An ecological carbon sink monitoring method based on data processing

Abstract

The application discloses an ecological carbon sink monitoring method based on data processing and relates to the technical field of ecological carbon sink monitoring, and comprises the following steps: S1, acquiring current physiological parameters of each plant in the vegetation of a region to be monitored, and determining a vegetation influence degree of the region to be monitored; S2, collecting carbon emission amounts of the region to be monitored in historical time periods; and S3, determining final carbon emission amounts at next time according to the vegetation influence degree of the region to be monitored and the carbon emission amounts in the historical time periods. The application comprehensively reflects the complexity and synergy of the carbon cycle of an ecological system, accurately restores the complex mechanism of the carbon cycle of the ecological system, and makes the monitoring result more capable of reflecting the real ecological nature of carbon sink.

Description

Technical Field

[0001] This invention relates to the field of ecological carbon sink monitoring technology, and specifically to an ecological carbon sink monitoring method based on data processing. Background Technology

[0002] Currently, ecological carbon sink monitoring technologies mainly cover satellite remote sensing monitoring and ground observation network monitoring, but existing technologies have significant shortcomings in practical applications: On the one hand, traditional monitoring methods focus on the direct measurement of carbon emissions or carbon sinks, without fully considering the dynamic impact of vegetation's own physiological characteristics (such as the mechanical properties of plant roots) on ecological carbon sinks, making it difficult to accurately reflect the complex interaction mechanism between vegetation and carbon emissions, resulting in the need to improve the accuracy and reliability of monitoring results; on the other hand, there is a lack of technical means to effectively integrate historical data, making it impossible to accurately depict the changing patterns of carbon emissions under the influence of vegetation, thus affecting the accuracy of carbon emission prediction. Summary of the Invention

[0003] To address the above problems, this invention proposes a data processing-based method for monitoring ecological carbon sinks.

[0004] The technical solution of this invention is: a data processing-based method for monitoring ecological carbon sinks, comprising the following steps:

[0005] S1. Obtain the current physiological parameters of each plant in the vegetation of the area to be monitored, and determine the vegetation impact degree of the area to be monitored;

[0006] S2. Collect carbon emissions of the area to be monitored over historical periods;

[0007] S3. Determine the final carbon emissions at the next moment based on the vegetation impact of the area to be monitored and the carbon emissions during historical periods.

[0008] Furthermore, S1 includes the following sub-steps:

[0009] S11. Grid the area to be monitored and extract the current physiological parameters of each plant in the grid;

[0010] S12. Based on the current physiological parameters of each plant in the grid, construct the competition coefficient between adjacent grids;

[0011] S13. Determine the vegetation impact degree of the area to be monitored based on all competition coefficients.

[0012] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, gridding is a common means of fine monitoring, which can realize the segmented management of the area; the competition coefficient between plants will directly affect the carbon metabolism capacity of the vegetation community. Based on the physiological parameters of plants in the grid, the competition coefficient can be constructed to accurately capture the effect of this competition on the vegetation, and realize the fine segmentation of the monitoring area and the quantification of the interaction between plants.

[0013] Furthermore, S12 includes the following sub-steps:

[0014] S121. Calculate the tensile coefficient of each plant in the grid based on the Young's modulus of the root of each plant in the grid.

[0015] S122. Cluster the stretching coefficients of all plants in the monitoring area to obtain several clusters and the center value of each cluster;

[0016] S123. Based on the cluster to which each plant belongs and the center value of the cluster, construct a competition coefficient between adjacent grids.

[0017] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, the Young's modulus of plant roots (reflecting the mechanical properties of roots resisting deformation) affects the plant's acquisition of soil resources (such as root depth and water absorption), thereby affecting plant growth and carbon sequestration capacity; cluster analysis can classify plants with similar stretching coefficients (determined by root mechanical and morphological parameters), which facilitates the analysis of inter-grid competition based on category characteristics.

[0018] Furthermore, in S121, the stretching coefficient of the plant within the grid... The expression is:

[0019] ;

[0020] in, This represents the cross-sectional area of ​​the plant root. Represents a constant. Young's modulus, representing the root of a plant. This represents the strain energy per unit cross-sectional area of ​​the plant root. This indicates the length of the root system in a plant.

[0021] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, the expression for the tensile coefficient integrates the cross-sectional area of ​​the plant root (morphological parameter), Young's modulus (mechanical parameter), strain energy per unit cross-sectional area (energy parameter), and root length (morphological parameter). These parameters together determine the tensile mechanical behavior of the plant root, which is directly related to ecological processes such as plant rooting and resource absorption, thereby affecting carbon sinks. The mechanical, morphological, and energy parameters of the plant root are unified into quantifiable indicators.

[0022] Furthermore, in S123, the grid and adjacent grids Competition coefficient between The expression is:

[0023] ;

[0024] in, Represents a grid Inner The elongation coefficient of a plant, Represents a grid Inner The elongation coefficient of a plant, Represents a grid The number of plants contained within. Represents a grid The number of plants contained within. Represents a grid In the clustering results, the first Cluster center value, Represents a grid In the clustering results, the first Cluster center value, Represents a grid The number of clusters contained in the clustering results. Represents a grid The number of clusters contained in the clustering results. This means generating a random number between 0 and 1. Indicates the first scaling factor. This represents the second scaling factor.

[0025] Furthermore, S13 includes the following sub-steps:

[0026] S131. Extract the ratio between the maximum competition coefficient and the minimum competition coefficient;

[0027] S132. The results of the comparison using the arctangent function are processed, and the vegetation influence is obtained based on the parameters of each soil depth in the area to be monitored.

[0028] The beneficial effects of the above-mentioned further scheme are as follows: In this invention, the ratio of the maximum to the minimum competition coefficient reflects the degree of difference in the competition relationship, and the arctangent function can map this ratio to a reasonable range; soil depth parameters (moisture content, temperature) are key environmental factors affecting vegetation growth and carbon sink, and the vegetation influence is determined by combining these parameters, thus incorporating the influence of the soil environment on vegetation.

[0029] Furthermore, in S132, the vegetation impact... The expression is:

[0030] ;

[0031] ;

[0032] in, This represents the ratio between the maximum competition coefficient and the minimum competition coefficient. Represents the arctangent function. This represents the basic microbial respiration rate of the monitored area under ideal conditions. Indicates intermediate parameters. This indicates the maximum soil moisture content at all soil depths in the monitored area. This represents the minimum soil moisture content at all soil depths in the monitored area. This indicates the maximum soil temperature at all soil depths in the monitored area. This represents the minimum soil temperature at all soil depths in the monitored area. This represents the baseline microbial respiration rate of the monitored area at the current moment. This represents an exponential function.

[0033] The beneficial effects of the above-mentioned further scheme are as follows: In this invention, microbial respiration is the core pathway of soil carbon emission, and soil temperature and humidity directly affect microbial activity; the arctangent treatment makes the range of competition coefficient ratios more suitable, which can more realistically reflect the comprehensive regulatory role of vegetation on carbon sinks and provide reliable vegetation-side parameters for subsequent carbon emission prediction.

[0034] Furthermore, S3 includes the following sub-steps:

[0035] S31. Input the carbon emissions of the area to be monitored in historical periods into the moving average model to obtain the preliminary carbon emissions for the next time period.

[0036] S32. The product of the initial carbon emissions at the next moment and the vegetation impact of the area to be monitored shall be used as the final carbon emissions at the next moment.

[0037] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, the moving average model is suitable for capturing the trend of time series data (historical carbon emissions); the vegetation influence reflects the real-time regulatory effect of current vegetation on carbon emissions, and the preliminary emissions obtained by correcting the historical trend are used to both depict the long-term pattern of carbon emissions using historical data and reflect the dynamic changes of the ecosystem through vegetation parameter correction, thereby improving the accuracy and timeliness of carbon emissions prediction at the next moment.

[0038] The beneficial effects of this invention are as follows: This invention integrates multiple dimensions of factors such as vegetation physiological parameters (root mechanics and morphology, etc.), inter-plant competition, soil environmental parameters, and historical carbon emission trends. It covers not only the interactions between organisms and the influence of plants on community carbon metabolism, but also the constraints of the environment on carbon cycling and the constraints of soil temperature and humidity on the activity of microorganisms (the core carriers of soil carbon emissions). It comprehensively reflects the complexity and synergy of the carbon cycle in the ecosystem, accurately restores the complex mechanism of the carbon cycle in the ecosystem, and makes the monitoring results more reflective of the true ecological nature of carbon sinks. Attached Figure Description

[0039] Figure 1 This is a flowchart of a data processing-based method for monitoring ecological carbon sinks. Detailed Implementation

[0040] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0041] like Figure 1 As shown, this invention provides a data processing-based method for monitoring ecological carbon sinks, comprising the following steps:

[0042] S1. Obtain the current physiological parameters of each plant in the vegetation of the area to be monitored, and determine the vegetation impact degree of the area to be monitored;

[0043] S2. Collect carbon emissions of the area to be monitored over historical periods;

[0044] S3. Determine the final carbon emissions at the next moment based on the vegetation impact of the area to be monitored and the carbon emissions during historical periods.

[0045] In this embodiment of the invention, S1 includes the following sub-steps:

[0046] S11. Grid the area to be monitored and extract the current physiological parameters of each plant in the grid;

[0047] S12. Based on the current physiological parameters of each plant in the grid, construct the competition coefficient between adjacent grids;

[0048] S13. Determine the vegetation impact degree of the area to be monitored based on all competition coefficients.

[0049] In this invention, gridding is a common method for refined monitoring, which can realize the segmented management of areas; the competition coefficient between plants directly affects the carbon metabolism capacity of vegetation communities. Based on the physiological parameters of plants within the grid, the competition coefficient can be constructed to accurately capture the effect of this competition on vegetation, thus realizing the refined segmentation of the monitoring area and the quantification of the interaction between plants.

[0050] In this embodiment of the invention, S12 includes the following sub-steps:

[0051] S121. Calculate the tensile coefficient of each plant in the grid based on the Young's modulus of the root of each plant in the grid.

[0052] S122. Cluster the stretching coefficients of all plants in the monitoring area to obtain several clusters and the center value of each cluster;

[0053] S123. Based on the cluster to which each plant belongs and the center value of the cluster, construct a competition coefficient between adjacent grids.

[0054] In this invention, the Young's modulus of plant roots (reflecting the mechanical properties of roots to resist deformation) affects the plant's acquisition of soil resources (such as root depth and water absorption), thereby affecting plant growth and carbon sequestration capacity; cluster analysis can classify plants with similar stretching coefficients (determined by root mechanical and morphological parameters), which facilitates the analysis of inter-grid competition based on category characteristics.

[0055] In this embodiment of the invention, in S121, the stretching coefficient of the plant within the grid is... The expression is:

[0056] ;

[0057] in, This represents the cross-sectional area of ​​the plant root. Represents a constant. Young's modulus, representing the root of a plant. This represents the strain energy per unit cross-sectional area of ​​the plant root. This indicates the length of the root system in a plant.

[0058] In this invention, the expression for the tensile coefficient integrates the cross-sectional area of ​​the plant root (morphological parameter), Young's modulus (mechanical parameter), strain energy per unit cross-sectional area (energy parameter), and root length (morphological parameter). These parameters together determine the tensile mechanical behavior of the plant root, which is directly related to ecological processes such as plant rooting and resource absorption, and thus affects carbon sinks. This invention unifies the mechanical, morphological, and energy parameters of the plant root into a quantifiable index.

[0059] In this embodiment of the invention, in S123, the mesh and adjacent grids Competition coefficient between The expression is:

[0060] ;

[0061] in, Represents a grid Inner The elongation coefficient of a plant, Represents a grid Inner The elongation coefficient of a plant, Represents a grid The number of plants contained within. Represents a grid The number of plants contained within. Represents a grid In the clustering results, the first Cluster center value, Represents a grid In the clustering results, the first Cluster center value, Represents a grid The number of clusters contained in the clustering results. Represents a grid The number of clusters contained in the clustering results. This means generating a random number between 0 and 1. Indicates the first scaling factor. This represents the second scaling factor.

[0062] After the stretching coefficients of the plants contained in each grid are clustered, they belong to a cluster, and the cluster has a cluster center value.

[0063] In this embodiment of the invention, S13 includes the following sub-steps:

[0064] S131. Extract the ratio between the maximum competition coefficient and the minimum competition coefficient;

[0065] S132. The results of the comparison using the arctangent function are processed, and the vegetation influence is obtained based on the parameters of each soil depth in the area to be monitored.

[0066] In this invention, the ratio of the maximum to the minimum competition coefficient reflects the degree of difference in the competition relationship, and the arctangent function can map this ratio to a reasonable range; soil depth parameters (moisture content, temperature) are key environmental factors affecting vegetation growth and carbon sink, and the vegetation influence is determined by combining these parameters, incorporating the influence of the soil environment on vegetation.

[0067] In this embodiment of the invention, in S132, the vegetation influence degree The expression is:

[0068] ;

[0069] ;

[0070] in, This represents the ratio between the maximum competition coefficient and the minimum competition coefficient. Represents the arctangent function. This represents the basic microbial respiration rate of the monitored area under ideal conditions. Indicates intermediate parameters. This indicates the maximum soil moisture content at all soil depths in the monitored area. This represents the minimum soil moisture content at all soil depths in the monitored area. This indicates the maximum soil temperature at all soil depths in the monitored area. This represents the minimum soil temperature at all soil depths in the monitored area. This represents the baseline microbial respiration rate of the monitored area at the current moment. This represents an exponential function.

[0071] In this invention, microbial respiration is the core pathway of soil carbon emissions, and soil temperature and humidity directly affect microbial activity. The arctangent treatment adapts the range of competition coefficient ratios, which can more realistically reflect the comprehensive regulatory role of vegetation on carbon sinks and provide reliable vegetation-side parameters for subsequent carbon emission prediction.

[0072] In this embodiment of the invention, S3 includes the following sub-steps:

[0073] S31. Input the carbon emissions of the area to be monitored in historical periods into the moving average model to obtain the preliminary carbon emissions for the next time period.

[0074] S32. The product of the initial carbon emissions at the next moment and the vegetation impact of the area to be monitored shall be used as the final carbon emissions at the next moment.

[0075] In this invention, the moving average model is suitable for capturing the trend of time series data (historical carbon emissions); the vegetation influence reflects the real-time regulatory effect of current vegetation on carbon emissions, and the preliminary emissions obtained by correcting the historical trend are used. This not only uses historical data to depict the long-term pattern of carbon emissions, but also reflects the dynamic changes of the ecosystem through vegetation parameter correction, thereby improving the accuracy and timeliness of carbon emissions prediction at the next moment.

[0076] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. An ecological carbon sink monitoring method based on data processing, characterized in that, The method comprises the following steps: S1, obtaining the current physiological parameters of each plant in the vegetation of the to-be-monitored area, and determining the vegetation influence degree of the to-be-monitored area; S2, collecting the carbon emission amount of the to-be-monitored area in a historical period; S3, determining the final carbon emission amount at the next moment according to the vegetation influence degree of the to-be-monitored area and the carbon emission amount in the historical period; The S1 comprises the following sub-steps: S11, griding the to-be-monitored area, and extracting the current physiological parameters of each plant in the grid; S12, constructing a competition coefficient between adjacent grids according to the current physiological parameters of each plant in the grid; S13, determining the vegetation influence degree of the to-be-monitored area according to all the competition coefficients; The S12 comprises the following sub-steps: S121, calculating the tensile coefficient of each plant in the grid according to the Young's modulus of the root of each plant in the grid; S122, clustering the tensile coefficients of all plants in the to-be-monitored area to obtain a plurality of clusters and the center value of each cluster; S123, constructing a competition coefficient between adjacent grids according to the cluster to which each plant in the grid belongs and the center value of the cluster; In the S121, the plant's tensile coefficient within the grid is expressed as: ； wherein, represents the cross-sectional area of the plant root, represents a constant, represents the Young's modulus of the plant root, represents the strain energy per unit cross-sectional area of the plant root, represents the root length of the plant root; In the S123, the grid and the adjacent grid between the competition coefficient The expression is: ； wherein, represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents the number of plants in the grid represents a random number between 0 and 1 represents a first scaling factor represents a second scaling factor The S13 comprises the following sub-steps: S131, extracting the ratio between the maximum competition coefficient and the minimum competition coefficient; S132, processing the ratio result by using an inverse tangent function, and obtaining the vegetation influence degree according to the parameters of each soil depth in the to-be-monitored area; In the S132, the vegetation influence degree The expression of the vegetation influence degree is: ； ； wherein, represents the ratio between the maximum competition coefficient and the minimum competition coefficient, represents the arc tangent function, represents the basal microbial respiration rate of the area to be monitored under ideal conditions, represents an intermediate parameter, represents the maximum soil moisture of the area to be monitored at all soil depths, represents the minimum soil moisture of the area to be monitored at all soil depths, represents the maximum soil temperature of the area to be monitored at all soil depths, represents the minimum soil temperature of the area to be monitored at all soil depths, represents the basal microbial respiration rate of the area to be monitored at the current time instant, represents the exponential function. 2.The data processing based ecological carbon sink monitoring method according to claim 1, characterized in that, The S3 comprises the following sub-steps: S31, inputting the carbon emission amount of the to-be-monitored area in the historical period into a moving average model to obtain a preliminary carbon emission amount at the next moment; S32, taking the product of the preliminary carbon emission amount at the next moment and the vegetation influence degree of the to-be-monitored area as the final carbon emission amount at the next moment.

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