Forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies

Abstract

The invention relates to the technical field of forestry measurement, in particular to a forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies. The forest sample plot investigation 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 extraction module, a database and a visualization module. According to the forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies, all-around point cloud data acquisition is carried out in the air, the ground and marked fixed points of a forest through fusion of ALS point cloud acquisition, MLS point cloud acquisition and TLS point cloud acquisition technologies, different technical advantages are fully played, and the forest sample plot investigation and measurement method and system based on fusion of multiple point cloud technologies have the advantages of being high in accuracy and high in accuracy. The forest sample plot survey and measurement precision and efficiency are improved, multiple point cloud acquisition modes are fused and dynamically adjusted, the fusion strategy is automatically adjusted according to the data acquisition characteristics of the acquirer in different scenes, and the acquisition efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry surveying, 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 to investigate the tree growth factors, site conditions, etc. of small plots (plots) through mathematical and statistical methods, in order to estimate the total amount of forest resources or the overall quantitative characteristics of related items. This process includes exploration, preliminary investigation, design of sampling plan, field measurement and internal analysis. The main purpose of forest plot survey and measurement is to fully grasp 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. 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 undulation in some forest plots is large, and manual measurement has great risks, making it impossible to carry out safe, efficient, and precise 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-mentioned background technology.

[0005] To achieve the above object, the first part of the present invention provides the following scheme, which is a forest plot survey and measurement method based on the fusion of multiple point cloud technologies, characterized in that it includes the following steps: A1. Establish a point cloud collection database for forest plots, then set up an aircraft equipped with point cloud collection equipment, then fly the aircraft above the forest plot to be collected, and then start cruising to collect point cloud data for the forest plot. At the same time, a handheld laser radar device is set up. The collection method of the handheld laser radar device is to use it for ground mobile collection of forest plots. Mobile point cloud data collection is performed in the ground environment of the forest plot. Finally, five fixed-point point cloud devices are set up to collect fixed ground point cloud data. A2. For the point cloud data collected by the aircraft and fixed points, the tree crown point cloud data is acquired, and the geometric characteristics and spectral characteristics of the forest crown are extracted. Then, the point cloud data of the tree trunk is obtained from the handheld laser radar device and the fixed point collection point cloud data. The central axis and radius information are accurately extracted based on the cylindrical shape characteristics, and rough alignment is performed; A3. Extract the height, slope, roughness and point cloud density geometry and spatial distribution features from the aircraft point cloud data as the training sample feature vector, and then use cross-validation and parameter optimization techniques to improve the model generalization ability and classification accuracy. Then, for the classified tree point cloud, calculate the vertical distance between its highest point and the ground point cloud to obtain the tree height, and correct the data in combination with the surrounding terrain undulations. A4. After registering the point cloud data collected by the fixed point and handheld LiDAR equipment, the contour recognition algorithm based on deep learning is combined with the least squares method to fit the circular contour and calculate the diameter at breast height of each tree. The tree height information extracted by the classification of the aircraft point cloud data is then combined with the measurement results of the point cloud data collected by the fixed point. The data fusion algorithm is then used for fusion and correction. At the same time, the ratio of the projected area of ​​the crown point cloud on the horizontal plane to the total area of ​​the sample plot is calculated from the point cloud data to preliminarily obtain the canopy density. Subsequently, the point cloud data is used to obtain detailed information on the crown structure, and a virtual model of the forest sample plot is constructed using 3D modeling technology. The crown coverage is observed from different angles to optimize and verify the canopy density value. A5. By collecting sample trees of forest plots using the above point cloud data, we can obtain complete factor information of forest plots. Then, we can generate a measurement model of the forest plot using the factor information. Then, we can optimize the model to obtain an accurate and clear forest plot model. Then, we can add past data and related ecological data into the model to predict the ecological environment and development trend of the forest plot, which will facilitate subsequent plant growth predictions and future resource planning for the forest plot.

[0006] In a further embodiment, the contour recognition algorithm is combined with the least squares fitting algorithm as follows: For a circle on a two-dimensional plane, its equation can be expressed as:

[0007] in, are the two-dimensional coordinates of the center of the tree, 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 as follows:

[0008] 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 partial derivatives:

[0009] right Find partial derivatives:

[0010] right Find partial derivatives:

[0011] Among them, when the radius is obtained After, DBH That is twice the radius, that is: .

[0012] In a further embodiment, the virtual model building method of the forest plot is as follows: A401, first remove the noise points in the point cloud data, and remove the outliers that are far away from the group through statistical analysis, then use the systematic grid downsampling method to streamline the data, reduce the data volume while ensuring the basic shape of the model, and finally, through translation and rotation transformation operations, unify the point cloud data from different sources into the same coordinate system; A402. Reconstruct the surface of the processed point cloud data through triangulation to build a continuous surface model. Use height threshold or machine learning classification methods to separate tree and terrain point cloud data to achieve separate modeling. Use coordinate mapping to attach the calibrated and enhanced high-resolution aerial images or ground texture images to the three-dimensional surface model to complete texture 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 folding 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.

[0013] In a further embodiment, the method for calculating the ratio of the projection 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: The calculation method of the projected area on the horizontal plane is as follows: Project the three-dimensional tree crown point cloud data onto a 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:

[0014] The edge detection algorithm is used to process the projected point cloud data, extract the contour of the crown projection, and obtain the contour point set. ,in ; Green's formula is used to calculate the area enclosed by the contour. The calculation formula for the enclosed area is as follows:

[0015] For discrete contour points, numerical integration is used for approximate calculation, and then the contour points are connected in sequence to form a closed curve. The calculation process is as follows:

[0016] in, is the contour point set, is the number of contour points; The total area of ​​the sample plot is calculated as follows: When the sample plot is a regular rectangular site, its length is known to be , width is , then the total area of ​​the plot is:

[0017] When the plot is a circular plot, the radius is known to be , then the total area of ​​the plot is:

[0018] When the sample plot has irregular terrain, the coordinates of the boundary points of the sample plot 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:

[0019] The ratio of the projected area on the horizontal plane to the total area of ​​the plot is calculated as follows:

[0020] in, is the canopy density of the forest plot.

[0021] In a further embodiment, the point cloud device starts a real-time processing flow while collecting point cloud data, uses parallel computing technology to simultaneously perform pre-processing such as denoising and filtering, and processes and cross-matches point cloud data from different sources.

[0022] The second part of the present invention provides the following scheme: 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, including: a forest plot survey and measurement system, the forest plot survey and measurement system including 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 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 simultaneously optimize the collection paths during the movement of the collectors; 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 registration module is used to automatically select a suitable 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, fuse 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, and at the same time combine the surrounding terrain undulation correction data to obtain accurate environmental information of the forest sample plot; The database manages the collected and processed point cloud data, forest plot factor data, etc. in a unified manner, realizes data storage, query, backup and recovery functions, and supports the import and export of multiple data formats; The visualization module displays the point cloud data, tree distribution, forest plot factors and other information of the forest plot in a three-dimensional visualization manner. Users can observe and analyze the plot from multiple angles through interactive operations.

[0023] 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; The data acquisition module is used for parameter setting, data acquisition control and real-time data preview of ALS point cloud collector, MLS point cloud collector and TLS point cloud collector. Through the controller, the user 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.

[0024] In a further embodiment, the registration module includes a data verification module and an environment monitoring module; The data verification module sets 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, ensures the integrity of the data, and 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 timely discover abnormal data fluctuations and ensure data accuracy and reliability; The environmental monitoring module, combined with the meteorological sensor network, collects information on factors such as temperature, humidity, and light intensity 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.

[0025] In a further embodiment, the classification 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.

[0026] In a further embodiment, the forest plot survey and measurement system further includes a resource planning decision module; The resource planning decision module estimates the biomass of each tree based on the tree species, breast diameter, 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 breast diameter and tree height parameters into the corresponding model, and then the carbon storage of the forest sample plot is calculated based on the conversion relationship between biomass and carbon storage. At the same time, the point cloud data and forest growth model are combined to predict the changing trend of carbon storage in the future, providing data support for carbon sink trading, ecological compensation and future resource planning decisions.

[0027] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention integrates ALS point cloud collection, MLS point cloud collection and TLS point cloud collection technologies to collect all-round forest point cloud data 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, and through the point cloud data registration method, it can effectively realize the 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.

[0028] 2. The present invention integrates and dynamically adjusts multiple point cloud acquisition methods by setting ALS point cloud acquisition, MLS point cloud acquisition and TLS point cloud acquisition technologies. According to the characteristics of ALS, MLS and TLS data in different scenarios, the fusion strategy is automatically adjusted. 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 leading 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. 3. The present invention has real-time dynamic processing capabilities through preprocessing after data collection. During the point cloud data collection process, data denoising, preliminary classification and other preprocessing operations are performed simultaneously. Once the collection is completed, the complete data processing flow can be completed quickly, and the preliminary results of forest sample 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 sample survey and measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the survey and measurement method of the forest sample plot survey and measurement system of the present invention; Figure 2 It is a flowchart of a virtual model building method of a forest plot of the present invention; Figure 3 It is a system block diagram of the forest sample plot survey and measurement system of the present invention. DETAILED DESCRIPTION

[0030] The following will be described clearly and completely in conjunction with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1 Referring to FIGS. 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: A1. Establish a point cloud collection database for forest plots, then set up an aircraft equipped with point cloud collection equipment, then fly the aircraft above the forest plot to be collected, and then start cruising to collect point cloud data for the forest plot. At the same time, a handheld laser radar device is set up. The collection method of the handheld laser radar device is to use it for ground mobile collection of forest plots. Mobile point cloud data collection is performed in the ground environment of the forest plot. Finally, five fixed-point point cloud devices are set up to collect fixed ground point cloud data. A2. For the point cloud data collected by the aircraft and fixed points, the tree crown point cloud data is acquired, and the geometric characteristics and spectral characteristics of the forest crown are extracted. Then, the point cloud data of the tree trunk is obtained from the handheld laser radar device and the fixed point collection point cloud data. The central axis and radius information are accurately extracted based on the cylindrical shape characteristics, and rough alignment is performed; A3. Extract the height, slope, roughness and point cloud density geometry and spatial distribution features from the aircraft point cloud data as the training sample feature vector, and then use cross-validation and parameter optimization techniques to improve the model generalization ability and classification accuracy. Then, for the classified tree point cloud, calculate the vertical distance between its highest point and the ground point cloud to obtain the tree height, and correct the data in combination with the surrounding terrain undulations. A4. After registering the point cloud data collected by the fixed point and handheld LiDAR equipment, the contour recognition algorithm based on deep learning is combined with the least squares method to fit the circular contour and calculate the diameter at breast height of each tree. The tree height information extracted by the classification of the aircraft point cloud data is then combined with the measurement results of the point cloud data collected by the fixed point. The data fusion algorithm is then used for fusion and correction. At the same time, the ratio of the projected area of ​​the crown point cloud on the horizontal plane to the total area of ​​the sample plot is calculated from the point cloud data to preliminarily obtain the canopy density. Subsequently, the point cloud data is used to obtain detailed information on the crown structure, and a virtual model of the forest sample plot is constructed using 3D modeling technology. The crown coverage is observed from different angles to optimize and verify the canopy density value. A5. By collecting sample trees of forest plots using the above point cloud data, we can obtain complete factor information of forest plots. Then, we can generate a measurement model of the forest plot using the factor information. Then, we can optimize the model to obtain an accurate and clear forest plot model. Then, we can add past data and related ecological data into the model to predict the ecological environment and development trend of the forest plot, which will facilitate subsequent plant growth predictions and future resource planning for the forest plot.

[0032] The contour recognition algorithm combined with the least squares fitting algorithm is as follows: For a circle on a two-dimensional plane, its equation can be expressed as:

[0033] in, are the two-dimensional coordinates of the center of the tree, 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 as follows:

[0034] 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 partial derivatives:

[0035] right Find partial derivatives:

[0036] right Find partial derivatives:

[0037] Among them, when the radius is obtained After, DBH That is twice the radius, that is: .

[0038] The method for calculating the ratio of the projection 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 a 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:

[0039] The edge detection algorithm is used to process the projected point cloud data, extract the contour of the crown projection, and obtain the contour point set. ,in ; Green's formula is used to calculate the area enclosed by the contour. The calculation formula for the enclosed area is as follows:

[0040] For discrete contour points, numerical integration is used for approximate calculation, and then the contour points are connected in sequence to form a closed curve. The calculation process is as follows:

[0041] in, is the contour point set, is the number of contour points; The total area of ​​the sample plot is calculated as follows: When the sample plot is a regular rectangular site, its length is known to be , width is , then the total area of ​​the plot is:

[0042] When the plot is a circular plot, the radius is known to be , then the total area of ​​the plot is:

[0043] When the sample plot has irregular terrain, the coordinates of the boundary points of the sample plot 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:

[0044] The ratio of the projected area on the horizontal plane to the total area of ​​the plot is calculated as follows:

[0045] in, is the canopy density of the forest plot.

[0046] The point cloud device starts the real-time processing process while collecting point cloud data, and uses parallel computing technology to simultaneously perform pre-processing such as denoising and filtering to process and cross-match point cloud data from different sources.

[0047] 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, including: a forest plot survey and measurement system, the forest plot survey and measurement system including 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 extraction module, a database and a visualization module; 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 optimize the collection paths synchronously during the movement of the collector. It is designed with a dedicated route planning software, which automatically generates the optimal flight path based on the geographical boundaries of the forest plot, terrain undulation data and the required point cloud density requirements, thereby improving the collection efficiency of the collector; 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, a suitable filtering method is selected to smooth the point cloud data. Normalization unifies the point cloud data into a specific coordinate range and scale for subsequent processing. The above method is used to preprocess the collected data, which facilitates the subsequent application of the collected data. The registration module is used to automatically select the 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 the height, slope, roughness, point cloud density geometry and spatial distribution features from the point cloud data as the training sample feature vector, fuse 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, and at the same time combine the surrounding terrain undulation correction data to obtain accurate environmental information of the forest sample plot; The database manages the collected and processed point cloud data, forest plot factor data, etc. in a unified manner, realizes 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 design 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 retrieval and management of data; The visualization module displays the point cloud data, tree distribution, forest plot factors and other information of the forest plot in a three-dimensional visualization way. 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 realized for forest plot data of different scales. On a macro scale, the distribution and topography of the entire forest plot are displayed. On a micro scale, detailed features of a single tree, such as trunk texture, leaf distribution, etc., can be viewed in depth, making it easier for surveyors to view the situation of the forest plot more intuitively and efficiently.

[0048] Example 2 Reference Figure 2 , further improvements were made on the basis of Example 1: The virtual modeling method of the forest plot is as follows: A401, first remove the noise points in the point cloud data, and remove the outliers that are far away from the group through statistical analysis, then use the systematic grid downsampling method to streamline the data, reduce the data volume while ensuring the basic shape of the model, and finally, through translation and rotation transformation operations, unify the point cloud data from different sources into the same coordinate system; A402. Reconstruct the surface of the processed point cloud data through triangulation to build a continuous surface model. Use height threshold or machine learning classification methods to separate tree and terrain point cloud data to achieve separate modeling. Use coordinate mapping to attach the calibrated and enhanced high-resolution aerial images or ground texture images to the three-dimensional surface model to complete texture 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 folding 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.

[0049] Example 3 Reference Figure 1 , further improvements were made on the basis of Example 1: ALS point cloud collector, MLS point cloud collector and TLS point cloud collector are connected with 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, the user can select different acquisition devices through the interface and set the 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, which facilitates the control personnel to make quick adjustments to ensure that the collected data is accurate and effective.

[0050] The registration module includes a data verification module and an environment monitoring module; The data verification module sets 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, ensures the integrity of the data, and 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 timely discover 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, and then performs auxiliary optimization on the point cloud data. For point cloud data with large errors, re-collection is adopted to improve the accuracy of point cloud data; The environmental monitoring module, combined with the meteorological sensor network, collects real-time information on factors such as temperature, humidity, and light intensity in forest plots. By correlating and analyzing point cloud data, it explores the impact of environmental factors on tree growth and distribution. Meteorological sensors are reasonably distributed in forest plots to ensure that factor information in each area can be effectively collected. Meteorological data and point cloud data are associated, stored, and analyzed. The relationship 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 all-round monitoring of forest plots to ensure the ecological health of forest plots.

[0051] The classification 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 plot data and tree growth models, combined with the tree characteristic data obtained from the current survey. By collecting a large amount of historical forest plot data, suitable tree growth models are screened, such as the logistic model and 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, so as to predict the future growth of trees in the corresponding forest plots and the development of the plot ecology, thereby facilitating the subsequent management of the corresponding forest plots.

[0052] 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, breast diameter, tree height and biomass model of the forest plot. Different tree species have different biomass estimation models. The biomass is calculated by substituting the measured breast diameter and tree height parameters into the corresponding model, and then the carbon storage of the forest plot is calculated based on the conversion relationship between biomass and carbon storage. At the same time, the point cloud data and forest growth model are combined to predict the changing trend of carbon storage in the future, providing data support for carbon sink trading, ecological compensation and future resource planning decisions.

[0053] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present 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, characterized in that: The following steps are involved: A1. Establish a point cloud collection database for forest plots, then set up an aircraft equipped with a point cloud collection device, then fly the aircraft above the forest plot to be collected, and then start cruising to collect point cloud data for the forest plot. At the same time, a handheld laser radar device is set up. The collection mode of the handheld laser radar device is used for ground mobile collection of forest plots. The collection mode of the handheld laser radar device is used for ground mobile collection of forest plots. Mobile point cloud data collection is performed in the ground environment of the forest plot. Finally, five fixed-point point cloud devices are set up to collect fixed ground point cloud data. A2. For the point cloud data collected by the aircraft and fixed points, the tree crown point cloud data is acquired, and the geometric characteristics and spectral characteristics of the forest crown are extracted. Then, the point cloud data of the tree trunk is obtained from the handheld laser radar device and the point cloud data collected by the fixed point. The central axis and radius information are accurately extracted based on the cylindrical shape characteristics, and rough registration is performed; A3. Extract the height, slope, roughness and point cloud density geometry and spatial distribution features from the aircraft point cloud data as the training sample feature vector, and then use cross-validation and parameter optimization techniques to improve the model generalization ability and classification accuracy. Then, for the classified tree point cloud, calculate the vertical distance between its highest point and the ground point cloud to obtain the tree height, and correct the data in combination with the surrounding terrain undulations. A4. After registering the point cloud data collected by the fixed point and handheld LiDAR equipment, the contour recognition algorithm based on deep learning is combined with the least squares method to fit the circular contour and calculate the diameter at breast height of each tree. The tree height information extracted by the classification of the aircraft point cloud data is then combined with the measurement results of the point cloud data collected by the fixed point. The data fusion algorithm is then used for fusion and correction. At the same time, the ratio of the projected area of ​​the crown point cloud on the horizontal plane to the total area of ​​the sample plot is calculated from the point cloud data to preliminarily obtain the canopy density. Subsequently, the point cloud data is used to obtain detailed information on the crown structure, and a virtual model of the forest sample plot is constructed using 3D modeling technology. The crown coverage is observed from different angles to optimize and verify the canopy density value. A5. By collecting sample trees of forest plots using the above point cloud data, we can obtain complete factor information of forest plots. Then, we can generate a measurement model of the forest plot using the factor information. Then, we can optimize the model to obtain an accurate and clear forest plot model. Then, we can add past data and related ecological data into the model to predict the ecological environment and development trend of the forest plot, which will facilitate subsequent plant growth predictions and future resource planning for the forest plot.

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 can be expressed as: ;in, are the two-dimensional coordinates of the center of the tree, 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 as follows: ; 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 partial derivatives: ； right Find partial derivatives: ； right Find 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 the noise points in the point cloud data, and remove the outliers that are far away from the group through statistical analysis, then use the systematic grid downsampling method to streamline the data, reduce the data volume while ensuring the basic shape of the model, and finally, through translation and rotation transformation operations, unify the point cloud data from different sources into the same coordinate system; A402. Reconstruct the surface of the processed point cloud data through triangulation to build a continuous surface model. Use height threshold or machine learning classification methods to separate tree and terrain point cloud data to achieve separate modeling. Use coordinate mapping to attach the calibrated and enhanced high-resolution aerial images or ground texture images to the three-dimensional surface model to complete texture 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 folding 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 projection 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 a 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 contour of the crown projection, and obtain the contour point set. ,in ; Green's formula is used to calculate the area enclosed by the contour. The calculation formula for the enclosed area is as follows: ; For discrete contour points, numerical integration is used for approximate calculation, and then the contour points are connected in sequence 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 sample plot is calculated as follows: When the sample plot is a regular rectangular site, its length is known to be , width is , then the total area of ​​the plot is: ； When the plot is a circular plot, the radius is known to be , then the total area of ​​the plot is: ； When the sample plot has irregular terrain, the coordinates of the boundary points of the sample plot 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 sample 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 flow while collecting point cloud data, uses parallel computing technology to synchronously perform pre-processing such as denoising and filtering, 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 is applied to the forest plot survey and measurement method based on the fusion of multiple point cloud technologies as described 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 simultaneously optimize the collection paths during the movement of the collectors; 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 registration module is used to automatically select a suitable 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, fuse 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, and at the same time combine the surrounding terrain undulation correction data to obtain accurate environmental information of the forest sample plot; The database manages the collected and processed point cloud data, forest plot factor data, etc. in a unified manner, realizes data storage, query, backup and recovery functions, and supports the import and export of multiple data formats; The visualization module displays the point cloud data, tree distribution, forest plot factors and other 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, the MLS point cloud collector and the TLS point cloud collector are connected with 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 collector, MLS point cloud collector and TLS point cloud collector. Through the controller, the user 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 sets 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, ensures the integrity of the data, and 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 timely discover abnormal data fluctuations and ensure data accuracy and reliability; The environmental monitoring module, combined with the meteorological sensor network, collects information on factors such as temperature, humidity, and light intensity 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 decision module; The resource planning decision module estimates the biomass of each tree based on the tree species, breast diameter, 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 breast diameter and tree height parameters into the corresponding model, and then the carbon storage of the forest sample plot is calculated based on the conversion relationship between biomass and carbon storage. At the same time, the point cloud data and forest growth model are combined to predict the changing trend of carbon storage in the future, providing data support for carbon sink trading, ecological compensation and future resource planning decisions.

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