A method for rapid calculation of carbon source and sink of mine area scale ecosystem

Abstract

The present application relates to the technical field of data measurement, and more particularly to a method for rapid calculation of carbon source and sink of mine area scale ecosystem. The method comprises the following steps: obtaining mine area remote sensing image data, ground measured data and mine area mining activity records, and constructing a multi-source mine area basic data set; performing spatial registration and time alignment on the multi-source mine area basic data set to obtain an aligned multi-source fusion data set; identifying mine land occupation patches based on the aligned multi-source fusion data set, extracting land use type information of the patches, forming a land use type data set, and combining with mine area carbon pool type definition to generate a carbon pool classification data set; therefore, the present application integrates multi-source data, optimizes spatial and time alignment, and refines the carbon storage calculation process, solves the problems of data dispersion and inaccurate calculation in the traditional method, and improves the scientificity and reliability of the mine area ecosystem carbon source and sink measurement.

Description

Technical Field

[0001] This invention relates to the field of data measurement technology, and in particular to a rapid calculation method for ecosystem carbon sources and sinks at the mining area scale. Background Technology

[0002] Existing research has significant biases in the specific carbon source summation intensity for different land use types within mining areas, such as undisturbed land, mine pits, spoil heaps, reclaimed forest land, reclaimed grassland, and industrial squares. Furthermore, quantitative understanding of key issues such as the loss of ecosystem carbon sources and sinks caused by land use changes in mining areas, including the accurate estimation of ecosystem carbon storage losses due to the direct occupation and damage of land by mining activities, remains insufficient. Therefore, there is an urgent need for a method that can rapidly and quantitatively measure the carbon sources and sinks of mining area ecosystems. Summary of the Invention

[0003] Therefore, it is necessary to provide a rapid calculation method for ecosystem carbon sources and sinks at the mining area scale to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a rapid calculation method for ecosystem carbon sources and sinks at the mining area scale is provided, the method comprising the following steps:

[0005] Step S1: Acquire remote sensing image data, ground measurement data and mining activity records of the mining area to construct a multi-source mining area basic dataset; perform spatial registration and temporal alignment on the multi-source mining area basic dataset to obtain an aligned multi-source fusion dataset;

[0006] Step S2: Identify mining-occupied patches based on the aligned multi-source fusion dataset, extract land use type information from the patches to form a land use type dataset, and generate a carbon pool classification dataset by combining the definition of mining area carbon pool type;

[0007] Step S3: Based on the land use type dataset and the carbon pool classification dataset, call the carbon density parameter table and the land use type conversion carbon source-sink coefficient matrix to generate the land use carbon parameter dataset;

[0008] Step S4: Calculate the area-based carbon source and sink input data and the volume-based carbon source and sink input data based on the land use carbon parameter dataset to form the initial calculation dataset for carbon storage change;

[0009] Step S5: Summarize and merge the initial calculation dataset of carbon storage changes to obtain the total carbon source and sink measurement results of the mining area ecosystem.

[0010] The beneficial effects of this invention lie in the construction of a multi-source mining area basic dataset by acquiring remote sensing image data, ground measurement data, and mining activity records of the mining area. This dataset not only integrates data from different sources, enhancing the comprehensiveness and reliability of the information, but also lays the foundation for subsequent spatial registration and temporal alignment. Through spatial registration and temporal alignment, the effective fusion of multi-source data within the same coordinate system and time frame is ensured, enabling subsequent analysis to be based on the same data foundation, thereby improving data consistency. Identifying mining-occupied land parcels and extracting land use type information, the resulting land use type dataset and carbon pool classification dataset provide important classification criteria and basic data for subsequent carbon reserve calculations. Precise definitions of land use types and carbon pool classifications help to better understand the impact of different land uses on carbon reserves and provide necessary support for carbon density calculations. By calling the carbon density parameter table and the land use type to convert carbon source and sink coefficient matrices, a land use carbon parameter dataset is generated, allowing carbon reserve calculations to be based on more detailed and accurate data. This process ensures higher accuracy and scientific rigor in carbon storage calculations for different land use types. By calculating and integrating carbon source and sink input data, the final result of the total carbon source and sink of the mining area ecosystem is obtained. This result not only intuitively reflects the dynamic changes in carbon within the mining area ecosystem but also provides a quantitative basis for ecological environmental protection and resource management. Therefore, this invention, by integrating multi-source data, optimizing spatial and temporal alignment, and refining the carbon storage calculation process, solves the problems of data dispersion and inaccurate calculations in traditional methods, thus improving the scientific rigor and reliability of carbon source and sink measurement in mining area ecosystems. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the steps of a rapid calculation method for ecosystem carbon sources and sinks at the mining area scale.

[0012] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4.

[0013] Figure 3 A schematic diagram showing the changes in carbon storage for different land use types;

[0014] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0016] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] To achieve the above objectives, please refer to Figures 1 to 3 A rapid calculation method for ecosystem carbon sources and sinks at the mining area scale, the method comprising the following steps:

[0019] Step S1: Acquire remote sensing image data, ground measurement data and mining activity records of the mining area to construct a multi-source mining area basic dataset; perform spatial registration and temporal alignment on the multi-source mining area basic dataset to obtain an aligned multi-source fusion dataset;

[0020] Step S2: Identify mining-occupied patches based on the aligned multi-source fusion dataset, extract land use type information from the patches to form a land use type dataset, and generate a carbon pool classification dataset by combining the definition of mining area carbon pool type;

[0021] Step S3: Based on the land use type dataset and the carbon pool classification dataset, call the carbon density parameter table and the land use type conversion carbon source-sink coefficient matrix to generate the land use carbon parameter dataset;

[0022] Step S4: Calculate the area-based carbon source and sink input data and the volume-based carbon source and sink input data based on the land use carbon parameter dataset to form the initial calculation dataset for carbon storage change;

[0023] Step S5: Summarize and merge the initial calculation dataset of carbon storage changes to obtain the total carbon source and sink measurement results of the mining area ecosystem.

[0024] The beneficial effects of this invention lie in the construction of a multi-source mining area basic dataset by acquiring remote sensing image data, ground measurement data, and mining activity records of the mining area. This dataset not only integrates data from different sources, enhancing the comprehensiveness and reliability of the information, but also lays the foundation for subsequent spatial registration and temporal alignment. Through spatial registration and temporal alignment, the effective fusion of multi-source data within the same coordinate system and time frame is ensured, enabling subsequent analysis to be based on the same data foundation, thereby improving data consistency. Identifying mining-occupied land parcels and extracting land use type information, the resulting land use type dataset and carbon pool classification dataset provide important classification criteria and basic data for subsequent carbon reserve calculations. Precise definitions of land use types and carbon pool classifications help to better understand the impact of different land uses on carbon reserves and provide necessary support for carbon density calculations. By calling the carbon density parameter table and the land use type to convert carbon source and sink coefficient matrices, a land use carbon parameter dataset is generated, allowing carbon reserve calculations to be based on more detailed and accurate data. This process ensures higher accuracy and scientific rigor in calculating carbon storage for different land use types. By calculating and summarizing carbon source and sink input data, the final result of the total carbon source and sink of the mining area ecosystem is obtained. This result not only reflects the dynamic changes in carbon in the mining area ecosystem but also provides a quantitative basis for ecological environmental protection and resource management.

[0025] In this embodiment of the invention, reference is made to Figure 1 The diagram illustrates the steps of a rapid calculation method for ecosystem carbon sources and sinks at the mining area scale according to the present invention. In this example, the rapid calculation method for ecosystem carbon sources and sinks at the mining area scale includes the following steps:

[0026] Step S1: Acquire remote sensing image data, ground measurement data and mining activity records of the mining area to construct a multi-source mining area basic dataset; perform spatial registration and temporal alignment on the multi-source mining area basic dataset to obtain an aligned multi-source fusion dataset;

[0027] Step S2: Identify mining-occupied patches based on the aligned multi-source fusion dataset, extract land use type information from the patches to form a land use type dataset, and generate a carbon pool classification dataset by combining the definition of mining area carbon pool type;

[0028] Step S3: Based on the land use type dataset and the carbon pool classification dataset, call the carbon density parameter table and the land use type conversion carbon source-sink coefficient matrix to generate the land use carbon parameter dataset;

[0029] Step S4: Calculate the area-based carbon source and sink input data and the volume-based carbon source and sink input data based on the land use carbon parameter dataset to form the initial calculation dataset for carbon storage change;

[0030] Step S5: Summarize and merge the initial calculation dataset of carbon storage changes to obtain the total carbon source and sink measurement results of the mining area ecosystem.

[0031] Preferably, step S1 includes:

[0032] Step S11: Acquire high-resolution remote sensing image data and extract image band information reflecting vegetation and surface characteristics;

[0033] Step S12: Obtain ground quadrat measured data, including measured carbon density values ​​for different carbon pools;

[0034] Step S13: Collect records of mining activities in the mining area, including mining scope, project type, and time information;

[0035] Step S14: Unify the coordinate reference of remote sensing image data, ground quadrat measured data and mining activity records in the mining area;

[0036] Step S15: Perform timestamp matching on the data from the unified benchmark to form a multi-source fusion dataset.

[0037] In embodiments of this invention, key band information reflecting vegetation and surface characteristics is extracted through the acquisition of high-resolution remote sensing image data. This process typically utilizes remote sensing sensors, such as multispectral or hyperspectral sensors, to obtain detailed ground information. Subsequently, ground sample plot measurement data is obtained by setting up sample plots within the study area and employing standardized soil and vegetation sampling techniques to measure and record the measured carbon density values ​​of different carbon pools. This data provides the foundation for subsequent carbon storage assessment. Collecting records of mining activities in the mining area requires integrating multi-source data, including mining scope, project type, and time information. This process typically relies on Geographic Information System (GIS) technology to effectively locate and describe mining activities in the spatial context. To achieve a unified coordinate reference for remote sensing image data, ground sample plot measurement data, and mining activity records, coordinate transformation of different data sources is first required to ensure they are analyzed under the same spatial reference system. Common methods include using geographic coordinate systems (such as WGS-84) and projected coordinate systems (such as UTM). Timestamp matching technology is used to synchronize data with a unified reference, ensuring effective integration of data from different time points. This process involves standardizing data time stamps, typically using time series analysis methods to ensure consistency across datasets over time, thus creating a multi-source fused dataset. This dataset not only reflects vegetation change and carbon storage dynamics but also reveals the impact of mining activities on the ecological environment. In summary, this series of steps, through remote sensing technology, GIS analysis, and time series matching, constructs a comprehensive data framework, providing solid data support for subsequent ecological environment assessments and carbon cycle research.

[0038] Preferably, step S2 includes:

[0039] Step S21: Identify the mining patches in the fused dataset and generate a unique number for each patch;

[0040] Step S22: Extract the land use type of each patch, including undisturbed land, mine pit, spoil heap, reclaimed forest land, reclaimed grassland and industrial square;

[0041] Step S23: Establish a correspondence between each land use type and the four carbon pools to generate a carbon pool classification dataset.

[0042] In embodiments of this invention, the mining area is automatically identified and classified using image processing and machine learning algorithms within the fused dataset. In particular, deep learning models such as Convolutional Neural Networks (CNNs) effectively distinguish mining-occupied land parcels. Each identified parcel is assigned a unique number for subsequent analysis and data management. For each parcel, a land use type is extracted using a land cover classification algorithm. This process may combine spectral features from remote sensing images with measured ground sample data, applying classifiers such as decision trees, random forests, or support vector machines (SVMs) to achieve accurate identification of land types. Specifically, the extraction of different land use types, such as undisturbed land, mine pits, spoil heaps, reclaimed forest land, reclaimed grassland, and industrial plazas, relies on the construction of a training dataset and model training to ensure the accuracy of the classification results. After completing the extraction of land use types, step S23 involves establishing a correspondence between each land use type and four carbon pools (such as soil carbon pool, biological carbon pool, dead organic matter carbon pool, and atmospheric carbon pool). This process is typically achieved by establishing a classification database, utilizing existing literature and measured data to develop carbon density correlation models between various land use types and their corresponding carbon pools. In this way, a carbon pool classification dataset can be generated, comprehensively reflecting the impact of different land use types on carbon pools, thereby providing a scientific basis for ecological environment monitoring and management.

[0043] Preferably, step S3 includes:

[0044] Step S31: Match the corresponding carbon density value for each land use type to form a carbon density parameter table;

[0045] Step S32: Call the land use type conversion carbon source-sink coefficient matrix to obtain the coefficient values ​​corresponding to the changes in different land use types;

[0046] Step S33: Combine the carbon density value with the coefficient value to form a set of land use carbon parameters.

[0047] In embodiments of this invention, a carbon density value is matched for each land use type, a process that relies on a previously established carbon database classification dataset. By comparing with existing literature and measured data, the carbon density values ​​corresponding to each land use type (such as mine pits, reclaimed forest land, etc.) are determined, and these values ​​are integrated to form a carbon density parameter table. This table not only provides carbon storage information for each land use type in a specific region but also provides basic data for subsequent analysis. The land use type conversion carbon source-sink coefficient matrix involves techniques including matrix operations and data mapping. This matrix is ​​typically constructed using multinomial regression analysis or statistical models, aiming to describe the carbon source-sink coefficients corresponding to changes in different land use types. These coefficients represent the degree of impact of land use change on carbon storage and can provide necessary parameters for subsequent carbon emission estimation. Using matrix operation libraries in programming languages ​​(such as Python or R), the coefficient values ​​corresponding to changes in different land use types can be efficiently extracted and calculated, ensuring the efficiency and accuracy of data processing. The carbon density values ​​are combined with the coefficient values ​​to form a land use carbon parameter set. This process typically involves data integration and merging techniques. By correlating carbon density parameters and carbon source-sink coefficients from different data sources, and using data frameworks (such as Pandas) for integration, a comprehensive dataset containing land use types, carbon density values, and their corresponding coefficients is ultimately generated. This dataset provides a foundation for subsequent carbon dynamic monitoring and ecological assessment, and can reveal the characteristics of the carbon cycle and its environmental impacts under different land use patterns.

[0048] As an example of the present invention, reference is made to... Figure 2 As shown, step S4 in this example includes:

[0049] Step S41: For the three carbon pools of aboveground vegetation, root system, and animals, generate area-based carbon source and sink calculation data based on the area of ​​each patch and the corresponding carbon density value;

[0050] Step S42: Sum the area-based carbon source and sink calculation data to obtain the area-based carbon storage change;

[0051] Step S43: For the soil carbon pool, generate volumetric carbon source and sink calculation data based on the soil volume and corresponding carbon density value of each patch;

[0052] Step S44: Sum the volumetric carbon source and sink calculation data to obtain the volumetric carbon storage change;

[0053] Step S45: Store the area-based carbon storage changes and volume-based carbon storage changes into a unified carbon storage change calculation dataset.

[0054] In embodiments of this invention, for three carbon pools—above-ground vegetation, roots, and animals—area-based carbon source and sink calculation data are generated based on the area of ​​each patch and its corresponding carbon density value. This process typically relies on a previously established carbon density parameter table; by multiplying the area of ​​each patch by its corresponding carbon density value, the carbon storage of each carbon pool can be calculated. Geographic Information Systems (GIS) and spatial analysis tools can efficiently extract patch area data and perform corresponding carbon storage calculations. The area-based carbon source and sink calculation data are summed to obtain the area-based carbon storage change. This operation typically employs data aggregation and statistical analysis methods, such as using data processing software for aggregation calculations, to ensure that the carbon storage of different carbon pools accurately reflects the overall changes in each patch. By summing the carbon storage of each patch, the total area-based carbon storage change is finally obtained. For the soil carbon pool, volume-based carbon source and sink calculation data are generated based on the soil volume of each patch and its corresponding carbon density value. This process also relies on GIS technology. First, soil volume data for each map patch is extracted, typically through soil profile analysis or geophysical exploration. This data is then multiplied by the corresponding carbon density value to calculate the carbon storage of the soil carbon pool. The volumetric carbon source and sink calculation data are then summed to obtain the volumetric carbon storage change. This process also employs data aggregation methods to ensure that the carbon storage of all soil carbon pools is effectively integrated and reflects the overall change. Area-based and volumetric carbon storage changes are then uniformly stored in the carbon storage change calculation dataset. This integration process involves data framework management and storage, typically using structured databases or tables to integrate different types of carbon storage change data and ensure data traceability and accessibility for subsequent analysis and decision support.

[0055] Preferably, the change in area-based carbon storage is calculated using the following formula:

[0056]

[0057] Where, ΔC a A represents the change in carbon storage by area. i,j d represents the area of ​​the j-th land use category in the i-th map patch. j,k r represents the carbon density of the k-th carbon library under this type. u,v This represents the carbon source-sink coefficient when the land use type changes from u to v.

[0058] In this embodiment of the invention, a spatial analysis-based mathematical formula is used to calculate area-based carbon storage changes. This formula comprehensively considers the area, carbon density, and changes of each patch. A preliminary estimate of carbon storage is generated by multiplying the area of ​​each patch by its corresponding carbon density. The Σ symbol in the formula represents the summation of all relevant patches, thereby calculating the overall carbon storage. This process typically relies on Geographic Information Systems (GIS) and remote sensing technology to accurately acquire spatial information and attribute data of the patches. At the data level, the geographic coordinates of each patch are first standardized to ensure effective analysis within the same coordinate system. Next, carbon density values ​​for different land use types are extracted using image processing techniques, and this data is stored and managed through a database management system. Furthermore, the conversion coefficient involved in the formula represents the mutual conversion relationship between different land use types. This coefficient is typically established through historical data analysis and statistical models to ensure its applicability under different environmental conditions. Through this scientific model construction and data analysis, the impact of land use change on carbon storage can be effectively assessed, thereby providing data support for ecosystem management and protection.

[0059] Preferably, the soil volume and corresponding carbon density are calculated using the following formula:

[0060]

[0061] Where, d soil Soil carbon density (SOC) l Indicates the organic carbon content of the l-th soil layer, BD l H represents the bulk density of the l-th soil layer. l denoted by l, where represents the thickness of the l-th soil layer, and p represents the total number of soil layers.

[0062] In this embodiment of the invention, multiple soil sampling points are set up within the study area, and standardized soil collection and analysis methods are used to measure the soil carbon content at each sampling point, thereby obtaining basic SOC data. To ensure data accuracy, multiple repeated measurements are usually required, and the average value is calculated to eliminate random errors. Next, BD (soil dry density) and H (sample layer thickness) in the formula represent the physical properties of the soil, respectively. BD can be obtained by weighing or volumetric methods, while H is determined through soil profile analysis. At the data level, the carbon density, dry density, and layer thickness data of each soil sampling point must first be standardized to facilitate subsequent analysis and calculation. By combining the soil carbon content with the corresponding BD value and sample layer thickness, carbon storage estimates for each sampling point can be generated. Furthermore, a database management system is used to integrate these data to form a systematic soil carbon density dataset, facilitating further spatial analysis and statistics. Additionally, the Σ symbol in the formula represents summation over all sampling points; this process typically relies on the collaborative application of Geographic Information Systems (GIS) and statistical analysis software to achieve efficient data processing and visualization. By summarizing all relevant sampling points, the total soil carbon storage within the study area can be obtained. This calculation not only reveals the spatial distribution characteristics of soil carbon storage but also provides important data support for subsequent carbon cycle research and ecological management.

[0063] Preferably, the soil volume and corresponding carbon density are calculated using the following formula:

[0064]

[0065] Where, ΔC v V represents the change in carbon storage by volume. i,j d represents the soil volume of the j-th land use type in the i-th patch. soil This represents the corresponding soil carbon density, r. u,v This represents the carbon source-sink coefficient when the land use type changes from u to v.

[0066] In embodiments of this invention, by integrating soil volume and carbon density data for each patch, changes in soil carbon storage can be effectively assessed. Soil volume data acquisition typically relies on Geographic Information System (GIS) technology. Detailed spatial sampling analysis of soil profiles, combined with soil cubic volume measurement methods, allows for accurate acquisition of soil volume information for each patch. Simultaneously, carbon density data originates from field measurements and laboratory analysis of soil samples. Standardized soil sampling techniques are typically employed to obtain soil samples at different depths, followed by chemical analysis to determine their carbon content, thus forming a corresponding carbon density dataset. All collected soil volume, carbon density, and land use type conversion coefficients are standardized to ensure data consistency and comparability. This data is then integrated through a database management system to form a systematic dataset of soil carbon storage changes, facilitating subsequent analysis and calculation. The Σ symbol in the formula represents the summation over all relevant patches; this process typically relies on the spatial analysis functions of GIS software to achieve efficient data processing and visualization. Finally, by multiplying the soil volume of each patch by its corresponding carbon density value and summing the results with a conversion coefficient, the change in soil volumetric carbon storage across the entire study area can be obtained. This calculation not only reveals the spatial distribution characteristics of soil carbon storage but also provides important data support for subsequent carbon cycle research and ecosystem management.

[0067] Preferably, step S5 includes:

[0068] Step S51: Sum the changes in area-based carbon storage and the changes in volume-based carbon storage to obtain the total change in carbon storage.

[0069] Step S52: Determine the sign of the change in total carbon reserves and record the attribute identifier of the carbon source or carbon sink;

[0070] Step S53: Generate a dataset of calculation results containing changes in total carbon storage and attribute identifiers.

[0071] In embodiments of this invention, the changes in area-based carbon storage and volume-based carbon storage are summed to obtain the total change in carbon storage. This process relies on previously calculated carbon storage data of various types, typically employing data aggregation and statistical analysis methods. Using a database management system, the two types of carbon storage data are integrated, and the total change in carbon storage is calculated using simple mathematical operations (such as addition). This process must ensure data accuracy and consistency, usually requiring data verification to eliminate potential errors. The sign of the total change in carbon storage is determined; this step mainly involves logical analysis of the total change in carbon storage value to determine whether the value is positive, negative, or zero. Positive values ​​typically indicate a carbon sink attribute, suggesting that the ecosystem absorbed more carbon within a specific time period; negative values ​​indicate a carbon source attribute, meaning that the ecosystem released carbon. This process can be implemented programmatically, using conditional statements (such as IF-THEN structures) to classify the change and generate corresponding attribute identifiers. A dataset containing the total change in carbon storage and attribute identifiers is generated. This process typically employs data structuring software (such as Pandas or data frames in R) to ensure that the generated dataset clearly displays all indicators. The resulting dataset includes not only changes in total carbon storage but also corresponding attribute labels, facilitating subsequent analysis and visualization. Simultaneously, this dataset can be stored in a database, ensuring data traceability and usability to support subsequent ecological assessments and management decisions.

[0072] Preferably, the summation of area-based carbon storage changes and volume-based carbon storage changes in step S51 includes:

[0073] The change in total carbon reserves is calculated using the following formula:

[0074] ΔC t =ΔC a +ΔC v ;

[0075] ΔC t ΔC represents the change in total carbon reserves. a ΔC represents the change in carbon storage by area. v This represents the change in carbon reserves by volume.

[0076] In the embodiments of the present invention, ΔC in the formula t This represents the change in total carbon storage, and its calculation depends on the area-based change in carbon storage (ΔC). a ) and volumetric carbon storage changes (ΔC v The sum of these two types of carbon storage changes. At the data level, it is first necessary to ensure the accuracy and comparability of these two types of carbon storage change data. To this end, standardized data processing methods are usually adopted to unify the units for various types of carbon storage, in order to avoid calculation errors caused by inconsistent units. Area-based carbon storage change (ΔC)a ) and volumetric carbon storage changes (ΔC v All of these data are obtained through previous steps and are typically stored in a database for easy retrieval and access. Data management systems (such as SQL databases or data framework software) efficiently process and store this information, ensuring data integrity and accessibility. When performing addition operations, data processing libraries in programming languages ​​(such as Python or R) are usually used. Simple addition operators are employed to summarize the changes in the two types of carbon reserves, thereby calculating the total change in carbon reserves. Furthermore, ensuring the accuracy and consistency of the data is crucial. Therefore, data validation and preprocessing are usually performed before calculation, such as removing outliers and handling missing data, to improve the reliability of the final result. Through these methods, the final generated ΔC... t This not only accurately reflects changes in the carbon storage of an ecosystem over a specific time period, but also provides a solid data foundation for subsequent analysis and decision-making. Finally, the calculation results are typically stored in a new dataset or table to facilitate subsequent visualization analysis and report generation. This dataset will include the total change in carbon storage and its related attribute information for further research and application.

[0077] In one implementation of the present invention, such as Figure 3 As shown, the carbon storage changes for six land use types are illustrated. Green represents carbon sinks (positive values), and orange represents carbon sources (negative values).

[0078] The calculation is based on the calculation method in step S4.

[0079] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0080] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A rapid calculation method for ecosystem carbon sources and sinks at the mining area scale, characterized in that, Includes the following steps: Step S1: Acquire remote sensing image data, ground measurement data, and mining activity records of the mining area to construct a multi-source mining area basic dataset; Spatial registration and temporal alignment are performed on the basic datasets of multi-source mining areas to obtain aligned multi-source fusion datasets. Step S2: Identify mining-occupied patches based on the aligned multi-source fusion dataset, extract land use type information of the patches to form a land use type dataset, and generate a carbon pool classification dataset by combining the definition of carbon pool type in the mining area; Step S3: Based on the land use type dataset and the carbon pool classification dataset, call the carbon density parameter table and the land use type conversion carbon source-sink coefficient matrix to generate the land use carbon parameter dataset; Step S4: Based on the land use carbon parameter dataset, calculate the area-based carbon source and sink input data and the volume-based carbon source and sink input data respectively to form the initial calculation dataset for carbon storage change; Step S4 includes: Step S41: For the three carbon pools of aboveground vegetation, root system, and animals, generate area-based carbon source and sink calculation data based on the area of ​​each patch and the corresponding carbon density value; Step S42: Sum the area-based carbon source and sink calculation data to obtain the area-based carbon storage change; Step S43: For the soil carbon pool, generate volumetric carbon source and sink calculation data based on the soil volume and corresponding carbon density value of each patch; Step S44: Sum the volumetric carbon source and sink calculation data to obtain the volumetric carbon storage change; Step S45: Store both area-based and volume-based carbon storage changes in the carbon storage change calculation dataset; volume-based carbon storage changes are calculated using the following formula: ； in, This represents the change in carbon storage by volume. Indicates the first The first of the map spots Soil volume of different land uses This indicates the corresponding soil carbon density. Indicates land use type by Transform into Carbon source-sink coefficient at that time; Step S5: Summarize and merge the initial calculation dataset of carbon storage changes to obtain the total carbon source and sink measurement results of the mining area ecosystem; Step S5 includes: Step S51: Sum the changes in area-based carbon storage and the changes in volume-based carbon storage to obtain the total change in carbon storage. Step S52: Determine the sign of the change in total carbon reserves and record the attribute identifier of the carbon source or carbon sink; Step S53: Generate a dataset of calculation results containing changes in total carbon storage and attribute identifiers.

2. The rapid calculation method for ecosystem carbon sources and sinks at the mining area scale as described in claim 1, characterized in that, Step S1 includes: Step S11: Acquire high-resolution remote sensing image data and extract image band information reflecting vegetation and surface characteristics; Step S12: Obtain ground quadrat measured data, including measured carbon density values ​​for different carbon pools; Step S13: Collect records of mining activities in the mining area, including mining scope, project type, and time information; Step S14: Unify the coordinate reference of remote sensing image data, ground quadrat measured data and mining activity records in the mining area; Step S15: Perform timestamp matching on the data from the unified benchmark to form a multi-source fusion dataset.

3. The rapid calculation method for ecosystem carbon sources and sinks at the mining area scale as described in claim 1, characterized in that, Step S2 includes: Step S21: Identify the mining patches in the fused dataset and generate a unique number for each patch; Step S22: Extract the land use type of each patch, including undisturbed land, mine pit, spoil heap, reclaimed forest land, reclaimed grassland and industrial square; Step S23: Establish a correspondence between each land use type and the four carbon pools to generate a carbon pool classification dataset.

4. The rapid calculation method for ecosystem carbon sources and sinks at the mining area scale as described in claim 1, characterized in that, Step S3 includes: Step S31: Match the corresponding carbon density value for each land use type to form a carbon density parameter table; Step S32: Call the land use type conversion carbon source-sink coefficient matrix to obtain the coefficient values ​​corresponding to the changes in different land use types; Step S33: Combine the carbon density value with the coefficient value to form a set of land use carbon parameters.

5. The rapid calculation method for ecosystem carbon sources and sinks at the mining area scale as described in claim 1, characterized in that, Changes in carbon storage by area are calculated using the following formula: ； in, This represents the change in carbon storage by area. Indicates the first The first of the map spots Area of ​​land use type Indicates the first of this type Carbon density of carbon-like libraries Indicates land use type by Transform into The carbon source-sink coefficient at that time.

6. The rapid calculation method for ecosystem carbon sources and sinks at the mining area scale as described in claim 1, characterized in that, Soil volume and corresponding carbon density are calculated using the following formula: ； in, Indicates soil carbon density, Indicates the first Soil organic carbon content in the upper layer Indicates the first Soil bulk density, Indicates the first Soil layer thickness, This indicates the total number of soil layers.

7. The rapid calculation method for ecosystem carbon sources and sinks at the mining area scale as described in claim 1, characterized in that, Step S51, which involves summing the changes in area-based carbon storage with the changes in volume-based carbon storage, includes: The change in total carbon reserves is calculated using the following formula: ； This indicates the change in total carbon reserves. This represents the change in carbon storage by area. This represents the change in carbon reserves by volume.

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