Overground carbon reserve assessment method and device based on laser radar point cloud data

Abstract

The invention provides an overground carbon reserve assessment method and device based on laser radar point cloud data. The method provided by the invention comprises the following steps: acquiring point cloud data of a target area; processing the point cloud data; constructing a digital elevation model and a digital surface model based on the processed point cloud data; determining a canopy height model based on the digital elevation model and the digital surface model; inverting the above-ground biomass based on the canopy height model and the biomass empirical equation, and generating an above-ground biomass distribution diagram; on the basis of the above-ground biomass distribution diagram, carbon content conversion is conducted on the above-ground biomass, an above-ground carbon reserve distribution diagram is generated, and the above-ground carbon reserve of the target area is evaluated on the basis of the above-ground carbon reserve distribution diagram. According to the method and the device provided by the invention, the carbon reserve difference of different vegetation types in the target area is considered, the problems of insufficient precision and poor spatial heterogeneity of an overground carbon reserve assessment method are solved, and the reliability and the adaptability of carbon reserve assessment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of carbon storage assessment, and particularly to a method and device for assessing above-ground carbon storage based on lidar point cloud data. Background Art

[0002] With the increasing severity of global climate change problems, the carbon sequestration functions of ecosystems such as forests and wetlands have received unprecedented attention. Above-ground biomass (AGB), as an important component of the carbon storage in terrestrial ecosystems, plays a core role in assessing carbon sequestration capacity, formulating carbon trading policies, and guiding ecological restoration and management. Especially in coastal areas, ecological types such as mangroves and salt marshes are playing an increasingly prominent role in the carbon cycle. As an important ecological unit of the Zhoushan Archipelago, Xiaogan Island's ecosystem plays an important role in blue carbon storage and terrestrial carbon sequestration. Therefore, it is necessary to assess the above-ground carbon storage in coastal areas.

[0003] However, the current assessment of above-ground carbon storage in coastal areas mainly relies on traditional ground plot surveys or low-resolution remote sensing estimates, which cannot effectively reflect small-scale spatial heterogeneity and three-dimensional structural characteristics. Therefore, there is an urgent need for new technologies to achieve higher-precision and scalable carbon storage estimation methods. The current methods for assessing carbon storage mainly rely on ground plot surveys, collecting data such as vegetation types, heights, breast diameters, and biomass within the plots, or estimating through low-resolution remote sensing data. However, these methods are difficult to meet the requirements for assessing the carbon storage of ecosystems with high detail and precision, especially in complex coastal ecosystems. Lidar technology, with its high resolution and three-dimensional penetration ability, can provide detailed vegetation vertical structure information, providing rich data support for above-ground biomass inversion modeling. And the application of statistical modeling or machine learning methods can further improve the accuracy of carbon storage assessment. However, the application of lidar technology in small-scale and complex ecological environments such as coastal islands is still relatively rare. Especially in areas with significant variations in multiple vegetation types and terrain, how to accurately assess the carbon storage in these areas remains a challenge.

[0004] Therefore, there is an urgent need for a method that takes into account the carbon storage differences of different vegetation types in the target area, overcomes the problems of insufficient accuracy and poor spatial heterogeneity of above-ground carbon storage assessment methods, and improves the reliability and adaptability of carbon storage assessment. Summary of the Invention

[0005] In view of this, the present application provides a method and device for assessing above-ground carbon storage based on lidar point cloud data, which is used to take into account the carbon storage differences of different vegetation types in the target area, overcome the problems of insufficient accuracy and poor spatial heterogeneity of above-ground carbon storage assessment methods, and improve the reliability and adaptability of carbon storage assessment.

[0006] Specifically, this application is implemented through the following technical solutions:

[0007] In the first aspect of this application, a method for evaluating aboveground carbon storage based on lidar point cloud data is provided. The method includes:

[0008] Obtain the point cloud data of the target area;

[0009] Process the point cloud data. The processed point cloud data contains the elevation information of different land cover types on the ground surface;

[0010] Based on the processed point cloud data, construct a digital elevation model and a digital surface model; the digital elevation model contains the elevation information of the lowest point of the point cloud data of the land cover type in each grid cell, and the digital surface model contains the elevation information of the highest point of the point cloud data of all land cover types in each grid cell;

[0011] Based on the digital elevation model and the digital surface model, determine the canopy height model; the canopy height model includes the height of all objects on the ground surface relative to the ground surface;

[0012] Based on the canopy height model, invert the aboveground biomass based on the biomass empirical equation to generate an aboveground biomass distribution map;

[0013] Based on the aboveground biomass distribution map, perform carbon content conversion on the aboveground biomass to generate an aboveground carbon storage distribution map, and evaluate the aboveground carbon storage of the target area based on the aboveground carbon storage distribution map.

[0014] In the second aspect of this application, an apparatus for evaluating aboveground carbon storage based on lidar point cloud data is provided. The apparatus includes an acquisition module, a processing module, a construction module, a determination module, a generation module, and an evaluation module;

[0015] Among them, the acquisition module is used to obtain the point cloud data of the target area;

[0016] The processing module is used to process the point cloud data. The processed point cloud data contains the elevation information of different land cover types on the ground surface;

[0017] The construction module is used to construct a digital elevation model and a digital surface model based on the processed point cloud data; the digital elevation model contains the elevation information of the lowest point of the point cloud data of the land cover type in each grid cell, and the digital surface model contains the elevation information of the highest point of the point cloud data of all land cover types in each grid cell;

[0018] The determination module is used to determine the canopy height model based on the digital elevation model and the digital surface model; the canopy height model includes the height of all objects on the ground surface relative to the ground surface;

[0019] The generating module is configured to invert the aboveground biomass based on the biomass empirical equation based on the canopy height model, and generate an aboveground biomass distribution map;

[0020] The evaluation module is configured to perform carbon content conversion on the aboveground biomass based on the aboveground biomass distribution map, generate an aboveground carbon storage distribution map, and evaluate the aboveground carbon storage of the target area based on the aboveground carbon storage distribution map.

[0021] The above-ground carbon stock assessment method and device based on lidar point cloud data provided by this application. In the first aspect, this application makes full use of the high precision and rich three-dimensional information of lidar point cloud data, making the assessment of above-ground carbon stocks more accurate and refined. Traditional carbon stock estimation methods mainly rely on remote sensing images, field plot surveys or estimation methods based on empirical formulas, and are often limited by insufficient data resolution, limited measurement range or large human errors. This application obtains the point cloud data of the target area and processes it, enabling the data to accurately distinguish the elevation information of different ground object types, thus avoiding the errors caused by factors such as surface undulation and vegetation overlap in traditional methods. During the data processing process, this application constructs a digital elevation model and a digital surface model, which record the lowest elevation of the ground surface and the highest elevation of all ground objects respectively, making the subsequent calculation of canopy height more accurate. In addition, based on the canopy height model, the true height information of vegetation relative to the ground surface can be effectively obtained, and then biomass inversion can be carried out in combination with the biomass empirical equation. Compared with traditional estimation methods that only rely on a single data source, this application utilizes the high-resolution characteristics of lidar data and combines physical models for multi-level data processing, making the finally obtained biomass and carbon stock data more accurate and reliable. At the same time, this application can generate spatial distribution maps of biomass and carbon stocks, enabling the data not only to stay at the level of numerical calculation, but also to intuitively show the spatial distribution of carbon stocks in the region, which is helpful for subsequent analysis, prediction and management. In the second aspect, from data acquisition, preprocessing, model construction to the final carbon stock calculation and visualization output, each step is carefully designed to ensure the reasonable utilization of data and the scientific nature of the calculation. First, high-precision point cloud data is obtained through lidar, laying the foundation for accurate carbon stock assessment; subsequently, the elevation information of the ground surface and vegetation is distinguished based on the digital elevation model and the digital surface model, providing support for subsequent canopy height calculation; then, through the canopy height model, the accuracy of vegetation height data is ensured, reducing errors caused by terrain undulation or low vegetation. In terms of carbon stock calculation, this application introduces a biomass empirical equation for biomass inversion and further converts it into carbon stocks, ensuring the scientific nature of carbon stock estimation. Finally, this application can generate a carbon stock distribution map to realize the visual expression of data, making the distribution of carbon stocks in the region clear at a glance. Compared with traditional methods that rely on plot surveys and empirical formula calculations, this application greatly reduces human intervention and improves the degree of automation, making it possible to monitor carbon stocks over a large range and for a long time. In addition, because this application can perform repeated calculations based on the dynamic update of point cloud data and has good adaptability, it can be used for carbon stock monitoring at different time periods and support long-term carbon sink dynamic assessment. This systematic process design makes carbon stock assessment not only more accurate, but also more operable, providing an efficient assessment tool for forest management, ecological protection and carbon sink trading. Brief Description of the Drawings

[0022] Figure 1 This is a flowchart of the above-ground carbon storage assessment method based on lidar point cloud data provided by the first embodiment of the present application;

[0023] Figure 2 This is a schematic diagram of the target area provided by the present application;

[0024] Figure 3 This is an effect diagram of the lidar point cloud data of the target area provided by the present application;

[0025] Figure 4 This is a schematic diagram of the elevation information of the lidar point cloud data of the target area provided by the present application;

[0026] Figure 5 This is a vegetation point cloud map of the target area provided by the present application;

[0027] Figure 6 This is a canopy height distribution map of the target area provided by the present application;

[0028] Figure 7 This is a map of above-ground carbon storage distribution of the target area provided by the present application;

[0029] Figure 8 This is a schematic structural diagram of the above-ground carbon storage assessment device based on lidar point cloud data provided by the second embodiment of the present application. Detailed implementation manners

[0030] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application.

[0031] The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the present application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0032] It should be understood that although terms such as first, second, and third may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0033] Specific embodiments are given below to introduce the technical solutions of this application in detail.

[0034] Figure 1 It is a flowchart of the method for evaluating aboveground carbon storage based on lidar point cloud data provided by this application. Please refer to Figure 1 , the method provided in this embodiment may include:

[0035] S101. Obtain the point cloud data of the target area.

[0036] Specifically, the target area is set according to actual needs, and in this embodiment, it is not limited thereto. For example, Figure 2 It is a schematic diagram of the target area provided by this application. Please refer to Figure 2 , the target area targeted by this application is the vegetation distribution test area of Xiaoqiandao and Danzhi Island. Considering that Xiaoqiandao, as an important ecological unit in the Zhoushan Archipelago New Area, has a typical mixed landscape of coastal wetlands and artificial forests, and its ecosystem plays an important role in both blue carbon storage and terrestrial carbon sinks. The vegetation in the Xiaoqiandao area is mainly mangroves, arbor forests, and salt marsh herbaceous vegetation, with complex terrain changes and diverse ecological types, having a high carbon sequestration potential. Therefore, this application conducts an evaluation of aboveground carbon storage for Xiaoqiandao.

[0037] Furthermore, the point cloud data is three-dimensional spatial data obtained through remote sensing technologies such as Light Detection and Ranging (LiDAR), usually composed of a large number of "points", and each point represents a position in three-dimensional space. Each point usually contains multiple attribute information, such as position coordinates (X, Y, Z), intensity, number of echoes, etc. These points are generated during the scanning process through laser ranging technology and can be used to represent the morphology and structural characteristics of the ground, objects, buildings, etc. The point cloud data usually contains position coordinates (X, Y, Z), intensity values, number of returns (number of echoes), classification information, timestamp, etc. Among them, the position coordinates are the three-dimensional coordinates of each point, which are the basic attributes of the point cloud data and represent the position of the point in space, usually represented in the world coordinate system or local coordinate system. The intensity value is the intensity of laser reflection, characterizing the reflection situation between the laser beam and the object surface. The number of returns refers to the fact that during the scanning process of the LiDAR, each laser pulse may reflect multiple echoes from the object surface, and the point cloud data records the number of these echoes, which helps to distinguish the surfaces of different objects, such as distinguishing the objects between the ground and the canopy. The classification information refers to that in the processed point cloud data, points can be classified according to different land cover types, such as ground, vegetation, buildings, etc., and points of different categories can be distinguished by different identifiers. The timestamp refers to the acquisition time of the point cloud data, which is used to identify the moment of data acquisition. Figure 3 This is the effect diagram of the point cloud data of the target area provided by this application.

[0038] In specific implementation, the obtaining of the point cloud data of the target area includes: obtaining the geographical location of the target area; determining the flight altitude based on the terrain characteristics of the target area; determining the point cloud density according to the evaluation requirements of the target area and the point cloud data processing capability; determining the grid cell size based on the point cloud density; controlling the LiDAR to reach the target area at the flight altitude, collecting the point cloud data according to the point cloud density, and performing rasterization processing on the collected point cloud data based on the grid cell size to obtain rasterized data.

[0039] Specifically, the geographical coordinate information (longitude and latitude) of the target area is obtained through a GPS device or Geographic Information System (GIS) software. The position of the target area during the point cloud data collection process is ensured to be accurate by using the GNSS device built into the drone. The appropriate flight altitude is determined by analyzing the terrain data of the target area or by field investigation of the height differences in the target area. The determination of the flight altitude takes into account the range and coverage of the lidar to ensure that the lidar can comprehensively scan the ground surface and obtain clear point cloud data. For example, if the terrain of the target area has large undulations, the flight altitude can be appropriately increased to avoid the laser beam being blocked by the ground or vegetation. When determining the point cloud density, the assessment requirements of the target area (such as accuracy requirements and target analysis) and the data processing capabilities (storage space, processing speed, etc.) need to be considered. For example, in areas with high detail requirements, such as forests or wetlands, the point cloud density can be set to 15 points / m 2 or higher. If the target area is large and the accuracy requirement is low, a lower point cloud density (such as 5 points / m 2 ) can be determined by setting the scan frequency and scan angle of the lidar. Further, the grid cell size is determined based on the point cloud density. First, the number of point clouds per square meter is calculated, and then the appropriate grid cell size is set based on the point cloud density value. For example, if the point cloud density is 15 points / m 2 , the size of each grid cell can be set to 1m×1m or smaller to ensure that each grid cell contains sufficient point cloud data for subsequent analysis. During the flight, the flight path and flight altitude are set through the drone control system. The lidar system will scan according to the preset path to ensure coverage of the target area. Through the cooperation of the flight control system and the lidar system, the lidar will emit laser beams at the set frequency and scan angle and receive the reflected signals, recording the spatial position (X, Y, Z coordinates) and intensity value of each point. During the flight, the lidar emits a certain number of laser beams and receives the reflected signals each time according to the set scan frequency, scan range, and point cloud density. Each measurement records the position and other attribute data (such as intensity, number of returns, etc.) of a point. After obtaining the point cloud data, the point cloud data is rasterized using GIS software or point cloud processing software (such as CloudCompare, LASTools, etc.). First, the target area is divided into multiple grid cells according to the size of the grid cell. Then, the point cloud data within each grid cell is statistically analyzed to obtain the minimum, elevation value, maximum elevation value, or other statistical values within the grid cell. These statistical values will represent the spatial characteristics of each grid cell. After rasterization, the data of each grid cell (for example: elevation, vegetation density, number of point clouds, etc.) of each grid cell is obtained, and these data are organized into rasterized point cloud data.

[0040] S102. Process the point cloud data, and the processed point cloud data contains the elevation information of different ground object types on the ground surface.

[0041] Specifically, the ground object type refers to the categories of various natural or artificial objects on the ground surface, usually referring to objects related to the environment, terrain, vegetation, or artificial structures. In the point cloud data, different ground object types have different spatial characteristics, and these characteristics can help analyze the diversity of the ground surface. Common ground object types include: Ground surface (ground): Refers to the bare land, rocks, or other non-vegetation-covered parts, usually referring to the lowest point on the Earth's surface. Vegetation: Trees / forests: Tall vegetation, usually represented by higher points in the point cloud data, representing the height of the tree canopy. Shrubs / herbs: Usually lower, and the reflection points in the point cloud data will be concentrated closer to the ground. Mangroves / wetland plants: Plants in special ecosystems, with a lower height and unique point cloud characteristics. Buildings: Refers to artificial structures such as houses, bridges, etc., which are represented as high and flat structures in the point cloud data. Infrastructure such as roads and bridges: Has a flat surface, and the height and spatial position of these infrastructures will be reflected in the point cloud data. Water bodies: Such as lakes, rivers, or the ocean, usually have less reflection or no return points, and there is usually no obvious elevation information in these areas of the point cloud.

[0042] Furthermore, the elevation information refers to the vertical distance between each point in the point cloud data and a reference plane (such as sea level or the ground / ground surface). In the point cloud data, each point contains X, Y, and Z coordinates, and the Z coordinate represents the position of the point in the vertical direction, that is, the elevation. The accuracy of the elevation information is crucial for the ground surface model and terrain analysis, especially when conducting terrain undulation, vegetation height, or carbon storage assessment. Figure 4 It is a schematic diagram of the elevation information of the point cloud data of the target area provided by this application.

[0043] It should be noted that the processed point cloud data contains the elevation information of different ground object types, which is mainly related to the working principle of lidar. Lidar obtains the information of the object surface by emitting laser beams and receiving the reflected signals. Different ground object types have different reflection characteristics for laser signals, resulting in different intensities, times, and reflection angles of the returned signals. Therefore, each point in the point cloud data represents the vertical distance from the lidar emission point to the reflection surface, and the reflected signals come from different ground objects. Figure 5 It is a point cloud map of the vegetation in the target area provided by this application.

[0044] In a specific implementation, the processing of the point cloud data includes: merging point cloud data from different acquisition devices or time periods; performing thinning processing on the merged point cloud data to remove redundant points in the point cloud data; performing denoising processing on the processed point cloud data; traversing the denoised point cloud data to filter out data points in non-target areas of the point cloud data.

[0045] Specifically, point cloud data from different acquisition devices or time periods are obtained. The point cloud data obtained from different devices or time periods are aligned and matched according to spatial coordinates (X, Y, Z). When merging point cloud data, ensure that the data format and attributes of each point are consistent (such as coordinate system 1). If there are duplicate points, keep the latest data points and remove other data points. Further, the point cloud is filtered according to the set point distance threshold (such as point cloud density requirements). If the distance between adjacent points is less than the threshold, these points are merged or deleted. The distance-based thinning algorithm or grid thinning algorithm is used to filter according to the preset grid size or point density. The thinned point cloud data is denoised to remove noise points that do not conform to the actual ground feature. Filtering algorithms (such as statistical filtering, bilateral filtering, etc.) are used to remove outliers or points that do not conform to the spatial distribution law. The spatial neighborhood relationship of the point cloud is used in the denoising process to analyze the neighboring points around each point, identify and remove noise points. The denoised point cloud data is traversed and the point cloud data is filtered according to the preset geographic range or target type of the target area. Use geographic information (such as the bounding box of the target area, the rasterized area boundary, etc.) to spatially clip the point cloud data to remove points that are not in the target area. Filter the point cloud data based on grid cells, coordinate ranges, or other spatial constraints to ensure that only data points in the target area are retained.

[0046] The method provided in this embodiment can significantly improve the data quality, processing efficiency, and reliability of the final application by processing the point cloud data. First, merging the point cloud data from different acquisition devices or time periods can ensure data consistency, reduce the deviation caused by multiple acquisitions, and lay a foundation for subsequent processing steps. Thinning processing reduces the data volume by removing redundant points, which not only improves the processing speed but also effectively reduces the complexity of calculation and storage, making the data analysis of large-scale areas more efficient. Denoising processing helps to remove the noise points caused by measurement errors, equipment failures, or other external interferences, making the point cloud data more accurately reflect the surface features, thereby improving the accuracy of subsequent modeling. Filtering the data points in the non-target area can ensure the relevance of the data, only retain the information in the target area, and further improve the data accuracy. The combined effect of these steps not only improves the data quality but also optimizes subsequent tasks such as land cover classification, canopy height model, and carbon stock assessment. Finally, by reducing redundant points and noise points, the storage space requirement is reduced, the data storage and transmission costs are lowered, and the calculation efficiency is improved. These processing steps make the final point cloud data more reliable and provide accurate and effective support for subsequent applications such as ecological monitoring and carbon stock assessment.

[0047] Optionally, after traversing the denoised point cloud data and filtering the data points in the non-target area of the point cloud data, the method further includes: classifying the point cloud data by using a classification algorithm based on the spatial distribution characteristics and point cloud intensity information of the point cloud data to determine the point cloud data of different land cover types; correcting the land cover classification result based on the ground sample plot information of the target area.

[0048] Specifically, when implementing, the spatial distribution characteristics of the point cloud data are extracted, including the coordinate position (X, Y, Z) of each point and its relative neighborhood relationship, and the intensity information of the point cloud is extracted. The extracted spatial distribution characteristics and intensity information are input into the classification algorithm. Common classification algorithms include support vector machine (SVM), K-means clustering, random forest, etc. The classification algorithm divides the point cloud data into different land cover types according to the spatial characteristics and intensity information of the point cloud, such as ground, tree crown, building, etc. Further, the ground sample plot information of the target area is collected, including the annotations of different land cover types, such as the vegetation type and ground type recorded in the field survey. The classified point cloud data is matched with the sample plot information to check whether the classification result of each point cloud data is consistent with the actual ground type, and correct the inconsistent classification results. For example, if a certain area is misclassified as a tree crown, but the sample plot information indicates that the area is a water body, the classification result of this point is adjusted. Optionally, the correction can be performed by retraining the classification model or manual intervention to ensure the accuracy and consistency of the classification result.

[0049] The method provided in this embodiment can improve data accuracy, optimize subsequent processing, identify feature characteristics of ground objects, reduce the influence of noise, ensure precision, support various applications, and enhance the generalization ability of the model by performing primary classification and calibration on point cloud data. First of all, through classification, different types of ground objects can be distinguished, such as the ground, tree canopies, and buildings, which makes data analysis more accurate and targeted. The classified data can provide more accurate ground object information for subsequent processing. For example, when constructing a canopy height model, only the tree canopy area is concerned, thereby improving the accuracy and efficiency of the model. At the same time, classification helps to identify the spatial characteristics of each type of ground object, facilitating the extraction of specific spatial information and supporting more detailed analysis. During the denoising process, classification can effectively remove redundant data and noise, making subsequent analysis more focused on the ground object data actually needed. In addition, the calibration step corrects potential errors in classification by comparing ground sample information, ensuring that the classification results conform to the actual situation. The classified and calibrated point cloud data can also support various applications, such as carbon storage assessment, ecological monitoring, and urban planning, providing more accurate ground object information for specific applications. In a complex environment with multiple ground objects, classification and calibration can also enhance the generalization ability of the model, enabling this method to adapt to the data analysis requirements of different regions. Therefore, by classifying and calibrating point cloud data, not only the reliability of the analysis results is improved, but also strong support is provided for subsequent analysis and decision-making.

[0050] S103. Based on the processed point cloud data, construct a digital elevation model and a digital surface model.

[0051] Specifically, a digital elevation model (DEM) is a digital model representing the elevation information of the earth's surface topography. In the digital elevation model, the elevation value of each point on the earth's surface is recorded. Specifically, the digital elevation model usually only contains data of ground points, that is to say, it records the elevation values of the ground (earth's surface). By sampling the elevation of the earth's surface at different positions, the digital elevation model forms a spatial distribution map of elevation. A digital surface model (DSM) is a digital model representing the elevation information of the surface of ground objects. Different from the digital elevation model, the digital surface model not only records the elevation information of the earth's surface, but also includes the surface elevation of ground objects (such as trees, buildings, etc.). Therefore, the digital surface model shows the height from the ground to the top of the ground objects, including all the surfaces of the ground objects.

[0052] The digital elevation model contains the elevation information of the lowest point of the point cloud data of the surface type in each grid cell, and the digital surface model contains the elevation information of the highest point of the point cloud data of all ground object types in each grid cell.

[0053] Furthermore, a grid cell is the smallest unit used for dividing a spatial region in spatial data processing, usually a rectangular or square grid cell. Grid cells are used to represent data values within a specific region. For example, digital elevation models and digital surface models typically divide the target region into multiple grid cells, and each grid cell contains elevation data at a specific location. The size of a grid cell is usually determined by the data acquisition requirements and resolution.

[0054] It should be noted that the purpose of rasterization is to convert spatial data from a continuous form to a discrete form for subsequent calculation, analysis, and visualization. The introduction of grid cells can transform continuous spatial data into a discrete data structure that is easy to process, store, and analyze. Since in geographic information systems (GIS) and remote sensing applications, data usually involves large-scale geographic regions, using grid cells can split these large-scale regions into equal small units, making it convenient to perform independent calculations and analyses for each unit. The selection of the grid cell size determines the spatial resolution of the data. Smaller grid cells provide higher resolution and can represent the spatial information of the target region in more detail, but also require higher computational and storage resources.

[0055] In specific implementation, constructing a digital elevation model and a digital surface model based on the processed point cloud data includes: traversing the processed point cloud data, classifying the point cloud data based on the feature type to obtain point cloud data of different feature types; for the target point cloud data of the surface type, extracting the lowest elevation value within each grid cell of the target point metadata, and constructing a digital elevation model based on the lowest elevation value; for the point cloud data of each feature type, extracting the highest elevation value within each grid cell of the point cloud data, and constructing a digital surface model based on the highest elevation value.

[0056] Specifically, extract the point cloud data of the surface type (ground) from the classified point cloud data. Divide the target region into multiple grid cells. Each grid cell is a rectangular region of a fixed size. Within each grid cell, determine the lowest point in the point cloud data of the surface type (i.e., the elevation value of the ground point). By comparing the elevations of all surface points within the grid cell, select the minimum value as the elevation value of the grid cell. Record the lowest elevation value of each grid cell to form a digital elevation model. For feature types other than the surface type (such as trees, buildings, roads, etc.) in the classification, repeat the above operations. Divide the target region into multiple grid cells and extract the point cloud data of each feature type. For each grid cell, determine the highest point within the grid cell (i.e., the top elevation of the feature type). By comparing the elevations of all points within the grid cell, select the maximum value as the elevation value of the grid cell. Record the highest elevation value within each grid cell to form a digital surface model.

[0057] S104. Determine a canopy height model based on the digital elevation model and the digital surface model.

[0058] Specifically, the canopy height model (CHM) is a three-dimensional model that describes the distribution of vegetation canopy height above the ground. In forest or vegetation research, the canopy height generally refers to the vertical height between the top of the plant canopy and the ground. The canopy height model reflects the canopy height differences in different regions through elevation information and can effectively characterize the vertical structure of vegetation. Each grid cell of the canopy height model represents the height difference between the highest point of the plant canopy and the ground in that area and is usually used for evaluating the structural characteristics of forests, estimating carbon storage, and biomass analysis, etc.

[0059] Furthermore, the canopy height model is determined based on the digital elevation model and the digital surface model. The calculation method of the canopy height model is: Canopy height = Elevation value of the digital surface model - Elevation value of the digital elevation model. The canopy height model obtains the canopy height of the corresponding area by calculating the difference between the digital surface model value (i.e., the elevation of the highest point) and the digital elevation model value (i.e., the elevation of the ground point) of each grid cell. Figure 6 This is the canopy height distribution map of the target area provided by this application. Please refer to Figure 6 , the canopy height distribution of the target area, with a height range from 0 to 1 meter. As can be seen from Figure 6 , the overall distribution of the canopy height is uneven. There are more high-value areas (blue) in the north and the middle, indicating that the Spartina alterniflora grows densely and stably in these areas; while the middle and the southeast of the image are mainly green, indicating that the vegetation is shorter, which may be the expansion edge, disturbed area, or the community in the initial growth stage. This spatial heterogeneity reflects the stage and topographic environment dependence of the expansion of Spartina alterniflora in the target area. From the perspective of ecological management, the areas with higher height can be regarded as the mature core areas of Spartina alterniflora and should be monitored and controlled keyly; while the areas with lower height are the key areas for potential expansion or ecological restoration intervention. Combining this height distribution map, it can be further overlaid and analyzed with remote sensing indices (such as NDVI), topographic factors, or soil salinity data to support the research on the diffusion mechanism of Spartina alterniflora and the formulation of scientific management strategies, such as precise removal, habitat restoration, and ecological safety assessment.

[0060] In specific implementation, determining the canopy height model based on the digital elevation model and the digital surface model includes: matching the digital elevation model and the digital surface model based on grid cells to determine corresponding grid cell pairs; each grid cell pair includes a first grid cell in the digital elevation model and a second grid cell in the digital surface model; for each grid cell pair, calculating the canopy height of the grid cell pair based on the highest elevation information in the second grid cell and the lowest elevation information in the first grid cell; the canopy height is the difference between the highest elevation information and the lowest elevation information; integrating the canopy heights of all grid cell pairs to construct the canopy height model.

[0061] Specifically, traverse all grid cells of the digital elevation model and the digital surface model. According to the spatial coordinates, match each grid cell in the digital elevation model with the grid cell at the same position in the digital surface model to form grid cell pairs. Record each matched grid cell pair, where the first grid cell is from the digital elevation model and contains the lowest elevation information of the ground within the grid cell. The second grid cell is from the digital surface model and contains the highest elevation information of all ground objects within the grid cell. For each grid cell pair, extract the highest elevation value in the second grid cell and the lowest elevation value in the first grid cell. Calculate the difference between the highest elevation value and the lowest elevation value to obtain the canopy height of the grid cell pair. Arrange all the calculated canopy height values according to the spatial coordinates of the original grid cells to generate a complete canopy height data matrix. Store the canopy height information in the form of a spatial grid, and finally form the canopy height model.

[0062] S105. Based on the canopy height model, invert the aboveground biomass based on the biomass empirical equation to generate an aboveground biomass distribution map.

[0063] Specifically, the biomass empirical equation is a mathematical model for estimating the aboveground biomass of vegetation, usually fitted based on measured sample data. The biomass empirical equation usually uses the structural parameters of vegetation (such as canopy height) as input variables and combines statistical methods (such as exponential regression, power function, etc.) for modeling. The biomass empirical equation is an exponential equation regarding the aboveground biomass and the canopy height, and the biomass empirical equation includes a first empirical coefficient and a second empirical coefficient. The biomass empirical equation can be expressed as:

[0064] AGB = a × H^b.

[0065] Wherein, the AGB is the aboveground biomass; the H is the canopy height; the a and b are the first empirical coefficient and the second empirical coefficient, which are obtained by regression fitting of measured data.

[0066] Furthermore, aboveground biomass (AGB) refers to the total dry mass of the above-ground part of vegetation (such as tree trunks, branches, leaves, etc.), usually measured in tons per hectare (t / ha) or kilograms per square meter (kg / m 2 ) as the unit. Aboveground biomass is an important indicator for measuring the carbon storage and productivity of ecosystems such as forests, grasslands, and wetlands, and is widely used in carbon sink assessment, ecological monitoring, and carbon trading research. The aboveground biomass distribution map is a spatialized visualization map that represents the numerical distribution of aboveground biomass at different locations within a specific area. The aboveground biomass distribution map is usually generated based on remote sensing data, ground plot survey data, and biomass inversion models, and is presented in raster or vector form.

[0067] Optionally, the determination process of the biomass empirical equation includes: arranging multiple plots within the target area, and collecting the aboveground biomass data and canopy height data within each plot; determining the form of the exponential equation for the relationship between the canopy height and the aboveground biomass; for each plot, using the regression analysis method to fit the canopy height data and the aboveground biomass data to determine the first empirical coefficient and the second empirical coefficient; setting different weights based on the point cloud density of different plots, adjusting the first empirical coefficient and the second empirical coefficient of each plot based on the weights, and synthesizing the first empirical coefficient and the second empirical coefficient of different plots to determine the first empirical coefficient and the second empirical coefficient of the target area, thereby obtaining the biomass empirical equation.

[0068] In specific implementation, multiple representative quadrats are selected within the target area to ensure coverage of different vegetation types and terrain conditions. The aboveground biomass data of each quadrat is obtained by using ground survey methods (such as standard quadrat measurement, destructive sampling or remote sensing data verification). The canopy height information within each quadrat is extracted from lidar point cloud data. Combining existing research or empirical formulas, assuming that the aboveground biomass has an exponential relationship with the canopy height, a suitable form of exponential function is selected to reflect the vegetation growth characteristics and the law of biomass change. For each quadrat, the regression analysis method is used to fit the canopy height data and the aboveground biomass data to determine the first empirical coefficient and the second empirical coefficient. For the data of each quadrat, the nonlinear regression analysis method (such as least squares method, Bayesian regression, etc.) is used for fitting. Calculate the first empirical coefficient and the second empirical coefficient in the exponential equation to minimize the error between the fitting curve and the sample data. The point cloud data density of each quadrat is statistically calculated, that is, the number of points per unit area. Different weights are assigned to the quadrats according to the point cloud density. The greater the point cloud density, the greater the weight. The data credibility of high-density quadrats is higher and the weight is larger, while the weight of low-density quadrats is relatively smaller. According to the set weights, the empirical coefficients calculated for each quadrat are adjusted by weighted average, and the first empirical coefficient and the second empirical coefficient of the target area are determined by integrating the first empirical coefficient and the second empirical coefficient of different quadrats, and a biomass empirical equation is obtained. Combining the adjusted empirical coefficients of all quadrats, the unified empirical coefficient of the target area is calculated to determine the final biomass empirical equation applicable to the target area.

[0069] For the method provided in this embodiment, first, multiple quadrats are arranged within the target area, and the aboveground biomass data and the canopy height data are collected, so that the model construction is based on real measurement data rather than a single empirical formula, ensuring the rationality and scientificity of the equation. Secondly, selecting the form of the exponential equation can better match the nonlinear relationship between the aboveground biomass and the canopy height, so that the inversion result can reflect the vegetation growth characteristics. By using the regression analysis method to fit the data of each quadrat, the rationality of the equation parameters can be ensured, so that the empirical coefficients can optimally match the quadrat data and improve the reliability of biomass calculation. At the same time, introducing the point cloud density as a weighting factor makes the quadrats with high-density point cloud data occupy a greater weight in parameter calculation, which helps to reduce the error caused by low-density data and improve the stability of the model. Finally, by integrating the data of multiple quadrats, the empirical coefficients are adjusted and optimized, so that the equation is applicable to the entire target area rather than just a local area, enhancing the applicability and generalization of biomass estimation. In addition, this method is not only applicable to a specific area, but also can be adaptively adjusted according to different ecological environments, making the estimation results more universal. Generally speaking, this method makes full use of the advantages of ground measurement data and lidar remote sensing data, ensuring the accuracy and adaptability of the biomass empirical equation, and providing a scientific basis for more accurate carbon storage assessment.

[0070] In specific implementation, based on the canopy height model, the aboveground biomass is inversely calculated using the biomass empirical equation to generate an aboveground biomass distribution map, including: based on the canopy height model, for the canopy height within each grid cell, substituting the canopy height into the biomass empirical equation to calculate the aboveground biomass within each grid cell; mapping the aboveground biomass within each grid cell to the corresponding coordinate points according to the spatial position of the target area; performing spatial interpolation on the coordinate points and aboveground biomass of all grid cells to generate the aboveground biomass distribution map of the target area.

[0071] Specifically, traverse all grid cells in the canopy height model to obtain the canopy height value of each grid cell. Substitute the obtained canopy height value into the biomass empirical equation and use the biomass empirical equation to calculate the aboveground biomass value within each grid cell. According to the spatial coordinate information of the target area, assign the corresponding spatial coordinates to the aboveground biomass of each grid cell. Match the calculated aboveground biomass of each grid cell with its spatial coordinate points to form a spatial distribution dataset of aboveground biomass. Further, adopt a spatial interpolation method (such as Kriging interpolation, inverse distance weighted interpolation, etc.), based on the aboveground biomass data of existing grid cells, to estimate the biomass value of unobserved areas, so that the biomass data is smoothly distributed throughout the target area. Based on the interpolated data, use a Geographic Information System (GIS) or visualization tool to present the aboveground biomass value in the form of a grid to generate the aboveground biomass distribution map of the target area.

[0072] S106. Based on the aboveground biomass distribution map, perform carbon content conversion on the aboveground biomass to generate an aboveground carbon storage distribution map, and evaluate the aboveground carbon storage of the target area based on the aboveground carbon storage distribution map.

[0073] Specifically, aboveground carbon storage (AGC) refers to the carbon content stored in vegetation biomass. Plants absorb carbon dioxide through photosynthesis, convert it into organic matter and store it in the aboveground parts such as tree trunks, branches, and leaves. Therefore, aboveground carbon storage is an important part of forest carbon sinks, can be used to evaluate the carbon absorption capacity of ecosystems, and is a key indicator for formulating carbon trading, forest management, and climate change response strategies.

[0074] Further, the aboveground carbon storage distribution map is a map that visually shows the carbon storage distribution at different locations within the target area in a spatial visualization manner. The aboveground carbon storage distribution map is usually based on aboveground biomass data, calculates the carbon storage of each grid cell through a carbon conversion factor, and shows the carbon storage distribution of the entire area in the form of colors, contour lines, or raster images. It can intuitively reflect the differences in carbon storage in different regions, providing a scientific basis for carbon sink management, ecological compensation, and the formulation of carbon emission policies. Figure 7 This is the aboveground carbon storage distribution map of the target area provided by this application. Please refer to Figure 7 , for the spatial distribution of the aboveground carbon storage in the target area, the numerical range is from approximately 0.1448 to 0.1485 kg / m 2 . Overall, it shows a relatively high spatial consistency, with relatively small differences in carbon storage in space, but some local high-value aggregation areas can still be observed, mainly distributed in the northern, central, and southeastern edge regions of the image, which coincide with the areas with relatively high vegetation heights in the vegetation height distribution map. From the perspective of the carbon storage distribution characteristics, the high-value areas of aboveground carbon storage usually correspond to areas where Spartina alterniflora grows vigorously, has a high density, and has a long growth period, indicating that these areas have a strong carbon fixation ability. The low-value areas of carbon storage may be the initial growth communities, disturbed areas, or exposed mudflat zones. This layer provides spatial basic data for evaluating the carbon sink effect brought about by the ecological invasion of Spartina alterniflora, and also provides important references for wetland carbon management, the control of invasive species, and the assessment of "blue carbon" potential.

[0075] Specifically, when implementing, based on the aboveground biomass distribution map, converting the carbon content of the aboveground biomass to generate an aboveground carbon storage distribution map includes: based on the aboveground biomass distribution map, for the aboveground biomass at each coordinate point, multiplying the aboveground biomass by the carbon content coefficient to obtain the aboveground carbon storage corresponding to each coordinate point; the carbon content coefficient represents the proportion of aboveground carbon storage in aboveground biomass; performing spatial interpolation processing on the aboveground carbon storage of each coordinate point to generate the aboveground carbon storage distribution map of the target area.

[0076] Specifically, the carbon content coefficient refers to the proportion of carbon elements in aboveground biomass, that is, the proportion of carbon contained in unit mass of biomass. The carbon content coefficient is usually expressed as a dimensionless ratio value (such as 0.47 or 47%). The carbon content coefficients of different vegetation types are different, mainly affected by factors such as tree species, forest age, and growth environment. In the process of calculating aboveground carbon storage, the carbon content coefficient is used to convert aboveground biomass (AGB) to aboveground carbon storage (AGC), and the calculation formula is as follows:

[0077] AGC = AGB × C f ;

[0078] where, the AGC is the aboveground carbon storage; the AGB is the aboveground biomass; the Cf is the carbon content coefficient.

[0079] In specific implementation, each coordinate point in the above-ground biomass distribution map is traversed. The above-ground biomass value corresponding to each coordinate point is extracted, and the carbon content coefficient applicable to the vegetation type in this area is selected. The above-ground biomass value corresponding to each coordinate point is multiplied by the carbon content coefficient to calculate the above-ground carbon storage of each coordinate point. Further, spatial interpolation processing is performed on the above-ground carbon storage of each coordinate point to generate the above-ground carbon storage distribution map of the target area. A suitable spatial interpolation method (such as Kriging interpolation, inverse distance weighting method, etc.) is selected. Based on the calculated above-ground carbon storage data of each coordinate point, interpolation operations are carried out to fill the carbon storage values in the unmeasured areas. A complete above-ground carbon storage distribution map is generated, and the spatial distribution of the carbon storage in this area is visually displayed.

[0080] In the method provided by this embodiment, on the one hand, this application makes full use of the high precision and rich three-dimensional information of lidar point cloud data, making the assessment of above-ground carbon storage more accurate and refined. Traditional carbon storage estimation methods mainly rely on remote sensing images, field plot surveys, or estimation methods based on empirical formulas, and are often limited by insufficient data resolution, limited measurement range, or large human errors. However, this application obtains the point cloud data of the target area and processes it, enabling the data to accurately distinguish the elevation information of different ground object types, thus avoiding the errors caused by factors such as surface undulation and vegetation overlap in traditional methods. During the data processing, this application constructs a digital elevation model and a digital surface model, which record the lowest elevation of the ground surface and the highest elevation of all ground objects respectively, making the subsequent calculation of canopy height more accurate. In addition, based on the canopy height model, the true height information of vegetation relative to the ground surface can be effectively obtained, and then biomass inversion can be carried out in combination with the biomass empirical equation. Compared with the traditional estimation method that only relies on a single data source, this application uses the high-resolution characteristics of lidar data and combines physical models for multi-level data processing, making the final biomass and carbon storage data more accurate and reliable. At the same time, this application can generate spatial distribution maps of biomass and carbon storage, making the data not only stay at the level of numerical calculation, but also visually show the spatial distribution of carbon storage in the area, which is helpful for subsequent analysis, prediction, and management. On the other hand, from data acquisition, preprocessing, model construction, to the final carbon storage calculation and visualization output, each step is carefully designed to ensure the reasonable utilization of data and the scientific nature of calculation. First, high-precision point cloud data is obtained through lidar, laying the foundation for accurate assessment of carbon storage; then, the elevation information of the ground surface and vegetation is distinguished based on the digital elevation model and the digital surface model, providing support for the subsequent calculation of canopy height; next, through the canopy height model, the accuracy of vegetation height data is ensured, reducing errors caused by terrain undulation or low vegetation. In terms of carbon storage calculation, this application introduces a biomass empirical equation for biomass inversion and further converts it into carbon storage, ensuring the scientific nature of carbon storage estimation. Finally, this application can generate a carbon storage distribution map to realize the visual expression of data, making the distribution of carbon storage in the area clear at a glance. Compared with the traditional method that relies on plot surveys and empirical formula calculations, this application greatly reduces human intervention and improves the degree of automation, making it possible to monitor carbon storage over a large range and for a long time. In addition, because this application can perform repeated calculations based on the dynamic update of point cloud data, it has good adaptability and can be used for carbon storage monitoring at different time periods to support long-term carbon sink dynamic assessment. This systematic process design makes the carbon storage assessment not only more accurate, but also more operable, providing an efficient assessment tool for forest management, ecological protection, and carbon sink trading.Thirdly, by processing the point cloud data, the quality, processing efficiency, and reliability of the final application can be significantly improved. First of all, merging the point cloud data from different acquisition devices or time periods can ensure data consistency, reduce the deviation caused by multiple acquisitions, and at the same time lay a foundation for subsequent processing steps. Thinning processing reduces the data volume by removing redundant points, which not only improves the processing speed but also effectively reduces the complexity of calculation and storage, making the data analysis of large-scale areas more efficient. Denoising processing helps to remove the noise points caused by measurement errors, equipment failures, or other external interferences, making the point cloud data more accurately reflect the surface features, thereby improving the accuracy of subsequent modeling. Filtering out the data points in non-target areas can ensure the relevance of the data, only retain the information in the target area, and further improve the data accuracy. The combined effect of these steps not only improves the data quality but also optimizes subsequent tasks such as ground object classification, canopy height model, and carbon storage assessment. Finally, by reducing redundant points and noise points, the storage space requirements are reduced, the data storage and transmission costs are lowered, and at the same time the calculation efficiency is improved. These processing steps make the final point cloud data more reliable and provide accurate and effective support for subsequent applications such as ecological monitoring and carbon storage assessment.

[0081] Corresponding to the foregoing embodiment of a method for assessing above-ground carbon storage based on lidar point cloud data, the present application also provides an embodiment of an apparatus for assessing above-ground carbon storage based on lidar point cloud data.

[0082] Figure 8 It is a schematic structural diagram of an apparatus for assessing above-ground carbon storage based on lidar point cloud data provided in the second embodiment of the present application. Please refer to Figure 8 , the apparatus provided in this embodiment includes an acquisition module 210, a processing module 220, a construction module 230, a determination module 240, a generation module 250, and an evaluation module 260;

[0083] Among them, the acquisition module 210 is used to acquire the point cloud data of the target area;

[0084] The processing module 220 is used to process the point cloud data, and the processed point cloud data contains the elevation information of different ground object types on the ground surface;

[0085] The construction module 230 is used to construct a digital elevation model and a digital surface model based on the processed point cloud data; the digital elevation model contains the lowest elevation information of the point cloud data of the surface type in each grid cell, and the digital surface model contains the highest elevation information of the point cloud data of all ground object types in each grid cell;

[0086] The determination module 240 is configured to determine a canopy height model based on the digital elevation model and the digital surface model; the canopy height model includes the heights of all objects on the ground surface relative to the ground surface.

[0087] The generation module 250 is configured to invert the aboveground biomass based on the biomass empirical equation and generate an aboveground biomass distribution map based on the canopy height model.

[0088] The evaluation module 260 is configured to perform carbon content conversion on the aboveground biomass based on the aboveground biomass distribution map, generate an aboveground carbon storage distribution map, and evaluate the aboveground carbon storage in the target area based on the aboveground carbon storage distribution map.

[0089] The device in this embodiment can be used to execute Figure 1 the steps of the method embodiment shown. The specific implementation principle and process are similar and will not be elaborated here.

[0090] For the implementation process of the functions and roles of each unit in the above device, please refer to the implementation process of the corresponding steps in the above method for details, which will not be elaborated here.

[0091] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only illustrative. The units described as separated components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0092] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating aboveground carbon storage based on lidar point cloud data, characterized in that, The method includes: Obtaining the point cloud data of the target area; Processing the point cloud data, and the processed point cloud data contains the elevation information of different ground object types on the ground surface; Based on the processed point cloud data, constructing a digital elevation model and a digital surface model; the digital elevation model contains the lowest elevation information of the point cloud data of the ground surface type in each grid cell, and the digital surface model contains the highest elevation information of the point cloud data of all ground object types in each grid cell; Based on the digital elevation model and the digital surface model, determining a canopy height model; the canopy height model includes the height of all objects on the ground surface relative to the ground surface; Based on the canopy height model, inversely calculating the above-ground biomass based on the biomass empirical equation, and generating an above-ground biomass distribution map; Based on the above-ground biomass distribution map, performing carbon content conversion on the above-ground biomass, generating an above-ground carbon storage distribution map, and evaluating the above-ground carbon storage of the target area based on the above-ground carbon storage distribution map.

2. The method according to claim 1, wherein The constructing a digital elevation model and a digital surface model based on the processed point cloud data includes: Traversing the processed point cloud data, classifying the point cloud data based on the ground object type, and obtaining the point cloud data of different ground object types; For the target point cloud data of the ground surface type, extracting the lowest elevation value in each grid cell of the target point metadata, and constructing a digital elevation model based on the lowest elevation value; For the point cloud data of each ground object type, extracting the highest elevation value in each grid cell of the point cloud data, and constructing a digital surface model based on the highest elevation value.

3. The method according to claim 1, wherein The determining a canopy height model based on the digital elevation model and the digital surface model includes: Based on the grid cell, matching the digital elevation model and the digital surface model to determine the corresponding grid cell pair; each grid cell pair includes a first grid cell in the digital elevation model and a second grid cell in the digital surface model; For each grid cell pair, based on the highest elevation information in the second grid cell and the lowest elevation information in the first grid cell, calculating the canopy height of the grid cell pair; the canopy height is the difference between the highest elevation information and the lowest elevation information; Integrating the canopy heights of all grid cell pairs to construct a canopy height model.

4. The method according to claim 1, wherein The inversely calculating the above-ground biomass based on the biomass empirical equation and generating an above-ground biomass distribution map based on the canopy height model includes: Based on the canopy height model, for the canopy height in each grid cell, substituting the canopy height into the biomass empirical equation to calculate the above-ground biomass in each grid cell; Mapping the above-ground biomass in each grid cell to the corresponding coordinate point according to the spatial position of the target area; Performing spatial interpolation processing on the coordinate points and the above-ground biomass of all grid cells to generate the above-ground biomass distribution map within the target area.

5. The method according to claim 4, characterized in that, The biomass empirical equation is an exponential equation about the above-ground biomass and the canopy height. The biomass empirical equation includes a first empirical coefficient and a second empirical coefficient. The determination process of the biomass empirical equation includes: Lay out multiple quadrats within the target area and collect aboveground biomass data and canopy height data for each quadrat; Determine the form of an exponential equation for the relationship between the canopy height and the aboveground biomass; For each quadrat, use the regression analysis method to fit the canopy height data and the aboveground biomass data, and determine the first empirical coefficient and the second empirical coefficient; Set different weights based on the point cloud density of different quadrats, adjust the first empirical coefficient and the second empirical coefficient of each quadrat based on the weights, and synthesize the first empirical coefficient and the second empirical coefficient of different quadrats to determine the first empirical coefficient and the second empirical coefficient of the target area, and obtain the biomass empirical equation.

6. The method according to claim 1, wherein The conversion of the aboveground biomass to carbon content based on the aboveground biomass distribution map to generate an aboveground carbon storage distribution map includes: Based on the aboveground biomass distribution map, for the aboveground biomass at each coordinate point, multiply the aboveground biomass by the carbon content coefficient to obtain the aboveground carbon storage corresponding to each coordinate point; the carbon content coefficient represents the proportion of aboveground carbon storage in aboveground biomass; Perform spatial interpolation processing on the aboveground carbon storage of each coordinate point to generate the aboveground carbon storage distribution map of the target area.

7. The method according to claim 1, characterized in that, The processing of the point cloud data includes: Merge the point cloud data from different acquisition devices or time periods; Perform thinning processing on the merged point cloud data to remove redundant points in the point cloud data; Perform denoising processing on the processed point cloud data; Traverse the denoised point cloud data and filter out the data points in the non-target area of the point cloud data.

8. The method according to claim 7, wherein After traversing the denoised point cloud data and filtering out the data points in the non-target area of the point cloud data, the method further includes: Based on the spatial distribution characteristics and point cloud intensity information of the point cloud data, use a classification algorithm to classify the ground objects of the point cloud data and determine the point cloud data of different ground object types; Correct the ground object classification result based on the ground quadrat information of the target area.

9. The method according to claim 1, wherein The acquisition of the point cloud data of the target area includes: Obtain the geographical location of the target area; Determine the flight altitude based on the terrain characteristics of the target area; Determine the point cloud density according to the evaluation requirements of the target area and the point cloud data processing ability; Determine the grid cell size based on the point cloud density; Control the lidar to reach the target area at the flight altitude, collect point cloud data according to the point cloud density, and perform rasterization processing on the collected point cloud data based on the grid cell size to obtain rasterized data.

10. An above-ground carbon storage assessment device based on lidar point cloud data, characterized in that, The device includes an acquisition module, a processing module, a construction module, a determination module, a generation module, and an evaluation module; Among them, the acquisition module is used to acquire the point cloud data of the target area; The processing module is used to process the point cloud data, and the processed point cloud data contains elevation information of different ground object types on the ground surface; The construction module is used to construct a digital elevation model and a digital surface model based on the processed point cloud data; the digital elevation model contains the lowest elevation information of the point cloud data of the surface type in each grid cell, and the digital surface model contains the highest elevation information of the point cloud data of all feature types in each grid cell; The determination module is used to determine a canopy height model based on the digital elevation model and the digital surface model; the canopy height model includes the height of all objects on the surface relative to the surface; The generation module is used to invert the aboveground biomass based on the biomass empirical equation based on the canopy height model and generate an aboveground biomass distribution map; The evaluation module is used to convert the carbon content of the aboveground biomass based on the aboveground biomass distribution map to generate an aboveground carbon storage distribution map, and evaluate the aboveground carbon storage of the target area based on the aboveground carbon storage distribution map.

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