Forest stand factor estimation method, system and terminal based on airborne laser radar data

Abstract

The application provides a forest stand factor estimation method and system based on airborne laser radar data and a terminal, relates to the technical field of image processing, and comprises the following steps: performing normalization processing on laser radar point cloud data of a target forest area to obtain standard point cloud data of the target forest area; extracting a key feature vector in the standard point cloud data; determining a crown volume and a crown surface area of the target forest area according to the key feature vector; and performing forest resource evaluation on the target forest area according to a predicted tree species in the target forest area, the crown volume and the crown surface area to obtain forest stand factors of the target forest area. The application has the advantages of improving the accuracy of estimated forest stand factors.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to stand factor estimation methods, systems and terminals based on airborne lidar data. Background Technology

[0002] Existing technologies have significant shortcomings in the data acquisition and processing stages of stand factor estimation. When relying on optical remote sensing technology to acquire raw images of forest areas, only reflectance spectral data of the ground surface and the top of the vegetation canopy can be captured. This fails to directly obtain spatial information about the understory structure and the ground, resulting in incomplete data coverage and an inability to fully reflect the overall structural characteristics of the forest. Furthermore, the preprocessing of raw images only involves basic quality improvement operations, without specific processing for key structural information such as forest vertical layers and three-dimensional canopy morphology. The extracted features are mostly limited to two-dimensional planar features, lacking effective characterization of core three-dimensional parameters such as canopy volume and surface area. This results in low-quality basic data for subsequent stand factor estimation, directly affecting the completeness and accuracy of the estimation results.

[0003] Existing technologies have significant shortcomings in the model construction and calculation stages of stand factor estimation. They rely solely on ground-measured data and simple statistical models for estimation, failing to establish specific correlation models for the growth characteristics and biomass distribution patterns of different tree species. General models cannot adapt to the differences in biomass accumulation among tree species, leading to significant deviations in individual tree biomass calculations. Furthermore, the calculation of stand factors does not integrate key three-dimensional parameters such as canopy volume and surface area with tree species characteristic parameters, relying only on single reflectance spectral characteristics or ground-measured data for derivation. This fails to quantify the impact of canopy structure on biomass and does not correct or optimize for data biases during the estimation process. The resulting stand factors are ultimately insufficient to accurately reflect the true situation of core indicators such as forest stock volume and carbon storage, failing to meet the needs of precise forest resource assessment. Summary of the Invention

[0004] To improve the accuracy of estimating stand factors, this application provides a method, system, and terminal for estimating stand factors based on airborne lidar data.

[0005] Firstly, this application provides a method for estimating stand factors based on airborne lidar data, employing the following technical solution:

[0006] Methods for estimating stand factors based on airborne lidar data include:

[0007] S1. Normalize the lidar point cloud data of the target forest area to obtain the standard point cloud data of the target forest area.

[0008] S2. Extract the key feature vectors from the standard point cloud data;

[0009] S3. Determine the canopy volume and canopy surface area of ​​the target forest area based on the key feature vectors;

[0010] S4. Based on the estimated tree species, canopy volume, and canopy surface area in the target forest area, conduct a forest resource assessment of the target forest area to obtain the stand factor of the target forest area.

[0011] In a preferred embodiment, the canopy volume and canopy surface area of ​​the target forest area are determined based on the key feature vector:

[0012] Determine the canopy height model of the target forest area based on key feature vectors;

[0013] The canopy height model is divided according to preset voxel division parameters to obtain a uniform voxel grid of the target forest area;

[0014] Obtain lidar pulse parameters;

[0015] By analyzing the parameters of the lidar pulse, the three-dimensional straight path of the lidar pulse is obtained;

[0016] The uniform voxel grid is traversed sequentially according to the three-dimensional straight path to obtain the occupied voxel grid, the number of pulse hits on the occupied voxel grid, the unoccupied voxel grid, the occluded voxel grid, and the number of pulse occlusions on the occluded voxel grid.

[0017] The canopy volume and canopy surface area of ​​the target forest region are obtained by analyzing the pulse hit counts of occupied voxel grids, occupied voxel grids, unoccupied voxel grids, occluded voxel grids, and occluded voxel grids.

[0018] In a preferred embodiment, the step of normalizing the lidar point cloud data of the target forest area to obtain standard point cloud data of the target forest area includes:

[0019] Noise points are removed from the lidar point cloud data of the target forest area to obtain preliminary purified point cloud data of the target forest area.

[0020] The preliminary purified point cloud data is converted to a preset global coordinate system to obtain point cloud data with unified coordinates.

[0021] The coordinate-unified point cloud data is subjected to scale standardization processing to match the distribution of the point cloud data with the standard range, thereby obtaining the standard point cloud data of the target forest area.

[0022] In a preferred embodiment, the step of extracting key feature vectors from the standard point cloud data includes:

[0023] The standard point cloud data is segmented to distinguish between the ground point cloud subset and the vegetation point cloud subset in the target forest area.

[0024] Based on the vegetation point cloud subset, structural information representing the three-dimensional spatial distribution of the tree canopy is extracted;

[0025] Based on the structural information, determine the height distribution statistics of the vertical layers of the forest stands in the target forest area;

[0026] By fusing the height distribution statistics with the point cloud intensity information of the vegetation point cloud subset, the key feature vector of the standard point cloud data is obtained.

[0027] In a preferred embodiment, the step of conducting a forest resource assessment of the target forest area based on the estimated tree species, canopy volume, and canopy surface area to obtain the stand factors of the target forest area includes:

[0028] Obtain the tree species characteristic parameters corresponding to the estimated tree species;

[0029] Based on the tree species characteristic parameters, establish the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees;

[0030] Based on the aforementioned correlation, and the tree canopy volume and tree canopy surface area, determine the biomass per unit area of ​​different tree species in the target forest area;

[0031] The stand factors of the target forest area are obtained by summing up the biomass per unit area.

[0032] In a preferred embodiment, the step of establishing the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees based on the tree species characteristic parameters includes:

[0033] Obtain the synergistic influencing factors of the tree species characteristic parameters regarding the contribution of canopy volume and canopy surface area to biomass in the target forest area;

[0034] Based on the synergistic influencing factors, determine the relative weights of the canopy volume and the canopy surface area in biomass estimation;

[0035] Based on the relative weights, a biomass estimation model framework is constructed with the canopy volume and the canopy surface area as input variables.

[0036] The growth model parameters in the tree species characteristic parameters are used to initialize the parameters of the biomass estimation model framework to obtain the initial biomass estimation model.

[0037] The initial biomass estimation model is constrained and corrected by the biomass conversion factor in the tree species characteristic parameters to obtain an optimized biomass estimation model.

[0038] The optimized biomass estimation model is established as the correlation between the canopy volume and the canopy surface area and the biomass of a single tree.

[0039] In a preferred embodiment, the calculation formula for the optimized biomass estimation model is as follows:

[0040] ;

[0041] In the formula, Biomass per tree The biomass conversion factor, The relative weights of the canopy volumes are... The volume of the tree crown, The relative weight of the canopy surface area. These are nonlinear adjustment parameters determined based on the growth model parameters. The area of ​​the tree canopy is denoted as .

[0042] Secondly, this application provides a stand factor estimation system based on airborne lidar data, which adopts the following technical solution:

[0043] A stand factor estimation system based on airborne lidar data includes:

[0044] The data processing module is used to normalize the lidar point cloud data of the target forest area to obtain the standard point cloud data of the target forest area.

[0045] The feature extraction module is used to extract key feature vectors from the standard point cloud data;

[0046] The canopy volume determination module is used to determine the canopy volume and canopy surface area of ​​the target forest area based on the key feature vector.

[0047] The forest stand analysis module is used to assess the forest resources of the target forest area based on the estimated tree species, the canopy volume, and the canopy surface area, and to obtain the forest stand factors of the target forest area.

[0048] Thirdly, this application provides a terminal that adopts the following technical solution:

[0049] A terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the stand factor estimation method based on airborne lidar data as described in any of the preceding claims.

[0050] By adopting the above technical solution, through an operating terminal, the processor loads and executes a computer program stored in the memory for a stand factor estimation method based on airborne lidar data. This normalizes the lidar point cloud data of the target forest area to obtain standard point cloud data for the target forest area. Key feature vectors are extracted from the standard point cloud data. Based on the key feature vectors, the canopy volume and canopy surface area of ​​the target forest area are determined. Based on the estimated tree species, canopy volume, and canopy surface area in the target forest area, forest resource assessment is performed on the target forest area to obtain the stand factors of the target forest area. This application improves the accuracy of stand factor estimation.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] 1. This invention lays a high-quality data foundation for forest stand factor estimation through precise point cloud data processing and feature extraction. First, the lidar point cloud data of the target forest area undergoes normalization processing, including noise removal, coordinate unification, and scale standardization, to obtain standard point cloud data that accurately reflects the spatial distribution of the forest, effectively eliminating data interference and format differences. Then, the standard point cloud data is segmented to obtain ground and vegetation point cloud subsets. The three-dimensional canopy structure information of the vegetation point cloud is extracted, and combined with the statistical measures of the vertical layer height distribution of the forest stand and the point cloud intensity information, a multi-dimensional key feature vector is generated. This comprehensively captures the spatial and reflective characteristics of the forest structure, providing accurate feature support for subsequent canopy parameter calculations.

[0053] 2. This invention significantly improves the accuracy of stand factor estimation by utilizing a scientific canopy parameter calculation and biomass correlation model. A canopy height model is constructed based on key feature vectors, and a uniform voxel grid is divided according to preset parameters. Combining this with a three-dimensional straight-line path traversal of the grid using lidar pulses, occupied, unoccupied, and shaded voxels are accurately identified, and canopy volume and surface area are quantitatively calculated, fully capturing the spatial morphology of the canopy. Then, characteristic parameters of the predicted tree species are obtained, and an optimized biomass estimation model is constructed, incorporating synergistic influencing factors, relative weights, and nonlinear adjustment parameters. The canopy volume and surface area are substituted into the model to obtain the biomass per tree, and the biomass per unit area and stand factors are summarized. This achieves precise control over the entire process from data processing to resource assessment, greatly improving the reliability and scientific rigor of stand factor estimation. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the stand factor estimation method based on airborne lidar data in the embodiments of this application.

[0055] Figure 2 This is a functional block diagram of the stand factor estimation system based on airborne lidar data in the embodiments of this application. Detailed Implementation

[0056] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0057] This application discloses a method for estimating stand factors based on airborne lidar data. Specifically, it discloses a processing terminal and an airborne lidar. The processing terminal is communicatively connected to the airborne lidar to achieve data interaction and control. The airborne lidar is controlled to scan a target forest area, thereby obtaining lidar point cloud data, which is then sent to the processing terminal. The processing terminal normalizes the lidar point cloud data of the target forest area to obtain standard point cloud data for the target forest area. Key feature vectors are extracted from the standard point cloud data. Based on the key feature vectors, the canopy volume and canopy surface area of ​​the target forest area are determined. Based on the estimated tree species, canopy volume, and canopy surface area in the target forest area, a forest resource assessment is performed on the target forest area to obtain the stand factors of the target forest area, thereby improving the accuracy of stand factor estimation.

[0058] Reference Figure 1 This application discloses a method for estimating stand factors based on airborne lidar data, including the following steps:

[0059] S1. Normalize the lidar point cloud data of the target forest area to obtain the standard point cloud data of the target forest area.

[0060] In this embodiment of the invention, the step of normalizing the lidar point cloud data of the target forest area to obtain standard point cloud data of the target forest area includes:

[0061] Noise points are removed from the lidar point cloud data of the target forest area to obtain preliminary purified point cloud data of the target forest area.

[0062] The preliminary purified point cloud data is converted to a preset global coordinate system to obtain point cloud data with unified coordinates.

[0063] The coordinate-unified point cloud data is subjected to scale standardization processing to match the distribution of the point cloud data with the standard range, thereby obtaining the standard point cloud data of the target forest area.

[0064] Specifically, to remove noise points from the lidar point cloud data of the target forest area and obtain preliminary purified point cloud data, the process begins by traversing all point cloud data collected by the lidar, observing the spatial distribution of each point cloud and its distance relationship with neighboring point clouds. A fixed neighborhood range is defined, and the number of neighboring point clouds within this range for each point cloud is counted. If the number of neighboring point clouds for a particular point cloud is significantly lower than the preset normal number standard, it indicates that the point cloud has extremely low correlation with its surrounding point clouds and is considered an isolated noise point. Similarly, if the spatial coordinates of a point cloud deviate from the coordinates of most surrounding point clouds beyond the normal range, it is also identified as a noise point. All data identified as noise points are removed from the original point cloud data, and the remaining point cloud data constitutes the preliminary purified point cloud data for the target forest area.

[0065] Furthermore, when converting the preliminary purified point cloud data to a preset global coordinate system to obtain coordinate-unified point cloud data, the origin position, coordinate axis direction, and coordinate unit of the preset global coordinate system are first determined. This coordinate system is a unified reference system pre-set based on the geographic spatial location of the target forest area.

[0066] Furthermore, the local coordinate system parameters of the preliminary purified point cloud data are obtained, including the offset of the origin of the local coordinate system relative to the origin of the preset global coordinate system, and the angular deviation between the coordinate axes of the local coordinate system and the coordinate axes of the preset global coordinate system.

[0067] Furthermore, based on these offsets and angular deviations, the local coordinates of each point in the preliminary purified point cloud data are sequentially converted into coordinates in a preset global coordinate system. During the conversion process, it is ensured that the spatial positional relationship of each point remains unchanged in the new coordinate system. The point cloud data formed after all points have been converted is the coordinate unified point cloud data.

[0068] Furthermore, the coordinate-unified point cloud data is scaled to match the distribution of the point cloud data with the standard range. When obtaining the standard point cloud data of the target forest area, the distribution range of the coordinate-unified point cloud data in the three coordinate axes of the preset global coordinate system is first analyzed to determine the maximum and minimum values ​​of the point cloud data in each coordinate axis direction, and the distribution span of the current point cloud data in each coordinate axis direction is calculated.

[0069] Furthermore, a preset standard distribution range is retrieved. This range is a reasonable interval set based on the common distribution characteristics of a large amount of forest area point cloud data. Based on the difference between the current distribution span and the standard distribution range, the scaling ratio in each coordinate axis direction is calculated to ensure that the distribution of the scaled point cloud data in each coordinate axis direction can completely fall within the standard distribution range.

[0070] Furthermore, according to the calculated scaling ratio, the coordinate values ​​of each point in the coordinate unified point cloud data are adjusted proportionally. After adjustment, the distribution of all point cloud data is completely matched with the standard range, and the resulting point cloud data is the standard point cloud data of the target forest area.

[0071] In summary, removing noise points from lidar point cloud data yields preliminarily purified point cloud data. This process eliminates isolated and off-normally distributed noise points, preventing noise interference with subsequent data processing and ensuring that the point cloud data accurately reflects the spatial distribution of the land surface and vegetation in the target forest area. This addresses the problem of noise in the original point cloud data causing bias in subsequent analysis.

[0072] In summary, converting the initially purified point cloud data to a preset global coordinate system to obtain point cloud data with unified coordinates can unify the spatial reference of point cloud data under different collection batches and different local coordinate systems, eliminate spatial misalignment caused by coordinate differences, ensure the spatial consistency of point cloud data in the global scope, and provide a unified coordinate basis for subsequent cross-regional or overall forest area analysis.

[0073] In summary, standardizing point cloud data with uniform coordinates to obtain standard point cloud data allows the distribution of point cloud data in all directions of three-dimensional space to match the preset standard range. This avoids the impact of data scale differences on feature extraction accuracy and ensures that the key feature vectors extracted from the standard point cloud data can accurately represent the forest structure. This lays a reliable data foundation for accurately determining canopy volume, surface area, and subsequent stand factor estimation, thereby improving the overall accuracy of stand factor estimation.

[0074] S2. Extract the key feature vectors from the standard point cloud data;

[0075] In this embodiment of the invention, the step of extracting key feature vectors from the standard point cloud data includes:

[0076] The standard point cloud data is segmented to distinguish between the ground point cloud subset and the vegetation point cloud subset in the target forest area.

[0077] Based on the vegetation point cloud subset, structural information representing the three-dimensional spatial distribution of the tree canopy is extracted;

[0078] Based on the structural information, determine the height distribution statistics of the vertical layers of the forest stands in the target forest area;

[0079] By fusing the height distribution statistics with the point cloud intensity information of the vegetation point cloud subset, the key feature vector of the standard point cloud data is obtained.

[0080] Specifically, when performing point cloud segmentation on standard point cloud data to distinguish between the ground point cloud subset and the vegetation point cloud subset in the target forest area, the elevation information of each point in the standard point cloud data is first analyzed. Ground point clouds usually exhibit a continuous and relatively flat distribution in space, while vegetation point clouds extend upwards on the basis of ground point clouds to form a discrete distribution with large height differences.

[0081] Furthermore, the initial ground region is selected from the standard point cloud data where the elevation is low and the elevation change between adjacent points is small. By gradually expanding this region, point clouds whose elevation difference from the initial ground region is within a preset range and which are spatially continuous are classified as ground point clouds; point clouds whose elevation is significantly higher than that of ground point clouds and which are discontinuous with ground point clouds and exhibit a scattered or clustered upward growth pattern are classified as vegetation point clouds.

[0082] Furthermore, all points classified as ground point clouds are organized into a ground point cloud subset, and all points classified as vegetation point clouds are organized into a vegetation point cloud subset.

[0083] Furthermore, when extracting structural information representing the three-dimensional spatial distribution of the tree canopy based on the vegetation point cloud subset, all point clouds in the vegetation point cloud subset are traversed first, and the three-dimensional coordinates of each point cloud in the preset global coordinate system are recorded.

[0084] Furthermore, based on the spatial characteristics of canopy growth, the vegetation point cloud subset is horizontally divided into multiple continuous spatial regions, each corresponding to a potential canopy area. Within each region, the vertical distribution density of the point cloud is statistically analyzed, i.e., the proportion of point clouds in different elevation intervals, with higher density intervals corresponding to the main distribution layer of the canopy. Simultaneously, the maximum horizontal span and vertical height of the point cloud within each region, as well as the degree of horizontal aggregation, are recorded. Integrating the information on vertical distribution density, maximum horizontal span, vertical height, and horizontal aggregation degree for each region forms structural information that reflects the canopy's distribution morphology in three-dimensional space.

[0085] Furthermore, when determining the height distribution statistics of the vertical layers of forest stands in the target forest area based on structural information, the vertical height data corresponding to all vegetation point clouds are first extracted from the structural information, and these data are sorted in order of height from low to high. Referring to the principle of dividing the vertical layers of forest stands, that is, dividing the layers according to the growth characteristics of vegetation within different height ranges, such as low shrub layer, mid-level tree layer, and high-level tree layer, and combining the vertical distribution density in the structural information, the height division range of each layer is determined. Higher density and continuous height intervals correspond to different vertical layers.

[0086] Furthermore, within the height range of each vertical level, the average height, maximum height, minimum height of the vegetation point cloud within that level, as well as the proportion of the number of point clouds within that level to the total number of vegetation point clouds, are statistically analyzed. The statistical information, including the average height, maximum height, minimum height, and proportion of point clouds for each vertical level, is then summarized to form a statistical measure of the height distribution of forest stand vertical levels in the target forest area.

[0087] Furthermore, when fusing height distribution statistics with point cloud intensity information from vegetation point cloud subsets to obtain key feature vectors for standard point cloud data, the intensity information of each point cloud is first extracted from the vegetation point cloud subset. This information reflects the reflection intensity of the lidar signal after it shines on the vegetation surface, and the reflection intensity varies among different vegetation types or vegetation locations.

[0088] Furthermore, the average intensity value, maximum deviation of intensity value, and proportion of point cloud quantity within different intensity intervals of the vegetation point cloud subset are statistically analyzed to form point cloud intensity statistics. The average height, maximum height, minimum height, and proportion of point cloud quantity in the height distribution statistics are arranged in a preset order along with the average intensity value, maximum intensity deviation, and proportion of intensity intervals in the point cloud intensity statistics.

[0089] Furthermore, the sorted data is standardized and organized to ensure that the format of each data item is consistent. All the organized data are combined into an ordered data set, which is the key feature vector of the standard point cloud data.

[0090] In summary, segmenting standard point cloud data to distinguish between ground point cloud subsets and vegetation point cloud subsets can accurately separate point clouds of different attributes in forest areas, avoiding interference from ground point clouds in the extraction of vegetation-related features. This ensures that subsequent analysis focuses on vegetation structure, eliminates the influence of irrelevant data to accurately obtain canopy information, and solves the problem of vegetation feature extraction bias caused by mixed point cloud data.

[0091] In summary, extracting structural information representing the three-dimensional spatial distribution of tree canopies from vegetation point cloud subsets can capture the spatial morphological characteristics of tree canopies in terms of horizontal span, vertical height, and point cloud distribution density, fully reconstructing the three-dimensional growth state of tree canopies, providing detailed structural support for subsequent analysis of the vertical layers of forest stands, and avoiding the problem of incomplete tree canopy feature representation due to lack of three-dimensional structural data.

[0092] In summary, by determining the height distribution statistics of the vertical layers of forest stands in the target forest area based on structural information, different forest stand layers can be divided according to height differences, such as the low shrub layer, the middle tree layer, and the high tree layer. The average height, maximum height, minimum height, and point cloud quantity of each layer can be quantified, clearly presenting the stratification pattern of the forest's vertical structure and providing ecologically significant high-dimensional data for subsequent feature fusion.

[0093] In summary, the key feature vector obtained by fusing height distribution statistics with point cloud intensity information from vegetation point cloud subsets can integrate spatial dimension information of forest structure with intensity dimension information of laser reflection to form a multi-dimensional and comprehensive feature set. This avoids the information bias caused by single-dimensional features and provides high-quality feature input for subsequent accurate determination of canopy volume, canopy surface area and stand factor estimation, further improving the accuracy of stand factor estimation. This meets the core requirement of improving the accuracy of forest resource assessment based on airborne lidar data.

[0094] S3. Determine the canopy volume and canopy surface area of ​​the target forest area based on the key feature vectors;

[0095] In this embodiment of the invention, the canopy volume and canopy surface area of ​​the target forest area are determined based on the key feature vector:

[0096] Determine the canopy height model of the target forest area based on key feature vectors;

[0097] The canopy height model is divided according to preset voxel division parameters to obtain a uniform voxel grid of the target forest area;

[0098] Obtain lidar pulse parameters;

[0099] By analyzing the parameters of the lidar pulse, the three-dimensional straight path of the lidar pulse is obtained;

[0100] The uniform voxel grid is traversed sequentially according to the three-dimensional straight path to obtain the occupied voxel grid, the number of pulse hits on the occupied voxel grid, the unoccupied voxel grid, the occluded voxel grid, and the number of pulse occlusions on the occluded voxel grid.

[0101] The canopy volume and canopy surface area of ​​the target forest region are obtained by analyzing the pulse hit counts of occupied voxel grids, occupied voxel grids, unoccupied voxel grids, occluded voxel grids, and occluded voxel grids.

[0102] Specifically, when determining the canopy height model of the target forest area based on key feature vectors, the height distribution statistics of the vertical layers of the forest stand are first extracted from the key feature vectors, including the average height, maximum height, and minimum height of each vertical layer. These data directly reflect the growth range and distribution characteristics of the tree canopy in the vertical direction. Combining the horizontal spatial range information of the target forest area, a three-dimensional spatial model is constructed with the horizontal spatial coordinates as the horizontal and vertical axes and the extracted height data as the vertical axis.

[0103] Furthermore, in the model, the maximum vegetation core height corresponding to each horizontal coordinate point is taken as the canopy height value of that point and filled into the corresponding position in the three-dimensional space to ensure that the model can fully present the height change of the tree canopy from the ground to the top. The final three-dimensional model is the canopy height model of the target forest area.

[0104] Furthermore, when dividing the canopy height model into a uniform voxel grid for the target forest region according to preset voxel partitioning parameters, the preset voxel partitioning parameters are first defined. These parameters specify the side lengths of the voxels in the horizontal and vertical directions, ensuring that each voxel has the same spatial volume. Starting from the minimum horizontal range and minimum vertical height of the canopy height model, continuous cubic units are sequentially divided in the horizontal direction according to the voxel side lengths. Simultaneously, cubic units of the same side length are divided upwards from the ground level in the vertical direction.

[0105] Furthermore, each cubic unit is a voxel, and all voxels are uniformly distributed in the three-dimensional space of the canopy height model and completely cover the entire spatial range of the canopy height model. These voxels together constitute a uniform voxel grid of the target forest region.

[0106] Furthermore, when obtaining lidar pulse parameters, the operation log file of the lidar device when collecting point cloud data of the target forest area is retrieved first. This file records in detail the relevant information of the lidar emitted pulses.

[0107] Furthermore, the emission angle of the lidar pulses (i.e., the angle between the pulse emission direction and the horizontal and vertical directions) is extracted from the recording files; the emission intensity (i.e., the energy level of the pulse during emission) is extracted; the spatial resolution (i.e., the horizontal distance between two adjacent pulses) is extracted; and the flight time range (i.e., the time interval from pulse emission to the reception of the reflected signal) is extracted. These extracted information, including emission angle, emission intensity, spatial resolution, and flight time range, are then compiled and summarized to form a complete set of lidar pulse parameters.

[0108] Furthermore, when analyzing the lidar pulse parameters to obtain the three-dimensional straight path of the lidar pulse, the position of the lidar device is first taken as the pulse emission starting point. Based on the extracted pulse emission angle, the emission direction of the pulse in three-dimensional space is determined. Combining the spatial resolution of the pulse, the positional differences of adjacent pulses in the horizontal direction are clarified to ensure that the emission starting points of each pulse are distributed at fixed intervals on the horizontal plane.

[0109] Furthermore, based on the flight time range of the pulse, the spatial position of the pulse at different distances from its emission point is calculated. Since the pulse propagates in a straight line in space, the emission point of each pulse is connected sequentially with the spatial position corresponding to different flight times. The resulting straight line is the propagation path of the pulse in three-dimensional space. The propagation paths of all pulses together constitute the set of three-dimensional straight-line paths of the lidar pulse.

[0110] Furthermore, when traversing the uniform voxel grid sequentially according to the three-dimensional straight path to obtain the number of pulse hits on occupied voxel grids, the number of pulse occlusions on unoccupied voxel grids, the number of pulse occlusions on occupied voxel grids, and the number of pulse occlusions on occupied voxel grids, a three-dimensional straight path of a lidar pulse is first selected. Starting from the pulse emission point, the path is checked sequentially along the straight direction to see if it passes through voxels in the uniform voxel grid.

[0111] Furthermore, if a voxel intersects with the pulse's straight path and the corresponding position of the voxel in the canopy height model contains vegetation height data (i.e., the voxel contains a canopy), then the voxel is determined to have occupied a voxel grid. At the same time, the pulse's hit on the voxel is recorded, and the number of pulse hits on the voxel grid that has been occupied is accumulated. If a voxel intersects with the pulse's straight path but the corresponding position of the voxel does not contain vegetation height data (i.e., the voxel does not contain a canopy), then the voxel grid is determined to not have occupied a voxel grid.

[0112] Furthermore, if a voxel does not directly intersect the pulse's straight-line path but is located behind an already occupied voxel grid along the pulse propagation direction, and the pulse cannot reach that voxel due to being blocked by an already occupied voxel grid in front, it is determined to be an occluded voxel grid. Simultaneously, one instance of pulse occlusion of that voxel is recorded, and the number of pulse occlusions for occluded voxel grids is accumulated. This process is repeated for all three-dimensional straight-line paths of LiDAR pulses to complete the determination and counting of all voxels in a uniform voxel grid, ultimately obtaining data on various types of voxel grids and their corresponding counts.

[0113] Furthermore, by analyzing the occupied voxel grids, the number of pulse hits on occupied voxel grids, the unoccupied voxel grids, the occluded voxel grids, and the number of pulse occlusions on occluded voxel grids, the canopy volume and canopy surface area of ​​the target forest area are obtained. First, the canopy volume is calculated, and the number of all occupied voxel grids in the uniform voxel grid is counted. Since each voxel has a fixed spatial volume, the number of occupied voxel grids is multiplied by the volume of a single voxel, and the result is the canopy volume of the target forest area. This calculation method ensures that all voxel spaces containing the canopy are included in the volume.

[0114] Further, the canopy surface area is calculated by first identifying the faces adjacent to unoccupied and occluded voxel grids within the occupied voxel grid. For each occupied voxel grid, the adjacent faces to the unoccupied or occluded voxel grids constitute the surface portion of the canopy. The number of such adjacent faces for all occupied voxel grids is counted. Each adjacent face has a fixed area, calculated as the square of the voxel's side length. Multiplying the number of adjacent faces by the area of ​​a single face yields the canopy surface area of ​​the target forest region, ensuring complete coverage of all surfaces in contact with the air and occluded areas of the canopy.

[0115] In summary, determining the canopy height model based on key feature vectors can construct a realistic three-dimensional canopy model by relying on the height distribution statistics of the vertical layers of the forest stand, providing an accurate basis for subsequent calculations and reducing errors caused by model bias. Dividing the canopy height model according to preset parameters to obtain a uniform voxel grid transforms the continuous canopy space into standardized voxel units, solving the problem of difficulty in quantifying continuous space and providing a unified calculation basis for the measurement.

[0116] In summary, by acquiring and analyzing the LiDAR pulse parameters to obtain the three-dimensional straight path, the actual detection trajectory is restored, ensuring that the voxel traversal is consistent with the actual scene and avoiding judgment deviations caused by inaccurate path simulation. By traversing the voxel grid to obtain various voxel and frequency data, the canopy-occupied, canopy-free, and occluded voxels are accurately distinguished, the spatial relationship of the canopy is fully captured, and the calculation loopholes of occluded canopy layers are avoided.

[0117] In summary, the analysis of various voxel and frequency data yields canopy volume and surface area. Volume is calculated by multiplying the number of occupied voxels by the volume of a single voxel, and the surface area is quantified by identifying adjacent surfaces. Combined with the results of interaction frequency verification, the accuracy of the calculation is improved, laying a key data foundation for subsequent forest resource assessment and obtaining accurate stand factors.

[0118] S4. Based on the estimated tree species, canopy volume, and canopy surface area in the target forest area, conduct a forest resource assessment of the target forest area to obtain the stand factor of the target forest area.

[0119] In this embodiment of the invention, the step of assessing the forest resources of the target forest area based on the estimated tree species, the canopy volume, and the canopy surface area to obtain the stand factors of the target forest area includes:

[0120] Obtain the tree species characteristic parameters corresponding to the estimated tree species;

[0121] Based on the tree species characteristic parameters, establish the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees;

[0122] Based on the aforementioned correlation, and the tree canopy volume and tree canopy surface area, determine the biomass per unit area of ​​different tree species in the target forest area;

[0123] The stand factors of the target forest area are obtained by summing up the biomass per unit area.

[0124] The steps for establishing the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees based on the tree species characteristic parameters include:

[0125] Obtain the synergistic influencing factors of the tree species characteristic parameters regarding the contribution of canopy volume and canopy surface area to biomass in the target forest area;

[0126] Based on the synergistic influencing factors, determine the relative weights of the canopy volume and the canopy surface area in biomass estimation;

[0127] Based on the relative weights, a biomass estimation model framework is constructed with the canopy volume and the canopy surface area as input variables.

[0128] The growth model parameters in the tree species characteristic parameters are used to initialize the parameters of the biomass estimation model framework to obtain the initial biomass estimation model.

[0129] The initial biomass estimation model is constrained and corrected by the biomass conversion factor in the tree species characteristic parameters to obtain an optimized biomass estimation model.

[0130] The optimized biomass estimation model is established as the correlation between the canopy volume and the canopy surface area and the biomass of a single tree.

[0131] The calculation formula for the optimized biomass estimation model is as follows:

[0132] ;

[0133] In the formula, Biomass per tree The biomass conversion factor, The relative weights of the canopy volumes are... The volume of the tree crown, The relative weight of the canopy surface area. These are nonlinear adjustment parameters determined based on the growth model parameters. The area of ​​the tree canopy is denoted as .

[0134] Specifically, when obtaining the tree species characteristic parameters corresponding to the estimated tree species, the preset tree species characteristic database is first consulted. This database stores key characteristic information of different tree species during the growth process, including parameters related to biomass calculation such as wood density, growth rate, proportion of branch and leaf biomass, and proportion of trunk biomass.

[0135] Furthermore, based on the estimated tree species names of the target forest area, a precise search is performed in the database to find entries that perfectly match the estimated tree species. From these entries, the wood density (i.e., the weight of wood per unit volume) is extracted; the proportion of branch and leaf biomass (i.e., the proportion of branch and leaf biomass in the total biomass of a single tree) is extracted; and the proportion of trunk biomass (i.e., the proportion of trunk biomass in the total biomass of a single tree) is extracted. These extracted parameters are then compiled and summarized to form a set of tree species characteristic parameters corresponding to the estimated tree species.

[0136] Furthermore, when establishing the relationship between crown volume, crown surface area and single tree biomass based on tree species characteristic parameters, we first analyze the intrinsic relationship between each indicator in the tree species characteristic parameters and single tree biomass. Wood density directly determines the biomass per unit volume of wood, while the proportion of branch and leaf biomass and the proportion of trunk biomass clarify the distribution ratio of biomass in different parts.

[0137] Furthermore, by combining the overall size of the canopy space reflected by canopy volume and the extent of branch and leaf spread reflected by canopy surface area, a basic rule is established that larger canopy volumes and wider surface areas generally result in higher individual tree biomass. Combining wood density with canopy volume allows for the calculation of the potential biomass of the trunk; combining the proportion of branch and leaf biomass with canopy surface area allows for the calculation of the potential biomass of the branches and leaves. By integrating these correlations, it is clarified which tree species characteristic parameters influence individual tree biomass through canopy volume and canopy surface area, forming a clear correlation rule—the relationship between canopy volume, canopy surface area, and individual tree biomass.

[0138] Furthermore, when determining the biomass per unit area of ​​different tree species in the target forest area based on the correlation relationships, canopy volume, and canopy surface area, the established correlation relationships are first invoked for each estimated tree species in the target forest area. Based on the species characteristic parameters, the canopy volume is substituted into the calculation logic related to wood density in the correlation relationships to obtain the biomass of a single tree trunk; the canopy surface area is substituted into the calculation logic related to the proportion of branch and leaf biomass in the correlation relationships to obtain the biomass of a single tree branch and leaf. The biomass of a single tree trunk and the biomass of a single tree branch and leaf are added together to obtain the total biomass of a single tree of that species.

[0139] Furthermore, the number of individual trees of this species in the target forest area is counted, and the total biomass of all individual trees of this species is calculated. At the same time, the total area of ​​the target forest area is measured and determined. The total biomass of this species is divided by the total area of ​​the area to obtain the biomass per unit area of ​​this species. The biomass per unit area of ​​all different estimated tree species in the target forest area is calculated in the same way.

[0140] Furthermore, when summarizing the biomass per unit area to obtain the stand factors of the target forest area, it is first clarified that the stand factors are the core indicators that comprehensively reflect the status of forest biomass resources in the region, and it is necessary to integrate the biomass per unit area information of all tree species. The biomass per unit area of ​​different tree species in the target forest area is classified and statistically analyzed, and the specific value of the biomass per unit area of ​​each tree species and the distribution area ratio of that tree species in the region are recorded.

[0141] Furthermore, the biomass per unit area is categorized and organized according to the ecological function or economic value of tree species. For example, the biomass per unit area of ​​arbor species is grouped into one category, and the biomass per unit area of ​​shrub species into another. The biomass per unit area of ​​all categorized species is summed to obtain the total biomass per unit area of ​​the target forest area. At the same time, information such as the distribution and proportion of biomass per unit area of ​​each tree species is included, forming a comprehensive data set that includes the overall biomass level and internal structural characteristics. This data set is the stand factor of the target forest area.

[0142] Specifically, when obtaining the synergistic influencing factors of canopy volume and canopy surface area on the biomass contribution of the target forest area from the tree species characteristic parameters, information related to canopy growth and biomass accumulation should first be screened from the tree species characteristic parameters, including the change patterns of canopy volume and surface area of ​​the tree species at different growth stages, and the distribution ratio of biomass in different canopy parts.

[0143] Furthermore, we analyzed the correlation between canopy volume and surface area in biomass. For example, when the canopy volume increases and the surface area expands simultaneously, the increase in photosynthetic area of ​​branches and leaves will promote biomass growth. The characteristic parameter that both affect biomass accumulation is called the synergistic influencing factor. By sorting out such characteristic parameters in tree species characteristics, we extracted specific factors that clearly reflect the synergistic effect of the two, forming a set of synergistic influencing factors of canopy volume and canopy surface area on biomass contribution.

[0144] Furthermore, when determining the relative weights of canopy volume and canopy surface area in biomass estimation based on synergistic influencing factors, the differences in the degree of contribution of canopy volume and surface area to biomass under each synergistic influencing factor are first analyzed. For example, if a certain synergistic influencing factor shows that canopy volume contributes more significantly to trunk biomass, while canopy surface area contributes more prominently to branch and leaf biomass, the relative importance of the two in the overall biomass estimation can be determined by combining the proportion of trunk and branch / leaf biomass in the total biomass of this tree species.

[0145] Furthermore, a comprehensive evaluation of all synergistic influencing factors was conducted, and the frequency and degree of the dominant contribution of canopy volume and canopy surface area in each factor were statistically analyzed. Based on the evaluation results, weight values ​​for both were assigned to ensure that the weights accurately reflect their actual impact on biomass estimation, thus obtaining the relative weights of canopy volume and canopy surface area in biomass estimation.

[0146] Furthermore, based on relative weights, when constructing a biomass estimation model framework with canopy volume and canopy surface area as input variables, the core structure of the model framework is first clarified, and canopy volume and canopy surface area are set as the two core input variables of the model, while the biomass of a single tree is set as the output variable of the model.

[0147] Furthermore, based on the determined relative weights, corresponding weight percentages are assigned to the two input variables within the model framework to ensure that the influence of the input variables on the output variables is consistent with their relative weights. The computational logic of the model framework is designed as follows: first, corresponding weights are assigned to the input canopy volume and surface area, respectively; then, the two weighted variables are combined and calculated through an integration module, ultimately outputting the corresponding single biomass estimate, forming a complete biomass estimation model framework centered on the two input variables.

[0148] Furthermore, by using the growth model parameters in the tree species characteristic parameters, the biomass estimation model framework is initialized. When obtaining the initial biomass estimation model, the growth model parameters are first extracted from the tree species characteristic parameters, including the tree species growth rate parameters, the crown development parameters corresponding to different tree ages, and the correlation parameters between biomass growth and tree age.

[0149] Furthermore, these growth model parameters are substituted into the various computational modules of the biomass estimation model framework. For example, the growth rate parameter is substituted into the integration module to adjust the comprehensive calculation coefficients of the weighted variables; the canopy development parameter is substituted into the input variable processing module to standardize the input format and range of canopy volume and surface area. By substituting these parameters, initial operating parameters are assigned to each computational stage in the model framework, enabling the model framework to possess basic computational capabilities and forming an initial biomass estimation model capable of preliminary biomass estimation.

[0150] Furthermore, when the initial biomass estimation model is constrained and corrected by the biomass conversion factor in the tree species characteristic parameters to obtain the optimized biomass estimation model, the biomass conversion factor is first extracted from the tree species characteristic parameters. This factor is a parameter that reflects the conversion relationship between biomass in different parts of the tree species and the overall biomass, such as the coefficient of converting branch and leaf biomass into total biomass, the coefficient of converting trunk biomass into total biomass, etc.

[0151] Furthermore, samples of this tree species with known actual biomass are selected, and the canopy volume and surface area of ​​the samples are input into the initial biomass estimation model to obtain the estimated biomass value of the model. The estimated biomass value is compared with the actual biomass value of the sample, and the deviation between the two is calculated. The weight allocation and calculation coefficients in the model are adjusted according to the biomass conversion factor. For example, if the estimated value is higher than the actual value, the deviation is reduced by decreasing the calculation coefficient related to the biomass conversion factor of the corresponding part. Sample testing and parameter adjustment are repeated until the deviation between the model's estimated value and the actual value stabilizes within the preset range, resulting in an optimized biomass estimation model.

[0152] Furthermore, when establishing the optimized biomass estimation model as the correlation between canopy volume and canopy surface area and individual tree biomass, the stability and accuracy of the optimized biomass estimation model were first verified. Multiple groups of tree species samples in different growth states were selected, and the canopy volume and surface area of ​​the samples were input respectively. The estimated individual tree biomass value output by the model was recorded and compared with the actual biomass value of the samples to confirm that the model can accurately estimate biomass under different conditions.

[0153] Furthermore, the model's correlation logic between canopy volume and surface area and individual tree biomass was examined to ensure its clarity, guaranteeing that the mapping relationship between the model's input and output conformed to the biological growth patterns reflected by the tree species' characteristic parameters. After verifying the model's accuracy and the reasonableness of its correlation logic, this optimized biomass estimation model was formally established as the link between canopy volume, canopy surface area, and individual tree biomass, defining the correlation among the three.

[0154] Specifically, the value of single-tree biomass comes from the calculation results of the optimized biomass estimation model. This result is obtained by inputting the canopy volume and canopy surface area, combined with the biomass conversion factor, relative weight and nonlinear adjustment parameters, and according to the calculation logic set by the model, directly reflecting the total biomass of a single tree.

[0155] Furthermore, the biomass conversion factor originates from tree species characteristic parameters. When extracting tree species characteristic parameters, entries matching the estimated tree species are retrieved from the preset tree species characteristic database. Parameters reflecting the conversion relationship between biomass in different parts of the tree species and the overall biomass are extracted from these parameters, which are the biomass conversion factors used to constrain and correct the initial biomass estimation model.

[0156] Furthermore, the relative weight of canopy volume is derived from the analysis of synergistic influencing factors. First, synergistic influencing factors are extracted from tree species characteristic parameters. Then, the degree of contribution of canopy volume to biomass in each synergistic influencing factor is analyzed. Combining the evaluation results of all synergistic influencing factors, the weight value of canopy volume in biomass estimation is assigned. This value is the relative weight of canopy volume.

[0157] Furthermore, the canopy volume is derived from the measurement of the canopy of the target forest area. By constructing a canopy height model, dividing it into uniform voxel grids, counting the number of voxel grids already occupied and multiplying by the volume of a single voxel, the result is the canopy volume, which directly reflects the spatial size of the canopy.

[0158] Furthermore, the relative weight of canopy surface area also comes from the analysis of synergistic influencing factors. When analyzing synergistic influencing factors, the degree of contribution of canopy surface area to biomass is simultaneously calculated. Combined with the comprehensive evaluation of all synergistic influencing factors, the weight value of canopy surface area in biomass estimation is assigned, and this value is the relative weight of canopy surface area.

[0159] Furthermore, the nonlinear adjustment parameter is derived from the growth model parameter. The growth model parameter is extracted from the tree species characteristic parameters, including parameters such as tree species growth rate, crown development and tree age. Based on the nonlinear influence characteristics of these parameters on biomass growth, the parameter used to adjust the nonlinear relationship of the model is determined, and this parameter is the nonlinear adjustment parameter.

[0160] Furthermore, the canopy surface area is derived from the measurement of the canopy of the target forest area. When counting the occupied voxel grids, the faces adjacent to the occupied voxel grids and the non-occupied voxel grids are identified. The number of these adjacent faces is counted and multiplied by the area of ​​a single face. The result is the canopy surface area, which reflects the degree of canopy branch and leaf spread.

[0161] Furthermore, the significance of this formula lies in establishing a precise correlation between canopy volume, canopy surface area, and individual tree biomass. It corrects the overall calculation results using a biomass conversion factor to ensure the results conform to the biomass conversion patterns of tree species. The relative weights of canopy volume and canopy surface area reflect their different degrees of influence on biomass accumulation. By adjusting parameters nonlinearly, it adapts to the nonlinear characteristics of tree species biomass growth, ultimately achieving the goal of accurately calculating individual tree biomass based on canopy volume and canopy surface area, providing a reliable basis for calculating biomass per unit area in subsequent forest resource assessments.

[0162] Furthermore, the trend of this formula is reflected in several aspects. When the canopy volume increases, with other parameters remaining constant, the product of the canopy volume and the corresponding relative weight increases, which in turn increases the value within the brackets, ultimately leading to an increase in the biomass of individual trees. This increase is affected by the nonlinear adjustment parameter, exhibiting a nonlinear change consistent with the growth pattern of the tree species. Similarly, when the canopy surface area increases, the product of the canopy surface area and the corresponding relative weight increases, the value within the brackets increases accordingly, and the biomass of individual trees also increases accordingly, following the same nonlinear trend. When the biomass conversion factor increases, with the value within the brackets and the nonlinear adjustment parameter remaining constant, the biomass of individual trees increases proportionally with the increase of the biomass conversion factor. When the nonlinear adjustment parameter changes, it changes the result of the power operation of the value within the brackets, thus causing the biomass of individual trees to exhibit different growth or decay rates, in order to match the biomass change characteristics of the tree species at different growth stages.

[0163] In summary, obtaining the tree species characteristic parameters for the estimated tree species can provide species-specific basic data for establishing biomass correlations, ensuring that the correlations conform to the tree species' growth patterns and avoiding deviations caused by general parameters. Establishing the correlation between crown volume, surface area and single tree biomass based on tree species characteristic parameters can accurately connect crown morphology and biomass, providing a scientific basis for biomass measurement.

[0164] In summary, determining the biomass per unit area of ​​different tree species based on correlations and canopy parameters enables targeted calculation of biomass for each tree species, reflecting the impact of species differences on forest resources. Summarizing the biomass per unit area yields stand factors, which can integrate information on all tree species resources within the region, forming a comprehensive forest resource assessment result and providing reliable process support for accurately obtaining stand factors.

[0165] In summary, obtaining the synergistic influencing factors among tree species characteristic parameters can clarify the joint effects of canopy volume and surface area on biomass, providing a basis for subsequent weight allocation; determining the relative weights based on the synergistic influencing factors can reflect the different levels of importance of the two in biomass estimation and avoid the subjectivity of weight setting.

[0166] In summary, the biomass estimation model framework based on relative weights can build a model foundation that fits the logic of biomass formation, with canopy volume and surface area as the core inputs. The initial model can be obtained by initializing the model using growth model parameters, which can endow the model with basic calculation capabilities and ensure that the model conforms to the growth law of tree species.

[0167] In summary, calibrating the initial model with biomass conversion factors to obtain an optimized model can reduce estimation bias and improve model accuracy. Establishing the optimized model as a correlation can create a precise mapping between canopy parameters and individual tree biomass, providing a reliable basis for biomass measurement in subsequent forest resource assessments.

[0168] In summary, the formula corrects the overall results through a biomass conversion factor to ensure that it conforms to the biomass conversion law of tree species; it distinguishes the degree of influence of canopy volume and surface area on biomass by relative weights, which is consistent with the actual difference in their contributions.

[0169] In summary, by adapting nonlinear adjustment parameters to the nonlinear characteristics of tree species biomass growth, the canopy parameters and individual tree biomass can be accurately linked, providing a reliable basis for subsequent calculation of biomass per unit area and obtaining accurate stand factors.

[0170] Based on the same inventive concept, embodiments of this application provide a stand factor estimation system based on airborne lidar data, including:

[0171] The data processing module is used to normalize the lidar point cloud data of the target forest area to obtain the standard point cloud data of the target forest area.

[0172] The feature extraction module is used to extract key feature vectors from the standard point cloud data;

[0173] The canopy volume determination module is used to determine the canopy volume and canopy surface area of ​​the target forest area based on the key feature vector.

[0174] The forest stand analysis module is used to assess the forest resources of the target forest area based on the estimated tree species, the canopy volume, and the canopy surface area, and to obtain the forest stand factors of the target forest area.

[0175] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0176] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a stand factor estimation method based on airborne lidar data.

[0177] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0178] Based on the same inventive concept, embodiments of this application provide a terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as a stand factor estimation method based on airborne lidar data.

[0179] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0180] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for estimating stand factors based on airborne lidar data, characterized in that, include: S1. Normalize the lidar point cloud data of the target forest area to obtain the standard point cloud data of the target forest area. S2. Extract key feature vectors from the standard point cloud data, including: The standard point cloud data is segmented to distinguish between the ground point cloud subset and the vegetation point cloud subset in the target forest area. Based on the vegetation point cloud subset, structural information representing the three-dimensional spatial distribution of the tree canopy is extracted; Based on the structural information, determine the height distribution statistics of the vertical layers of the forest stands in the target forest area; By fusing the height distribution statistics with the point cloud intensity information of the vegetation point cloud subset, the key feature vector of the standard point cloud data is obtained; S3. Determine the canopy volume and canopy surface area of ​​the target forest area based on the key feature vectors; S4. Based on the estimated tree species, canopy volume, and canopy surface area in the target forest area, conduct a forest resource assessment of the target forest area to obtain the stand factors of the target forest area, including: Obtain the tree species characteristic parameters corresponding to the estimated tree species; Based on the tree species characteristic parameters, establish the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees; Based on the aforementioned correlation, and the tree canopy volume and tree canopy surface area, the biomass per unit area of ​​different tree species in the target forest area is determined, wherein the formula for calculating the biomass per unit area is as follows: ； In the formula, Biomass per unit area The biomass conversion factor, The relative weights of the canopy volumes are... The volume of the tree crown, The relative weight of the canopy surface area. These are nonlinear adjustment parameters determined based on the growth model parameters. The area of ​​the tree canopy; The stand factors of the target forest area are obtained by summing up the biomass per unit area.

2. The stand factor estimation method based on airborne lidar data according to claim 1, characterized in that, Based on the key feature vectors, determine the canopy volume and canopy surface area of ​​the target forest area: Determine the canopy height model of the target forest area based on key feature vectors; The canopy height model is divided according to preset voxel division parameters to obtain a uniform voxel grid of the target forest area; Obtain lidar pulse parameters; By analyzing the parameters of the lidar pulse, the three-dimensional straight path of the lidar pulse is obtained; The uniform voxel grid is traversed sequentially according to the three-dimensional straight path to obtain the occupied voxel grid, the number of pulse hits on the occupied voxel grid, the unoccupied voxel grid, the occluded voxel grid, and the number of pulse occlusions on the occluded voxel grid. The canopy volume and canopy surface area of ​​the target forest region are obtained by analyzing the pulse hit counts of occupied voxel grids, occupied voxel grids, unoccupied voxel grids, occluded voxel grids, and occluded voxel grids.

3. The stand factor estimation method based on airborne lidar data according to claim 2, characterized in that, The steps for normalizing the lidar point cloud data of the target forest area to obtain standard point cloud data of the target forest area include: Noise points are removed from the lidar point cloud data of the target forest area to obtain preliminary purified point cloud data of the target forest area. The preliminary purified point cloud data is converted to a preset global coordinate system to obtain point cloud data with unified coordinates. The coordinate-unified point cloud data is subjected to scale standardization processing to match the distribution of the point cloud data with the standard range, thereby obtaining the standard point cloud data of the target forest area.

4. The stand factor estimation method based on airborne lidar data according to claim 1, characterized in that, The steps for establishing the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees based on the tree species characteristic parameters include: Obtain the synergistic influencing factors of the tree species characteristic parameters regarding the contribution of canopy volume and canopy surface area to biomass in the target forest area; Based on the synergistic influencing factors, determine the relative weights of the canopy volume and the canopy surface area in biomass estimation; Based on the relative weights, a biomass estimation model framework is constructed with the canopy volume and the canopy surface area as input variables. The growth model parameters in the tree species characteristic parameters are used to initialize the parameters of the biomass estimation model framework to obtain the initial biomass estimation model. The initial biomass estimation model is constrained and corrected by the biomass conversion factor in the tree species characteristic parameters to obtain an optimized biomass estimation model. The optimized biomass estimation model is established as the correlation between the canopy volume and the canopy surface area and the biomass of a single tree.

5. A stand factor estimation system based on airborne lidar data, characterized in that, include: The data processing module is used to normalize the lidar point cloud data of the target forest area to obtain the standard point cloud data of the target forest area. The feature extraction module is used to extract key feature vectors from the standard point cloud data, including: The standard point cloud data is segmented to distinguish between the ground point cloud subset and the vegetation point cloud subset in the target forest area. Based on the vegetation point cloud subset, structural information representing the three-dimensional spatial distribution of the tree canopy is extracted; Based on the structural information, determine the height distribution statistics of the vertical layers of the forest stands in the target forest area; By fusing the height distribution statistics with the point cloud intensity information of the vegetation point cloud subset, the key feature vector of the standard point cloud data is obtained; The canopy volume determination module is used to determine the canopy volume and canopy surface area of ​​the target forest area based on the key feature vector. The forest stand analysis module is used to assess forest resources in the target forest area based on the estimated tree species, canopy volume, and canopy surface area, and to obtain the stand factors of the target forest area, including: Obtain the tree species characteristic parameters corresponding to the estimated tree species; Based on the tree species characteristic parameters, establish the correlation between the canopy volume, the canopy surface area, and the biomass of individual trees; Based on the aforementioned correlation, and the tree canopy volume and tree canopy surface area, the biomass per unit area of ​​different tree species in the target forest area is determined, wherein the formula for calculating the biomass per unit area is as follows: ； In the formula, Biomass per unit area The biomass conversion factor, The relative weights of the canopy volumes are... The volume of the tree crown, The relative weight of the canopy surface area. These are nonlinear adjustment parameters determined based on the growth model parameters. The area of ​​the tree canopy; The stand factors of the target forest area are obtained by summing up the biomass per unit area.

6. A terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 4, for estimating stand factors based on airborne lidar data.

Images