Mangrove forest vegetation carbon reserve estimation method based on multi-source data

Abstract

The invention relates to a mangrove forest vegetation carbon reserve estimation method based on multi-source data. The method comprises the following steps: extracting a ground surface characteristic parameter set of ground surface multi-source remote sensing data of a research area, and dividing mangrove forest ecological units according to the ground surface characteristic parameter set; acquiring measured data of the underground carbon library of the representative sample plot in the unit, training by combining with the parameter set to obtain an underground carbon distribution inference model, and inverting an underground carbon distribution proportion map; and the above-ground carbon reserve map and the above-ground carbon reserve map are superposed to calculate a mangrove forest vegetation all-carbon library spatial distribution map. By adopting the method, the problems of low precision and large underground carbon conversion deviation of traditional estimation can be solved, and spatial explicit quantitative accurate estimation of the mangrove forest vegetation all-carbon library is realized.

Description

Technical Field

[0001] This invention belongs to the field of carbon storage estimation technology, and in particular relates to a method for estimating carbon storage of mangrove vegetation based on multi-source data. Background Technology

[0002] With the deepening of research on ecological environmental protection and climate change, the accurate estimation of carbon storage in mangrove vegetation has gradually become a research hotspot in the fields of ecology and environmental science. As an important coastal ecosystem, mangroves have a strong carbon sink function, and the accurate assessment of their carbon storage is of great significance for ecosystem management and climate change response.

[0003] Traditionally, the estimation of carbon storage in mangrove vegetation has relied primarily on field surveys and limited sample plot data, estimating carbon storage by measuring mangrove biomass and carbon content in the field.

[0004] However, this method is time-consuming and labor-intensive, and it is difficult to achieve rapid assessments on a large scale, making it difficult to meet the needs of modern ecological research and management for high-precision, large-scale carbon storage estimation. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for estimating mangrove vegetation carbon storage based on multi-source data that can achieve explicit spatial quantification of mangrove vegetation carbon storage, addressing the aforementioned technical issues.

[0006] Firstly, this application provides a method for estimating mangrove vegetation carbon storage based on multi-source data, including:

[0007] Feature extraction was performed on the surface of the study area using multi-source remote sensing data to obtain a set of surface feature parameters;

[0008] Ecological zoning is performed based on a set of surface feature parameters to generate multiple mangrove ecological units;

[0009] Obtain measured data of underground carbon pools from representative plots in multiple mangrove ecosystems;

[0010] A correlation model was trained based on the set of surface feature parameters and measured data of underground carbon pool to obtain an inference model for underground carbon allocation.

[0011] The surface feature parameter set is input into the subsurface carbon allocation inference model for spatial inversion, generating a spatial distribution map of the subsurface carbon allocation ratio in the study area.

[0012] Based on the set of surface characteristic parameters, aboveground biomass was estimated to obtain a spatial distribution map of aboveground carbon storage in the study area.

[0013] Spatial overlay calculations were performed on the spatial distribution maps of aboveground carbon storage and underground carbon allocation ratio to obtain the spatial distribution map of the total carbon pool of mangrove vegetation in the study area. The spatial distribution map of the total carbon pool of mangrove vegetation was used for spatial explicit quantitative estimation of the carbon storage distribution of mangrove vegetation.

[0014] In one embodiment, a correlation model is trained based on a set of surface feature parameters and measured data of the underground carbon pool to obtain an underground carbon allocation inference model, including:

[0015] A training sample set was obtained by matching the surface feature parameter set and the measured data of the underground carbon pool.

[0016] The initial underground carbon allocation model is obtained by training a random forest regression model based on the training sample set.

[0017] The hyperparameters of the initial underground carbon allocation model were optimized using cross-validation to generate an inference model for underground carbon allocation.

[0018] In one embodiment, feature extraction is performed based on multi-source remote sensing data of the study area to obtain a set of surface feature parameters, including:

[0019] Vegetation index data were obtained by calculating vegetation index based on multispectral remote sensing images of the study area.

[0020] Backscattering features were extracted from radar remote sensing images of the study area to obtain radar scattering feature data;

[0021] Spatial registration is performed on vegetation index data and radar scattering feature data to generate a spatially aligned feature dataset.

[0022] Numerical standardization is performed on the spatially aligned feature dataset to generate a set of surface feature parameters.

[0023] In one embodiment, ecological zoning is performed based on a set of surface feature parameters to generate multiple mangrove ecological units, including:

[0024] Principal component analysis was performed based on the set of surface feature parameters to obtain the dimensionality-reduced principal component features.

[0025] Unsupervised clustering analysis was performed on the principal component features to generate an initial ecological zoning map;

[0026] Based on prior knowledge of mangrove species distribution, the boundaries of the initial ecological zoning map were optimized to obtain multiple mangrove ecological units.

[0027] In one embodiment, aboveground biomass is estimated based on a set of surface characteristic parameters to obtain a spatial distribution map of aboveground carbon storage in the study area, including:

[0028] Biomass equations were calculated based on vegetation height characteristics in the set of surface feature parameters to obtain estimated aboveground biomass values ​​for each pixel.

[0029] The estimated aboveground biomass values ​​of each pixel are spatially rasterized and assigned values ​​to generate a spatial distribution map of aboveground biomass.

[0030] Based on the carbon content coefficient of mangrove vegetation, the spatial distribution map of aboveground biomass is converted into carbon storage to generate an initial spatial distribution map of aboveground carbon storage.

[0031] Spatial interpolation optimization was performed on the initial spatial distribution map of aboveground carbon storage to obtain the spatial distribution map of aboveground carbon storage in the study area.

[0032] In one embodiment, a biomass equation is calculated based on vegetation height characteristics in a set of surface feature parameters to obtain an estimated aboveground biomass value for each pixel, including:

[0033] The vegetation height value of each pixel is extracted from the set of surface feature parameters to generate vegetation height raster data.

[0034] Tree species classification was performed on the vegetation height raster data based on mangrove tree species-specific parameters to obtain tree species classification raster data.

[0035] Based on the tree species classification raster data, the corresponding biomass equation parameter set is matched to generate the parameterized biomass equation;

[0036] The aboveground biomass of each pixel is estimated by performing pixel-by-pixel calculations on the vegetation height raster data using a parameterized biomass equation.

[0037] In one embodiment, the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio are spatially overlaid to obtain a spatial distribution map of the total carbon pool of mangrove vegetation in the study area, including:

[0038] Spatial registration was performed on the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio to obtain a spatially unified spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio.

[0039] Based on the spatial distribution map of underground carbon allocation ratio with unified spatial benchmark, the spatial distribution map of aboveground carbon storage with unified spatial benchmark is calculated pixel by pixel to generate the spatial distribution map of underground carbon storage.

[0040] The spatial distribution map of aboveground carbon storage, which is based on a unified spatial benchmark, is overlaid and summed at the pixel level to generate a spatial distribution map of the entire carbon pool of mangrove vegetation in the study area.

[0041] Secondly, this application also provides a mangrove vegetation carbon storage estimation device based on multi-source data, comprising:

[0042] The surface feature extraction module is used to extract features from the multi-source remote sensing data of the surface of the study area to obtain a set of surface feature parameters.

[0043] The ecological zoning module is used to divide the land into ecological zones based on a set of surface feature parameters, generating multiple mangrove ecological units.

[0044] The underground carbon pool data acquisition module is used to acquire measured underground carbon pool data from representative plots in multiple mangrove ecosystems.

[0045] The correlation modeling module is used to train the correlation model based on the set of surface feature parameters and measured data of the underground carbon pool, so as to obtain the underground carbon allocation inference model.

[0046] The spatial inversion module is used to input the set of surface feature parameters into the subsurface carbon allocation inference model for spatial inversion and generate a spatial distribution map of the subsurface carbon allocation ratio in the study area.

[0047] The aboveground biomass estimation module is used to estimate aboveground biomass based on a set of surface characteristic parameters, and to obtain a spatial distribution map of aboveground carbon storage in the study area.

[0048] The full carbon pool coupling module is used to perform spatial overlay calculations on the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio to obtain the spatial distribution map of the full carbon pool of mangrove vegetation in the study area; the spatial distribution map of the full carbon pool of mangrove vegetation is used for spatial explicit quantitative estimation of the carbon storage distribution of mangrove vegetation.

[0049] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the mangrove vegetation carbon storage estimation method based on multi-source data as described in the first aspect.

[0050] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the mangrove vegetation carbon storage estimation method based on multi-source data as described in the first aspect.

[0051] The aforementioned method for estimating mangrove vegetation carbon storage based on multi-source data, through feature extraction and ecological zoning of multi-source remote sensing data, can accurately reflect the spatial differences of mangrove ecological units, providing a more precise geographical basis for carbon storage estimation. Combining measured data of the underground carbon pool with model training can effectively compensate for the shortcomings of traditional methods in estimating underground carbon storage, achieving accurate inferences of the underground carbon allocation ratio. Furthermore, through aboveground biomass estimation and spatial overlay calculations, a spatial distribution map of the entire carbon pool is generated, comprehensively quantifying the carbon storage distribution of mangrove vegetation. This method solves the problems of one-sidedness and low precision in existing technologies for estimating mangrove carbon storage, achieving refined and spatially explicit quantitative estimation of carbon storage from aboveground to underground, providing more scientific and efficient technical support for mangrove ecosystem management and carbon sink research. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying 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.

[0053] Figure 1 A flowchart illustrating a method for estimating mangrove vegetation carbon storage based on multi-source data provided by this invention;

[0054] Figure 2 A schematic flowchart of a method for generating a spatial distribution map of aboveground carbon storage in a study area, provided as an optional embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of a mangrove vegetation carbon storage estimation device based on multi-source data provided by the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] In one embodiment, such as Figure 1 As shown, a method for estimating mangrove vegetation carbon storage based on multi-source data is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0058] S101. Based on the multi-source remote sensing data of the surface of the study area, feature extraction is performed to obtain the surface feature parameter set.

[0059] Optionally, the study area is selected from multi-source remote sensing data encompassing both multispectral and radar remote sensing images. Vegetation indices are obtained by calculating vegetation-sensitive feature bands in the images, exemplarily calculating commonly used indices such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Radar remote sensing images undergo radiometric and geometric correction preprocessing before extracting core radar scattering feature data of the backscattering coefficient. Spatial registration of the two types of data is performed using a matching method with corresponding control points to ensure a one-to-one correspondence between pixel positions, forming a spatially aligned feature dataset. Subsequently, the Z-score normalization method is used to process this dataset, eliminating differences in data units, ultimately generating a set of surface feature parameters.

[0060] S102. Based on the set of surface feature parameters, ecological zoning is carried out to generate multiple mangrove ecological units.

[0061] Optionally, principal component analysis is first performed based on the set of surface feature parameters. Orthogonal transformations are used to convert multiple highly correlated parameters into a few uncorrelated principal component features, preserving core information while reducing data dimensionality. K-means clustering is then used to perform unsupervised clustering analysis on the principal component features. Based on feature similarity, the study area is divided into several initial ecological regions, generating an initial ecological zoning map. Prior knowledge of mangrove species distribution includes existing species survey literature and historical distribution maps. Referring to this information, areas with ambiguous boundaries or cross-species distributions in the initial zoning are adjusted, ultimately resulting in multiple mangrove ecological units with clear boundaries that conform to the ecological characteristics of mangroves.

[0062] S103. Obtain measured data of underground carbon pools from representative plots in multiple mangrove ecosystems.

[0063] Optionally, for each generated mangrove ecological unit, sample plots are selected based on the uniformity of vegetation growth within the unit. The selected sample plots should reflect the typical growth state of the mangroves within the unit to be considered representative. The coordinates of the sample plots are initially located using remote sensing imagery, and then the actual location is pinpointed using the Global Positioning System (GPS). Soil samples are collected layer by layer using a soil drill, with the sampling depth covering the main root distribution layer of the mangroves. The samples are brought back to the laboratory, dried, ground, and the organic carbon content is determined using the potassium dichromate oxidation-external heating method. The underground carbon storage per unit area is calculated by combining soil bulk density and sampling volume. The measurement results of all representative sample plots are then compiled to form the measured data of the underground carbon pool.

[0064] S104. Based on the set of surface feature parameters and measured data of underground carbon pool, a correlation model is trained to obtain an inference model for underground carbon allocation.

[0065] Optionally, a subset of parameters corresponding to representative sample plot locations in the surface feature parameter set is used as the independent variable, and the measured data of the underground carbon pool of the corresponding sample plot is used as the dependent variable. Sample matching is completed through spatial location association to construct a one-to-one corresponding training sample set. A random forest regression model is trained based on this sample set. Specifically, multiple decision trees are initialized, and samples and features are randomly selected to construct individual decision trees. Each decision tree outputs a prediction result. The K-fold cross-validation method is used to divide the training sample set into K equal parts. The model is trained using K-1 parts and validated using 1 part. The number of trees and the hyperparameter of maximum depth are iteratively adjusted until the model prediction error stabilizes, and finally, an underground carbon allocation inference model is generated.

[0066] S105. Input the set of surface feature parameters into the underground carbon allocation inference model for spatial inversion to generate a spatial distribution map of the underground carbon allocation ratio in the study area.

[0067] Optionally, the preprocessed set of surface feature parameters is completely input into the pre-trained subsurface carbon allocation inference model. The model calculates the parameter combination corresponding to each pixel based on the mapping relationship between features and subsurface carbon allocation ratios formed during internal training. The spatial inversion process transforms the pixel-by-pixel feature parameters into corresponding subsurface carbon allocation ratio values ​​through model computation. This process utilizes raster data processing tools for batch processing, ensuring that each spatial location receives the corresponding allocation ratio result. The final output is a spatial distribution map of subsurface carbon allocation ratios covering the entire study area, with pixel values ​​representing subsurface carbon allocation ratios.

[0068] S106. Based on the set of surface characteristic parameters, the aboveground biomass was estimated to obtain a spatial distribution map of aboveground carbon storage in the study area.

[0069] Optionally, vegetation height feature data are extracted from the land surface feature parameter set, and substituted into the corresponding biomass equation to calculate the estimated aboveground biomass value for each pixel. These estimated values ​​are then spatially rasterized using a raster data generation tool according to their corresponding spatial coordinates, ensuring that each raster cell corresponds to a unique aboveground biomass value, thus forming a spatial distribution map of aboveground biomass. The mangrove vegetation carbon content coefficient is a fixed proportion coefficient of carbon in the dry matter of mangrove vegetation. This coefficient is multiplied by the raster values ​​in the aboveground biomass spatial distribution map to complete the carbon storage conversion, obtaining an initial spatial distribution map of aboveground carbon storage. Kriging interpolation is then used to optimize areas with missing or abnormal data in the map, ultimately obtaining the spatial distribution map of aboveground carbon storage for the study area.

[0070] S107. Spatial overlay calculations are performed on the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio to obtain the spatial distribution map of the total carbon pool of mangrove vegetation in the study area; the spatial distribution map of the total carbon pool of mangrove vegetation is used for spatial explicit quantitative estimation of the carbon storage distribution of mangrove vegetation.

[0071] Optionally, a universally accepted geographic coordinate system is selected as the benchmark. Spatial registration is performed on the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio. Coordinate correction ensures that the raster cells of the two types of maps correspond completely in spatial location, resulting in two types of maps with a unified spatial benchmark. Using the underground carbon allocation ratio of the unified benchmark as a coefficient, a pixel-by-pixel multiplication calculation is performed with the corresponding raster value in the aboveground carbon storage map to obtain the underground carbon storage value of each raster and generate an underground carbon storage spatial distribution map. Furthermore, the raster overlay operation function is invoked to sum the raster values ​​at corresponding positions in the aboveground and underground carbon storage spatial distribution maps pixel by pixel, finally generating a spatial distribution map of the entire carbon pool of mangrove vegetation covering the study area.

[0072] The above-mentioned method for estimating mangrove vegetation carbon storage based on multi-source data first extracts the set of surface feature parameters from multi-source remote sensing data of the study area and divides mangrove ecological units. Then, it obtains measured data of underground carbon pool in sample plots to train the model and invert the underground carbon distribution ratio map. At the same time, it estimates the aboveground carbon storage map. Finally, it overlays the data to obtain the full carbon pool distribution map. Through multi-source data fusion and zonal targeted modeling, it solves the problems of insufficient data support and disconnect between aboveground and underground carbon accounting in traditional estimation, and realizes the spatial explicit quantitative and accurate estimation of the full carbon pool of mangrove vegetation.

[0073] In one embodiment, a correlation model is trained based on a set of surface feature parameters and measured data of the underground carbon pool to obtain an underground carbon allocation inference model, including:

[0074] S201. Perform sample matching between the surface feature parameter set and the measured data of the underground carbon pool to obtain the training sample set.

[0075] Optionally, parameter information for all grids in the surface feature parameter set is extracted first, and the spatial coordinates of each grid are recorded. Simultaneously, the spatial coordinates and measured values ​​of the sample plots corresponding to the measured data of the underground carbon pool are organized. Using a spatial coordinate association algorithm, the measured data corresponding to each sample plot coordinate is matched with parameter combinations at the same coordinate positions in the surface feature parameter set. Invalid samples with mismatched coordinates, missing parameters, or abnormal measured data are eliminated. Finally, a training sample set consisting of feature parameter combinations and corresponding underground carbon pool data is formed, providing a qualified data foundation for model training.

[0076] S202. Train the random forest regression model based on the training sample set to obtain the initial underground carbon allocation model.

[0077] Optionally, the basic parameters of the random forest regression model are initialized. A portion of samples is randomly selected from the training sample set as training data for individual decision trees, and some feature parameters are randomly selected to participate in the construction of individual decision trees. Each decision tree selects split nodes according to the information gain or Gini coefficient of the feature parameters, and grows gradually until a preset stopping condition is reached. This process is repeated to construct multiple independent decision trees. All decision trees are combined to form an initial underground carbon allocation model. When predicting data using this model in subsequent processes, the average of the prediction results from multiple decision trees is used as the final output.

[0078] S203. The hyperparameters of the initial underground carbon allocation model are optimized by cross-validation to generate an underground carbon allocation inference model.

[0079] Optionally, a K-fold cross-validation method is used, dividing the training sample set into K mutually exclusive subsets. One subset is selected sequentially as the validation set, and the remaining K-1 subsets are used as the training set. An initial underground carbon allocation model is trained based on each training set, and the model's prediction accuracy is tested using the corresponding validation set, with error values ​​recorded. This process is repeated K times. Based on the error values ​​obtained from each validation, hyperparameters such as the number of decision trees and node splitting thresholds are adjusted accordingly. This iteration continues until the model's prediction error on the validation set is minimized and stabilized, thus generating a performance-optimized underground carbon allocation inference model.

[0080] In the above embodiments, valid samples are selected by matching the set of surface feature parameters with the measured data of underground carbon pool through spatial coordinate association. Then, a random forest regression model is trained based on the samples to obtain the initial model. Finally, the hyperparameters are iteratively optimized through cross-validation. Through the standardized sample processing and model optimization process, the stability and accuracy of the model's prediction of underground carbon allocation ratio are improved.

[0081] In one embodiment, feature extraction is performed based on multi-source remote sensing data of the study area to obtain a set of surface feature parameters, including:

[0082] S301. Calculate vegetation index data based on multispectral remote sensing images of the study area.

[0083] Optionally, after acquiring multispectral remote sensing images of the study area, atmospheric correction and geometric fine correction preprocessing are performed to eliminate image distortion caused by atmospheric scattering and topographic relief. The red and near-infrared bands in the images, which are sensitive to vegetation growth status, are selected, and band calculations are performed according to a preset vegetation index calculation formula. Specifically, the normalized vegetation index is calculated by dividing (near-infrared band reflectance - red band reflectance) by (near-infrared band reflectance + red band reflectance). Similarly, other required vegetation indices are calculated, and finally, all calculation results are integrated to obtain vegetation index data.

[0084] S302. Backscattering features are extracted from radar remote sensing images of the study area to obtain radar scattering feature data.

[0085] Optionally, the radar remote sensing images of the study area are first denoised using an adaptive filtering algorithm to remove speckle noise. Then, radiometric calibration is performed to convert the image's grayscale values ​​into physically meaningful backscattering coefficients, which reflect the ground cover's ability to scatter radar waves. For the scattering characteristics of mangrove areas, backscattering coefficients under different polarization modes (HH (Horizontal-Horizontal, horizontally polarized transmission and horizontally polarized reception) and HV (Horizontal-Vertical, horizontally polarized transmission and vertically polarized reception) are extracted. These coefficients can characterize the mangrove canopy structure and biomass information, and the corresponding coefficients are integrated to form radar scattering characteristic data.

[0086] S303. Spatial registration is performed on vegetation index data and radar scattering feature data to generate a spatially aligned feature dataset.

[0087] Optionally, using geometrically corrected multispectral remote sensing imagery as a reference, several corresponding control points are determined from radar scattering feature data and vegetation index data through manual selection or automatic matching. These control points should be selected as points with significant features and stable locations, such as road intersections and river bends. A polynomial transformation algorithm is used to perform a geometric transformation on the radar scattering feature data to unify the spatial coordinates of the two types of data. Then, resampling technology is used to adjust the spatial resolution of the data to ensure that the pixels of the two types of data are completely aligned, thereby generating a spatially aligned feature dataset.

[0088] S304. Perform numerical standardization on the spatially aligned feature dataset to generate a set of surface feature parameters.

[0089] Optionally, for different types of parameters in the spatially aligned feature dataset, the Z-score normalization method can be used, with the specific calculation formula: Standardized data = (Original data - Mean of the parameter type) ÷ Standard deviation of the parameter type. This calculation eliminates the influence of differences in units and numerical ranges between vegetation index and radar scattering feature data, ensuring all parameters are of the same numerical order of magnitude. After processing, the data integrity is checked, outliers are removed, and missing values ​​are added, ultimately generating a set of surface feature parameters that can be directly used for modeling.

[0090] In the above embodiments, vegetation index and radar scattering feature data are extracted from multispectral and radar remote sensing images, respectively. Spatial registration is then performed to ensure spatial alignment of the two types of data. Finally, standardization is used to eliminate dimensional differences. This approach not only fully integrates the advantages and features of different remote sensing data, but also solves the problem of inconsistent formats and difficulty in collaborative use of multi-source data. The resulting set of surface feature parameters provides a high-quality and highly adaptable data foundation for ecological zoning and carbon storage-related modeling.

[0091] In one embodiment, ecological zoning is performed based on a set of surface feature parameters to generate multiple mangrove ecological units, including:

[0092] S401. Principal component analysis is performed based on the set of surface feature parameters to obtain the dimensionality-reduced principal component features.

[0093] Optionally, all parameters in the surface feature parameter set are organized into a data matrix, and the correlation between different parameters is analyzed by calculating the covariance matrix between the parameters. Based on the covariance matrix, eigenvalues ​​and eigenvectors are solved. The larger the eigenvalue, the more important the principal component represented by the corresponding eigenvector. Several eigenvectors with eigenvalues ​​greater than 1 are selected as principal components. These principal components can retain the key information of the original parameters to the greatest extent while significantly reducing the parameter dimensionality, ultimately obtaining the dimensionality-reduced principal component features.

[0094] S402. Perform unsupervised clustering analysis on the principal component features to generate an initial ecological zoning map.

[0095] Optionally, a reasonable number of clusters is preset, which is determined with reference to the approximate number of mangrove ecological types in the study area. The K-means clustering algorithm is used, and a preset number of cluster centers are randomly selected. The Euclidean distance from each principal component feature sample to each cluster center is calculated, and the samples are assigned to the nearest cluster. The cluster centers are iteratively updated repeatedly until the positions of the cluster centers no longer change significantly or the maximum number of iterations is reached. At this point, each cluster represents a region with similar ecological characteristics, and an initial ecological zoning map is generated accordingly.

[0096] S403. Based on prior knowledge of mangrove species distribution, the initial ecological zoning map is optimized to obtain multiple mangrove ecological units.

[0097] Optionally, prior knowledge of mangrove species distribution includes existing mangrove species survey reports and long-term monitoring data on species distribution in the region. This data clarifies the suitable growth environments and distribution ranges of different mangrove species. By comparing these findings with the existing knowledge, the boundaries of the initial ecological zoning map are checked one by one. Boundaries that cross the core distribution area of ​​a single species are adjusted, and areas containing multiple species with significantly different ecological conditions are broken down. Ultimately, multiple mangrove ecological units with boundaries matching species distribution characteristics are obtained.

[0098] In the above embodiments, principal component analysis is performed on the set of surface feature parameters to reduce dimensionality and simplify calculations. Then, unsupervised clustering is used to obtain an initial ecological zoning map. Finally, the zoning boundaries are optimized by combining prior knowledge of mangrove species distribution. This avoids the blindness of traditional zoning based on administrative or topographical methods and makes the divided mangrove ecological units more consistent with the ecological growth characteristics of the vegetation itself. This effectively improves the pertinence and reliability of selecting representative plots and related measured data.

[0099] In an optional embodiment, such as Figure 2 As shown, aboveground biomass was estimated based on the set of surface characteristic parameters, resulting in a spatial distribution map of aboveground carbon storage in the study area, including:

[0100] S501. Biomass equations are calculated based on vegetation height characteristics in the set of surface feature parameters to obtain estimated aboveground biomass values ​​for each pixel.

[0101] Optionally, vegetation height-related data can be selected from the set of surface feature parameters, and the vegetation height value corresponding to each pixel can be extracted. Based on the growth characteristics of different mangrove tree species, the corresponding allometric growth equation is selected as the biomass equation. This equation uses vegetation height as the core variable to establish a mathematical relationship between height and aboveground biomass. The vegetation height value of each pixel is substituted into the corresponding equation, and the aboveground biomass estimate of each pixel is obtained through pixel-by-pixel calculation, ensuring that the estimate matches the actual growth state of the vegetation.

[0102] S502. Spatial rasterization is performed on the estimated aboveground biomass values ​​of each pixel to generate a spatial distribution map of aboveground biomass.

[0103] Optionally, the spatial resolution corresponding to the remote sensing data of the study area is determined, and the raster cell size is set according to this resolution. The estimated ground biomass value of each pixel is written into the corresponding raster cell according to its spatial coordinates in the original remote sensing image through a raster assignment program. During the assignment process, it is ensured that the estimated value of each spatial location accurately corresponds to a unique raster. After all pixels are assigned, a ground biomass spatial distribution map that can intuitively reflect the spatial distribution differences of ground biomass in the study area is automatically generated.

[0104] S503. Based on the carbon content coefficient of mangrove vegetation, the spatial distribution map of aboveground biomass is converted into carbon storage to generate an initial spatial distribution map of aboveground carbon storage.

[0105] Optionally, the mangrove vegetation carbon content coefficient is a fixed percentage of carbon in mangrove dry matter that has been verified by the scientific community. This coefficient is applicable to the carbon storage conversion of most mangrove vegetation. A raster calculation tool is used to multiply the biomass value of each grid cell in the aboveground biomass spatial distribution map by this coefficient, completing the conversion from biomass to carbon storage through batch processing. The resulting initial aboveground carbon storage spatial distribution map is generated, where each grid cell value directly represents the aboveground carbon storage of the corresponding area.

[0106] S504. Spatial interpolation optimization is performed on the initial aboveground carbon storage spatial distribution map to obtain the aboveground carbon storage spatial distribution map of the study area.

[0107] Optionally, Kriging interpolation is used to optimize the initial aboveground carbon storage spatial distribution map. This method is based on the principle of spatial autocorrelation and calculates reasonable values ​​for surrounding missing or anomalous grids by analyzing the carbon storage values ​​of known grids. Specifically, it searches for several normal grids surrounding each anomalous grid, calculates the interpolation results based on distance weights, replaces the anomalous values, and fills in the missing values. After interpolation, the distribution map is smoothed to ensure that the spatial distribution of carbon storage is continuous and reasonable, ultimately obtaining a more accurate aboveground carbon storage spatial distribution map of the study area.

[0108] In the above embodiments, the aboveground biomass is estimated by combining vegetation height characteristics with the biomass equation. After being rasterized to generate a biomass distribution map, it is converted into an initial aboveground carbon storage map. Then, the data is optimized by spatial interpolation, and the conversion and optimization from biomass to carbon storage is gradually completed. This solves the problems of discontinuous spatial distribution of data and outliers affecting accuracy in traditional aboveground carbon storage estimation, and forms a high-precision aboveground carbon storage spatial distribution map.

[0109] In one embodiment, a biomass equation is calculated based on vegetation height characteristics in a set of surface feature parameters to obtain an estimated aboveground biomass value for each pixel, including:

[0110] S601. Extract the vegetation height value of each pixel based on the set of surface feature parameters to generate vegetation height raster data.

[0111] Optionally, vegetation height parameters are extracted from a set of surface feature parameters, typically obtained through interferometric techniques using radar remote sensing imagery. Based on the spatial extent of the study area and the preset raster resolution, the vegetation height parameters are assigned to each raster cell according to their corresponding geographic coordinates. Each raster cell records the vegetation height value at its corresponding location. All raster cells are then integrated using a raster data generation tool to ultimately form vegetation height raster data, clearly presenting the spatial distribution of mangrove vegetation height in the study area.

[0112] S602. Based on the tree species-specific parameters of mangrove trees, classify the vegetation height raster data to obtain tree species classification raster data.

[0113] Optionally, mangrove species-specific parameters include distinguishing features such as typical vegetation height ranges and growth density thresholds for different tree species. The height value of each raster in the vegetation height raster data is compared with preset species-specific parameters such as height ranges. A threshold judgment and feature matching algorithm is used to determine the mangrove species corresponding to each raster. After determination, a unique identifier value is assigned to each tree species. The identifier values ​​of all raster data are integrated to generate tree species classification raster data that can distinguish the spatial distribution of different tree species.

[0114] S603. Match the corresponding biomass equation parameter set with the tree species classification raster data to generate a parameterized biomass equation.

[0115] Optionally, a database mapping mangrove tree species to biomass equation parameters is pre-established. Each tree species in the database is associated with a set of suitable biomass equation parameters, including key parameters such as equation coefficients and exponents. The tree species identifier of each grid cell in the tree species classification raster data is read, and the corresponding biomass equation parameters are retrieved from the database based on the identifier. The parameters are then substituted into a general biomass equation template to generate parameterized biomass equations for each tree species, ensuring that the equations match the growth characteristics of the corresponding tree species.

[0116] S604. The vegetation height raster data is calculated pixel by pixel using the parameterized biomass equation to obtain the estimated aboveground biomass value for each pixel.

[0117] Optionally, the raster cell-by-cell calculation function is invoked to sequentially read the vegetation height value of each cell in the vegetation height raster data, while simultaneously matching the parameterized biomass equation corresponding to that cell. The height value is then substituted into the equation for calculation to obtain the estimated aboveground biomass value for that cell. This calculation is repeated for all cells, recording the estimation result for each cell, and finally summarizing to form a set of aboveground biomass estimates covering all cells in the study area.

[0118] In the above embodiments, vegetation height raster data is first extracted, classified according to tree species-specific parameters, and then matched with corresponding biomass equation parameters. Biomass is then calculated pixel by pixel using parameterized equations. This breaks the limitation of traditional biomass estimation using general equations that ignore tree species differences. It ensures that the aboveground biomass estimation of each pixel is consistent with the growth characteristics of the corresponding tree species, significantly improving the accuracy of aboveground biomass estimation and providing a strong guarantee for the accurate calculation of aboveground carbon storage.

[0119] In one embodiment, the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio are spatially overlaid to obtain a spatial distribution map of the total carbon pool of mangrove vegetation in the study area, including:

[0120] S701. Spatial registration is performed on the spatial distribution map of aboveground carbon reserves and the spatial distribution map of underground carbon allocation ratio to obtain a spatially unified spatial distribution map of aboveground carbon reserves and a spatial distribution map of underground carbon allocation ratio.

[0121] Optionally, a universal geodetic coordinate system is selected as the unified spatial reference, and the coordinate information of the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio are read separately. The coordinate systems of the two types of maps are uniformly transformed to the preset reference using a coordinate transformation algorithm. Then, image registration technology is used to adjust the pixel positions of the spatial distribution map of underground carbon allocation ratio with the aboveground carbon storage spatial distribution map as a reference, so that the pixels of the same geographical location in the two types of maps completely overlap, resulting in two types of distribution maps with a unified spatial reference.

[0122] S702. Based on the unified spatial benchmark spatial distribution map of underground carbon allocation ratio, perform pixel-by-pixel multiplication calculation on the unified spatial benchmark spatial distribution map of aboveground carbon storage to generate the underground carbon storage spatial distribution map.

[0123] Optionally, the raster multiplication function can be enabled, using a spatially unified subsurface carbon allocation ratio spatial distribution map as the coefficient layer and a surface carbon storage spatial distribution map as the numerical layer. A pixel-by-pixel multiplication operation is performed on both layers, that is, the surface carbon storage value of each raster location is multiplied by the corresponding subsurface carbon allocation ratio, and the result is the subsurface carbon storage value at that location. After batch processing all raster operations, a subsurface carbon storage spatial distribution map reflecting the spatial distribution of subsurface carbon storage in the study area is automatically generated.

[0124] S703. The spatial distribution map of aboveground carbon storage, which is based on a unified spatial benchmark, and the spatial distribution map of underground carbon storage are superimposed and summed at the pixel level to generate a spatial distribution map of the entire carbon pool of mangrove vegetation in the study area.

[0125] Optionally, a raster overlay and summation tool can be used to read spatially unified maps of aboveground and belowground carbon storage. Addition operations are performed on the raster values ​​at the same spatial location in both maps; the result for each location represents the total carbon pool of mangrove vegetation in that area. After completing the overlay and summation operation for all pixels, the results are integrated to generate a complete raster map, which visually represents the spatial distribution of the total carbon pool of mangrove vegetation in the study area.

[0126] In the above embodiments, a unified spatial registration benchmark is established between the aboveground carbon storage map and the underground carbon distribution ratio map. Then, the underground carbon storage map is obtained through pixel-by-pixel multiplication. Finally, the maps are superimposed and summed to generate a complete carbon pool distribution map. Through standardized spatial benchmark unification and step-by-step calculation, the problems of spatial mismatch between aboveground and underground carbon data and unclear accounting logic are solved. The final generated complete carbon pool distribution map can clearly and accurately present the complete spatial distribution of carbon storage in mangrove vegetation in the study area.

[0127] The aforementioned method for estimating mangrove vegetation carbon storage based on multi-source data extracts features from multi-source remote sensing data and generates parameter sets through standardized processing. It then enhances the representativeness of sample plot data by combining ecological zoning, constructs a groundwater carbon allocation inference model using a random forest regression model, and estimates aboveground carbon storage using a tree species-specific parameterized biomass equation. Finally, it obtains a full carbon pool distribution map through spatial registration and overlay operations. This technical solution effectively solves the problems of single data source, coarse groundwater carbon estimation, and unreasonable zoning in traditional methods, achieving accurate spatial explicit quantitative estimation of the entire mangrove vegetation carbon pool and providing reliable data support for mangrove ecological protection and carbon sink accounting.

[0128] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0129] Based on the same inventive concept, this application also provides an apparatus for implementing the aforementioned method for estimating mangrove vegetation carbon storage based on multi-source data. The solution provided by this apparatus is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the mangrove vegetation carbon storage estimation apparatus based on multi-source data provided below can be found in the limitations of the mangrove vegetation carbon storage estimation method based on multi-source data described above, and will not be repeated here.

[0130] In one exemplary embodiment, such as Figure 3 As shown, a mangrove vegetation carbon storage estimation device 10 based on multi-source data is provided, comprising:

[0131] The surface feature extraction module 11 is used to extract features from the multi-source remote sensing data of the surface of the study area to obtain a set of surface feature parameters.

[0132] Ecological zoning module 12 is used to perform ecological zoning based on a set of surface feature parameters, generating multiple mangrove ecological units;

[0133] The underground carbon pool data acquisition module 13 is used to acquire measured underground carbon pool data from representative plots in multiple mangrove ecosystems.

[0134] The correlation modeling module 14 is used to train the correlation model based on the set of surface feature parameters and measured data of underground carbon pool, so as to obtain the underground carbon allocation inference model.

[0135] Spatial inversion module 15 is used to input the set of surface feature parameters into the underground carbon allocation inference model for spatial inversion and generate a spatial distribution map of the underground carbon allocation ratio in the study area.

[0136] The aboveground biomass estimation module 16 is used to estimate aboveground biomass based on the set of surface characteristic parameters, and to obtain a spatial distribution map of aboveground carbon storage in the study area.

[0137] The full carbon pool coupling module 17 is used to perform spatial overlay calculations on the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio to obtain the spatial distribution map of the full carbon pool of mangrove vegetation in the study area; the spatial distribution map of the full carbon pool of mangrove vegetation is used for spatial explicit quantitative estimation of the carbon storage distribution of mangrove vegetation.

[0138] In one embodiment, the association modeling module includes:

[0139] The sample matching unit is used to match the measured data of the surface feature parameter set and the underground carbon pool to obtain the training sample set.

[0140] The model training unit is used to train a random forest regression model based on the training sample set to obtain the initial underground carbon allocation model.

[0141] The hyperparameter optimization unit is used to optimize the hyperparameters of the initial subsurface carbon allocation model using cross-validation to generate a subsurface carbon allocation inference model.

[0142] In one embodiment, the surface feature extraction module includes:

[0143] The vegetation index calculation unit is used to calculate vegetation index based on multispectral remote sensing images of the study area to obtain vegetation index data.

[0144] The radar scattering feature extraction unit is used to extract backscattering features from radar remote sensing images of the study area to obtain radar scattering feature data.

[0145] Spatial registration unit is used to spatially register vegetation index data and radar scattering feature data to generate a spatially aligned feature dataset.

[0146] The numerical standardization processing unit is used to perform numerical standardization processing on the spatially aligned feature dataset to generate a set of surface feature parameters.

[0147] In one embodiment, the ecological partitioning module includes:

[0148] Principal component analysis unit is used to perform principal component analysis based on the set of surface feature parameters to obtain the dimensionality-reduced principal component features.

[0149] The unsupervised clustering analysis unit is used to perform unsupervised clustering analysis on principal component features to generate an initial ecological zoning map;

[0150] Boundary optimization units are used to optimize the boundaries of the initial ecological zoning map based on prior knowledge of mangrove species distribution, resulting in multiple mangrove ecological units.

[0151] In one embodiment, the aboveground biomass estimation module includes:

[0152] The biomass equation calculation unit is used to calculate the biomass equation based on the vegetation height characteristics in the set of surface feature parameters, and obtain the estimated value of aboveground biomass for each pixel.

[0153] The spatial rasterization assignment unit is used to perform spatial rasterization assignment on the estimated aboveground biomass value of each pixel to generate a spatial distribution map of aboveground biomass.

[0154] The carbon storage conversion unit is used to convert the aboveground biomass spatial distribution map into carbon storage based on the carbon content coefficient of mangrove vegetation, and generate an initial aboveground carbon storage spatial distribution map.

[0155] The spatial interpolation optimization unit is used to perform spatial interpolation optimization on the initial aboveground carbon storage spatial distribution map to obtain the aboveground carbon storage spatial distribution map of the study area.

[0156] In one embodiment, the biomass equation calculation unit includes:

[0157] The vegetation height extraction subunit is used to extract the vegetation height value of each pixel based on the set of surface feature parameters and generate vegetation height raster data.

[0158] The tree species classification subunit is used to classify tree species in vegetation height raster data based on mangrove tree species-specific parameters to obtain tree species classification raster data.

[0159] The biomass equation parameter matching subunit is used to match the corresponding biomass equation parameter set based on tree species classification raster data and generate parameterized biomass equations.

[0160] The pixel-by-pixel calculation subunit is used to perform pixel-by-pixel calculations on vegetation height raster data using parameterized biomass equations to obtain the estimated aboveground biomass value for each pixel.

[0161] In one embodiment, the full carbon library coupling module includes:

[0162] The spatial registration unit is used to spatially register the spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio, so as to obtain a spatially unified spatial distribution map of aboveground carbon storage and the spatial distribution map of underground carbon allocation ratio.

[0163] The pixel-by-pixel multiplication calculation unit is used to perform pixel-by-pixel multiplication calculations on the aboveground carbon storage spatial distribution map based on the spatially unified underground carbon allocation ratio spatial distribution map, and generate the underground carbon storage spatial distribution map.

[0164] The pixel-level overlay summation unit is used to overlay and sum the spatial distribution maps of aboveground carbon storage and underground carbon storage, which are based on a unified spatial benchmark, to generate a spatial distribution map of the entire carbon pool of mangrove vegetation in the study area.

[0165] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the mangrove vegetation carbon storage estimation method based on multi-source data as described above.

[0166] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the mangrove vegetation carbon storage estimation method based on multi-source data as described above.

[0167] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0168] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A mangrove vegetation carbon storage estimation method based on multi-source data, characterized by, The method comprises: feature extraction is performed on surface multi-source remote sensing data of a study area to obtain a set of surface feature parameters; ecological zoning is performed according to the set of surface feature parameters to generate a plurality of mangrove ecological units; measured data of an underground carbon pool of representative sample plots in the plurality of mangrove ecological units are obtained; an association model is trained based on the set of surface feature parameters and the measured data of the underground carbon pool to obtain an underground carbon distribution inference model; the set of surface feature parameters is input into the underground carbon distribution inference model for spatial inversion to generate a spatial distribution map of the proportion of underground carbon distribution in the study area; aboveground biomass estimation is performed according to the set of surface feature parameters to obtain a spatial distribution map of aboveground carbon storage in the study area; spatial superposition calculation is performed on the spatial distribution map of aboveground carbon storage and the spatial distribution map of the proportion of underground carbon distribution to obtain a spatial distribution map of a total carbon pool of mangrove vegetation in the study area; the spatial distribution map of the total carbon pool of mangrove vegetation is used for spatial explicit quantitative estimation of the distribution of carbon storage of mangrove vegetation.

2. The method of claim 1, wherein, The association model is trained based on the set of surface feature parameters and the measured data of the underground carbon pool to obtain an underground carbon distribution inference model, which comprises: sample matching is performed on the set of surface feature parameters and the measured data of the underground carbon pool to obtain a training sample set; a random forest regression model is trained based on the training sample set to obtain an initial underground carbon distribution model; hyperparameter optimization is performed on the initial underground carbon distribution model by a cross-validation method to generate the underground carbon distribution inference model.

3. The method of claim 1, wherein, The feature extraction is performed on surface multi-source remote sensing data of a study area to obtain a set of surface feature parameters, which comprises: vegetation index calculation is performed on multi-spectral remote sensing images of the study area to obtain vegetation index data; backscattering feature extraction is performed on radar remote sensing images of the study area to obtain radar scattering feature data; spatial registration is performed on the vegetation index data and the radar scattering feature data to generate a set of spatially aligned feature data; numerical standardization processing is performed on the set of spatially aligned feature data to generate the set of surface feature parameters.

4. The method of claim 3, wherein, The ecological zoning is performed according to the set of surface feature parameters to generate a plurality of mangrove ecological units, which comprises: principal component analysis is performed based on the set of surface feature parameters to obtain principal component features after dimension reduction; unsupervised clustering analysis is performed on the principal component features to generate an initial ecological zoning map; boundary optimization is performed on the initial ecological zoning map according to prior knowledge of mangrove species distribution to obtain the plurality of mangrove ecological units.

5. The method of claim 1, wherein, The aboveground biomass estimation is performed according to the set of surface feature parameters to obtain a spatial distribution map of aboveground carbon storage in the study area, which comprises: biomass equation calculation is performed based on vegetation height features in the set of surface feature parameters to obtain aboveground biomass estimation values of each pixel; spatial rasterization assignment is performed on the aboveground biomass estimation values of each pixel to generate an aboveground biomass spatial distribution map; carbon storage conversion is performed on the aboveground biomass spatial distribution map according to a mangrove vegetation carbon content coefficient to generate an initial aboveground carbon storage spatial distribution map; The initial aboveground carbon storage spatial distribution map is spatially interpolated and optimized to obtain the aboveground carbon storage spatial distribution map of the study area.

6. The method of claim 5, wherein, The vegetation height feature in the set of surface feature parameters is used for biomass equation calculation to obtain the aboveground biomass estimation value of each pixel, including: The vegetation height value of each pixel is extracted from the set of surface feature parameters to generate vegetation height raster data; The vegetation height raster data is classified into tree species based on specific parameters of mangrove tree species to obtain tree species classification raster data; The corresponding biomass equation parameter set is matched according to the tree species classification raster data to generate a parameterized biomass equation; The vegetation height raster data is calculated pixel by pixel through the parameterized biomass equation to obtain the aboveground biomass estimation value of each pixel.

7. The method of claim 6, wherein, The aboveground carbon storage spatial distribution map and the underground carbon allocation proportion spatial distribution map are spatially overlaid and calculated to obtain the mangrove vegetation total carbon pool spatial distribution map of the study area, including: The aboveground carbon storage spatial distribution map and the underground carbon allocation proportion spatial distribution map are spatially registered to obtain the aboveground carbon storage spatial distribution map and the underground carbon allocation proportion spatial distribution map with unified spatial reference; The aboveground carbon storage spatial distribution map with unified spatial reference is multiplied pixel by pixel based on the underground carbon allocation proportion spatial distribution map with unified spatial reference to generate an underground carbon storage spatial distribution map; The aboveground carbon storage spatial distribution map with unified spatial reference and the underground carbon storage spatial distribution map are pixel-level overlaid and summed to generate the mangrove vegetation total carbon pool spatial distribution map of the study area.

8. A mangrove vegetation carbon stock estimation device based on multi-source data, characterized by, The device comprises: A surface feature extraction module configured to extract features from surface multi-source remote sensing data of a study area to obtain a set of surface feature parameters; An ecological zoning module configured to perform ecological zoning based on the set of surface feature parameters to generate a plurality of mangrove ecological units; An underground carbon pool data acquisition module configured to acquire measured data of underground carbon pools of representative sample plots in the plurality of mangrove ecological units; An association modeling module configured to train an association model based on the set of surface feature parameters and the measured data of underground carbon pools to obtain an underground carbon allocation inference model; A spatial inversion module configured to input the set of surface feature parameters into the underground carbon allocation inference model for spatial inversion to generate an underground carbon allocation proportion spatial distribution map of the study area; An aboveground biomass estimation module configured to estimate aboveground biomass based on the set of surface feature parameters to obtain an aboveground carbon storage spatial distribution map of the study area; A total carbon pool coupling module configured to spatially overlay and calculate the aboveground carbon storage spatial distribution map and the underground carbon allocation proportion spatial distribution map to obtain a mangrove vegetation total carbon pool spatial distribution map of the study area; the mangrove vegetation total carbon pool spatial distribution map is used for spatial explicit quantification and estimation of mangrove vegetation carbon storage distribution. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1 to 7.

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