Single wood volume and sample plot volume estimation method and device, medium and product
By combining foundation and airborne lidar point cloud data, a single wood area estimation model is constructed, which solves the problem of traditional methods relying on traditional ground measurements, and achieves lossless and accurate forest stock estimation, reducing costs and man-made interference.
Patent Information
- Application Number
- CN202510551595.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-29
- Publication Date
- 2025-08-12
AI Technical Summary
The existing forest stock estimation methods rely on traditional ground measurement data, resulting in high time and economic costs, and human factors are interfered with, making it difficult to meet the needs of large-scale forest monitoring.
Combining ground-based lidar and airborne lidar point cloud data, a single wood area estimation model is constructed through preprocessing, registration, single wood segmentation and biomass index calculation to achieve accurate estimation without traditional ground measurement.
The lossless and accurate estimation of single wood volume and sample accumulation is achieved, reducing the dependence on traditional ground measurements, reducing time and labor costs, and improving data consistency.
Smart Images
Figure CN120472315A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of forest stock estimation, and in particular to a method, equipment, medium and product for estimating the stock volume of a single tree and a sample plot. Background Art
[0002] Accurate forest standing stock estimation is fundamental to effective forest resource management, precise carbon storage assessment, and the development of scientific and sustainable development strategies. Traditional standing stock estimation methods based on parse wood and allometric growth models (ASM), while widely used, rely on extensive destructive sampling and field surveys.
[0003] Light Detection and Ranging (LiDAR) technology has been widely used in forest resource monitoring and stock estimation due to its efficient, non-contact data collection capabilities. Among them, ground-based laser scanning (TLS) technology, with its high precision and automation advantages, can achieve detailed reconstruction of the three-dimensional structure of individual trees and is widely used for individual tree stock estimation. However, due to the limited sampling range, this method is difficult to meet the needs of large-scale forest monitoring. In contrast, airborne laser scanning (ALS) technology can cover a wider area and obtain structural parameters such as forest canopy height, which can achieve stock estimation at the plot scale, but it is highly dependent on traditional field measurement data.
[0004] The combination of multi-platform LiDAR data, such as TLS, ALS, and UAV-LS, has made significant progress in non-destructive estimation at the scale of individual trees to hectares, significantly improving estimation accuracy. However, existing models still rely heavily on field data for calibration, which increases time and financial investment. The LiDAR Biomass Index (LBI) has been proven to accurately estimate aboveground biomass at the individual tree and plot scales using canopy and tree height information obtained by ALS. However, model calibration still relies on traditional field measurements. Summary of the Invention
[0005] The purpose of this application is to provide a method, equipment, medium and product for estimating the volume of a single tree and the volume of a sample plot, which can accurately and non-destructively estimate the volume of a single tree and the volume of a sample plot without the need for traditional ground measurement data.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides a method for estimating the volume of a single tree and the volume of a sample plot, the method comprising:
[0008] Obtain ground-based lidar point clouds and airborne lidar point clouds;
[0009] Preprocess the ground-based lidar point cloud and the airborne lidar point cloud separately;
[0010] Use the pre-processed airborne lidar point cloud to register the pre-processed ground-based lidar point cloud;
[0011] Perform single tree segmentation on the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud to obtain the single tree segmentation results;
[0012] Determine the sample trees based on the tree segmentation results; each sample tree includes the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud corresponding to each tree;
[0013] The volume of a single tree is determined based on the registered ground-based lidar point cloud corresponding to the sample tree using the TreeQSM algorithm.
[0014] Determine the individual tree lidar biomass index and tree height based on the pre-processed airborne lidar point cloud corresponding to the sample individual tree;
[0015] A single tree volume estimation model was constructed based on the single tree lidar biomass index, tree height and single tree volume corresponding to the sample trees.
[0016] Based on the LiDAR biomass index and tree height of all individual trees in the sample plot, the individual tree volume estimation model is used to determine the volume of all individual trees in the sample plot; and the volume per unit area of the sample plot is determined.
[0017] Optionally, the preprocessing of the ground-based lidar point cloud and the airborne lidar point cloud respectively includes:
[0018] The ground-based lidar point cloud and the airborne lidar point cloud are subjected to point cloud denoising, ground point and non-ground point classification, and height normalization in turn.
[0019] Optionally, registering the pre-processed ground-based lidar point cloud using the pre-processed airborne lidar point cloud specifically includes:
[0020] The pre-processed ground-based lidar point cloud was registered to the pre-processed airborne lidar point cloud using CloudCompare 2.13.2 software.
[0021] Optionally, determining the single tree lidar biomass index and tree height based on the preprocessed lidar point cloud corresponding to the sample single tree specifically includes:
[0022] Using the formula Determine the individual tree lidar biomass index;
[0023] Among them, LBI is the lidar biomass index, H Tis the tree height, H B is the height value of a certain position of the crown, U L (H) is the volume density of the crown at height H, r(H) is the radius of the crown at height H, π is the circumference of a circle, and ΔH is a preset height interval used to calculate the cumulative value of LBI.
[0024] Optionally, constructing a tree volume estimation model based on the tree lidar biomass index, tree height, and tree volume corresponding to the sample trees specifically includes:
[0025] Sort the single tree lidar biomass index corresponding to the sample trees;
[0026] A single tree volume estimation model was constructed using nonlinear regression analysis based on the single tree lidar biomass index, tree height and single tree volume corresponding to the odd-numbered sorted sample trees.
[0027] Optionally, the single wood volume estimation model is:
[0028]
[0029] Among them, V is the volume of a single wood, H T is the tree height, LBI is the LiDAR biomass index, and α, β, and k are the input parameters of the single tree volume estimation model.
[0030] Optionally, the method further includes: determining the volume of all individual trees in the sample plot using a single tree volume estimation model based on the LiDAR biomass index and tree height of all individual trees in the sample plot; and determining the volume per unit area of the sample plot;
[0031] Obtain the scope of the sample plot;
[0032] According to the scope of the sample site, the The single tree crown segmentation technology based on spectral clustering technology performs single tree segmentation on the airborne LiDAR point cloud to obtain the LiDAR point cloud corresponding to the single tree in the sample plot;
[0033] The lidar biomass index and tree height were determined based on the lidar point cloud corresponding to individual trees in the sample plot.
[0034] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for estimating the volume of a single tree and the volume of a sample plot.
[0035] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for estimating the volume of a single tree and the volume of a sample plot.
[0036] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for estimating the volume of a single tree and the volume of a sample plot.
[0037] According to the specific embodiments provided in this application, this application has the following technical effects:
[0038] The present application provides a method, equipment, medium and product for estimating the volume of individual trees and the stock volume of sample plots. By using multi-platform LiDAR data of ground-based LiDAR and airborne LiDAR, combined with the LiDAR biomass index, it is possible to achieve non-destructive and accurate estimation of the volume of individual trees and the stock volume of sample plots without relying on traditional ground measurement data. Compared with traditional ground measurement methods, the individual tree volume estimation based on ground-based LiDAR in the present application has higher accuracy, and the constructed individual tree volume estimation model can show good performance in estimating the volume of individual trees and the stock volume of sample plots. In addition, the present application can solve the problem of the high dependence of existing stock volume estimation methods on traditional field measurement data; it can significantly reduce the dependence on traditional ground data, reduce the time cost and labor intensity of manual sampling, and at the same time reduce the interference of human factors and enhance the consistency of data. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0040] Figure 1 This is a flow chart of a method for estimating the volume of a single tree and the volume of a sample plot in one embodiment of the present application;
[0041] Figure 2 This is a schematic diagram showing a comparison between the estimated timber volume using TreeQSM and the analyzed timber volume based on TLS data provided in one embodiment of the present application;
[0042] Figure 3 A schematic diagram showing a comparison between the estimated timber volume and the analyzed timber volume using the one-dimensional and two-dimensional ASM methods provided in an embodiment of the present application;
[0043] Figure 4 A schematic diagram of the calibration accuracy of a single wood scale model provided in one embodiment of the present application;
[0044] Figure 5 A schematic diagram of the verification accuracy of a single-wood scale model provided in one embodiment of the present application;
[0045] Figure 6 This is a schematic diagram of the estimation accuracy of the accumulation volume of the sample site scale model provided in one embodiment of the present application. DETAILED DESCRIPTION
[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0047] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0048] In an exemplary embodiment, Figure 1 As shown, a method for estimating the volume of a single tree and the volume of a sample plot is provided, which includes the following S101 to S109.
[0049] S101, obtain ground-based lidar point cloud (TLS) and airborne lidar point cloud (ALS);
[0050] S102, pre-processing the ground-based lidar point cloud and the airborne lidar point cloud respectively;
[0051] S102 specifically includes:
[0052] The ground-based lidar point cloud and the airborne lidar point cloud are subjected to point cloud denoising, ground point and non-ground point classification, and height normalization in turn;
[0053] S103, registering the pre-processed ground-based lidar point cloud using the pre-processed airborne lidar point cloud;
[0054] S103 specifically includes:
[0055] Use CloudCompare 2.13.2 software to register the pre-processed ground-based lidar point cloud to the pre-processed airborne lidar point cloud. The specific registration steps are as follows:
[0056] First, the “Translate / Rotate” function in CloudCompare 2.13.2 software was used to translate and rotate the TLS data to align the TLS data with the ALS data. Then, based on the “Global Shift” parameter of the ALS data, the “Translation” tool in the “Apply Transformation” module in CloudCompare 2.13.2 software was used to transform the coordinates of the TLS data, thereby completing the high-precision registration of the TLS and ALS data.
[0057] S104, performing single tree segmentation on the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud to obtain a single tree segmentation result;
[0058] Before single tree segmentation, labels are assigned to the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud to distinguish data from different platforms (for example, the “Platform” label of TLS data is set to “1” and that of ALS data is set to “2”).
[0059] Single tree segmentation is performed using the point cloud processing software LiDAR360.
[0060] S105, determining sample trees based on the tree segmentation results; each sample tree includes the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud corresponding to each tree;
[0061] Among them, the principle of selecting sample single trees is to select single trees with relatively complete trunk point clouds of the aligned ground-based lidar point cloud and crown point clouds of the pre-processed airborne lidar point cloud.
[0062] S106, determining the volume of a single tree based on the registered ground-based lidar point cloud corresponding to the sample tree using the TreeQSM algorithm;
[0063] The point cloud of a single tree trunk is to remove the noise and branch point cloud of the single tree, retaining only the trunk point cloud; CloudCompare 2.13.2 software is used to remove the noise, branches and leaves of the single tree through a combination of automatic and manual methods. The specific removal process is as follows:
[0064] First, the linearity and verticality of the point cloud are calculated using the "Compute Geometric Features" function. This identifies tree trunks (high linearity and strong verticality) and branches (low linearity and irregular distribution).
[0065] Next, we used the "Filter Points by Value" function to filter the trunk and branch points, and then used the "Label Connected Components" function to separate and label the branches and leaves, automatically completing the separation of most trunks and branches.
[0066] Finally, the separation results need to be manually adjusted to refine the separation effect.
[0067] The TreeQSM algorithm is used to reconstruct quantitative structure models (QSM) from single tree trunk point clouds using different parameter sets multiple times, and the best reconstruction result is selected by minimizing the distance between the original point cloud and the reconstructed QSM.
[0068] In the TreeQSM algorithm, key parameters for QSM reconstruction include PatchDiam1, PatchDiam2Min, and PatchDiam2Max, which control the size of the segments during reconstruction. Each tree uses a different parameter set to generate a different QSM, taking the trunk point cloud of each tree as input. The TreeQSM algorithm uses the minimum distance between the original point cloud and the reconstructed QSM as the selection criterion. The select_optimum function uses this minimum distance as the default parameter set selection criteria.
[0069] Because the QSM reconstruction process involves random factors, even if the same set of parameters is used for each tree, the resulting QSM will be slightly different each time the algorithm is repeated. Therefore, five different QSMs were generated for each tree, using the same optimal parameter set, and the average of these QSMs was used as the final tree volume estimate.
[0070] The accuracy of TLS-based volume estimation is evaluated below by calculating the volume of a single log using two other conventional methods and taking the analytical log volume as a reference.
[0071] Among them, the other two conventional methods are: Method 1 uses TLS to extract the DBH and tree height of individual trees and combines it with ASM to estimate timber volume; Method 2 uses traditional non-destructive measurement technology, using manually measured DBH and pre-felling tree height as independent variables and combining it with ASM to estimate timber volume. The ASM is shown in Table 1.
[0072] Table 1 Allometric growth model of Japanese larch (taking Dagujia Forest Farm in Qingyuan County, Liaoning Province as an example)
[0073]
[0074] Among them, the local model of Dagujia Forest Farm comes from the study published by Gao Huilin et al. (2019) Modelling outer-and inner-crown profiles based on tree status and cardinal directions in relation to competition for Larix kaempferi plantations in northern China; the sources of the relevant literature models are Zheng Junbao (1978)'s study on the one-dimensional volume table of standing timber in larch plantations and Wang Weibin et al. (2021)'s study on the development of compatible standing timber volume and aboveground biomass equations for northeastern larch. DBH is the diameter at breast height (cm); V is the volume of individual trees (m 3 ); H is the tree height (m); D 轮 is the wheel diameter (cm); D 围 is the girth (cm); g is the cross-sectional area at chest height (m 2 );d is the diameter at breast height (cm).
[0075] Under the premise of ensuring that the accuracy meets the requirements, the single wood volume value based on TLS is used to replace the traditional non-destructive measurement method;
[0076] Taking the analytical wood volume as a reference, the single wood volume estimated by ground-based lidar has a higher accuracy (R 2 =0.97, rRMSE=12.90%, RSME=0.03m 3 ,like Figure 2 The results are comparable to the best results of Method 1 and Method 2 (R 2 =0.97, rRMSE=12.76%, RSME=0.03m 3 ,like Figure 3 and Table 2).
[0077] Table 2 Comparison of tree volume estimation accuracy of different allometric models and methods
[0078] Method 1 Method 2 <![CDATA[RMSE(m 3 )]]> rRMSE (%) <![CDATA[R 2 ]]> <![CDATA[RMSE(m 3 )]]> rRMSE (%) <![CDATA[R 2 ]]> Univariate allometric model LY / T2654-2016 0.06 26.97 0.86 0.05 22.95 0.90 DB21-778-814-94 0.07 31.65 0.80 0.06 28.11 0.84 Zheng Junbao 0.18 80.02 -0.26 0.18 78.93 0.22 Binary allometric model LY / T1353-1999 0.04 19.67 0.92 0.05 23.77 0.89 LY / T2654-2016 0.03 12.76 0.97 0.03 14.34 0.96 DB / 21 / T2780-1-2017 0.03 14.51 0.96 0.04 17.14 0.94 Local model of Dagujia Forest Farm 0.06 25.08 0.88 0.06 28.55 0.84 Wang Weibin et al. 0.04 16.07 0.95 0.04 16.64 0.95
[0079] S107, determining the single tree lidar biomass index and tree height based on the pre-processed airborne lidar point cloud corresponding to the sample single tree;
[0080] S107 specifically includes:
[0081] Using the formula Determine the individual tree lidar biomass index;
[0082] Among them, LBI is the lidar biomass index, which is derived by combining the allometric relationship with the lidar echo equation, and H T is the tree height, H B is the height value of a certain position of the crown, U L (H) is the volume density of the crown at height H (m 2 / m 3 ), r(H) is the radius of the crown at height H, π is the circumference of a circle, and ΔH is a preset height interval used to calculate the cumulative value of LBI.
[0083] S108, constructing a single tree volume estimation model based on the single tree lidar biomass index, tree height and single tree volume corresponding to the sample single tree;
[0084] S108 specifically includes:
[0085] Sort the single tree lidar biomass index corresponding to the sample trees;
[0086] A single tree volume estimation model was constructed using nonlinear regression analysis based on the single tree lidar biomass index, tree height and single tree volume corresponding to the odd-numbered sorted sample trees.
[0087] The single wood volume estimation model is:
[0088]
[0089] Among them, V is the volume of a single wood, H T is the tree height, LBI is the LiDAR biomass index, and α, β, and k are the input parameters of the single tree volume estimation model, which are obtained by regression with the measured data.
[0090] like Figure 4 As shown in Figure 2, the single wood volume estimation model has a high fitting accuracy (R 2 =0.84, RMSE=0.08m 3 , rRMSE=25.94%).
[0091] The timber volume was estimated using the single-tree volume estimation model based on the LBI and tree height of even-numbered sample trees. The accuracy of the model's single-tree volume estimation was evaluated using the volume calculated using the TreeQSM method as a reference.
[0092] like Figure 5 As shown in Figure 2, the single-tree volume estimation model showed a high estimation accuracy (R 2 =0.79, RMSE=0.10m 3 , rRMSE = 29.85%).
[0093] S109: Determine the volume of all individual trees in the plot based on the LiDAR biomass index and tree height of all individual trees in the plot using a tree volume estimation model; and determine the volume per unit area of the plot.
[0094] Before S109, it also included:
[0095] S1, obtain the sample plot range;
[0096] S2, based on the sample area, The individual tree crown segmentation (ITCS) technology based on non-spectral clustering (NSC) technology performs individual tree segmentation on the airborne LiDAR point cloud to obtain the LiDAR point cloud corresponding to the individual tree in the sample plot;
[0097] S3: Determine the LiDAR biomass index and tree height based on the LiDAR point cloud corresponding to each tree in the plot. Sum the estimated volume of all individual trees in the plot to calculate the volume per unit area, which serves as the estimated volume of the plot. The volume per unit area calculated using the allometric model is used as a reference to assess the accuracy of the model's volume estimation.
[0098] Each tree in the plot was measured one by one, and the volume of each tree was calculated using the ASM method. The volume of each tree in the plot was added up to calculate the volume per unit area, which was used as the reference value of the volume of the plot. The estimation accuracy of this application at the plot scale is relatively high (R 2 =0.70, RMSE=51.32m 3 / ha, rRMSE = 24.40%, as Figure 6 shown).
[0099] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory is an operating system and a computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for estimating the volume of a single tree and the volume of a sample plot is implemented.
[0100] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.
[0101] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0103] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0104] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.
[0105] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.
[0106] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0107] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for estimating the volume of a single tree and the volume of a sample plot, characterized in that: The method for estimating the volume of a single tree and the volume of a sample plot includes: Obtain ground-based lidar point clouds and airborne lidar point clouds; Preprocess the ground-based lidar point cloud and the airborne lidar point cloud separately; Use the pre-processed airborne lidar point cloud to register the pre-processed ground-based lidar point cloud; Perform single tree segmentation on the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud to obtain the single tree segmentation results; Determine the sample trees based on the tree segmentation results; each sample tree includes the pre-processed airborne lidar point cloud and the registered ground-based lidar point cloud corresponding to each tree; The volume of a single tree is determined based on the registered ground-based lidar point cloud corresponding to the sample tree using the TreeQSM algorithm. Determine the individual tree lidar biomass index and tree height based on the pre-processed airborne lidar point cloud corresponding to the sample individual tree; A single tree volume estimation model was constructed based on the single tree lidar biomass index, tree height and single tree volume corresponding to the sample trees. Based on the LiDAR biomass index and tree height of all individual trees in the sample plot, the individual tree volume estimation model is used to determine the volume of all individual trees in the sample plot; and the volume per unit area of the sample plot is determined.
2. The method for estimating the volume of a single tree and the volume of a sample plot according to claim 1, characterized in that: The pre-processing of the ground-based laser radar point cloud and the airborne laser radar point cloud respectively specifically includes: The ground-based lidar point cloud and the airborne lidar point cloud are subjected to point cloud denoising, ground point and non-ground point classification, and height normalization in turn.
3. The method for estimating the volume of a single tree and the volume of a sample plot according to claim 1, characterized in that: The registering of the pre-processed ground-based lidar point cloud with the pre-processed airborne lidar point cloud specifically includes: The pre-processed ground-based lidar point cloud was registered to the pre-processed airborne lidar point cloud using CloudCompare 2.13.2 software.
4. The method for estimating the volume of a single tree and the volume of a sample plot according to claim 1, characterized in that: The method of determining the single tree lidar biomass index and tree height based on the pre-processed airborne lidar point cloud corresponding to the sample single tree specifically includes: Using the formula Determine the individual tree lidar biomass index; Among them, LBI is the lidar biomass index, H T is the tree height, H B is the height value of a certain position of the crown, U L (H) is the volume density of the crown at height H, r(H) is the radius of the crown at height H, π is the circumference of a circle, and ΔH is a preset height interval used to calculate the cumulative value of LBI.
5. The method for estimating the volume of a single tree and the volume of a sample plot according to claim 1, characterized in that: The individual tree volume estimation model is constructed based on the individual tree lidar biomass index, tree height, and individual tree volume corresponding to the sample individual trees, specifically including: Sort the single tree lidar biomass index corresponding to the sample trees; A single tree volume estimation model was constructed using nonlinear regression analysis based on the single tree lidar biomass index, tree height and single tree volume corresponding to the odd-numbered sorted sample trees.
6. The method for estimating the volume of a single tree and the volume of a sample plot according to claim 1 or claim 5, characterized in that: The single wood volume estimation model is: Among them, V is the volume of a single wood, H T is the tree height, LBI is the LiDAR biomass index, and α, β, and k are the input parameters of the single tree volume estimation model.
7. The method for estimating the volume of a single tree and the volume of a sample plot according to claim 1, characterized in that: The volume of all individual trees in the sample plot is determined using a single tree volume estimation model based on the LiDAR biomass index and tree height of all individual trees in the sample plot; And determine the volume of sample plot per unit area, which also includes: Obtain the scope of the sample plot; According to the scope of the sample site, the The single tree crown segmentation technology based on spectral clustering technology performs single tree segmentation on the airborne LiDAR point cloud to obtain the LiDAR point cloud corresponding to the single tree in the sample plot; The lidar biomass index and tree height were determined based on the lidar point cloud corresponding to individual trees in the sample plot.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for estimating the volume of a single tree and the volume of a sample plot according to any one of claims 1 to 7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for estimating the volume of a single tree and the volume of a sample plot according to any one of claims 1 to 7 is implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for estimating the volume of a single tree and the volume of a sample plot according to any one of claims 1 to 7 is implemented.