Soil carbon sink accounting and optimized layout system based on GIS (Geographic Information System) analysis

Abstract

The invention relates to the technical field of environmental science and land management, and discloses a GIS analysis-based soil carbon sink accounting and optimization layout system, which comprises a data acquisition module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module is used for acquiring high-resolution remote sensing data through laser radar equipment and a hyperspectral camera; the data processing module is used for performing radiation correction, atmospheric correction, geometric correction and data fusion processing on the remote sensing data; the data analysis module is used for carrying out carbon reserve estimation and analysis on the processed data through geographic information system software and a machine learning algorithm; the optimization layout module is used for carrying out land utilization optimization layout based on the carbon reserve analysis result; and the visual display module is used for displaying the carbon sink accounting result and the optimized layout scheme through an interactive interface. According to the method, data accuracy is improved through advanced data verification, land use is optimized by comprehensively considering multiple factors through a multi-objective optimization model, and decision-making efficiency is improved through a modern user interface and a data visualization technology.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and land management technology, and in particular to a soil carbon sink accounting and optimization layout system based on GIS analysis. Background Art

[0002] The soil carbon sink accounting and optimization layout system is a key tool in the field of environmental science and land management, which aims to quantify and manage carbon storage on land resources and optimize land use to support ecological protection and socio-economic development. This system analyzes factors such as soil, vegetation and terrain, evaluates the impact of different land use strategies on carbon storage, and puts forward land use optimization suggestions to enhance ecosystem services and improve the comprehensive utilization efficiency of land.

[0003] At present, most soil carbon sink accounting systems rely on remote sensing technology and geographic information systems for data collection and analysis. These systems usually integrate soil type, vegetation cover and climate data to simulate the carbon cycle process, and use statistical or model methods to estimate regional carbon storage. Optimization layout uses simple mathematical models or decision support tools to consider ecological, economic and social factors and propose the best land use plan. These systems support environmental management and policy making to a certain extent.

[0004] Although the existing system has a certain functional foundation, there are still some shortcomings. First, the existing soil carbon sink accounting and optimization layout system relies on data input from remote sensing technology and geographic information systems, and these data sources include satellite images, ground monitoring stations, etc. Although these technologies are extremely useful in data collection, they often face data loss due to cloud cover, instrument failure, or data transmission errors. In addition, traditional land use optimization usually focuses on a single or a few goals, such as maximizing economic benefits or minimizing environmental impacts, while ignoring the comprehensive management of other important goals such as carbon storage, biodiversity, and water resources. This approach shows obvious limitations when dealing with complex interactions between ecosystems and socio-economic systems. Finally, the user interface and data visualization functions of existing systems are usually insufficient and fail to provide sufficient interactivity and intuitiveness, which reduces the user experience and the efficiency of decision support. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a soil carbon sink accounting and optimization layout system based on GIS analysis. The present invention improves data integrity and accuracy through advanced data verification tools, and solves the data loss and error problems in the prior art. A multi-objective optimization model is used to comprehensively consider factors such as carbon storage and biodiversity to optimize land use decisions. Modern user interfaces and data visualization technologies enhance user experience and interactivity, and improve decision-making efficiency.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a soil carbon sink accounting and optimization layout system based on GIS analysis, comprising:

[0007] Data acquisition module, used to obtain high-resolution remote sensing data through lidar equipment and hyperspectral cameras;

[0008] A data processing module, used for performing radiation correction, atmospheric correction, geometric correction and data fusion processing on the remote sensing data;

[0009] Data analysis module for carbon stock estimation and analysis of processed data using geographic information system software and machine learning algorithms;

[0010] Optimization layout module, used to optimize land use layout based on carbon storage analysis results;

[0011] Visual display module, used to display carbon sink accounting results and optimized layout plans through an interactive interface;

[0012] The data verification module is used to perform quality control on the collected data to ensure the accuracy and consistency of the data.

[0013] Preferably, the data acquisition module includes:

[0014] LiDAR equipment to obtain point cloud data of terrain and vegetation structure;

[0015] Hyperspectral cameras to obtain spectral data on vegetation health and biochemical properties;

[0016] UAV platform used to carry lidar equipment and hyperspectral cameras for data collection.

[0017] Preferably, the data processing module includes:

[0018] A tool for denoising and classifying LiDAR point cloud data, the tool being LAStools;

[0019] A processing tool for generating digital elevation models and digital surface models, wherein the tool is ArcGIS;

[0020] The invention relates to software for performing radiation correction, atmospheric correction and geometric correction on hyperspectral data, wherein the software is environmental visualization image analysis software.

[0021] Preferably, the data analysis module includes:

[0022] A carbon stock estimation model based on a machine learning algorithm that is trained and predicted using input data on topography, vegetation structure, and health;

[0023] A tool for generating carbon stock reports containing graphical and map representations of carbon stocks.

[0024] Preferably, the machine learning algorithm is a random forest model, and the formula of the random forest model is:

[0025]

[0026] in, is the predicted value, N is the number of trees, T i (x) is the predicted value of the i-th tree.

[0027] Preferably, the optimized layout module includes:

[0028] Land use planning tools based on multi-objective optimization models that include multiple objective functions such as carbon storage, water resources, biodiversity and socio-economic benefits;

[0029] Tools for generating optimized layout scenarios that balance various land use requirements and maximize ecological and socio-economic benefits.

[0030] Preferably, the objective function formula of the multi-objective optimization model is:

[0031]

[0032] Among them, Z is the objective function, w i is the target weight, O i is the target value.

[0033] Preferably, the constraints of the multi-objective optimization model are:

[0034]

[0035] Among them, A j is the area of ​​the jth land use type, A total is the total land area.

[0036] Preferably, the visual display module includes:

[0037] Web front-end developed based on HTML5, CSS3 and JavaScript, using React.js and Vue.js frameworks;

[0038] The backend application program interface is developed based on Node.js and Express framework, using PostgreSQL database to store spatial data;

[0039] Use D3.js for chart visualization and Leaflet and Mapbox for map visualization.

[0040] Preferably, the data verification module includes:

[0041] Data integrity checking tools to detect loss and errors during data collection;

[0042] Data consistency verification tools are used to ensure the consistency of data from different data sources in space and time;

[0043] Data accuracy verification tool, used to verify the accuracy of remote sensing data by comparing it with ground truth data.

[0044] The present invention provides a soil carbon sink accounting and optimization layout system based on GIS analysis. It has the following beneficial effects:

[0045] 1. By integrating advanced data verification tools such as data integrity check, consistency check and accuracy verification, the system significantly improves the accuracy of remote sensing and ground data processing, ensures the quality of data before analysis, thereby improving the accuracy of model predictions and the scientific nature of decision-making, and reducing the risks caused by data errors.

[0046] 2. The present invention uses a multi-objective optimization model to systematically balance multiple objectives such as carbon storage, water resource protection, biodiversity and socio-economic benefits, which not only improves the efficiency of land resource use, but also promotes the sustainable development of ecological protection and socio-economy.

[0047] 3. The present invention provides an intuitive and interactive user interface by adopting HTML5, CSS3, JavaScript, D3.js, Leaflet and Mapbox, thereby making complex data and analysis results easier to understand and accept, enhancing user participation and transparency of the decision-making process. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the specification 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.

[0050] Please see attached Figure 1 The embodiment of the present invention provides a soil carbon sink accounting and optimization layout system based on GIS analysis, including:

[0051] The data acquisition module is used to obtain high-resolution remote sensing data through lidar equipment and hyperspectral cameras. Lidar establishes a high-precision three-dimensional surface model by accurately measuring the flight time of laser pulses. The hyperspectral camera records the spectral characteristics of light reflected from the surface, which is used to analyze the composition of surface materials and the physiological state of vegetation. By providing high-precision and multi-dimensional surface data, it provides reliable input for subsequent data processing and analysis, thereby improving the accuracy of carbon storage estimation and the scientific nature of land use planning;

[0052] The data processing module is used to perform radiation correction, atmospheric correction, geometric correction and data fusion processing on remote sensing data. Radiation correction compensates for sensor errors and environmental changes by adjusting the spectral curve of sensor response. Atmospheric correction is based on the atmospheric model to calculate and eliminate the scattering and absorption effects of the atmosphere on the light signal. The angle and position of the geometric correction image are guaranteed to ensure the accuracy of geographic positioning, thereby significantly improving the quality and availability of the data and ensuring the accuracy and reliability of subsequent analysis;

[0053] Data analysis module, which is used to estimate and analyze carbon storage of processed data through geographic information system software and machine learning algorithms. The data analysis module provides spatial analysis tools to visualize and analyze geographic location data. Machine learning algorithms can identify the complex relationship between surface features and carbon storage by training large amounts of geographic and spectral data, thereby providing accurate carbon storage estimates, providing a scientific basis for policy making and resource management, and enhancing data support for decision-making;

[0054] The optimization layout module is used to optimize the layout of land use based on the results of carbon storage analysis. Through a multi-objective optimization algorithm, it comprehensively considers factors such as carbon storage, ecological protection and land development to form the optimal plan for land use. The algorithm is used to find the optimal solution set to achieve a balance between different goals, thereby improving the utilization efficiency of land resources, while protecting and increasing carbon sinks, and promoting sustainable development of ecology and social economy;

[0055] Visualization display module, which is used to display carbon sink accounting results and optimization layout plans through an interactive interface. It uses network technology to intuitively display complex data in the form of graphics and maps, so that non-professional users can easily understand the analysis results and decision-making basis, thereby improving the accessibility and comprehensibility of data and enhancing the participation and transparency of policymakers and the public;

[0056] The data verification module is used to perform quality control on the collected data to ensure the accuracy and consistency of the data. By comprehensively verifying the integrity, consistency and accuracy of the data, errors and inconsistencies in the data collection and processing process can be discovered and corrected, thereby improving the accuracy and reliability of the entire system carbon sink estimation and land planning, and providing users with more reliable decision-making support.

[0057] Please see attached Figure 1 In a preferred embodiment of the present invention, the data acquisition module includes:

[0058] LiDAR equipment is used to obtain point cloud data of terrain and vegetation structure. LiDAR uses the principle of constant light speed to measure distance. A light beam is emitted from the device, hits the object and is reflected back. The device determines the distance by measuring the round-trip time of the light beam. With these distance data, accurate three-dimensional models of the surface and objects can be constructed, which provides the necessary basic data for accurate assessment of soil carbon storage, making carbon sink estimation more accurate and reliable. In addition, high-precision terrain data is essential for planning land use and assessing vegetation health, enhancing the effectiveness of land management and environmental monitoring;

[0059] Hyperspectral cameras are used to obtain spectral data on vegetation health and biochemical properties. Hyperspectral imaging technology works by recording how different wavelengths of light are absorbed or reflected by surface materials. Each material, especially different types of vegetation, has its own unique spectral signature. Analyzing these signatures, surface materials can be identified and classified, and the health of vegetation can be assessed. The detailed spectral information obtained by hyperspectral cameras can greatly improve the accuracy of vegetation classification and the meticulousness of ecological health assessments. In addition, timely acquisition of this data can help respond quickly to changes in vegetation ecology and optimize environmental protection and resource management strategies;

[0060] UAV platforms are used to carry lidar equipment and hyperspectral cameras for data collection. UAV platforms are operated by remote control or automatic navigation systems according to preset flight paths. Able to fly at low altitudes, providing a more detailed data collection perspective than satellites, using drones for data collection has the advantages of high cost-effectiveness, flexible operation, and fast data acquisition. It can quickly collect the required data without disturbing the ground environment, which is particularly suitable for inaccessible or sensitive ecological areas, thereby increasing the frequency of data updates and providing instant and accurate data support for land use planning and environmental monitoring.

[0061] Please see attached Figure 1 In a preferred embodiment of the present invention, the data processing module includes:

[0062] Tools for denoising and classifying LiDAR point cloud data. The tool is LAStools. LAStools is a set of software tools for processing LiDAR data. It can perform denoising, classification and other preprocessing steps on the raw point cloud data collected from LiDAR equipment. Through effective data cleaning and classification, LAStools improves the quality and usability of data, which is crucial for subsequent soil carbon storage analysis and terrain modeling. Accurate point cloud data processing can significantly improve the accuracy of terrain modeling and provide a solid data foundation for land use planning and environmental management;

[0063] ArcGIS is a processing tool used to generate digital elevation models and digital surface models. ArcGIS is used to generate digital elevation models and digital surface models from processed LIDAR point cloud data. Digital elevation models represent the exposed surface of the ground, while digital surface models include all objects on the ground, such as trees and buildings. The generated digital elevation models and digital surface models provide important terrain information for soil carbon sink estimation, affecting the calculation of carbon reserves, such as the impact of terrain undulations on the carbon storage capacity of ecosystems. In addition, accurate terrain models are equally important for other applications such as flood control, urban planning, and disaster management, enhancing the scientificity and effectiveness of decision-making;

[0064] Software used to perform radiometric, atmospheric and geometric corrections on hyperspectral data. The software is environmental visualization image analysis software. Environmental visualization image analysis software is used to perform radiometric, atmospheric and geometric corrections on hyperspectral data. These correction steps ensure that the data acquired from the hyperspectral camera conforms to the actual surface conditions before analysis, without distortion or error. Radiometric correction corrects data errors caused by sensor errors and environmental factors. Atmospheric correction deals with the effects of atmospheric scattering and absorption of light signals, and geometric correction ensures that each pixel of the image accurately corresponds to a specific location on the ground.

[0065] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the data analysis module includes:

[0066] Carbon storage estimation model based on machine learning algorithm. The model is trained and predicted by inputting terrain, vegetation structure and health data. The input terrain, vegetation structure and health data are processed and analyzed by machine learning algorithm to improve the accuracy and efficiency of estimation. It can also handle a large number of complex variables and learn how vegetation and soil types affect carbon storage, thus providing more scientific carbon management recommendations and land use planning.

[0067] A tool for generating carbon storage reports, which contain charts and maps of carbon storage. The reporting tool creates dynamic and static views that can show in detail the geographical distribution of carbon storage and its changes over time. By integrating and interpreting the output of the model, the tool can automatically generate reports containing explanatory text and graphics, providing users with intuitive data interpretation, thereby helping policymakers, researchers and the public to better understand carbon sink dynamics and promote the formulation and implementation of environmental protection and sustainable development policies. In addition, this automated report generation tool greatly reduces the workload of manual data processing and report writing, and improves work efficiency and the frequency of report updates.

[0068] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the machine learning algorithm is a random forest model, and the formula of the random forest model is:

[0069]

[0070] in, is the predicted value, N is the number of trees, T i (x) is the predicted value of the i-th tree. Random forest is an ensemble learning method that improves the accuracy and robustness of the model by constructing multiple decision trees and aggregating their prediction results. Each tree is trained on a different random sample of the data set, and a randomly selected feature set is used to determine the split point. The use of random forest models in carbon storage estimation can effectively handle complex nonlinear relationships and high-dimensional data, providing more accurate and stable prediction results. In addition, it also reduces the risk of overfitting the model, making the model more robust and easy to generalize to new and unseen data.

[0071] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the layout optimization module includes:

[0072] Land use planning tools based on multi-objective optimization models, including multiple objective functions such as carbon storage, water resources, biodiversity and socio-economic benefits. Multi-objective optimization is a technique for finding the optimal compromise between multiple conflicting objectives. In this system, this technique is used to comprehensively consider the needs of carbon storage maximization, water resource protection, biodiversity maintenance and socio-economic development. By optimizing carbon storage and biodiversity objectives, the model helps identify the most beneficial land use strategies for the ecosystem and promote ecological balance;

[0073] A tool for generating optimized layout plans that balance various land use needs and maximize ecological and socio-economic benefits. Based on geographic information systems and advanced computing technologies, the tool generates land use recommendations through spatial data processing and algorithm optimization. Geographic information systems provide spatial analysis capabilities to help identify areas suitable for different uses, while optimization algorithms find the best combination and layout based on the set objectives and constraints, and can analyze and plan the use of land resources to ensure that land use decisions are scientific, reasonable, and in line with long-term sustainable development goals.

[0074] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the objective function formula of the multi-objective optimization model is:

[0075]

[0076] Among them, Z is the objective function, w i is the target weight, O i The multi-objective optimization model handles multiple competing objectives by assigning weights to each objective. The weight represents the importance of each objective. By setting different weight values, the model's focus on different objectives can be adjusted. The optimization process uses an algorithm to find a solution that maximizes the comprehensive objective function. This solution is the optimal land use solution that satisfies the trade-offs between different objectives. Using a multi-objective optimization model, multiple objectives such as environmental protection and economic development can be considered simultaneously, providing a comprehensive decision support tool.

[0077] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the constraints of the multi-objective optimization model are:

[0078]

[0079] Among them, A j is the area of ​​the jth land use type, A total The total land area is the total land area. This constraint ensures the practical feasibility of the planning scheme and prevents over-allocation of land resources. The use of the total land area constraint not only ensures the legality and practical enforceability of land use planning, but also protects the environment and other key resources from being over-exploited. This constraint prompts decision makers to consider more precisely how to allocate land resources to maximize environmental protection, socio-economic development and other possible benefits.

[0080] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the visualization display module includes:

[0081] The web front-end is developed based on HTML5, CSS3 and JavaScript, using React.js and Vue.js frameworks. HTML5 provides the structure for building web page content, CSS3 is used for style design, and JavaScript is responsible for the interactive logic of the page. React.js and Vue.js frameworks enhance the responsiveness and interactivity of web applications, and optimize page rendering performance through virtual DOM, making the user interface more responsive and the user experience smoother. These technologies make front-end development more efficient and dynamic, and can reflect back-end data changes in real time, thereby providing users with an intuitive and responsive interface. This not only makes the presentation of carbon sink data and land use plans more attractive, but also makes it easier for users to understand complex data and analysis results;

[0082] The back-end application program interface developed based on Node.js and Express framework uses PostgreSQL database to store spatial data. The back-end application program interface is developed based on Node.js and Express framework and connected to PostgreSQL database. Node.js provides an efficient, event-driven platform that can handle multiple connections, while the Express framework simplifies the development of routing and middleware. PostgreSQL is used as a database to store and manage spatial data and other related data, so that the front-end application can quickly and reliably access the latest data;

[0083] Use D3.js for chart visualization, and Leaflet and Mapbox for map visualization. D3.js allows developers to create highly customized data visualizations by binding data to the DOM and using the widely used SVG format. Leaflet is a lightweight map library that provides a variety of map interaction functions; Mapbox provides more powerful map services that support large-scale spatial data processing and complex map styles, allowing complex carbon sink data and land use analysis results to be clearly presented in the form of graphics and maps, greatly improving the acceptability and comprehensibility of information. Through interactive charts and maps, users can intuitively see data patterns and trends, which promotes deeper data exploration and analysis.

[0084] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the data verification module includes:

[0085] Data integrity check tools are used to detect loss and errors in the data collection process. Data integrity check tools use CRC and hash check algorithms to ensure that data has not been tampered with or damaged during storage or transmission, so that data problems can be discovered in advance, preventing erroneous data from being used for subsequent analysis, and improving the quality and reliability of data processing;

[0086] Data consistency check tool, used to ensure the consistency of data from different data sources in space and time, uses advanced comparison algorithms and rule engines to compare data items recorded in geographic information systems. By automatically identifying and adjusting differences between data, such as time zone differences and mismatched coordinate systems, it ensures that all data are integrated into a unified analysis framework, thereby improving the accuracy and credibility of model predictions;

[0087] The data accuracy verification tool is used to verify the accuracy of remote sensing data by comparing it with ground measured data. The tool uses regression analysis and error analysis statistical methods to compare the consistency of ground measurement data and remote sensing data. By regularly calibrating with ground data, the performance of the data processing algorithm can be continuously improved to ensure the scientificity and practicality of soil carbon sink estimation.

[0088] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A soil carbon sink accounting and optimization layout system based on GIS analysis, characterized by: include: Data acquisition module, used to obtain high-resolution remote sensing data through lidar equipment and hyperspectral cameras; A data processing module, used for performing radiation correction, atmospheric correction, geometric correction and data fusion processing on the remote sensing data; Data analysis module for carbon stock estimation and analysis of processed data using geographic information system software and machine learning algorithms; Optimization layout module, used to optimize land use layout based on carbon storage analysis results; Visual display module, used to display carbon sink accounting results and optimized layout plans through an interactive interface; The data verification module is used to perform quality control on the collected data to ensure the accuracy and consistency of the data.

2. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The data acquisition module comprises: LiDAR equipment to obtain point cloud data of terrain and vegetation structure; Hyperspectral cameras to obtain spectral data on vegetation health and biochemical properties; UAV platform used to carry lidar equipment and hyperspectral cameras for data collection.

3. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The data processing module comprises: A tool for denoising and classifying LiDAR point cloud data, the tool being LAStools; A processing tool for generating digital elevation models and digital surface models, wherein the tool is ArcGIS; The invention relates to software for performing radiation correction, atmospheric correction and geometric correction on hyperspectral data, wherein the software is environmental visualization image analysis software.

4. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The data analysis module includes: A carbon stock estimation model based on a machine learning algorithm that is trained and predicted using input data on topography, vegetation structure, and health; A tool for generating carbon stock reports containing graphical and map representations of carbon stocks.

5. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The machine learning algorithm is a random forest model, and the formula of the random forest model is: in, is the predicted value, N is the number of trees, T i (x) is the predicted value of the i-th tree.

6. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The optimized layout module includes: Land use planning tools based on multi-objective optimization models that include multiple objective functions such as carbon storage, water resources, biodiversity and socio-economic benefits; Tools for generating optimized layout scenarios that balance various land use requirements and maximize ecological and socio-economic benefits.

7. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 6 is characterized in that: The objective function formula of the multi-objective optimization model is: Among them, Z is the objective function, w i is the target weight, O i is the target value.

8. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 7 is characterized in that: The constraints of the multi-objective optimization model are: Among them, A j is the area of ​​the jth land use type, A total is the total land area.

9. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The visual display module includes: Web front-end developed based on HTML5, CSS3 and JavaScript, using React.js and Vue.js frameworks; The backend application program interface is developed based on Node.js and Express framework, using PostgreSQL database to store spatial data; Use D3.js for chart visualization and Leaflet and Mapbox for map visualization.

10. The soil carbon sink accounting and optimization layout system based on GIS analysis according to claim 1 is characterized in that: The data verification module comprises: Data integrity checking tools to detect loss and errors during data collection; Data consistency verification tools are used to ensure the consistency of data from different data sources in space and time; Data accuracy verification tool, used to verify the accuracy of remote sensing data by comparing it with ground truth data.

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