Forest carbon sink function monitoring method and system based on multi-source data

Abstract

The invention discloses a forest carbon sink function monitoring method based on multi-source data. The method comprises the following steps: collecting data such as vegetation, soil, weather, landform and interference factors; the collected data are preprocessed; the preprocessed data are input into a Biome-BGC model for processing, and ecological system carbon flux parameters are obtained; determining a vegetation carbon sink amount and a soil carbon sink amount based on an ecological system carbon flux parameter, and obtaining a comprehensive carbon sink amount by combining artificial and natural interference carbon emission amounts; determining the forest carbon sink potential through a constructed forest carbon sink potential calculation formula based on the calculated comprehensive carbon sink amount; based on the calculated comprehensive carbon sink amount, time stability, space stability and anti-interference stability are determined, weighted summation is carried out on the time stability, the space stability and the anti-interference stability, and the carbon sink stability is determined; according to the method, the forest carbon sink function is monitored through multi-source data, the monitoring accuracy is improved, and the forest carbon sink potential, the carbon sink stability and the investment value are accurately quantified.

Description

Technical Field

[0001] This application relates to the field of forest carbon sink monitoring technology, and relates to, but is not limited to, a method and system for monitoring forest carbon sink functions based on multi-source data. Background Technology

[0002] Forest carbon sinks refer to the natural phenomenon in which forest ecosystems absorb carbon dioxide (CO2) from the atmosphere through photosynthesis and fix it in vegetation tissues, soil, and wood products, thereby reducing the concentration of greenhouse gases in the atmosphere. This process not only plays a crucial role in regulating the global carbon cycle and mitigating climate change, but is also an important natural solution to addressing global warming. According to statistics from authoritative international organizations, global forests absorb 25% to 30% of human emissions annually.

[0003] As the largest carbon sink in terrestrial ecosystems, forests are playing an increasingly prominent strategic role in carbon trading and ecological investment. Forest carbon sinks not only absorb and fix carbon dioxide from the atmosphere through photosynthesis, thus improving the ecological environment, but also, through carbon market trading mechanisms, can be transformed into economically valuable carbon assets, injecting new financial vitality into forestry projects and helping to achieve a win-win situation for both ecological and economic benefits.

[0004] However, current investment in forest carbon sequestration projects faces numerous challenges. On the one hand, traditional assessment methods often rely on single-dimensional data or static models, making it difficult to comprehensively consider the dynamic impacts of complex factors such as vegetation, soil, weather, topography, and human and natural disturbances on forest carbon sequestration functions. This results in significant estimation errors regarding forest carbon sequestration functions and makes it difficult to accurately control investment risks. On the other hand, the carbon sequestration market lacks a scientifically unified investment value assessment standard, making it impossible for investors to accurately judge the potential returns and stability of projects, thus hindering the large-scale and sustainable development of the forest carbon sequestration industry. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and system for monitoring forest carbon sink function based on multi-source data. The aim is to monitor forest carbon sink function through multi-source data, avoiding the problem that traditional assessment methods rely on single-dimensional data or static models, which make it difficult to comprehensively consider the dynamic impact of complex factors such as vegetation, soil, meteorology, topography, and human and natural disturbances on forest carbon sink function, resulting in large deviations in the estimation of forest carbon sink function.

[0006] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a method for monitoring forest carbon sink function based on multi-source data, the method comprising: Collect vegetation data, soil data, meteorological data, topographic data, and data on interfering factors; perform format unification and standardization processing on the collected data, and perform spatiotemporal matching on the processed data; The spatiotemporally matched data is input into the Biome-BGC model for processing to obtain ecosystem carbon flux parameters. Based on these parameters, vegetation carbon sink and soil carbon sink are determined. Combining anthropogenic and natural disturbance carbon emissions, a comprehensive carbon sink calculation formula is used to obtain the comprehensive carbon sink. Based on the calculated comprehensive carbon sink, the forest carbon sink potential is determined using a constructed formula. Based on the calculated comprehensive carbon sink, temporal stability, spatial stability, and disturbance resistance stability are determined. These factors are then weighted and summed to determine the carbon sink stability.

[0007] In some embodiments, the vegetation data includes: vegetation type, spatial distribution of vegetation, and vegetation growth parameters; the vegetation type includes: coniferous forest, broad-leaved forest, and shrubland; the soil data includes: soil type, soil properties, and soil organic carbon content; the soil type includes: sandy soil, clay soil, and loam soil; the soil properties include: soil bulk density and pH value; the meteorological data includes: basic meteorological elements and special meteorological event data; the basic meteorological elements include: air temperature, precipitation, photosynthetically active radiation, wind speed, and air humidity; the special meteorological events include: the occurrence time, intensity, and impact range of special events such as typhoons, droughts, and forest fires; the topographic data includes: topographic factors and landform types; the topographic factors include: elevation, slope, and aspect; the landform types include: mountains, plains, and hills; the disturbance factor data includes: human disturbance activity data and natural disturbance factors; the human disturbance activity data includes the area of ​​human logging and fertilization intensity; the natural disturbance factors include: the spatiotemporal distribution of the area affected by pests and diseases and the spatiotemporal distribution of the volume of fallen trees.

[0008] In some embodiments, the determination of vegetation carbon sink and soil carbon sink based on ecosystem carbon flux parameters, combined with anthropogenic and natural disturbance carbon emissions, and the obtaining of the comprehensive carbon sink through a constructed comprehensive carbon sink calculation formula, includes: ; ; ; The ecosystem carbon flux parameters include: net primary productivity. heterotrophic respiration value and net ecosystem productivity ; This refers to the carbon sequestration of vegetation. Soil carbon sequestration; For comprehensive carbon sequestration; The carbon emissions caused by human interference were obtained through on-site observations. The carbon emissions from natural disturbances were obtained through on-site observations.

[0009] In some embodiments, the method further includes: A progressive automatic calibration mechanism is adopted to optimize the Biome-BGC model step by step on weekly, monthly, semi-annual, and annual scales.

[0010] In some embodiments, the formula for calculating the forest carbon sequestration potential is: ; in, This represents the projected carbon sink in T years. The total carbon sink is represented by T, which indicates the planning period. This represents the interference risk coefficient, with a value between 0 and 1.

[0011] In some embodiments, determining time stability, spatial stability, and anti-interference stability based on the calculated comprehensive carbon sink, and then weighting and summing these three factors to determine carbon sink stability, includes: ; in, This represents the standard deviation of annual carbon sequestration. This represents the average annual carbon sequestration. For time stability; This represents the standard deviation of spatial carbon sequestration. This represents the average amount of carbon sequestration in space. For spatial stability; For comprehensive carbon sequestration; This indicates the carbon sink amount before the interference; This indicates the time required for carbon sequestration to return to the average level of carbon sequestration before the occurrence of the disturbance event. This indicates the maximum permissible recovery time, which is adjusted according to industry standards and project timelines. For anti-interference stability; , , The weighting coefficients and ; For carbon sink stability.

[0012] In some embodiments, the method further includes: The calculated comprehensive carbon sink, forest carbon sink potential, and carbon sink stability are standardized to obtain the first comprehensive carbon sink, the first forest carbon sink potential, and the first carbon sink stability. A risk correction term is introduced, and the carbon sink investment value score is calculated by combining the three factors of the first comprehensive carbon sink, the first forest carbon sink potential, and the first carbon sink stability. The calculated carbon sink investment value score is converted to a percentage system, and the carbon sink investment value is evaluated based on the percentile of the carbon sink investment value score.

[0013] In some embodiments, the carbon sink investment value score is calculated using the following formula, including: ; ; in, Assess the investment value of carbon sinks; , , The weighting coefficients and ; It is the largest comprehensive carbon sink; The first forest carbon sequestration potential; For the first carbon sink stability; This is a risk correction item; This represents the interference risk coefficient, with a value between 0 and 1. For adjustment coefficients, ; This is the maintenance cost coefficient.

[0014] In some embodiments, assessing the investable value of carbon sinks based on percentiles of carbon sink investable value scores includes: When the percentile of the carbon sink investment value score is greater than or equal to 80, the investment level is assessed as priority, and the management strategy is to focus on long-term carbon sink projects. When the percentile of the carbon sink investment value score is between 60 and 79, the investment level is assessed as optimization, and the management strategy is to invest after improving management measures. When the percentile of the carbon sink investment value score is between 40 and 59, the investment level is assessed as wait-and-see, and the management strategy is to require risk control or policy support. When the percentile of the carbon sink investment value score is less than 40, the investment level is assessed as avoidance, and the management strategy is to not invest for the time being.

[0015] Secondly, embodiments of this application provide a forest carbon sink function monitoring system based on multi-source data, the system comprising: The data acquisition module is used to collect vegetation data, soil data, meteorological data, topographic data, and interference factor data; The data preprocessing module is used to perform format unification and standardization on the collected data, and to perform spatiotemporal matching on the processed data; The Biome-BGC model processing module is used to input the spatiotemporally matched data into the Biome-BGC model for processing to obtain ecosystem carbon flux parameters. The integrated carbon sink calculation module is used to determine vegetation carbon sink and soil carbon sink based on ecosystem carbon flux parameters. It combines anthropogenic and natural disturbance carbon emissions and obtains the integrated carbon sink through the constructed integrated carbon sink calculation formula. The forest carbon sequestration potential determination module is used to determine the forest carbon sequestration potential based on the calculated comprehensive carbon sequestration amount and the constructed forest carbon sequestration potential calculation formula. The carbon sink stability determination module is used to determine the temporal stability, spatial stability, and anti-interference stability based on the calculated comprehensive carbon sink amount, and to perform a weighted summation of the temporal stability, spatial stability, and anti-interference stability to determine the carbon sink stability.

[0016] The beneficial effects of the technical solutions provided in this application include at least the following: In this embodiment, forest carbon sink function is monitored through multi-source data, avoiding the problem that traditional assessment methods often rely on single-dimensional data or static models, making it difficult to comprehensively consider the dynamic impact of complex factors such as vegetation, soil, meteorology, topography, and human and natural disturbances on forest carbon sink function, leading to large estimation biases. Furthermore, a comprehensive method and system encompassing multi-source data acquisition, dynamic model simulation, and multi-dimensional assessment accurately quantifies forest carbon sink potential, carbon sink stability, and investment value, providing scientific basis and technical support for carbon sink project investment decisions, resource optimization, and the healthy development of the carbon market. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart of a method for monitoring forest carbon sink function based on multi-source data; Figure 2 This is a system block diagram of a forest carbon sink function monitoring system based on multi-source data. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0020] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0021] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0022] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for monitoring forest carbon sink function based on multi-source data. The method provided in this embodiment includes: Step S1: Collect vegetation data, soil data, meteorological data, topographic data, and data on interfering factors.

[0023] Here, by monitoring forest carbon sink function through multi-source data, we avoid the problem of traditional assessment methods relying heavily on single-dimensional data or static models. These methods struggle to comprehensively consider the dynamic impacts of complex factors such as vegetation, soil, meteorology, topography, and human and natural disturbances on forest carbon sink function, leading to significant estimation biases. Comprehensive data collection makes the monitoring and assessment of forest carbon sink volume more accurate, providing a more reliable data foundation for subsequent model simulations and investment value analysis. It fully considers the impact of various factors on forest carbon sink function, improving the accuracy and credibility of the assessment results.

[0024] Step S2 involves standardizing and unifying the format of the collected data, and then performing spatiotemporal matching on the processed data.

[0025] Here, the collected data is categorized into vector data and numerical data, and each data type is processed accordingly. Specifically, vector data (such as vegetation distribution and soil sampling point location data) is converted to GeoJSON format, raster data (such as remote sensing imagery and DEM data) is uniformly converted to TIFF format, and time series data (such as meteorological data) is stored in CSV format to ensure data format consistency. Numerical data (such as soil bulk density and precipitation) is Z-score standardized; categorical data (such as vegetation type and landform type) is one-hot encoded to facilitate data application in the model.

[0026] All collected data are projected onto the WGS84 / UTM coordinate system to unify the geospatial benchmark; the temporal resolution is unified to the daily scale, and daily statistics are performed on high-frequency data (such as meteorological data), while low-frequency data (such as soil sampling data) are extended to the annual scale through interpolation methods; the alignment of data with different spatial resolutions is achieved through bilinear interpolation algorithm to ensure the consistency of data in the spatiotemporal dimensions.

[0027] Comprehensive data collection enables more accurate monitoring and assessment of forest carbon sinks, providing a more reliable data foundation for subsequent model simulations and investment value analysis. It also allows for full consideration of the impact of various factors on forest carbon sink functions, improving the accuracy and reliability of assessment results.

[0028] Step S3: Input the spatiotemporally matched data into the Biome-BGC model for processing to obtain ecosystem carbon flux parameters.

[0029] Here, in constructing the Biome-BGC model, the monitoring boundary is first divided into equally spaced grids and land use types are labeled using a Geographic Information System (GIS). The vegetation types interpreted from remote sensing, along with growth parameters such as leaf area index (LAI) and biomass from ground plot surveys, are rasterized to generate vegetation data. Then, the soil type, bulk density, pH value, and soil organic carbon (SOC) from stratified sampling are analyzed. Carbon content is rasterized according to soil depth and input into the model as soil parameters. Basic meteorological elements collected by micro-weather stations are corrected by topographic data (elevation, slope, aspect) to generate daily meteorological raster, and special event interference pulses are constructed in combination with meteorological database. Based on topographic data, the impact of microclimate on carbon cycle is quantified. Interference factors quantified by ground patrol and remote sensing monitoring include human interference (logging area, fertilization intensity) and natural interference (pest and disease area, fallen tree volume). Finally, all data are input into the model, and the carbon flux parameters in the model are simulated by a dynamic response mechanism to achieve precise matching between data collection and model construction.

[0030] The ecosystem carbon flux parameters include net ecosystem productivity (NEP), net primary productivity (NPP), and heterotrophic respiration. Net ecosystem productivity (NEP) refers to the difference between carbon input and carbon output in an ecosystem (such as forest, grassland, or farmland) over a certain period (e.g., one day, one year), reflecting the overall function of the ecosystem as a "carbon source" or "carbon sink." Net primary productivity (NPP) refers to the amount of carbon remaining after subtracting the carbon consumed by plants' respiration (autotrophic respiration, Ra) from the amount of carbon fixed by plants through photosynthesis (gross primary productivity, GPP) over a certain period (e.g., one day, one year). It reflects the actual biomass accumulated by plants and is a fundamental indicator of energy flow and carbon cycling in an ecosystem. Heterotrophic respiration value... Carbon output refers to the amount of carbon consumed by heterotrophic organisms (non-autotrophic organisms) in an ecosystem as they decompose organic matter and release carbon dioxide (CO2). It is an important component of ecosystem carbon output. Heterotrophic organisms include animals, fungi, and bacteria. Their respiration does not depend on photosynthesis. Instead, they obtain energy by decomposing plant residues, animal feces, and soil organic matter, while converting organic carbon into inorganic carbon (CO2) and returning it to the atmosphere.

[0031] Step S4: Based on ecosystem carbon flux parameters, determine vegetation carbon sink and soil carbon sink. Combine anthropogenic and natural carbon emissions, and obtain the comprehensive carbon sink using the constructed comprehensive carbon sink calculation formula.

[0032] Here, based on the results of step S3, vegetation carbon sequestration and soil carbon sequestration are determined. By using multi-source data to determine these values, we avoid the problem of traditional assessment methods relying heavily on single-dimensional data or static models, which struggle to comprehensively consider the dynamic impacts of vegetation, soil, meteorology, and topography on vegetation and soil carbon sequestration, leading to significant estimation errors. Furthermore, by incorporating complex factors such as anthropogenic and natural disturbances, we estimate the overall carbon sequestration, thus improving the accuracy of the overall carbon sequestration estimate.

[0033] The vegetation carbon sink refers to the net accumulation of carbon dioxide (CO2) absorbed from the atmosphere by vegetation through photosynthesis and converted into organic carbon (such as carbohydrates and cellulose), and is an important component of ecosystem carbon sinks. Essentially, it is the difference between the amount of carbon fixed by vegetation over a certain period (e.g., year, month) and the amount of carbon consumed by its own respiration, reflecting the net absorption capacity of vegetation for atmospheric carbon. The soil carbon sink refers to the net accumulation of carbon dioxide (CO2) and other carbon-containing gases absorbed from the atmosphere by soil through physical, chemical, and biological processes, and is an important component of terrestrial ecosystem carbon sinks. Essentially, it is the difference between soil carbon input (such as plant litter, root exudates, and organic fertilizer return) and carbon output (such as microbial decomposition, soil respiration, and erosion loss), reflecting the net carbon fixation capacity of soil as a "carbon pool" for atmospheric carbon.

[0034] The comprehensive carbon sequestration is the sum of vegetation carbon sequestration and soil carbon sequestration minus the sum of anthropogenic and natural carbon emissions.

[0035] The amount of carbon emissions caused by human interference Human activities such as logging, fires, and land-use changes lead to carbon emissions through various mechanisms, and these emissions collectively constitute anthropogenic carbon emissions. Anthropogenic carbon emissions include emissions from logging. Carbon emissions from fires Carbon emissions from land use change Among them, carbon emissions from human activities such as logging are derived from field survey data through specific coefficient calibration and biomass loss calculations; carbon emissions from fires are mainly estimated based on biomass loss caused by fires and the burning of soil organic matter; and carbon emissions from land use change are calculated by comparing the differences in carbon storage before and after land use change. The calculation formula is as follows: .

[0036] The carbon emissions from natural disturbances In forest ecosystems, pests and diseases, and fallen trees are two major natural disturbances. Therefore, their carbon emissions constitute a significant portion of total natural disturbance carbon emissions. In other words, natural disturbance carbon emissions include carbon emissions from pests and diseases. Carbon emissions from fallen wood Carbon emissions from pests and diseases are primarily calculated based on forest biomass loss caused by pests and diseases; carbon emissions from wind-fallen trees are also primarily calculated based on biomass loss caused by wind-fallen trees. The calculation formulas are as follows: .

[0037] Step S5: Based on the calculated comprehensive carbon sequestration, the forest carbon sequestration potential is determined using the constructed forest carbon sequestration potential calculation formula.

[0038] Here, based on the results of step S4, the forest carbon sequestration potential is determined. By using multi-source data to determine the forest carbon sequestration potential, we avoid the problem that traditional assessment methods rely heavily on single-dimensional data or static models, which makes it difficult to comprehensively consider the dynamic impact of vegetation, soil, weather, and topography on the forest carbon sequestration potential, resulting in large errors in the estimation of the forest carbon sequestration potential.

[0039] Step S6: Based on the calculated comprehensive carbon sink, determine the time stability, spatial stability, and anti-interference stability, and perform a weighted summation of the time stability, spatial stability, and anti-interference stability to determine the carbon sink stability.

[0040] Here, based on the results of step S4, temporal stability, spatial stability, and robustness stability are determined. These are achieved using multi-source data, avoiding the problem of traditional assessment methods relying heavily on single-dimensional data or static models, which struggle to comprehensively consider the dynamic impacts of vegetation, soil, meteorology, and topography on temporal stability, spatial stability, and robustness stability, leading to significant estimation errors in these metrics. Furthermore, a weighted summation of temporal stability, spatial stability, and robustness stability is used to determine the comprehensive carbon sink, improving the accuracy of the comprehensive carbon sink estimation.

[0041] In some embodiments, the vegetation data includes: vegetation type, spatial distribution of vegetation, and vegetation growth parameters; the vegetation type includes: coniferous forest, broad-leaved forest, and shrubland; the soil data includes: soil type, soil properties, and soil organic carbon content; the soil type includes: sandy soil, clay soil, and loam soil; the soil properties include: soil bulk density and pH value; the meteorological data includes: basic meteorological elements and special meteorological event data; the basic meteorological elements include: air temperature, precipitation, photosynthetically active radiation, wind speed, and air humidity; the special meteorological events include: the occurrence time, intensity, and impact range of special events such as typhoons, droughts, and forest fires; the topographic data includes: topographic factors and landform types; the topographic factors include: elevation, slope, and aspect; the landform types include: mountains, plains, and hills; the disturbance factor data includes: human disturbance activity data and natural disturbance factors; the human disturbance activity data includes the area of ​​human logging and fertilization intensity; the natural disturbance factors include: the spatiotemporal distribution of the area affected by pests and diseases and the spatiotemporal distribution of the volume of fallen trees.

[0042] Here, by monitoring forest carbon sink function through multi-source data, we avoid the problem of traditional assessment methods relying heavily on single-dimensional data or static models. These methods struggle to comprehensively consider the dynamic impacts of complex factors such as vegetation, soil, meteorology, topography, and human and natural disturbances on forest carbon sink function, leading to significant estimation biases. Comprehensive data collection makes the monitoring and assessment of forest carbon sink volume more accurate, providing a more reliable data foundation for subsequent model simulations and investment value analysis. It fully considers the impact of various factors on forest carbon sink function, improving the accuracy and credibility of the assessment results.

[0043] In some embodiments, the determination of vegetation carbon sink and soil carbon sink based on ecosystem carbon flux parameters, combined with anthropogenic and natural disturbance carbon emissions, and the obtaining of the comprehensive carbon sink through a constructed comprehensive carbon sink calculation formula, includes: ; ; ; The ecosystem carbon flux parameters include: net primary productivity. heterotrophic respiration value and net ecosystem productivity ; This refers to the carbon sequestration of vegetation. Soil carbon sequestration; For comprehensive carbon sequestration; The carbon emissions caused by human interference were obtained through on-site observations. The carbon emissions from natural disturbances were obtained through on-site observations.

[0044] In some embodiments, the method further includes: employing a progressive automatic calibration mechanism to gradually optimize the Biome-BGC model on weekly, monthly, semi-annual, and annual scales.

[0045] Here, the earliest historical data on vegetation, soil, weather, topography, and disturbance factors are first input into the Biome-BGC model. The model's pre-defined initial parameters are then used to calculate NEP, NPP, and other parameters. Simulated data, such as values, is first generated. Then, this simulated data is compared with corresponding historical observational data to analyze the differences. Next, the model parameters are adjusted and calibrated based on these differences. After adjustment, the same or similar data is input again for calculation to see if the simulation results improve. This process is iterated until the simulation results achieve a satisfactory match with historical data, at which point the model parameter calibration is complete. After calibration, the calibrated model is used with new data for calculation and prediction.

[0046] Specifically, weekly data calibration involves running the model with the current parameter values ​​using relevant weekly monitoring data collected from historical data. The weekly simulation results are then meticulously compared with the weekly monitoring data from the corresponding Biome-BGC model, which uses the earliest data collected from historical data. Special attention is paid to indicators showing significant short-term changes, such as weekly changes in net ecosystem productivity (NEP) and soil respiration.

[0047] Based on the comparison results, manually fine-tune the parameters as needed. If the simulated net ecosystem productivity (NEP) this week is higher than the observed value, consider appropriately reducing the parameter values ​​related to photosynthetic efficiency and observe the changes in the simulation results next week. Each adjustment should not be too large to avoid excessive impact on other indicators.

[0048] Monthly data calibration involves integrating weekly monitoring data within each month and calculating statistical measures such as monthly averages or cumulative values ​​for comparison with the model's monthly-scale simulation results. In addition to focusing on indicators like NEP as in weekly calibration, it's also necessary to comprehensively consider the differences between simulated and observed values ​​for other indicators such as NPP and soil carbon change at the monthly scale. Multiple parameters are adjusted collaboratively; for example, if the simulated monthly NPP value is found to be low while the simulated soil carbon storage value is found to be high, it may be necessary to simultaneously adjust parameters related to plant growth and soil carbon decomposition to achieve synchronous optimization of multiple indicators. The parameter adjustments and simulation results trends during monthly calibration are recorded, and the interactions between parameters and their impact on different indicators are analyzed to provide a reference for subsequent calibrations.

[0049] Every six months, data is calibrated, and monthly data from the past six months are further summarized and analyzed to observe the changing trends of the ecosystem over a longer timescale. At this stage, the focus is on indicators with seasonal variations, such as seasonal fluctuations in net ecosystem productivity (NEP) and seasonal accumulation of vegetation biomass. Based on the comparison results of the six-month data, parameters are further optimized and adjusted. Adjustments may be needed for parameters that are not clearly reflected in short-term calibration but have a significant impact on a six-month scale, such as temperature response parameters and precipitation distribution parameters that consider seasonal variations. Simultaneously, the parameter combinations are optimized based on the understanding and experience gained during manual calibration to better simulate the seasonal dynamics of the ecosystem. The calibrated model is validated using six-month data. In addition to comparing the numerical differences between the simulation results and observed data, the model's ability to simulate ecosystem changing trends needs to be evaluated. For example, it is necessary to check whether the model can accurately simulate the seasonal peaks and troughs of vegetation growth, and whether the overall trend of soil carbon storage changes over the six months matches actual observations.

[0050] Annual data calibration involves comprehensively integrating monitoring data from the entire year, including annual averages of meteorological data and annual totals or rates of change of ecosystem indicators. This data is then compared with the model's annual simulation results to assess the model's simulation effectiveness of the entire ecosystem's annual carbon cycle and productivity from a macroscopic perspective. Based on the comparative analysis of annual data, model parameters are comprehensively adjusted. Considering long-term ecosystem dynamics and carbon balance, some key parameters are globally optimized, such as those related to vegetation growth rate and soil carbon pool turnover time, to ensure the model can accurately simulate carbon cycle processes and NEP, NPP, etc., on an annual scale. Key Indicators. After annual data calibration, the model undergoes a comprehensive evaluation. The model is run using the initial parameter values, and the preliminary simulation results are compared with existing historical observation data. Indicators such as root mean square error (RMSE) and mean absolute error (MAE) are used to assess accuracy. Simultaneously, considering the actual conditions and biological significance of the ecosystem, it is determined whether the model can reasonably simulate the long-term dynamics of the ecosystem. If the model can accurately simulate various indicators on an annual scale, and the parameter values ​​conform to ecological reality, then the model calibration can be considered to have achieved good results, and the final parameter combination can be confirmed.

[0051] In some embodiments, the formula for calculating the forest carbon sequestration potential is: ; in, This represents the projected carbon sink in T years. The total carbon sink is represented by T, which indicates the planning period. This represents the interference risk coefficient, with a value between 0 and 1.

[0052] Here, the interference risk coefficient, which is between 0 and 1, is the probability of fire / pest / disease occurrence multiplied by the loss rate. The loss rate is the proportion of carbon sink loss caused by the interference event to the potential carbon sink, reflecting the intensity and depth of the interference.

[0053] Fire loss rate: The proportion of forest biomass destroyed by fire to the total biomass before the fire (e.g., if a fire in a certain area results in the loss of 80% of the forest carbon pool, then the loss rate = 0.8). Pest and disease loss rate: The percentage of vegetation carbon sequestration capacity reduced due to pests and diseases (e.g., if pine caterpillar damage reduces LAI by 20%, which in turn leads to a 15% decrease in NPP, then the loss rate = 0.15).

[0054] In some embodiments, determining time stability, spatial stability, and anti-interference stability based on the calculated comprehensive carbon sink, and then weighting and summing the time stability, spatial stability, and anti-interference stability to obtain the carbon sink stability, includes: ; in, This represents the standard deviation of annual carbon sequestration. This represents the average annual carbon sequestration. For time stability; This represents the standard deviation of spatial carbon sequestration. This represents the average amount of carbon sequestration in space. For spatial stability; For comprehensive carbon sequestration; This indicates the carbon sink amount before the interference; This indicates the time required for carbon sequestration to return to the average level of carbon sequestration before the occurrence of the disturbance event. This indicates the maximum permissible recovery time, which is adjusted according to industry standards and project timelines. For anti-interference stability; , , The weighting coefficients and ; For carbon sink stability.

[0055] Here, the disruptive events referred to are fires, pests and diseases, and human logging, etc.

[0056] In some embodiments, the method further includes: The calculated comprehensive carbon sink, forest carbon sink potential, and carbon sink stability are standardized to obtain the first comprehensive carbon sink, the first forest carbon sink potential, and the first carbon sink stability. A risk correction term is introduced, and the carbon sink investment value score is calculated by combining the three factors of the first comprehensive carbon sink, the first forest carbon sink potential, and the first carbon sink stability. The calculated carbon sink investment value score is converted to a percentage system, and the carbon sink investment value is evaluated based on the percentile of the carbon sink investment value score.

[0057] Here, the calculated comprehensive carbon sink, forest carbon sink potential, and carbon sink stability are standardized to obtain the first comprehensive carbon sink, the first forest carbon sink potential, and the first carbon sink stability. Standardization can eliminate dimensional differences and ensure unit uniformity.

[0058] The risk adjustment item, used in finance, investment, and risk management, is a relevant item or parameter that adjusts the impact of risk factors on the underlying model, valuation, or expected results. Its core function is to quantify risk factors and integrate them into the analytical framework, making the final result more consistent with reality. The risk adjustment item focuses more on the adjustment process of the model or result, which can manifest as a premium on the revenue side (e.g., an increase in the discount rate) or a reduction on the cash flow side (e.g., a decrease in expected returns). Essentially, it is a specific treatment method after risk quantification.

[0059] By introducing a risk correction term and combining three factors—the first comprehensive carbon sink volume, the first forest carbon sink potential, and the first carbon sink stability—a carbon sink investment value score is calculated. Introducing the risk correction term makes the carbon sink investment value score more in line with reality and can intuitively represent the premium of the investment return segment and the reduction of the cash flow segment, providing a scientific basis and technical support for carbon sink project investment decisions, resource optimization and allocation, and the healthy development of the carbon market.

[0060] In some embodiments, the carbon sink investment value score is calculated using the following formula, including: ; ; in, Assess the investment value of carbon sinks; , , The weighting coefficients and ; It is the largest comprehensive carbon sink; The first forest carbon sequestration potential; For the first carbon sink stability; This is a risk correction item; This represents the interference risk coefficient, with a value between 0 and 1. For adjustment coefficients, ; This is the maintenance cost coefficient.

[0061] In some embodiments, assessing the investable value of carbon sinks based on percentiles of carbon sink investable value scores includes: When the percentile of the carbon sink investment value score is greater than or equal to 80, the investment level is assessed as priority, and the management strategy is to focus on long-term carbon sink projects. When the percentile of the carbon sink investment value score is between 60 and 79, the investment level is assessed as optimization, and the management strategy is to invest after improving management measures. When the percentile of the carbon sink investment value score is between 40 and 59, the investment level is assessed as wait-and-see, and the management strategy is to require risk control or policy support. When the percentile of the carbon sink investment value score is less than 40, the investment level is assessed as avoidance, and the management strategy is to not invest for the time being.

[0062] Here, investment levels are classified based on the percentile of the carbon sink's investable value score, and corresponding management strategies are formulated to provide a scientific basis and technical support for carbon sink project investment decisions, resource optimization, and the healthy development of the carbon market.

[0063] Example 2 Based on the foregoing embodiments, this application further provides a forest carbon sink function monitoring system based on multi-source data. The system includes various modules and units included in each module, which can be implemented by a processor in an electronic device; of course, it can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0064] Please see Figure 2 , Figure 2 This is a system block diagram of a forest carbon sink function monitoring system based on multi-source data. The forest carbon sink function monitoring system 200 based on multi-source data provided in this embodiment includes: The data acquisition module 201 is used to collect vegetation data, soil data, meteorological data, topographic data, and interference factor data; The data preprocessing module 202 is used to perform format unification and standardization processing on the collected data, and to perform spatiotemporal matching on the processed data; Biome-BGC model processing module 203 is used to input the spatiotemporally matched data into the Biome-BGC model for processing to obtain ecosystem carbon flux parameters. The integrated carbon sink calculation module 204 is used to determine the vegetation carbon sink and soil carbon sink based on ecosystem carbon flux parameters, and to obtain the integrated carbon sink by combining anthropogenic and natural disturbance carbon emissions through the constructed integrated carbon sink calculation formula. The forest carbon sink potential determination module 205 is used to determine the forest carbon sink potential based on the calculated comprehensive carbon sink amount and through the constructed forest carbon sink potential calculation formula. The carbon sink stability determination module 206 is used to determine the time stability, spatial stability and anti-interference stability based on the calculated comprehensive carbon sink amount, and to perform a weighted summation of the time stability, spatial stability and anti-interference stability to determine the carbon sink stability.

[0065] The entire system monitors forest carbon sequestration through multi-source data, avoiding the problem of traditional assessment systems relying on single-dimensional data or static models. These systems struggle to comprehensively consider the dynamic impacts of complex factors such as vegetation, soil, weather, topography, and human and natural disturbances on forest carbon sequestration, leading to significant estimation errors. Furthermore, through a comprehensive system encompassing multi-source data acquisition, dynamic model simulation, and multi-dimensional assessment, the system accurately quantifies forest carbon sequestration potential, stability, and investment value, providing scientific basis and technical support for carbon sequestration project investment decisions, resource optimization, and the healthy development of the carbon market.

[0066] It should be noted that the description of the above system embodiments is similar to the description of the above method embodiments, and has similar beneficial effects. For technical details not disclosed in the system embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0067] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0068] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0070] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0071] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, 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 units.

[0072] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause the device automatic test line to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0073] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0074] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0075] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring forest carbon sink function based on multi-source data, characterized in that, The method includes: Collect vegetation data, soil data, meteorological data, topographic data, and data on interfering factors; The collected data were processed to unify and standardize the format, and the processed data were then subjected to spatiotemporal matching. The spatiotemporally matched data is input into the Biome-BGC model for processing to obtain ecosystem carbon flux parameters. Based on ecosystem carbon flux parameters, vegetation carbon sink and soil carbon sink are determined. Combined with anthropogenic and natural carbon emissions, the comprehensive carbon sink is obtained through the constructed comprehensive carbon sink calculation formula. Based on the calculated comprehensive carbon sink, the forest carbon sink potential is determined by constructing a calculation formula for forest carbon sink potential. Based on the calculated comprehensive carbon sink, the temporal stability, spatial stability, and anti-interference stability are determined, and the carbon sink stability is determined by weighted summation of the temporal stability, spatial stability, and anti-interference stability.

2. The forest carbon sink function monitoring method based on multi-source data according to claim 1, characterized in that, The vegetation data includes: vegetation type, spatial distribution of vegetation, and vegetation growth parameters; the vegetation type includes: coniferous forest, broad-leaved forest, and shrub forest. The soil data includes: soil type, soil properties, and soil organic carbon content; the soil types include: sandy soil, clay soil, and loam soil; the soil properties include: soil bulk density and pH value; The meteorological data includes: basic meteorological elements and special meteorological event data; the basic meteorological elements include: temperature, precipitation, photosynthetically active radiation, wind speed, and air humidity; the special meteorological events include: the occurrence time, intensity, and impact range of special events such as typhoons, droughts, and forest fires. The topographic data includes: topographic factors and landform types; the topographic factors include: elevation, slope, and aspect; the landform types include: mountains, plains, and hills. The interference factor data includes: human interference activity data and natural interference factors; the human interference activity data includes the area of ​​human logging and the intensity of fertilization; the natural interference factors include: the spatiotemporal distribution of the area affected by pests and diseases and the spatiotemporal distribution of the volume of fallen trees.

3. The forest carbon sink function monitoring method based on multi-source data according to claim 2, characterized in that, The method involves determining vegetation carbon sink and soil carbon sink based on ecosystem carbon flux parameters, combining anthropogenic and natural disturbance carbon emissions, and obtaining the comprehensive carbon sink through a constructed comprehensive carbon sink calculation formula, including: ； ； ； The ecosystem carbon flux parameters include: net primary productivity. heterotrophic respiration value and net ecosystem productivity ; This refers to the carbon sequestration of vegetation. Soil carbon sequestration; For comprehensive carbon sequestration; The carbon emissions caused by human interference were obtained through on-site observations. The carbon emissions from natural disturbances were obtained through on-site observations.

4. The forest carbon sink function monitoring method based on multi-source data according to claim 1, characterized in that, The method further includes: A progressive automatic calibration mechanism is adopted to optimize the Biome-BGC model step by step on weekly, monthly, semi-annual, and annual scales.

5. The method for monitoring forest carbon sink function based on multi-source data according to any one of claims 1 to 4, characterized in that, The formula for calculating the forest carbon sequestration potential is as follows: ； in, This represents the predicted carbon sink amount after T years. The total carbon sink is represented by T, which indicates the planning period. This represents the interference risk coefficient, with a value between 0 and 1.

6. The method for monitoring forest carbon sink function based on multi-source data according to any one of claims 1 to 4, characterized in that, The calculated comprehensive carbon sink amount determines time stability, spatial stability, and anti-interference stability. A weighted sum of these three factors is then applied to determine carbon sink stability, including: ； in, This represents the standard deviation of annual carbon sequestration. This represents the average annual carbon sequestration. For time stability; This represents the standard deviation of spatial carbon sequestration. This represents the average amount of carbon sequestration in space. For spatial stability; For comprehensive carbon sequestration; This indicates the carbon sink amount before the interference; This indicates the time required for carbon sequestration to return to the average level of carbon sequestration before the occurrence of the disturbance event. This indicates the maximum permissible recovery time, which is adjusted according to industry standards and project timelines. For anti-interference stability; , , The weighting coefficients and ; For carbon sink stability.

7. The forest carbon sink function monitoring method based on multi-source data according to claim 1, characterized in that, The method further includes: The calculated comprehensive carbon sink, forest carbon sink potential, and carbon sink stability are standardized to obtain the first comprehensive carbon sink, the first forest carbon sink potential, and the first carbon sink stability. By introducing a risk correction term and combining three factors—the first comprehensive carbon sink volume, the first forest carbon sink potential, and the first carbon sink stability—a carbon sink investment value score is calculated. The calculated carbon sink investment value score is converted to a percentage system, and the investment value of carbon sink is evaluated based on the percentile of the carbon sink investment value score.

8. The forest carbon sink function monitoring method based on multi-source data according to claim 7, characterized in that, The investment value score of carbon sinks is calculated using the following formula, including: ； ； in, Assess the investment value of carbon sinks; , , The weighting coefficients and ; It is the largest comprehensive carbon sink; The first forest carbon sequestration potential; For the first carbon sink stability; This is a risk correction item; This represents the interference risk coefficient, with a value between 0 and 1. For adjustment coefficients, ; This is the maintenance cost coefficient.

9. The forest carbon sink function monitoring method based on multi-source data according to claim 7 or 8, characterized in that, The assessment of the investable value of carbon sinks based on percentile scores includes: When the percentile of the carbon sink investment value score is greater than or equal to 80, the investment level is rated as priority, the management strategy is to focus on investment, and the project is a long-term carbon sink project. When the percentile of the carbon sink investment value score is between 60 and 79, the investment level is rated as optimized, and the management strategy is to invest after improving management measures. When the percentile of the carbon sink investment value score is between 40 and 59, the investment level is assessed as "wait and see," and the management strategy is "risk control or policy support is required." When the percentile of the carbon sink investment value score is less than 40, the investment level is assessed as avoidance, and the management strategy is to not invest for the time being.

10. A forest carbon sequestration function monitoring system based on multi-source data, characterized in that, The system includes: The data acquisition module is used to collect vegetation data, soil data, meteorological data, topographic data, and interference factor data; The data preprocessing module is used to perform format unification and standardization on the collected data, and to perform spatiotemporal matching on the processed data; The Biome-BGC model processing module is used to input the spatiotemporally matched data into the Biome-BGC model for processing to obtain ecosystem carbon flux parameters. The integrated carbon sink calculation module is used to determine vegetation carbon sink and soil carbon sink based on ecosystem carbon flux parameters. It combines anthropogenic and natural disturbance carbon emissions and obtains the integrated carbon sink through the constructed integrated carbon sink calculation formula. The forest carbon sequestration potential determination module is used to determine the forest carbon sequestration potential based on the calculated comprehensive carbon sequestration amount and the constructed forest carbon sequestration potential calculation formula. The carbon sink stability determination module is used to determine the temporal stability, spatial stability, and anti-interference stability based on the calculated comprehensive carbon sink amount, and to perform a weighted summation of the temporal stability, spatial stability, and anti-interference stability to determine the carbon sink stability.

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