A big data-based forestry carbon sink monitoring system and method

Abstract

The present application belongs to the technical field of monitoring, and relates to a forestry carbon sink monitoring system and method based on big data. The system is composed of a multi-source data acquisition module, a data preprocessing and standardization module, a space-time registration module, a heterogeneous fusion module, a multi-scale carbon sink estimation module, a dynamic calibration module and a result output module. Satellite remote sensing, unmanned aerial vehicle remote sensing, ground sample investigation, Internet of Things environmental sensing data and historical forestry resource archive data are collected, and then denoising, format conversion, space-time registration and other treatments are performed to obtain standardized monitoring data. The system uses a two-stage estimation method to estimate carbon storage, and uses a dynamic calibration method to quickly adapt to changes in the forest area. Finally, the system generates a carbon storage distribution map, a carbon sink change map, abnormal change warning information and a monitoring report, and has good real-time performance, accuracy and traceability. The completeness and stability of the carbon storage and carbon sink estimation results are improved, and the system is suitable for fine management and decision support of forestry carbon sinks.

Description

Technical Field

[0001] This invention relates to the field of monitoring technology, specifically to a forestry carbon sequestration monitoring system and method based on big data. Background Technology

[0002] Forest ecosystems are among the largest carbon sinks in terrestrial ecosystems. Accurately understanding the baseline and dynamic changes of forest carbon storage is fundamental to achieving dual-carbon goals, ecological protection and restoration, and realizing the value of carbon sequestration. Simultaneously, the methodologies of afforestation carbon sequestration projects have introduced measurable, reportable, and verifiable requirements for emission reduction accounting, monitoring, and verification. Therefore, establishing a faster, more continuous, and standardized monitoring system based on forestry carbon sequestration scenarios has become a major direction for current technological development.

[0003] Currently, forestry carbon sequestration monitoring technologies mostly employ a variety of methods, including satellite remote sensing, UAV remote sensing, lidar, GIS, GNSS, ground plot surveys, and environmental sensors, to acquire information on forest cover changes, growth status, biomass parameters, and local ecological environment. Carbon econometric models are then used to estimate carbon storage or carbon sequestration. Research shows that remote sensing technology has advantages such as macroscopic, dynamic, rapid, and repeatable capabilities, making it suitable for large-scale, periodic monitoring. Ground sensor networks and plot surveys can supplement detailed information such as soil, temperature, humidity, and forest stand structure. Smart forestry platforms integrate remote sensing, GIS, IoT, and data analysis capabilities into a single information system.

[0004] However, existing forestry monitoring methods rely heavily on a single data source or a single model, failing to achieve a unified approach at both the regional and local fine scales, resulting in incomplete and unstable estimations of carbon storage and carbon sinks. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a forestry carbon sink monitoring system and method based on big data. By constructing a multi-source heterogeneous data fusion mechanism for forestry carbon sink scenarios, it helps to solve the problems of existing monitoring methods being heavily reliant on a single data source or a single model, and having difficulty in balancing regional scale and local fine scale, thereby improving the completeness and stability of carbon storage and carbon sink estimation results.

[0006] To achieve the above technical solution, the present invention provides a forestry carbon sink monitoring system based on big data, including a multi-source data acquisition module, a data preprocessing and standardization module, a spatiotemporal registration module, a heterogeneous fusion module, a multi-scale carbon sink estimation module, a dynamic calibration module, and a result output module; The multi-source data acquisition module is used to collect satellite remote sensing data, UAV remote sensing data, ground sample plot survey data, IoT environmental sensing data, and historical forestry resource archive data for the target forest area; The data preprocessing and standardization module is connected to the multi-source data acquisition module and is used to denoise, unify coordinate systems, unify timestamps, convert formats, fill in missing data, and mark quality data to obtain standardized monitoring data. The spatiotemporal registration module is linked with the data preprocessing and standardization module to perform spatial and temporal alignment of standardized monitoring data according to preset spatial units and time series. The heterogeneous fusion module is linked with the spatiotemporal registration module to perform feature association, weight allocation and fusion processing on the monitoring data that has been aligned spatially and temporally, forming a spatiotemporal fusion database of forestry carbon sinks; The multi-scale carbon sink estimation module is connected to the heterogeneous fusion module to extract regional-scale carbon sink estimation features and local fine-scale carbon sink estimation features from the forestry carbon sink spatiotemporal fusion database, so as to obtain regional-scale carbon storage estimation results and local fine-scale carbon storage correction results. The dynamic calibration module is connected to the multi-scale carbon sink estimation module to update the regional-scale carbon storage estimate and the local fine-scale carbon storage correction results, so as to obtain the carbon storage results and carbon sink dynamic change results of the target forest area. The results output module is linked to the dynamic calibration module to output carbon storage distribution maps, carbon sink change maps, abnormal change early warning information, and monitoring reports.

[0007] Furthermore, the multi-source data acquisition module includes: a satellite remote sensing acquisition submodule, an UAV remote sensing acquisition submodule, a ground sample plot acquisition submodule, an IoT sensing acquisition submodule, and a historical archive access submodule; The satellite remote sensing acquisition submodule is used to collect multispectral images, radar images, and lidar altitude data of the target forest area at different time points; The UAV remote sensing acquisition submodule is used to acquire low-altitude images, point cloud data, or canopy structure data of the target forest area; The ground plot collection submodule is used to collect data on target location, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, and growth status. The IoT sensing and acquisition submodule is used to collect environmental factor data, including temperature, humidity, soil moisture, light intensity, and carbon dioxide concentration. The historical archive access submodule is used to collect forest resource inventory data, management operation records, afforestation records, and disturbance event records; The multi-source data acquisition module adds acquisition time, spatial coordinates, resolution level, source identifier, and initial credibility label to each piece of data during acquisition.

[0008] Furthermore, the process of denoising, unifying coordinate systems, unifying timestamps, converting formats, completing missing data, and marking quality data to obtain standardized monitoring data includes the following steps: Perform radiometric correction, atmospheric correction, geometric correction, and orthorectification on satellite remote sensing data and UAV remote sensing data; Outlier removal, coordinate correction, and field completion were performed on the ground sample plot survey data. Perform time-series denoising, missing value imputation, and sensor drift correction on IoT environmental sensing data; perform field mapping, format conversion, and semantic unification on historical forestry resource archive data. The processed data is uniformly mapped to a preset data structure, which includes at least a spatial location field, a time field, a tree species field, a forest stand structure field, an environmental factor field, a quality score field, and a source tracing field.

[0009] Furthermore, the spatiotemporal registration module is used to: establish a composite spatial unit composed of forest compartment / compartment boundaries and regular grid superposition, treat the composite spatial unit as a unified monitoring unit, and generate a spatiotemporally aligned dataset for the same monitoring period and the same composite spatial unit; The process of generating a spatiotemporally aligned dataset with the same monitoring period and the same composite spatial unit includes the following steps: For high-resolution data, aggregate statistics are performed according to composite spatial units; For low-resolution data, pushdown allocation is performed using spatial neighborhood constraints and land class consistency constraints. Data collected at different times are time-aligned according to a preset monitoring cycle; Data acquired asynchronously is matched with proximity data or interpolated to form a unified time sequence segment.

[0010] Furthermore, the multi-scale carbon sink estimation module adopts a two-stage estimation method of regional-scale coarse estimation and local fine-scale correction; Among them, the regional-scale rough estimation uses tree height, canopy closure, canopy coverage, and tree species information within the composite spatial unit to calculate the initial value of regional carbon storage. Local fine-scale correction: Using survey data of the target forest area and point cloud data from UAVs, parameters such as diameter at breast height, tree height, crown width or single tree structure are extracted to locally correct the initial value of carbon storage in the target forest area. The multi-scale carbon sink estimation module also includes: cross-scale consistency constraints. When the deviation between the aggregated value of the local fine-scale correction result and the regional scale coarse estimation result in the preset area is greater than the preset tolerance, the local fine-scale correction result is consistently reverted according to the fusion weight and spatial neighborhood relationship to obtain the regional scale carbon storage estimation result and the local fine-scale carbon storage correction result.

[0011] Furthermore, the dynamic calibration module is also used to perform incremental calibration on the carbon storage results based on survey data and time-series monitoring data; and to trigger dynamic calibration when any of the following conditions are met: a) The changes in tree height, canopy density, canopy coverage, or plot boundary exceed the preset threshold within two adjacent monitoring periods; b) Identify events such as fires, pests and diseases, logging, afforestation, or land use changes; c) The deviation between the current estimated result and the actual measured result of the sample plot exceeds the preset error threshold.

[0012] Furthermore, the results output module is used to generate spatial distribution maps of carbon storage, thematic maps of carbon sink changes, plot-level statistical tables, periodic monitoring reports, and early warning information for areas of abnormal changes, and to generate result traceability files based on the results of dynamic calibration. The early warning information should include the location of the abnormal area, the type of abnormality, the magnitude of the change, and the recommended level of investigation. The results traceability file should include source data identifiers, preprocessing parameters, fusion weights, estimation model version, calibration timestamps, and anomaly handling records.

[0013] Secondly, the present invention provides a forestry carbon sequestration monitoring method based on big data, comprising the following steps: Step 1: Acquire satellite remote sensing data, UAV remote sensing data, ground sample plot survey data, IoT environmental sensing data, and historical forestry resource archive data for the target forest area; Step 2: Denoise the acquired data, unify the coordinate system, unify the timestamps, convert the format, fill in missing data, and mark the quality to obtain standardized monitoring data; Step 3: Based on the preset spatial units and time series, perform spatial and temporal alignment on the standardized monitoring data to form a spatiotemporally aligned dataset; Step 4: Perform feature association, weight allocation, and fusion processing on the spatiotemporally aligned dataset to form a spatiotemporally fused forestry carbon sink database; Step 5: Extract regional-scale carbon sequestration estimation features and local fine-scale carbon sequestration estimation features from the forestry carbon sequestration spatiotemporal fusion database to obtain regional-scale carbon storage estimation results and local fine-scale carbon storage correction results; Step Six: Update the regional-scale carbon storage estimate and the local fine-scale carbon storage correction results based on the survey data and time-series monitoring data to obtain the carbon storage results and carbon sink dynamic changes of the target forest area; Step 7: Output carbon storage distribution map, carbon sink change map, abnormal change early warning information and monitoring report.

[0014] Thirdly, the present invention provides a computer-readable storage medium including a stored program that, when the program is running, controls the device where the computer-readable storage medium is located to execute the above-described forestry carbon sink monitoring method based on big data.

[0015] The beneficial effects of this invention are as follows: This invention employs multi-source data acquisition, standardized processing, spatiotemporal registration, heterogeneous fusion, multi-scale estimation, and dynamic calibration to overcome current problems in forestry carbon sink monitoring, such as single data sources, difficulty in considering spatial scales, poor dynamic updating capabilities, and low traceability of results. It achieves the goal of monitoring carbon storage and carbon sink changes in target forest areas with high completeness, high stability, high timeliness, and high verifiability. (See attached figures.) The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0016] Figure 1 This is the electrical schematic diagram of the forestry carbon sink monitoring system based on big data according to the present invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, each technical and scientific term used in these embodiments has the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0020] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0021] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0022] Example 1: like Figure 1 As shown, this invention provides a forestry carbon sequestration monitoring system based on big data, comprising: (i) Multi-source data acquisition module, used to collect satellite remote sensing data, UAV remote sensing data, ground sample plot survey data, Internet of Things environmental perception data and historical forestry resource archive data of the target forest area, so as to obtain multi-source heterogeneous basic data of the target forest area; The multi-source data acquisition module includes: a satellite remote sensing acquisition submodule, an UAV remote sensing acquisition submodule, a ground sample plot acquisition submodule, an Internet of Things sensing acquisition submodule, and a historical archive access submodule.

[0023] A1-1: Satellite remote sensing acquisition submodule, used to acquire multispectral images, radar images and lidar altitude data of the target forest area at different time points.

[0024] Specifically, the satellite remote sensing acquisition submodule acquires data on a monthly or quarterly basis to reflect changes in canopy cover, vegetation growth, and disturbance in the target forest area during each monitoring period.

[0025] A1-2: UAV remote sensing acquisition submodule, used to collect low-altitude images, point cloud data or canopy structure data of key areas in the target forest area.

[0026] UAV remote sensing observations are mainly deployed in areas sensitive to carbon sink changes, areas prone to pests and diseases, post-fire recovery areas, or areas with artificial operations to improve the ability to conduct detailed local observations.

[0027] A1-3: Ground plot data collection submodule, used to collect data on target location, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, and growth status.

[0028] Specifically, the ground sample collection submodule adopts a combination of fixed and temporary sample plots. Fixed sample plots are used for long-term comparative analysis, while temporary sample plots are used for supplementary verification of areas with abnormal changes.

[0029] A1-4: IoT sensing and acquisition submodule, used to collect environmental factor data.

[0030] The environmental data include temperature, humidity, soil moisture, light intensity, and carbon dioxide concentration.

[0031] Specifically, the IoT sensing and data collection submodule collects data at an hourly or higher frequency to reflect the impact of changes in the target forest area's ecological environment on the carbon sink estimation results.

[0032] A1-5: Historical Archives Access Submodule, used to access forest resource inventory data, management operation records, afforestation records and disturbance event records to provide long-term background information and historical baselines for the target forest area.

[0033] A1-6: Data tag generation unit, used to automatically attach acquisition time, spatial coordinates, resolution level, source identifier and initial credibility tag to each piece of data during the acquisition phase.

[0034] Specifically, for any piece of collected data Its initial confidence value The following method is used to determine:

[0035] in, Score the reliability of the data source. To score the resolution quality, Rate the time effectiveness. Scoring for data integrity Scoring is given for spatial positioning accuracy. to Let be the weight coefficients for each scoring item, and satisfy:

[0036] This method allows for a basic quantitative evaluation of the quality of multi-source data during the data acquisition phase, providing a basis for subsequent heterogeneous fusion and conflict identification.

[0037] (ii) Data preprocessing and standardization module, used to denoise, unify coordinate system, unify timestamp, convert format, fill missing data and mark quality of multi-source heterogeneous basic data collected by multi-source data acquisition module, so as to obtain standardized monitoring data; A2-1: Remote sensing data processing unit, used to perform radiometric correction, atmospheric correction, geometric correction and orthorectification on satellite remote sensing data and UAV remote sensing data.

[0038] A2-2: Sample plot data processing unit, used to remove outliers, correct coordinates, and complete fields in ground sample plot survey data.

[0039] A2-3: Sensor data processing unit, used for time-series denoising, missing value imputation, and sensor drift correction of IoT environmental sensing data.

[0040] A2-4: Historical Archives Processing Unit, used for field mapping, format conversion, and semantic unification of historical forestry resource archive data.

[0041] A2-5: Unified mapping unit, used to map the processed data to a preset data structure.

[0042] The preset data structure includes at least the following fields: spatial location, time, tree species, forest stand structure, environmental factors, quality score, and source tracing.

[0043] (iii) Spatiotemporal registration module, used to spatially and temporally align standardized monitoring data according to preset spatial units and time series, so as to generate a spatiotemporally aligned dataset under a unified period; A3-1: Composite spatial unit construction unit, used to establish composite spatial units formed by superimposing forest compartment / compartment boundaries and regular grids, and to use composite spatial units as unified monitoring units.

[0044] A3-2: High-resolution aggregation unit, used to aggregate and statistically analyze high-resolution data according to composite spatial units.

[0045] A3-3: Low-resolution pushdown unit, used to push down and allocate low-resolution data using spatial neighborhood constraints and land use consistency constraints.

[0046] A3-4: Time alignment unit, used to align data from different acquisition times according to a preset monitoring cycle.

[0047] A3-5: Timing compensation unit, used to form a unified timing segment by using proximity matching or interpolation compensation to compensate asynchronously acquired data.

[0048] After processing by the spatiotemporal registration module, a spatiotemporally aligned dataset for the same monitoring period and the same composite spatial unit can be obtained.

[0049] (iv) Heterogeneous fusion module, used to perform feature association, weight allocation and fusion processing on monitoring data that have been aligned spatially and temporally to form a spatiotemporal fusion database of forestry carbon sinks; A4-1: Feature association unit, used to extract tree height features, canopy closure features, canopy cover features, tree species distribution features, topographic features, and environmental factor features.

[0050] A4-2: Fusion weight allocation unit, used to determine the fusion weight of different data sources based on data quality score, temporal proximity, spatial resolution level, plot matching degree and historical stability.

[0051] For the first within the same composite spatial unit Class features, their fusion values It can be represented as:

[0052] in, For the first The data source provides the first Class feature values, For the first The fusion weights of each data source satisfy the following conditions:

[0053] A4-3: Conflict detection unit, used to prioritize retaining the high-confidence data as the master value and write the remaining data into the verification record when different data sources give inconsistent results for the same feature.

[0054] A4-4: Database generation unit, used to write the fused data into the forestry carbon sink spatiotemporal fusion database.

[0055] (v) Multi-scale carbon sink estimation module, used to extract regional scale carbon sink estimation features and local fine scale carbon sink estimation features from the forestry carbon sink spatiotemporal fusion database, so as to obtain regional scale carbon storage estimation results and local fine scale carbon storage correction results. A5-1: Regional-scale coarse estimation unit, used to calculate the initial value of regional carbon storage based on tree height, canopy closure, canopy coverage and tree species information within the composite spatial unit; Preferably, for the first Each composite spatial unit, with its initial regional carbon storage value It can be represented as:

[0056] in, For the first Estimated biomass of a composite spatial unit, is the carbon conversion coefficient.

[0057] A5-2: Local fine-scale correction unit, used to extract diameter at breast height, tree height, crown width or single tree structure parameters from target forest area survey data and UAV point cloud data, and to locally correct the initial value of carbon storage in the target forest area.

[0058] A5-3: Cross-scale consistency constraint unit, used to perform consistency callback of local fine-scale correction results based on fusion weight and spatial neighborhood relationship when the aggregated value of local fine-scale correction results in the preset area deviates from the regional scale rough estimation result by more than the preset tolerance.

[0059] Specifically, the corrected carbon storage results It can be represented as:

[0060] in, This represents the initial value for carbon storage at the regional scale. These are local fine-scale correction values. This is the scale balance coefficient.

[0061] (vi) Dynamic calibration module, used to perform incremental calibration of carbon storage results based on survey data and time-series monitoring data, so as to obtain carbon storage results and dynamic changes in carbon sink in the target forest area; A6-1: Triggering determination unit, used to trigger dynamic calibration when any of the following conditions are met: (a) the change in tree height, canopy closure, canopy coverage or plot boundary exceeds a preset threshold within two adjacent monitoring periods; (b) fire, pests and diseases, logging, afforestation or land use change events are identified; (c) the deviation between the current estimated result and the actual measured result of the sample plot exceeds a preset error threshold.

[0062] A6-2: Change region identification unit, used to identify change regions that need to be recalculated, and only performs incremental recalculation on the composite spatial units corresponding to the change regions.

[0063] A6-3: Incremental calibration unit, used to update regional-scale carbon storage estimates and local fine-scale carbon storage correction results based on the latest sample plot survey data and the latest time-series monitoring data.

[0064] (vii) Results output module, used to output carbon storage spatial distribution map, carbon sink change thematic map, plot-level statistical table, periodic monitoring report and early warning information of abnormal change areas according to the results after dynamic calibration, and generate result traceability file.

[0065] A7-1: Graphics output unit, used to generate spatial distribution maps of carbon reserves and thematic maps of carbon sink changes.

[0066] A7-2: Report output unit, used to generate plot-level statistical tables and periodic monitoring reports.

[0067] A7-3: Early warning output unit, used to output the location of abnormal areas, types of abnormalities, magnitude of changes, and recommended investigation level.

[0068] A7-4: Traceability document generation unit, used to record source data identifier, preprocessing parameters, fusion weights, estimation model version, calibration timestamp, and anomaly handling records.

[0069] With the above settings, the system provided in this embodiment can achieve dynamic, precise and traceable monitoring of changes in carbon storage and carbon sink in the target forest area.

[0070] Example 2: This embodiment provides a forestry carbon sink monitoring method based on big data, including the following steps: Step 1: Acquire satellite remote sensing data, UAV remote sensing data, ground sample plot survey data, IoT environmental sensing data, and historical forestry resource archive data for the target forest area; Step 2: Denoise the acquired data, unify the coordinate system, unify the timestamps, convert the format, fill in missing data, and mark the quality to obtain standardized monitoring data; Step 3: Based on the preset spatial units and time series, perform spatial and temporal alignment on the standardized monitoring data to form a spatiotemporally aligned dataset; Step 4: Perform feature association, weight allocation, and fusion processing on the spatiotemporally aligned dataset to form a spatiotemporally fused forestry carbon sink database; Step 5: Extract regional-scale carbon sequestration estimation features and local fine-scale carbon sequestration estimation features from the forestry carbon sequestration spatiotemporal fusion database to obtain regional-scale carbon storage estimation results and local fine-scale carbon storage correction results; Step Six: Update the regional-scale carbon storage estimate and the local fine-scale carbon storage correction results based on the survey data and time-series monitoring data to obtain the carbon storage results and carbon sink dynamic changes of the target forest area; Step 7: Output carbon storage distribution map, carbon sink change map, abnormal change early warning information and monitoring report.

[0071] Step one includes: S1: Acquire multispectral images, radar images, and lidar altitude data of the target forest area at different time points through the satellite remote sensing acquisition submodule; S2: Acquire low-altitude images, point cloud data, or canopy structure data of key areas in the target forest area through the UAV remote sensing acquisition submodule; S3: Obtain data on target location, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, and growth status through the ground sample plot collection submodule; S4: Acquire data on temperature, humidity, soil moisture, light intensity, and carbon dioxide concentration through the IoT sensing and acquisition submodule; S5: Access forest resource inventory data, management operation records, afforestation records, and disturbance event records through the historical archive access submodule; S6: Add the collection time, spatial coordinates, resolution level, source identifier, and initial credibility label to each piece of collected data.

[0072] Step two includes: Q1: Perform radiometric correction, atmospheric correction, geometric correction, and orthorectification on satellite remote sensing data and UAV remote sensing data; Q2: Perform outlier removal, coordinate correction, and field completion on ground sample plot survey data; Q3: Perform time-series denoising, missing value imputation, and sensor drift correction on IoT environmental sensing data; Q4: Perform field mapping, format conversion, and semantic unification on historical forestry resource archive data; Q5: Map the processed data into a pre-defined data structure to obtain standardized monitoring data.

[0073] Step three includes: R1: Establish a composite spatial unit formed by superimposing forest compartment / compartment boundaries and regular grids; R2: Aggregate and statistically analyze high-resolution data according to composite spatial units; R3: Push down the allocation of low-resolution data using spatial neighborhood constraints and land class consistency constraints; R4: Align data from different collection times according to a preset monitoring cycle; R5: For asynchronously acquired data, a unified time sequence segment is formed by using proximity matching or interpolation compensation.

[0074] Step five includes: T1: Calculate the initial value of regional carbon storage based on tree height, canopy closure, canopy coverage and tree species information within the composite spatial unit; T2: Extract parameters such as diameter at breast height, tree height, crown width, or single-tree structure from target forest area survey data and UAV point cloud data to make local corrections to the initial value of regional carbon storage; T3: When the deviation between the aggregated value of the local fine-scale correction result and the regional-scale coarse estimation result in the preset area is greater than the preset tolerance, the local fine-scale correction result is consistently adjusted according to the fusion weight and spatial neighborhood relationship to obtain the regional-scale carbon storage estimation result and the local fine-scale carbon storage correction result.

[0075] Step six includes: U1: Determine whether the dynamic calibration trigger condition is met; U2: Identify the changing area when the triggering condition is met; U3: Performs incremental recalculation only on the changed regions; U4: Update the carbon storage results and dynamic changes in carbon sinks corresponding to the changed areas; U5: Outputs the updated results to the results output module.

[0076] Example 3: The present invention provides a computer-readable storage medium, which includes a stored program that, when the program is running, controls the device where the computer-readable storage medium is located to execute the forestry carbon sink monitoring method based on big data as described in Embodiment 2.

[0077] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Additionally, it should be noted that the flowcharts in the accompanying drawings illustrate methods according to embodiments of this disclosure. In the descriptions corresponding to the flowcharts or block diagrams in the drawings, the operations or steps corresponding to different blocks may occur in a different order than disclosed in the description; sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the function involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0080] 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 forestry carbon sequestration monitoring system based on big data, characterized in that, It includes a multi-source data acquisition module, a data preprocessing and standardization module, a spatiotemporal registration module, a heterogeneous fusion module, a multi-scale carbon sink estimation module, a dynamic calibration module, and a result output module; The multi-source data acquisition module is used to collect satellite remote sensing data, UAV remote sensing data, ground sample plot survey data, IoT environmental sensing data, and historical forestry resource archive data for the target forest area; The data preprocessing and standardization module is connected to the multi-source data acquisition module and is used to denoise, unify coordinate systems, unify timestamps, convert formats, fill in missing data, and mark quality data to obtain standardized monitoring data. The spatiotemporal registration module is linked with the data preprocessing and standardization module to perform spatial and temporal alignment of standardized monitoring data according to preset spatial units and time series. The heterogeneous fusion module is linked with the spatiotemporal registration module to perform feature association, weight allocation and fusion processing on the monitoring data that has been aligned spatially and temporally, forming a spatiotemporal fusion database of forestry carbon sinks; The multi-scale carbon sink estimation module is connected to the heterogeneous fusion module to extract regional-scale carbon sink estimation features and local fine-scale carbon sink estimation features from the forestry carbon sink spatiotemporal fusion database, so as to obtain regional-scale carbon storage estimation results and local fine-scale carbon storage correction results. The dynamic calibration module is connected to the multi-scale carbon sink estimation module to update the regional-scale carbon storage estimate and the local fine-scale carbon storage correction results, so as to obtain the carbon storage results and carbon sink dynamic change results of the target forest area. The results output module is linked to the dynamic calibration module to output carbon storage distribution maps, carbon sink change maps, abnormal change early warning information, and monitoring reports.

2. The forestry carbon sequestration monitoring system based on big data according to claim 1, characterized in that, The multi-source data acquisition module includes: a satellite remote sensing acquisition submodule, an UAV remote sensing acquisition submodule, a ground sample plot acquisition submodule, an IoT sensing acquisition submodule, and a historical archive access submodule; The satellite remote sensing acquisition submodule is used to collect multispectral images, radar images, and lidar altitude data of the target forest area at different time points; The UAV remote sensing acquisition submodule is used to acquire low-altitude images, point cloud data, or canopy structure data of the target forest area; The ground plot collection submodule is used to collect data on target location, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, and growth status. The IoT sensing and acquisition submodule is used to collect environmental factor data, including temperature, humidity, soil moisture, light intensity, and carbon dioxide concentration. The historical archive access submodule is used to collect forest resource inventory data, management operation records, afforestation records, and disturbance event records; The multi-source data acquisition module adds acquisition time, spatial coordinates, resolution level, source identifier, and initial credibility label to each piece of data during acquisition.

3. The forestry carbon sequestration monitoring system based on big data according to claim 1, characterized in that, The process of denoising, unifying coordinate systems, unifying timestamps, converting formats, completing missing data, and marking quality data to obtain standardized monitoring data includes the following steps: Perform radiometric correction, atmospheric correction, geometric correction, and orthorectification on satellite remote sensing data and UAV remote sensing data; Outlier removal, coordinate correction, and field completion were performed on the ground sample plot survey data. Perform time-series denoising, missing value imputation, and sensor drift correction on IoT environmental sensing data; Perform field mapping, format conversion, and semantic unification on historical forestry resource archive data; The processed data is uniformly mapped to a preset data structure, which includes at least a spatial location field, a time field, a tree species field, a forest stand structure field, an environmental factor field, a quality score field, and a source tracing field.

4. The forestry carbon sequestration monitoring system based on big data according to claim 1, characterized in that, The spatiotemporal registration module is used to: establish composite spatial units formed by superimposing forest compartment / compartment boundaries and regular grids, treat the composite spatial units as unified monitoring units, and generate a spatiotemporal aligned dataset for the same monitoring period and the same composite spatial unit; The process of generating a spatiotemporally aligned dataset with the same monitoring period and the same composite spatial unit includes the following steps: For high-resolution data, aggregate statistics are performed according to composite spatial units; For low-resolution data, pushdown allocation is performed using spatial neighborhood constraints and land class consistency constraints. Data collected at different times are time-aligned according to a preset monitoring cycle; Data acquired asynchronously is matched with proximity data or interpolated to form a unified time sequence segment.

5. The forestry carbon sequestration monitoring system based on big data according to claim 1, characterized in that, The multi-scale carbon sink estimation module adopts a two-stage estimation method of regional-scale coarse estimation and local fine-scale correction; Among them, the regional-scale rough estimation uses tree height, canopy closure, canopy coverage, and tree species information within the composite spatial unit to calculate the initial value of regional carbon storage. Local fine-scale correction: Using survey data of the target forest area and point cloud data from UAVs, parameters such as diameter at breast height, tree height, crown width or single tree structure are extracted to locally correct the initial value of carbon storage in the target forest area. The multi-scale carbon sink estimation module also includes: cross-scale consistency constraints. When the deviation between the aggregated value of the local fine-scale correction result and the regional scale coarse estimation result in the preset area is greater than the preset tolerance, the local fine-scale correction result is consistently reverted according to the fusion weight and spatial neighborhood relationship to obtain the regional scale carbon storage estimation result and the local fine-scale carbon storage correction result.

6. The forestry carbon sequestration monitoring system based on big data according to claim 1, characterized in that, The dynamic calibration module is also used to perform incremental calibration on the carbon storage results based on survey data and time-series monitoring data; and to trigger dynamic calibration when any of the following conditions are met: a) The changes in tree height, canopy density, canopy coverage, or plot boundary exceed the preset threshold within two adjacent monitoring periods; b) Identify events such as fires, pests and diseases, logging, afforestation, or land use changes; c) The deviation between the current estimated result and the actual measured result of the sample plot exceeds the preset error threshold.

7. The forestry carbon sequestration monitoring system based on big data according to claim 1, characterized in that, The results output module is used to generate carbon storage spatial distribution maps, carbon sink change thematic maps, plot-level statistical tables, periodic monitoring reports, and early warning information for abnormal change areas based on the results of dynamic calibration, and to generate result traceability files. The early warning information should include the location of the abnormal area, the type of abnormality, the magnitude of the change, and the recommended level of investigation. The results traceability file should include source data identifiers, preprocessing parameters, fusion weights, estimation model version, calibration timestamps, and anomaly handling records.

8. A forestry carbon sequestration monitoring method based on big data, characterized in that, Includes the following steps: Step 1: Acquire satellite remote sensing data, UAV remote sensing data, ground sample plot survey data, IoT environmental sensing data, and historical forestry resource archive data for the target forest area; Step 2: Denoise the acquired data, unify the coordinate system, unify the timestamps, convert the format, fill in missing data, and mark the quality to obtain standardized monitoring data; Step 3: Based on the preset spatial units and time series, perform spatial and temporal alignment on the standardized monitoring data to form a spatiotemporally aligned dataset; Step 4: Perform feature association, weight allocation, and fusion processing on the spatiotemporally aligned dataset to form a spatiotemporally fused forestry carbon sink database; Step 5: Extract regional-scale carbon sequestration estimation features and local fine-scale carbon sequestration estimation features from the forestry carbon sequestration spatiotemporal fusion database to obtain regional-scale carbon storage estimation results and local fine-scale carbon storage correction results; Step Six: Update the regional-scale carbon storage estimate and the local fine-scale carbon storage correction results based on the survey data and time-series monitoring data to obtain the carbon storage results and carbon sink dynamic changes of the target forest area; Step 7: Output carbon storage distribution map, carbon sink change map, abnormal change early warning information and monitoring report.

9. A computer-readable storage medium comprising a stored program, characterized in that, During program execution, the device containing the computer-readable storage medium is controlled to perform the forestry carbon sink monitoring method based on big data as described in claim 8.

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