Carbon cycle analysis method and system for artificial vegetation in desert region

Abstract

The invention discloses a carbon cycle analysis method and system for artificial vegetation in a desert region, and relates to the technical field of environment monitoring and data processing, and the method comprises the steps: obtaining basic carbon emission data corresponding to each historical monitoring period of a target region; acquiring extra carbon emission data of each historical monitoring period of the target area; calculating actual carbon emission data by combining the basic carbon emission data and the additional carbon emission data in the same historical monitoring period; obtaining a prediction function of the actual carbon emission data according to the sequence of the historical monitoring periods; and calculating corrected carbon emission data according to the predicted carbon emission data and the actual carbon emission data so that the corrected carbon emission data participates in net carbon sink calculation of the target area. According to the analysis method and system, the carbon emission increment condition caused by different illumination intensities is considered, the accuracy of net carbon sink evaluation calculation is guaranteed, and the calculation evaluation error caused by illumination abrupt change but carbon emission lagging change can be effectively reduced in combination with the predicted carbon emission data and the actual carbon emission data.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and data processing technology, and more specifically, to a method and system for analyzing the carbon cycle of artificial vegetation in desert areas. Background Technology

[0002] In desert regions, the original soil is barren and biomass is low. The carbon pool is mainly concentrated in the inorganic carbon portion of the soil. If the inorganic carbon storage in the soil is insufficient, it will easily weaken the carbon cycle capacity of the entire ecosystem. Therefore, based on the current carbon storage status and carbon flux characteristics of desert areas, artificial intervention measures (such as artificial planting and irrigation to form vegetation) should be taken to enhance their carbon sequestration capacity. The key link is to accurately assess the current carbon cycle status, especially the net carbon sink, in order to formulate effective carbon sequestration strategies.

[0003] Traditional monitoring methods (such as soil respiration chambers) primarily focus on microbial respiration as a carbon emission pathway, neglecting the crucial process of photochemical degradation of soil organic carbon (SOC) under intense sunlight in desert areas. Photochemical degradation is an abiotic pathway in which SOC decomposes and releases CO2 under sunlight (especially ultraviolet light), constituting another significant carbon emission source in desert ecosystems. Ignoring this process leads to an underestimation of total carbon emissions in desert areas, resulting in an overestimation of net carbon sink measurements and impacting the scientific validity and effectiveness of subsequent carbon management measures.

[0004] In existing technologies, most net carbon sequestration analysis and assessment processes lack consideration of the additional carbon emissions generated by photochemical degradation. Even if some existing technologies take photochemical degradation into account, they treat it as a static influencing factor in the assessment, without taking into account the dynamic changes in photochemical degradation and the lag in the impact of carbon emissions. Therefore, the final carbon sequestration capacity assessment results are still inaccurate.

[0005] In view of this, this application is hereby filed. Summary of the Invention

[0006] The purpose of this invention is to provide a carbon cycle analysis method and system for artificial vegetation in desert areas. This analysis method and system ensures the accuracy of the final net carbon sink assessment calculation by considering the carbon emission increment caused by different light intensities on the basis of the basic carbon emissions generated by soil respiration. Furthermore, it uses historical monitoring data to predict future carbon emission data. Combining predicted carbon emission data with actual carbon emission data can effectively reduce the calculation and assessment errors caused by sudden changes in light intensity but lagging changes in carbon emissions.

[0007] The embodiments of the present invention are implemented as follows:

[0008] Firstly, a method for analyzing the carbon cycle of artificial vegetation in desert areas includes the following steps: acquiring basic carbon emission data corresponding to each historical monitoring period of a target area, wherein the target area refers to one of the areas obtained by gridding the desert area to be analyzed; acquiring light intensity data of the target area for each historical monitoring period, and calculating additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period; wherein the additional carbon emission data refers to the additional carbon emissions generated by photochemical degradation under different light intensities; calculating actual carbon emission data by combining the basic carbon emission data and the additional carbon emission data under the same historical monitoring period; arranging all actual carbon emission data in time sequence according to the order of historical monitoring periods, and performing fitting training on the time sequenced data to obtain a prediction function for actual carbon emission data; predicting the predicted carbon emission data of the target area in the next monitoring period based on the prediction function, calculating corrected carbon emission data by combining the predicted carbon emission data and the actual carbon emission data of the target area in the next monitoring period, and using the corrected carbon emission data to participate in the calculation of the net carbon sink of the target area.

[0009] In some optional embodiments, calculating the additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period includes the following steps: collecting the light intensity data for that historical monitoring period and determining the ultraviolet radiation intensity; determining a first impact level based on the ultraviolet radiation intensity; determining a second impact level based on the soil photosensitivity of the target area; determining a third impact level based on the surface soil microparameters of the target area; merging the first impact level, the second impact level, and the third impact level into a carbon emission impact level; and determining the additional carbon emission data based on the carbon emission impact level and a carbon emission experience table; wherein the surface soil microparameters include at least one of surface soil temperature parameters, surface soil moisture parameters, surface soil color parameters, and surface soil texture parameters.

[0010] In some optional embodiments, the surface soil microparameters may further include a litter cover parameter, and a fourth impact level is determined based on the litter cover parameter. The first impact level, the second impact level, the third impact level, and the fourth impact level are then combined into the carbon emission impact level.

[0011] In some optional implementations, the litter coverage parameter includes a coverage breadth parameter and a coverage depth parameter. The coverage area ratio is determined based on the coverage breadth parameter, and a first adjustment ratio is determined based on the coverage area ratio. The litter layer height difference is determined based on the coverage depth parameter, and a second adjustment ratio is determined based on the litter layer height difference. The product of the first adjustment ratio and the fourth impact level is obtained, and the result of multiplying the product by the second adjustment ratio is used as the comprehensive fourth impact level. The comprehensive fourth impact level is used in the combined calculation of the carbon emission impact level.

[0012] In some optional embodiments, the litter coverage parameter further includes a litter coverage transmittance parameter, a transmittance range is determined based on the litter coverage transmittance parameter, a third adjustment ratio is determined based on the transmittance range, and the product of the comprehensive fourth impact level and the third adjustment ratio is used as the basis for participating in the combined calculation of the carbon emission impact level.

[0013] In some optional implementations, after calculating the additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period, the method further includes a step of anomaly judgment on the additional carbon emission data: determining at least one similar region of the target area, obtaining reference carbon emission data of the similar region under the same monitoring period, judging whether the additional carbon emission data is abnormal based on all reference carbon emission data, and if it is judged to be abnormal, adjusting the weight ratio of the additional carbon emission data in the subsequent calculation of actual carbon emission data; wherein, in the anomaly judgment, the judgment is made based on the average difference between all reference carbon emission data and the additional carbon emission data.

[0014] In some alternative implementations, adjusting the weighting of the additional carbon emission data in the subsequent calculation of actual carbon emission data includes the steps of first determining the range of the mean difference and then determining the weighting based on the range.

[0015] In some optional implementations, determining at least one similar region of the target region includes the following steps: obtaining an ultraviolet radiation reference map of the desert region; calculating the similarity of the ultraviolet radiation time-series curves of each pixel region based on the ultraviolet radiation reference map; merging consecutive pixel regions that meet the ultraviolet radiation time-series curve similarity requirements into an initial grid; calculating the ultraviolet radiation difference for each initial grid; if the difference uniformity requirement is met, then using the initial grid as the selection basis for the target region; otherwise, continuing to segment the initial grid until the difference uniformity requirement is met; assigning a radiation level label and a surface attribute label to each initial grid; matching similar initial grids with the same radiation level label and the same surface attribute label to the target initial grid; using the similar initial grids as the selection basis for the similar region; wherein, the target initial grid refers to the initial grid contained in the target region; the radiation level label represents the ultraviolet radiation intensity, and the surface attribute label represents the soil type, topography, and vegetation cover.

[0016] In some optional implementations, after determining at least one similar region of the target region, the method further includes a step of filtering the similar regions: obtaining a reference prediction function for the similar regions, and using the comparison between the reference prediction function and the actual carbon emission judgment results to determine whether the reference carbon emission data for the same monitoring period is abnormal. If abnormal, the similar region is filtered out; otherwise, it is retained. The reference prediction function is obtained by fitting and training all the actual carbon emission data of the similar region in a time-series arrangement according to the order of historical monitoring periods.

[0017] Secondly, a carbon cycle analysis system for artificial vegetation in desert areas includes:

[0018] The first acquisition unit is used to acquire basic carbon emission data corresponding to each historical monitoring period of the target area, wherein the target area refers to one of the areas obtained by grid division of the desert area to be analyzed;

[0019] The second acquisition unit is used to acquire light intensity data of the target area for each historical monitoring period, and calculate additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period; wherein, the additional carbon emission data refers to the amount of additional carbon emissions generated by photochemical degradation under different light intensities;

[0020] The first calculation unit is used to calculate the actual carbon emission data by combining the basic carbon emission data and the additional carbon emission data under the same historical monitoring period; it arranges all the actual carbon emission data in time sequence according to the order of the historical monitoring period, and performs fitting training on the time sequence data to obtain the prediction function of the actual carbon emission data.

[0021] The second calculation unit is used to predict the predicted carbon emission data of the target area for the next monitoring period according to the prediction function, and to calculate the corrected carbon emission data by combining the predicted carbon emission data of the target area in the next monitoring period with the actual carbon emission data, and to use the corrected carbon emission data to participate in the calculation of the net carbon sink of the target area.

[0022] The beneficial effects of the embodiments of the present invention are:

[0023] The carbon cycle analysis method and system for artificial vegetation in desert areas provided in this invention utilizes data from various historical monitoring periods, combining basic carbon emission data (carbon emissions from soil respiration) and additional carbon emission data (carbon emissions from photochemical degradation) to calculate actual carbon emission data. This method is applicable to situations where additional carbon emissions from the photochemical degradation of soil organic carbon (SOC) caused by strong sunlight in desert areas are calculated more accurately, enabling a more accurate calculation of net carbon sinks. Furthermore, it uses data from historical monitoring periods for regularity prediction, preserving the regularity of historical data while mitigating the problem of delayed feedback of additional carbon emission data when only considering different light intensities, thus making the actual carbon emission data for the current monitoring period more accurate and reliable.

[0024] Overall, the carbon cycle analysis method and system for artificial vegetation in desert areas provided by the embodiments of the present invention not only additionally considers and quantifies the carbon emissions generated by the photochemical degradation of soil organic carbon (SOC) in desert areas, thus providing a calculation basis for the final accurate assessment of the net carbon sink in desert areas, but also incorporates the regularity characteristics of historical data, avoiding the problem of overly distorted carbon emission calculation results for the current monitoring period due to the lag in carbon emissions caused by ignoring changes in light intensity but photochemical degradation. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of the main steps of the analysis method provided in the embodiments of the present invention;

[0027] Figure 2 for Figure 1 The flowchart shows one step S200 of the analysis method shown.

[0028] Figure 3 for Figure 2The flowchart of some sub-steps of step S200 is shown below;

[0029] Figure 4 This is a modular schematic diagram of the analysis system provided in an embodiment of the present invention.

[0030] Icons: 500 - Analysis system; 510 - First acquisition unit; 520 - Second acquisition unit; 530 - First calculation unit; 540 - Second calculation unit. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0032] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0033] It should be understood that the terms "system," "device," and / or "module" used in this invention are methods for distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0034] As indicated in this invention and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0035] Flowcharts are used in this invention to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0036] Example

[0037] In carbon cycle analysis studies of desert areas, compared to conventional regional carbon cycle studies, field measurements of soil respiration are significantly affected by high temperatures, the carbon costs of irrigation are easily overlooked, and the photodegradation unique to arid regions are all key aspects that need to be considered in carbon cycle analysis of desert environments. Especially regarding photodegradation, neglecting the photochemical degradation of soil organic carbon (SOC) in desert areas can systematically overestimate the net carbon sink of artificial vegetation and may even distort the certification results of carbon sink projects. Therefore, in addition to using soil respiration chamber data for carbon emission measurements, it is necessary to additionally consider and quantify the photochemical degradation of soil organic carbon (SOC) in desert areas and its impact on the carbon cycle.

[0038] Previously, the additional carbon emissions from photochemical degradation were mainly considered based on radiation conditions and soil microenvironment. Field surveys were used to establish a correlation table between ultraviolet light, soil environment, and carbon emission data. While this approach could compensate for the omission in the calculation and assessment of photochemical degradation of soil organic carbon (SOC) under strong light conditions in desert areas, which mainly focused on microbial respiration as a carbon emission pathway, the actual survey process revealed that the photochemical degradation of soil organic carbon caused by changes in light intensity is a continuous process (i.e., a multi-step process of photon absorption → electronic excitation → chemical bond breaking → small molecule mineralization). If the additional carbon emission data obtained directly from the empirical table based on the light intensity data at that time, the results would be inaccurate and distorted. To overcome the distortion of assessment results caused by this situation, we found that by controlling the data acquisition cycle to match the aforementioned photochemical degradation cycle, the analysis results of the current data monitoring cycle are strongly correlated with the analysis results of the previous data monitoring cycle (or other historical data monitoring cycles). Therefore, this embodiment proposes a carbon cycle analysis method for artificial vegetation in desert areas, which not only takes into account the additional carbon emissions caused by photochemical degradation, but also comprehensively considers the lag of the additional carbon emissions relative to the light intensity changes, combined with the regularity of historical data, thereby ensuring the accuracy of the final analysis results to a large extent.

[0039] See also Figure 1 This embodiment provides a carbon cycle analysis method for artificial vegetation in desert areas, which includes the following steps:

[0040] S100: Obtain basic carbon emission data corresponding to each historical monitoring period of the target area. The target area refers to one of the areas obtained by gridding the desert area to be analyzed. This step means that the desert area to be analyzed for carbon cycle is divided into grids. The grid division principle can be uniform division, concentrated division, or typical division. This embodiment uses concentrated division combined with typical division to illustrate the process. A smaller area with concentrated sunlight and typical soil is taken as a grid, which is represented as the target area. Based on the carbon cycle analysis of each target area, the carbon cycle situation of the entire desert area is finally determined.

[0041] First, it is necessary to obtain the data monitored in the target area over all historical monitoring periods (i.e., each manually set monitoring time period; if the degree of automation is high, it can be in the hourly unit, and if the degree of automation is relatively low, it can be in the daily unit). Among these, the basic carbon emission data should be protected. This basic carbon emission data mainly refers to the carbon emission data through soil ecological respiration (including plant respiration, soil respiration, heterotrophic respiration, root respiration, etc.) (e.g., obtained by using a soil respiration chamber). In some other implementation methods, carbon emission data caused by vegetation disturbance may also be included.

[0042] S200: Obtain light intensity data for each historical monitoring period of the target area, and calculate the additional carbon emission data for each historical monitoring period based on the light intensity data for each historical monitoring period; wherein, the additional carbon emission data refers to the additional carbon emissions generated by photochemical degradation under different light intensities; this step indicates that in the above-mentioned historical monitoring periods, it is also necessary to simultaneously obtain light intensity data (e.g., using ultraviolet radiation sensors, full-spectrum radiometers, thermal infrared cameras, etc.), convert the light intensity data of each historical monitoring period into additional carbon emission data, that is, calculate the additional carbon emissions caused by photochemical degradation. The calculation method can be to use existing radiation-degradation linear models for approximate calculation; it can also be to train a machine learning dynamic model based on historical data for subsequent prediction; or it can be to use the differential method of comparative experiments to invert the calculation results, etc. The purpose is to estimate the carbon emission data of photochemical degradation based on the light intensity data collected in the current time period of the target area.

[0043] S300: Combine basic carbon emission data and additional carbon emission data from the same historical monitoring period to calculate actual carbon emission data; arrange all actual carbon emission data in time series according to the order of historical monitoring periods, and perform fitting training on the time-series sorted data to obtain the prediction function for actual carbon emission data; this step means adding the basic carbon emission data and additional carbon emission data collected in the same historical monitoring period to calculate actual carbon emission data, so that the additional carbon emissions generated by photochemical degradation of soil organic carbon in desert areas can be included, ensuring that the final carbon emission data is more accurate and reliable.

[0044] In addition, it is necessary to conduct planned analysis using actual carbon emission data obtained from historical monitoring cycles to explore the correlation characteristics between changes in additional carbon emission data caused by changes in sunlight. Further analysis of the feedback time and correlation degree of photochemical degradation carbon emissions caused by changes in sunlight is needed. This means utilizing all actual carbon emission data from all historical monitoring cycles as a whole, arranging all actual carbon emission data in chronological order according to the historical monitoring cycles, and then fitting and training all the sorted actual carbon emission data to obtain a prediction function. First, the actual carbon emission data measured in each historical monitoring cycle are arranged in chronological order to form a curve that changes over time. Then, a learning model (e.g., linear regression, ARIMA time series model, or LSTM machine learning model) is selected to analyze the patterns of this curve. Finally, the algorithm automatically learns these patterns and generates a mathematical formula (i.e., the required prediction function), which can be input with future sunlight data to predict the corresponding actual carbon emissions (the model can be continuously iterated).

[0045] S400: Based on the prediction function, predict the carbon emission data for the target area in the next monitoring period. Combine the predicted carbon emission data and the actual carbon emission data of the target area in the next monitoring period to calculate the corrected carbon emission data. Use the corrected carbon emission data to participate in the calculation of the net carbon sink of the target area. This step means that the prediction function obtained above is used to predict the actual carbon emission data of the target area in the next monitoring period. The prediction result is recorded as the predicted carbon emission data. The predicted carbon emission data is calculated based on the historical changes in actual carbon emission data and has a strong correlation with historical data changes. Then, the predicted carbon emission data is combined with the actual carbon emission data for the next monitoring period (collected and calculated using the same method as steps S100-S300). This retains the characteristics of the predicted carbon emission data while also incorporating the characteristics of actual detection and calculation. Thus, the corrected carbon emission data is finally calculated by combining the predicted and actual carbon emission data, possessing both of the aforementioned characteristics. Therefore, using the corrected carbon emission data in the calculation of the net carbon sink of the target area can, on the one hand, additionally consider and quantify the carbon emissions generated by the photochemical degradation of soil organic carbon in desert areas, providing a calculation basis for the final accurate assessment of the net carbon sink of desert areas. On the other hand, it incorporates the regularity of historical data, avoiding the problem of overly distorted carbon emission calculation results for the current monitoring period due to the lag in carbon emissions caused by ignoring changes in light intensity but photochemical degradation.

[0046] The above technical solution first divides the desert region into grids to obtain the target area to be analyzed. Then, it combines basic carbon emission data with additional carbon emission data driven by light intensity to calculate more accurate actual carbon emission data. A prediction function is established using time-series fitting, thereby generating corrected carbon emission data that integrates historical patterns and real-time monitoring. This approach quantifies the contribution of photochemical degradation to carbon emissions in desert areas for the first time, solving the underestimation problem caused by traditional methods neglecting light factors in desert regions. Secondly, a dynamic prediction model overcomes the lag effect of photochemical degradation carbon emissions, improving monitoring timeliness. Finally, the corrected data mechanism takes into account both historical patterns and real-time data, ensuring that net carbon sink assessment reflects actual emissions while maintaining trend stability, providing a more accurate analytical basis for assessing the carbon sink capacity of artificial vegetation in deserts.

[0047] When calculating additional carbon emissions using light intensity, in addition to light intensity itself as the calculation indicator, it is also necessary to consider the photosensitivity of the soil and some soil environmental factors to ensure a more accurate calculation of carbon emissions generated by photochemical degradation. Please refer to [link / reference needed] for details. Figure 2 The calculation of additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period includes the following steps:

[0048] S210: Collect the light intensity data for the historical monitoring period and determine the ultraviolet radiation intensity, and determine the first impact level based on the ultraviolet radiation intensity; this step means directly calculating the ultraviolet radiation intensity using the light intensity data collected in the corresponding historical monitoring period (for example, data directly collected and processed by an ultraviolet radiation sensor can be used), and determining the first layer of impact on carbon decomposition based on the ultraviolet radiation intensity (for example, judging the UV radiation level as high, medium, or low based on the sunshine hours, cloud coverage, and seasonal changes in the target area, and determining the impact level according to an experience table established based on experimental data), that is, determining the first impact level.

[0049] S220: Determine the second level of influence based on the soil photosensitivity of the target area; this step means taking into account the soil photosensitivity, for example, combining the light absorption characteristics of soil organic matter (which can be determined through laboratory spectral analysis), judging the sensitivity of organic carbon to UV-B / UV-A (such as highly sensitive aromatic compounds, etc.), and determining the second layer of influence, that is, determining the second level of influence.

[0050] Next, step S230: Determine the third level of influence based on the surface soil micro-parameters of the target area. This step indicates that by further considering the surface soil environmental parameters, practical verification shows that parameters such as soil temperature, humidity, texture, and color have a significant impact on photochemical degradation. Specifically, surface soil micro-parameters include at least one of the following: surface soil temperature, surface soil humidity, surface soil color, and surface soil texture. Among these parameters, high temperatures in the surface soil accelerate photochemical reactions, while low temperatures significantly inhibit them; excessively low soil humidity increases light penetration; dark soils absorb more heat, while light-colored soils reflect more light; fine particles (clay) obscure SOCs, while coarse particles (sand) expose more SOCs. Therefore, additional consideration needs to be given to the aforementioned surface soil micro-parameters to roughly quantify the impact of various micro-parameters on photodegradation of carbon and determine the degree of influence, i.e., the third level of influence.

[0051] S240: Combine the first impact level, the second impact level, and the third impact level into a carbon emission impact level, and determine the additional carbon emission data based on the carbon emission impact level and the carbon emission experience table. This step means that the above-mentioned quantified first impact level, second impact level, and third impact level are directly combined and calculated. The combination method can be direct addition or weighted addition to obtain the total carbon emission impact level data. The additional carbon emission data can be determined by establishing a carbon emission experience table (a table of corresponding total carbon emissions for different carbon emission levels) based on daily experimental results.

[0052] The above technical solution utilizes a multi-level influencing factor synergistic quantification of photochemical degradation carbon emissions. Specifically, it establishes a three-level impact assessment system comprising ultraviolet radiation intensity (level 1), soil photosensitivity (level 2), and surface soil microparameters (level 3), comprehensively covering key environmental variables of the photodegradation reaction. Through hierarchical quantification and empirical table comparison, the complex light-soil interaction is transformed into a calculable increase in carbon emissions. Compared to traditional single-light-intensity models, this approach has broader applicability, making the estimation of photochemical degradation carbon emissions in desert areas more consistent with the actual environmental coupling mechanism.

[0053] Based on the above technical solutions, since the surface soil environment is an important influencing factor besides light intensity and soil photosensitivity, its temperature, humidity, texture, and color all become important factors affecting the photodegradation of organic carbon. Furthermore, we found that not every area's surface soil is directly exposed to sunlight; some areas experience soil cover due to vegetation shedding. This litter cover also becomes an important factor affecting the photodegradation of organic carbon, so it needs further consideration. Specifically, the surface soil micro-parameters also include litter cover parameters. A fourth influence level is determined based on this litter cover parameter, that is, by quantifying litter cover (e.g., through UAV multispectral image analysis or ground quadrat photogrammetry) to determine another degree of influence on the photodegradation of organic carbon, i.e., the fourth influence level. The first, second, and third influence levels calculated above are combined with this fourth influence level (e.g., directly added or weighted added) to form the carbon emission influence level.

[0054] By introducing litter cover as the fourth impact level through the above technical solution, the quantitative model of photochemical degradation carbon emissions is further improved. To make the impact assessment of litter cover more accurate, at least two dimensions should be considered: one is the coverage area (the percentage of litter cover area to the total land surface area calculated by UAV multispectral image analysis or ground quadrat photogrammetry), and the other is the coverage depth (combined with lidar or probe-measured cover layer thickness). That is, the litter cover parameter includes a coverage area parameter and a coverage depth parameter. The coverage area percentage is determined based on the coverage area parameter (for example, for every 1% increase in coverage, the degradation amount decreases by 0.8%). A first adjustment ratio is determined based on the coverage area percentage (determined according to preset empirical rules or deep learning methods). The litter layer height difference is determined based on the coverage depth parameter (by measuring the basic average height difference; for every 1cm increase in depth, the light transmittance decreases exponentially). A second adjustment ratio is determined based on the litter layer height difference (also determined according to preset empirical rules or deep learning methods).

[0055] The product of the first adjustment ratio and the fourth impact level, obtained above, is multiplied by the second adjustment ratio to obtain the comprehensive fourth impact level. This means the comprehensive fourth impact level is the sum of the first adjustment ratio, the second adjustment ratio, and the fourth impact level. This comprehensive fourth impact level is then used in the combined calculation of carbon emission impact levels. Through the aforementioned technical solution, litter coverage (i.e., coverage breadth and depth) is quantified in two dimensions, enabling refined modeling of the photochemical degradation shading effect. This allows the comprehensive fourth impact level to characterize both the spatial distribution characteristics of litter and the light attenuation patterns under different accumulation thicknesses, thereby improving the accuracy of the conversion parameters for photochemical carbon emission suppression.

[0056] In addition to coverage breadth and depth parameters, the morphology of the litter also needs to be considered. Different types of litter have different light transmittance or reflectance, requiring further consideration of their light transmittance. Specifically, the litter coverage parameters also include litter coverage transmittance parameters (achieved through one or more methods, such as spectrometers, UAV multispectral imaging, or AI visual estimation). The transmittance range is determined based on the litter coverage transmittance parameters, meaning that the range of transmittance it falls within is locked by measuring the transmittance parameter value (such as typical ranges like 30-50%, 50-80%, 80-100%).

[0057] Then, based on this transmittance range, a third adjustment ratio is determined, and this third adjustment ratio is used in the calculation of the comprehensive fourth impact level; that is, the product of the comprehensive fourth impact level and the third adjustment ratio is used as the basis for the combined calculation of carbon emission impact levels. Through the aforementioned technical solution, litter transmittance is introduced as a third key parameter, enabling precise quantification of the photochemical degradation shading effect in three dimensions. This allows the comprehensive fourth impact level to simultaneously reflect the physical shading effect of vegetation cover and the optical properties of materials, making it particularly suitable for complex scenarios with mixed litter types.

[0058] By considering multiple dimensions such as light intensity, soil photosensitivity, and surface soil microenvironment parameters in the target area, the influence of these dimensions on the photolysis of organic carbon can be directly detected and calculated. This model is primarily based on calculations using actual parameters. Furthermore, to ensure the reliability of the final calculation of the influence of these actual parameters on the photolysis of organic carbon and to eliminate accidental factors such as data acquisition or calculation errors, similar calculation results from related areas can be used as corroborating references. This further ensures the reliability of the final photolysis organic carbon calculation results from another perspective. Please refer again. Figure 2 The process of calculating additional carbon emission data for each historical monitoring period based on light intensity data also includes a step of anomaly detection for the additional carbon emission data.

[0059] S250: Identify at least one similar region to the target region, meaning a region in the desert area identified in the carbon cycle analysis that is highly similar to the target region in terms of light intensity, soil, and microenvironment, so that the calculation results of both regions can be used as a reference. Specifically, obtain reference carbon emission data for the similar region within the same historical monitoring period (ensuring that the data is collected and calculated within the same historical monitoring period as the target region to ensure data alignment). This reference carbon emission data refers to additional carbon emission data calculated using light intensity data from the similar region (or obtained using the above multi-dimensional calculation method). This additional carbon emission data is recorded as the reference carbon emission data for the similar region.

[0060] Then, based on all similar regions, i.e. all reference carbon emission data, it is determined whether the additional carbon emission data of the target region is abnormal. In the abnormality judgment, the average difference between all reference carbon emission data and the additional carbon emission data is used for judgment. If it is judged to be abnormal, the weight ratio of the additional carbon emission data in the subsequent calculation of actual carbon emission data is adjusted. That is, the judgment of whether the additional carbon emission data is abnormal is based on the above average difference and the preset value. If it is abnormal, it means that there may be random errors in the data collection or calculation method in the calculation of additional carbon emission data. In order to reduce its impact, the weight ratio of the additional carbon emission data in the subsequent calculation of actual carbon emission data is adjusted so that the actual carbon emission data is closer to the basic carbon emission data. Furthermore, the process of adjusting the weighting of the additional carbon emission data in the calculation of subsequent actual carbon emission data includes the steps of first determining the range of the mean difference and then determining the weighting based on the range. That is, the size range of the mean difference is first determined, and then the weighting is adjusted based on the size range (when the mean difference is in the 0-10% range, the original weight is maintained at 90%; in the 10-20% range, it is reduced to 70%; in the 20-30% range, it is reduced to 50%; and if it exceeds 30%, the abnormal data is directly removed).

[0061] By introducing a similar region reference verification mechanism through the above technical solution (establishing a regional comparison verification system based on environmental similarity), the reliability of photocatalytic carbon emission data can be improved. Finally, when judging anomalies, the influence of abnormal data is balanced through a weight adjustment mechanism, so that the final calculation results retain the characteristics of the region and conform to the overall pattern of the regional group.

[0062] Since the selection of similar regions directly affects the reliability of anomaly detection in additional carbon emission data, and considering that similar regions need to have a high degree of similarity to the target region, it is necessary to refine and quantify the criteria for high similarity. Please refer to [link / reference needed] for details. Figure 3 Determining at least one similar region of the target region includes the following steps:

[0063] S251: Obtain the ultraviolet radiation reference map of the desert region (e.g., obtain the daily average radiation intensity data of the ultraviolet band of the target area through satellite remote sensing, generate a radiation distribution heat map after atmospheric correction and terrain correction, and perform local calibration using a ground-based ultraviolet sensor network). Based on the ultraviolet radiation reference map, calculate the similarity of the ultraviolet radiation time-series curves of each pixel region. Merge the continuous pixel regions that meet the similarity requirements of the ultraviolet radiation time-series curves into an initial grid. That is, first analyze the similarity of the ultraviolet radiation of each pixel region, for example, use the dynamic time warping algorithm to calculate the similarity of the UV radiation time-series curves of each pixel point. Then, merge the continuous pixel regions that meet the similarity of the ultraviolet radiation time-series curves into an initial grid. That is, perform region growing and merging on adjacent pixels with similarity higher than a set threshold to form an initial grid with uniform radiation characteristics.

[0064] Furthermore, the ultraviolet radiation difference of each initial grid is calculated. If the difference uniformity requirement is met, the initial grid is used as the basis for selecting the target region. Otherwise, the initial grid is further divided until the difference uniformity requirement is met. That is, the initial grid can be tested for ultraviolet radiation uniformity by recursive segmentation algorithm. Grids that do not meet the standard are divided twice according to the direction of the maximum gradient until the preset difference threshold is met. For example, if it exceeds 30% of the average value of the whole region, recursive segmentation is carried out along the direction of the maximum radiation gradient until the radiation difference inside all sub-grids is lower than the threshold. Finally, the grids that meet the standard are retained as candidates for the target region.

[0065] S252: Assign a radiation level label and a surface attribute label to each initial grid. Match similar initial grids with the same radiation level label and surface attribute label to the target initial grid. Use these similar initial grids as the basis for selecting the similar region. That is, assign a radiation level label (representing ultraviolet radiation intensity) and a surface attribute label (representing soil type, topography, and vegetation cover) to each initial grid to facilitate comparison of similarity. Then, filter out similar initial grids with the same radiation level label and surface attribute label as the target initial grid (the target initial grid refers to the initial grids contained in the target region) as candidates for similar regions, providing a basis for similarity reference comparison (considering both radiation and surface attributes).

[0066] By employing the aforementioned technical solutions, the temporal similarity of ultraviolet radiation in each pixel region is precisely quantified. Continuous pixels with similar radiation characteristics are merged into an initial grid. A recursive segmentation algorithm ensures that the difference in ultraviolet radiation within each grid is controlled within a threshold range. Simultaneously, by combining multi-dimensional matching of radiation level labels and surface attribute labels (soil type, topography, vegetation cover), high-precision screening of similar regions is achieved. This not only ensures the uniformity of radiation characteristics within the grid but also ensures a high degree of consistency between similar regions and the target region in key factors affecting photochemical degradation through the dual constraints of surface attributes. This significantly improves the reliability of anomaly detection in additional carbon emission data.

[0067] Based on the above technical solutions, in order to eliminate the random errors in the collection or calculation of carbon emission data from similar regions, especially when there are very few similar regions, it cannot be ruled out that the reliability of data from similar regions is lower than that of data from the target region. Therefore, please refer to [the relevant documentation / resources] again. Figure 3 After determining at least one similar region of the target region, the method further includes a step of filtering the similar regions:

[0068] S253: Obtain the reference prediction function for the similar region. Use the relationship between the reference prediction function and the actual carbon emission judgment results to determine whether the reference carbon emission data for the same monitoring period is abnormal. If abnormal, the similar region is filtered out; otherwise, it is retained. The reference prediction function is obtained by fitting and training all the actual carbon emission data of the similar region in a time-series arrangement according to the order of historical monitoring periods. That is, the reference prediction function for the similar region is calculated in a manner similar to step S300, which calculates the prediction function for the target region. By comparing the correlation between the judgment result of the reference prediction function and the actual carbon emission judgment result (i.e., the reference actual carbon emission data) of the corresponding monitoring period, the reliability of the reference actual carbon emission data is determined, and then the reliability of the reference carbon emission data is further calculated.

[0069] The above technical solution establishes an independent reference prediction function for each similar region and cross-validates its prediction results with the actual carbon emission data of the corresponding monitoring period to verify the reliability of the reference carbon emission data. This can effectively identify and screen out abnormal similar regions caused by data collection or calculation errors, thus ensuring the reliability of the reference data even when the number of similar regions is limited.

[0070] This embodiment also provides a carbon cycle analysis system 500 for artificial vegetation in desert areas. Please refer to [link / reference]. Figure 4This is a modular schematic diagram of the carbon cycle analysis system 500 for artificial vegetation in a desert area. It is mainly used to divide the system into functional modules according to the embodiments of the above method. For example, it can be divided into individual functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or software. It should be noted that the module division in this invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation. For example, in the case of dividing each functional module according to its corresponding function... Figure 4 The diagram shown is only a schematic of a system / device. The carbon cycle analysis system 500 for artificial vegetation in the desert area may include a first acquisition unit 510, a second acquisition unit 520, a first calculation unit 530, and a second calculation unit 540. The functions of each unit module are described below.

[0071] The first acquisition unit 510 is used to acquire basic carbon emission data corresponding to each historical monitoring period of the target area, wherein the target area refers to one of the areas obtained by grid division of the desert area to be analyzed.

[0072] The second acquisition unit 520 is used to acquire light intensity data for each historical monitoring period of the target area, and calculate additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period; wherein, the additional carbon emission data refers to the additional carbon emissions generated by photochemical degradation under different light intensities; in some embodiments, the second acquisition unit 520 is also used to collect light intensity data for the historical monitoring period and determine the ultraviolet radiation intensity, determine a first impact level based on the ultraviolet radiation intensity; determine a second impact level based on the soil photosensitivity of the target area; determine a third impact level based on the surface soil microparameters of the target area; combine the first impact level, the second impact level, and the third impact level into a carbon emission impact level, and determine the additional carbon emission data based on the carbon emission impact level and a carbon emission experience table; and is used to determine the additional carbon emission data based on the litter cover parameter. The method involves defining a fourth impact level, merging the first, second, third, and fourth impact levels into a single carbon emission impact level; determining a coverage area percentage based on the coverage breadth parameter, and determining a first adjustment ratio based on the coverage area percentage; determining the litter layer height difference based on the coverage depth parameter, and determining a second adjustment ratio based on the litter layer height difference; obtaining the product of the first adjustment ratio and the fourth impact level, and then multiplying the product by the second adjustment ratio to obtain a comprehensive fourth impact level, which is then used in the combined calculation of the carbon emission impact level; and determining a transmittance range based on the litter coverage transmittance parameter, and determining a third adjustment ratio based on the transmittance range, using the product of the comprehensive fourth impact level and the third adjustment ratio as the basis for participating in the combined calculation of the carbon emission impact level.

[0073] In some embodiments, the second acquisition unit 520 is further configured to: determine at least one similar region of the target region; obtain reference carbon emission data of the similar region under the same monitoring period; determine whether the additional carbon emission data is abnormal based on all reference carbon emission data; if it is determined to be abnormal, adjust the weight ratio of the additional carbon emission data in the subsequent calculation of actual carbon emission data; and perform the steps of first determining the interval range of the mean difference and then determining the weight ratio based on the interval range; and acquire the ultraviolet radiation reference map of the desert region, calculate the similarity of the ultraviolet radiation time-series curves of each pixel region based on the ultraviolet radiation reference map, and determine the similarity of the ultraviolet radiation time-series curves. The required continuous pixel regions are merged into an initial grid; ultraviolet radiation difference is calculated for each initial grid, and if the difference uniformity requirement is met, the initial grid is used as the selection basis for the target region; otherwise, the initial grid is further segmented until the difference uniformity requirement is met; each initial grid is assigned a radiation level label and a surface attribute label, and similar initial grids with the same radiation level label and the same surface attribute label are matched with the target initial grid, and these similar initial grids are used as the selection basis for the similar region; and a reference prediction function is used to obtain the similar region, and the reference prediction function is used to determine whether the reference carbon emission data of the same monitoring period is abnormal. If it is abnormal, the similar region is screened out; otherwise, it is retained.

[0074] The first calculation unit 530 is used to calculate the actual carbon emission data by combining the basic carbon emission data and the additional carbon emission data under the same historical monitoring period; it arranges all the actual carbon emission data in time sequence according to the order of the historical monitoring period, and performs fitting training on the time sequence data to obtain the prediction function of the actual carbon emission data.

[0075] The second calculation unit 540 is used to predict the predicted carbon emission data of the target area in the next monitoring period according to the prediction function, and to calculate the corrected carbon emission data by combining the predicted carbon emission data of the target area in the next monitoring period with the actual carbon emission data, and to use the corrected carbon emission data to participate in the calculation of the net carbon sink of the target area.

[0076] In the above embodiments, the more specific working processes of each functional unit can be referred to the corresponding content disclosed in the foregoing method embodiments. Furthermore, each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0077] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for analyzing the carbon cycle of artificial vegetation in desert areas, characterized in that, Includes the following steps: Acquire basic carbon emission data for each historical monitoring period of the target area, wherein the target area refers to one of the areas obtained by grid division of the desert area to be analyzed; Acquire light intensity data for each historical monitoring period of the target area, and calculate additional carbon emission data for each historical monitoring period based on the light intensity data for each historical monitoring period; wherein, the additional carbon emission data refers to the additional carbon emissions generated by photochemical degradation under different light intensities; The actual carbon emission data is calculated by combining the basic carbon emission data and the additional carbon emission data under the same historical monitoring period; all the actual carbon emission data are arranged in time series according to the order of the historical monitoring period, and the time series data is fitted and trained to obtain the prediction function of the actual carbon emission data. The predicted carbon emission data for the target area in the next monitoring period is predicted based on the prediction function. The predicted carbon emission data and the actual carbon emission data for the target area in the next monitoring period are combined to calculate the corrected carbon emission data. The corrected carbon emission data is then used to calculate the net carbon sink of the target area.

2. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 1, characterized in that, The calculation of additional carbon emission data for each historical monitoring period based on the light intensity data for each historical monitoring period includes the following steps: Collect light intensity data for the historical monitoring period and determine the ultraviolet radiation intensity; determine the first impact level based on the ultraviolet radiation intensity; determine the second impact level based on the soil photosensitivity of the target area; determine the third impact level based on the surface soil micro-parameters of the target area; combine the first impact level, the second impact level, and the third impact level into a carbon emission impact level; determine the additional carbon emission data based on the carbon emission impact level and a carbon emission experience table; wherein, the surface soil micro-parameters include at least one of surface soil temperature parameter, surface soil moisture parameter, surface soil color parameter, and surface soil texture parameter.

3. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 2, characterized in that, The surface soil micro-parameters also include litter cover parameters. A fourth impact level is determined based on the litter cover parameters. The first impact level, the second impact level, the third impact level, and the fourth impact level are combined into the carbon emission impact level.

4. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 3, characterized in that, The litter coverage parameters include coverage breadth parameters and coverage depth parameters. The coverage area ratio is determined based on the coverage breadth parameter, and a first adjustment ratio is determined based on the coverage area ratio. The litter layer height difference is determined based on the coverage depth parameter, and a second adjustment ratio is determined based on the litter layer height difference. The product of the first adjustment ratio and the fourth impact level is obtained, and the result of multiplying the product by the second adjustment ratio is used as the comprehensive fourth impact level. The comprehensive fourth impact level is used in the combined calculation of the carbon emission impact level.

5. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 4, characterized in that, The litter coverage parameter also includes the litter coverage transmittance parameter. The transmittance range is determined based on the litter coverage transmittance parameter. A third adjustment ratio is determined based on the transmittance range. The product of the comprehensive fourth impact level and the third adjustment ratio is used as the basis for participating in the combined calculation of the carbon emission impact level.

6. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 1 or 5, characterized in that, After calculating the additional carbon emission data for each historical monitoring period based on the light intensity data for each historical monitoring period, the process also includes a step of anomaly detection for the additional carbon emission data: Identify at least one similar region of the target region, obtain reference carbon emission data of the similar region under the same monitoring period, determine whether the additional carbon emission data is abnormal based on all reference carbon emission data, and if it is determined to be abnormal, adjust the weight ratio of the additional carbon emission data in the subsequent calculation of actual carbon emission data; wherein, when determining abnormality, the determination is made based on the average difference between all reference carbon emission data and the additional carbon emission data.

7. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 6, characterized in that, Adjusting the weighting of this additional carbon emission data in the calculation of subsequent actual carbon emission data includes the steps of first determining the range of the mean difference and then determining the weighting based on the range.

8. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 6, characterized in that, Determining at least one similar region of the target region includes the following steps: Obtain the ultraviolet radiation reference map of the desert region, calculate the similarity of the ultraviolet radiation time series curves of each pixel region based on the ultraviolet radiation reference map, and merge the continuous pixel regions that meet the requirements of ultraviolet radiation time series curve similarity into an initial grid. The ultraviolet radiation difference is calculated for each initial grid. If the difference uniformity requirement is met, the initial grid is used as the basis for selecting the target region. Otherwise, the initial grid is further divided until the difference uniformity requirement is met. Each initial grid is assigned a radiation level label and a surface attribute label. Similar initial grids with the same radiation level label and the same surface attribute label are matched with the target initial grid. These similar initial grids are used as the basis for selecting the similar region. The target initial grid refers to the initial grids contained in the target region. The radiation level label indicates the intensity of ultraviolet radiation, and the surface attribute label indicates the soil type, topography, and vegetation cover.

9. The carbon cycle analysis method for artificial vegetation in desert areas according to claim 6, characterized in that, After determining at least one similar region of the target region, the method further includes a step of filtering the similar regions: A reference prediction function for the similar region is obtained. The reference prediction function is compared with the actual carbon emission judgment results to determine whether the reference carbon emission data for the same monitoring period is abnormal. If it is abnormal, the similar region is screened out; otherwise, it is retained. The reference prediction function is obtained by fitting and training all the actual carbon emission data of the similar region in a time sequence according to the order of historical monitoring periods.

10. A carbon cycle analysis system for artificial vegetation in desert areas, characterized in that, include: The first acquisition unit is used to acquire basic carbon emission data corresponding to each historical monitoring period of the target area, wherein the target area refers to one of the areas obtained by grid division of the desert area to be analyzed; The second acquisition unit is used to acquire light intensity data of the target area for each historical monitoring period, and calculate additional carbon emission data for each historical monitoring period based on the light intensity data of each historical monitoring period; wherein, the additional carbon emission data refers to the amount of additional carbon emissions generated by photochemical degradation under different light intensities; The first calculation unit is used to calculate the actual carbon emission data by combining the basic carbon emission data and the additional carbon emission data under the same historical monitoring period; it arranges all the actual carbon emission data in time sequence according to the order of the historical monitoring period, and performs fitting training on the time sequence data to obtain the prediction function of the actual carbon emission data. The second calculation unit is used to predict the predicted carbon emission data of the target area for the next monitoring period according to the prediction function, and to calculate the corrected carbon emission data by combining the predicted carbon emission data of the target area in the next monitoring period with the actual carbon emission data, and to use the corrected carbon emission data to participate in the calculation of the net carbon sink of the target area.

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