A method, device, equipment and medium for retrieving carbon storage of ground vegetation

Abstract

The application discloses a method and device for retrieving aboveground vegetation carbon storage, equipment and medium, and relates to the field of ecological monitoring. The method comprises the following steps: obtaining hyperspectral images of a study area; classifying each pixel in the hyperspectral images by using a pre-trained classification model to determine the vegetation type of each pixel; extracting features from the hyperspectral images to determine the remote sensing features of each pixel; retrieving the biomass in the study area by using a retrieval model according to the remote sensing features of each pixel to obtain the biomass of each pixel; the retrieval model is obtained by training a training sample set in advance, the training sample set comprises the remote sensing features and real biomass of each pixel in multiple sample plots, and the real biomass is determined based on the hyperspectral images and three-dimensional structure parameters of the sample plots; and the aboveground vegetation carbon storage of the study area is determined according to the carbon conversion coefficient of each vegetation, the vegetation type and the biomass of each pixel. The application realizes accurate retrieval of the aboveground vegetation carbon storage.

Description

Technical Field

[0001] This application relates to the field of ecological monitoring, and in particular to a method, apparatus, equipment and medium for retrieving carbon storage in aboveground vegetation. Background Technology

[0002] Aboveground biomass and carbon storage are important indicators for assessing carbon sinks and monitoring ecological conditions in forest ecosystems. However, traditional biomass / carbon storage inversion methods based on single spectral data from remote sensing are limited by factors such as missing structural information and scale inconsistencies, making it difficult to meet the needs of refined ecological monitoring. With the development of remote sensing and oblique photogrammetry technologies, high spatial resolution spectral and three-dimensional data can be acquired, but how to effectively integrate multi-source information and how to accurately map plot biomass to the image pixel scale remain research challenges. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, equipment and medium for retrieving aboveground vegetation carbon storage, which can realize pixel-level biomass retrieval and improve the accuracy of aboveground vegetation carbon storage.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a method for inverting aboveground vegetation carbon storage, including:

[0006] Acquire hyperspectral images of the study area;

[0007] A pre-trained classification model is used to classify each pixel in the hyperspectral image to determine the vegetation type of each pixel;

[0008] Feature extraction is performed on the hyperspectral image to determine the remote sensing features of each pixel;

[0009] Based on the remote sensing characteristics of each pixel, an inversion model is used to invert the biomass in the study area to obtain the biomass of each pixel; wherein, the inversion model is trained in advance using a training sample set, which includes the remote sensing characteristics of each pixel in multiple sample plots and the actual biomass of each pixel; the actual biomass is determined based on the hyperspectral image and three-dimensional structural parameters of the sample plot.

[0010] The aboveground vegetation carbon storage in the study area was determined based on the carbon conversion coefficient of each vegetation type, the vegetation type of each pixel, and the biomass of each pixel.

[0011] Secondly, this application provides a device for retrieving aboveground vegetation carbon storage, comprising:

[0012] The image acquisition module is used to acquire hyperspectral images of the study area.

[0013] The pixel classification module is used to classify each pixel in the hyperspectral image using a pre-trained classification model and determine the vegetation type of each pixel.

[0014] The feature extraction module is used to extract features from the hyperspectral image and determine the remote sensing features of each pixel;

[0015] The biomass inversion module is used to invert the biomass of the study area based on the remote sensing characteristics of each pixel using an inversion model, thereby obtaining the biomass of each pixel. The inversion model is pre-trained using a training sample set, which includes the remote sensing characteristics of each pixel in multiple sample plots and the actual biomass of each pixel. The actual biomass is determined based on the hyperspectral image and three-dimensional structural parameters of the sample plot.

[0016] The carbon storage determination module is used to determine the aboveground vegetation carbon storage in the study area based on the carbon conversion coefficient of each vegetation, the vegetation type of each pixel, and the biomass of each pixel.

[0017] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for retrieving aboveground vegetation carbon storage.

[0018] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for retrieving aboveground vegetation carbon storage.

[0019] According to the specific embodiments provided in this application, this application achieves the following technical effects: This application realizes the accurate inversion of aboveground vegetation carbon storage by combining hyperspectral imagery with classification models, inversion models, and carbon conversion coefficients. Specifically, firstly, a pre-trained classification model is used to accurately classify pixels in the hyperspectral imagery, clarifying vegetation types and providing a basis for subsequent carbon storage calculations; simultaneously, pixel-level remote sensing features are obtained through feature extraction, and combined with an inversion model trained on the actual biomass of the sample plots, the biomass of each pixel can be efficiently and accurately inverted. Furthermore, the actual biomass is determined based on the hyperspectral imagery and three-dimensional structural parameters of the sample plots, further improving the reliability of biomass inversion. Finally, carbon storage is calculated by combining the carbon conversion coefficient, vegetation type, and biomass of each type of vegetation, achieving accurate quantification of aboveground vegetation carbon storage in the study area. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is an application environment diagram of a method for retrieving aboveground vegetation carbon storage in one embodiment of this application.

[0022] Figure 2 This is a flowchart illustrating a method for retrieving aboveground vegetation carbon storage according to an embodiment of this application.

[0023] Figure 3 This is a schematic diagram of the functional modules of a ground vegetation carbon storage inversion device provided in an embodiment of this application.

[0024] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] This application uses multi-source data fusion to perform pixel-based allocation of plot biomass and combines different vegetation types to perform carbon storage conversion, thus constructing a complete and more refined method for estimating aboveground carbon storage.

[0027] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] The aboveground vegetation carbon storage inversion method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send hyperspectral images of the study area to server 102, and server 102 can retrieve the aboveground vegetation carbon storage of the study area based on the received hyperspectral images. Server 102 can then feed back the aboveground vegetation carbon storage of the study area to terminal 101.

[0029] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0030] In one exemplary embodiment, such as Figure 2 As shown, a method for inverting aboveground vegetation carbon storage is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 201 to 205.

[0031] Step 201: Obtain hyperspectral images of the study area.

[0032] In a specific application example, a high spatial resolution hyperspectral image covering the study area was acquired using a hyperspectral imaging system mounted on a drone, providing rich spectral information for subsequent feature extraction and classification.

[0033] The hyperspectral image is then further subjected to Gaussian filtering and noise band removal processes.

[0034] Step 202: Classify each pixel in the hyperspectral image using a pre-trained classification model to determine the vegetation type of each pixel. The classification model is a random forest model.

[0035] Specifically, a random forest model is used to perform refined vegetation classification on the hyperspectral images of the study area, outputting distribution patches of dominant tree species or vegetation types within the study area (including the vegetation type of each pixel). These distribution patches are used for subsequent determination of carbon storage coefficients, which can improve the ecological significance and accuracy of carbon storage estimation.

[0036] Step 203: Extract features from the hyperspectral image to determine the remote sensing features of each pixel.

[0037] Step 204: Based on the remote sensing characteristics of each pixel, the biomass in the study area is inverted using an inversion model to obtain the biomass of each pixel.

[0038] The inversion model is pre-trained using a training sample set, which includes remote sensing features of each pixel within multiple sample plots and the actual biomass of each pixel. The actual biomass is determined based on hyperspectral imagery and three-dimensional structural parameters of the sample plots.

[0039] In a specific application example, the training process of the inversion model includes the following steps (1) to (6).

[0040] (1) Obtain hyperspectral images, multi-view images and sample plot survey data from multiple sample plots.

[0041] Specifically, hyperspectral images of the sample plots were collected using a hyperspectral imaging system mounted on a drone. At the same time, multi-view images were collected using an oblique photography system on the drone, providing accurate monitoring data for subsequent 3D reconstruction to obtain digital surface models (DSMs) and 3D structural parameters such as canopy surface area.

[0042] In addition, data collected from ground-based sample plot surveys, including tree species type, diameter at breast height (DBH), and height, were used. Individual tree biomass was calculated based on DBH and height, and the total biomass within the sample plots was tallied based on the individual tree biomass, providing necessary real-world sample support for subsequent inversion model construction.

[0043] (2) For any sample site, the hyperspectral image, multi-view image and sample site survey data of the sample site are spatially calibrated.

[0044] First, the hyperspectral images were sequentially processed with Gaussian filtering and noise band removal. Then, the hyperspectral images, multi-view images, and sample plot survey data were spatially calibrated to ensure that data from different sources could be uniformly projected onto the same coordinate system, laying the foundation for subsequent analysis.

[0045] (3) Calculate the vegetation index of each pixel in the plot based on the calibrated hyperspectral image, and calculate the canopy surface area of ​​each pixel in the plot based on the calibrated multi-view image.

[0046] Specifically, a digital surface model is generated based on the calibrated multi-view images. The digital surface model includes an elevation grid for each pixel and the dimensions of each pixel, i.e., three-dimensional structural parameters. The slope of each elevation grid in the digital surface model is calculated and converted into radians to obtain the radian value of each pixel. Calculate the canopy surface area of ​​each pixel based on its curvature and size: .in, For the first in the digital surface model k Line number j The radians of the corresponding pixels in the elevation raster. For the first in the digital surface modelk Line number j The slope of the elevation grid. For the first in the digital surface model k Line number j The canopy surface area of ​​the corresponding pixels in the column elevation raster. The size of the pixel.

[0047] (4) Calculate the actual biomass of each pixel in the sample plot based on the vegetation index, canopy surface area of ​​each pixel and sample plot survey data.

[0048] The following formula is used to calculate the first... i The actual biomass of a single pixel:

[0049] ;

[0050] in, For the first i The true biomass of a single pixel For the first i The canopy surface area of ​​each pixel For the first i The vegetation index of each pixel This represents the total biomass within the sample plot.

[0051] In view of the inconsistency between the plot scale and the image pixel scale in traditional biomass inversion, this application calculates the vegetation index and canopy surface area of ​​each pixel in the plot, and then weights the total biomass of the plot according to the proportion of vegetation index and structural features (canopy surface area) within the pixel. This realizes the mapping of the measured biomass of the plot to the pixel scale, generates pixel-level real biomass, and provides accurate labeled data for subsequent model training.

[0052] (5) Extract features from the calibrated hyperspectral image to obtain the remote sensing features of each pixel in the sample plot.

[0053] Specifically, spectral and textural features of pixels within the sample plots are extracted from hyperspectral imagery. Then, based on the importance of each feature to biomass, all features are selected, and the top 50 features are retained as remote sensing features for model construction to improve computational efficiency and enhance model stability.

[0054] The feature selection process involves training a random forest regression model using all features. The random forest regression model, through an ensemble learning mechanism of multiple decision trees, automatically evaluates the contribution of each feature to the predicted target variable (e.g., biomass) during training. Then, the importance of each feature is calculated. During model training, the random forest quantifies the contribution of each feature to improving predictive performance based on the reduction in the Gini index or the decrease in the mean squared error at node splits. A higher index indicates a stronger explanatory power for the target variable. Finally, all features are sorted from highest to lowest according to their contribution scores, forming a complete list of feature importance. This list reflects the degree of influence of each feature relative to the overall model in predicting biomass.

[0055] (6) Construct a training sample set based on the actual biomass of each pixel in each sample plot and the remote sensing features of each pixel, and use the training sample set to train the inversion model.

[0056] Furthermore, this application can construct various inversion models based on the selected remote sensing features and actual biomass, including multiple linear regression, random forest, XGBoost, LightGBM, support vector regression, and artificial neural networks. The performance of each model is comprehensively evaluated through cross-validation and accuracy metrics, and the optimal model is selected for aboveground biomass inversion in the study area, outputting biomass results. The model ultimately selected in this application is the random forest model.

[0057] To address the challenges of accurately mapping biomass samples from sample plots to remote sensing pixels and the differences in carbon conversion coefficients among different vegetation types, this application utilizes hyperspectral imagery and three-dimensional structural data (such as canopy surface area) acquired by unmanned aerial vehicles (UAVs) to perform refined spatial allocation of biomass within sample plots at the pixel scale, achieving precise mapping of biomass samples to the pixel level. Based on this, an inversion model is constructed by combining multi-source remote sensing features, and carbon conversion coefficients for different vegetation types are determined using vegetation classification patches, ultimately achieving a high-precision estimation of aboveground vegetation carbon storage. Compared to traditional uniform allocation methods based on sample plot mean, this application preserves the structural differences within sample plots, improves the consistency and representativeness of training samples, and thus significantly enhances the reliability and accuracy of remote sensing inversion results.

[0058] Step 205: Determine the aboveground vegetation carbon storage in the study area based on the carbon conversion coefficient of each vegetation type, the vegetation type of each pixel, and the biomass of each pixel.

[0059] In a specific application example, for any given pixel, the carbon conversion coefficient of that pixel is determined based on its vegetation type. The carbon storage of that pixel is calculated based on its carbon conversion coefficient and biomass. The carbon storage is the product of the carbon conversion coefficient and biomass. The aboveground vegetation carbon storage of the study area is determined based on the carbon storage of each pixel. The aboveground vegetation carbon storage of the study area is the sum of the carbon storage of each pixel.

[0060] Specifically, based on the distribution patches of vegetation types obtained in step 202, the biomass inversion results are processed into blocks according to different vegetation types. Subsequently, based on the carbon conversion coefficients of various vegetation types, the aboveground vegetation carbon storage in the study area is calculated to form the final spatial data product of carbon storage, providing a scientific basis for regional ecological monitoring and carbon sink assessment.

[0061] This application significantly improves the accuracy and stability of aboveground biomass inversion by fusing hyperspectral imagery with three-dimensional structural parameters. The proposed plot biomass pixelation method effectively solves the problem of inconsistency between plot scale and pixel scale in traditional methods, making model training more accurate. In addition, the hierarchical inversion method based on vegetation classification makes the final biomass and carbon storage results more consistent with ecological characteristics, achieving high-precision and high-resolution biomass estimation.

[0062] Based on the same inventive concept, this application also provides an apparatus for implementing the method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, specific limitations in one or more apparatus embodiments provided below can be found in the limitations of the method described above, and will not be repeated here.

[0063] In one exemplary embodiment, such as Figure 3 As shown, a method for retrieving aboveground vegetation carbon storage includes the following functional modules.

[0064] Image acquisition module 301 is used to acquire hyperspectral images of the study area.

[0065] The pixel classification module 302 is used to classify each pixel in the hyperspectral image using a pre-trained classification model to determine the vegetation type of each pixel.

[0066] The feature extraction module 303 is used to extract features from the hyperspectral image and determine the remote sensing features of each pixel.

[0067] The biomass inversion module 304 is used to invert the biomass of the study area based on the remote sensing characteristics of each pixel using an inversion model, thereby obtaining the biomass of each pixel. The inversion model is pre-trained using a training sample set, which includes the remote sensing characteristics of each pixel within multiple sample plots and the actual biomass of each pixel; the actual biomass is determined based on the hyperspectral imagery and three-dimensional structural parameters of the sample plots.

[0068] The carbon storage determination module 305 is used to determine the aboveground vegetation carbon storage in the study area based on the carbon conversion coefficient of each vegetation, the vegetation type of each pixel, and the biomass of each pixel.

[0069] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores hyperspectral images of the study area. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for retrieving aboveground vegetation carbon storage.

[0070] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0071] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0072] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0073] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0074] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0075] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0076] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for inverting aboveground vegetation carbon storage, characterized in that, The method includes: Acquire hyperspectral images of the study area; specifically, hyperspectral images covering the study area are acquired using a hyperspectral imaging system mounted on a drone. A pre-trained random forest model is used to classify each pixel in the hyperspectral image and determine the vegetation type of each pixel; Feature extraction is performed on the hyperspectral image to determine the remote sensing features of each pixel; Based on the remote sensing characteristics of each pixel, an inversion model is used to invert the biomass in the study area to obtain the biomass of each pixel; the inversion model is a random forest model; wherein, the inversion model is trained in advance using a training sample set, the training sample set including the remote sensing characteristics of each pixel in multiple sample plots and the actual biomass of each pixel; the actual biomass is determined based on the hyperspectral image and three-dimensional structural parameters of the sample plot; The training process of the inversion model includes: Acquire hyperspectral images, multi-view images, and plot survey data from multiple sample plots; the plot survey data represents the total biomass within the sample plot. For any given plot, the hyperspectral imagery, multi-view imagery, and plot survey data of the plot are spatially calibrated. A digital surface model is generated based on the calibrated multi-view images; the digital surface model includes an elevation grid for each pixel and the size of each pixel; the slope of each elevation grid in the digital surface model is calculated and converted into radians to obtain the radian of each pixel; the canopy surface area of ​​each pixel is calculated based on the radian and the size of each pixel respectively. Based on the vegetation index, canopy surface area, and plot survey data of each pixel within the plot, the true biomass of each pixel within the plot is calculated; the following formula is used to calculate the... i The actual biomass of a single pixel: ;in, For the first i The true biomass of a single pixel For the first i The canopy surface area of ​​each pixel For the first i The vegetation index of each pixel This represents the total biomass within the sample plot; Feature extraction was performed on the calibrated hyperspectral imagery to obtain the remote sensing features of each pixel within the sample plot. Specifically, spectral and textural features of pixels within the sample plot were extracted from the hyperspectral imagery. Then, all features were selected based on their importance to biomass, with the top 50 features retained for model construction. The feature selection process involved training a random forest regression model using all features. The importance of each feature was then calculated. During model training, the random forest quantified the contribution of each feature to improving prediction performance based on the reduction in the Gini index or the decrease in the mean squared error at node splits. A higher index indicates a stronger explanatory power for the target variable. All features were then sorted from highest to lowest according to their contribution scores to form a complete list of feature importance. This list reflects the degree of influence of each feature relative to the overall model in predicting biomass. A training sample set is constructed based on the actual biomass of each pixel in each sample plot and the remote sensing features of each pixel, and the inversion model is trained using the training sample set. The aboveground vegetation carbon storage in the study area was determined based on the carbon conversion coefficient of each vegetation type, the vegetation type of each pixel, and the biomass of each pixel.

2. The method for retrieving aboveground vegetation carbon storage according to claim 1, characterized in that, Before classifying each pixel in the hyperspectral image using a pre-trained classification model, the method further includes: The hyperspectral image is then subjected to Gaussian filtering and noise band removal processes in sequence.

3. The method for retrieving aboveground vegetation carbon storage according to claim 1, characterized in that, Based on the carbon conversion coefficient of each vegetation type, the vegetation type of each pixel, and the biomass of each pixel, the aboveground vegetation carbon storage in the study area was determined, including: For any given pixel, the carbon conversion coefficient of that pixel is determined based on the vegetation type of that pixel; The carbon storage of a pixel is calculated based on the pixel's carbon conversion coefficient and the pixel's biomass. The carbon storage of aboveground vegetation in the study area was determined based on the carbon storage of each pixel.

4. A device for retrieving carbon storage in aboveground vegetation, characterized in that, The apparatus performs the aboveground vegetation carbon storage inversion method according to any one of claims 1-3, and the apparatus comprises: The image acquisition module is used to acquire hyperspectral images of the study area. The pixel classification module is used to classify each pixel in the hyperspectral image using a pre-trained classification model and determine the vegetation type of each pixel. The feature extraction module is used to extract features from the hyperspectral image and determine the remote sensing features of each pixel; The biomass inversion module is used to invert the biomass of the study area based on the remote sensing characteristics of each pixel using an inversion model, thereby obtaining the biomass of each pixel. The inversion model is pre-trained using a training sample set, which includes the remote sensing characteristics of each pixel in multiple sample plots and the actual biomass of each pixel. The actual biomass is determined based on the hyperspectral image and three-dimensional structural parameters of the sample plot. The carbon storage determination module is used to determine the aboveground vegetation carbon storage in the study area based on the carbon conversion coefficient of each vegetation, the vegetation type of each pixel, and the biomass of each pixel.

5. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the aboveground vegetation carbon storage inversion method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the aboveground vegetation carbon storage inversion method as described in any one of claims 1-3.

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