A method and system for estimating carbon storage of shrub vegetation based on unmanned aerial vehicle technology

Abstract

The application discloses a shrub vegetation carbon storage estimation method and system based on unmanned aerial vehicle technology, and the method comprises the following steps: a laser radar is carried on an unmanned aerial vehicle to scan a target region, initial point cloud data is obtained, and a three-dimensional coordinate set of shrub vegetation is obtained based on the initial point cloud data; noise points are removed by using a filtering algorithm to obtain clean point cloud data according to the obtained three-dimensional coordinate set, if the point density in the clean point cloud data is lower than a first preset threshold, missing points are supplemented by using an interpolation algorithm to obtain complete point cloud data; for the complete point cloud data, a voxel method is used to divide a spatial grid, and vegetation volume features are obtained based on the number of points in each spatial grid; height and coverage range parameters are extracted from the vegetation volume features, shrub cluster boundaries are obtained by using a density clustering algorithm; biomass distribution indexes are calculated according to the shrub cluster boundaries, and regional carbon storage estimation values are calculated based on the biomass distribution indexes.

Description

Technical Field

[0001] This invention relates to the field of carbon storage calculation technology, specifically to a method and system for estimating the carbon storage of shrub vegetation based on unmanned aerial vehicle (UAV) technology. Background Technology

[0002] In the fields of ecological conservation and climate change research, estimating the carbon storage of shrub vegetation is a crucial task. As an important component of terrestrial ecosystems, the carbon storage capacity of shrubs directly relates to the balance of regional carbon cycles and the stability of ecosystems. However, accurately estimating the carbon storage of shrub vegetation is not only a key focus of scientific research but also an important basis for formulating environmental protection policies, urgently requiring efficient and precise technical means to support this goal.

[0003] Currently, although some methods exist for assessing carbon storage in shrub vegetation, these methods often face limitations in data acquisition and analysis. Traditional methods rely heavily on ground surveys or remote sensing imagery, making it difficult to obtain detailed spatial information in complex terrains and densely vegetated areas. This is especially true when shrub vegetation is unevenly distributed and morphologically varied, often limiting the comprehensiveness and accuracy of the data. This limitation leads to an insufficient understanding of vegetation structure and biomass distribution, thus affecting the reliability of carbon storage estimation.

[0004] Against this backdrop, the main technical challenge of the research lies in how to efficiently acquire spatial structural information of shrub vegetation. Shrub vegetation is morphologically complex and highly heterogeneous, with significant differences in leaf distribution and volume characteristics across different regions. Traditional measurement methods struggle to capture these subtle variations. The lack of spatial structural information further exacerbates the difficulty in determining vegetation volume and density characteristics, making the rapid and accurate reconstruction of the true morphology of vegetation over a vast area a pressing problem. For example, in a shrub-covered hillside, due to undulating terrain and vegetation obstruction, conventional tools struggle to comprehensively scan the three-dimensional morphology of the vegetation, leading to significant deviations in the judgment of key parameters such as vegetation height and coverage.

[0005] Therefore, how to efficiently acquire three-dimensional spatial information of shrub vegetation in complex environments and accurately reconstruct its morphological characteristics has become a key problem that urgently needs to be solved in this study. Solving this problem will directly affect the accuracy and applicability of carbon storage estimation, providing more reliable data support for ecological protection. Summary of the Invention

[0006] To address the above technical problems, this invention provides a method for estimating the carbon storage of shrub vegetation based on unmanned aerial vehicle (UAV) technology, comprising the following steps:

[0007] The target area is scanned by a drone equipped with a lidar to obtain initial point cloud data, and a set of three-dimensional coordinates of shrub vegetation is obtained based on the initial point cloud data.

[0008] Based on the acquired three-dimensional coordinate set, a filtering algorithm is used to remove noise points to obtain clean point cloud data. If the point density in the clean point cloud data is lower than the first preset threshold, the missing points are supplemented by an interpolation algorithm to obtain complete point cloud data.

[0009] For complete point cloud data, a voxelization method is used to divide the space into grids, and vegetation volume features are obtained based on the number of points in each space grid.

[0010] Height and coverage parameters are extracted from vegetation volume features, and shrub cluster boundaries are obtained through density clustering algorithm.

[0011] Biomass distribution indices are calculated based on the boundaries of shrub clusters, and regional carbon storage estimates are calculated based on these biomass distribution indices.

[0012] Preferred methods for obtaining a set of three-dimensional coordinates include:

[0013] The target area is fully scanned by a drone equipped with a lidar to acquire scan data and form an initial point cloud.

[0014] Extract initial point cloud data from the initial point cloud and perform noise reduction on the initial point cloud data to obtain the processed point cloud dataset;

[0015] The three-dimensional coordinates of shrub vegetation are constructed based on the processed point cloud dataset, resulting in a set of three-dimensional coordinates.

[0016] Preferred methods for obtaining complete point cloud data include:

[0017] The three-dimensional coordinate set is initially organized, and the data format is standardized to obtain a structured point cloud dataset;

[0018] A voxel grid filtering algorithm is used to denoise the structured point cloud dataset, and a density detection method is used to calculate the density value of the point cloud after denoising, thus obtaining the density distribution result of the point cloud data.

[0019] If there are areas in the density distribution results that are below the first preset threshold, then the missing points in these areas are marked, and the interpolation algorithm is called to calculate and process these positions to generate supplementary points;

[0020] The supplementary points are integrated with the original point cloud data to obtain complete point cloud data.

[0021] Preferred methods for obtaining vegetation volume characteristics include:

[0022] For complete point cloud data, the voxelization method is used to divide the three-dimensional space into meshes to obtain several voxel units;

[0023] For the divided voxel units, the number of point cloud data in each voxel unit is counted. If the number of points in a voxel unit exceeds the second preset threshold, it is marked as a high-density region, and preliminary density distribution information is obtained.

[0024] The spatial location of high-density areas is extracted from the preliminary density distribution information, and clustering methods are used to group adjacent high-density areas to identify potential vegetation clustering areas.

[0025] Based on the potential vegetation clustering areas, the spatial volume of each group is calculated, the corresponding volume data is obtained, and the vegetation volume characteristics are obtained.

[0026] Preferred methods for obtaining the boundary of shrub clusters include:

[0027] Height parameters and coverage data are obtained from vegetation volume features and then denoised and standardized to obtain a normalized vegetation parameter dataset.

[0028] Density clustering was used to analyze the normalized vegetation parameter dataset, and regional division was performed based on height parameter and coverage data to determine the set of similar regions.

[0029] Based on a set of similar regions, the distribution characteristics of vegetation volume data within similar regions are obtained. By comparing the differences in distribution characteristics between adjacent regions, the preliminary range of shrub clusters is determined.

[0030] For the data within the initial range, if the difference in height parameters and coverage between adjacent areas is less than the third preset threshold, these areas are merged to obtain the adjusted range of the shrub cluster.

[0031] By smoothing the boundaries of the adjusted shrub clusters, the boundary point set data is obtained, and the final shrub cluster boundary is determined.

[0032] Preferred methods for obtaining regional carbon storage estimates include:

[0033] By defining the boundaries of shrub clusters and obtaining corresponding biomass data within those boundaries, and combining this with the influence weights of environmental factors, the biomass distribution characteristics of each sub-region are calculated.

[0034] Key indicators were extracted from the biomass distribution characteristics, and the random forest algorithm was used to process the indicator data to determine the correlation between the biomass distribution characteristics of each sub-region and environmental factors.

[0035] If the correlation is higher than the fourth preset threshold, then the estimated value of regional carbon storage within the region is calculated by combining the biomass distribution characteristics and environmental factors.

[0036] The present invention also provides a shrub vegetation carbon storage estimation system based on UAV technology. The system applies the above-mentioned method and includes: a data acquisition module, a data processing module, a volume feature acquisition module, a boundary acquisition module, and a carbon storage calculation module.

[0037] The data acquisition module scans the target area using a lidar mounted on a drone to obtain initial point cloud data, and then obtains a set of three-dimensional coordinates of the shrub vegetation based on the initial point cloud data.

[0038] The data processing module uses a filtering algorithm to remove noise points from the acquired three-dimensional coordinate set to obtain clean point cloud data. If the point density in the clean point cloud data is lower than the first preset threshold, the missing points are supplemented by an interpolation algorithm to obtain complete point cloud data.

[0039] In the volume feature acquisition module, for complete point cloud data, the voxelization method is used to divide the spatial grid, and the vegetation volume feature is obtained based on the number of points in each spatial grid.

[0040] The boundary acquisition module is used to extract height and coverage parameters from vegetation volume features and obtain the shrub cluster boundary through a density clustering algorithm.

[0041] The carbon storage calculation module calculates the biomass distribution index based on the shrub cluster boundary, and then calculates the estimated regional carbon storage based on the biomass distribution index.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] This invention addresses the problems of noise interference in point cloud data, inaccurate structural analysis due to insufficient density, and large deviations between estimated values ​​and historical data that are difficult to optimize in traditional vegetation monitoring. It solves these problems by integrating a logical process of scanning acquisition, filtering, interpolation, voxelization, density clustering, and support vector machine adjustment. This invention significantly improves the accuracy and efficiency of vegetation carbon sink assessment, promoting ecological monitoring and the achievement of carbon neutrality goals. Attached Figure Description

[0044] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0047] Explanation of reference numerals in the attached figures:

[0048] 1010, Processor; 1020, Memory; 1030, Input / Output Interface; 1040, Communication Interface; 1050, Bus. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example 1

[0052] In this embodiment, as Figure 1 As shown, a method for estimating carbon storage in shrub vegetation based on unmanned aerial vehicle (UAV) technology includes the following steps:

[0053] S1. Scan the target area using a drone equipped with a lidar to obtain initial point cloud data, and obtain a set of three-dimensional coordinates of the shrub vegetation based on the initial point cloud data.

[0054] The method for obtaining the three-dimensional coordinate set includes: using a drone equipped with a lidar to perform a comprehensive scan of the target area, acquiring scan data to form an initial point cloud; extracting initial point cloud data from the initial point cloud and performing noise reduction processing on the initial point cloud data to obtain a processed point cloud dataset; and constructing the three-dimensional coordinates of the shrub vegetation based on the processed point cloud dataset to obtain the three-dimensional coordinate set.

[0055] In this embodiment, a drone equipped with a lidar system performs high-precision scanning of the target shrub vegetation area to acquire initial point cloud data. Specifically, the drone uses a multi-rotor platform equipped with a high-performance lidar sensor. Its scanning parameters include a scanning frequency of 10-20Hz, a vertical field of view of 30°, and a horizontal field of view of 360° to ensure comprehensive coverage of the target area. The flight altitude is controlled between 30-100 meters, and the flight speed is maintained at 3-6 meters per second. Real-time positioning and attitude correction are performed using GPS and IMU modules to generate high-density point cloud data. The initial point cloud is acquired through laser pulse return signals. Each point contains three-dimensional coordinates (X, Y, Z) and reflection intensity information, forming a raw dataset. After data acquisition, the point cloud is imported using CloudCompare for preliminary format conversion and coordinate system unification. Subsequently, vegetation-related data is extracted from the initial point cloud and denoising is performed. First, a statistical outlier removal algorithm is applied to calculate the average k-nearest neighbor distance for each point (k=50 in this embodiment), and noise points with a distance exceeding twice the standard deviation of the average are removed to eliminate aircraft jitter or environmental interference. Next, a radius filtering method is used, setting the search radius to 0.1-0.3 meters to filter out isolated points and non-vegetated points (such as ground or buildings). The processed point cloud dataset is then segmented using Euclidean clustering, grouping adjacent points into potential shrub objects, and constructing a 3D coordinate set based on this, i.e., extracting the X, Y, and Z values ​​of each vegetation point to form a structured coordinate matrix.

[0056] S2. Based on the obtained three-dimensional coordinate set, a filtering algorithm is used to remove noise points to obtain clean point cloud data. If the point density in the clean point cloud data is lower than the first preset threshold, the missing points are supplemented by an interpolation algorithm to obtain complete point cloud data.

[0057] The method for obtaining complete point cloud data includes: initially organizing the three-dimensional coordinate set and unifying the data format to obtain a structured point cloud dataset; using a voxel grid filtering algorithm to denoise the structured point cloud dataset and using a density detection method to calculate the density value of the denoised point cloud to obtain the density distribution result of the point cloud data; if there are areas in the density distribution result that are lower than a first preset threshold, then the missing points in these areas are marked, and an interpolation algorithm is called to calculate and process these positions to generate supplementary points; finally, the supplementary points are integrated with the original point cloud data to obtain complete point cloud data.

[0058] In this embodiment, the acquired 3D coordinate set is first preliminarily organized and formatted to generate a structured point cloud dataset. Specifically, the point cloud data is converted to a standardized format, ensuring that the coordinate system is WGS84 or UTM, and data cleaning operations are applied to remove duplicate points and invalid values. Next, a voxel grid filtering algorithm is used for noise reduction: the voxel size is set to 0.1 meters, the 3D space is divided into uniform grid cells, the point cloud within each voxel is downsampled, and the original points are replaced by the calculated centroid, thereby reducing noise and redundant data while preserving vegetation structure features. After noise reduction, a density detection method is used to analyze the point cloud distribution. The space is divided into 0.2m × 0.2m × 0.2m cubic grids, the number of points in each grid is counted, and a density distribution map is generated. By calculating the overall point density (e.g., points per cubic meter), if the density of some areas is found to be lower than a first preset threshold (in this embodiment, the first preset threshold is set to less than 100 points per cubic meter), these areas are marked as missing points. For the marked missing areas, an interpolation algorithm is called to supplement the points. This embodiment employs the inverse distance weighted (IDW) interpolation method, setting the search radius to 0.5 meters and the power parameter to 2. Based on the 3D coordinates of surrounding known points and distance weights, the location of missing points is calculated to ensure that interpolated points smoothly fill low-density areas while avoiding excessive distortion of vegetation morphology. During interpolation, high reflectivity data from neighboring points are prioritized to improve accuracy. After generating supplementary points, they are merged with the original clean point cloud using a data integration module. Coordinate alignment and overlapping point removal are performed using a point cloud library, ultimately forming a complete point cloud dataset.

[0059] S3. For complete point cloud data, a voxelization method is used to divide the spatial grid, and vegetation volume features are obtained based on the number of points in each spatial grid.

[0060] The method for obtaining vegetation volume characteristics includes: for complete point cloud data, using a voxelization method to divide the three-dimensional space into grids to obtain several voxel units; for the divided voxel units, counting the number of point cloud data in each voxel unit; if the number of points in a voxel unit exceeds a second preset threshold, it is marked as a high-density area, obtaining preliminary density distribution information; extracting the spatial location of high-density areas from the preliminary density distribution information, using a clustering method to group adjacent high-density areas, and determining potential vegetation clustering areas; calculating the spatial volume of each group based on the potential vegetation clustering areas, obtaining the corresponding volume data, and obtaining vegetation volume characteristics.

[0061] In this embodiment, voxelization is first applied to the complete point cloud data to divide the three-dimensional space into uniform grid cells for systematic analysis of vegetation structure. Specifically, the voxel size is set to 0.1 meters to ensure uniform spatial resolution, with each voxel representing a small cube of 0.1 meters × 0.1 meters × 0.1 meters. After the point cloud data is assigned to the corresponding voxels, the number of points within each voxel is counted: by traversing all points, their coordinates are mapped to voxel grid indices, and the number of points under each index is counted to generate a point density distribution map. Based on the point density statistics, a second preset threshold of 5 points per voxel is set. All voxels are traversed, and voxels with more than this threshold are marked as high-density areas, and their three-dimensional coordinates and density values ​​are recorded to form preliminary density distribution information. Subsequently, a density clustering algorithm is used to group high-density voxels to identify potential vegetation clustering areas. This embodiment uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, with parameters set as follows: neighborhood radius ε = 0.2 meters, minimum number of points MinPts = 5. Specifically, the Euclidean distance between each high-density voxel is first calculated. Voxels with a distance less than ε and satisfying MinPts are grouped into the same cluster, forming clusters, while isolated noise points are filtered out. For each cluster, its spatial volume is calculated to obtain vegetation volume characteristics. Volume calculation is achieved by determining the 3D bounding box of the cluster: first, the minimum and maximum X, Y, and Z coordinates of all points within the cluster are extracted, and then the bounding box volume is calculated. If the cluster shape is complex, a convex hull algorithm can be used to construct a minimum convex polyhedron before calculating its volume to improve accuracy. Volume data is stored in cubic meters and output as a structured dataset, including the ID, volume value, and center coordinates of each cluster. Finally, the vegetation volume characteristics are obtained.

[0062] S4. Extract height and coverage parameters from vegetation volume features, and obtain the shrub cluster boundary through density clustering algorithm.

[0063] The method for obtaining the shrub cluster boundary includes: obtaining height parameters and coverage data from vegetation volume features and performing denoising and standardization to obtain a normalized vegetation parameter dataset; analyzing the normalized vegetation parameter dataset using density clustering, dividing the height parameter and coverage data into regions, and determining a set of similar regions; based on the set of similar regions, obtaining the distribution characteristics of vegetation volume data within the similar regions, and determining the preliminary range of the shrub cluster by comparing the differences in distribution characteristics between adjacent regions; for the data within the preliminary range, if the difference in height parameters and coverage between adjacent regions is less than a third preset threshold, merging these regions to obtain the adjusted shrub cluster range; and obtaining the boundary point set data by performing boundary smoothing processing on the adjusted shrub cluster range to determine the final shrub cluster boundary.

[0064] In this embodiment, height and cover parameters are first extracted from vegetation volume features. Specifically, the height parameter is obtained by calculating the vertical extent of the point cloud in each cluster, i.e., taking the difference between the maximum and minimum Z-coordinates of all points within each cluster as the height value; the cover parameter is defined by projecting the point cloud onto a horizontal plane (XY plane) and calculating the convex hull area of ​​each cluster to reflect the horizontal distribution of vegetation. After extraction, the Z-score normalization method is used to calculate the mean and standard deviation of the height and cover data, converting each value into the form of (original value - mean) / standard deviation to eliminate the influence of dimensions; simultaneously, a moving average filter is applied to smooth the height data to reduce noise caused by local fluctuations. Next, density clustering algorithm is also used to analyze the normalized vegetation parameter dataset to divide similar regions and determine the preliminary extent of shrub clusters. This embodiment uses the DBSCAN algorithm with the following parameters: neighborhood radius ε = 0.3 meters (adjusted based on data scale), and minimum number of points MinPts = 3. Specifically, firstly, the Euclidean distance of each data point in the parameter space is calculated. Points with a distance less than ε and satisfying MinPts are grouped into the same cluster, forming a set of similar regions. Then, based on these sets, the distribution characteristics of vegetation volume data within each similar region are analyzed, and the preliminary range of the shrub cluster is determined by comparing the differences in distribution characteristics between adjacent regions (e.g., using an independent samples t-test to compare the mean height and coverage area). If the difference in height parameters between adjacent regions is less than a third preset threshold (set to 10% in this embodiment) and the difference in coverage area is less than 15%, these regions are merged to optimize the cluster range. Finally, the adjusted shrub cluster boundaries were smoothed, and the Alpha shape algorithm was used to extract the boundaries from the merged point cloud data. Specifically, the convex hull of the point set was first calculated, and then the long sides were removed based on the α value to generate a more natural boundary contour. Simultaneously, a B-spline curve fitting method was applied to interpolate and smooth the boundary points, with an order of 3 and a control point spacing of 0.1 meters to reduce jagged edges. Finally, iterative optimization was used to ensure the continuity and accuracy of the boundary point set data, outputting the shrub cluster boundaries for subsequent biomass calculations.

[0065] S5. Calculate the biomass distribution index based on the shrub cluster boundary, and calculate the estimated regional carbon storage based on the biomass distribution index.

[0066] The method for obtaining the estimated regional carbon storage includes: defining the boundary of the shrub cluster, obtaining the corresponding biomass data within the boundary, and calculating the biomass distribution characteristics of each sub-region by combining the influence weights of environmental factors; extracting key indicators from the biomass distribution characteristics, processing the indicator data using the random forest algorithm, and determining the correlation between the biomass distribution characteristics and environmental factors in each sub-region; if the correlation is higher than a fourth preset threshold, then calculating the estimated regional carbon storage within the region by combining the biomass distribution characteristics and environmental factors.

[0067] In this embodiment, biomass data for each sub-region is first extracted based on the shrub cluster boundary, and biomass distribution indices are calculated in conjunction with environmental factors. Specifically, vegetation volume characteristics are obtained from within the boundary, and the volume is converted into biomass using the allometric growth equation. The formula is:

[0068]

[0069] in, B i Indicates the first i Biomass of each subregion V i Indicates the first i The vegetation volume of each sub-region a and b This represents species-specific parameters; in this embodiment, the parameters are calibrated based on typical shrubs. a =0.5、 b =1.2; Simultaneously, environmental factors (such as soil organic carbon content, annual average temperature and precipitation) are integrated, and biomass calculation is adjusted through weighted fusion to generate biomass distribution characteristics, which are then output as a structured dataset:

[0070]

[0071] in, B adj,i Indicates the characteristics of biomass distribution. S i This represents the standardized soil factors. T i This represents the standardized temperature factor. P i The standardized precipitation factor is represented in this embodiment, with the weights set as follows: w 1 = 0.3 w 2 = 0.2 w3=0.2. Subsequently, key indicators (such as average biomass density, maximum biomass, and gradient value) are extracted from biomass distribution characteristics and integrated with environmental factor data (including altitude, slope aspect, and soil pH) into a multi-feature matrix. The random forest algorithm is used to analyze this matrix to determine the correlation between biomass distribution and environmental factors. If the correlation score is higher than the fourth preset threshold (set to 0.7 in this embodiment), the correlation is considered significant, indicating that environmental factors have a dominant influence on biomass distribution; otherwise, the model parameters need to be recalibrated. This process ensures robustness through cross-validation and outputs the importance ranking of key environmental variables for subsequent carbon storage calculations. Finally, based on the high correlation results, the estimated regional carbon storage value is calculated by combining biomass distribution indicators and environmental factors. First, the adjusted biomass of all sub-regions is summarized, and the carbon conversion formula is applied:

[0072]

[0073] in, C total This represents the total carbon storage in the region. CF This represents the carbon fraction; in this embodiment, it is taken as... CF =0.48 (based on the average carbon content of shrub vegetation). Simultaneously, environmental adjustment factors were introduced. E factor (For example, use 0.9 for arid regions and 1.1 for humid regions) to refine the estimation:

[0074]

[0075] Cfinal represents the final estimated regional carbon storage value. Finally, through uncertainty analysis to assess the error, a spatial distribution map of carbon storage and a total report are output to provide data support for ecological management.

[0076] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0077] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the sequence number of each step in the above embodiments does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The actions or steps recorded in the claims can be performed in a different order than that in the above embodiments and can still achieve the desired result. In addition, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] Example 2

[0079] In this embodiment, a shrub vegetation carbon storage estimation system based on UAV technology includes: a data acquisition module, a data processing module, a volume feature acquisition module, a boundary acquisition module, and a carbon storage calculation module.

[0080] The data acquisition module scans the target area using a lidar mounted on a drone to obtain initial point cloud data, and then obtains a set of three-dimensional coordinates of the shrub vegetation based on the initial point cloud data.

[0081] The data processing module uses a filtering algorithm to remove noise points from the acquired three-dimensional coordinate set to obtain clean point cloud data. If the point density in the clean point cloud data is lower than the first preset threshold, the missing points are supplemented by an interpolation algorithm to obtain complete point cloud data.

[0082] In the volume feature acquisition module, for complete point cloud data, a voxelization method is used to divide the space into grids, and vegetation volume features are obtained based on the number of points in each space grid.

[0083] The boundary acquisition module is used to extract height and coverage parameters from vegetation volume features and obtain the shrub cluster boundary through a density clustering algorithm.

[0084] The carbon storage calculation module calculates the biomass distribution index based on the shrub cluster boundary, and then calculates the estimated regional carbon storage based on the biomass distribution index.

[0085] The system described in the above embodiments is used to implement the corresponding shrub vegetation carbon storage estimation method based on UAV technology in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0086] It should be noted that the aforementioned shrub vegetation carbon storage estimation system based on UAV technology is presented in the form of functional units. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0087] For example, a "module" can be a software program, hardware circuit, or a combination of both that implements the above functions. Hardware circuits may include application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functions.

[0088] Example 3

[0089] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the shrub vegetation carbon storage estimation method based on UAV technology described in any of the above embodiments.

[0090] Figure 2 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0091] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0092] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0093] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0094] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB (Universal Serial Bus), network cable, etc.) or wireless means (such as mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0095] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0096] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0097] The system described in the above embodiments is used to implement the corresponding shrub vegetation carbon storage estimation method based on UAV technology in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0098] Example 4

[0099] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the shrub vegetation carbon storage estimation method based on UAV technology as described in any of the above embodiments.

[0100] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0101] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the shrub vegetation carbon storage estimation method based on UAV technology as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0102] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0103] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) have been set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that the embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0104] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0105] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] This disclosure is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for estimating carbon storage in shrub vegetation based on unmanned aerial vehicle (UAV) technology, characterized in that, Includes the following steps: The target area is scanned by a drone equipped with a lidar to obtain initial point cloud data, and a set of three-dimensional coordinates of shrub vegetation is obtained based on the initial point cloud data. Based on the acquired three-dimensional coordinate set, a filtering algorithm is used to remove noise points to obtain clean point cloud data. If the point density in the clean point cloud data is lower than the first preset threshold, the missing points are supplemented by an interpolation algorithm to obtain complete point cloud data. For complete point cloud data, a voxelization method is used to divide the space into grids, and vegetation volume features are obtained based on the number of points in each space grid. Height and coverage parameters are extracted from vegetation volume features, and shrub cluster boundaries are obtained through density clustering algorithm. Biomass distribution indexes are calculated based on shrub cluster boundaries, and regional carbon storage estimates are calculated based on biomass distribution indexes. Methods for obtaining a set of three-dimensional coordinates include: The target area is fully scanned by a drone equipped with a lidar to acquire scan data and form an initial point cloud. Extract initial point cloud data from the initial point cloud and perform noise reduction on the initial point cloud data to obtain the processed point cloud dataset; The three-dimensional coordinates of shrub vegetation are constructed based on the processed point cloud dataset, resulting in a set of three-dimensional coordinates; Methods for obtaining complete point cloud data include: The three-dimensional coordinate set is initially organized, and the data format is standardized to obtain a structured point cloud dataset; A voxel grid filtering algorithm is used to denoise the structured point cloud dataset, and a density detection method is used to calculate the density value of the point cloud after denoising, thus obtaining the density distribution result of the point cloud data. If there are areas in the density distribution results that are below the first preset threshold, then the missing points in these areas are marked, and the interpolation algorithm is called to calculate and process these positions to generate supplementary points; The supplementary points are integrated with the original point cloud data to obtain complete point cloud data; Methods for obtaining vegetation volume characteristics include: For complete point cloud data, the voxelization method is used to divide the three-dimensional space into meshes to obtain several voxel units; For the divided voxel units, the number of point cloud data in each voxel unit is counted. If the number of points in a voxel unit exceeds the second preset threshold, it is marked as a high-density region, and preliminary density distribution information is obtained. The spatial location of high-density areas is extracted from the preliminary density distribution information, and clustering methods are used to group adjacent high-density areas to identify potential vegetation clustering areas. Based on the potential vegetation clustering areas, calculate the spatial volume of each group, obtain the corresponding volume data, and obtain the vegetation volume characteristics; Methods for obtaining the boundaries of shrub clusters include: Height parameters and coverage data are obtained from vegetation volume features and then denoised and standardized to obtain a normalized vegetation parameter dataset. Density clustering was used to analyze the normalized vegetation parameter dataset, and regional division was performed based on height parameter and coverage data to determine the set of similar regions. Based on a set of similar regions, the distribution characteristics of vegetation volume data within similar regions are obtained. By comparing the differences in distribution characteristics between adjacent regions, the preliminary range of shrub clusters is determined. For the data within the initial range, if the difference in height parameters and coverage between adjacent areas is less than the third preset threshold, these areas are merged to obtain the adjusted range of the shrub cluster. By smoothing the boundaries of the adjusted shrub clusters, the boundary point set data is obtained, and the final shrub cluster boundary is determined. Methods for obtaining regional carbon storage estimates include: By defining the boundaries of shrub clusters and obtaining corresponding biomass data within those boundaries, and combining this with the influence weights of environmental factors, the biomass distribution characteristics of each sub-region are calculated. Key indicators were extracted from the biomass distribution characteristics, and the random forest algorithm was used to process the indicator data to determine the correlation between the biomass distribution characteristics of each sub-region and environmental factors. If the correlation is higher than the fourth preset threshold, then the estimated value of regional carbon storage within the region is calculated by combining the biomass distribution characteristics and environmental factors.

2. A system for estimating carbon storage in shrub vegetation based on unmanned aerial vehicle (UAV) technology, wherein the system applies the method described in claim 1, characterized in that, include: The module includes a data acquisition module, a data processing module, a volume feature acquisition module, a boundary acquisition module, and a carbon storage calculation module. The data acquisition module scans the target area using a lidar mounted on a drone to obtain initial point cloud data, and then obtains a set of three-dimensional coordinates of the shrub vegetation based on the initial point cloud data. The data processing module uses a filtering algorithm to remove noise points from the acquired three-dimensional coordinate set to obtain clean point cloud data. If the point density in the clean point cloud data is lower than the first preset threshold, the missing points are supplemented by an interpolation algorithm to obtain complete point cloud data. In the volume feature acquisition module, for complete point cloud data, the voxelization method is used to divide the spatial grid, and the vegetation volume feature is obtained based on the number of points in each spatial grid. The boundary acquisition module is used to extract height and coverage parameters from vegetation volume features and obtain the shrub cluster boundary through a density clustering algorithm. The carbon storage calculation module calculates the biomass distribution index based on the shrub cluster boundary, and then calculates the estimated regional carbon storage based on the biomass distribution index.

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