Ecological carbon sink monitoring method based on data processing

Abstract

The invention discloses an ecological carbon sink monitoring method based on data processing, and relates to the technical field of ecological carbon sink monitoring, and the method comprises the following steps: S1, obtaining the current physiological parameters of each plant in vegetation of a to-be-monitored region, and determining the vegetation influence degree of the to-be-monitored region; s2, collecting the carbon emission of the to-be-monitored area in the historical time period; and S3, determining the final carbon emission at the next moment according to the vegetation influence degree of the to-be-monitored area and the carbon emission at the historical time period. According to the method, the complexity and the collaboration of the carbon circulation of the ecological system are comprehensively reflected, and the complex mechanism of the carbon circulation of the ecological system is precisely restored, so that the monitoring result can better reflect the real ecological nature of carbon sink.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological carbon sink monitoring, and in particular to an ecological carbon sink monitoring method based on data processing. Background Art

[0002] At present, ecological carbon sink monitoring technology mainly covers satellite remote sensing monitoring, ground observation network monitoring and other methods, but the existing technology has obvious shortcomings in practical applications: on the one hand, traditional monitoring methods mostly focus on the direct measurement of carbon emissions or carbon sinks, and do not fully consider the dynamic impact of vegetation's own physiological characteristics (such as the mechanical properties of plant roots) on ecological carbon sinks. It is difficult to accurately reflect the complex interaction mechanism between vegetation and carbon emissions, resulting in the accuracy and reliability of monitoring results need to be improved; on the other hand, there is a lack of technical means to effectively integrate historical data, and it is impossible to accurately depict the changing laws of carbon emissions under the influence of vegetation, which in turn affects the accuracy of carbon emission forecasts. Summary of the Invention

[0003] In order to solve the above problems, the present invention proposes an ecological carbon sink monitoring method based on data processing.

[0004] The technical solution of the present invention is: a data processing-based ecological carbon sink monitoring method comprises the following steps: S1. Obtain the current physiological parameters of each plant in the vegetation in the monitored area and determine the vegetation impact of the monitored area; S2. Collect carbon emissions from the monitored area during historical periods; S3. Determine the final carbon emissions at the next moment based on the vegetation impact of the monitored area and the carbon emissions in the historical period.

[0005] Furthermore, S1 includes the following sub-steps: S11, gridding the area to be monitored and extracting the current physiological parameters of each plant in the grid; S12, constructing a competition coefficient between adjacent grids based on the current physiological parameters of each plant in the grid; S13. Determine the vegetation impact of the area to be monitored based on all competition coefficients.

[0006] The beneficial effects of the above further scheme are: in the present invention, gridding is a common means of refined monitoring, which can realize block management of the region; the competition coefficient between plants will directly affect the carbon metabolism capacity of the vegetation community. The competition coefficient is constructed based on the physiological parameters of plants in the grid, which can accurately capture the effect of this competition on vegetation, and realize the refined segmentation of the monitoring area and the quantification of the interaction between plants.

[0007] Furthermore, S12 includes the following sub-steps: S121. Calculating the stretch coefficient of each plant in the grid based on the Young's modulus of the root of each plant in the grid; S122, clustering the stretch coefficients of all plants in the monitoring area to obtain a number of clusters and the center value of each cluster; S123. Construct a competition coefficient between adjacent grids based on the clusters to which each plant in the grid belongs and the center value of the cluster.

[0008] The beneficial effects of the above further scheme are: in the present invention, the Young's modulus of the plant roots (reflecting the mechanical properties of the roots' resistance to deformation) will affect the plant's acquisition of soil resources (such as rooting depth and water absorption), thereby affecting the plant's growth and carbon sequestration capacity; cluster analysis can classify plants with similar stretch coefficients (determined by root mechanical and morphological parameters), facilitating the analysis of inter-grid competition based on category characteristics.

[0009] Furthermore, in S121, the stretching coefficient of the plant in the grid The expression is: ; in, 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, Indicates the root length of the plant.

[0010] The beneficial effect of the above further scheme is: in the present invention, the expression of 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 jointly determine the tensile mechanical behavior of the plant roots, and the mechanical behavior of the roots is directly related to ecological processes such as plant rooting and resource absorption, which in turn affects carbon sequestration, unifying the mechanical, morphological and energy parameters of the plant roots into quantifiable indicators.

[0011] Furthermore, in S123, the grid and adjacent grids Competition coefficient between The expression is: ; in, Representation Grid Neidi The stretch factor of a plant, Representation Grid Neidi The stretch factor of a plant, Representation Grid The number of plants contained, Representation Grid The number of plants contained, Representation Grid The clustering results of Cluster center values, Representation Grid The clustering results of Cluster center values, Representation Grid The number of clusters contained in the clustering result, Representation Grid The number of clusters contained in the clustering result, Indicates taking a random number between 0 and 1. represents the first scaling factor, Indicates the second scaling factor.

[0012] Furthermore, S13 includes the following sub-steps: S131, extracting the ratio between the maximum competition coefficient and the minimum competition coefficient; S132. Process the comparison value result using an inverse tangent function, and obtain the vegetation impact degree based on the parameters of each soil depth in the area to be monitored.

[0013] The beneficial effects of the above further scheme are: in the present invention, the ratio of the maximum and minimum competition coefficients reflects the degree of difference in the competition relationship, and the inverse tangent function can map the ratio to a reasonable range; soil depth parameters (moisture content, temperature) are key environmental factors affecting vegetation growth and carbon sequestration. These parameters are combined to determine the vegetation impact degree, and the impact of the soil environment on vegetation is incorporated.

[0014] Furthermore, in S132, the vegetation impact The expression is: ; ; in, It represents the ratio between the maximum competition coefficient and the minimum competition coefficient, represents the inverse tangent function, Indicates the basic microbial respiration rate in the monitored area under ideal conditions. represents the intermediate parameters, Indicates the maximum soil moisture content at all soil depths in the monitored area. Indicates the minimum soil moisture content at all soil depths in the monitored area. Indicates the maximum soil temperature at all soil depths in the area to be monitored, Indicates the minimum soil temperature at all soil depths in the area to be monitored, Indicates the basic microbial respiration rate of the monitored area at the current moment, Represents the exponential function.

[0015] The beneficial effects of the above further scheme are: in the present invention, microbial respiration is the core pathway of soil carbon emission, and soil temperature and humidity directly affect microbial activity; the inverse tangent treatment adapts the range of the competition coefficient ratio, which can more truly reflect the comprehensive regulatory effect of vegetation on carbon sinks, and provide reliable vegetation-end parameters for subsequent carbon emission predictions.

[0016] Furthermore, S3 includes the following sub-steps: S31. Inputting the carbon emissions of the monitored area in the historical period into the moving average model to obtain the preliminary carbon emissions at the next moment; S32. The product of the preliminary carbon emissions at the next moment and the vegetation impact degree of the monitored area is used as the final carbon emissions at the next moment.

[0017] The beneficial effects of the above further scheme are: in the present invention, the moving average model is suitable for capturing the trend of time series data (historical carbon emissions); the vegetation impact reflects the real-time regulatory effect of current vegetation on carbon emissions, and the preliminary emissions obtained by correcting the historical trend use historical data to depict the long-term laws of carbon emissions, and reflect the dynamic changes of the ecosystem through vegetation parameter correction, thereby improving the accuracy and timeliness of the carbon emissions prediction at the next moment.

[0018] The beneficial effects of the present invention are: the present invention integrates multi-dimensional factors such as vegetation physiological parameters (root mechanics and morphology, etc.), competition between plants, soil environmental parameters and historical carbon emission trends, covering both the interactions between organisms and the impact of plants on community carbon metabolism, and incorporating environmental constraints on the carbon cycle, soil temperature and humidity restricting the activity of microorganisms (core carriers of soil carbon emissions), comprehensively reflecting the complexity and synergy of the ecosystem carbon cycle, accurately restoring the complex mechanism of the ecosystem carbon cycle, and enabling the monitoring results to better reflect the true ecological nature of carbon sinks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the ecological carbon sink monitoring method based on data processing. DETAILED DESCRIPTION

[0020] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0021] like Figure 1 As shown, the present invention provides an ecological carbon sink monitoring method based on data processing, comprising the following steps: S1. Obtain the current physiological parameters of each plant in the vegetation in the monitored area and determine the vegetation impact of the monitored area; S2. Collect carbon emissions from the monitored area during historical periods; S3. Determine the final carbon emissions at the next moment based on the vegetation impact of the monitored area and the carbon emissions in the historical period.

[0022] In this embodiment of the present invention, S1 includes the following sub-steps: S11, gridding the area to be monitored and extracting the current physiological parameters of each plant in the grid; S12, constructing a competition coefficient between adjacent grids based on the current physiological parameters of each plant in the grid; S13. Determine the vegetation impact of the area to be monitored based on all competition coefficients.

[0023] In the present invention, gridding is a commonly used means of refined monitoring, which can realize block management of the region; the competition coefficient between plants will directly affect the carbon metabolism capacity of the vegetation community. The competition coefficient is constructed based on the physiological parameters of plants in the grid, which can accurately capture the effect of this competition on vegetation, and realize the refined segmentation of the monitoring area and the quantification of the interaction between plants.

[0024] In this embodiment of the present invention, S12 includes the following sub-steps: S121. Calculating the stretch coefficient of each plant in the grid based on the Young's modulus of the root of each plant in the grid; S122, clustering the stretch coefficients of all plants in the monitoring area to obtain a number of clusters and the center value of each cluster; S123. Construct a competition coefficient between adjacent grids based on the clusters to which each plant in the grid belongs and the center value of the cluster.

[0025] In the present invention, the Young's modulus of plant roots (reflecting the mechanical properties of the roots' resistance to deformation) affects the plants' acquisition of soil resources (such as rooting depth and water absorption), thereby affecting plant growth and carbon sequestration capacity; cluster analysis can classify plants with similar stretch coefficients (determined by root mechanical and morphological parameters), facilitating the analysis of inter-grid competition based on category characteristics.

[0026] In the embodiment of the present invention, in S121, the stretching coefficient of the plant in the grid The expression is: ; in, 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, Indicates the root length of the plant.

[0027] In the present invention, the expression of the tensile coefficient integrates the cross-sectional area (morphological parameter), Young's modulus (mechanical parameter), strain energy per unit cross-sectional area (energy parameter) and root length (morphological parameter) of the plant root. These parameters jointly determine the tensile mechanical behavior of the plant root. The mechanical behavior of the root is directly related to ecological processes such as plant rooting and resource absorption, which in turn affects carbon sequestration, unifying the mechanical, morphological and energy parameters of the plant root into quantifiable indicators.

[0028] In the embodiment of the present invention, in S123, the grid and adjacent grids Competition coefficient between The expression is: ; in, Representation Grid Neidi The stretch factor of a plant, Representation Grid Neidi The stretch factor of a plant, Representation Grid The number of plants contained, Representation Grid The number of plants contained, Representation Grid The clustering results of Cluster center values, Representation Grid The clustering results of Cluster center values, Representation Grid The number of clusters contained in the clustering result, Representation Grid The number of clusters contained in the clustering result, Indicates taking a random number between 0 and 1. represents the first scaling factor, Indicates the second scaling factor.

[0029] After the stretch coefficients of the plants contained in each grid are clustered, each cluster has a cluster center value.

[0030] In this embodiment of the present invention, S13 includes the following sub-steps: S131, extracting the ratio between the maximum competition coefficient and the minimum competition coefficient; S132. Process the comparison value result using an inverse tangent function, and obtain the vegetation impact degree based on the parameters of each soil depth in the area to be monitored.

[0031] In the present invention, the ratio of the maximum and minimum competition coefficients reflects the degree of difference in the competitive relationship, and the inverse tangent function can map this ratio to a reasonable range; soil depth parameters (moisture content, temperature) are key environmental factors affecting vegetation growth and carbon sequestration. These parameters are combined to determine the vegetation impact degree, incorporating the impact of the soil environment on vegetation.

[0032] In the embodiment of the present invention, in S132, the vegetation impact The expression is: ; ; in, It represents the ratio between the maximum competition coefficient and the minimum competition coefficient, represents the inverse tangent function, Indicates the basic microbial respiration rate in the monitored area under ideal conditions. represents the intermediate parameters, Indicates the maximum soil moisture content at all soil depths in the monitored area. Indicates the minimum soil moisture content at all soil depths in the monitored area. Indicates the maximum soil temperature at all soil depths in the area to be monitored, Indicates the minimum soil temperature at all soil depths in the area to be monitored, Indicates the basic microbial respiration rate of the monitored area at the current moment, Represents the exponential function.

[0033] In the present invention, microbial respiration is the core pathway of soil carbon emission, and soil temperature and humidity directly affect microbial activity; the inverse tangent treatment adapts the range of the competition coefficient ratio, which can more realistically reflect the comprehensive regulatory effect of vegetation on carbon sinks and provide reliable vegetation-end parameters for subsequent carbon emission predictions.

[0034] In this embodiment of the present invention, S3 includes the following sub-steps: S31. Inputting the carbon emissions of the monitored area in the historical period into the moving average model to obtain the preliminary carbon emissions at the next moment; S32. The product of the preliminary carbon emissions at the next moment and the vegetation impact degree of the monitored area is used as the final carbon emissions at the next moment.

[0035] In the present invention, the moving average model is suitable for capturing the trend of time series data (historical carbon emissions); the vegetation impact reflects the real-time regulatory effect of current vegetation on carbon emissions, and the preliminary emissions obtained by correcting the historical trend use historical data to depict the long-term laws of carbon emissions, and reflect the dynamic changes of the ecosystem through vegetation parameter correction, thereby improving the accuracy and timeliness of the carbon emissions forecast at the next moment.

[0036] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for monitoring ecological carbon sinks based on data processing, characterized in that: The following steps are involved: S1. Obtain the current physiological parameters of each plant in the vegetation in the monitored area and determine the vegetation impact of the monitored area; S2. Collect carbon emissions from the monitored area during historical periods; S3. Determine the final carbon emissions at the next moment based on the vegetation impact of the monitored area and the carbon emissions in the historical period.

2. The ecological carbon sink monitoring method based on data processing according to claim 1 is characterized in that: The S1 includes the following sub-steps: S11, gridding the area to be monitored and extracting the current physiological parameters of each plant in the grid; S12, constructing a competition coefficient between adjacent grids based on the current physiological parameters of each plant in the grid; S13. Determine the vegetation impact of the area to be monitored based on all competition coefficients.

3. The ecological carbon sink monitoring method based on data processing according to claim 2 is characterized in that: The S12 includes the following sub-steps: S121. Calculating the stretch coefficient of each plant in the grid based on the Young's modulus of the root of each plant in the grid; S122, clustering the stretch coefficients of all plants in the monitoring area to obtain a number of clusters and the center value of each cluster; S123. Construct a competition coefficient between adjacent grids based on the clusters to which each plant in the grid belongs and the center value of the cluster.

4. The ecological carbon sink monitoring method based on data processing according to claim 3 is characterized in that: In S121, the stretching coefficient of the plant in the grid The expression is: ； in, 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, Indicates the root length of the plant.

5. The ecological carbon sink monitoring method based on data processing according to claim 3 is characterized in that: In the S123, the grid and adjacent grids Competition coefficient between The expression is: ； in, Representation Grid Neidi The stretch factor of a plant, Representation Grid Neidi The stretch factor of a plant, Representation Grid The number of plants contained, Representation Grid The number of plants contained, Representation Grid The clustering results of Cluster center values, Representation Grid The clustering results of Cluster center values, Representation Grid The number of clusters contained in the clustering result, Representation Grid The number of clusters contained in the clustering result, Indicates taking a random number between 0 and 1. represents the first scaling factor, Indicates the second scaling factor.

6. The ecological carbon sink monitoring method based on data processing according to claim 2 is characterized in that: The S13 includes the following sub-steps: S131, extracting the ratio between the maximum competition coefficient and the minimum competition coefficient; S132. Process the comparison value result using an inverse tangent function, and obtain the vegetation impact degree based on the parameters of each soil depth in the area to be monitored.

7. The ecological carbon sink monitoring method based on data processing according to claim 6 is characterized in that: In S132, the vegetation impact The expression is: ； ； in, It represents the ratio between the maximum competition coefficient and the minimum competition coefficient, represents the inverse tangent function, Indicates the basic microbial respiration rate in the monitored area under ideal conditions. represents the intermediate parameters, Indicates the maximum soil moisture content at all soil depths in the monitored area. Indicates the minimum soil moisture content at all soil depths in the monitored area. Indicates the maximum soil temperature at all soil depths in the area to be monitored, Indicates the minimum soil temperature at all soil depths in the area to be monitored, Indicates the basic microbial respiration rate of the monitored area at the current moment, Represents the exponential function.

8. The ecological carbon sink monitoring method based on data processing according to claim 1 is characterized in that: The S3 includes the following sub-steps: S31. Inputting the carbon emissions of the monitored area in the historical period into the moving average model to obtain the preliminary carbon emissions at the next moment; S32. The product of the preliminary carbon emissions at the next moment and the vegetation impact degree of the monitored area is used as the final carbon emissions at the next moment.

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