Tree analysis system, tree analysis method, and computer program

Abstract

To grasp a state of an environment where a plurality of trees such as forests exist while reducing time and cost required for work.SOLUTION: The tree analysis system 110 according to the embodiment analyzes a state of a tree by using three dimensional point group data 120 obtained by forest measurement. The processing device 111 determines the position of the tree and the height of the tree indicated by the three dimensional point group data 120 on the basis of continuity along the height direction of the plurality of points indicated by the three dimensional point group data 120. The processing device 111 determines a crown portion of the tree based on the position of the tree, and calculates a crown area based on the crown portion. The processing device 111 calculates the state of the tree based on the tree height and the tree crown area.SELECTED DRAWING: Figure 18

Description

[Technical Field]

[0001] The present invention relates to a tree analysis system, a tree analysis method, and a computer program. [Background technology]

[0002] Forest measurement is conducted for the management and utilization of forest resources. Forest measurement can be performed using various methods. For example, Patent Document 1 discloses a method of photographing a forest with a camera from the sky and extracting the tree crown circle from the obtained image by image processing. Patent Document 2 discloses a method of irradiating a forest with radar waves of different wavelengths from the sky and determining the height of trees from the difference between the heights calculated from the waves reflected from the tops of the trees and the waves reflected from the ground. Patent Document 3 discloses a method of mounting a laser ranging device on an aircraft and using the laser ranging device to acquire three-dimensional data of the ground, including trees, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-66050 [Patent Document 2] Japanese Patent Application Publication No. 9-184880 [Patent Document 3] Japanese Patent Application Laid-Open No. 2016-70708 Summary of the Invention [Problem to be solved by the invention]

[0004] By measuring forests using a laser ranging device, 3D point cloud data of the forest can be obtained. This 3D point cloud data of the forest can be used to analyze the condition of the forest. However, measuring forests and analyzing the condition of the forest using the obtained 3D point cloud data is very time-consuming and costly.

[0005] There is a need to understand the state of an environment where multiple trees exist, such as a forest, while reducing the time and cost required for the work. [Means for solving the problem]

[0006] This specification discloses a tree analysis system, a tree analysis method, and a computer program described in the following items.

[0007] [Item 1] A tree analysis system that analyzes the state of trees using 3D point cloud data obtained by forest measurement, a processing device; a storage device that stores a computer program that controls the operation of the processing device; Equipped with The processing device, in accordance with the computer program, determining the positions and heights of the trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data; determining a crown portion of the tree based on the position of the tree; Calculating a crown area based on the crown portion; A tree analysis system that calculates the condition of the tree based on the tree height and the crown area.

[0008] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0009] According to one embodiment of the present invention, the positions and heights of trees are determined based on the continuity of multiple points indicated by the 3D point cloud data along the height direction, the crown areas are determined based on the tree positions, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data, thereby reducing the time and cost required for forest measurement and data analysis.

[0010] [Item 2] Item 2. The tree analysis system of item 1, wherein the processing device calculates the diameter of the tree's trunk based on the tree height and the crown area.

[0011] This makes it possible to obtain data on tree trunk diameters while reducing the time and cost required for forest measurement and data analysis.

[0012] [Item 3] Item 3. The tree analysis system according to item 2, wherein the processing device calculates the diameter of the trunk of the tree using table information showing the relationship between tree height, crown area, and trunk diameter.

[0013] Using the table information, the trunk diameter can be calculated based on the correlation between tree height, crown area and trunk diameter.

[0014] [Item 4] Item 3. The tree analysis system according to item 2, wherein the processing device calculates the diameter of the trunk of the tree using an estimation model that takes at least tree height and crown area as input and trunk diameter as output.

[0015] Using an estimation model generated through machine learning, trunk diameter can be calculated based on the correlation between tree height, crown area, and trunk diameter.

[0016] [Item 5] Item 5. The tree analysis system according to item 4, wherein the estimation model is an estimation model that further inputs one or more of the tree species, the topography of the area where the forest measurements were performed, and the region where the forest measurements were performed.

[0017] This allows the diameter of the trunk to be calculated with greater accuracy.

[0018] [Item 6] Item 2. The tree analysis system according to item 1, wherein the processing device calculates the volume of the tree based on the tree height and the crown area.

[0019] This makes it possible to obtain data on tree volume while reducing the time and cost required for forest measurement and data analysis.

[0020] [Item 7] 7. The tree analysis system according to item 6, wherein the processing device calculates the volume of the tree using table information showing the relationship between tree height, crown area, and volume.

[0021] By using the table information, the volume can be calculated based on the correlation between tree height, crown area and volume.

[0022] [Item 8] 7. The tree analysis system according to item 6, wherein the processing device calculates the volume of the tree using an estimation model that takes at least tree height and crown area as input and takes volume as output.

[0023] By using an estimation model generated through machine learning, it is possible to calculate tree volume based on the correlation between tree height, crown area, and volume.

[0024] [Item 9] Item 9. The tree analysis system according to item 8, wherein the estimation model is an estimation model that further inputs one or more of the tree species, the topography of the area where the forest measurements were performed, and the region where the forest measurements were performed.

[0025] This allows for more accurate calculation of the volume of timber.

[0026] [Item 10] The processing device includes: determining a plurality of points representing the trunk of the tree based on the continuity of the plurality of points represented by the three-dimensional point cloud data along a height direction; 10. A tree analysis system according to any one of items 1 to 9, which determines the position of the tree based on a plurality of points indicating the trunk of the tree.

[0027] This allows the location of the trunk and the location of the tree to be determined using low-density 3D point cloud data.

[0028] [Item 11] The processing device includes: determining a position where the continuity along the height direction of a plurality of points including the point indicating the trunk of the tree is lost as the position of the tree apex; Item 11. The tree analysis system according to item 10, wherein the height of the tree is determined based on the position of the tree apex.

[0029] This allows tree height to be determined using low-density 3D point cloud data.

[0030] [Item 12] The processing device includes: determining the position of the highest point among a plurality of points located within a predetermined range from the position of the tree as the position of the tree apex; Item 11. The tree analysis system according to item 10, wherein the height of the tree is determined based on the position of the tree apex.

[0031] This allows tree height to be determined using low-density 3D point cloud data.

[0032] [Item 13] The processing device includes: extracting a plurality of points highly related to the plurality of points at the tree positions based on predetermined rules; 13. The tree analysis system according to any one of items 1 to 12, wherein the tree crown portion is determined based on the extracted plurality of points.

[0033] This allows the crown portion of the tree to be determined from its position.

[0034] [Item 14] 14. The tree analysis system according to any one of items 1 to 13, wherein the processing device calculates the number of trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the multiple points indicated by the three-dimensional point cloud data.

[0035] This allows the number of trees to be calculated with higher accuracy than a method of calculating the number of trees using data on the crown of the forest measured from above.

[0036] [Item 15] 15. The tree analysis system according to any one of items 1 to 14, wherein the processing device calculates the crown area as the crown area when the crown portion is projected onto a predetermined plane.

[0037] This allows the crown area to be calculated based on the crown portion.

[0038] [Item 16] Item 16. The tree analysis system according to item 15, wherein the predetermined plane is a plane that is approximately perpendicular to the height direction.

[0039] This makes it possible to calculate the area of ​​the tree crown when the tree crown portion is projected onto a substantially horizontal plane.

[0040] [Item 17] 15. The tree analysis system according to any one of items 1 to 14, wherein the processing device calculates the crown area based on a distribution in three-dimensional space of a plurality of points corresponding to the crown portion in the three-dimensional point cloud data.

[0041] This allows the crown area to be calculated based on the crown portion.

[0042] [Item 18] Item 18. The tree analysis system according to item 17, wherein the processing device approximates a collection of multiple points corresponding to the tree crown portion distributed in the three-dimensional space to a predetermined shape, and calculates the tree crown area based on the predetermined shape.

[0043] This makes it possible to calculate the tree crown area based on multiple points corresponding to tree crown portions distributed in three-dimensional space.

[0044] [Item 19] 19. A tree analysis system according to any one of items 1 to 18, wherein the three-dimensional point cloud data is three-dimensional point cloud data obtained by forest measurement performed by flying an unmanned aerial vehicle equipped with a LiDAR sensor.

[0045] This makes it possible to obtain 3D point cloud data that includes not only tree crown point clouds but also trunk point clouds and ground surface point clouds.

[0046] [Item 20] A computer-implemented tree analysis method for analyzing the state of trees using three-dimensional point cloud data obtained by forest measurement, comprising: The tree analysis method includes: determining the positions and heights of the trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data; determining a crown portion of the tree based on the location of the tree; calculating a crown area based on the crown portion; calculating the state of the tree based on the tree height and the crown area; A tree analysis method, including:

[0047] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0048] According to one embodiment of the present invention, the positions and heights of trees are determined based on the continuity of multiple points indicated by the 3D point cloud data along the height direction, the crown areas are determined based on the tree positions, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data, thereby reducing the time and cost required for forest measurement and data analysis.

[0049] [Item 21] A computer program that causes a computer to analyze the state of trees using 3D point cloud data obtained by forest measurement, The computer program comprises: determining the positions and heights of the trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data; determining a crown portion of the tree based on the location of the tree; calculating a crown area based on the crown portion; calculating the state of the tree based on the tree height and the crown area; A computer program that causes the computer to execute the above.

[0050] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0051] According to one embodiment of the present invention, the positions and heights of trees are determined based on the continuity of multiple points indicated by the 3D point cloud data along the height direction, the crown areas are determined based on the tree positions, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data, thereby reducing the time and cost required for forest measurement and data analysis. [Effects of the Invention]

[0052] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0053] According to one embodiment of the present invention, the positions and heights of trees are determined based on the continuity of multiple points indicated by the 3D point cloud data along the height direction, the crown areas are determined based on the tree positions, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data, thereby reducing the time and cost required for forest measurement and data analysis. [Brief explanation of the drawings]

[0054] [Figure 1] 1 is a diagram showing a state of measurement of a measurement target area 50 from which three-dimensional point cloud data is to be acquired according to an embodiment of the present invention. [Figure 2] 1 is a side view of an unmanned helicopter 1 according to an embodiment of the present invention. [Figure 3] 1 is a front view of an unmanned helicopter 1 according to an embodiment of the present invention. [Figure 4] 1 is a diagram illustrating an example of the hardware configuration of a flight control box 15 of an unmanned helicopter 1 according to an embodiment of the present invention. [Figure 5] FIG. 1 is a diagram showing a state of forest measurement for acquiring three-dimensional point cloud data according to an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram illustrating another example of a LiDAR sensor 20 according to an embodiment of the present invention. [Figure 7] 1 is a diagram showing an example of a forest measurement system 100 including an unmanned helicopter 1 and a terminal device 110 according to an embodiment of the present invention. [Figure 8] FIG. 2 is a diagram illustrating another example of the terminal device 110 according to the embodiment of the present invention. [Figure 9] 2 is a block diagram showing an example of the hardware configuration of a terminal device 110 according to the embodiment of the present invention. FIG. [Figure 10] 10 is a flowchart showing an example of a process for setting flight conditions for flying the unmanned helicopter 1 according to the embodiment of the present invention to perform forest measurement. [Figure 11] 1 is a diagram showing an example of a measurement target area 50 displayed on a display device 117 according to an embodiment of the present invention. [Figure 12] FIG. 2 is a diagram showing an example of a plot area 50a according to an embodiment of the present invention. [Figure 13] FIG. 1 is a diagram showing an example of a flight path 60 set by a processing device 111 according to an embodiment of the present invention. [Figure 14] 1 is an enlarged view of a portion of a flight path 60 set by a processing device 111 according to an embodiment of the present invention. [Figure 15] 3 is a flowchart showing an example of flight operations of the unmanned helicopter 1 according to the embodiment of the present invention. [Figure 16] FIG. 10 is a diagram showing another example of a flight path 60 set by the processing device 111 according to an embodiment of the present invention. [Figure 17] FIG. 10 is a diagram showing yet another example of a flight path 60 set by the processing device 111 according to an embodiment of the present invention. [Figure 18] 10 is a flowchart illustrating an example of processing for analyzing the state of a tree using three-dimensional point cloud data 120 according to an embodiment of the present invention. [Figure 19] FIG. 2 is a diagram schematically illustrating an example of three-dimensional point cloud data 120 according to the embodiment of the present invention. [Figure 20] FIG. 10 is a diagram illustrating an example of processing for determining the position of a tree in a space where a plurality of points indicated by three-dimensional point cloud data 120 are distributed according to an embodiment of the present invention. [Figure 21] FIG. 10 is a diagram illustrating an example of a process for determining a tree vertex according to an embodiment of the present invention. [Figure 22] FIG. 10 is a diagram illustrating another example of a process for determining a tree vertex according to an embodiment of the present invention. [Figure 23] FIG. 10 is a diagram illustrating an example of a process for determining a tree crown portion according to an embodiment of the present invention. [Figure 24] FIG. 10 is a diagram showing an example of a plurality of points 123 projected onto a predetermined plane according to an embodiment of the present invention. [Figure 25] 10 is a flowchart showing another example of processing for analyzing the state of a tree using the three-dimensional point cloud data 120 according to the embodiment of the present invention. [Figure 26] FIG. 10 is a diagram illustrating an example of a process for calculating the number of trees indicated by the three-dimensional point cloud data 120 according to the embodiment of the present invention. [Figure 27] FIG. 10 is a diagram showing another example of a process for calculating a tree crown area according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] In order to manage and utilize forest resources, it is important to conduct what is known as "forest measurement." This "forest measurement" can include surveying forest structure, estimating forest volume, and understanding the amount of change in the forest over a certain period of time. Conventional forest measurement, which involves measuring each tree individually and then compiling and analyzing the resulting data, is extremely labor-intensive and time-consuming. Therefore, efforts are being made to install laser ranging devices (LiDAR sensors) on unmanned aerial vehicles and use LiDAR sensors to conduct forest measurement from the air.

[0056] The inventors of the present application discovered that by flying an unmanned aerial vehicle equipped with a LiDAR sensor at a relatively low altitude (for example, an absolute altitude of 150 m or less, preferably an absolute altitude of 80 m or less) and performing forest measurements, it is possible to obtain 3D point cloud data including not only tree crown point clouds but also trunk point clouds, understory vegetation point clouds, and ground surface point clouds. Here, the tree crown point cloud is a point cloud corresponding to the tree crowns, the trunk point cloud is a point cloud corresponding to the tree trunks, the understory vegetation point cloud is a point cloud corresponding to the understory vegetation, and the ground surface point cloud is a point cloud corresponding to the ground surface. Laser pulses emitted from the LiDAR sensor reach and are reflected not only by the tree crowns but also by the tree trunks, understory vegetation, and ground surface, etc., thereby obtaining 3D point cloud data including the tree crown point cloud, trunk point cloud, understory vegetation point cloud, and ground surface point cloud.

[0057] Using these 3D point cloud data, including crown point clouds, trunk point clouds, understory vegetation point clouds, and ground surface point clouds, it is possible to estimate the condition of the forest and the condition of each tree.

[0058] When each of the multiple points included in the 3D point cloud data is projected onto a horizontal plane, the density of the multiple points on the horizontal plane is, for example, 100 points / m 2 By increasing the density of the point cloud, it is possible to increase the amount of information that can be obtained from positions below the canopy, allowing for more detailed estimation of the forest condition.

[0059] In this specification, "forest measurement" includes the act of scanning a forest from the air using a LiDAR sensor and acquiring scan data. The scan data is typically expressed as the position coordinates of each point constituting a point cloud acquired for each scan. The position coordinates of the points acquired for each scan are defined in a local coordinate system that moves with the unmanned aerial vehicle. Such a local coordinate system may be referred to as a mobile coordinate system or a sensor coordinate system. Generally, "forest measurement" includes converting the positions of each reflection point expressed in the local coordinate system into a geographic coordinate system. "Forest measurement" may further include, after conversion to the geographic coordinate system, analyzing the forest structure, visually displaying the shape of the forest and trees, determining the abundance ratio of each tree species in the forest, and determining the forest volume density.

[0060] An "unmanned aerial vehicle" (UAV) is an aircraft without a human pilot, sometimes called a drone. Aircraft can include rotary-wing and fixed-wing aircraft. An example of an unmanned aerial vehicle with rotors is an unmanned helicopter or an unmanned multicopter. The rotors may be rotated by an internal combustion engine or an electric motor. The flight of an unmanned aerial vehicle may be autonomous, partially automated, or remotely controlled by a human using a radio. Unmanned aerial vehicles can measure their current position in three dimensions using a Global Navigation Satellite System (GNSS) and correct that position as they fly. In the exemplary embodiment described below, the "unmanned aerial vehicle" is an "unmanned helicopter." The term "unmanned" means that no human pilot is required to operate the aircraft, and does not exclude unmanned aerial vehicles from carrying non-pilots.

[0061] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention will not be described in more detail than necessary. For example, detailed descriptions of well-known matters and redundant descriptions of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Furthermore, the following embodiments are merely examples, and the present invention is not limited to the following embodiments.

[0062] (Measurement of the measurement area) First, an example of measurement of a measurement target area will be described.

[0063] 1 is a diagram showing how a measurement target area 50, from which 3D point cloud data is to be acquired, is measured. In an exemplary embodiment, the measurement target area 50 is a forest 54 spreading over a mountain slope 52, and forest measurement is performed using an unmanned helicopter 1. In the forest 54, multiple trees rise from the ground surface. The forest 54 as the measurement target area 50 is not limited to a forest on a slope (slope forest), but may also be a forest on flat land.

[0064] Fig. 2 is a side view of the unmanned helicopter 1 provided with a LiDAR sensor (lidar sensor) 20. Fig. 3 is a front view of the unmanned helicopter 1.

[0065] The LiDAR sensor 20 emits laser beam pulses (hereinafter abbreviated as "laser pulses") 22 one after another while changing the emission direction, and can measure the distance to each reflection point from the time difference between the emission time and the acquisition time of the reflected pulse of each laser pulse. The "reflection points" can be the crowns and trunks of each tree that makes up the forest 54, or the ground surface such as slopes and flat areas.

[0066] The LiDAR sensor 20 can measure the distance from the aircraft to the forest using any method. Measurement methods for the LiDAR sensor 20 include, for example, mechanical rotation, MEMS, and phased array methods. These measurement methods differ in the way they emit laser pulses (scanning methods). For example, a mechanical rotation LiDAR sensor rotates a cylindrical head that emits laser pulses and detects the reflected light of the laser pulses to scan the measurement target in all directions 360 degrees around the rotation axis. A MEMS LiDAR sensor uses a MEMS mirror to oscillate the emission direction of the laser pulses and scan the measurement target within a predetermined angular range centered on the oscillation axis. A phased array LiDAR sensor controls the phase of light to oscillate the emission direction of light and scan the measurement target within a predetermined angular range centered on the oscillation axis.

[0067] The unmanned helicopter 1 comprises an airframe 4 having a main body 2 and a tail body 3. A LiDAR sensor 20 is attached to the bottom of the airframe 4 via a bracket 25. A main rotor 5 is provided on top of the main body 2, and a tail rotor 6 is provided at the rear of the tail body 3. A radiator 7 is provided at the front of the main body 2. A flight control box 15 is provided at the rear of the main body 2. An engine 8, which is an internal combustion engine, and a power generator 9 are provided within the main body 2. Also housed within the main body 2 are an intake system, main rotor shaft, and fuel tank, all of which are not shown. The rotation generated by the engine 8 is transmitted to the main rotor 5 and tail rotor 6, and the rotation of the main rotor 5 and tail rotor 6 causes the unmanned helicopter 1 to fly.

[0068] A control panel 10 is provided on the upper rear side of the main body 2, and indicator lights 11 are provided on the lower rear side. The control panel 10 displays pre-flight checkpoints, self-check results, etc. The displays on the control panel 10 can also be confirmed at the ground station. The indicator lights 11 display the GNSS control status and aircraft abnormality warnings, etc. Skids 12, which are legs that support the aircraft 4 during landing, are provided on the lower center side of the main body 2.

[0069] FIG. 4 shows an example of the hardware configuration of the flight control box (control device) 15. The flight control box 15 houses a positioning module 15a, an acceleration sensor 15b, a barometric pressure sensor 15c, a geomagnetic sensor 15d, an ultrasonic sensor 15e, a communication device 15f, a processing device 15g, and a storage device 15j. The storage device 15j includes a read-only memory (ROM) 15h, a random access memory (RAM) 15i, and the like. The components can transmit and receive data to and from each other via, for example, wiring or an internal bus 15k. Note that the various sensors, including the positioning module 15a, do not necessarily need to be located inside the flight control box 15. For example, the positioning module 15a may be located on the top of the tail body 3 to make it easier to acquire signals from GNSS satellites.

[0070] The positioning module 15a receives GNSS signals transmitted from GNSS satellites and performs positioning based on the GNSS signals. The positioning module 15a outputs position data indicating the geographic coordinates of the current position of the unmanned helicopter 1. GNSS is a general term for satellite positioning systems such as GPS (Global Positioning System), QZSS (Quasi-Zenith Satellite System, e.g., Michibiki), GLONASS, Galileo, and BeiDou. Any positioning method that can obtain position information with the required accuracy may be adopted as the positioning method. For example, an interferometric positioning method or a relative positioning method may be adopted as the positioning method. The number of positioning modules 15a may be one or more (e.g., two).

[0071] The acceleration sensor 15b is a triaxial acceleration sensor that detects acceleration in the X-, Y-, and Z-axes. If the acceleration sensor 15b is a six-axis acceleration sensor, it can also detect the roll acceleration, pitch angular velocity, and yaw acceleration of the unmanned helicopter 1. The acceleration sensor 15b may include multiple uniaxial or biaxial acceleration sensors that detect acceleration in the X, Y, and Z directions. The barometric pressure sensor 15c detects barometric pressure. The current altitude can be determined from the detected barometric pressure. Since the relationship between barometric pressure and altitude is publicly known, its explanation is omitted here. The geomagnetic sensor 15d detects the current orientation of the unmanned helicopter 1. The ultrasonic sensor 15e is used to detect absolute altitude during low-altitude flight. The current attitude of the unmanned helicopter 1 can be determined by using the data (aircraft data) output from the acceleration sensor 15b and the geomagnetic sensor 15d. The flight data and aircraft data are provided to the processing device 15g.

[0072] The communication device 15f has a communication circuit that performs wireless communication in accordance with the Bluetooth (registered trademark) and / or Wi-Fi (registered trademark) standards. The communication device 15f may also perform wireless communication using a mobile phone line or a line via an artificial satellite. The communication device 15f receives flight route data before flight and performs necessary communication with the ground via wireless during flight. The flight route data includes the coordinates of the route that the unmanned helicopter 1 should fly and absolute altitude data.

[0073] The processing device 15g may be, for example, a semiconductor integrated circuit including a central processing unit (CPU). The processing device 15g may be realized by a microprocessor or a microcontroller. Alternatively, the processing device 15g may be realized by a field programmable gate array (FPGA) equipped with a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), or a combination of two or more circuits selected from these circuits.

[0074] The storage device 15j stores a computer program that controls the operation of the processing device 15g. The storage device 15j may store a computer program that causes the processing device 15g to control the flight of the unmanned helicopter 1 and the forest measurement. Such a computer program may be provided to the unmanned helicopter 1 via a storage medium (e.g., a semiconductor memory or an optical disk) or a telecommunications line (e.g., the Internet). The computer program may also be provided to the unmanned helicopter 1 via wireless communication. Such a computer program may be sold as commercial software.

[0075] The processing device 15g executes a control program stored in the storage device 15j to fly the unmanned helicopter 1. More specifically, the processing device 15g flies the unmanned helicopter 1 along a flight path prepared in advance while monitoring the above-mentioned flight data, aircraft data, and operating condition data such as engine RPM and throttle opening.

[0076] 4, the flight control box 15 is connected to the LiDAR sensor 20. The LiDAR sensor 20 outputs scan results (a set of time data, direction data, distance data, etc.) to the flight control box 15. Using the geographic coordinate data indicating the flight position of the unmanned helicopter 1 output from the positioning module 15a and the scan results output from the LiDAR sensor 20, the position of the measurement target expressed in, for example, a geographic coordinate system can be calculated.

[0077] The LiDAR sensor 20 does not have to be connected to the flight control box 15. In this case, the scan results of the LiDAR sensor 20 may be stored in a storage device within the LiDAR sensor 20. The scan results of the LiDAR sensor 20 may be output from the LiDAR sensor 20 to the outside via wireless communication. The flight control box 15 and the LiDAR sensor 20 may share a power source.

[0078] Alternatively, the positioning module 15a may be provided in the unmanned helicopter 1 independently of the flight control box 15, and the positioning module 15a may output position data to each of the flight control box 15 and the LiDAR sensor 20. In this case, the scan results of the LiDAR sensor 20 may be stored in a storage device within the LiDAR sensor 20. Furthermore, when a processor within the LiDAR sensor 20 calculates the position of the measurement object using the position data output by the positioning module 15a and the scan results of the LiDAR sensor 20, the calculation results may be stored in a storage device within the LiDAR sensor 20.

[0079] An operator who manages the flight and operation of the unmanned helicopter 1 may fly the unmanned helicopter 1 along a pre-prepared flight path while visually monitoring the flight conditions. A remote control receiving antenna 13 that receives command signals from a remote control device is provided at the rear end of the tail body 3 (Fig. 2).

[0080] 2 and 3, the LiDAR sensor 20 is an optical device that emits (radiates), for example, near-infrared laser pulses 22 and detects reflected light of the laser pulses 22 to measure the distance to a reflection point.

[0081] In the exemplary embodiment, the LiDAR sensor 20 is a mechanically rotating type, and a head 23 that emits laser pulses 22 and detects reflected light of the laser pulses 22 rotates around a rotation axis 21. The rotation of the head 23 enables scanning in all directions of 360 degrees. In this embodiment, the range of the scannable range of the LiDAR sensor 20 that is blocked by the body 4 of the unmanned helicopter 1, etc., is not reflected in the measurement results. For convenience of description, FIG. 3 shows only a portion of the laser pulses 22 emitted in all directions of 360 degrees. In this specification, the rotation of the head 23 of the LiDAR sensor 20 may be referred to as "rotation of the LiDAR sensor 20."

[0082] The head 23 of the LiDAR sensor 20 rotates to change the direction of the emission port and simultaneously emits multiple laser pulses 22 at a predetermined angular pitch α (rad). FIG. 2 shows, as an example, N laser pulses 22 simultaneously emitted along a plane through which the rotation axis 21 of the LiDAR sensor 20 passes. For convenience of description, the laser pulses 22 are depicted as beams rather than pulses. Note that "simultaneously" does not necessarily mean exactly the same time, but also includes approximately the same time. The value of N is arbitrary and may be, for example, 12, 16, 32, or 64, but is not limited to these values. The head 23 of the LiDAR sensor 20 has, for example, N laser light sources arranged, and the laser pulses 22 are emitted from the emission ports of the N laser pulses 22. The laser light sources may be, for example, laser diodes, but are not limited to these.

[0083] With reference to Figure 3, a laser pulse 22 emitted from one particular emission port will be described. The head 23 of the LiDAR sensor 20 rotates to change the direction of the emission port, emits laser pulses 22 at a predetermined angular pitch α (rad), and detects the reflected light of each laser pulse 22 reflected by the forest. This makes it possible to obtain data on the distance to the reflection point in the direction at each predetermined angular pitch α. The predetermined angular pitch α may be a fixed value or a variable value.

[0084] The LiDAR sensor 20 may be a LiDAR sensor that oscillates the emission direction of a laser pulse as described above and scans a measurement target within a predetermined angular range centered on an oscillation axis. In this specification, the "rotation" and "rotation axis" of the LiDAR sensor 20 also include such "oscillation" and "oscillation axis." The rotation angle range when oscillating the emission direction of a laser pulse is, for example, 180 degrees or less, but is not limited thereto.

[0085] 2, the LiDAR sensor 20 is mounted so that its rotation axis 21 faces in the fore-and-aft direction of the body 4 of the unmanned helicopter 1. The direction in which the rotation axis 21 faces is arbitrary, and for example, the LiDAR sensor 20 may be mounted so that the rotation axis 21 faces in the left-right direction of the body 4 of the unmanned helicopter 1. In addition, the rotation axis of the LiDAR sensor 20 may be inclined with respect to a plane perpendicular to the height direction of the unmanned helicopter 1.

[0086] FIG. 5 illustrates a forest measurement process for acquiring 3D point cloud data. In the example shown in FIG. 5, a forest 54 spreads across a slope 52. The forest 54 spread across the slope 52 can be irradiated with laser pulses 22 not only from an obliquely upward direction but also from a lateral direction or from a diagonally downward direction. When irradiated with laser pulses 22 from a lateral direction, the laser pulses 22 can easily pass through the leaves, which generally grow horizontally to receive sunlight. The laser pulses 22 reach and are reflected not only by the canopies 56a of the trees 56 but also by the trunks 56b of the trees 56, understory vegetation 58, and the ground surface 59. This allows for the acquisition of 3D point cloud data including not only the canopy point cloud but also the trunk point cloud, understory vegetation point cloud, and ground surface point cloud.

[0087] The LiDAR sensor 20 may be a LiDAR sensor with one scan line that emits one laser pulse 22 at a predetermined angular pitch α.

[0088] 6 illustrates a LiDAR sensor 20 with one scan line that emits one laser pulse 22 at every predetermined angular pitch α. By using a LiDAR sensor that emits one laser pulse 22 at every predetermined angular pitch α, rather than a LiDAR sensor that simultaneously emits multiple laser pulses 22 at every predetermined angular pitch α, the output power per laser pulse 22 can be increased, thereby increasing the measurable distance.

[0089] By not emitting multiple laser pulses 22 simultaneously, it is possible to obtain highly accurate point cloud data because reflected light from one laser pulse 22 does not become noise in the detection of reflected light from another laser pulse 22. When multiple laser pulses 22 are emitted simultaneously, variations in intensity may occur among the multiple laser pulses 22. In this embodiment, by not emitting multiple laser pulses 22 simultaneously, such variations do not affect the detection results, and it is possible to obtain highly accurate point cloud data.

[0090] In the example shown in Fig. 6, the rotation axis 21 of the LiDAR sensor 20 is tilted relative to the body of the unmanned helicopter 1. A height direction 36 of the unmanned helicopter 1 is a direction parallel to the vertical direction when the unmanned helicopter 1 is stationary on horizontal ground. A plane 37 perpendicular to the height direction 36 is indicated by a dotted line. The rotation axis 21 of the LiDAR sensor 20 of this embodiment is tilted relative to the plane 37. The plane 37, which is the reference for the angle of the rotation axis 21, is parallel to the horizontal ground (horizontal plane).

[0091] The angle θ1 between the plane 37 and the rotation axis 21 is, for example, 20 to 60 degrees, and the rotation axis 21 is inclined at an angle of 20 to 60 degrees relative to the plane 37. As another example, the angle θ1 is, for example, 25 to 50 degrees, and the rotation axis 21 is inclined at an angle of 25 to 50 degrees relative to the plane 37.

[0092] By tilting the rotation axis 21 of the LiDAR sensor 20 relative to the plane 37, it is possible to emit the laser pulses 22 at an angle that allows most of the laser pulses 22 to easily pass between the leaves of the trees when performing forest measurements. As a result, the laser pulses 22 can be incident on the interior of the tree canopy. Furthermore, the laser pulses 22 reach and are reflected not only by the tree canopy, but also by the tree trunks, understory vegetation, and the ground surface. This makes it possible to obtain 3D point cloud data that includes a wealth of tree canopy point clouds, trunk point clouds, understory vegetation point clouds, and ground surface point clouds.

[0093] When a LiDAR sensor 20 with a single scan line is used, if the rotation axis 21 of the LiDAR sensor 20 is parallel to the plane 37, the trajectory of the laser spot formed on the surface of the trunk will extend roughly along the direction in which the trunk extends (up and down). This makes it difficult for the trajectory of the laser spot to cross the trunk, making it difficult to accurately estimate the trunk diameter from the obtained point cloud data. On the other hand, the rotation axis 21 of the LiDAR sensor 20 of this embodiment is tilted with respect to the plane 37. This makes it easier for the trajectory of the laser spot to cross the trunk, making it easier to accurately estimate the trunk diameter from the obtained point cloud data.

[0094] In the example shown in Figure 6, the LiDAR sensor 20 is installed at an angle where many laser pulses 22 are irradiated onto an area extending forward of the unmanned helicopter 1, but the LiDAR sensor 20 may also be installed at an angle where many laser pulses 22 are irradiated onto an area extending rearward of the unmanned helicopter 1.

[0095] (Flight Condition Decision System) Next, a flight condition determination system for determining flight conditions when flying the unmanned helicopter 1 in forest measurement will be described.

[0096] The unmanned helicopter 1 is programmed to fly according to set flight conditions. A user can set the flight conditions using, for example, a terminal device. The terminal device is a computer device used by the user. The flight condition determination system of this embodiment includes such a terminal device.

[0097] FIG. 7 shows an example of a forest measurement system 100 including an unmanned helicopter 1 and a terminal device 110. The terminal device 110 is, for example, a mobile terminal such as a smartphone or a tablet computer, or a laptop computer. The terminal device 110 may also be a stationary computer such as a desktop PC (Personal Computer). FIG. 8 shows another example of the terminal device 110. In the example shown in FIG. 8, the terminal device 110 is a base station control device.

[0098] The terminal device 110 operates as a flight condition determination system. The terminal device 110 has a built-in or external storage device, and stores map data 90 in the storage device. The terminal device 110 displays a measurement area 50 for forest measurement on a display device based on the map data 90, and determines flight conditions.

[0099] Fig. 9 is a block diagram showing an example of the hardware configuration of the terminal device 110. The terminal device 110 shown in Fig. 9 includes a processing device 111, a storage device 115, a communication device 116, a display device 117, and an input device 118. These components are connected to each other via a bus so that they can communicate with each other. The processing device 111 includes a processor 112, a ROM (Read Only Memory) 113, and a RAM (Random Access Memory) 114.

[0100] The processor 112 may be, for example, a semiconductor integrated circuit including a central processing unit (CPU). The processor 112 may be implemented by a microprocessor or a microcontroller. Alternatively, the processor 112 may be implemented by a field programmable gate array (FPGA) equipped with a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), or a combination of two or more circuits selected from these circuits. The processor 112 sequentially executes a computer program stored in the ROM 113, which describes a group of instructions for executing one or more processes, to achieve the desired processing.

[0101] The ROM 113 is, for example, a writable memory (e.g., a PROM), a rewritable memory (e.g., a flash memory), or a read-only memory. The ROM 113 stores a program that controls the operation of the processor 112. The RAM 114 provides a working area for temporarily loading the control program stored in the ROM 113 at boot time.

[0102] The storage device 115 includes one or more storage media such as a flash memory or a magnetic disk. The storage device 115 is, for example, a semiconductor storage device, a magnetic storage device, an optical storage device, or a combination thereof. An example of a semiconductor storage device is a solid-state drive (SSD). An example of a magnetic storage device is a hard disk drive (HDD). An example of an optical storage device is an optical disk drive or a magneto-optical disk (MD) drive. The storage device 115 stores various data such as 3D point cloud data and image data generated using the 3D point cloud data. The data stored in the storage device 115 may include map data of a predetermined environment measured using the unmanned helicopter 1 equipped with the LiDAR sensor 20.

[0103] The storage device 115 may store computer programs that cause the processor 112 to perform various operations. Such computer programs may be provided to the terminal device 110 via a storage medium (e.g., a semiconductor memory or an optical disk) or a telecommunications line (e.g., the Internet). Such computer programs may be sold as commercial software.

[0104] The communication device 116 is a communication module for performing data communication with external devices and the unmanned helicopter 1, etc. The communication device 116 can perform wired communication and / or wireless communication. The communication device 116 can perform wired communication in accordance with communication standards such as USB, IEEE1394 (registered trademark), or Ethernet (registered trademark). The communication device 116 can perform wireless communication in accordance with the Bluetooth (registered trademark) standard and / or the Wi-Fi (registered trademark) standard. The communication device 116 may be a communication module capable of performing wireless communication in accordance with a communication method such as Bluetooth Low Energy (BLE) or Low Power Wide Area (LPWA). By using a communication method such as BLE or LPWA, long-distance, wide-area communication can be achieved with low power consumption. The communication device 116 may perform wireless communication using a mobile phone line or a line via an artificial satellite.

[0105] The display device 117 may be, for example, a liquid crystal display or an OLED (Organic Light-Emitting Diode) display. The display device 117 may display a map of a predetermined environment such as the forest 54. The input device 118 is a device for converting instructions from a user into data and inputting the data into the computer. The input device 118 may be, for example, a keyboard, a mouse, a touch panel, a microphone, or a combination thereof.

[0106] Next, a process for setting flight conditions for flying the unmanned helicopter 1 to perform forest measurement will be described.

[0107] In forest measurement, if a large number of laser pulses 22 are irradiated over the entire measurement area 50, the time and cost required to fly the unmanned helicopter 1 increases, and the amount of 3D point cloud data obtained becomes enormous. If the amount of data is large, data analysis also requires a lot of time and cost.

[0108] In this embodiment, a plot area is set in a part of the measurement target area 50. The plot area is an area where measurements are performed in relatively more detail compared to other areas of the measurement target area 50. For example, by performing measurements with a large total number of laser pulses 22 irradiated per unit area only in the plot area, it is possible to reduce the time and cost required for forest measurement and data analysis.

[0109] By measuring the plot area in detail, the condition of the trees in the plot area can be analyzed with high accuracy. Based on the analysis results of the trees in the plot area, the condition of the trees in areas other than the plot area can be estimated with high accuracy.

[0110] FIG. 10 is a flowchart showing an example of a process for setting flight conditions for flying the unmanned helicopter 1 to perform forest measurement.

[0111] The processing device 111 acquires map data of the measurement target area 50 and displays the map of the measurement target area 50 on the display device 117 (step S10). The processing device 111 reads, from the storage device 115, map data corresponding to the measurement target area 50 selected by the user operating the input device 118, and displays the map of the measurement target area 50 on the display device 117, for example.

[0112] 11 is a diagram showing an example of a measurement target area 50 displayed on the display device 117. In this example, the measurement target area 50 is a forest. Dashed lines 62 represent contour lines.

[0113] The processing device 111 receives a plot area designation from the user (step S11). For example, the user uses the input device 118, such as a touch panel or a mouse, to enclose a part of the measurement target area 50 displayed on the display device 117. The processing device 111 sets the enclosed area as the plot area. Also, for example, the processing device 111 sets an area of ​​a predetermined size that includes the position where the user has marked a point on the measurement target area 50 using the input device 118 as the plot area. The plot area may be automatically set by the processing device 111 based on a preset rule.

[0114] FIG. 12 is a diagram showing an example of a set plot area 50a. The area surrounded by a dashed line in FIG. 12 is the plot area 50a. The number of plot areas 50a to be set is arbitrary, and one or more plot areas 50a may be set. In the example shown in FIG. 12, two plot areas 50a are set. In the example shown in FIG. 12, the shape of the plot area 50a is rectangular, but the shape of the plot area 50a is not limited to this. For example, the shape of the plot area 50a may be circular.

[0115] The processing device 111 sets flight conditions for the measurement target area 50 in which the plot area 50a is set (step S12). The processing device 111 sets the flight conditions so that the total number of laser pulses 22 irradiated per unit area differs between the plot area 50a and an outside-plot area, which is an area of ​​the measurement target area 50 other than the plot area 50a. For example, the flight conditions are set so that the average value of the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a differs from the average value of the total number of laser pulses 22 irradiated per unit area of ​​the outside-plot area. For example, the processing device 111 sets flight conditions so that the way the unmanned helicopter 1 is flown in the plot area 50a differs from the way the unmanned helicopter 1 is flown in the outside-plot area. For example, the processing device 111 sets a flight path for the measurement target area 50 to achieve such flight conditions.

[0116] Fig. 13 is a diagram showing an example of a flight path 60 set by the processing device 111. Fig. 14 is a diagram showing an enlarged view of a part of the flight path 60 set by the processing device 111.

[0117] The processing device 111 sets the flight path 60 so that the total number of laser pulses 22 irradiated per unit area in the plot area 50a is greater than that in the outside-plot area 50b. For example, the flight path 60 is set so that the average total number of laser pulses 22 irradiated per unit area in the plot area 50a is greater than that in the outside-plot area 50b. In the example shown in FIGS. 13 and 14 , the flight path 60 is set so that the frequency with which the unmanned helicopter 1 makes round trips over the plot area 50a is greater than the frequency with which the unmanned helicopter 1 makes round trips over the outside-plot area 50b. More specifically, the flight path 60 includes a plurality of path segments, and the flight path 60 is set so that the interval W1 between adjacent path segments over the plot area 50a is smaller than the interval W2 between adjacent path segments over the outside-plot area 50b. Here, each path segment is a portion of the flight path 60. The size of the interval W1 is, for example, ½ to 1 / 10 of the interval W2, but is not limited thereto. As an example, the flight path 60 is set so that the interval W1 is 5 m and the interval W2 is 25 m.

[0118] As the unmanned helicopter 1 travels back and forth through the measurement target area 50, laser pulses 22 may be irradiated onto the same area on both the outbound flight and the return flight. By reducing the interval W1 between adjacent path segments in the plot area 50a, the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a can be increased. Increasing the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a makes it possible to obtain high-density 3D point cloud data for the plot area 50a.

[0119] The processing device 111 outputs information indicating the flight path 60 to an external device. For example, the processing device 111 outputs the information indicating the flight path 60 to the unmanned helicopter 1. The processing device 15g of the unmanned helicopter 1 stores the received information indicating the flight path 60 in, for example, the storage device 15j. The unmanned helicopter 1 performs forest measurement while flying based on the information indicating the flight path 60.

[0120] 15 is a flowchart showing an example of the flight operation of the unmanned helicopter 1. The flight operation shown in FIG.

[0121] In step S20, the processing device 15g of the unmanned helicopter 1 flies the unmanned helicopter 1 to the start position of the forest measurement while acquiring information about its own position using the positioning module 15a, etc. Note that the "start position" here includes the absolute altitude as well as the longitude and latitude of the position on the flight path 60 where forest measurement starts. When the unmanned helicopter 1 reaches the start position of the forest measurement, in step S21, the processing device 15g starts forest measurement using the LiDAR sensor 20.

[0122] In step S22, the processing device 15g causes the unmanned helicopter 1 to fly along the set flight path 60.

[0123] In step S23, the processing device 15g determines whether the forest measurement has been completed. Specifically, the processing device 15g determines whether the flight along the preset flight path 60 has been completed. When the forest measurement has been completed, the unmanned helicopter 1 returns, for example, by autonomous flight.

[0124] High-density 3D point cloud data can be generated by combining the sensor data output from the LiDAR sensor 20 while flying each of the multiple route segments included in the flight path 60. The process of generating high-density 3D point cloud data may be executed by the terminal device 110 or any other computer.

[0125] In this embodiment, by increasing the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a, high-density 3D point cloud data of the plot area 50a can be obtained, which allows the state of the trees in the plot area 50a to be analyzed with high accuracy.

[0126] By reducing the total number of laser pulses 22 irradiated per unit area of ​​the outside-plot area 50b, it is possible to reduce the time and cost required for forest measurement and data analysis. Since the condition of the trees in the plot area 50a can be analyzed with high accuracy, the condition of the trees in the outside-plot area 50b can be estimated with high accuracy based on the analysis results of the trees in the plot area 50a.

[0127] 13 and 14, the processing device 111 may set a flight path 60 so that, when the unmanned helicopter 1 makes a round trip over the plot area 50a, the unmanned helicopter 1 moves from above the plot area 50a to above the outside-plot area 50b and then returns to above the plot area 50a. This allows sufficient measurement of the edge of the plot area 50a even if the flight attitude of the unmanned helicopter 1 becomes unstable. The unmanned helicopter 1 does not have to fly from above the plot area 50a to above the outside-plot area 50b and then return to above the plot area 50a on every round trip, and a flight path 60 may be set so that the unmanned helicopter 1 does so at least once.

[0128] As a method for increasing the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a, the processing device 111 may set the flight altitude so that the absolute altitude at which the unmanned helicopter 1 flies above the plot area 50a is lower than the absolute altitude at which it flies above the outside-plot area 50b. By lowering the absolute altitude at which the unmanned helicopter 1 flies above the plot area 50a, it is possible to increase the total number of laser pulses 22 irradiated per unit area, and also to irradiate the laser pulses 22 to the measurement object with a small degree of diffusion of the laser pulses 22.

[0129] As another method for increasing the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a, the processing device 111 may set the flight speed of the unmanned helicopter 1 so that the flight speed when flying above the plot area 50a is lower than the flight speed when flying above the outside-plot area 50b. By reducing the flight speed when the unmanned helicopter 1 flies above the plot area 50a, the total number of laser pulses 22 irradiated per unit area can be increased.

[0130] Furthermore, two or more of the above methods may be combined. The frequency with which the unmanned helicopter 1 travels back and forth over the plot area 50a is made higher than the frequency with which the unmanned helicopter 1 travels back and forth over the outside-plot area 50b. The absolute altitude of the unmanned helicopter 1 when flying over the plot area 50a is set lower than the absolute altitude when flying over the outside-plot area 50b; The flight speed of the unmanned helicopter 1 when flying over the plot area 50a is set lower than the flight speed when flying over the outside-plot area 50b; Two or more of the above settings may be performed.

[0131] Fig. 16 is a diagram showing another example of a flight path 60 set by the processing device 111. In the example shown in Fig. 16, the flight path 60 along which the unmanned helicopter 1 flies when measuring the plot area 50a includes two or more path segments that intersect with each other in a planar view seen from the vertical direction. By including path segments that intersect with each other in the flight path 60 of the plot area 50a, it is possible to increase the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a.

[0132] Fig. 17 is a diagram showing yet another example of a flight path 60 set by the processing device 111. In the example shown in Fig. 17, the flight path 60 along which the unmanned helicopter 1 flies when measuring the plot area 50a includes a spiral flight path. By including the spiral flight path 60 of the plot area 50a, it is possible to increase the total number of laser pulses 22 irradiated per unit area of ​​the plot area 50a.

[0133] (Tree analysis system) Next, we will explain a tree analysis system that analyzes the condition of trees using 3D point cloud data obtained by forest measurement.

[0134] As described above, three-dimensional point cloud data is obtained by forest measurement performed by flying the unmanned helicopter 1 equipped with the LiDAR sensor 20. The obtained three-dimensional point cloud data is stored, for example, in the storage device 115 of the terminal device 110. The three-dimensional point cloud data may also be stored in a server.

[0135] The terminal device 110 operates as a tree analysis system that analyzes the state of trees using three-dimensional point cloud data. Alternatively, a server may operate as the tree analysis system. An example in which the terminal device 110 operates as the tree analysis system will be described below.

[0136] In the process of analyzing the condition of trees, high-density 3D point cloud data can be used to calculate the condition of the trees (for example, trunk diameter and volume). For example, by using high-density 3D point cloud data that is high enough to detect the outline of the tree trunk, it is possible to calculate the diameter of the trunk. However, there is an issue with obtaining high-density 3D point cloud data, as it increases the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained becomes enormous, making data analysis time and costly.

[0137] In this embodiment, the tree positions and tree heights are determined based on the continuity along the height direction of multiple points indicated by the 3D point cloud data, the tree crown areas are determined based on the tree positions, and the tree conditions are calculated based on the tree heights and crown areas. This makes it possible to calculate the tree conditions from low-density 3D point cloud data, reducing the time and cost required for forest measurement and data analysis.

[0138] For example, the 3D point cloud data obtained by measuring the above-described off-plot area 50b may not be high-density data. In this embodiment, for example, it is possible to calculate the condition of a tree from such low-density 3D point cloud data. Note that the process for analyzing the condition of a tree in this embodiment may also be applied to the analysis of 3D point cloud data obtained by measuring the plot area 50a.

[0139] 18 is a flowchart showing an example of processing for analyzing the state of a tree using 3D point cloud data according to this embodiment. In the example shown in Fig. 18, the diameter of the trunk is calculated as the analysis of the state of the tree.

[0140] The processing device 111 of the terminal device 110 reads out the three-dimensional point cloud data from the storage device 115 or the server (step S30).

[0141] The processing device 111 generates 3D point cloud data with the ground surface flattened using the read 3D point cloud data. The processing device 111 determines the altitude of each of the multiple points included in the 3D point cloud data. The altitude is, for example, an absolute altitude, which is the height of the roots of trees from the ground surface. For example, the processing device 111 generates a DTM (Digital Terrain Model) from the 3D point cloud data and determines the absolute altitude of each of the multiple points using data obtained by subtracting the DTM from the 3D point cloud data. Note that a DSM (Digital Surface Model) and the DTM may be generated from the 3D point cloud data, and the absolute altitude of each of the multiple points may be determined using data obtained by subtracting the DTM from the DSM. Data obtained by subtracting the DTM from the DSM is called a DCHM (Digital Canopy Height Model). The 3D point cloud data with the ground surface flattened may be generated in advance and stored in the storage device 115 or a server.

[0142] Fig. 19 is a diagram showing a schematic example of 3D point cloud data 120. The ground surface where forests spread generally includes slopes. By subtracting the DTM from the 3D point cloud data, 3D point cloud data 120 in which the ground surface is flattened is obtained. Points 121 shown in Fig. 19 indicate the ground surface.

[0143] The processing device 111 determines the position of the tree in a space where a plurality of points indicated by the three-dimensional point cloud data 120 are distributed (step S31).

[0144] FIG. 20 is a diagram showing an example of processing for determining the position of a tree in a space where a plurality of points indicated by the three-dimensional point cloud data 120 are distributed. First, the processing device 111 determines a plurality of points indicating the trunk of the tree based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data 120. For example, a plurality of points that are continuously distributed along the height direction (Z direction) from the positions of a plurality of points 121 that indicate a flat ground surface are extracted. The plurality of continuously distributed points are, for example, a plurality of points where the distance between each point is equal to or less than a first predetermined distance. The size of the first predetermined distance is arbitrary and is determined in advance.

[0145] In the example shown in FIG. 20 , a plurality of points surrounded by a dashed dotted line are extracted as a plurality of points continuously distributed along the height direction. The processing device 111 determines these plurality of points continuously distributed along the height direction as a plurality of points 122 indicating the trunk of the tree. The processing device 111 can determine the position 132 of the tree based on the plurality of points 122 indicating the trunk of the tree. The tree position 132 indicates the position of the tree on, for example, an XY plane. For example, the tree position 132 indicates the position of a point corresponding to the base of the trunk. The tree position 132 may also indicate the position of the tree in three-dimensional space. For example, the tree position 132 may indicate the positions of the plurality of points 122 indicating the trunk.

[0146] Next, the processing device 111 determines the tree height (step S32). First, the processing device 111 determines the position of the tree apex.

[0147] Fig. 21 is a diagram showing an example of processing for determining the tree apex. The processing device 111 determines the position of the tree apex, which is the position surrounded by the dashed line in Fig. 20 where the continuity in the height direction of the multiple points 122 indicating the trunk is lost. In the example shown in Fig. 21, point 124, which is the highest point among the multiple points 122 indicating the trunk, becomes the point indicating the tree apex. The processing device 111 determines the position of point 124 as the position of the tree apex.

[0148] Figure 22 is a diagram showing another example of the process for determining the tree apex. In the example shown in Figure 22, the processing device 111 determines the position of the highest point among multiple points located within a predetermined range R1 from the tree position 132 as the position of the tree apex. The predetermined range R1 may be a range extending a second predetermined distance in the X and Y directions from the tree position 132 as a reference, and may also be a range extending in the height direction. The size of the second predetermined distance is arbitrary and is determined in advance. The processing device 111 determines the position of the highest point 124 among multiple points located within the predetermined range R1 as the position of the tree apex.

[0149] The processing device 111 determines the height of the tree based on the position of the tree apex. For example, the tree height is the length in the vertical direction between point 121, which indicates the ground surface, and point 124. Alternatively, the tree height may be the length between point 122, which corresponds to the base of the trunk, and point 124.

[0150] Next, the processing device 111 determines the tree crown portion (step S33). Fig. 23 is a diagram showing an example of the process of determining the tree crown portion. For example, the processing device 111 extracts, based on a predetermined rule, a plurality of points that are highly related to the plurality of points 122 and 124 at the tree position 132, as points indicating the tree crown.

[0151] For example, the processing device 111 extracts points 122 and 124 whose distances to the extracted points are equal to or less than a third predetermined distance. The processing device 111 further extracts points whose distances to the extracted points are equal to or less than the third predetermined distance. By sequentially extracting points whose distances to the extracted points are equal to or less than the third predetermined distance, a plurality of points 123 representing the tree crown are extracted. The size of the third predetermined distance is arbitrary and is determined in advance.

[0152] Points belonging to a range extending downward at a predetermined angle from point 124 indicating the tree apex as a reference, as indicated by dashed line 134 in Fig. 23, may be extracted as points 123 indicating the tree crown. Of the points belonging to the range indicated by dashed line 134 in Fig. 23, a plurality of points whose distance between the above-mentioned points is equal to or less than the third predetermined distance may be extracted as points 123 indicating the tree crown.

[0153] Next, the processing device 111 calculates the tree crown area (step S34). For example, the processing device 111 calculates the tree crown area when a plurality of points 123 indicating the tree crown are projected onto a predetermined plane. The predetermined plane is, for example, a plane approximately perpendicular to the height direction (XY plane).

[0154] 24 is a diagram showing a plurality of points 123 projected onto a predetermined plane. The processing device 111 calculates the area of ​​a range 123a in which the plurality of points 123 are distributed, for example, to obtain the value of the tree crown area.

[0155] The processing device 111 calculates the diameter of the tree trunk based on the calculated tree height and crown area (step S35). The trunk diameter calculated by the processing device 111 is, for example, but not limited to, the diameter at breast height (the diameter of the trunk at chest height of an adult).

[0156] The storage device 115 or server stores in advance table information showing the relationship between tree height, crown area, and trunk diameter. The processing device 111 can use the table information to calculate trunk diameter from tree height and crown area. By using the table information, it is possible to calculate trunk diameter based on the correlation between tree height, crown area, and trunk diameter.

[0157] The processing device 111 may calculate the trunk diameter of a tree using an estimation model that receives tree height and crown area as input and outputs trunk diameter. Such an estimation model is generated in advance by machine learning using data on tree height, crown area, and trunk diameter as training data. The estimation model is stored in advance in the storage device 115 or a server. The processing device 111 can calculate the trunk diameter using the estimation model.

[0158] In addition to data on tree height, crown area, and trunk diameter, one or more of the following may be used as training data for machine learning: tree species data, data on the topography (slope, orientation, etc.) of the area where forest measurements were performed, and data on the region where forest measurements were performed (climate, etc.). In this case, the tree trunk diameter is calculated using an estimation model that inputs one or more of tree species, topography, and region, as well as tree height and crown area, and outputs trunk diameter. This allows for more accurate calculation of trunk diameter.

[0159] In this embodiment, the positions and heights of trees are determined based on the continuity of multiple points indicated by the 3D point cloud data 120 along the height direction, and the crown areas are calculated by determining the tree positions. The condition of the trees is then calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data 120, reducing the time and cost required for forest measurement and data analysis.

[0160] In the above example, the diameter of the trunk is calculated as an analysis of the condition of the tree. As another example, the volume of the tree may be calculated as an analysis of the condition of the tree.

[0161] Fig. 25 is a flowchart showing another example of processing for analyzing the condition of trees using three-dimensional point cloud data. In the example shown in Fig. 25, the analysis of the condition of trees involves calculating the volume of the trees.

[0162] The processing of steps S30 to S34 shown in Figure 25 is the same as the processing of steps S30 to S34 shown in Figure 18. In the example shown in Figure 25, the processing device 111 calculates the timber volume based on the calculated tree height and crown area (step S35a).

[0163] The storage device 115 or server stores table information in advance that indicates the relationship between tree height, crown area, and volume. The processing device 111 can use the table information to calculate the volume from the tree height and crown area. By using the table information, it is possible to calculate the volume based on the correlation between tree height, crown area, and volume.

[0164] The processing device 111 may calculate the volume using an estimation model that takes tree height and crown area as input and outputs volume. Such an estimation model is generated in advance by machine learning using data on tree height, crown area, and volume as training data. The estimation model is stored in advance in the storage device 115 or a server. The processing device 111 can calculate the volume using the estimation model.

[0165] In addition to data on tree height, crown area, and volume, one or more of the following may be used as training data for machine learning: tree species data, data on the topography (slope, orientation, etc.) of the area where forest measurements were performed, and data on the region where forest measurements were performed (climate, etc.). In this case, the volume is calculated using an estimation model that inputs one or more of tree species, topography, and region, as well as tree height and crown area, and outputs the volume. This allows for more accurate calculation of the volume.

[0166] Furthermore, the processing of this embodiment can be used to calculate the number of trees represented by the three-dimensional point cloud data 120. FIG. 26 is a diagram showing an example of processing for calculating the number of trees represented by the three-dimensional point cloud data 120. As described using FIG. 20, among the points represented by the three-dimensional point cloud data 120, a plurality of points that are continuously distributed along the height direction are determined as a plurality of points 122 that represent tree trunks. As shown in FIG. 26, the processing device 111 extracts the plurality of points 122 that are continuously distributed along the height direction at each of a plurality of different positions, thereby extracting a plurality of trunks. The number of extracted trunks is the number of trees.

[0167] Furthermore, the crown area may be calculated by a process different from the example shown in FIG. 24. FIG. 27 is a diagram showing another example of a process for calculating the crown area. In the example shown in FIG. 27, the processing device 111 calculates the crown area based on the distribution in three-dimensional space of a plurality of points 123 corresponding to the crown portion of the three-dimensional point cloud data 120. For example, the processing device 111 approximates a collection of the plurality of points 123 distributed in three-dimensional space to a predetermined shape, and calculates the crown area based on the predetermined shape. In the example shown in FIG. 27, the processing device 111 approximates the collection of the plurality of points 123 to a cone 123b. The processing device 111 may calculate the lateral area of ​​the cone 123b as the crown area.

[0168] The present specification discloses a tree analysis system, a tree analysis method, and a computer program product described in the following items.

[0169] [Item 1] A tree analysis system 110 that analyzes the state of trees using three-dimensional point cloud data 120 obtained by forest measurement, a processing unit 111; storage devices 113, 115 for storing computer programs that control the operation of the processing unit 111; Equipped with The processing unit 111 executes the following steps in accordance with the computer program: determining the positions and heights of trees indicated by the three-dimensional point cloud data 120 based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data 120; determining the canopy portion of the tree based on the tree's location; Calculate the crown area based on the crown area; A tree analysis system 110 calculates the condition of the tree based on tree height and crown area.

[0170] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0171] According to an embodiment of the present invention, the positions and heights of trees are determined based on the continuity along the height direction of multiple points indicated by the 3D point cloud data 120, the crown areas are determined based on the positions of the trees, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data 120, thereby reducing the time and cost required for forest measurement and data analysis.

[0172] [Item 2] The tree analysis system 110 of item 1, wherein the processing unit 111 calculates the diameter of the tree trunk based on the tree height and crown area.

[0173] This makes it possible to obtain data on tree trunk diameters while reducing the time and cost required for forest measurement and data analysis.

[0174] [Item 3] Item 3. The tree analysis system 110 according to item 2, wherein the processing device 111 calculates the diameter of the tree trunk using table information indicating the relationship between tree height, crown area, and trunk diameter.

[0175] Using the table information, the trunk diameter can be calculated based on the correlation between tree height, crown area and trunk diameter.

[0176] [Item 4] Item 3. The tree analysis system 110 according to item 2, wherein the processing device 111 calculates the diameter of the tree trunk using an estimation model that takes at least the tree height and the crown area as input and the diameter of the trunk as output.

[0177] Using an estimation model generated through machine learning, trunk diameter can be calculated based on the correlation between tree height, crown area, and trunk diameter.

[0178] [Item 5] Item 5. The tree analysis system 110 according to item 4, wherein the estimation model is an estimation model that further inputs one or more of the tree species, the topography of the area where the forest measurements were performed, and the region where the forest measurements were performed.

[0179] This allows the diameter of the trunk to be calculated with greater accuracy.

[0180] [Item 6] Item 1. The tree analysis system 110 according to item 1, wherein the processing device 111 calculates the volume of the tree based on the tree height and the crown area.

[0181] This makes it possible to obtain data on tree volume while reducing the time and cost required for forest measurement and data analysis.

[0182] [Item 7] Item 7. The tree analysis system 110 according to item 6, wherein the processing device 111 calculates the volume of the tree using table information indicating the relationship between tree height, crown area, and volume.

[0183] By using the table information, the volume can be calculated based on the correlation between tree height, crown area and volume.

[0184] [Item 8] Item 7. The tree analysis system 110 according to item 6, wherein the processing device 111 calculates the volume of the tree using an estimation model that receives at least the tree height and the crown area as input and outputs the volume.

[0185] By using an estimation model generated through machine learning, it is possible to calculate tree volume based on the correlation between tree height, crown area, and volume.

[0186] [Item 9] Item 10. The tree analysis system 110 of item 8, wherein the estimation model is an estimation model that further inputs one or more of the tree species, the topography of the area where the forest measurements were taken, and the region where the forest measurements were taken.

[0187] This allows for more accurate calculation of the volume of timber.

[0188] [Item 10] The processing device 111 determining a plurality of points representing tree trunks based on the continuity along the height direction of the plurality of points represented by the three-dimensional point cloud data; 10. The tree analysis system 110 of any of items 1 to 9, which determines the position of a tree based on a plurality of points indicating the trunk of the tree.

[0189] This allows the location of the trunk and the location of the tree to be determined using the 3D point cloud data 120 that is not dense.

[0190] [Item 11] The processing device 111 The position where the continuity in the height direction of the plurality of points including the point indicating the trunk of the tree is lost is determined as the position of the tree apex; Item 11. The tree analysis system 110 of item 10, which determines the height of a tree based on the position of the tree apex.

[0191] This allows the tree height to be determined using the three-dimensional point cloud data 120 that is not high density.

[0192] [Item 12] The processing device 111 determining the position of the highest point among a plurality of points located within a predetermined range from the position of the tree as the position of the tree apex; Item 11. The tree analysis system 110 of item 10, which determines the height of a tree based on the position of the tree apex.

[0193] This allows the tree height to be determined using the three-dimensional point cloud data 120 that is not high density.

[0194] [Item 13] The processing device 111 Based on predetermined rules, multiple points that are highly related to the multiple points at the tree locations are extracted, 13. The tree analysis system 110 of any one of items 1 to 12, wherein the tree crown portion is determined based on the extracted plurality of points.

[0195] This allows the crown portion of the tree to be determined from its position.

[0196] [Item 14] A tree analysis system 110 described in any one of items 1 to 13, wherein the processing device 111 calculates the number of trees indicated by the three-dimensional point cloud data 120 based on the continuity along the height direction of multiple points indicated by the three-dimensional point cloud data 120.

[0197] This allows the number of trees to be calculated with higher accuracy than a method of calculating the number of trees using data on the crown of the forest measured from above.

[0198] [Item 15] 15. The tree analysis system 110 according to any one of items 1 to 14, wherein the processing device 111 calculates the crown area as the crown area when the crown portion is projected onto a predetermined plane.

[0199] This allows the crown area to be calculated based on the crown portion.

[0200] [Item 16] Item 16. A tree analysis system 110 according to item 15, wherein the predetermined plane is a plane substantially perpendicular to the height direction.

[0201] This makes it possible to calculate the area of ​​the tree crown when the tree crown portion is projected onto a substantially horizontal plane.

[0202] [Item 17] A tree analysis system 110 according to any one of items 1 to 14, wherein the processing device 111 calculates the tree crown area based on the distribution in three-dimensional space of a plurality of points corresponding to the tree crown portion in the three-dimensional point cloud data 120.

[0203] This allows the crown area to be calculated based on the crown portion.

[0204] [Item 18] Item 18. The tree analysis system 110 according to item 17, wherein the processing device 111 approximates a collection of multiple points corresponding to tree crown portions distributed in three-dimensional space to a predetermined shape, and calculates the tree crown area based on the predetermined shape.

[0205] This makes it possible to calculate the tree crown area based on multiple points corresponding to tree crown portions distributed in three-dimensional space.

[0206] [Item 19] A tree analysis system 110 described in any one of items 1 to 18, wherein the three-dimensional point cloud data 120 is three-dimensional point cloud data 120 obtained by forest measurement performed by flying an unmanned aerial vehicle equipped with a LiDAR sensor.

[0207] This makes it possible to obtain three-dimensional point cloud data 120 that includes not only a tree crown point cloud but also a trunk point cloud and a ground surface point cloud.

[0208] [Item 20] A computer-implemented tree analysis method for analyzing the state of trees using three-dimensional point cloud data 120 obtained by forest measurement, comprising: The tree analysis method is determining the positions and heights of trees indicated by the three-dimensional point cloud data 120 based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data 120; determining a tree crown portion based on the tree's location; calculating a canopy area based on the canopy portion; Calculating the condition of trees based on tree height and crown area; A tree analysis method, including:

[0209] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0210] According to an embodiment of the present invention, the positions and heights of trees are determined based on the continuity along the height direction of multiple points indicated by the 3D point cloud data 120, the crown areas are determined based on the positions of the trees, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data 120, thereby reducing the time and cost required for forest measurement and data analysis.

[0211] [Item 21] A computer program that causes a computer to execute analysis of the state of trees using three-dimensional point cloud data 120 obtained by forest measurement, The computer program is determining the positions and heights of trees indicated by the three-dimensional point cloud data 120 based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data 120; determining a tree crown portion based on the tree's location; calculating a canopy area based on the canopy portion; Calculating the condition of trees based on tree height and crown area; A computer program that causes a computer to execute the following.

[0212] Using high-density 3D point cloud data, it is possible to calculate the condition of trees (for example, trunk diameter and volume). For example, using high-density 3D point cloud data that is high enough to detect the outline of a tree trunk, it is possible to calculate the diameter of the trunk. However, obtaining high-density 3D point cloud data poses the problem of increasing the time and cost required for forest measurement. Furthermore, the volume of 3D point cloud data obtained is enormous, making data analysis time and costly.

[0213] According to an embodiment of the present invention, the positions and heights of trees are determined based on the continuity along the height direction of multiple points indicated by the 3D point cloud data 120, the crown areas are determined based on the positions of the trees, and the condition of the trees is calculated based on the tree heights and crown areas. This makes it possible to calculate the condition of trees from low-density 3D point cloud data 120, thereby reducing the time and cost required for forest measurement and data analysis. [Industrial Applicability]

[0214] According to an embodiment of the present invention, it is possible to grasp the state of an environment where multiple trees exist, such as a forest, while reducing the time and cost required for the work. [Explanation of symbols]

[0215] 1: Unmanned aerial vehicle (unmanned helicopter), 2: Main body, 3: Tail body, 4: Airframe, 5: Main rotor, 6: Tail rotor, 7: Radiator, 8: Engine, 9: Power generator, 10: Control panel, 11: Indicator light, 12: Skid, 13: Remote control receiving antenna, 15: Flight control box, 15a: Positioning module, 15b: Acceleration sensor, 15c: Barometric pressure sensor, 15d: Geomagnetic sensor, 15e: Ultrasonic sensor, 15f: Communication device, 15g: Processing device, 15j: Storage device, 15k: Internal bus, 20: LiDAR sensor, 21: Rotation axis, 22: Laser pulse, 23: Head, 25: Bracket, 36: Height direction, 37: Plane, 50: Measurement target area, 50a: Plot area, 50b: Area outside the plot, 52: Slope, 54: Forest, 56: Tree, 56a: Tree crown, 56b: Trunk, 58: Understory vegetation, 59: Ground surface, 60: Flight path, 62: Contour lines, 90: Map data, 100: Forest measurement system, 110: Terminal device (tree analysis system), 111: Processing device, 112: Processor, 113: ROM, 114: RAM, 115: Storage device, 116: Communication device, 117: Display device, 118: Input device, 120: 3D point cloud data, 121: Points indicating ground surface, 122: Points indicating trunk, 123: Points indicating tree crown, 124: Points indicating tree apex, 132: Tree position, 134: Tree crown extraction range

Claims

1. A tree analysis system that analyzes the state of trees using three-dimensional point cloud data obtained by forest measurement, a processing device; a storage device that stores a computer program that controls the operation of the processing device; Equipped with The processing device, in accordance with the computer program, determining the positions and heights of the trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data; determining a crown portion of the tree based on the position of the tree; Calculating a crown area based on the crown portion; A tree analysis system that calculates the condition of the tree based on the tree height and the crown area.

2. The tree analysis system according to claim 1 , wherein the processing device calculates a diameter of the trunk of the tree based on the tree height and the crown area.

3. 3. The tree analysis system according to claim 2, wherein the processing device calculates the diameter of the trunk of the tree using table information indicating the relationship between tree height, crown area, and trunk diameter.

4. 3. The tree analysis system according to claim 2, wherein the processing device calculates the diameter of the trunk of the tree using an estimation model that receives at least tree height and crown area as input and outputs trunk diameter.

5. The tree analysis system of claim 4 , wherein the estimation model is an estimation model that further inputs one or more of the tree species, the topography of the area where the forest measurement was performed, and the region where the forest measurement was performed.

6. The tree analysis system according to claim 1 , wherein the processing device calculates the volume of the tree based on the tree height and the crown area.

7. 7. The tree analysis system according to claim 6, wherein the processing device calculates the volume of the tree using table information indicating the relationship between tree height, crown area, and volume.

8. 7. The tree analysis system according to claim 6, wherein the processing device calculates the volume of the tree using an estimation model that receives at least tree height and crown area as input and outputs volume.

9. The tree analysis system of claim 8 , wherein the estimation model is an estimation model that further inputs one or more of the tree species, the topography of the area where the forest measurement was performed, and the region where the forest measurement was performed.

10. The processing device includes: determining a plurality of points representing the trunk of the tree based on the continuity of the plurality of points represented by the three-dimensional point cloud data along a height direction; The tree analysis system according to claim 1 or 2, wherein the position of the tree is determined based on a plurality of points representing the trunk of the tree.

11. The processing device includes: determining a position where the continuity along the height direction of a plurality of points including the point indicating the trunk of the tree is lost as the position of the tree apex; The tree analysis system of claim 10 , further comprising: determining a height of the tree based on the position of the tree apex.

12. The processing device includes: determining the position of the highest point among a plurality of points located within a predetermined range from the position of the tree as the position of the tree apex; The tree analysis system of claim 10 , further comprising: determining a height of the tree based on the position of the tree apex.

13. The processing device includes: extracting a plurality of points highly related to the plurality of points at the tree positions based on predetermined rules; The tree analysis system according to claim 1 or 2, wherein the crown portion is determined based on the extracted points.

14. The tree analysis system according to claim 1 or 2, wherein the processing device calculates the number of trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the multiple points indicated by the three-dimensional point cloud data.

15. 3. The tree analysis system according to claim 1, wherein the processing device calculates, as the tree crown area, a tree crown area obtained by projecting the tree crown portion onto a predetermined plane.

16. The tree analysis system according to claim 15 , wherein the predetermined plane is a plane that is substantially perpendicular to a height direction.

17. 3. The tree analysis system according to claim 1, wherein the processing device calculates the crown area based on a distribution in three-dimensional space of a plurality of points corresponding to the crown portion in the three-dimensional point cloud data.

18. 18. The tree analysis system according to claim 17, wherein the processing device approximates a collection of a plurality of points corresponding to the tree crown portion distributed in the three-dimensional space to a predetermined shape, and calculates the tree crown area based on the predetermined shape.

19. The tree analysis system according to claim 1 or 2, wherein the three-dimensional point cloud data is three-dimensional point cloud data obtained by forest measurement performed by flying an unmanned aerial vehicle equipped with a LiDAR sensor.

20. A computer-implemented tree analysis method for analyzing tree conditions using three-dimensional point cloud data obtained by forest measurement, comprising: The tree analysis method includes: determining the positions and heights of the trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data; determining a crown portion of the tree based on the location of the tree; calculating a crown area based on the crown portion; calculating the state of the tree based on the tree height and the crown area; A tree analysis method, including:

21. A computer program that causes a computer to analyze the state of trees using three-dimensional point cloud data obtained by forest measurement, The computer program comprises: determining the positions and heights of the trees indicated by the three-dimensional point cloud data based on the continuity along the height direction of the plurality of points indicated by the three-dimensional point cloud data; determining a crown portion of the tree based on the location of the tree; calculating a crown area based on the crown portion; calculating the state of the tree based on the tree height and the crown area; A computer program that causes the computer to execute the above.

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