source auxiliary data related to the sample plot are collected, including remote sensing image data, meteorological data, and soil data of the area where the sample plot is located; the effective parameters of forest vegetation are correlated and matched with the multi-source auxiliary data, based on the geographical coordinates and administrative affiliation of the sample plot. For example, the effective parameters of a certain sample plot are compared with the interpretation results of the remote sensing image corresponding to the coordinates of the sample plot. If there are differences, they are verified by combining field survey photos and records to determine the final vegetation type; the vegetation growth status in the effective parameters is correlated with meteorological data and soil data to analyze the impact of meteorological conditions and soil conditions on vegetation growth, and relevant impact descriptions are added to the parameters; all the correlated and matched data are integrated to form a comprehensive dataset containing basic information of the sample plot, core parameters of forest vegetation, multi-source auxiliary data, and correlation analysis descriptions. This dataset is the vegetation data of the sample plot.

[0127] In summary, the geographic coordinates and attribute information of the sample plots are extracted based on the layout plan, and a clear spatial distribution map is generated using a geographic information system, providing precise guidance for field surveys. Surveyors use this information to locate the sample plots and, following standard procedures, measure the diameter at breast height (DBH) and height of trees in each plot, and collect statistics on vegetation parameters such as bamboo, shrubs, and grasses, forming initial data that comprehensively reflects the vegetation status. This effectively solves the data deviation problems caused by ambiguous location and non-standard measurement in traditional methods.

[0128] In summary, after obtaining the initial parameters, they are classified and calibrated according to the hierarchical structure to clarify the level to which the parameters belong, laying the foundation for hierarchical carbon storage calculation. The hierarchical parameters are strictly verified and screened in accordance with industry standards to check their completeness and accuracy, correct or remove invalid data, and form a set of real and reliable effective forest vegetation parameters, fundamentally avoiding the risk of subsequent calculation chaos due to data quality issues.

[0129] In summary, to enhance the dimensionality and interpretability of the data, effective parameters were integrated with multi-source auxiliary data such as remote sensing imagery, meteorology, and soil. The data was then correlated and verified using geographic coordinates and administrative affiliations to form a complete vegetation dataset containing core parameters, environmental factors, and comprehensive analysis. This process greatly enriched the data's content and provided more comprehensive support for accurate carbon storage calculations and comprehensive ecological risk analysis.

[0130] S5. Based on the vegetation data, the forest vegetation carbon storage of the largest unit and the smallest unit in the basic unit is summarized step by step to obtain the annual carbon storage of the largest unit and the smallest unit.

[0131] In this embodiment of the invention, the step of summarizing the forest vegetation carbon storage of the largest and smallest units in the basic unit based on the vegetation data to obtain the annual carbon storage of the largest and smallest units includes:

[0132] The vegetation data is transformed to obtain the forest vegetation carbon storage of the sample plot;

[0133] Based on the aforementioned stratified structure, the forest vegetation carbon storage of the sample plot is aggregated within each stratum to obtain the average stratified carbon storage of the sample plot.

[0134] The parameters of the layered structure are analyzed and deduced to obtain the layer area and layer adjustment coefficient of the layered structure;

[0135] The forest vegetation carbon storage of the smallest unit is obtained by calculating the layer area, layer adjustment coefficient, and average layer carbon storage. The formula for calculating the forest vegetation carbon storage of the smallest unit is as follows:

[0136] ;

[0137] in, This represents the carbon storage of the forest vegetation in the smallest unit. Indicates the first The average carbon storage of the layer described above. Indicates the first The area of ​​the layer described above, Indicates the first The layer adjustment coefficient described in the layer description. Indicates the number of layers in a hierarchical structure;

[0138] The forest vegetation carbon storage of the smallest unit is coupled in multiple dimensions to obtain the forest vegetation carbon storage of the largest unit in the basic unit.

[0139] The forest vegetation carbon storage of the largest unit and the forest vegetation carbon storage of the smallest unit are fused at multiple scales to obtain the annual carbon storage of the largest unit and the smallest unit.

[0140] In this embodiment of the invention, the step of multi-dimensionally coupling the forest vegetation carbon storage of the smallest unit to obtain the forest vegetation carbon storage of the largest unit in the basic unit includes:

[0141] The forest vegetation carbon storage of the smallest unit is correlated and weighted to obtain the area weight coefficient of the smallest unit.

[0142] Based on the area weighting coefficient, the forest vegetation carbon storage of the smallest unit is scaled and corrected to obtain the standardized carbon storage of the smallest unit.

[0143] By coupling the standardized carbon storage in multiple dimensions, the forest vegetation carbon storage of the largest unit is obtained, wherein the calculation formula for the forest vegetation carbon storage of the largest unit is:

[0144] ;

[0145] in, This represents the forest vegetation carbon storage of the largest unit. Indicates the first The carbon storage of forest vegetation in the smallest unit. Indicates the first The area weighting coefficient of the smallest unit, This indicates the number of the smallest units in the basic unit. This represents the sum of the area weight coefficients of the smallest unit.

[0146] Specifically, when converting the vegetation data to obtain the forest vegetation carbon storage of the sample plot, the core growth parameters of different vegetation types within the sample plot are first extracted from the vegetation data. For arbor forests, the diameter at breast height (DBH), tree height, and tree species information of each tree are extracted; for bamboo forests, the average DBH, average height, and number of trees are extracted. Referring to industry standard methods for forest resource carbon storage measurement, corresponding biomass models are used for different tree species. The DBH and tree height data of individual trees are substituted into the model to calculate the biomass of each tree. Then, the sample plot... The total biomass of the arbor forest within the sample plot is obtained by summing the biomass of each individual tree. For bamboo forests, the biomass per unit area is determined based on the average diameter at breast height (DBH) and average height. Combined with the actual area of ​​the bamboo forest within the sample plot, the total biomass of the bamboo forest is calculated. Since forest vegetation carbon storage is usually 50% of the biomass, the forest vegetation carbon storage of the sample plot is obtained by multiplying the sum of the total biomass of the arbor forest and the total biomass of the bamboo forest by 0.5. This method is used to complete the data conversion for all sample plots, resulting in the forest vegetation carbon storage corresponding to each sample plot.

[0147] Furthermore, based on the aforementioned hierarchical structure, when aggregating the forest vegetation carbon storage of the sample plots within each layer to obtain the average stratified carbon storage of the sample plots, the division range of each layer in the hierarchical structure is first clarified, such as the sample plots included in the "arbor forest" layer, the sample plots included in the "bamboo forest" layer, and the sample plots included in the "other types" layer. Forest vegetation carbon storage data of all sample plots are collected according to the hierarchical classification. For example, the carbon storage data of all sample plots belonging to the "arbor forest" layer are grouped into one group, and the carbon storage data of sample plots belonging to the "bamboo forest" layer are grouped into another group. The carbon storage data of sample plots in each group are summed to obtain the total carbon storage of that layer, and the number of sample plots in that layer is counted. The average value obtained by dividing the total carbon storage of that layer by the number of sample plots is the average stratified carbon storage of that layer. The calculation of all layers is completed in sequence to obtain the average stratified carbon storage of each layer.

[0148] Furthermore, when analyzing and deducing the parameters of the hierarchical structure to obtain the hierarchical area and adjustment coefficient, the total land area data of the smallest unit is first obtained. Then, the administrative unit information contained in each level is extracted from the hierarchical structure. The land area of ​​each administrative unit is queried through the administrative division database. The land areas of all administrative units within the same level are summed to obtain the preliminary area of ​​that level. Combined with the interpretation results of remote sensing images, the preliminary area is corrected. For example, if there are areas in a certain administrative village within a certain level that are not of the vegetation type of that level, the area of ​​the non-target vegetation area is determined through remote sensing images and extracted from the area of ​​the administrative village. After deduction, the accurate actual coverage area of ​​each level is finally obtained, that is, the level area of ​​the stratified structure. The determination of the level adjustment coefficient needs to be combined with the vegetation growth status and historical data within the level. By comparing the average diameter at breast height (DBH) and tree height of the current sample plot of the level with the average growth index of the same level of vegetation in the past 5 years, if the current growth index is 10% higher than the historical average, the level adjustment coefficient is set to 1.1; if it is basically consistent with the historical average, the level adjustment coefficient is set to 1.0; if it is 8% lower than the historical average, the level adjustment coefficient is set to 0.92, and so on to complete the derivation of the level adjustment coefficient of each level, thus obtaining the level adjustment coefficient of the stratified structure.

[0149] Furthermore, when calculating the forest vegetation carbon storage of the smallest unit by taking into account the layer area, layer adjustment coefficient, and average layer carbon storage, the calculation is performed separately for each layer. For each layer, the average layer carbon storage of that layer is multiplied by the layer area to obtain the preliminary carbon storage of that layer. Then, the preliminary carbon storage is multiplied by the layer adjustment coefficient of that layer to obtain the actual carbon storage of that layer after growth condition correction. The actual carbon storage of all layers is added together, and the sum is the forest vegetation carbon storage of the smallest unit. For example, the actual carbon storage of the "arbor forest" layer is 500,000 tons, the "bamboo forest" layer is 150,000 tons, and the "other types" layer is 20,000 tons. The sum of these three results in the forest vegetation carbon storage of the county-level administrative region being 670,000 tons.

[0150] Furthermore, when coupling the forest vegetation carbon storage of the smallest unit in multiple dimensions to obtain the forest vegetation carbon storage of the largest unit in the basic unit, firstly, identify all the smallest units included in the largest unit in the basic unit, collect the forest vegetation carbon storage data of each smallest unit, and simultaneously obtain the land area and forest coverage data of each smallest unit; use the proportion of the land area of ​​the smallest unit to the total land area of ​​the largest unit as the first weight, and the proportion of the forest coverage of the smallest unit to the average forest coverage of the largest unit as the second weight, setting the two weights to a weight ratio of 0.6 and 0.4 respectively, determined according to the uniformity of vegetation distribution within the largest unit. If the distribution is relatively uniform, the weight ratios are the same; if the difference is large, the land area weight ratio is adjusted; calculate the comprehensive weight of each smallest unit, i.e., land area ratio × 0.6 + forest coverage ratio × 0.4; multiply the forest vegetation carbon storage of each smallest unit by its corresponding comprehensive weight to obtain the weighted carbon storage of that smallest unit in the largest unit; sum the weighted carbon storage of all the smallest units, and the sum is the forest vegetation carbon storage of the largest unit in the basic unit.

[0151] Furthermore, when multi-scale fusion is performed on the forest vegetation carbon storage of the largest unit and the forest vegetation carbon storage of the smallest unit to obtain the annual carbon storage of the largest and smallest units, the logical consistency of the carbon storage data of the largest unit and each smallest unit is first checked to ensure that the carbon storage of the largest unit is equal to the sum of the carbon storage of each smallest unit according to reasonable weights. If there are slight differences, the carbon storage of the largest unit is fine-tuned to make it consistent with the sum of the carbon storage of the smallest units. Then, combined with the dynamic change data of vegetation within the year, such as the annual increase in carbon storage due to afforestation in the largest unit, the reduction in carbon storage due to logging, and the loss of carbon storage due to pests and diseases, these dynamic changes are respectively included in the carbon storage of the largest unit and the corresponding smallest unit. For example, if the annual increase in carbon storage due to afforestation in a certain smallest unit is 20,000 tons, then the carbon storage of the smallest unit increases by 20,000 tons, and the carbon storage of the largest unit increases by 20,000 tons simultaneously. Finally, the updated carbon storage of the largest unit is the annual carbon storage of the largest unit, and the updated carbon storage of each smallest unit is the annual carbon storage of the corresponding smallest unit.

[0152] Specifically, when assigning weights to the forest vegetation carbon storage of the smallest unit to obtain the area weight coefficient of the smallest unit, it is first determined that the smallest unit in the basic unit is a county-level administrative region and the largest unit is a city-level administrative region. The total land area data of all county-level administrative regions within the city-level administrative region, as well as the land area data of each county-level administrative region, are collected. The proportion of the land area of ​​each county-level administrative region to the total land area of ​​the city-level administrative region is calculated. The specific calculation method is: the area weight coefficient of a county-level administrative region = the land area of ​​the county-level administrative region ÷ the total land area of ​​the city-level administrative region. The area weight coefficients of all county-level administrative regions within the city-level administrative region are calculated in this way.

[0153] Furthermore, based on the area weighting coefficient, the forest vegetation carbon storage of the smallest unit is scaled and corrected to obtain the standardized carbon storage of the smallest unit. First, the calculated forest vegetation carbon storage data for each county-level administrative region is obtained. The forest vegetation carbon storage of each county-level administrative region is multiplied by its corresponding area weighting coefficient. This calculation is used to achieve the scaling correction of carbon storage for different county-level administrative regions, eliminating the impact of differences in carbon storage values ​​caused by differences in the area of ​​county-level administrative regions on subsequent aggregation. In this way, the calculation of standardized carbon storage for all county-level administrative regions is completed.

[0154] Furthermore, when obtaining the forest vegetation carbon storage of the largest unit by multi-dimensional coupling of the standardized carbon storage, it is first confirmed that the standardized carbon storage of all county-level administrative regions within the municipal administrative region has been calculated without omission or error; the standardized carbon storage of all county-level administrative regions is summed, and the summation result is the forest vegetation carbon storage of the municipal administrative region. Through this multi-dimensional coupling method, the accurate summation of forest vegetation carbon storage from the county-level administrative region to the municipal administrative region is achieved.

[0155] Specifically, no. To obtain the stratified carbon storage mean, the vegetation data of the sample plots must first be transformed to obtain the forest vegetation carbon storage of the sample plots. Then, based on the stratified structure of forest vegetation cover area, the forest vegetation carbon storage of the sample plots is aggregated within each stratum, ultimately generating the mean value of the stratified carbon storage mean. Average carbon reserves across all layers; Obtaining the area of ​​each layer requires analytical deduction of the parameters of the layered structure of forest vegetation cover area, and extracting the area of ​​the first layer from the analytical deduction results. Layer-by-layer area; Obtaining the layered adjustment coefficients requires analytical deduction of the parameters of the layered structure of forest vegetation cover area, and extracting the first adjustment coefficient from the analytical deduction results. Adjustment coefficients at each level.

[0156] Furthermore, determining the number of layers in the hierarchical structure requires first normalizing the forest vegetation coverage area of ​​the smallest administrative unit and the total area of ​​the smallest administrative unit to obtain the forest vegetation coverage index. Then, based on the forest vegetation coverage index, the smallest administrative unit is hierarchically defined to obtain the primary level. Finally, the primary levels are structurally merged to obtain the hierarchical structure. The number of levels contained in this hierarchical structure is the number of layers in the hierarchical structure.

[0157] Furthermore, the first The average carbon storage of each layer and the first layer Multiplying the areas of each level yields an intermediate result, which reflects the... The relationship between the average carbon storage of each layer and the area of ​​the corresponding layer; calculate the sum of the areas of all layers, using the product of the first layer and the second layer. Dividing the area of ​​each layer by the sum gives the first layer. The proportion of the area of ​​each layer to the total area of ​​all layers, and this proportion is compared with the first layer. The adjustment coefficients are added together to obtain a new value, which incorporates the values ​​of the previous layer. Layer-by-layer adjustment coefficient and the first Information on the percentage of area at each level.

[0158] Furthermore, the previously obtained first... The average carbon storage of each layer and the first layer The product of the areas of each level is merged with the newly obtained result. Layer-by-layer adjustment coefficient and the first Multiplying the area percentages of each layer yields another intermediate result, which further integrates the information from the previous two steps. Dividing the number of layers in the hierarchical structure by the sum of the number of layers plus one yields a coefficient used to scale and adjust the intermediate result obtained earlier. Multiplying the previously integrated intermediate result by this scaling and adjustment coefficient yields the first... The contribution of each layer to the carbon storage of the smallest unit of forest vegetation is calculated by summing the contribution values ​​of all layers. The summation result is the carbon storage of the smallest unit of forest vegetation. This result is obtained by gradually integrating relevant data from each layer and performing calculations, and it can accurately reflect the actual situation of the carbon storage of the smallest unit of forest vegetation.

[0159] Furthermore, when the first When the average carbon storage of each layer increases, in the first layer... Layer-level area, first With the adjustment coefficients and the number of layers in the hierarchical structure remaining unchanged, the first... The contribution of each layer to the carbon storage of the smallest unit of forest vegetation increases, which in turn leads to an increase in the carbon storage of the smallest unit of forest vegetation; when the first layer... As the area of ​​each level increases, at the th Average carbon reserves of each layer With the adjustment coefficients and the number of layers in the hierarchical structure remaining unchanged, the first... The contribution of each layer to the carbon storage of the smallest unit of forest vegetation increases, which in turn leads to an increase in the carbon storage of the smallest unit of forest vegetation; when the first layer... When the adjustment coefficient increases layer by layer, at the first... Average carbon reserves of each layer With the area of ​​each level and the number of levels in the hierarchical structure remaining unchanged, the first The contribution of each layer to the carbon storage of the smallest unit of forest vegetation increases, thus increasing the carbon storage of the smallest unit of forest vegetation. When the number of layers in the stratified structure increases, if the parameters of the newly added layer can make the layer produce a positive contribution value, and the parameters of other layers remain unchanged, then the sum of the contribution values ​​of all layers will increase, thus increasing the carbon storage of the smallest unit of forest vegetation. If the parameters of the newly added layer make the layer produce a negative contribution value, or a small contribution value, and the parameters of other layers remain unchanged, then the sum of the contribution values ​​of all layers may decrease or increase only slightly, thus potentially decreasing or increasing the carbon storage of the smallest unit of forest vegetation.

[0160] Specifically, no. Obtaining the forest vegetation carbon storage of the smallest unit requires first transforming the vegetation data of the sample plots to obtain the forest vegetation carbon storage of the sample plots. Then, based on the stratified structure of forest vegetation cover area, the forest vegetation carbon storage of the sample plots is aggregated within each stratum to obtain the stratified carbon storage mean. Next, the parameters of the stratified structure are analyzed and deduced to obtain the stratified area and stratified adjustment coefficient. Finally, the forest vegetation carbon storage of the smallest unit is obtained through specific calculations, where the first... The forest vegetation carbon storage of the smallest unit is the corresponding value extracted from these calculation results; the... To obtain the area weight coefficient of the smallest unit, a correlation weighting operation must first be performed on the forest vegetation carbon storage of the smallest unit. This correlation weighting involves assigning a corresponding weight to each smallest unit based on its relevant characteristics. This assignment process comprehensively considers the actual situation of the smallest unit to ensure that the weight reflects its relative importance within the largest unit. The final result is the area weight coefficient of the smallest unit. The weight corresponding to the smallest unit is the weight of the th smallest unit. The area weighting coefficient of the smallest unit;

[0161] Furthermore, determining the number of the smallest units within a basic unit requires first defining the scope of the basic unit, then identifying all the smallest units contained within that basic unit, and finally counting these smallest units one by one. The result of this counting is the number of the smallest units within the basic unit. Obtaining the sum of the area weight coefficients of the smallest units requires first collecting all the... The area weight coefficients of each smallest unit are calculated, and then these area weight coefficients are summed up to obtain the total area weight coefficients of the smallest unit.

[0162] Furthermore, the first The carbon storage of forest vegetation in the smallest unit and the first Multiplying the area weight coefficients of the smallest units yields an intermediate result, which reflects the... The relationship between the forest vegetation carbon storage of the smallest unit and its area weighting coefficient; calculate the sum of the area weighting coefficients of the smallest units, and use the product of the smallest unit and its area weighting coefficient. Dividing the area weight coefficient of the smallest unit by the sum yields the result of the first smallest unit. The proportion of the area weight coefficient of the smallest unit to the sum of the area weight coefficients of all smallest units is added to 1 to obtain a new value that combines the values ​​of 1 and the smallest unit. Information on the proportion of the area weight coefficient of each smallest unit;

[0163] Furthermore, take the previously obtained first... The carbon storage of forest vegetation in the smallest unit and the first The product of the area weight coefficients of the smallest unit and the newly obtained fusion of 1 and the first Multiplying the values ​​of the area weight coefficients of the smallest units yields another intermediate result, which further integrates the information from the first two steps and reflects the results of the first step. The contribution of each smallest unit to the carbon storage of the forest vegetation in the largest unit; for all The intermediate results mentioned above are summed to obtain the forest vegetation carbon storage of the largest unit. This result is obtained by gradually integrating the relevant data of each smallest unit and calculating it, which can accurately reflect the actual situation of the forest vegetation carbon storage of the largest unit.

[0164] Furthermore, when the first When the carbon storage of forest vegetation in the smallest unit increases, in the th... With the area weighting coefficient of the smallest unit, the number of smallest units in the basic unit, and the sum of the area weighting coefficients of the smallest units remaining unchanged, the th... The contribution of the smallest unit to the carbon storage of the largest unit's forest vegetation will increase, which in turn will lead to an increase in the carbon storage of the largest unit's forest vegetation.

[0165] Furthermore, when the first When the area weight coefficient of the smallest unit increases, in the th... With the sum of the forest vegetation carbon storage of the smallest unit, the number of smallest units in the basic unit, and the area weighting coefficient of the smallest unit remaining unchanged, the th The contribution of the smallest unit to the carbon storage of the largest unit's forest vegetation will increase, which in turn will lead to an increase in the carbon storage of the largest unit's forest vegetation.

[0166] Furthermore, when the number of minimum units in the basic unit increases, if the contribution value of the newly added minimum unit is positive and the relevant data of other minimum units remain unchanged, then the sum of the contribution values ​​of all minimum units will increase, thereby increasing the forest vegetation carbon storage of the largest unit; if the contribution value of the newly added minimum unit is negative or small, and the relevant data of other minimum units remain unchanged, then the sum of the contribution values ​​of all minimum units may decrease or increase only slightly, thereby potentially decreasing or increasing the forest vegetation carbon storage of the largest unit.

[0167] Furthermore, when the sum of the area weight coefficients of the smallest unit increases, if the first... If the area weight coefficient of the smallest unit remains unchanged, then the... The proportion of the area weight coefficient of the smallest unit to the total will decrease, which in turn leads to a decrease in the value that combines 1 with this proportion, in the th... The smallest unit of forest vegetation carbon storage, the first With the area weight coefficient of the smallest unit remaining unchanged, the th The contribution of each smallest unit to the carbon storage of the largest unit's forest vegetation will decrease. If the situation is the same for other smallest units, then the carbon storage of the largest unit's forest vegetation will decrease.

[0168] In summary, based on industry standards, the core growth parameters of vegetation such as trees and bamboo forests are converted into biomass through a biomass model, and the carbon storage of each sample plot is calculated at a ratio of 50%. Based on the stratified structure, the carbon storage of the sample plots is aggregated within each layer to obtain the average carbon storage of each layer, thus clearly presenting the carbon storage level of different vegetation layers and providing a stratified quantitative basis for subsequent calculations.

[0169] In summary, based on the stratified aggregation, the actual area of ​​each stratum is determined by combining data such as remote sensing, and the stratification adjustment coefficient is set by comparing the vegetation growth status. These parameters that accurately match the actual situation are substituted into the calculation formula. By "average carbon storage × stratum area × stratification adjustment coefficient" and scaling the number of strata, a scientific and accurate minimum unit carbon storage is finally obtained, which effectively avoids the calculation deviation caused by parameters deviating from reality.

[0170] In summary, to calculate the carbon storage of larger units, a multi-dimensional coupling method is adopted. First, objective weighting coefficients are assigned to each smallest unit based on the land area, and the carbon storage is scaled to eliminate the influence of differences in area base. Then, the standardized carbon storage is weighted and summed to obtain the carbon storage of the largest unit. The logical consistency of multi-level data is checked, and annual dynamic changes are incorporated for updating to ensure that the final annual carbon storage result accurately reflects the actual changes in forest vegetation.

[0171] S6. Based on a preset threshold, perform a risk assessment on the annual carbon storage to obtain an early warning report on the carbon storage of the forest vegetation.

[0172] In this embodiment of the invention, the step of performing a risk assessment on the annual carbon storage based on a preset threshold to obtain an early warning report on the forest vegetation carbon storage includes:

[0173] Dynamic features are extracted from the annual carbon storage and the historical carbon storage trends of the sample plots to obtain the changing characteristics of the forest vegetation carbon storage.

[0174] The abnormal change characteristics of the forest vegetation carbon storage are obtained by comparing and verifying the change characteristics with the preset threshold.

[0175] A comprehensive analysis of the abnormal change characteristics yields an early warning report on the carbon storage of the forest vegetation.

[0176] Specifically, to obtain the changing characteristics of forest vegetation carbon storage, dynamic feature extraction is performed on the annual carbon storage and the historical carbon storage trends of the sample plots. First, historical carbon storage data of the sample plots over the past 5 to 10 years are collected. These data must be consistent with the statistical caliber of the annual carbon storage, using the sample plot as the basic unit and the year as the time unit, and covering the same vegetation type statistical range. The annual carbon storage data and historical carbon storage data are organized into a time series dataset in chronological order. For each sample plot, the change in carbon storage and the rate of change between two adjacent years are calculated. By drawing a time series line graph, the fluctuation of carbon storage in each sample plot over time is presented intuitively. At the same time, the average change, average rate of change, and standard deviation of change of all sample plots in different years are statistically analyzed to analyze the overall trend, magnitude, and consistency of carbon storage changes. This information reflecting the time-varying patterns, magnitude, and consistency of carbon storage together constitutes the changing characteristics of forest vegetation carbon storage.

[0177] Furthermore, the changes are compared and verified against preset thresholds. When obtaining the abnormal changes in forest vegetation carbon storage, the basis for setting the preset thresholds is first clarified. Referring to industry standards in the field of forest resource management, historical patterns of regional forest vegetation carbon storage changes, and ecological protection requirements, multi-dimensional thresholds are set, including annual carbon storage change thresholds, annual carbon storage change rate thresholds, and abnormal sample plot proportion thresholds. The extracted changes are then compared one by one with the corresponding thresholds. If the annual carbon storage change of a certain sample plot is -12 tons, exceeding the set lower limit threshold of -5 tons, then the sample plot is determined to have an abnormal change. If the proportion of sample plots with abnormal changes or change rates within an administrative unit reaches 40%, exceeding the 30% abnormal sample plot proportion threshold, then the administrative unit is determined to have an overall abnormal change. All individual sample plot characteristics and overall abnormal characteristics of administrative units that are determined to be abnormal are collected to form the abnormal changes in forest vegetation carbon storage.

[0178] Furthermore, when comprehensively analyzing the abnormal change characteristics to obtain an early warning report on forest vegetation carbon storage, the abnormal change characteristics are first classified and sorted. By anomaly type, they are categorized into abnormally low carbon storage, abnormally high carbon storage, and regionally concentrated anomalies; by impact range, they are categorized into single-plot anomalies, village-level anomalies, county-level anomalies, and city-level anomalies. For each type of anomaly, possible causes are analyzed. For example, a significant decrease in carbon storage may be due to excessive logging, outbreaks of pests and diseases, or forest fires; an abnormally high carbon storage may be due to large-scale afforestation or significant natural vegetation recovery; and regionally concentrated anomalies... It may be related to the implementation of specific policies or natural disasters in the region; the rationality of the causal analysis should be further verified by combining the field survey records of sample plots and regional ecological event reports; the warning level should be divided according to the severity of the anomaly, such as a slight anomaly in a single sample plot is set as a "general warning", and a severe decline in a large number of sample plots in a county-level area is set as a "major warning"; finally, the anomaly type, impact range, causal analysis, warning level and response recommendations should be compiled into a structured report. The report should include a list of abnormal sample plots, a regional anomaly distribution map, the basis for causal analysis and specific response measures, forming an early warning report on forest vegetation carbon storage.

[0179] In general, to extract the dynamic characteristics of annual and historical carbon storage change trends in sample plots, it is necessary to collect historical data of the sample plots over the past 5 to 10 years, ensuring consistency with the annual carbon storage statistics. All data should be collected using sample plots as units and annual data as time units, covering the same vegetation type. After compiling the data into a time series dataset, the changes and rates of change between adjacent years should be calculated. Line graphs should be plotted, and the average change, average rate of change, and standard deviation of change should be statistically analyzed to clarify the patterns, magnitudes, and consistency of changes over time. During verification, thresholds for annual carbon storage changes, rates of change, and the proportion of abnormal sample plots should be set with reference to forest resource management industry standards, historical patterns of regional carbon storage changes, and ecological protection requirements. Change characteristics should be compared against these thresholds one by one to screen for abnormal characteristics in individual sample plots and overall administrative units, forming abnormal change characteristics to identify key areas of concern.

[0180] In general, a comprehensive assessment of abnormal changes requires categorizing and analyzing them according to their type and scope of impact. This should be combined with field survey records from sample plots and regional ecological event reports to determine the causes. For example, a decline in carbon storage may be due to over-logging or pests and diseases, while a surge may be due to large-scale afforestation. Furthermore, warning levels should be assigned based on the severity of the anomalies. For instance, a minor anomaly in a single sample plot is classified as a "general warning," while a severe decline in a large number of sample plots at the county level is classified as a "major warning." Finally, the anomaly type, scope of impact, causes, warning level, and response recommendations should be integrated to form a structured warning report, providing a basis for decision-making in forest carbon resource management.

[0181] like Figure 2 The diagram shown is a functional block diagram of an annual monitoring and early warning system for forest vegetation carbon storage provided in an embodiment of the present invention.

[0182] The annual monitoring and early warning system 100 for forest vegetation carbon storage described in this invention can be installed in an electronic device. Depending on the functions implemented, the annual monitoring and early warning system 100 for forest vegetation carbon storage may include a parameter encryption module 101, a scheme deployment module 102, a structural layering module 103, a data survey module 104, a data aggregation module 105, and a risk early warning module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0183] In this embodiment, the functions of each module / unit are as follows:

[0184] The parameter encryption module is used to determine the encryption spacing of the sample plots based on the annual monitoring accuracy standard for forest vegetation carbon storage, with administrative regions as the basic unit, and to obtain the encryption spacing parameters of the sample plots.

[0185] The scheme deployment module is used to spatially configure the encryption spacing parameters to obtain the deployment scheme of the smallest unit in the basic unit;

[0186] The structural hierarchical module is used to hierarchically classify the forest vegetation coverage area of ​​the smallest administrative unit in the smallest unit to obtain the hierarchical structure of the forest vegetation coverage area.

[0187] The data survey module is used to accurately measure the forest vegetation parameters in the sample plot based on the layout scheme and the layered structure, and obtain the vegetation data of the sample plot.

[0188] The data aggregation module is used to aggregate the forest vegetation carbon storage of the largest unit and the smallest unit in the basic unit according to the vegetation data, so as to obtain the annual carbon storage of the largest unit and the smallest unit.

[0189] The risk warning module is used to assess the risk of the annual carbon storage based on a preset threshold and obtain a warning report on the carbon storage of the forest vegetation.

[0190] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0191] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0192] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0193] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0194] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A forest vegetation carbon storage annual monitoring and early warning method, characterized in that, The method comprises: S1, based on the annual monitoring accuracy standard of forest vegetation carbon storage, determining the encryption interval of the sample plot based on the administrative region as the basic unit, obtaining the encryption interval parameter of the sample plot; S2, spatially configuring the encryption interval parameter to obtain the layout scheme of the smallest unit in the basic unit, comprising: carrying out grid analysis on the encryption interval parameter to obtain the layout grid of the sample plot, comprising: scaling the encryption interval parameter to obtain the basic size of the basic unit; based on the basic size, discretizing the basic unit to obtain the initial grid of the basic unit; mapping the topographic features of the sample plot to the initial grid to obtain the layout grid of the sample plot; spatially superimposing the layout grid and the geographical boundary of the smallest unit in the basic unit to obtain the preliminary layout point of the smallest unit, wherein the smallest unit is a county-level administrative region; adjusting the preliminary layout point to obtain the optimized layout point of the smallest unit; integrating the optimized layout point to obtain the layout scheme of the smallest unit; S3, classifying the forest vegetation coverage area of the smallest administrative unit in the smallest unit by level to obtain the hierarchical structure of the forest vegetation coverage area; S4, based on the layout scheme and the hierarchical structure, accurately measuring the forest vegetation parameters in the sample plot to obtain the vegetation data of the sample plot; S5, based on the vegetation data, the forest vegetation carbon storage of the largest unit and the smallest unit in the basic unit is summarized step by step to obtain the annual carbon storage of the largest unit and the smallest unit, comprising: carrying out data conversion on the vegetation data to obtain the forest vegetation carbon storage of the sample plot; based on the hierarchical structure, aggregating the forest vegetation carbon storage of the sample plot within the layer to obtain the average carbon storage of the sample plot in the layer; analyzing and deducing the parameters of the hierarchical structure to obtain the hierarchical area and layer adjustment coefficient of the hierarchical structure; calculating the hierarchical area, layer adjustment coefficient and average carbon storage in the layer to obtain the forest vegetation carbon storage of the smallest unit; carrying out multi-dimensional coupling on the forest vegetation carbon storage of the smallest unit to obtain the forest vegetation carbon storage of the largest unit in the basic unit, wherein the largest unit is a municipal administrative region; multi-scale fusion of the forest vegetation carbon storage of the largest unit and the forest vegetation carbon storage of the smallest unit to obtain the annual carbon storage of the largest unit and the smallest unit; S6, based on the preset threshold, the annual carbon storage is judged to obtain the early warning report of the forest vegetation carbon storage.

2. The method for monitoring and early warning of forest vegetation carbon storage according to claim 1, wherein, The method comprises: carrying out feature analysis on the historical data of forest vegetation carbon storage in the administrative region to obtain the distribution characteristics of the forest vegetation carbon storage; The sampling accuracy of the administrative region is obtained by checking the administrative region based on the annual monitoring accuracy standard of the forest vegetation carbon storage; The topology optimization of the sample plot of the basic unit is obtained based on the distribution characteristics and the sampling accuracy; The encryption interval of the sample plot is obtained by deducing and verifying the encryption interval based on the annual monitoring accuracy standard of the forest vegetation carbon storage.

3. The method for monitoring and early warning of forest vegetation carbon storage according to claim 1, characterized in that, The hierarchical structure of the forest vegetation coverage area is obtained by classifying the forest vegetation coverage area of the smallest administrative unit in the smallest unit, including: The forest vegetation coverage index of the forest vegetation coverage area and the total area of the smallest administrative unit is obtained by normalizing the forest vegetation coverage area of the smallest administrative unit in the smallest unit and the total area of the smallest administrative unit; The primary level of the smallest administrative unit is obtained by classifying the smallest administrative unit based on the forest vegetation coverage index; The hierarchical structure of the forest vegetation coverage area is obtained by merging the structure of the primary level.

4. The forest vegetation carbon storage annual monitoring and early warning method according to claim 1, characterized in that, The vegetation data of the sample plot is obtained by accurately measuring the forest vegetation parameters in the sample plot based on the layout scheme and the hierarchical structure, including: The spatial distribution of the sample plot is obtained by scheme shaping the location information of the sample plot based on the layout scheme; The initial parameters of the forest vegetation of the sample plot are obtained by cooperatively extracting the forest vegetation parameters of the sample plot based on the spatial distribution; The hierarchical parameters of the forest vegetation are obtained by classifying the initial parameters of the forest vegetation based on the hierarchical structure; The effective parameters of the forest vegetation of the sample plot are obtained by checking and screening the hierarchical parameters; The vegetation data of the sample plot is obtained by fusing the effective parameters of the forest vegetation with multi-source data.

5. The forest vegetation carbon storage annual monitoring and early warning method according to claim 1, characterized in that, The forest vegetation carbon storage calculation formula of the smallest unit is: ； wherein, represents the forest vegetation carbon storage of the smallest unit, represents the average of the stratified carbon storage of the first layer, represents the hierarchical area of the first layer, represents the layer adjustment coefficient of the first layer, represents the number of layers in the stratified structure.

6. The method for monitoring and early warning of forest vegetation carbon storage according to claim 1, characterized in that, The forest vegetation carbon storage of the largest unit in the basic unit is obtained by coupling the forest vegetation carbon storage of the smallest unit in multiple dimensions, including: The area weight coefficient of the smallest unit is obtained by associating and empowering the forest vegetation carbon storage of the smallest unit; The standardized carbon storage of the smallest unit is obtained by scaling and correcting the forest vegetation carbon storage of the smallest unit based on the area weight coefficient; The forest vegetation carbon storage of the largest unit is obtained by coupling the standardized carbon storage in multiple dimensions, wherein the forest vegetation carbon storage calculation formula of the largest unit is: ； wherein, represents the forest vegetation carbon storage of the maximum unit, represents the forest vegetation carbon storage of the minimum unit, represents the forest vegetation carbon storage of the minimum unit, represents the area weight coefficient of the minimum unit, represents the area weight coefficient of the minimum unit, represents the number of minimum units in the basic unit, represents the sum of the minimum unit area weight coefficients.

7. The method for monitoring and early warning of forest vegetation carbon storage according to claim 1, characterized in that, The early warning report of the forest vegetation carbon storage is obtained by risk research and judgment of the annual carbon storage based on the preset threshold, including: The change characteristics of the forest vegetation carbon storage are obtained by dynamically extracting the change characteristics of the annual carbon storage and the historical carbon storage of the sample plot; The abnormal change characteristics of the forest vegetation carbon storage are obtained by comparing and verifying the change characteristics with the preset threshold; The early warning report of the forest vegetation carbon storage is obtained by comprehensively researching and judging the abnormal change characteristics.

8. A forest vegetation carbon storage annual monitoring and early warning system, characterized in that, The system comprises: The parameter encryption module is used to determine the encryption spacing of the sample plots based on the annual monitoring accuracy standard for forest vegetation carbon storage, with administrative regions as the basic unit, and to obtain the encryption spacing parameters of the sample plots. The scheme deployment module is used to spatially configure the encryption spacing parameters to obtain the deployment scheme of the smallest unit in the basic unit, including: The grid layout of the sample plot is obtained by performing grid analysis on the density spacing parameters, including: The basic dimensions of the basic unit are obtained by calibrating the encryption spacing parameters. Based on the basic dimensions, the basic unit is discretized to obtain the initial mesh of the basic unit; The topographic features of the sample plot are mapped to the initial grid to obtain the layout grid of the sample plot; The deployment grid is spatially overlaid with the geographical boundary of the smallest unit in the basic unit to obtain the preliminary deployment points of the smallest unit, wherein the smallest unit is a county-level administrative region; Spatial adaptation adjustments are made to the initial deployment points to obtain the optimized deployment points of the smallest unit; The optimized layout points are integrated and designed to obtain the layout scheme of the smallest unit; The structural hierarchical module is used to hierarchically classify the forest vegetation coverage area of ​​the smallest administrative unit in the smallest unit to obtain the hierarchical structure of the forest vegetation coverage area. The data survey module is used to accurately measure the forest vegetation parameters in the sample plot based on the layout scheme and the layered structure, and obtain the vegetation data of the sample plot. The data aggregation module is used to aggregate the forest vegetation carbon storage of the largest and smallest units in the basic unit based on the vegetation data, to obtain the annual carbon storage of the largest and smallest units, including: The vegetation data is transformed to obtain the forest vegetation carbon storage of the sample plot; Based on the aforementioned stratified structure, the forest vegetation carbon storage of the sample plot is aggregated within each stratum to obtain the average stratified carbon storage of the sample plot. The parameters of the layered structure are analyzed and deduced to obtain the layer area and layer adjustment coefficient of the layered structure; The forest vegetation carbon storage of the smallest unit is obtained by calculating the layer area, layer adjustment coefficient and the average carbon storage of the layer. The forest vegetation carbon storage of the smallest unit is coupled in multiple dimensions to obtain the forest vegetation carbon storage of the largest unit in the basic unit, wherein the largest unit is a municipal administrative region. The forest vegetation carbon storage of the largest unit and the forest vegetation carbon storage of the smallest unit are fused at multiple scales to obtain the annual carbon storage of the largest unit and the smallest unit. The risk warning module is used to assess the risk of the annual carbon storage based on a preset threshold and obtain a warning report on the carbon storage of the forest vegetation.

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