Method and system for measuring and calculating angles and distances of sample trees in first-class checking of forest resources

Abstract

The invention relates to the technical field of forest resource metering, and discloses a method and a system for measuring and calculating the angle and the distance of a sample tree in first-class checking of forest resources, and the method for measuring and calculating the angle and the distance of the sample tree in first-class checking of the forest resources comprises the following steps: obtaining original measurement data and environment parameters through a mobile laser radar and an RTK system; establishing a space coordinate system model; calculating theoretical coordinates, azimuth angles and distances; completing data compensation based on the error correction coefficient; accurate azimuth angles and distance values are obtained. Meanwhile, a data acquisition mode of combining an RTK system and a mobile laser radar is adopted, so that the sample wood can be quickly and accurately positioned, and a uniform space reference is provided for subsequent data processing through construction of a space coordinate system. The method is high in reliability and adaptability, can meet the actual requirements of forest resource checking in a complex forest region environment, and has a good application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of forest resource measurement, and more specifically, it relates to a method and system for measuring the angle and distance of sample trees in the first-class inventory of forest resources. Background Art

[0002] The first-class inventory of forest resources is crucial for accurately grasping the forest resource situation, and the accurate measurement of the angle and distance of sample trees provides key data support for forest resource assessment. However, there are some technical problems in this field currently. For example, some existing measurement methods are greatly affected by vegetation coverage, terrain undulation, and environmental factors (such as temperature, humidity, atmospheric pressure, etc.) in complex forest environments. Taking the traditional measurement method based on a single lidar as an example, when the forest vegetation coverage density is high, the laser signal will undergo multiple reflections and scatterings, resulting in large deviations in the acquired original azimuth angle and distance data. Moreover, this method does not fully consider the influence of terrain elevation changes on the measurement results. In mountainous areas and other complex terrain regions, the measurement error increases, making it difficult to meet the requirements of accurate inventory of forest resources for the accuracy of the finally measured angle and distance of sample trees, thus affecting the scientific assessment and management decision-making of forest resources. Summary of the Invention

[0003] The present invention provides a method and system for measuring the angle and distance of sample trees in the first-class inventory of forest resources, which solves the technical problems of the influence of terrain elevation changes on the measurement results and the insufficient accuracy of measuring the angle and distance in related technologies.

[0004] The present invention provides a method for measuring the angle and distance of sample trees in the first-class inventory of forest resources, including:

[0005] Using an RTK system to obtain the measurement position coordinates, using a mobile lidar to measure the original azimuth angle and distance data, and obtaining the environmental parameters of temperature, humidity, and air pressure;

[0006] Performing spatial coordinate system modeling, and calculating the theoretical spatial coordinates of the sample trees according to the RTK coordinates through coordinate transformation;

[0007] Performing correction for the influence of vegetation coverage, establishing a functional relationship between the vegetation coverage density and the lidar measurement error, and calculating the vegetation coverage density correction coefficient;

[0008] Performing correction for the influence of environmental parameters, establishing a functional relationship between the environmental parameters and the lidar measurement error, and calculating the environmental parameter correction coefficient;

[0009] Performing time synchronization optimization, installing clock modules on the lidar and RTK, synchronizing and correcting the time deviation regularly, establishing a functional relationship between the time deviation and the measurement data, and correcting the original data with deviations;

[0010] Complex terrain correction: The UAV lidar obtains terrain elevation data, performs polynomial fitting to calculate the terrain correction coefficient, and corrects the theoretical coordinates of the sample trees.

[0011] Comprehensive error compensation: After completing various corrections, a comprehensive error compensation model is established. The corrected data, theoretical data, and correction coefficient are introduced to calculate the azimuth distance after compensation as the final measurement result.

[0012] In a preferred embodiment, in the vegetation coverage impact correction, the polynomial function relationship between the vegetation coverage density and the lidar azimuth error and distance error is obtained by fitting experimental data, and the vegetation coverage density impact correction coefficient is calculated therefrom.

[0013] In a preferred embodiment, in the environmental parameter impact correction, the polynomial function relationship between the environmental parameters of temperature, humidity, and atmospheric pressure and the lidar azimuth error and distance error is obtained by fitting experimental data, and the environmental parameter impact correction coefficient is calculated therefrom.

[0014] In a preferred embodiment, in the time synchronization optimization, the polynomial function relationship between the time deviation and the lidar azimuth error and distance error is obtained by fitting experimental data, and the original azimuth data and original distance data with time deviation are corrected therefrom.

[0015] In a preferred embodiment, in the complex terrain impact correction, by performing cubic polynomial fitting on the terrain elevation data, the function relationship between the terrain elevation and the offset of the theoretical coordinates of the sample trees is obtained, and the terrain correction coefficient is calculated therefrom to correct the theoretical coordinates of the sample trees.

[0016] In a preferred embodiment, a weight coefficient is introduced into the comprehensive error compensation model to balance the differences between the corrected original azimuth data, original distance data and the theoretical azimuth and theoretical distance. The weight coefficient is determined by optimizing multiple experiments to achieve the best error compensation effect.

[0017] In a preferred embodiment, in the basic data acquisition step, the scanning frequency of the mobile lidar used is not less than 100 Hz, the scanning angle range is not less than 360°, the ranging accuracy is better than 5 cm, the angle measurement accuracy is better than 0.1°, and the positioning accuracy of the supporting RTK system is better than 5 cm.

[0018] In a preferred embodiment, in the spatial coordinate system modeling step, the longitude and latitude coordinates are converted into plane rectangular coordinates by the Gauss-Krüger projection or UTM projection method, and combined with the three-dimensional coordinates of the RTK system, a spatial rectangular coordinate system adapted to the actual geographical environment is established.

[0019] In a preferred embodiment, a system for measuring the angles and distances of sample trees in the first-class inventory of forest resources includes:

[0020] A mobile lidar for obtaining original azimuth data and original distance data;

[0021] An RTK system for obtaining real-time coordinate information of the measurement location;

[0022] An environmental parameter sensor for obtaining environmental-related parameters such as temperature, humidity, and atmospheric pressure measurements;

[0023] A multi-spectral remote sensing device for obtaining vegetation cover density data of the measurement area;

[0024] An airborne lidar for obtaining high-precision topographic elevation data of the measurement area;

[0025] A data processing module for receiving and processing the output data of the above devices and calculating the measurement results of the angles and distances of the sample trees.

[0026] In a preferred embodiment, the mobile lidar, RTK system, environmental parameter sensor, and data processing module are integrated into a portable device, which also includes a data storage module and a data communication module for storing measurement data and exchanging data with external devices.

[0027] The beneficial effects of the present invention are as follows:

[0028] Improve measurement accuracy: By comprehensively considering various factors, such as obtaining environmental-related parameters (temperature, humidity, atmospheric pressure, etc.) of the measurement environment to perform error compensation on the original measurement data, the present invention effectively reduces the interference of environmental factors on the measurement of the angles and distances of sample trees, improves the accuracy of the measurement results, and provides a more reliable data basis for the inventory of forest resources.

[0029] Targeted solutions are proposed for different vegetation cover densities and terrain conditions. For example, under different vegetation cover densities, relevant models are established to analyze and correct the data on the reflection and penetration of lidar signals; in areas with complex terrain, an airborne lidar is used to obtain high-precision topographic elevation data to perform terrain correction on the spatial coordinate system, enabling the method to stably and accurately measure the angles and distances of sample trees in various complex forest environments and expanding the scope of application. Description of the Drawings

[0030] Figure 1 is a flowchart of the method for measuring the angles and distances of sample trees in the first-class inventory of forest resources according to the present invention;

[0031] Figure 2 is an example diagram of sample tree data according to the present invention. Detailed Implementation Modes

[0032] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein, and changes can be made to the functions and arrangements of the elements discussed without departing from the scope of protection of the content of this specification. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described in some examples can also be combined in other examples.

[0033] In at least one embodiment of the present invention, a method for measuring the angle and distance of sample trees in the first-class inventory of forest resources is disclosed. As Figure 1 shown, it includes the following steps:

[0034] Step100: Use the RTK system to obtain the measurement position coordinates, measure the original azimuth angle and distance data using a mobile lidar, and obtain the temperature, humidity, and atmospheric pressure environment parameters;

[0035] Obtain the original azimuth angle data measured by the mobile lidar and the original distance data ; obtain the real-time coordinate information of the measurement position obtained by the Real-Time Kinematic (RTK) system ; obtain the measurement environment-related parameters, including temperature 、humidity 、atmospheric pressure , and set the environmental parameter vector ;

[0036] Step200: Model the spatial coordinate system, and calculate the theoretical spatial coordinates of the sample trees through coordinate transformation according to the RTK coordinates;

[0037] Calculate the fluctuation range of the azimuth angle and the fluctuation range of the distance ;

[0038] In one embodiment of the present invention, the calculation formulas for the fluctuation range of the azimuth angle and the fluctuation range of the distance are as follows:

[0039] ;

[0040] Wherein, represents the maximum value in the original azimuth angle data , represents the minimum value in the original azimuth angle data ;

[0041] ;

[0042] Among them, represents the maximum value in the original distance data , represents the minimum value in the original distance data .

[0043] Based on the real-time coordinate information obtained by RTK , combined with the known geographical information of the measurement area, a spatial coordinate system model is established;

[0044] In an embodiment of the present invention, let the theoretical coordinates of the sample tree in this spatial coordinate system be , which is determined by the approximate position information of the known sample tree and the geographical coordinate conversion relationship. The specific conversion formula is set according to the actual geographical coordinate system, and the calculation formula is:

[0045] ;

[0046] Among them, is a geographical-related constant vector, represents a constant related to the parameters of the Earth ellipsoid, is an offset calculated according to the specific geographical conditions of the measurement area (such as the longitude of the central meridian, etc.). The function represents the relationship for calculating the theoretical coordinate in the spatial coordinate system of the sample tree based on the given real-time coordinate ;

[0047] ;

[0048] Similarly, is another geographical-related constant, is also an offset calculated based on the Gauss-Kruger projection formula. The function represents the relationship for calculating the theoretical coordinate in the spatial coordinate system of the sample tree;

[0049] ;

[0050] Here, can be understood as a constant related to elevation. Under the assumption of a simple terrain, complex elevation corrections can be ignored. The function represents the relationship for calculating the theoretical coordinate in the spatial coordinate system of the sample tree.

[0051] In an embodiment of the present invention, a data sample of a set of sample tree measurement points and an example calculation are provided. The data sample of the sample tree measurement points is as shown in Figure 2 ; The example calculation formula is as follows:

[0052] ;

[0053] ;

[0054] Combined with the known geographical information, using the formula in the spatial coordinate system model establishment step, the theoretical coordinates of the sample tree in the spatial coordinate system can be further calculated. Assuming the geographical related constant vector , taking sample tree measurement point 1 as an example:

[0055] ;

[0056] ;

[0057] ;

[0058] In an embodiment of the present invention, the geographical related constant vector is a vector composed of a set of constants related to the geographical characteristics of the measurement area; its specific value is usually determined by the geographical information of the measurement area, such as factors like the earth ellipsoid parameters and the longitude of the central meridian; for example, in a specific measurement area, by analyzing and calculating the geographical data of the area, the constant values closely related to the geographical characteristics are obtained, and then the geographical related constant vector is formed.

[0059] In an embodiment of the present invention, for the differences in the signal reflection and penetration of lidar under different vegetation coverage densities, which may affect the accuracy of the original measurement data and thus the measurement results after final error compensation, the method for calculating the angles and distances of sample trees in the first-class inventory of forest resources further includes the following steps:

[0060] Step300, Vegetation coverage impact correction, establish the functional relationship between vegetation coverage density and lidar measurement error, and calculate the vegetation coverage density correction coefficient;

[0061] Specifically, it includes the following steps:

[0062] s301, Obtain the vegetation coverage density data of the measurement area through multispectral remote sensing equipment ;

[0063] s302, Establish a vegetation coverage density calculation model to evaluate the vegetation coverage density;

[0064] In an embodiment of the present invention, the evaluation of vegetation coverage density includes the following steps:

[0065] Determine the model form and establish a vegetation coverage density calculation model ; Here is a simplified form based on the pixel dichotomy model, and after simplification, it is ;

[0066] Obtain the model coefficients. Through linear regression of the historical data of the measurement area, obtain the coefficients related to the measurement position and , and these coefficients are used to adapt to the vegetation characteristics of different regions;

[0067] Calculate the vegetation coverage density. Substitute the obtained vegetation coverage density data , coefficients and into the simplified model to calculate the vegetation coverage density at the current measurement position .

[0068] s303, Spatial coordinate system modeling, calculate the theoretical coordinates of the sample tree, calculate the theoretical azimuth angle, and calculate the theoretical distance;

[0069] In an embodiment of the present invention, the calculation formulas for the theoretical azimuth angle and the theoretical distance are as follows:

[0070] ;

[0071] Wherein, represents the arctangent function, and respectively represent the theoretical horizontal and vertical coordinates of the sample tree in the spatial coordinate system; and respectively represent the real-time horizontal and vertical coordinates obtained by the RTK system;

[0072] ;

[0073] Wherein, , , respectively represent the theoretical horizontal, vertical, and vertical coordinates of the sample tree in the spatial coordinate system; , , respectively represent the real-time horizontal, vertical, and vertical coordinates obtained by the RTK system.

[0074] s304, Vegetation influence correction, calculate the azimuth vegetation influence correction coefficient and the distance vegetation influence correction coefficient;

[0075] In an embodiment of the present invention, the azimuth vegetation influence correction coefficient ; is a function obtained by fitting experimental data. Through a large number of lidar measurement experiments in environments with different vegetation coverage densities, the function is fitted using the least squares method , where and represent the first and second fitting coefficients respectively. The function represents the relationship for calculating the azimuth vegetation influence correction coefficient based on the vegetation coverage density .

[0076] In an embodiment of the present invention, the distance vegetation influence correction coefficient , is also a function fitted based on experimental data , and represent the third and fourth fitting coefficients obtained by fitting experimental data respectively, and are used to correct the influence of vegetation coverage density on the lidar measurement distance; the function represents the relationship for calculating the distance vegetation influence correction coefficient based on the vegetation coverage density .

[0077] Step400, environmental parameter influence correction, establish the functional relationship between environmental parameters and lidar measurement error, and calculate the environmental parameter correction coefficient;

[0078] Specifically, it includes the following steps:

[0079] s401, environmental parameter error correction, obtain the environmental parameter vector ; calculate the azimuth error correction coefficient and the distance error correction coefficient;

[0080] In an embodiment of the present invention, the calculation formulas for the azimuth error correction coefficient and the distance error correction coefficient are as follows:

[0081] ;

[0082] ;

[0083] Among them, represents the azimuth error correction coefficient, represents the function for calculating the azimuth error correction coefficient based on environmental factors; , , represent temperature, humidity, and atmospheric pressure respectively; represents the distance error correction coefficient; is the function for calculating the distance error correction coefficient based on environmental factors.

[0084] In an embodiment of the present invention, aiming at the possible time synchronization error between the RTK system and the lidar, which may cause deviation in data fusion processing, the method for calculating the angle and distance of sample trees in the first-class inventory of forest resources further includes the following steps:

[0085] Step500, Time synchronization optimization. Install clock modules on the lidar and the RTK, synchronize and correct the time deviation regularly, establish the functional relationship between the time deviation and the measurement data, and correct the original data with deviation.

[0086] Specifically, it includes the following steps:

[0087] s501, Introduction of high-precision clock modules. Install high-precision clock modules on the RTK system and the lidar device respectively to ensure that the time accuracy of both reaches the nanosecond level.

[0088] s502, Use the Network Time Protocol (NTP) or the Precision Time Protocol (PTP) for time synchronization, and set the synchronization period to ;

[0089] s503, Regularly detect the time deviation between the RTK system and the lidar ; If (the set time deviation threshold), then perform time correction;

[0090] In an embodiment of the present invention, the time correction includes:

[0091] Correct the original azimuth angle:

[0092] ;

[0093] Wherein, is the corrected original azimuth angle, is the original azimuth angle before correction, is the azimuth angle error correction coefficient, is the offset of the time deviation, used for azimuth angle correction calculation;

[0094] Correct the original distance:

[0095] ;

[0096] Wherein, is the corrected original distance, is the original distance before correction, is the distance error correction coefficient under a certain specific condition, is the offset of the time deviation, used for distance correction calculation;

[0097] s504, Calculate the fluctuation range of the corrected azimuth angle ; Calculate the corrected distance fluctuation range: ;

[0098] s505, Comprehensive error compensation, calculate the compensated azimuth and the compensated distance;

[0099] In an embodiment of the present invention, the calculation formulas for the compensated azimuth and the compensated distance are as follows:

[0100] ;

[0101] Wherein, is the azimuth error correction coefficient, is the first compensation weight coefficient, used to balance the difference between the theoretical azimuth and the corrected original azimuth ;

[0102] ;

[0103] Wherein, represents the distance error correction coefficient, is another weight coefficient used to adjust the difference between the theoretical distance and the corrected original distance ;

[0104] In an embodiment of the present invention, for the complex terrain (such as mountains, hills, etc.) environment, the relative position relationship of the sample trees may change due to the terrain undulation, resulting in an inaccurate spatial coordinate system model. The method for measuring the angles and distances of the sample trees in the first-class inventory of forest resources further includes the following steps:

[0105] Step600, Complex terrain correction, the UAV lidar obtains the terrain elevation data, and calculates the terrain correction coefficient by polynomial fitting to correct the theoretical coordinates of the sample trees;

[0106] Specifically, it includes the following steps:

[0107] s601, Terrain data collection, use the UAV equipped with lidar to conduct terrain mapping to obtain high-precision terrain elevation data ;

[0108] s602, Terrain correction of the spatial coordinate system, calculate the terrain correction coefficient: , , , which respectively represent the offsets of the theoretical coordinates of the sample trees in the , , directions due to the terrain undulation.

[0109] The following functional relationship is obtained by polynomial fitting of the terrain data in the measurement area:

[0110] ;

[0111] Among them, , , respectively represent the fifth, sixth, and seventh fitting coefficients in the direction. is the high-precision terrain elevation data obtained by topographic surveying with a lidar carried by an unmanned aerial vehicle. This formula represents calculating the terrain correction coefficient in the direction based on the terrain elevation ;

[0112] ;

[0113] Among them, , , respectively represent the eighth, ninth, and tenth fitting coefficients in the direction. This formula is used to calculate the terrain correction coefficient in the direction based on ;

[0114] ;

[0115] Among them, , , respectively represent the eleventh, twelfth, and thirteenth fitting coefficients in the direction, and are used to calculate the terrain correction coefficient in the direction based on .

[0116] s603, calculate the corrected theoretical azimuth and the corrected theoretical distance;

[0117] ;

[0118] Among them, represents the corrected theoretical azimuth, , respectively represent the component and component of the corrected theoretical coordinates of the sample tree; , respectively represent the component and component of the RTK system coordinate data;

[0119] ;

[0120] Among them, represents the corrected theoretical distance, , , respectively represent the , , components of the corrected theoretical coordinates of the sample tree; , , respectively represent the , , components of the RTK system coordinate data.

[0121] Step700, Comprehensive error compensation. After completing various corrections, establish a comprehensive error compensation model, introduce the corrected data, theoretical data, and correction coefficients, and calculate the azimuth distance after compensation as the final measurement result;

[0122] Specifically, it includes the following steps:

[0123] s701, Comprehensive error compensation under complex terrain, calculate the azimuth after compensation and the distance after compensation;

[0124] ;

[0125] Among them, is the azimuth after compensation, is the corrected original azimuth, is the azimuth error correction coefficient, represents the fluctuation range of the original azimuth data after preliminary processing under complex terrain conditions, represents the second compensation weight coefficient under complex terrain conditions, represents the theoretical azimuth of the sample tree in the space coordinate system established under complex terrain;

[0126] ;

[0127] Among them, represents the distance after compensation under complex terrain environment, is the corrected original distance, is the distance error correction coefficient, represents the fluctuation range of the original distance data after preliminary processing under complex terrain conditions, represents the third compensation weight coefficient under complex terrain conditions, represents the theoretical distance of the sample tree in the space coordinate system established under complex terrain;

[0128] s702, Data verification and storage, verify the data after compensation and The accuracy can be compared with the measurement results of equipment such as high-precision total stations; the final measurement results are stored in the database for subsequent forest resource inventory work.

[0129] By introducing high-precision topographic surveying data and correcting the theoretical coordinates of sample trees based on the terrain elevation, a more accurate spatial coordinate system model can be established, reducing measurement errors in complex terrain environments and improving the accuracy and reliability of forest resource inventory.

[0130] The above describes the embodiments of the present invention, but the embodiments are not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make more equivalent embodiments in various forms, all of which fall within the protection scope of this embodiment.

Claims

1. A method for measuring the angles and distances of sample trees in the first-class inventory of forest resources, characterized in that, Including the following steps: Using an RTK system to obtain the measured position coordinates, adopting a mobile lidar to measure the original azimuth angle and distance data, and obtaining the environmental parameters of temperature, humidity, and air pressure; Spatial coordinate system modeling, according to the RTK coordinates, through coordinate transformation, calculating the theoretical spatial coordinates of the sample trees; Vegetation coverage impact correction, establishing the functional relationship between vegetation coverage density and lidar measurement error, and calculating the vegetation coverage density correction coefficient; Environmental parameter impact correction, establishing the functional relationship between environmental parameters and lidar measurement error, and calculating the environmental parameter correction coefficient; Time synchronization optimization, installing clock modules on the lidar and RTK, regularly synchronizing and correcting the time deviation, establishing the functional relationship between the time deviation and measurement data, and correcting the original data with deviations; Complex terrain correction, using an unmanned aerial vehicle lidar to obtain terrain elevation data, performing polynomial fitting to calculate the terrain correction coefficient, and correcting the theoretical coordinates of the sample trees; Comprehensive error compensation, after completing various corrections, establishing a comprehensive error compensation model, introducing the corrected data, theoretical data, and correction coefficients, and calculating the compensated azimuth angle and distance as the final measurement result.

2. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, characterized in that, In the vegetation coverage impact correction, the polynomial functional relationships between vegetation coverage density and lidar azimuth angle error, distance error are obtained by fitting experimental data, and the vegetation coverage density impact correction coefficient is calculated therefrom.

3. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, wherein In the environmental parameter impact correction, the polynomial functional relationships between environmental parameters of temperature, humidity, and atmospheric pressure and lidar azimuth angle error, distance error are obtained by fitting experimental data, and the environmental parameter impact correction coefficient is calculated therefrom.

4. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, characterized in that, In the time synchronization optimization, the polynomial functional relationships between time deviation and lidar azimuth angle error, distance error are obtained by fitting experimental data, and the original azimuth angle data and original distance data with time deviation are corrected therefrom.

5. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, characterized in that, In the complex terrain impact correction, by performing cubic polynomial fitting on the terrain elevation data, the functional relationship between terrain elevation and the offset of the theoretical coordinates of the sample trees is obtained, and the terrain correction coefficient is calculated therefrom to correct the theoretical coordinates of the sample trees.

6. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, characterized in that, A weight coefficient is introduced into the comprehensive error compensation model to balance the differences between the corrected original azimuth angle data, original distance data and the theoretical azimuth angle, theoretical distance.

7. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, characterized in that, In the basic data acquisition step, the scanning frequency of the adopted mobile lidar is greater than or equal to 100 Hz, the scanning angle range is greater than or equal to 360°, the ranging accuracy is greater than 5 cm, the angle measurement accuracy is greater than 0.1°, and the positioning accuracy of the supporting RTK system is greater than 5 cm.

8. The method for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 1, characterized in that, In the spatial coordinate system modeling step, through the Gauss-Kruger projection or UTM projection method, the longitude and latitude coordinates are converted into plane rectangular coordinates, and combined with the three-dimensional coordinates of the RTK system, a spatial rectangular coordinate system adapted to the actual geographical environment is established.

9. A system for measuring the angles and distances of sample trees in the first-class inventory of forest resources, characterized in that, For implementing the method for measuring the angle and distance of sample trees in the first-class inventory of forest resources as described in any one of claims 1-8, including: A mobile lidar for obtaining the original azimuth angle data and original distance data; An RTK system for obtaining the real-time coordinate information of the measurement position; An environmental parameter sensor for obtaining the environmental parameters related to the measurement of temperature, humidity, and atmospheric pressure; Multispectral remote sensing equipment for obtaining vegetation cover density data of a measurement area; Unmanned aerial vehicle (UAV)-borne lidar for obtaining high-precision topographic elevation data of a measurement area; A data processing module for receiving and processing the output data of the above-mentioned equipment and calculating the measurement results of the sample tree angles and distances.

10. The system for measuring the angle and distance of sample trees in the first-class inventory of forest resources according to claim 9, characterized in that, A mobile lidar, RTK system, environmental parameter sensor, and data processing module are integrated into a portable device, which also includes a data storage module and a data communication module for storing measurement data and exchanging data with external devices.

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