System and method for measuring carbon sequestration of forest land along highway

Abstract

The invention relates to the technical field of carbon sink metering, and particularly discloses a carbon sink metering system and method for forest land along an expressway, and the method comprises the steps: 1, collecting remote sensing image data of the forest land along the expressway; 2, carrying out image recognition on the collected remote sensing image data to obtain a vegetation type and a growth condition of the forest land; 3, based on the intercommunication hub area of the expressway, carrying out regional division on the forest land along the expressway to obtain a plurality of project hub areas; 4, calculating regional carbon reserves of each project hub region; 5, identifying key stable vegetation types which are slightly influenced and key fluctuating vegetation types which are greatly influenced; and 6, based on the key stable vegetation type and the key fluctuating vegetation type, correcting the carbon content of the key fluctuating vegetation type in the regional carbon reserve calculation process so as to correct the regional carbon reserve.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon sink measurement, and in particular to a carbon sink measurement system and method for forest land along a highway. Background Art

[0002] With the rapid development of highway construction, its impact on the ecological environment along the line has attracted widespread attention. In terms of assessing forest carbon sinks, although some existing technologies have realized the importance of the dynamic factor of highway operation and tried to take it into consideration, most methods still fail to comprehensively and deeply quantify the specific impact of highway operation on forest carbon sinks along the line. In particular, these methods often lack a detailed analysis of the complex environmental changes and vegetation response mechanisms generated during highway operation, resulting in deviations in the assessment results and failure to accurately reflect the actual situation. Summary of the invention

[0003] The purpose of the present invention is to provide a system and method for measuring carbon sinks in forest land along a highway to solve the technical problems in the above background.

[0004] The purpose of the present invention can be achieved by the following technical solutions: In a first aspect, the present invention provides a method for measuring carbon sinks in forest land along a highway, comprising: Step 1: Collect remote sensing image data of forest land along the highway; Step 2: Perform image recognition on the collected remote sensing image data to obtain the vegetation type and growth status of the forest land; Step 3: Based on the interchange hub area of ​​the expressway, divide the forest land along the expressway into regions to obtain multiple project hub areas; Step 4: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, calculate the regional carbon storage of each project hub area; Step 5: Assess the impact of highway operation on carbon storage in forest land along the highway, and identify key stable vegetation types that are less affected and key fluctuating vegetation types that are more affected, specifically: Based on the historical records of regional carbon storage in the project hub area, assess whether vehicles have an impact on regional carbon storage during highway operation, and generate relevant signals based on the assessment results; Based on the relevant signals, in-depth analysis is conducted to identify key stable vegetation types that are less affected by highway operations and key fluctuating vegetation types that are more affected; Step 6: Based on the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the regional carbon stock calculation process is corrected, thereby correcting the regional carbon stock.

[0005] In a second aspect, the present invention provides a carbon sink measurement system for forest land along a highway, the system comprising: Data acquisition module: collects remote sensing image data of forest land along the highway; Image recognition module: Perform image recognition on the collected remote sensing image data to obtain the vegetation type and growth status of the forest land; Hierarchical processing module: Based on the interchange hub area of ​​the highway, the forest land along the highway is divided into regions to obtain multiple project hub areas; Carbon storage calculation module: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, the regional carbon storage of each project hub area is calculated; Data analysis module: Evaluate the impact of highway operation on carbon storage in forest land along the highway, and identify key stable vegetation types with less impact and key fluctuating vegetation types with greater impact, specifically: Correlation analysis submodule: Based on the historical records of regional carbon storage in the project hub area, evaluate whether vehicles have an impact on regional carbon storage during highway operation, and generate correlation signals based on the evaluation results; Key vegetation type analysis submodule: Based on the relevant signals, in-depth analysis is performed to determine the key stable vegetation types that are less affected by highway operations and the key fluctuating vegetation types that are more affected; Correction module: Based on the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the regional carbon storage calculation process is corrected, thereby correcting the regional carbon storage.

[0006] Beneficial effects of the present invention: The data acquisition module is used to obtain remote sensing image data of the forest land, and then the image recognition technology is used to determine the vegetation type and growth status; based on the highway interchange hub area, the forest land along the line is divided into multiple project hub areas; in each project hub area, the carbon storage calculation module is used to accurately calculate the regional carbon storage by combining the biomass model, soil carbon pool model and vegetation carbon content; including calculating the vegetation carbon storage according to the vegetation type and growth parameters, and calculating the soil carbon storage by analyzing the soil organic matter content, soil bulk density and soil layer depth; further, the method evaluates the impact of highway operation on the carbon storage of the forest land along the line; by extracting the regional carbon storage data and operation data in the historical period, calculating the correlation coefficient, and analyzing the highway operation The synchronization of carbon storage changes in highway operation and forest land along the highway is analyzed; on this basis, the key stable vegetation types that are less affected by highway operation and the key fluctuating vegetation types that are more affected are identified; finally, according to the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the process of regional carbon storage calculation is corrected to improve the accuracy of regional carbon storage calculation; the present invention realizes the accurate measurement and dynamic evaluation of carbon storage in forest land along the highway by comprehensively using remote sensing technology, image recognition technology, biogeochemical models and other means; it not only provides a scientific basis for ecological environmental protection in highway construction and operation, but also helps to promote the green and sustainable development of transportation infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present invention will be further described below in conjunction with the accompanying drawings.

[0008] Figure 1 is a flowchart of Embodiment 1 of the present invention; Figure 2 It is a system block diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1:

[0010] See also Figure 1 As shown, a method for measuring carbon sinks in forest land along a highway according to an embodiment of the present invention includes: Step 1: Collect remote sensing image data of forests along the highway, so as to obtain image data of forests along the highway efficiently and on a large scale, and provide basic data support for subsequent analysis; Step 2: Perform image recognition on the collected remote sensing image data to obtain the vegetation type and growth status (such as breast diameter and tree height) of the forest land, providing accurate vegetation information for the calculation of regional carbon storage; Step 3: Based on the interchange hub areas of the highway, divide the forest land along the highway into regions to obtain multiple project hub areas, so as to facilitate the calculation and analysis of carbon storage in each area; Step 4: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, the regional carbon storage of each project hub area is calculated. Combined with the biomass model and the soil carbon pool model, the regional carbon storage of each project hub area can be accurately calculated, providing reliable data support for evaluating the impact of highway operations on the carbon storage of forest land along the route. The preset carbon content of each vegetation type makes the calculation of carbon storage more accurate and targeted. For example, the process of obtaining the regional carbon storage of each project hub area is as follows: Based on any project hub area, remote sensing technology and ground surveys were used to identify the main vegetation types as pine trees and shrubs. Further growth information such as the diameter at breast height and tree height of pine trees, as well as ground diameter, height and number of branches of shrubs were collected. The calculation process of pine carbon storage is: Select a biomass model suitable for pine trees, such as a biomass model based on DBH and tree height: ,in, is the pine biomass, is the breast diameter of the pine tree, is the height of the pine tree; It should be explained that the diameter at breast height of a pine tree is the diameter 1.3 meters above the ground; the height of a pine tree is the vertical distance from the ground to the top of the crown; The diameter at breast height and tree height of pine trees were input into the pine biomass model to calculate the pine biomass; The carbon content of pine trees is assumed to be 0.5 (i.e. half of the pine tree biomass is carbon), and the pine tree biomass is multiplied by the carbon content of the pine tree, thereby converting the pine tree biomass into pine tree carbon storage to obtain the pine tree carbon storage; The calculation process of shrub carbon storage is: Select a biomass model that is appropriate for the shrubland, such as a biomass model based on ground diameter, height, and number of branches: ,in, is the biomass of the shrubs, The diameter of the shrub. is the plant height of the shrub, and B is the number of branches of the shrub; It should be explained that the ground diameter of a shrub is the diameter 10 cm above the ground; the plant height of a shrub is the vertical distance from the ground to the top of the crown; The ground diameter, height and number of branches of the bush were input into the bush biomass model to calculate the bush biomass; Similarly, the carbon content of the bush is preset to be 0.5, and the bush biomass is multiplied by the bush carbon content, so as to convert the bush biomass into bush carbon storage and obtain bush carbon storage; The calculation process of soil carbon storage is: Collect soil samples, analyze soil organic matter content, soil bulk density, soil layer depth and soil area, and input soil organic matter content, soil bulk density, soil layer depth and soil area into the soil carbon pool model to calculate soil carbon storage; The soil carbon pool model is: , where C is soil carbon storage, TOC is soil organic matter content, BD is soil bulk density, DS is soil depth, and A is the soil area of ​​the project hub area; Then add up the carbon stocks of all the pines and shrubs in the project hub area, and add the soil carbon stock to get the regional carbon stock in the project hub area; Step 5: Evaluate the impact of highway operation on the carbon storage of forest land along the route, and identify the key stable vegetation types that are less affected and the key fluctuating vegetation types that are more affected. By evaluating the impact of highway operation on the carbon storage of forest land along the route, we can gain an in-depth understanding of the potential impact of highway operation on the ecological environment, and identify the key stable vegetation types and key fluctuating vegetation types, which will help to revise the regional carbon storage in the future and improve the accuracy of the calculation. In some implementation schemes, based on the historical records of regional carbon storage in the project hub area, it is assessed whether vehicles have an impact on regional carbon storage during highway operation, and a relevant signal is generated based on the assessment results. The specific process is as follows: S1: Extract the historical records of regional carbon storage in the project hub area during the historical period, which provides basic data for subsequent analysis, ensuring that accurate historical data is available to support the assessment of the impact of highway operation on carbon storage in forest land along the route, thus enhancing the reliability and accuracy of the analysis; Among them, the historical period can be 1 year, 5 years, or 10 years; The historical records of regional carbon storage include: regional carbon storage at different collection time points and the corresponding collection time during the historical period; Mark the time period between two adjacent collection times in the historical period as the collection interval period; S2: Extract the operation data of the project hub area in each collection interval period, and calculate the carbon emission coefficient in each collection interval period. Combine the operation data of the highway with the carbon storage data, which provides key indicators for the subsequent analysis of the impact of highway operation on carbon storage, and helps to deeply understand the interaction between operation activities and the ecological environment; Among them, operational data include: the number and type of vehicles traveling within the project hub area; The calculation process of the carbon emission coefficient is as follows: the number of vehicles of the same vehicle type is multiplied by the corresponding carbon emission factor to obtain the carbon emission amount, and then the carbon emissions of all vehicle types are summed up to obtain the carbon emission coefficient; It should be explained that the carbon emission factor is an indicator used to estimate the amount of greenhouse gases emitted in a specific activity or process. For vehicle emissions, it is necessary to collect carbon emission factors of different models and different fuel types (such as gasoline, diesel, natural gas, etc.). Carbon emission factors can be obtained from authoritative organizations or databases, such as the International Energy Agency and environmental protection departments; It should be explained that different types of vehicles contribute to different degrees of carbon emissions. Generally speaking, heavy trucks have higher carbon emissions, while passenger cars have relatively lower carbon emissions. S3: Analyze the changing trends of regional carbon storage and carbon emission coefficients at each sampling time point in the historical period, obtain the correlation coefficient, and reveal the changing trend relationship between regional carbon storage and carbon emission coefficient through quantitative analysis, which provides a scientific basis for the subsequent evaluation of the impact of highway operation on carbon storage. The specific process is as follows: By formula: , calculate the correlation coefficient R, where is the regional carbon storage at the i-th collection time point in the historical period, i takes values ​​of 1, 2, 3, ..., n, and n is the total number of collection time points in the historical period. is the average carbon storage of the region at all sampling time points during the historical period, is the carbon emission coefficient at the i-th collection time point in the historical period, It is the average value of carbon emission coefficients at all collection time points in the historical period; S4: Based on the correlation coefficient, evaluate whether vehicles have an impact on regional carbon storage during highway operation, and generate relevant signals based on the evaluation results. By comparing the correlation coefficient with the correlation coefficient threshold, it is clear whether highway operation has an impact on carbon storage, which provides directional guidance for subsequent in-depth analysis of affected vegetation types. The specific process is as follows: Preset a correlation coefficient threshold, and compare and analyze the correlation coefficient with the correlation coefficient threshold; If the correlation coefficient is less than or equal to the correlation coefficient threshold, it means that the synchronization of the change trends between the regional carbon storage and carbon emission coefficient is high, that is, a correlation signal is generated; If the correlation coefficient is greater than the correlation coefficient threshold, it means that the synchronization of the change trends between the regional carbon storage and carbon emission coefficient is low, that is, an uncorrelated signal is generated; Based on the correlation signal, that is, confirming that there is a high degree of synchronization between the operation of the expressway and the trend of carbon storage changes in the forestland along the expressway, in-depth analysis is conducted to determine the key stable vegetation types that are less affected by the operation of the expressway and the key fluctuating vegetation types that are more affected. The specific process is as follows: SS1: Make a more detailed classification of the identified vegetation types (e.g. pine trees, shrubs) in all project hub areas, such as considering factors such as tree species, tree age, and growth environment, so as to more accurately assess the carbon sink stability of each type of vegetation; SS2: Extract the regional carbon storage and carbon emission coefficient at each collection time point in all project hub areas, and calculate the regional carbon storage change and carbon emission coefficient change at each collection interval; Among them, the change in regional carbon stock is the difference between the regional carbon stock at the collection time point at the end of the collection interval and the regional carbon stock at the collection time point at the beginning of the collection interval; the change in carbon emission coefficient is the difference between the carbon emission coefficient at the collection time point at the end of the collection interval and the carbon emission coefficient at the collection time point at the beginning of the collection interval; The response coefficient is obtained by calculating the ratio of the change in regional carbon storage to the change in carbon emission coefficient during the corresponding sampling interval. The response coefficients of each collection interval in all project hub areas are sorted in ascending order of numerical value to obtain a response coefficient sequence, and the first quantile value in the response coefficient sequence is extracted to obtain a response coefficient stability threshold, and the third quantile value in the response coefficient sequence is extracted to obtain a response coefficient fluctuation threshold; Extract the vegetation type of the project hub area corresponding to the response coefficient being less than or equal to the response coefficient stability threshold, and mark it as the pre-key stable vegetation type; Extract the vegetation type of the project hub area corresponding to when the response coefficient is greater than the response coefficient fluctuation threshold, and mark it as the pre-key fluctuation vegetation type; SS3: Based on any pre-focused stable vegetation type, extract the response coefficient of the vegetation type that meets the pre-focused stable vegetation type in each sampling interval in all project hub areas, and mark it as the response stability verification coefficient; comparing the response stability verification coefficient to a response coefficient stability threshold; If the response stability verification coefficient is greater than the response coefficient stability threshold, the response stability verification coefficient is marked as a verification stability abnormal coefficient; Calculate the difference between all verification stability anomaly coefficients and the response coefficient stability threshold, and sum them up to obtain the response stability anomaly degree value; Extract the total number of verification stability anomaly coefficients, multiply the response stability anomaly degree value by the total number of verification stability anomaly coefficients to obtain the verification stability anomaly characterization value; Preset a verification stable abnormal characterization threshold, and compare and analyze the verification stable abnormal characterization value with the verification stable abnormal characterization threshold; If the verification stability anomaly characterization value is less than or equal to the verification stability anomaly characterization threshold, it means that the carbon sink of the pre-key stable vegetation type is relatively stable and is less affected by the operation of the expressway, that is, the pre-key stable vegetation type is marked as a key stable vegetation type; SS4: Based on any pre-focused fluctuation vegetation type, extract the response coefficient of the vegetation type that meets the pre-focused fluctuation vegetation type in each sampling interval period in all project hub areas, and mark it as the response fluctuation verification coefficient; comparing the response fluctuation verification coefficient with the response coefficient fluctuation threshold; If the response fluctuation verification coefficient is greater than the response coefficient fluctuation threshold, the response fluctuation verification coefficient is marked as a verification fluctuation abnormal coefficient; Calculate the difference between all verification fluctuation anomaly coefficients and the response coefficient fluctuation threshold respectively, and sum them up to obtain the response fluctuation anomaly degree value; Extract the total number of verification fluctuation anomaly coefficients, multiply the response fluctuation anomaly value by the total number of verification fluctuation anomaly coefficients to obtain the verification fluctuation anomaly characterization value; Preset a verification fluctuation abnormality characterization threshold, and compare and analyze the verification fluctuation abnormality characterization value with the verification fluctuation abnormality characterization threshold; If the verification fluctuation anomaly characterization value is less than or equal to the verification fluctuation anomaly characterization threshold, it means that the carbon sink of the pre-key fluctuation vegetation type is relatively fluctuating and is less affected by the operation of the highway, that is, the pre-key fluctuation vegetation type is marked as the key fluctuation vegetation type; Step 6: Based on the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the process of regional carbon storage calculation is corrected, so as to correct the regional carbon storage. Correcting the carbon content of the key fluctuating vegetation types can more accurately reflect the actual impact of highway operation on the carbon storage of forest land along the line. By correcting the regional carbon storage, the accuracy and reliability of carbon sink measurement can be improved, providing a scientific basis for ecological and environmental protection in highway construction and operation. In some implementation plans, the process of correcting the carbon content of key fluctuating vegetation types in the regional carbon stock calculation process is as follows: Extract the regional carbon storage changes of all key stable vegetation types and calculate the mean to obtain the stable change mean; Extract the regional carbon storage changes of all key fluctuating vegetation types and calculate the mean to obtain the mean of fluctuation changes; By formula: , calculate and obtain the carbon content correction value CO, where ST is the stable change mean, CJ is the fluctuating change mean, and CA is the carbon content; The technical solution of the embodiment of the present invention is mainly as follows: using a data acquisition module to obtain remote sensing image data of forest land, and then determining the vegetation type and growth status through image recognition technology; based on the highway interchange hub area, dividing the forest land along the line into multiple project hub areas; in each project hub area, combining the biomass model, soil carbon pool model and vegetation carbon content, using the carbon storage calculation module to accurately calculate the regional carbon storage; including calculating the vegetation carbon storage according to the vegetation type and growth parameters, and calculating the soil carbon storage by analyzing the soil organic matter content, soil bulk density and soil layer depth; further, the method evaluates the impact of highway operation on the carbon storage of forest land along the line; by extracting the regional carbon storage data and operation data in the historical period, the correlation coefficient is calculated. , analyze the synchronization between highway operation and the changing trend of carbon storage in forest land along the highway; on this basis, identify the key stable vegetation types that are less affected by highway operation and the key fluctuating vegetation types that are more affected; finally, according to the key stable vegetation types and the key fluctuating vegetation types, correct the carbon content of the key fluctuating vegetation types in the process of regional carbon storage calculation to improve the accuracy of regional carbon storage calculation; the present invention realizes the accurate measurement and dynamic evaluation of carbon storage in forest land along highways by comprehensively using remote sensing technology, image recognition technology and biogeochemical models; it not only provides a scientific basis for ecological environmental protection in highway construction and operation, but also helps to promote the green and sustainable development of transportation infrastructure. Embodiment 2:

[0011] Based on Example 1, please refer to Figure 2 As shown, a carbon sink measurement system for forest land along a highway according to an embodiment of the present invention includes: Data acquisition module: collects remote sensing image data of forest land along the highway; Image recognition module: Perform image recognition on the collected remote sensing image data to obtain the vegetation type and growth status of the forest land; Hierarchical processing module: Based on the interchange hub area of ​​the highway, the forest land along the highway is divided into regions to obtain multiple project hub areas; Carbon storage calculation module: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, the regional carbon storage of each project hub area is calculated; Data analysis module: Evaluate the impact of highway operation on carbon storage in forest land along the highway, and identify key stable vegetation types with less impact and key fluctuating vegetation types with greater impact, specifically: Correlation analysis submodule: Based on the historical records of regional carbon storage in the project hub area, evaluate whether vehicles have an impact on regional carbon storage during highway operation, and generate correlation signals based on the evaluation results; Key vegetation type analysis submodule: Based on the relevant signals, in-depth analysis is performed to determine the key stable vegetation types that are less affected by highway operations and the key fluctuating vegetation types that are more affected; Correction module: Based on the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the regional carbon storage calculation process is corrected, thereby correcting the regional carbon storage.

[0012] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for measuring carbon sinks in forest land along a highway, characterized in that: include: Step 1: Collect remote sensing image data of forest land along the highway; Step 2: Perform image recognition on the collected remote sensing image data to obtain the vegetation type and growth status of the forest land; Step 3: Based on the interchange hub area of ​​the expressway, divide the forest land along the expressway into regions to obtain multiple project hub areas; Step 4: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, calculate the regional carbon storage of each project hub area; Step 5: Assess the impact of highway operation on carbon storage in forest land along the highway, and identify key stable vegetation types with less impact and key fluctuating vegetation types with greater impact, specifically: Based on the historical records of regional carbon storage in the project hub area, assess whether vehicles have an impact on regional carbon storage during highway operation, and generate relevant signals based on the assessment results; Based on the relevant signals, in-depth analysis is conducted to identify key stable vegetation types that are less affected by highway operations and key fluctuating vegetation types that are more affected; Step 6: Based on the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the regional carbon stock calculation process is corrected, thereby correcting the regional carbon stock.

2. The method for measuring carbon sinks in forest land along highways according to claim 1, characterized in that: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, the process of calculating the regional carbon storage of each project hub area is as follows: Based on any project hub area, identify the main vegetation types through remote sensing technology and ground surveys, and further collect growth status information of the main vegetation types; Select biomass models suitable for various main vegetation types respectively, and input the growth status information of the corresponding vegetation types into the biomass model to obtain the biomass of the main vegetation types; The carbon content of each major vegetation type is preset respectively, and the biomass of the major vegetation type is multiplied by its carbon content to obtain the carbon storage of the major vegetation type; Collect soil samples, analyze soil organic matter content, soil bulk density, soil layer depth and soil area, and input soil organic matter content, soil bulk density, soil layer depth and soil area into the soil carbon pool model to calculate soil carbon storage; The soil carbon pool model is: , where C is soil carbon storage, TOC is soil organic matter content, BD is soil bulk density, DS is soil depth, and A is the soil area of ​​the project hub area; Then add up the carbon stocks of all major vegetation types in the project hub area, and add the soil carbon stock to obtain the regional carbon stock in the project hub area.

3. The method for measuring carbon sinks in forest land along highways according to claim 1, characterized in that: Based on the historical records of regional carbon storage in the project hub area, the process of evaluating whether vehicles have an impact on regional carbon storage during highway operation and generating relevant signals based on the evaluation results is as follows: S1: Extract the historical records of regional carbon storage in the project hub area during the historical period; The historical records of regional carbon storage include: regional carbon storage at different collection time points and the corresponding collection time during the historical period; Mark the time period between two adjacent collection times in the historical period as the collection interval period; S2: Extract the operation data of the project hub area in each collection interval period and calculate the carbon emission coefficient in each collection interval period; S3: Analyze the changing trends of regional carbon storage and carbon emission coefficients at each sampling time point in the historical period to obtain the correlation coefficient; S4: Based on the correlation coefficient, evaluate whether vehicles have an impact on regional carbon storage during highway operation, and generate relevant signals based on the evaluation results. The specific process is as follows: Preset a correlation coefficient threshold, and compare and analyze the correlation coefficient with the correlation coefficient threshold; If the correlation coefficient is less than or equal to the correlation coefficient threshold, a correlation signal is generated; If the correlation coefficient is greater than the correlation coefficient threshold, a no correlation signal is generated.

4. A method for measuring carbon sinks in forest land along highways according to claim 3, characterized in that: The process of obtaining the correlation coefficient is: By formula: , calculate the correlation coefficient R, where is the regional carbon storage at the i-th collection time point in the historical period, i takes values ​​of 1, 2, 3, ..., n, and n is the total number of collection time points in the historical period. is the average carbon storage of the region at all sampling time points during the historical period, is the carbon emission coefficient at the i-th collection time point in the historical period, It is the average value of the carbon emission coefficient at all collection time points in the historical period.

5. The method for measuring carbon sinks in forest land along highways according to claim 3 is characterized in that: Based on the relevant signals, the process of in-depth analysis to determine the key stable vegetation types that are less affected by highway operations and the key fluctuating vegetation types that are greatly affected is as follows: SS1: A more detailed classification of the identified vegetation types within all project hub areas; SS2: Extract the regional carbon storage and carbon emission coefficient at each collection time point in all project hub areas, and calculate the regional carbon storage change and carbon emission coefficient change at each collection interval; Based on the regional carbon storage changes and carbon emission coefficient changes in each sampling interval, the response coefficient is calculated, and then based on the response coefficient, the pre-focused stable vegetation type and pre-focused fluctuating vegetation type are analyzed and obtained; SS3: Based on any pre-focused stable vegetation type, verify the degree of its impact on highway operation and analyze and obtain the focus stable vegetation type; SS4: Based on any pre-selected key fluctuating vegetation type, verify the degree of its impact by highway operation and analyze to obtain the key fluctuating vegetation type.

6. A method for measuring carbon sinks in forest land along highways according to claim 5, characterized in that: Based on the regional carbon storage changes and carbon emission coefficient changes in each sampling interval, the response coefficient is calculated. Based on the response coefficient, the process of obtaining the pre-focused stable vegetation type and the pre-focused fluctuating vegetation type is analyzed as follows: The response coefficient is obtained by calculating the ratio of the change in regional carbon storage to the change in carbon emission coefficient during the corresponding sampling interval. The response coefficients of each collection interval in all project hub areas are sorted in ascending order of numerical value to obtain a response coefficient sequence, and the first quantile value in the response coefficient sequence is extracted to obtain a response coefficient stability threshold, and the third quantile value in the response coefficient sequence is extracted to obtain a response coefficient fluctuation threshold; Extract the vegetation type of the project hub area corresponding to the response coefficient being less than or equal to the response coefficient stability threshold, and mark it as the pre-key stable vegetation type; The vegetation type of the project hub area corresponding to the response coefficient being greater than the response coefficient fluctuation threshold is extracted and marked as the pre-key fluctuation vegetation type.

7. A method for measuring carbon sinks in forest land along highways according to claim 6, characterized in that: Based on any pre-focused stable vegetation type, the degree of its impact on highway operation is verified, and the process of obtaining the focus stable vegetation type is analyzed as follows: Based on any pre-focused stable vegetation type, extract the response coefficient of the vegetation type that meets the pre-focused stable vegetation type in each sampling interval period in all project hub areas, and mark it as the response stability verification coefficient; comparing the response stability verification coefficient to a response coefficient stability threshold; If the response stability verification coefficient is greater than the response coefficient stability threshold, the response stability verification coefficient is marked as a verification stability abnormal coefficient; Calculate the difference between all verification stability anomaly coefficients and the response coefficient stability threshold, and sum them up to obtain the response stability anomaly degree value; Extract the total number of verification stability anomaly coefficients, multiply the response stability anomaly degree value by the total number of verification stability anomaly coefficients to obtain the verification stability anomaly characterization value; Preset a verification stable abnormal characterization threshold, and compare and analyze the verification stable abnormal characterization value with the verification stable abnormal characterization threshold; If the verified stable anomaly characterization value is less than or equal to the verified stable anomaly characterization threshold, the pre-key stable vegetation type is marked as a key stable vegetation type.

8. The method for measuring carbon sinks in forest land along highways according to claim 6 is characterized in that: Based on any pre-focused fluctuating vegetation type, the degree of its impact on highway operation is verified, and the process of analyzing and obtaining the focus fluctuating vegetation type is as follows: Based on any pre-focused fluctuation vegetation type, extract the response coefficient of the vegetation type that meets the pre-focused fluctuation vegetation type in each sampling interval period in all project hub areas, and mark it as the response fluctuation verification coefficient; comparing the response fluctuation verification coefficient with the response coefficient fluctuation threshold; If the response fluctuation verification coefficient is greater than the response coefficient fluctuation threshold, the response fluctuation verification coefficient is marked as a verification fluctuation abnormal coefficient; Calculate the difference between all verification fluctuation anomaly coefficients and the response coefficient fluctuation threshold respectively, and sum them up to obtain the response fluctuation anomaly degree value; Extract the total number of verification fluctuation anomaly coefficients, multiply the response fluctuation anomaly value by the total number of verification fluctuation anomaly coefficients to obtain the verification fluctuation anomaly characterization value; Preset a verification fluctuation abnormality characterization threshold, and compare and analyze the verification fluctuation abnormality characterization value with the verification fluctuation abnormality characterization threshold; If the verification fluctuation anomaly characterization value is less than or equal to the verification fluctuation anomaly characterization threshold, the pre-key fluctuation vegetation type is marked as the key fluctuation vegetation type.

9. The method for measuring carbon sinks in forest land along highways according to claim 1, characterized in that: The process of correcting the carbon content of key fluctuating vegetation types in the process of regional carbon storage calculation is as follows: Extract the regional carbon storage changes of all key stable vegetation types and calculate the mean to obtain the stable change mean; Extract the regional carbon storage changes of all key fluctuating vegetation types and calculate the mean to obtain the mean of fluctuation changes; By formula: , calculate and obtain the carbon content correction value CO, where ST is the stable change mean, CJ is the fluctuating change mean, and CA is the carbon content.

10. A carbon sink measurement system for forest land along a highway, applied to a carbon sink measurement method for forest land along a highway as claimed in any one of claims 1 to 9, characterized in that: The system includes: Data acquisition module: collects remote sensing image data of forest land along the highway; Image recognition module: Perform image recognition on the collected remote sensing image data to obtain the vegetation type and growth status of the forest land; Hierarchical processing module: Based on the interchange hub area of ​​the highway, the forest land along the highway is divided into regions to obtain multiple project hub areas; Carbon storage calculation module: Based on the identified vegetation types and growth conditions, combined with the vegetation biomass model and soil carbon pool model, and the preset carbon content of each vegetation type, the regional carbon storage of each project hub area is calculated; Data analysis module: Evaluate the impact of highway operation on carbon storage in forest land along the highway, and identify key stable vegetation types with less impact and key fluctuating vegetation types with greater impact, specifically: Correlation analysis submodule: Based on the historical records of regional carbon storage in the project hub area, evaluate whether vehicles have an impact on regional carbon storage during highway operation, and generate correlation signals based on the evaluation results; Key vegetation type analysis submodule: Based on the relevant signals, in-depth analysis is performed to determine the key stable vegetation types that are less affected by highway operations and the key fluctuating vegetation types that are greatly affected; Correction module: Based on the key stable vegetation types and the key fluctuating vegetation types, the carbon content of the key fluctuating vegetation types in the regional carbon storage calculation process is corrected, thereby correcting the regional carbon storage.

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