A real-time monitoring method and system for forest carbon sink

Abstract

A real-time monitoring method and system for forest carbon sinks, relating to the field of data processing technology, achieves a leap from time-series two-dimensional imagery to automatic, continuous, and non-contact calculation of individual tree-level three-dimensional growth parameters by constructing a fixed multi-view visual monitoring network and utilizing network self-calibration and multi-view three-dimensional reconstruction technology. This enables high-precision, automated, and dynamic monitoring of forest carbon sinks without relying on fixed field calibration objects. The real-time monitoring method and system directly outputs time-series data of individual tree diameter at breast height (DBH) and tree height with true physical scales from multi-view image sequences of the same forest stand through motion recovery structures with known absolute distance constraints and multi-view geometric calculations. The obtained growth parameters directly support carbon sink calculations based on reliable biomass models, achieving automation of the monitoring process, objectivity of results, and auditability of data. This provides technical support for the accurate measurement and dynamic verification of forest carbon sinks.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method and system for real-time monitoring of forest carbon sequestration. Background Technology

[0002] As the largest carbon sink in terrestrial ecosystems, the accurate monitoring and quantification of forests' carbon sequestration function is crucial for addressing climate change. The core of forest carbon sequestration monitoring lies in the long-term, continuous, and accurate measurement of key tree growth parameters (mainly diameter at breast height and tree height), and the conversion of these parameters into changes in carbon storage based on reliable biomass models.

[0003] Currently, the mainstream forest carbon sequestration monitoring technologies mainly include the following categories: Manual sample plot survey method: Surveyors periodically enter fixed sample plots and use tools such as diameter at breast height (DBH) meters and height gauges to manually measure each tree. This method provides accurate data and is considered the benchmark method, but it has inherent drawbacks such as high labor costs, long monitoring cycles (usually once every 5 years), inability to achieve continuous observation, and high costs (each monitoring requires personnel to be on-site for measurement).

[0004] Remote sensing monitoring technologies include aerial photogrammetry and satellite remote sensing. These technologies can achieve large-scale, periodic observations, but their spatial resolution is limited, making it difficult to be accurate to the scale of individual trees. They are also susceptible to cloud cover and weather conditions, and the accuracy and reliability of parameter inversion under complex forest stand structures are insufficient.

[0005] Ground-based lidar scanning: High-precision 3D point clouds are acquired using ground-based lidar, enabling relatively accurate extraction of parameters for individual timber structures. However, this equipment is expensive, has limited coverage in a single scan, and is also difficult to implement for low-cost, long-term, continuous, automated monitoring.

[0006] In summary, the main problems faced by existing technologies can be summarized as follows: it is difficult to balance accuracy, timeliness, continuity, and cost. High-precision methods (such as manual inspection and lidar) cannot achieve low-cost continuous monitoring; while technologies that can achieve wide-area coverage (such as remote sensing) are difficult to meet the needs of precise measurement at the single-tree level. In addition, existing automated monitoring schemes mostly rely on pre-deployed calibration objects to achieve scale restoration of three-dimensional reconstruction, which has poor applicability in complex and long-term monitoring environments in the field and increases deployment and maintenance costs.

[0007] Therefore, there is an urgent need to develop a method and system for real-time automated monitoring of forest carbon sinks that can achieve single-tree scale, high precision, all-weather operation, low cost, and without relying on fixed calibration materials. Summary of the Invention

[0008] The purpose of this invention is to provide a real-time monitoring method and system for forest carbon sinks, which is relatively simple to deploy, robust, and can realize automated and continuous monitoring of forest carbon sinks.

[0009] The embodiments of the present invention are implemented as follows: A method for real-time monitoring of forest carbon sinks, comprising: S1. Set up multiple fixed monitoring points within the target forest monitoring plot, and collect time-series image data covering the target forest monitoring plot from multiple monitoring points in a coordinated manner; S2. Based on time-series image data, a sparse 3D point cloud of the sample plot with real physical scale is generated through multi-view geometric reconstruction technology, and the spatial pose of each monitoring point is solved. S3. Based on sparse 3D point cloud and spatial pose, identify and track individual trees in the sample plot, and integrate information from different monitoring points to directly calculate the diameter at breast height and tree height of individual trees; S4. Based on the time-series variation data of diameter at breast height (DBH) and tree height, calculate the carbon sink through the conversion relationship between biomass and carbon storage.

[0010] Furthermore, in other preferred embodiments of the present invention, step S2 includes: S21. Control all monitoring points to simultaneously acquire a set of initial multi-view images covering the target forest monitoring plots; S22. Based on the multi-view initial image sequence and introducing at least one known absolute true distance as a scale constraint, execute the motion recovery structure process to generate a sparse 3D point cloud of the sample plot with real physical scale, and complete the spatial pose calibration of each monitoring point.

[0011] Furthermore, in other preferred embodiments of the present invention, the absolute true distance is the straight-line distance between the two monitoring points that were pre-determined and input in step S1.

[0012] Furthermore, in other preferred embodiments of the present invention, in step S3, the method for calculating the diameter at breast height (DBH) and tree height of a single tree includes: For the tracked individual tree, determine its horizontal cross-section at a height of 1–1.5 meters above the fitted ground plane, and calculate its diameter at breast height (DBH) based on the spatial geometric characteristics of the horizontal cross-section; and, Calculate the vertical distance between the highest point and the base point of the root in the three-dimensional representation of a single tree, and use this distance as the tree height.

[0013] Furthermore, in other preferred embodiments of the present invention, step S4 includes: S41. Based on the tree species information of a single tree, call the corresponding biomass equation to convert the diameter at breast height (DBH) and tree height into biomass; S42. Calculate biomass increment based on time-series biomass data; S43. Multiply the biomass increment by the carbon content coefficient to obtain the carbon sink.

[0014] Furthermore, in other preferred embodiments of the present invention, it further includes: S5. Generate digital hash values ​​from the key data generated in steps S1 to S4 and store them in the blockchain network.

[0015] Furthermore, in other preferred embodiments of the present invention, the key data includes: time-series image data, sparse three-dimensional point cloud, calculated diameter at breast height and tree height, and calculated carbon sink.

[0016] Furthermore, in other preferred embodiments of the present invention, the method for identifying and tracking individual trees within the target forest monitoring plot in step S3 includes: Based on the semantic segmentation model, forest targets are identified from images of each monitoring point. Through multi-view geometric constraints and temporal correlation, each tree is given a unique identifier and continuously tracked.

[0017] Furthermore, in other preferred embodiments of the present invention, in step S1, when collecting time-series image data, all monitoring points are synchronized based on a unified time reference.

[0018] A real-time monitoring system for forest carbon sinks, comprising: The collaborative sensing network includes multiple fixed visual monitoring nodes deployed within the target forest monitoring plots, used to collaboratively acquire time-series images covering the target forest monitoring plots; The data processing platform is used to receive time-series images, perform multi-view geometric reconstruction to generate sparse 3D point clouds with real physical scale and solve node poses, as well as identify, track individual trees and solve their growth parameters. The carbon sequestration and certification platform is used to calculate carbon sequestration based on time-series data of growth parameters and to store key data as evidence.

[0019] The beneficial effects of the embodiments of the present invention are: This invention provides a real-time monitoring method and system for forest carbon sinks. By constructing a fixed multi-view visual monitoring network and utilizing network self-calibration and multi-view 3D reconstruction technology, it achieves a functional leap from time-series 2D images to automatic, continuous, and non-contact calculation of individual tree-level 3D growth parameters. This achieves high-precision, automated, and dynamic monitoring of forest carbon sinks without relying on fixed field calibration objects. Multi-view image sequences of the same forest stand are directly output as time-series data of individual tree diameter at breast height (DBH) and tree height with true physical scale through motion recovery structures with known absolute distance constraints and multi-view geometric calculations. The obtained growth parameters directly support carbon sink calculation based on a reliable biomass model, realizing automation of the monitoring process, objectification of results, and auditability of data. This provides a continuous, accurate, and traceable data foundation and core technical support for the precise measurement, dynamic verification, and credible trading of forest carbon sinks. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention. Therefore, the detailed description of the embodiments of the present invention provided below is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention.

[0021] The following examples further illustrate the real-time monitoring method and system for forest carbon sinks provided by the present invention. Example

[0022] This embodiment provides a method for real-time monitoring of forest carbon sinks, which includes: S1. Set up multiple fixed monitoring points within the target forest monitoring plot, and collect time-series image data covering the target forest monitoring plot from multiple monitoring points in a coordinated manner.

[0023] Optionally, each monitoring point integrates a high-definition optical camera, an attitude sensing unit, and an ambient light sensor. Vertical and horizontal scales are also deployed for calibration.

[0024] All monitoring points are connected via wired or wireless networks and access a unified time synchronization server (such as NTP) to ensure that the acquired time-series images have consistent and accurate timestamps. When acquiring time-series image data, all monitoring points are synchronized based on a unified time reference. This ensures that the data acquired by all nodes are strictly aligned on the timeline, providing a reliable time-series foundation for multi-view data fusion, 3D reconstruction, and accurate calculation of growth across cycles.

[0025] Each monitoring point has edge computing capabilities, which can adaptively adjust shooting parameters (such as exposure time and gain) based on ambient light sensor data, and temporarily store data when network conditions are poor.

[0026] Furthermore, the real-time monitoring method for forest carbon sinks provided in this embodiment also includes: S2. Based on time-series image data, a sparse 3D point cloud of the sample plot with real physical scale is generated through multi-view geometric reconstruction technology, and the spatial pose of each monitoring point is solved.

[0027] Specifically, step S2 includes: S21. Control all monitoring points to simultaneously acquire a set of initial multi-view images covering the target forest monitoring plots; S22. Based on the multi-view initial image sequence and introducing at least one known absolute true distance as a scale constraint, execute the Structure from Motion (SfM) process to generate a sparse 3D point cloud of the sample plot with a real physical scale, and complete the spatial pose calibration of each monitoring point.

[0028] Furthermore, the specific procedures for motor rehabilitation include: a. Feature detection and matching Thousands of unique “feature points” are extracted from each image using algorithms such as SIFT, ORB, or deep learning-based methods. Feature point matching is then performed across all image pairs to identify feature points in different images that correspond to the same physical point in the scene.

[0029] b. Incremental Reconstruction Select a pair of well-matched images, and calculate the first batch of 3D points and the initial poses of the two cameras using epipolar geometry and triangulation. Then, gradually add new images, using the correspondence between existing 3D points and 2D feature points in the new images to solve for the pose of the new camera, and then use the new camera to triangulate more 3D points. Repeat this process until all images have been processed.

[0030] c. Bundle adjustment optimization The pose parameters of all cameras and the coordinates of all 3D points are treated as a whole nonlinear optimization problem. By minimizing the reprojection error (that is, projecting 3D points back onto the image according to the currently estimated camera pose and calculating the difference between its position and the original 2D feature point position) and iteratively optimizing all parameters, the most consistent camera pose and 3D structure can be obtained.

[0031] d. Scale recovery By introducing at least one known absolute true distance (in this embodiment, the absolute true distance is the straight-line distance between two monitoring points that are pre-determined and input in step S1) during the reconstruction process, this distance is added as a constraint to the bundle adjustment optimization.

[0032] The optimization process forces the model to conform to the real-world measurements, thereby giving the entire reconstruction result the correct scale and ultimately outputting a "sparse 3D point cloud with real physical scale" and camera pose.

[0033] The final output of the motion reconstruction process is a sparse 3D point cloud, which encompasses the 3D coordinate set of key feature points in the scene, with real metric units. Each visual monitoring node has its precise position (X, Y, Z) and pose (rotation matrix R and translation vector t) in a unified world coordinate system.

[0034] Furthermore, the real-time monitoring method for forest carbon sinks provided in this embodiment also includes: S3. Based on sparse 3D point cloud and spatial pose, identify and track individual trees in the sample plot, and integrate information from different monitoring points to directly calculate the diameter at breast height (DBH) and tree height of individual trees.

[0035] Optionally, in step S3, the method for identifying and tracking individual trees within the target forest monitoring plot includes: Based on the semantic segmentation model, forest targets are identified from images of each monitoring point. Through multi-view geometric constraints and temporal correlation, each tree is given a unique identifier and continuously tracked.

[0036] The forest semantic segmentation model is a computer vision model based on deep learning. Its core task is to perform pixel-level classification on a single input forest scene image, labeling each pixel in the image, thereby accurately distinguishing pixels belonging to trees from the background (such as soil, grassland, fallen leaves, sky, rocks, etc.) and other non-target objects (such as equipment, animals, etc.).

[0037] Optionally, the forest tree semantic segmentation model can be built using semantic segmentation network architectures such as U-Net, DeepLab, and Transformer, through training or fine-tuning. Before model training, a large number of images are collected from fixed visual nodes in the target forest area (or similar environment). The images are then meticulously annotated manually, and the outlines of all visible trees are accurately delineated using polygons to form a ground truth mask. The model is then trained using the prepared data. The trained model can then perform inference and quickly generate segmentation result images after new images are uploaded.

[0038] Furthermore, in step S3, the method for calculating the diameter at breast height (DBH) and tree height of a single tree includes: For each tracked tree, a horizontal cross-section 1 to 1.5 meters above the fitted ground plane is determined, and the diameter at breast height (DBH) is calculated based on the spatial geometric characteristics of the horizontal cross-section. Additionally, the vertical distance between the highest point and the root base point in the three-dimensional representation of the tree is calculated as the tree height.

[0039] The specific methods for calculating chest diameter include: Generate dense point clouds based on multi-view stereo vision (MVS); To separate individual trees by clustering point clouds, methods such as DBSCAN and Euclidean clustering can be used. The RANSAC algorithm can be used to fit the ground plane. In the point cloud, determine a height layer of 1 to 1.5 meters above the ground plane, preferably 1.3 meters. For the point cloud cross-section of this layer, use cylindrical fitting (least square method) or calculate its convex hull / minimum enclosing circle to estimate the diameter.

[0040] The specific methods for calculating tree height include: In the segmented single-tree point cloud, the difference in Z coordinate between the highest point (with the largest Z coordinate) and the root base point (usually the intersection with the ground plane or the lowest point) is directly calculated.

[0041] This step enables direct and objective geometric calculation of key tree growth parameters based on accurate 3D point cloud data, avoiding errors and subjectivity in manual measurement and ensuring the accuracy and repeatability of diameter at breast height (DBH) and tree height data.

[0042] Furthermore, the real-time monitoring method for forest carbon sinks provided in this embodiment also includes: S4. Based on the time-series variation data of diameter at breast height (DBH) and tree height, calculate the carbon sink through the conversion relationship between biomass and carbon storage.

[0043] Specifically, step S4 includes: S41. Based on the tree species information of a single tree, call the corresponding biomass equation to convert the diameter at breast height (DBH) and tree height into biomass; S42. Calculate biomass increment based on time-series biomass data; S43. Multiply the biomass increment by the carbon content coefficient to obtain the carbon sink.

[0044] Furthermore, the general expression of the biomass equation is: M A = aD b H c ; In the formula, M A Where is biomass (kg), D is diameter at breast height (cm), H is tree height (m), and a, b, and c are model parameters. These parameters can be obtained by referring to relevant literature or by fitting a large amount of sample data for a specific tree species and region, and establishing a parameter library containing information related to tree species and region, which can be directly called upon in subsequent use.

[0045] Furthermore, the calculation method for carbon sequestration is as follows: C=ΔM A ×α; In the formula, ΔM A α is the increase in biomass (kg), C is the carbon sink, and α is the carbon content coefficient, which is an empirical parameter for a specific tree species. It can be obtained by consulting literature or by fitting a large amount of sample data, and its value is usually 0.45~0.5.

[0046] This step converts growth parameters into biomass by calling a specific biomass equation based on a single tree species, then calculates its increment and multiplies it by the carbon content coefficient. This scientifically and automatically converts geometric measurement data into carbon sinks, achieving the effect of standardizing and making the carbon sink accounting process traceable, and directly supporting the generation and trading of carbon credits.

[0047] Furthermore, in other preferred embodiments of the present invention, it further includes: S5. Generate digital hash values ​​from the key data generated in steps S1 to S4 and store them in the blockchain network.

[0048] Key data includes: time-series imagery, sparse 3D point clouds, calculated diameter at breast height (DBH) and tree height, and calculated carbon sequestration. By generating digital hash values ​​from key data throughout the monitoring and accounting process and storing them on a blockchain network, an immutable and fully traceable electronic evidence chain can be established for all raw data, intermediate results, and final conclusions, greatly improving the transparency and credibility of monitoring data.

[0049] This embodiment also provides a real-time monitoring system for forest carbon sequestration, which includes: The collaborative sensing network includes multiple fixed visual monitoring nodes deployed within the target forest monitoring plots, used to perform step S1 to collaboratively acquire time-series images covering the target forest monitoring plots. The data processing platform is used to receive time-series images, perform multi-view geometric reconstruction in step S2 to generate sparse 3D point clouds with real physical scale and solve node poses, and identify, track individual trees and solve their growth parameters in step S3. The carbon sequestration accounting and certification platform is used to calculate carbon sequestration by performing the S4 step based on time-series data of growth parameters and to store key data as evidence.

[0050] Experimental Example 1 A target forest monitoring plot was selected, and the method of the example was used to measure 30 tree samples in the target forest monitoring plot. The results were compared with the manual measurement values. The comparison results are shown in Table 1 and Table 2.

[0051] Table 1. Verification of the accuracy of single-tree growth parameter measurements Measurement parameters Mean of measurement in the example Manually measured mean Mean Absolute Error Root mean square error <![CDATA[Coefficient of determination R 2 > Breast diameter 24.3cm 24.5cm ±0.8cm ±1.1cm 0.991 Tree height 15.7m 15.9m ±0.3m ±0.4m 0.985 Table 2. Consistency Verification of Time-Series Monitoring (Period: One Growing Season) Measurement parameters Monitoring average of the examples Mean of manual monitoring results relative deviation Maximum individual deviation (absolute value) Increase in diameter at breast height (over one growing season) 0.82cm 0.85cm -3.5% 4.7% Tree height growth (in one growing season) 0.416m 0.428m -2.8% 4.3% As shown in Table 1, the diameter at breast height (DBH) and tree height of individual trees automatically measured by the method of this invention are in high agreement with the high-precision manual measurements. Specifically, the average absolute error of the DBH measurement is ±0.8 cm, the root mean square error is ±1.1 cm, and the coefficient of determination between the two data is as high as 0.991, showing a very strong linear correlation. The average absolute error of the tree height measurement is ±0.3 m, the root mean square error is ±0.4 m, and the coefficient of determination is 0.985. These quantitative indicators prove that the method of this invention has reached a level of accuracy in single-point measurement that can replace traditional manual measurement, and can provide reliable basic data for carbon sequestration accounting.

[0052] As shown in Table 2, continuous monitoring of fixed sample trees throughout a complete growing season reveals a high degree of consistency between the tree growth dynamics reflected by the method of this invention and the results of manual interval measurements. In terms of cumulative growth, the average diameter at breast height (DBH) increase monitored by this method was 0.82 cm, with a relative deviation of -3.5% compared to the manual measurement result (0.85 cm). For a single individual, the maximum deviation was 4.7%. The average tree height increase was 0.416 m, with a relative deviation of -2.8% compared to the manual result (0.428 m). For a single individual, the maximum deviation was 4.3%. More importantly, the continuous time-series growth curve output by this method perfectly matches the growth trend reflected by the discrete time-point data measured manually. This demonstrates that this invention not only achieves high-precision single measurements but also reliably captures and quantifies the true growth process of trees over time, achieving the core objective of automated continuous monitoring.

[0053] In summary, this invention provides a real-time monitoring method and system for forest carbon sinks. By constructing a fixed multi-view visual monitoring network and utilizing network self-calibration and multi-view 3D reconstruction technology, it achieves a functional leap from time-series 2D images to automatic, continuous, and non-contact calculation of individual tree-level 3D growth parameters. This achieves high-precision, automated, and dynamic monitoring of forest carbon sinks without relying on fixed field calibration objects. Multi-view image sequences of the same forest stand are directly output as time-series data of individual tree diameter at breast height (DBH) and tree height with true physical scale through motion recovery structures with known absolute distance constraints and multi-view geometric calculations. The obtained growth parameters directly support carbon sink calculation based on a reliable biomass model, realizing automation of the monitoring process, objectification of results, and auditability of data. This provides a continuous, accurate, and traceable data foundation and core technical support for the precise measurement, dynamic verification, and credible trading of forest carbon sinks.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of forest carbon sequestration, characterized in that, include: S1. Multiple fixed monitoring points are set up within the target forest monitoring plot, and time-series image data covering the target forest monitoring plot are collected collaboratively from the multiple monitoring points; S2. Based on the time-series image data, a sparse three-dimensional point cloud of the sample plot with real physical scale is generated by multi-view geometric reconstruction technology, and the spatial pose of each monitoring point is solved. S3. Based on the sparse three-dimensional point cloud and the spatial pose, identify and track individual trees in the sample plot, and fuse information from different monitoring points to directly calculate the diameter at breast height and tree height of the individual trees; S4. Based on the time-series variation data of the diameter at breast height (DBH) and the tree height, calculate the carbon sink through the conversion relationship between biomass and carbon storage.

2. The real-time monitoring method according to claim 1, characterized in that, Step S2 includes: S21. Control all monitoring points to simultaneously acquire a set of multi-view initial image sequences covering the target forest monitoring plots; S22. Based on the multi-view initial image sequence, and introducing at least one known absolute true distance as a scale constraint, execute the motion recovery structure process to generate the sparse three-dimensional point cloud of the sample plot with real physical scale, and complete the spatial pose calibration of each monitoring point.

3. The real-time monitoring method according to claim 2, characterized in that, The absolute true distance is the straight-line distance between the two monitoring points that were pre-determined and input in step S1.

4. The real-time monitoring method according to claim 3, characterized in that, In step S3, the method for calculating the diameter at breast height (DBH) and tree height of the individual tree includes: For the tracked individual tree, a horizontal cross-section at a height of 1-1.5 meters above the fitted ground plane is determined, and the diameter at breast height (DBH) is calculated based on the spatial geometric characteristics of the horizontal cross-section; and, The vertical distance between the highest point and the root base point in the three-dimensional representation of the single tree is calculated as the tree height.

5. The real-time monitoring method according to claim 1, characterized in that, Step S4 includes: S41. Based on the tree species information of the individual tree, call the corresponding biomass equation to convert the diameter at breast height (DBH) and the tree height into biomass; S42. Calculate the biomass increment based on the time-series biomass data; S43. Multiply the biomass increment by the carbon content coefficient to obtain the carbon sink.

6. The real-time monitoring method according to claim 1, characterized in that, Also includes: S5. Generate digital hash values ​​from the key data generated in steps S1 to S4 and store them in the blockchain network.

7. The real-time monitoring method according to claim 6, characterized in that, The key data includes: the time-series image data, the sparse 3D point cloud, the calculated diameter at breast height (DBH) and tree height, and the calculated carbon sequestration.

8. The real-time monitoring method according to claim 7, characterized in that, In step S3, the method for identifying and tracking individual trees within the target forest monitoring plot includes: Based on the semantic segmentation model, forest targets are identified from the images of each monitoring point, and each tree is assigned a unique identifier and continuously tracked through multi-view geometric constraints and temporal correlation.

9. The real-time monitoring method according to claim 1, characterized in that, In step S1, when collecting the time-series image data, all the monitoring points are synchronized based on a unified time reference.

10. A real-time monitoring system for forest carbon sequestration, characterized in that, include: The collaborative sensing network includes multiple fixed visual monitoring nodes deployed within the target forest monitoring plots, used to collaboratively acquire time-series images covering the target forest monitoring plots; The data processing platform is used to receive the time-series images, perform multi-view geometric reconstruction to generate sparse three-dimensional point clouds with real physical scale and solve the node poses, as well as identify and track individual trees and calculate their growth parameters. A carbon sequestration and certification platform is used to calculate carbon sequestration based on time-series data of the aforementioned growth parameters and to store key data as evidence.