Forestry investigation method and system for carbon sink forest management

Abstract

The invention relates to the technical field of forestry investigation, and discloses a forestry investigation system for carbon sink forest management, which comprises a data acquisition module, a data processing module, a data integration module, a model construction and analysis module, a decision and early warning module, an output module and an implementation monitoring module. Through multi-dimensional index investigation, information can be obtained from multiple levels of an ecological system, the carbon cycle process and ecological functions of the carbon sink forest are comprehensively known, more comprehensive data support is provided for scientific management, in addition, more variables influencing carbon sink are introduced, so that the model better conforms to the actual situation, the accuracy and generalization ability of the model are enhanced, and the method is suitable for popularization and application. According to the method, carbon reserve changes of the carbon sink forest under different conditions can be better predicted, finally, effect prediction of multiple management schemes is provided for managers through scene simulation and model analysis, scientific and reasonable management decision making is assisted, and meanwhile, an early warning mechanism can find potential risks in time and guarantee stable development of the carbon sink forest.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry surveys, and more specifically discloses a forestry survey method and system for carbon sink forest management. Background Art

[0002] A carbon sink forest refers to forestry activities that, through measures such as afforestation and forest protection, give full play to the carbon sink function of forests, absorb and fix carbon dioxide in the atmosphere, thereby reducing the concentration of carbon dioxide in the atmosphere and slowing down climate warming. Forestry surveys can accurately calculate and verify the carbon storage and emission reduction amounts of forestry carbon sink projects, provide a scientific basis for project decision-making, implementation, and evaluation, and at the same time help monitor and manage forest resources to ensure the health and sustainability of forests. Through regular forestry surveys, problems such as forest pests and diseases and illegal logging can be discovered and addressed in a timely manner, protecting and restoring forest vegetation, thereby enhancing the carbon sink capacity. In the prior art, the patent document with the authorization announcement number CN119152438B discloses "a method and system for remote sensing monitoring of forest carbon sinks"; it includes the following steps: setting monitoring content, selecting a monitoring area, where the monitoring content includes monitoring of carbon storage changes and forest health status; collecting high-resolution remote sensing images covering the entire monitoring area and obtaining multi-temporal images, and collecting ground survey data. The multi-temporal images are a set of images of the same area obtained at different time points, and the ground survey data includes tree height, tree diameter at breast height, tree species composition, and forest density.

[0003] Although the prior art has improved the accuracy and flexibility of prediction and generated high-resolution carbon storage distribution maps through efficient machine learning algorithms, the prior art mainly focuses on carbon storage changes and forest health status, only involving indicators such as tree height, diameter at breast height, tree species composition, forest density, and vegetation index, ignoring the impact of soil carbon dynamics, microbial activities on the carbon cycle, and the role of other organisms in the forest ecosystem, making it difficult to comprehensively evaluate the ecological functions and carbon sink potential of carbon sink forests. At the same time, external factors such as climate change and human activities are not fully considered, and it is impossible to accurately predict the changes in carbon storage of carbon sink forests under different scenarios. Summary of the Invention

[0004] The present invention mainly provides a forestry survey method and system for carbon sink forest management, which can solve the problems of single monitoring indicators and limited model construction proposed in the background art.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, it is a forestry survey method for carbon sink forest management, including: First, collect relevant data of the carbon sink forest with the help of satellites, drones, sensors and personnel, clean the collected data, remove outliers, fill in missing values by interpolation method and perform standardization processing to unify the dimension and format; Then, use GIS technology and data fusion technology to spatially associate and fuse multi-source heterogeneous data, give play to the advantages of each data, make the data complete and available, and lay the foundation for subsequent analysis; Then, use machine learning algorithms to construct a carbon storage prediction model, combine scenario analysis technology to simulate the response of the carbon sink forest, and analyze the changes of indicators; Again, according to the model analysis results, generate management decision suggestions through decision tree algorithms, set thresholds for key indicators, give early warnings when the monitored data exceeds the thresholds, and at the same time present the decision suggestions and analysis results in the form of maps, charts, etc. using visualization technology; Finally, use Internet of Things technology and image recognition technology to monitor the changes of indicators such as tree growth, soil environment, and vegetation cover of the carbon sink forest in real time after the implementation of management measures. When the expected goals are not achieved, feedback the monitoring information to the model construction and analysis links to re-optimize the model and decision-making, and realize the dynamic management of the carbon sink forest.

[0006] Furthermore, the satellites, drones, sensors and personnel can obtain macroscopic images of large areas and periodically of the carbon sink forest, high-resolution fine images of local areas, and information data related to meteorology, soil environment, trees, understory vegetation and animals in the carbon sink forest.

[0007] Furthermore, in the standardization processing of the data preprocessing, the minimum-maximum standardization and Z-score standardization methods are adopted to convert data with different dimensions into a unified standard form, which is convenient for subsequent analysis and model construction.

[0008] Furthermore, the machine learning algorithm constructs a model through regression algorithms and neural network algorithms. The regression algorithms and neural network algorithms cooperate with each other to improve the accuracy and generalization of the model, and at the same time enhance the non-linear modeling and feature learning ability to adapt to diverse data and task requirements.

[0009] Furthermore, the scenarios set in the scenario analysis technology include climate change scenarios and human activity scenarios, which help managers understand the possible changes of the carbon sink forest under different scenarios, formulate coping strategies in advance, and improve the foresight and scientificity of decision-making.

[0010] Furthermore, the Internet of Things technology uses sensors to monitor the changes of various indicators of the carbon sink forest in real time after the implementation of management measures, so as to realize continuous tracking of the management effect.

[0011] Furthermore, the image recognition technology uses the images obtained by satellites and drones for image recognition, analyzes the health status of trees and changes in vegetation cover, and improves the efficiency and accuracy of monitoring.

[0012] According to another aspect of the present invention, there is provided a forestry survey system for carbon sink forest management, which is implemented based on the above-mentioned forestry survey method for carbon sink forest management, and specifically includes: Through a data acquisition module composed of a satellite acquisition module, an unmanned aerial vehicle (UAV) acquisition module, a sensor acquisition module, and a personnel acquisition module, relevant data of the carbon sink forest is obtained. Subsequently, the data is cleaned in the data processing module to remove outliers, the interpolation method is used to fill in missing values, and normalization processing is performed to unify the data dimension and format. Then, using GIS technology and data fusion technology, multi-source heterogeneous data is spatially associated and fused in the data integration module to make the data complete and available. After that, in the model construction and analysis module, machine learning algorithms are used to construct prediction models such as carbon storage, and combined with scenario analysis technology to simulate the response of the carbon sink forest and analyze the changes of various indicators. Then, based on the model analysis results, in the decision-making and early warning module, management decision-making suggestions are generated through decision tree algorithms, thresholds are set for key indicators, and early warnings are given in a timely manner once the monitoring data exceeds the threshold. At the same time, the decision-making suggestions and analysis results are presented in the form of maps, charts, etc. using visualization technology and output through the output module. Finally, using Internet of Things technology and image recognition technology, in the implementation monitoring module, the changes in tree growth, soil environment, and vegetation coverage indicators of the carbon sink forest after the implementation of management measures are monitored in real time. If the expected goals are not achieved, the monitoring information is fed back to the model construction and analysis module to re-optimize the model and decision-making, realizing the dynamic management of the carbon sink forest.

[0013] Furthermore, the data acquisition module includes: a satellite acquisition module, an unmanned aerial vehicle (UAV) acquisition module, a sensor acquisition module, and a personnel acquisition module; The satellite acquisition module conducts large-area and periodic monitoring of the carbon sink forest from high altitude, and obtains rich spectral information through different electromagnetic wave bands, fully reflecting the different characteristics of the vegetation in the carbon sink forest; The unmanned aerial vehicle (UAV) acquisition module realizes the acquisition of high-resolution images of local areas of the carbon sink forest by controlling the UAV to fly at low altitude, providing timely and accurate data for the refined management of the carbon sink forest; The sensor acquisition module arranges meteorological sensors and soil sensors in the carbon sink forest to collect environmental data in real time, and transmits the data to the data center through wireless communication technology; The personnel acquisition module uses GPS equipment to determine the precise geographical locations of sample plots and trees, facilitating subsequent spatial data analysis and data integration.

[0014] The beneficial effects of the forestry survey method and system for carbon sink forest management based on the present invention are as follows: Through multi-dimensional index surveys, information can be obtained from multiple levels of the ecosystem, comprehensively understanding the carbon cycle process and ecological functions of carbon sink forests, providing more comprehensive data support for scientific management. In addition, more variables affecting carbon sinks are introduced to make the model more in line with the actual situation, enhancing the accuracy and generalization ability of the model, and being able to better predict the changes in carbon storage of carbon sink forests under different conditions. Finally, through scenario simulation and model analysis, the effects of multiple management plans are predicted for managers, assisting in formulating scientific and reasonable management decisions. At the same time, the early warning mechanism can timely detect potential risks and ensure the stable development of carbon sink forests. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described in detail below with reference to the drawings and specific implementation methods.

[0016] Figure 1 It is a schematic diagram of the system principle; Figure 2 It is a schematic diagram of the method flow. SPECIFIC IMPLEMENTATION MANNER

[0017] The present invention will be described in detail below with reference to the drawings and embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0018] According to one aspect of the present invention, as Figure 1-2 shown, a forestry survey method and system for carbon sink forest management are provided. First, through a data acquisition module composed of a satellite acquisition module, an unmanned aerial vehicle (UAV) acquisition module, a sensor acquisition module, and a personnel acquisition module, relevant data of the carbon sink forest are obtained; Among them, the satellite acquisition module, with its unique perspective of overlooking from high altitude, uses a large-area and periodic monitoring method to collect rich spectral information using different electromagnetic wave bands (for example, through the near-infrared band, it can keenly capture different characteristics of vegetation, clearly distinguish vegetation types, growth trends, and health conditions, providing key data support for macroscopically grasping the vegetation distribution of carbon sink forests); The UAV acquisition module, with its flexible low-altitude flight ability, conducts high-resolution image acquisition of local areas of the carbon sink forest. In the shooting of specific rare tree species areas or key monitoring plots, the high-definition images obtained by the UAV can be accurate to the details of each tree, providing indispensable data for refined management and helping managers deeply understand the vegetation conditions of local areas; The sensor acquisition module collects environmental data through meteorological sensors (collecting meteorological data such as temperature, humidity, and light) and soil sensors (environmental data such as soil temperature and humidity, pH value, and nutrient content). These sensors are like "antennae" distributed in the carbon sink forest, providing dynamic data for studying the interaction between the carbon sink forest and the environment; The personnel acquisition module uses GPS devices to accurately determine the geographical locations of sample plots and trees, which not only provides accurate coordinate information for spatial data analysis but also helps with subsequent data integration and precise positioning of on-site investigations, ensuring that all data can be accurately associated geographically; Through the mutual cooperation of the satellite acquisition module, the UAV acquisition module, the sensor acquisition module, and the personnel acquisition module, data is collected comprehensively from macro to micro, from static to dynamic, and from space to attribute, avoiding data missing and one-sidedness, and comprehensively reflecting the situation of the carbon sink forest (for example, when studying the vegetation distribution of the carbon sink forest, satellite data shows the overall pattern, UAV data presents the vegetation characteristics of specific areas, sensors provide environmental data for auxiliary analysis, and the positioning of staff ensures the spatial accuracy of data). In addition, satellites, UAVs, and sensors can obtain multi-source data, which can be mutually verified and supplemented to reduce errors. The data collected by staff on-site can calibrate and verify the data obtained by other technologies, and also meet the diverse needs of carbon sink forest management, improving the scientificity and effectiveness of management. At the same time, satellites and UAVs collect data periodically to monitor the dynamic changes of the carbon sink forest over time, sensors collect data in real-time to quickly feedback environmental changes, and on-site investigations by staff can discover potential problems. These data are transmitted and analyzed in real-time to provide timely information for managers to adjust management strategies, realizing the dynamic management and sustainable development of the carbon sink forest.

[0019] Then, the data processing module cleans the collected data, removes outliers, fills in missing values by interpolation method, and standardizes the data to unify the dimension and format; The data cleaning and outlier removal are based on statistical methods. Statistical quantities such as the mean and standard deviation of the data are calculated, and the 3σ principle (that is, data points that deviate from the mean by more than 3 times the standard deviation are regarded as outliers) is used to identify outliers (for example, in the data of the diameter at breast height of trees in the carbon sink forest, most tree diameters are between 10 - 30 cm. If there is a data point of 100 cm, far beyond the normal range, and after calculation, it deviates from the mean by more than 3 times the standard deviation, it can be determined as an outlier). Removing outliers can avoid their misleading effect on subsequent data analysis and model construction, ensure the accuracy and reliability of the data, and improve the stability and prediction accuracy of the model; The interpolation method to fill missing values ​​is to combine linear interpolation and spline interpolation. Linear interpolation is easy to calculate. When the data changes are relatively stable and the trend is approximately linear, it can quickly and accurately estimate the missing values. For example, when the diameter of the tree in the carbon sink forest grows steadily over a period of time, the missing values ​​can be filled in by linear relationships based on the previous and subsequent data, which can efficiently restore the continuity of the data. Spline interpolation can better fit the complex changing trends of the data by constructing piecewise polynomial functions. When the carbon sink forest is affected by seasonal changes, pests and diseases, etc., resulting in fluctuations in tree growth data, spline interpolation can accurately estimate the missing values ​​based on the complex trends of the surrounding data. The combination of the two not only gives play to the high efficiency of linear interpolation under simple trends, but also utilizes the accuracy of spline interpolation in dealing with complex changes, so that the filled data set can restore the authenticity of the data to the greatest extent while maintaining the overall trend of the data, providing a more reliable data basis for subsequent research on carbon sink forest growth analysis and carbon reserve prediction based on these data. The commonly used standardization methods for the final standardization process (unified dimensions and formats) are minimum-maximum standardization and Z-score standardization. The minimum-maximum standardization maps the data to the [0, 1] interval. The formula is: Where X is the original data, is the minimum value, For example, for the tree height data of carbon sink forest, the minimum value is 2 meters and the maximum value is 10 meters. If the minimum-maximum standardization is used, if the height of a tree is 5 meters, the standardized value is: (5-2) / (10-2)=0.375; Z-score standardization converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. The formula is: Where μ is the mean of the data and σ is the standard deviation. For example, suppose we have a set of DBH data (in centimeters) of trees in a carbon sink forest, which are 12, 15, 18, and 20 respectively. First, we calculate the mean μ of this set of data as (12+15+18+20) / 4=16.25), and the standard deviation σ is Taking 18 as an example, the standardized value is: (18-16.25) / 3.03=0.58; the combination of the two can not only take advantage of the Z-score standardization to handle outliers and stabilize data distribution, but also use the minimum-maximum standardization to accurately map the data to the required interval, thereby giving full play to their respective advantages in different data processing scenarios and analysis needs, improving the flexibility and accuracy of data processing, and providing a better data foundation for subsequent data analysis, modeling and other tasks; Data cleaning, interpolation filling, and standardization processing can improve data quality, eliminate the influence of dimensions, retain the data distribution characteristics, enhance the accuracy, comparability, and applicability of data, and provide high-quality and standardized data for the subsequent data integration module, facilitating the efficient and accurate fusion and analysis of data from different sources and types.

[0020] Next, the processed data in the data processing module is transmitted to the data integration module. Using GIS technology and data fusion technology, based on the vector boundary of the carbon sink forest area, the geographical coordinates of satellite images, UAV images are accurately matched with the geographical coordinates of the data collected by sensors, realizing the spatial association and fusion of multi-source heterogeneous data (for example, corresponding the vegetation distribution information in the satellite image with the soil data at the soil sensor collection points, so as to deeply analyze the mutual relationship between vegetation and soil environment and explore the ecological laws hidden behind the data), giving play to the advantages of each data, making the data complete and available, so as to deeply analyze the relationship between elements such as vegetation and soil environment and lay the foundation for subsequent analysis; Among them, when facing the analysis of specific elements such as soil and vegetation, the fusion process is as follows: First, geometric correction and radiometric calibration processing are carried out on satellite images and UAV images, and the conversion relationship between image coordinates and actual geographical coordinates is established through ground control points to ensure the spatial positioning accuracy; Second, for the attribute data such as soil temperature and humidity, pH value, and vegetation spectral reflectance collected by sensors, the discrete point data is converted into continuous raster data through a spatial interpolation algorithm (Kriging interpolation) to make it consistent with the image data in terms of spatial resolution and coordinate system; Finally, using the spatial overlay analysis function of GIS, the vegetation distribution raster layer, soil attribute raster layer, and carbon sink forest vector boundary layer are overlaid to construct a multi-dimensional spatial data cube, realizing the spatial coupling analysis of soil elements (such as nutrient content) and vegetation elements (such as leaf area index), and exploring the carbon cycle characteristics of the soil-vegetation system at different altitudes and slopes.

[0021] The model construction and analysis module uses machine learning algorithms to construct prediction models such as carbon storage, combines scenario analysis technology to simulate the response of carbon sink forests, and analyzes the changes of indicators; The machine learning algorithm organically combines the regression algorithm and the neural network algorithm. Among them, the regression algorithm can effectively analyze the linear relationship between carbon storage and factors such as tree height, diameter at breast height, and forest density, while the neural network algorithm is good at dealing with complex non-linear relationships, enhancing the non-linear modeling ability of the model. The two cooperate with each other, greatly improving the accuracy and generalization of the model. In practical applications, it can more accurately predict the change trend of carbon storage and provide a reliable basis for the scientific management of carbon sink forests; The specific model construction process is as follows: A hybrid model combining a regression algorithm and a neural network algorithm is adopted. First, variables with a Pearson correlation coefficient greater than 0.6 with carbon storage (such as tree diameter at breast height, tree height, soil moisture) are selected through the Pearson correlation coefficient. Then, 70% of the data is used as the training set and 30% as the test set. In the neural network model, the input layer (variable dimension), hidden layer (2 layers, with the number of neurons being 16 and 8 respectively), and output layer (predicted value of carbon storage) are set. The weights are optimized through the backpropagation algorithm, with the root mean square error (RMSE) as the loss function, and the model is iteratively trained until it converges. Finally, the model accuracy is verified through the test set to ensure that: R² > 0.85 Meanwhile, combined with scenario analysis techniques, multiple climate change scenarios (such as different degrees of temperature increase, changes in precipitation patterns) and human activity scenarios (such as different intensities of deforestation, afforestation activities, and changes in forest management methods) are set to simulate the responses of carbon sink forests under various circumstances. Through this simulation analysis, managers can gain insights into the possible change trends of carbon sink forests in advance, so as to formulate more targeted countermeasures. The specific analysis methods are as follows: In the climate change scenario, an ARIMA time series model is constructed based on historical meteorological data (temperature, precipitation) to predict the changes in meteorological factors under different greenhouse gas emission scenarios (such as high emission, medium emission, low emission), and the predicted values are input into the carbon storage prediction model to simulate the carbon sink response. In the human activity scenario, the rule branches of different harvesting intensities (such as retaining 70% of the trees in light harvesting and 30% of the trees in heavy harvesting) are set through the decision tree algorithm to simulate the change trends of carbon storage under different management measures, and a scenario analysis matrix is generated (such as the carbon storage fluctuation range under the combined scenario of "temperature + precipitation + harvesting intensity").

[0022] Then, the decision-making and early warning module generates management decision suggestions through the decision tree algorithm based on the model analysis results, providing managers with intuitive decision-making basis (for example, the root node of the decision tree is the change in carbon storage. According to different carbon storage change thresholds, the branch nodes correspond to different management measures, such as fertilization, irrigation, and logging, etc.), setting thresholds for key indicators, and issuing early warnings when monitoring data exceeds the threshold (taking carbon storage as an example, when carbon storage is close to or lower than the set reduction threshold, the system will quickly issue an alarm to remind managers to take timely measures). At the same time, the visualization technology in the output module presents decision suggestions and analysis results in the form of maps, charts, etc., so that managers can quickly and comprehensively understand the overall situation and change trend of carbon sink forests, so as to make scientific decisions. The specific implementation method is as follows: generate management strategies through the decision tree algorithm, for example, take the annual change rate of carbon storage as the root node, set threshold branches (such as <-5% triggers a first-level warning, -3%~-5% triggers a second-level warning); automatically generate "logging ban + artificial replanting" when the first-level warning occurs Strategy: When the second-level warning is issued, a "cutting restriction + fertilization" strategy is generated; at the same time, GIS visualization technology is used to generate a heat map of the warning area, superimposed with a soil fertility layer and a vegetation health index layer, to assist in locating key management areas; warning information is pushed to management personnel simultaneously through system pop-ups and text messages.

[0023] Finally, by implementing the Internet of Things technology (using sensor networks) and image recognition technology (using drones and satellites) in the monitoring module, the tree growth, soil environment, vegetation coverage and other indicators of the carbon sink forest after the management measures are implemented are monitored in real time. If the expected goals are not achieved, the monitoring information will be fed back to the model construction and analysis link to re-optimize the model and decision-making to achieve dynamic management of the carbon sink forest; IoT sensors are like "data collection stations" that continuously collect data on tree growth, soil environment, vegetation coverage and other indicators, and transmit these data to the data center in real time through wireless communication technology. Image recognition technology conducts in-depth analysis of images obtained by satellites and drones, and can quickly and accurately identify the occurrence area, scope and severity of tree diseases and insect pests, and monitor the dynamic changes of vegetation coverage. By combining the Internet of Things technology (using sensor networks) and image recognition technology (using drones and satellites), we can not only grasp the distribution and dynamics of carbon sink forests from a macro perspective, but also have an in-depth understanding of every detail at the micro level. We can also quickly detect abnormal changes in carbon sink forests through real-time data feedback, thereby providing solid support for timely adjustment of management strategies and ensuring the ecological functions and carbon sequestration capabilities of carbon sink forests.

[0024] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention also fall within the protection scope of the present invention.

Claims

1. A forestry survey method for carbon sink forest management, characterized in that, The method includes: First, collect relevant data of the carbon sink forest with the help of satellites, drones, sensors and personnel, clean the collected data, remove outliers, fill in missing values by interpolation method and perform standardization processing to unify the dimension and format; Then, use GIS technology and data fusion technology to spatially associate and fuse multi-source heterogeneous data, give play to the advantages of each data, make the data complete and available, and lay the foundation for subsequent analysis; Then, use machine learning algorithms to build a carbon storage prediction model, combine scenario analysis technology to simulate the response of the carbon sink forest, and analyze the changes of indicators; Again, according to the model analysis results, generate management decision suggestions through decision tree algorithms, set thresholds for key indicators, give early warnings when the monitored data exceeds the thresholds, and at the same time present the decision suggestions and analysis results in the form of maps, charts, etc. using visualization technology; Finally, use Internet of Things technology and image recognition technology to monitor in real time the changes of indicators such as tree growth, soil environment, and vegetation coverage of the carbon sink forest after the implementation of management measures. When the expected goals are not achieved, feedback the monitoring information to the model construction and analysis links to re-optimize the model and decision-making, and realize the dynamic management of the carbon sink forest.

2. The forestry survey method for carbon sink forest management according to claim 1, wherein: The satellites, drones, sensors and personnel can obtain macroscopic images of large areas and periodicity of the carbon sink forest, high-resolution fine images of local areas, and information data related to meteorology, soil environment, trees, understory vegetation and animals in the carbon sink forest.

3. The forestry survey method for carbon sink forest management according to claim 1, wherein: In the standardization processing in the data preprocessing, the minimum-maximum standardization and Z-score standardization methods are adopted to convert data with different dimensions into a unified standard form, which is convenient for subsequent analysis and model construction.

4. The forestry survey method for carbon sink forest management according to claim 1, wherein: The machine learning algorithms build models through regression algorithms and neural network algorithms. The regression algorithms and neural network algorithms cooperate with each other to improve the accuracy and generalization of the models, and at the same time enhance the non-linear modeling and feature learning capabilities to adapt to diverse data and task requirements.

5. The forestry survey method for carbon sink forest management according to claim 1, wherein: The scenarios set in the scenario analysis technology include climate change scenarios and human activity scenarios, which help managers understand the possible changes of the carbon sink forest under different scenarios, formulate coping strategies in advance, and improve the forward-looking and scientific nature of decision-making.

6. The forestry survey method for carbon sink forest management according to claim 1, wherein: The Internet of Things technology is to use sensors to monitor in real time the changes of various indicators of the carbon sink forest after the implementation of management measures, and realize continuous tracking of the management effects.

7. The forestry survey method for carbon sink forest management according to claim 1, characterized in that: The image recognition technology is to perform image recognition on the images obtained by satellites and drones, analyze the health status of trees and the changes in vegetation coverage, and improve the efficiency and accuracy of monitoring.

8. A forestry survey system for carbon sink forest management, characterized in that, The system is implemented based on a forestry survey method for carbon sink forest management described in any one of claims 1-7, and specifically includes: obtaining relevant data of the carbon sink forest through a data acquisition module composed of a satellite acquisition module, an unmanned aerial vehicle (UAV) acquisition module, a sensor acquisition module, and a personnel acquisition module. Subsequently, the data is cleaned in the data processing module to remove outliers, the interpolation method is used to fill in missing values, and normalization processing is performed to unify the data dimension and format. Then, using GIS technology and data fusion technology, multi-source heterogeneous data is spatially associated and fused in the data integration module to make the data complete and available. After that, in the model construction and analysis module, machine learning algorithms are used to construct prediction models such as carbon storage, and combined with scenario analysis technology to simulate the response of the carbon sink forest and analyze the changes of various indicators. Then, based on the model analysis results, in the decision-making and early warning module, management decision-making suggestions are generated through decision tree algorithms, thresholds are set for key indicators, and early warnings are given in a timely manner once the monitoring data exceeds the thresholds. At the same time, visualization technology is used to present the decision-making suggestions and analysis results in the form of maps, charts, etc. and output them through the output module. Finally, using Internet of Things technology and image recognition technology, in the implementation monitoring module, the changes in tree growth, soil environment, and vegetation coverage indicators of the carbon sink forest after the implementation of management measures are monitored in real time. If the expected goals are not achieved, the monitoring information is fed back to the model construction and analysis module to re-optimize the model and decision-making, realizing the dynamic management of the carbon sink forest.

9. The forestry survey system for carbon sink forest management according to claim 8, wherein: The data acquisition module includes: a satellite acquisition module, an unmanned aerial vehicle (UAV) acquisition module, a sensor acquisition module, and a personnel acquisition module; The satellite acquisition module conducts large-area and periodic monitoring of the carbon sink forest from high altitude, and obtains rich spectral information through different electromagnetic wave bands, fully reflecting the different characteristics of the vegetation in the carbon sink forest; The unmanned aerial vehicle (UAV) acquisition module realizes the acquisition of high-resolution images of local areas of the carbon sink forest by controlling the UAV to fly at low altitude, providing timely and accurate data for the refined management of the carbon sink forest; The sensor acquisition module arranges meteorological sensors and soil sensors in the carbon sink forest to collect environmental data in real time and transmits the data to the data center through wireless communication technology; The personnel acquisition module uses GPS devices to determine the precise geographical locations of sample plots and trees, facilitating subsequent spatial data analysis and data integration.

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