Forest plot survey and measurement method and system based on the fusion of multiple point cloud technologies

Abstract

The present invention relates to the field of forestry surveying technology, and in particular to a forest plot survey and measurement method and system based on the fusion of multiple point cloud technologies, including a forest plot survey and measurement system, wherein the forest plot survey and measurement system includes an ALS point cloud collector, an MLS point cloud collector, a TLS point cloud collector, a path planning module, a preprocessing module, a registration module, a classification and extraction module, a database, and a visualization module. The forest plot survey and measurement method and system based on the fusion of multiple point cloud technologies, by integrating ALS point cloud collection, MLS point cloud collection, and TLS point cloud collection technologies, respectively, performs all-round point cloud data collection in the air, on the ground, and at fixed points of forest markers, giving full play to the advantages of different technologies, improving the accuracy and efficiency of forest plot survey and measurement, fusing and dynamically adjusting multiple point cloud collection methods, and automatically adjusting the fusion strategy according to the data collection characteristics of the collectors in different scenarios, thereby improving collection efficiency.

Description

Technical Field

[0001] The present invention relates to the field of forestry surveying technology, and in particular to a forest plot survey and measurement method and system based on the fusion of multiple point cloud technologies. Background Art

[0002] Forest plot survey and measurement is the use of mathematical and statistical methods to investigate tree growth factors, site conditions, etc. of small plots (plots) in order to estimate the total amount of forest resources or the overall quantitative characteristics of related items. This process includes reconnaissance, preliminary investigation, design of sampling plans, field measurement and internal analysis. The main purpose of forest plot survey and measurement is to fully understand the current status and changes of forest resources, including the quantity, quality, structure and dynamic changes of forest resources. Through regular investigation and monitoring, the health status and service functions of forest ecosystems can be evaluated, providing a scientific basis for the protection and management of forest resources.

[0003] The current forest plot survey and measurement is carried out through a large number of field explorations, which requires a large amount of manpower and material resources and a long measurement time. In addition, the measurement accuracy is easily affected by human factors. In addition, due to the influence of factors such as the local conditions of forest resources and the large and complex content of forest survey information, there are problems such as great survey difficulty, low accuracy, and low degree of automation. At the same time, the ground in some forest plots has large undulations, and manual measurement has great risks, making it impossible to carry out safe, efficient, and detailed surveys, which increases the difficulty of forest plot survey and measurement. In addition, the measured factor information is prone to errors, affecting the actual judgment of the ecological environment of the forest plot. Summary of the Invention

[0004] The purpose of the present invention is to provide a forest plot survey and measurement method and system based on the fusion of multiple point cloud technologies to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the first part of the present invention provides the following solution: a forest plot survey and measurement method based on the fusion of multiple point cloud technologies, characterized by comprising the following steps:

[0006] A1. Establish a point cloud collection database for the forest plot. Then, set up an aircraft equipped with point cloud collection equipment. Then, fly the aircraft over the forest plot to be collected and begin a cruise flight to collect point cloud data from the forest plot. At the same time, set up a handheld laser radar device. The handheld laser radar device is used for ground mobile collection in the forest plot. Mobile point cloud data collection is performed in the ground environment of the forest plot. Finally, set up five fixed-point point cloud devices to collect fixed ground point cloud data.

[0007] A2. Acquire tree crown point cloud data from the point cloud data collected by the aircraft and fixed points, and extract the geometric and spectral characteristics of the forest crown. Then, obtain tree trunk point cloud data from the handheld LiDAR device and fixed-point point cloud data. Based on the cylindrical shape characteristics, accurately extract the central axis and radius information for coarse registration.

[0008] A3. We comprehensively extract the geometric and spatial distribution features of height, slope, roughness, and point cloud density from the aircraft point cloud data as training sample feature vectors. We then use cross-validation and parameter optimization techniques to improve the model's generalization and classification accuracy. For the classified tree point cloud, we calculate the vertical distance between its highest point and the ground point cloud to obtain the tree height, and then correct the data based on the surrounding terrain.

[0009] A4. After registering the point cloud data collected by fixed points and handheld LiDAR equipment, a deep learning-based contour recognition algorithm combined with the least squares method was used to fit circular contours and calculate the diameter at breast height of each tree. Tree height information extracted from the aircraft point cloud data classification was then combined with the point cloud data measurement results collected from the fixed points. A data fusion algorithm was then used to fuse and correct the data. The ratio of the horizontal projection area of ​​the crown point cloud to the total area of ​​the plot was calculated from the point cloud data to initially determine the canopy density. The point cloud data was then used to provide detailed information on the canopy structure. Three-dimensional modeling technology was then used to construct a virtual model of the forest plot. The canopy cover was observed from different angles to optimize and verify the canopy density value.

[0010] A5. By collecting sample trees from forest plots using the above point cloud data, we can obtain complete factor information of the forest plots. We can then generate a measurement model of the forest plot using the factor information. The model can then be optimized to obtain an accurate and clear forest plot model. This can then be used to add past data and related ecological data into the model to predict the ecological environment and development trends of the forest plots, facilitating subsequent plant growth predictions and future resource planning for the forest plots.

[0011] In a further embodiment, the contour recognition algorithm is combined with the least squares fitting algorithm as follows:

[0012] For a circle on a two-dimensional plane, its equation is expressed as:

[0013]

[0014] in, are the two-dimensional coordinates of the tree center, is the radius of the tree in two dimensions;

[0015] Suppose we have tree trunk point cloud data points , ;

[0016] The error function is defined here The sum of the squares of the distances from each data point to the circle is calculated using the following formula:

[0017]

[0018] In order to find smallest ,and , need to be separately and Find the partial derivatives and set them equal to zero. The specific partial derivative calculation formula is as follows:

[0019] right Find the partial derivatives:

[0020]

[0021] right Find the partial derivatives:

[0022]

[0023] right Find the partial derivatives:

[0024]

[0025] Among them, when the radius is obtained After, DBH That is twice the radius, that is: .

[0026] In a further embodiment, the virtual model of the forest plot is created as follows:

[0027] A401: First, remove noise points from the point cloud data and eliminate outliers through statistical analysis. Then, use a systematic grid downsampling method to streamline the data, reducing the data volume while maintaining the basic shape of the model. Finally, use translation and rotation transformation operations to unify the point cloud data from different sources into the same coordinate system.

[0028] A402. Surface reconstruction is performed on the processed point cloud data through triangulation to construct a continuous surface model. Tree and terrain point cloud data are separated using height threshold or machine learning classification methods to achieve separate modeling. Texture mapping is performed by attaching the rectified and enhanced high-resolution aerial imagery or ground texture imagery to the 3D surface model through coordinate mapping.

[0029] A403. The point cloud data obtained by different methods are fused, the gaps are eliminated by matching the overlapping areas, the model is simplified by edge collapse and vertex clustering, the number of triangular faces is reduced, and the error threshold is set to retain key features. The model is smoothed to improve the visual effect, and a complete virtual model of the forest plot can be established.

[0030] In a further embodiment, the method for calculating the ratio of the projected area of ​​the crown point cloud on the horizontal plane to the total area of ​​the sample plot using the point cloud data is as follows:

[0031] The calculation method of the projected area on the horizontal plane is as follows:

[0032] Project the three-dimensional tree crown point cloud data onto the horizontal two-dimensional plane, and set the coordinates of the point cloud data points to ,in is the number of point clouds. After projecting onto the horizontal plane, only and Coordinates, that is, the projection point is:

[0033]

[0034] The edge detection algorithm is used to process the projected point cloud data, extract the outline of the crown projection, and obtain the outline point set. ,in ;

[0035] Use Green's formula to calculate the area enclosed by the contour. The formula for calculating the enclosed area is as follows:

[0036]

[0037] For discrete contour points, numerical integration is used for approximate calculation, and then the contour points are sequentially connected to form a closed curve. The calculation process is as follows:

[0038]

[0039] in, is the contour point set, is the number of contour points;

[0040] The total area of ​​the plot is calculated as follows:

[0041] When the plot is a regular rectangular site, its length is known to be , width is , the total area of ​​the plot is:

[0042]

[0043] When the plot is circular, the radius is known to be , the total area of ​​the plot is:

[0044]

[0045] When the sample plot has irregular terrain, the coordinates of the sample plot boundary points are known. , ,in is the number of boundary points, and then the Green formula is used to calculate the area of ​​the sample plot. The area calculation formula is as follows:

[0046]

[0047] The ratio of the projected area on the horizontal plane to the total area of ​​the plot is calculated as follows:

[0048]

[0049] in, is the canopy density of the forest plot.

[0050] In a further embodiment, the point cloud device starts a real-time processing process while collecting point cloud data, uses parallel computing technology to simultaneously perform denoising and filtering preprocessing, and processes and cross-matches point cloud data from different sources.

[0051] The second part of the present invention provides the following solution: a forest plot survey and measurement system based on the fusion of multiple point cloud technologies is applied to the forest plot survey and measurement method based on the fusion of multiple point cloud technologies, comprising: a forest plot survey and measurement system, wherein the forest plot survey and measurement system comprises an ALS point cloud collector, an MLS point cloud collector, a TLS point cloud collector, a path planning module, a preprocessing module, a registration module, a classification and extraction module, a database, and a visualization module;

[0052] The ALS point cloud collector, MLS point cloud collector and TLS point cloud collector correspond to flight point cloud collection, ground mobile point cloud collection and fixed point cloud collection respectively;

[0053] The path planning module is used to plan the collection paths for the ALS point cloud collector and the MLS point cloud collector, and to optimize the collection paths synchronously during the movement of the collectors;

[0054] The preprocessing module is used to perform synchronous preprocessing on the collected point cloud data after collection, and perform denoising, filtering and normalization on the collected point cloud data;

[0055] The registration module is used to automatically select an appropriate registration algorithm for point cloud data from different sources according to their characteristics, and achieve fast and accurate registration through parameter optimization;

[0056] The classification and extraction module is used to comprehensively extract height, slope, roughness, point cloud density geometry and spatial distribution features from the point cloud data as training sample feature vectors, integrate the point cloud data classification, and for the classified tree point cloud, extract the vertical distance between the highest point and the ground point cloud to obtain the tree height. At the same time, combined with the surrounding terrain relief correction data, accurate environmental information of the forest sample plot is obtained;

[0057] The database manages the collected and processed point cloud data and forest plot factor data in a unified manner, realizes data storage, query, backup and recovery functions, and supports import and export of multiple data formats;

[0058] The visualization module displays the point cloud data, tree distribution, and forest plot factor information of the forest plot in a three-dimensional visualization manner. Users can observe and analyze the plot from multiple angles through interactive operations.

[0059] In a further embodiment, the ALS point cloud collector, the MLS point cloud collector and the TLS point cloud collector are connected to a data acquisition module;

[0060] The data acquisition module is used for parameter setting, data acquisition control and real-time data preview of ALS point cloud collectors, MLS point cloud collectors and TLS point cloud collectors. Through the controller, users can select different acquisition devices through the interface and set corresponding acquisition parameters. The collected data includes but is not limited to flight altitude, scanning angle and resolution.

[0061] In a further embodiment, the registration module includes a data verification module and an environment monitoring module;

[0062] The data verification module, by setting up a multi-source data cross-validation mechanism, compares the data analysis results obtained by different point cloud technologies for the same forest plot information to ensure data integrity. It also establishes a historical data comparison library to compare newly acquired data with past survey data from the same region and season, promptly detecting abnormal data fluctuations and ensuring data accuracy and reliability.

[0063] The environmental monitoring module, combined with the meteorological sensor network, collects temperature, humidity, and light intensity factor information in the forest sample plot in real time, and explores the impact of environmental factors on tree growth and distribution through correlation analysis with point cloud data.

[0064] In a further embodiment, the classification extraction module includes a growth prediction module;

[0065] The growth prediction module predicts the future growth trend of trees based on a large amount of historical forest sample data and tree growth models, combined with tree characteristic data obtained from current surveys.

[0066] In a further embodiment, the forest plot survey and measurement system further includes a resource planning and decision module;

[0067] The resource planning decision-making module estimates the biomass of each tree based on the tree species, diameter at breast height, tree height and biomass model of the forest sample plot. Different tree species have different biomass estimation models. The biomass is calculated by substituting the measured diameter at breast height and tree height parameters into the corresponding model. The carbon storage of the forest sample plot is then calculated based on the conversion relationship between biomass and carbon storage. At the same time, combined with point cloud data and forest growth models, the changing trend of carbon storage in the future is predicted, providing data support for carbon sink trading, ecological compensation and future resource planning decisions.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. The present invention integrates ALS point cloud acquisition, MLS point cloud acquisition and TLS point cloud acquisition technologies to conduct all-round forest point cloud data acquisition in the air, on the ground and at fixed points of forest markers, giving full play to the advantages of different technologies and improving the accuracy and efficiency of forest plot survey and measurement. In addition, through the point cloud data registration method, it can effectively achieve accurate spatial alignment of point cloud data obtained by different measurement systems, providing a reliable basis for subsequent feature extraction and data analysis, ensuring that the collected forest plot factor information is more accurate, and improving the accuracy of forest survey and measurement.

[0070] 2. This invention integrates and dynamically adjusts multiple point cloud acquisition methods by setting up ALS point cloud acquisition, MLS point cloud acquisition, and TLS point cloud acquisition technologies. It automatically adjusts the fusion strategy based on the characteristics of ALS, MLS, and TLS data in different scenarios. In areas with complex terrain, the weight of TLS and MLS data in the fusion is dynamically increased to improve the accuracy of local details. When a large-area overview is required, the dominant role of ALS data weight is strengthened to ensure overall coverage and efficiency. This adaptive fusion method greatly improves the quality and applicability of data fusion.

[0071] 3. The present invention has real-time dynamic processing capabilities through post-data collection preprocessing. During the point cloud data collection process, data preprocessing operations such as denoising and preliminary classification are performed simultaneously. Once the collection is completed, the complete data processing flow can be completed quickly, and the preliminary results of forest plot factors can be quickly output. This enables investigators to obtain key information at the first time and adjust the investigation strategy in time, which greatly shortens the entire investigation cycle and improves the efficiency of forest plot investigation and measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A flowchart of the survey and measurement method of the forest plot survey and measurement system of the present invention;

[0073] Figure 2 A flowchart of a virtual modeling method for a forest plot according to the present invention;

[0074] Figure 3 This is a system block diagram of the forest plot survey and measurement system of the present invention. DETAILED DESCRIPTION

[0075] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention. Example 1

[0076] Referring to Figures 1-3, this embodiment provides a forest plot survey and measurement method based on the fusion of multiple point cloud technologies, including the following steps:

[0077] A1. Establish a point cloud collection database for the forest plot. Then, set up an aircraft equipped with point cloud collection equipment. Then, fly the aircraft over the forest plot to be collected and begin a cruise flight to collect point cloud data from the forest plot. At the same time, set up a handheld laser radar device. The handheld laser radar device is used for ground mobile collection in the forest plot. Mobile point cloud data collection is performed in the ground environment of the forest plot. Finally, set up five fixed-point point cloud devices to collect fixed ground point cloud data.

[0078] A2. Acquire tree crown point cloud data from the point cloud data collected by the aircraft and fixed points, and extract the geometric and spectral characteristics of the forest crown. Then, obtain tree trunk point cloud data from the handheld LiDAR device and fixed-point point cloud data. Based on the cylindrical shape characteristics, accurately extract the central axis and radius information for coarse registration.

[0079] A3. We comprehensively extract the geometric and spatial distribution features of height, slope, roughness, and point cloud density from the aircraft point cloud data as training sample feature vectors. We then use cross-validation and parameter optimization techniques to improve the model's generalization and classification accuracy. For the classified tree point cloud, we calculate the vertical distance between its highest point and the ground point cloud to obtain the tree height, and then correct the data based on the surrounding terrain.

[0080] A4. After registering the point cloud data collected by fixed points and handheld LiDAR equipment, a deep learning-based contour recognition algorithm combined with the least squares method was used to fit circular contours and calculate the diameter at breast height of each tree. Tree height information extracted from the aircraft point cloud data classification was then combined with the point cloud data measurement results collected from the fixed points. A data fusion algorithm was then used to fuse and correct the data. The ratio of the horizontal projection area of ​​the crown point cloud to the total area of ​​the plot was calculated from the point cloud data to initially determine the canopy density. The point cloud data was then used to provide detailed information on the canopy structure. Three-dimensional modeling technology was then used to construct a virtual model of the forest plot. The canopy cover was observed from different angles to optimize and verify the canopy density value.

[0081] A5. By collecting sample trees from forest plots using the above point cloud data, we can obtain complete factor information of the forest plots. We can then generate a measurement model of the forest plot using the factor information. The model can then be optimized to obtain an accurate and clear forest plot model. This can then be used to add past data and related ecological data into the model to predict the ecological environment and development trends of the forest plots, facilitating subsequent plant growth predictions and future resource planning for the forest plots.

[0082] The contour recognition algorithm combined with the least squares fitting algorithm is as follows:

[0083] For a circle on a two-dimensional plane, its equation is expressed as:

[0084]

[0085] in, are the two-dimensional coordinates of the tree center, is the radius of the tree in two dimensions;

[0086] Suppose we have tree trunk point cloud data points , ;

[0087] The error function is defined here The sum of the squares of the distances from each data point to the circle is calculated using the following formula:

[0088]

[0089] In order to find smallest and , need to be separately and Find the partial derivatives and set them equal to zero. The specific partial derivative calculation formula is as follows:

[0090] right Find the partial derivatives:

[0091]

[0092] right Find the partial derivatives:

[0093]

[0094] right Find the partial derivatives:

[0095]

[0096] Among them, when the radius is obtained After, DBH That is twice the radius, that is: .

[0097] The method for calculating the ratio of the projected area of ​​the crown point cloud on the horizontal plane to the total area of ​​the sample plot is as follows:

[0098] The calculation method of the projected area on the horizontal plane is as follows:

[0099] Project the three-dimensional tree crown point cloud data onto the horizontal two-dimensional plane, and set the coordinates of the point cloud data points to ,in , is the number of point clouds. After projecting onto the horizontal plane, only and Coordinates, that is, the projection point is:

[0100]

[0101] The edge detection algorithm is used to process the projected point cloud data, extract the outline of the crown projection, and obtain the outline point set. ,in ;

[0102] Use Green's formula to calculate the area enclosed by the contour. The formula for calculating the enclosed area is as follows:

[0103]

[0104] For discrete contour points, numerical integration is used for approximate calculation, and then the contour points are sequentially connected to form a closed curve. The calculation process is as follows:

[0105]

[0106] in, is the contour point set, is the number of contour points;

[0107] The total area of ​​the plot is calculated as follows:

[0108] When the plot is a regular rectangular site, its length is known to be , width is , the total area of ​​the plot is:

[0109]

[0110] When the plot is circular, the radius is known to be , the total area of ​​the plot is:

[0111]

[0112] When the sample plot has irregular terrain, the coordinates of the sample plot boundary points are known. , ,in is the number of boundary points, and then the Green formula is used to calculate the area of ​​the sample plot. The area calculation formula is as follows:

[0113]

[0114] The ratio of the projected area on the horizontal plane to the total area of ​​the plot is calculated as follows:

[0115]

[0116] in, is the canopy density of the forest plot.

[0117] The point cloud device starts the real-time processing process while collecting point cloud data, uses parallel computing technology to simultaneously perform denoising and filtering preprocessing, and processes and cross-matches point cloud data from different sources.

[0118] A forest plot survey and measurement system based on the fusion of multiple point cloud technologies is applied to a forest plot survey and measurement method based on the fusion of multiple point cloud technologies, comprising: a forest plot survey and measurement system, the forest plot survey and measurement system comprising an ALS point cloud collector, an MLS point cloud collector, a TLS point cloud collector, a path planning module, a preprocessing module, a registration module, a classification and extraction module, a database, and a visualization module;

[0119] ALS point cloud collector, MLS point cloud collector and TLS point cloud collector correspond to flight point cloud collection, ground mobile point cloud collection and fixed point cloud collection respectively;

[0120] The path planning module is used to plan the collection paths for the ALS and MLS point cloud collectors and optimize the collection paths during the movement of the collectors. It is designed with dedicated route planning software that automatically generates the optimal flight path based on the geographical boundaries of the forest plot, terrain data, and the required point cloud density requirements, thereby improving the collection efficiency of the collectors.

[0121] The preprocessing module is used to perform synchronous preprocessing on the collected point cloud data after collection, and to perform denoising, filtering and normalization on the collected point cloud data. The denoising algorithm adopts a method based on statistical analysis, such as bilateral filtering and outlier removal algorithm, to effectively remove noise points. The filtering includes Gaussian filtering or median filtering. According to the characteristics of the data, the appropriate filtering method is selected to smooth the point cloud data. Normalization unifies the point cloud data to a specific coordinate range and scale for subsequent processing. The above methods are used to preprocess the collected data, which facilitates the subsequent application of the collected data.

[0122] The registration module is used to automatically select the appropriate registration algorithm for point cloud data from different sources based on their characteristics, and achieve fast and accurate registration through parameter optimization;

[0123] The classification and extraction module is used to comprehensively extract height, slope, roughness, point cloud density geometry and spatial distribution features from the point cloud data as training sample feature vectors, fuse the point cloud data classification, and extract the vertical distance between the highest point and the ground point cloud after classification to obtain the tree height. At the same time, combined with the surrounding terrain relief correction data, accurate environmental information of the forest sample plot is obtained;

[0124] The database manages the collected and processed point cloud data and forest plot factor data in a unified manner, implements data storage, query, backup, and recovery functions, and supports the import and export of multiple data formats. The database uses compressed storage to facilitate the compressed storage of massive point cloud data. The database is partitioned according to the source of the collected data (ALS point cloud collector, MLS point cloud collector, and TLS point cloud collector), collection time, plot number, and other dimensions to facilitate rapid data retrieval and management;

[0125] The visualization module displays the point cloud data, tree distribution, and forest plot factor information of the forest plot in a three-dimensional visualization manner. Users can observe and analyze the plot from multiple angles through interactive operations. Through hierarchical data loading and rendering technology, efficient multi-scale visualization effects are achieved. Multi-scale visualization functions are implemented for forest plot data of different scales. At the macro scale, the distribution and topography of the entire forest plot are displayed. At the micro scale, the detailed features of a single tree, such as trunk texture and leaf distribution, can be deeply viewed, making it easier for surveyors to view the situation of the forest plot more intuitively and efficiently. Example 2

[0126] Reference Figure 2 , further improvements were made on the basis of Example 1:

[0127] The virtual modeling method of the forest plot is as follows:

[0128] A401: First, remove noise points from the point cloud data and eliminate outliers through statistical analysis. Then, use a systematic grid downsampling method to streamline the data, reducing the data volume while maintaining the basic shape of the model. Finally, use translation and rotation transformation operations to unify the point cloud data from different sources into the same coordinate system.

[0129] A402. Surface reconstruction is performed on the processed point cloud data through triangulation to construct a continuous surface model. Tree and terrain point cloud data are separated using height threshold or machine learning classification methods to achieve separate modeling. Texture mapping is performed by attaching the rectified and enhanced high-resolution aerial imagery or ground texture imagery to the 3D surface model through coordinate mapping.

[0130] A403. The point cloud data obtained by different methods are fused, the gaps are eliminated by matching the overlapping areas, the model is simplified by edge collapse and vertex clustering, the number of triangular faces is reduced, and the error threshold is set to retain key features. The model is smoothed to improve the visual effect, and a complete virtual model of the forest plot can be established. Example 3

[0131] Reference Figure 1 , further improvements were made on the basis of Example 1:

[0132] ALS point cloud collector, MLS point cloud collector and TLS point cloud collector are connected with data acquisition module;

[0133] The data acquisition module is used for parameter setting, data acquisition control and real-time data preview of ALS point cloud collectors, MLS point cloud collectors and TLS point cloud collectors. Through the controller, users can select different acquisition devices through the interface and set corresponding acquisition parameters. The collected data includes but is not limited to flight altitude, scanning angle and resolution. Through the timely preview and viewing of real-time point cloud data acquisition, the control personnel can preview the integrity of the collected data, judge whether the point cloud acquisition data is complete, and whether there are inaccurate settings of the acquisition parameters of the acquisition equipment, thereby facilitating the control personnel to make quick adjustments to ensure that the collected data is accurate and effective.

[0134] The registration module includes a data verification module and an environmental monitoring module;

[0135] The data verification module sets up a multi-source data cross-validation mechanism. For the same forest plot information, it compares the data analysis results obtained by different point cloud technologies to ensure data integrity. It also establishes a historical data comparison library to compare the newly acquired data with the previous survey data of the same region and season, so as to promptly detect abnormal data fluctuations and ensure data accuracy and reliability. This module matches and analyzes the previous approximate data with the currently collected point cloud data, and then verifies the accuracy of the collected point cloud data. It then performs auxiliary optimization on the point cloud data and re-collects the point cloud data with large errors to improve the accuracy of the point cloud data.

[0136] The environmental monitoring module, combined with the meteorological sensor network, collects temperature, humidity, and light intensity factor information in the forest sample plot in real time. Through correlation analysis with point cloud data, it explores the impact of environmental factors on tree growth and distribution. Meteorological sensors are reasonably distributed in the forest sample plot to ensure that factor information in each area can be effectively collected. Meteorological data and point cloud data are associated, stored and analyzed. The correlation between factor information and forest resource data is displayed through a visual interface to assist researchers in analysis, thereby improving the ability of managers to conduct timely and efficient comprehensive monitoring of forest sample plots to ensure the ecological health of forest sample plots.

[0137] The classification extraction module includes a growth prediction module;

[0138] The growth prediction module predicts the future growth trend of trees based on a large amount of historical forest sample data and tree growth models, combined with the tree characteristic data obtained from the current survey. By collecting a large amount of historical forest sample data, suitable tree growth models are screened, such as the logistic model and the single tree growth model. Based on the currently collected tree characteristic data, the growth model parameters are calibrated, the prediction model is updated regularly, and the prediction results are adjusted according to the newly acquired data, thereby predicting the future growth of trees in the corresponding forest sample plots and the development of the sample plot ecology, thereby facilitating the subsequent management of the corresponding forest sample plots.

[0139] The forest plot survey and measurement system also includes a resource planning and decision-making module;

[0140] The resource planning decision-making module estimates the biomass of each tree based on the tree species, diameter at breast height, tree height and biomass model of the forest sample plot. Different tree species have different biomass estimation models. The biomass is calculated by substituting the measured diameter at breast height and tree height parameters into the corresponding model. The carbon storage of the forest sample plot is then calculated based on the conversion relationship between biomass and carbon storage. At the same time, combined with point cloud data and forest growth models, the changing trend of carbon storage in the future is predicted, providing data support for carbon sink trading, ecological compensation and future resource planning decisions.

[0141] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A forest plot survey and measurement method based on the fusion of multiple point cloud technologies is characterized by: The following steps are involved: A1. Establish a point cloud collection database for the forest plot. Then, set up an aircraft equipped with point cloud collection equipment. Then, fly the aircraft over the forest plot to be collected and begin a cruise flight to collect point cloud data from the forest plot. At the same time, set up a handheld laser radar device. The handheld laser radar device is used for ground mobile collection in the forest plot. Mobile point cloud data collection is performed in the ground environment of the forest plot. Finally, set up five fixed-point point cloud devices to collect fixed ground point cloud data. A2. Acquire tree crown point cloud data from the point cloud data collected by the aircraft and fixed points, and extract the geometric and spectral characteristics of the forest crown. Then, obtain tree trunk point cloud data from the handheld LiDAR device and fixed-point point cloud data. Based on the cylindrical shape characteristics, accurately extract the central axis and radius information for coarse registration. A3. We comprehensively extract the geometric and spatial distribution features of height, slope, roughness, and point cloud density from the aircraft point cloud data as training sample feature vectors. We then use cross-validation and parameter optimization techniques to improve the model's generalization and classification accuracy. For the classified tree point cloud, we calculate the vertical distance between its highest point and the ground point cloud to obtain the tree height, and then correct the data based on the surrounding terrain. A4. After registering point cloud data collected by fixed points and handheld LiDAR devices, a deep learning-based contour recognition algorithm combined with the least squares method was used to fit circular contours and calculate the diameter at breast height of each tree. Tree height information extracted from the aircraft point cloud data classification was then combined with the point cloud data measurement results collected from fixed points. A data fusion algorithm was then used to fuse and correct the data. The ratio of the horizontal projection area of ​​the crown point cloud to the total area of ​​the plot was calculated from the point cloud data to initially determine the canopy density. Detailed information on the canopy structure was then obtained from the point cloud data. Three-dimensional modeling technology was then used to construct a virtual model of the forest plot. The canopy cover was observed from different angles to optimize and verify the canopy density value. A5. By collecting sample trees from forest plots using the above point cloud data, we can obtain complete factor information of the forest plots. We can then generate a measurement model of the forest plot using the factor information. The model can then be optimized to obtain an accurate and clear forest plot model. This can then be used to add past data and related ecological data into the model to predict the ecological environment and development trends of the forest plots, facilitating subsequent plant growth predictions and future resource planning for the forest plots.

2. The forest plot survey and measurement method based on the fusion of multiple point cloud technologies according to claim 1 is characterized in that: The contour recognition algorithm combined with the least squares fitting algorithm is as follows: For a circle on a two-dimensional plane, its equation is expressed as: ； in, are the two-dimensional coordinates of the tree center, is the radius of the tree in two dimensions; Suppose we have tree trunk point cloud data points , ; The error function is defined here The sum of the squares of the distances from each data point to the circle is calculated using the following formula: ； In order to find smallest 、 and , need to be separately 、 and Find the partial derivatives and set them equal to zero. The specific partial derivative calculation formula is as follows: right Find the partial derivatives: ； right Find the partial derivatives: ； right Find the partial derivatives: ； Among them, when the radius is obtained After, DBH That is twice the radius, that is: .

3. The forest plot survey and measurement method and system based on the fusion of multiple point cloud technologies according to claim 1 is characterized in that: The virtual model building method of the forest plot is as follows: A401: First, remove noise points from the point cloud data and eliminate outliers through statistical analysis. Then, use a systematic grid downsampling method to streamline the data, reducing the data volume while maintaining the basic shape of the model. Finally, use translation and rotation transformation operations to unify the point cloud data from different sources into the same coordinate system. A402. Surface reconstruction is performed on the processed point cloud data through triangulation to construct a continuous surface model. Tree and terrain point cloud data are separated using height threshold or machine learning classification methods to achieve separate modeling. Texture mapping is performed by attaching the rectified and enhanced high-resolution aerial imagery or ground texture imagery to the 3D surface model through coordinate mapping. A403. The point cloud data obtained by different methods are fused, the gaps are eliminated by matching the overlapping areas, the model is simplified by edge collapse and vertex clustering, the number of triangular faces is reduced, and the error threshold is set to retain key features. The model is smoothed to improve the visual effect, and a complete virtual model of the forest plot can be established.

4. The forest plot survey and measurement method based on the fusion of multiple point cloud technologies according to claim 1 is characterized in that: The method for calculating the ratio of the projected area of ​​the crown point cloud on the horizontal plane to the total area of ​​the sample plot is as follows: The calculation method of the projected area on the horizontal plane is as follows: Project the three-dimensional tree crown point cloud data onto the horizontal two-dimensional plane, and set the coordinates of the point cloud data points to ,in , is the number of point clouds. After projecting onto the horizontal plane, only and Coordinates, that is, the projection point is: ； The edge detection algorithm is used to process the projected point cloud data, extract the outline of the crown projection, and obtain the outline point set. ,in ; Use Green's formula to calculate the area enclosed by the contour. The formula for calculating the enclosed area is as follows: ； For discrete contour points, numerical integration is used for approximate calculation, and then the contour points are sequentially connected to form a closed curve. The calculation process is as follows: ； in, is the contour point set, is the number of contour points; The total area of ​​the plot is calculated as follows: When the plot is a regular rectangular site, its length is known to be , width is , the total area of ​​the plot is: ； When the plot is circular, the radius is known to be , the total area of ​​the plot is: ； When the sample plot has irregular terrain, the coordinates of the sample plot boundary points are known. , ,in is the number of boundary points, and then the Green formula is used to calculate the area of ​​the sample plot. The area calculation formula is as follows: ； The ratio of the projected area on the horizontal plane to the total area of ​​the plot is calculated as follows: ； in, is the canopy density of the forest plot.

5. The forest plot survey and measurement method based on the fusion of multiple point cloud technologies according to claim 1 is characterized in that: The point cloud device starts a real-time processing process while collecting point cloud data, uses parallel computing technology to synchronously perform denoising and filtering preprocessing, and processes and cross-matches point cloud data from different sources.

6. A forest plot survey and measurement system based on the fusion of multiple point cloud technologies, applied to the forest plot survey and measurement method based on the fusion of multiple point cloud technologies as claimed in any one of claims 1 to 5, characterized in that: include: A forest plot survey and measurement system, comprising an ALS point cloud collector, an MLS point cloud collector, a TLS point cloud collector, a path planning module, a preprocessing module, a registration module, a classification and extraction module, a database, and a visualization module; The ALS point cloud collector, MLS point cloud collector and TLS point cloud collector correspond to flight point cloud collection, ground mobile point cloud collection and fixed point cloud collection respectively; The path planning module is used to plan the collection paths for the ALS point cloud collector and the MLS point cloud collector, and to optimize the collection paths synchronously during the movement of the collectors; The preprocessing module is used to perform synchronous preprocessing on the collected point cloud data after collection, and perform denoising, filtering and normalization on the collected point cloud data; The registration module is used to automatically select an appropriate registration algorithm for point cloud data from different sources according to their characteristics, and achieve fast and accurate registration through parameter optimization; The classification and extraction module is used to comprehensively extract height, slope, roughness, point cloud density geometry and spatial distribution features from the point cloud data as training sample feature vectors, integrate the point cloud data classification, and for the classified tree point cloud, extract the vertical distance between the highest point and the ground point cloud to obtain the tree height. At the same time, combined with the surrounding terrain relief correction data, accurate environmental information of the forest sample plot is obtained; The database manages the collected and processed point cloud data and forest plot factor data in a unified manner, realizes data storage, query, backup and recovery functions, and supports import and export of multiple data formats; The visualization module displays the point cloud data, tree distribution, and forest plot factor information of the forest plot in a three-dimensional visualization manner. Users can observe and analyze the plot from multiple angles through interactive operations.

7. The forest plot survey and measurement system based on the fusion of multiple point cloud technologies according to claim 6 is characterized in that: The ALS point cloud collector, MLS point cloud collector and TLS point cloud collector are connected to a data acquisition module; The data acquisition module is used for parameter setting, data acquisition control and real-time data preview of ALS point cloud collectors, MLS point cloud collectors and TLS point cloud collectors. Through the controller, users can select different acquisition devices through the interface and set corresponding acquisition parameters. The collected data includes but is not limited to flight altitude, scanning angle and resolution.

8. The forest plot survey and measurement system based on the fusion of multiple point cloud technologies according to claim 6 is characterized in that: The registration module includes a data verification module and an environment monitoring module; The data verification module, by setting up a multi-source data cross-validation mechanism, compares the data analysis results obtained by different point cloud technologies for the same forest plot information to ensure data integrity. It also establishes a historical data comparison library to compare newly acquired data with past survey data from the same region and season, promptly detecting abnormal data fluctuations and ensuring data accuracy and reliability. The environmental monitoring module, combined with the meteorological sensor network, collects temperature, humidity, and light intensity factor information in the forest sample plot in real time, and explores the impact of environmental factors on tree growth and distribution through correlation analysis with point cloud data.

9. The forest plot survey and measurement system based on the fusion of multiple point cloud technologies according to claim 6, characterized in that: The classification and extraction module includes a growth prediction module; The growth prediction module predicts the future growth trend of trees based on a large amount of historical forest sample data and tree growth models, combined with tree characteristic data obtained from current surveys.

10. The forest plot survey and measurement system based on the fusion of multiple point cloud technologies according to claim 6, characterized in that: The forest plot survey and measurement system also includes a resource planning and decision-making module; The resource planning decision-making module estimates the biomass of each tree based on the tree species, diameter at breast height, tree height and biomass model of the forest sample plot. Different tree species have different biomass estimation models. The biomass is calculated by substituting the measured diameter at breast height and tree height parameters into the corresponding model. The carbon storage of the forest sample plot is then calculated based on the conversion relationship between biomass and carbon storage. At the same time, combined with point cloud data and forest growth models, the changing trend of carbon storage in the future is predicted, providing data support for carbon sink trading, ecological compensation and future resource planning decisions.

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