Forest condition estimation system, forest condition estimation method, and computer program

Abstract

To provide a forest condition estimation system, a forest condition estimation method, and a computer program that easily grasp condition of a forest.SOLUTION: A forest condition estimation system (100) includes: a data acquisition unit (101) that acquires 3D point group data obtained by forest measurement; a number acquisition unit (102) that determines altitude of each of a plurality of points included in the 3D point cloud data, divides a plurality of points by step width of predetermined altitude, and acquires the number of points for each predetermined width of altitude; and an estimation unit (103) that estimates a condition of a forest on the basis of the number of points per predetermined width.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a forest condition estimation system, a forest condition estimation method, and a computer program. [Background technology]

[0002] Forest measurement is conducted to manage and utilize forest resources. Forest measurement can be performed by various methods. For example, Patent Document 1 discloses a method of photographing a forest with a camera from the sky and extracting a 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 calculating the height of a tree from the difference in height calculated from the wave reflected from the top of the tree and the wave reflected from the ground. Patent Document 3 discloses a method of mounting a laser distance measuring device on an aircraft and using the laser distance measuring device to obtain three-dimensional data of the ground, including trees, etc. [Prior art documents] [Patent documents]

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

[0004] When forest measurements are performed from the sky using a laser distance measuring device, point cloud data, which is data on the upper surface of the forest (top of the tree crown), can be obtained. However, it is difficult to obtain data on tree trunks because the laser light is blocked by leaves, etc.

[0005] Patent Document 3 mentions that humans bring a device such as a laser distance measuring device into the forest, acquire three-dimensional point cloud data, and fuse the point cloud data acquired by measurement from the sky with the point cloud data acquired by measurement on the ground. However, acquiring two types of point cloud data is very time-consuming.

[0006] In order to manage and utilize forest resources, it is necessary to easily grasp the condition of forests. [Means for solving the problem]

[0007] A forest condition estimation system according to one embodiment of the present invention comprises a data acquisition unit that acquires three-dimensional point cloud data obtained by forest measurement, a number acquisition unit that determines the altitude of each of a plurality of points included in the three-dimensional point cloud data, divides the plurality of points into predetermined altitude increments, and acquires the number of points for each predetermined altitude increment, and an estimation unit that estimates the forest condition based on the number of points for each predetermined increment.

[0008] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest.

[0009] In one embodiment, the three-dimensional point cloud data may be three-dimensional point cloud data obtained by forest measurement performed by flying an unmanned aerial vehicle equipped with a LiDAR sensor.

[0010] 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), the laser pulse emitted from the lidar sensor reaches and is reflected not only by the tree crowns but also by the tree trunks and understory vegetation, etc. This makes it possible to obtain 3D point cloud data including not only the tree crown point cloud but also the trunk point cloud, understory vegetation point cloud, and ground surface point cloud.

[0011] In one embodiment, the number acquisition unit may determine a digital elevation model from the three-dimensional point cloud data, and calculate an elevation of each of the plurality of points using the digital elevation model.

[0012] By using a digital elevation model determined from the latest 3D point cloud data, rather than using existing map data or digital elevation models, highly accurate values ​​for the elevation of each of multiple points can be obtained.

[0013] In one embodiment, when each of the plurality of points included in the three-dimensional point cloud data is projected onto a horizontal plane, the number density of the plurality of points on the horizontal plane is 100 points / m 2 It may be more than that.

[0014] By increasing the density of the point cloud, it is possible to increase the amount of information that can be obtained from below the canopy, enabling a more detailed understanding of the condition of the forest.

[0015] In one embodiment, the number acquisition unit may divide a forest measurement target into a plurality of areas and acquire the number of points for each of the predetermined widths for each area.

[0016] By acquiring altitude histograms of point clouds on an area-by-area basis, it is possible to understand the condition of the forest, which may differ depending on the location.

[0017] In one embodiment, the estimation unit may group the multiple points divided by the specified altitude increment into multiple groups having different altitudes, including an understory vegetation group, a trunk group, and a crown group, and estimate the condition of the forest based on the number of points included in each of the multiple groups.

[0018] The number of points included in each of the multiple groups reflects vegetation such as the presence of understory vegetation and trunks, and the amount of branches and leaves, and therefore makes it possible to estimate the type, amount, form, etc. of plants inhabiting the forest.

[0019] In one embodiment, the estimation unit may estimate the state of the forest based on a ratio of the number of points among the plurality of groups.

[0020] The ratio of the number of points contained in each of the multiple groups contains a characteristic pattern that indicates the condition of the forest, and from this pattern it is possible to estimate the degree of tree density and the proportion of inferior trees, etc.

[0021] In one embodiment, the estimation unit may estimate that the forest is an overcrowded stand if the ratio of the number of points included in the understory vegetation group and the trunk group to the number of points included in the crown group is below a predetermined value.

[0022] If the ratio of understory vegetation and trunks to the crown is equal to or less than a predetermined value, the forest can be estimated to be an overcrowded stand, and it can be determined that thinning of the forest is desirable.

[0023] In one embodiment, the forest condition estimation system further includes a memory unit that stores the number of points per specified width and the state of the forest as a dataset, and a model generation unit that uses the multiple datasets stored in the memory unit as training data and generates an estimation model through machine learning, the input of which is the number of points per specified width and the output of the forest state, and the estimation unit may use the estimation model to estimate the state of the forest from the number of points per specified width.

[0024] By using an estimation model generated by machine learning, it is possible to utilize correlations between features hidden in the distribution pattern of the number of points per specified width (point cloud altitude histogram) and the condition of the forest.

[0025] A forest condition estimation system according to one embodiment of the present invention is a system that estimates the condition of a forest using three-dimensional point cloud data obtained by forest measurement, and comprises a processor and a storage device that stores a program that controls the operation of the processor, and the processor performs the following operations in accordance with the program: acquires the three-dimensional point cloud data obtained by forest measurement, determines the altitude of each of a plurality of points contained in the three-dimensional point cloud data, divides the plurality of points into predetermined altitude increments, acquires the number of points for each predetermined altitude increment, and estimates the condition of the forest based on the number of points for each predetermined increment.

[0026] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest.

[0027] A forest condition estimation method according to one embodiment of the present invention is a method for estimating the condition of a forest using three-dimensional point cloud data obtained by forest measurement, and includes the steps of: acquiring three-dimensional point cloud data obtained by forest measurement; determining the altitude of each of a plurality of points contained in the three-dimensional point cloud data; dividing the plurality of points into predetermined altitude increments; acquiring the number of points for each predetermined altitude increment; and estimating the condition of the forest based on the number of points for each predetermined increment.

[0028] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest.

[0029] A computer program according to one embodiment of the present invention is a computer program that causes a computer to estimate the condition of a forest using three-dimensional point cloud data obtained by forest measurement, and the computer program causes the computer to acquire three-dimensional point cloud data obtained by forest measurement, determine the altitude of each of a plurality of points contained in the three-dimensional point cloud data, divide the plurality of points into predetermined altitude increments, acquire the number of points for each predetermined altitude increment, and estimate the condition of the forest based on the number of points for each predetermined increment.

[0030] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest. Effect of the Invention

[0031] According to one embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval of altitude. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it is possible to grasp the health and other conditions of the forest. [Brief description of the drawings]

[0032] [Figure 1] FIG. 1 is a diagram showing an unmanned helicopter 1 for performing forest measurement according to an embodiment of the present invention. [Diagram 2] 1 is an external side view of an unmanned helicopter 1 equipped with a LiDAR sensor 20 according to an embodiment of the present invention. [Diagram 3] FIG. 1 is a front view of an unmanned helicopter 1 according to an embodiment of the present invention. [Figure 4] FIG. 2 is a diagram illustrating an example of the hardware configuration of a flight control box 15 according to an embodiment of the present invention. [Diagram 5] FIG. 1 is a diagram showing an unmanned helicopter 1 for performing forest measurement according to an embodiment of the present invention. [Figure 6] 1 is a functional block diagram showing a forest condition estimation system 100 according to an embodiment of the present invention in functional blocks. [Figure 7] 1 is a block diagram showing an example of a hardware configuration of a forest condition estimation system 100 according to an embodiment of the present invention. [Figure 8] 1 is a flowchart illustrating an example of a process for estimating a forest condition according to an embodiment of the present invention. [Figure 9] FIG. 2 is a diagram showing an example of three-dimensional point cloud data according to the embodiment of the present invention. [Figure 10] FIG. 2 is a diagram showing an example of three-dimensional point cloud data according to the embodiment of the present invention. [Figure 11] FIG. 13 illustrates an example altitude histogram showing the number of points per given width according to an embodiment of the present invention. [Figure 12] FIG. 13 illustrates an example altitude histogram showing the number of points per given width according to an embodiment of the present invention. [Figure 13] 1 is a flowchart illustrating an example of a process for generating an estimation model by machine learning according to an embodiment of the present invention. [Figure 14] FIG. 2 is a diagram showing another example of the hardware configuration of the forest condition estimation system 100 according to the embodiment of the present invention. [Figure 15]FIG. 2 is a diagram showing a forest 54 including a recessive tree 561 according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] In order to manage and utilize forest resources, it is important to conduct what is called "forest measurement." This "forest measurement" can include investigating the structure of the forest, estimating the forest's volume, and understanding the amount of change in the forest over a certain period of time. Conventional forest measurement, which involves measuring each tree and then tabulating and analyzing the obtained data, is very labor-intensive and time-consuming. Therefore, efforts are being made to mount laser ranging devices (LiDAR sensors) on unmanned aerial vehicles and use the LiDAR sensors to conduct forest measurement from the air.

[0034] The inventors of the present application have found 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) to perform forest measurement, 3D point cloud data including not only a tree crown point cloud but also a trunk point cloud, an understory vegetation point cloud, and a ground surface point cloud can be obtained. Here, the tree crown point cloud is a point cloud corresponding to the tree crown, the trunk point cloud is a point cloud corresponding to the tree trunk, 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. The laser pulse emitted from the LiDAR sensor reaches and is reflected not only by the tree crown but also by the tree trunk, the understory vegetation, the ground surface, etc., thereby making it possible to obtain 3D point cloud data including a tree crown point cloud, a trunk point cloud, an understory vegetation point cloud, and a ground surface point cloud.

[0035] It is possible to estimate the condition of the forest using 3D point cloud data including crown point cloud, trunk point cloud, understory vegetation point cloud, and ground surface point cloud.

[0036] When each of the multiple points contained in the 3D point cloud data is projected onto a horizontal plane, the number density of the multiple points on the horizontal plane is 100 points / m 2By increasing the density of the point cloud, the amount of information that can be obtained from positions below the treetops can be increased, allowing for more detailed estimation of the forest condition.

[0037] In this specification, the term "forest measurement" includes scanning a forest from the sky using a LiDAR sensor and acquiring scan data. The scan data can typically be expressed by 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 by a local coordinate system that moves with the unmanned aerial vehicle. Such a local coordinate system can be called a moving body coordinate system or a sensor coordinate system. In general, "forest measurement" includes converting the positions of each reflection point expressed in the local coordinate system into a geographic coordinate system. "Forest measurement" can further include analyzing the structure of the forest after conversion to the geographic coordinate system, visually displaying the shape of the forest and trees, determining the abundance ratio of each type of tree in the forest, determining the volume density of the forest, and the like.

[0038] An "unmanned aerial vehicle" (UAV) is an aircraft that does not have a human pilot on board, and is sometimes called a drone. Aircraft may include rotorcraft 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 engine (internal combustion engine) or an electric motor. The flight of an unmanned aerial vehicle may be autonomous flight by a computer program, semi-autonomous flight with partial automation, or flight by remote control by a person using radio. An unmanned aerial vehicle can fly while measuring its current position in three dimensions and correcting its position with the aid of a Global Navigation Satellite System (GNSS). In the exemplary embodiment described below, the "unmanned aerial vehicle" is an "unmanned helicopter". The term "unmanned" means that a person does not need to be on board to operate the aircraft, and does not exclude the unmanned aerial vehicle from carrying a person other than the pilot.

[0039] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. However, more detailed description than necessary may be omitted. For example, detailed description of already well-known matters and duplicated description of substantially the same configuration may be omitted. This is to avoid the following description becoming unnecessarily redundant and to facilitate understanding by those skilled in the art. In addition, the following embodiment is an example, and the present invention is not limited to the following embodiment.

[0040] (Measurement of the measurement area) First, an embodiment of the measurement of the measurement target area will be described.

[0041] 1 shows an unmanned helicopter 1 that measures a measurement target area 50. 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 the unmanned helicopter 1. In the forest 54, a number of 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 be a forest on flat land.

[0042] Fig. 2 is an external 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.

[0043] The LiDAR sensor 20 successively emits laser beam pulses (hereinafter abbreviated as "laser pulses") 22 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 constitutes the forest 54, or the ground surface such as slopes and flat ground.

[0044] The LiDAR sensor 20 can measure the distance from the aircraft to the forest by any method. The measurement methods of the LiDAR sensor 20 include, for example, a mechanical rotation method, a MEMS method, and a phased array method. These measurement methods differ in the method of emitting laser pulses (scanning method). For example, a mechanical rotation type LiDAR sensor rotates a cylindrical head that emits laser pulses and detects reflected light of the laser pulses to scan the measurement object in all directions 360 degrees around the rotation axis. A MEMS type LiDAR sensor uses a MEMS mirror to swing the emission direction of the laser pulse and scan the measurement object within a predetermined angle range centered on the swing axis. A phased array type LiDAR sensor controls the phase of light to swing the emission direction of light and scans the measurement object within a predetermined angle range centered on the swing axis.

[0045] The unmanned helicopter 1 includes 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 the top of the main body 2, and a tail rotor 6 is provided on the rear of the tail body 3. A radiator 7 is provided on the front of the main body 2. A flight control box 15 is provided on the rear of the main body 2. An engine 8, which is an internal combustion engine, and a power generation device 9 are provided inside the main body 2. In addition, an intake system, a main rotor shaft, and a fuel tank, all of which are not shown, are housed inside the main body 2. The rotation generated by the engine 8 is transmitted to the main rotor 5 and the tail rotor 6, and the unmanned helicopter 1 flies as the main rotor 5 and the tail rotor 6 rotate.

[0046] 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 a ground station. The indicator lights 11 display the GNSS control status, aircraft abnormality warnings, etc. Skids 12, which are legs that support the aircraft 4 when landing, are provided on the lower central side of the main body 2.

[0047] 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, an air pressure sensor 15c, a geomagnetic sensor 15d, an ultrasonic sensor 15e, a communication circuit 15f, a signal processing circuit 15g, and a storage device 15j. The storage device 15j includes a ROM (Read Only Memory) 15h, a RAM (Random Access Memory) 15i, and the like. Each component can transmit and receive data to and from each other via, for example, wiring or an internal bus 15k. Note that the positioning module 15a and various other sensors do not always need to be provided inside the flight control box 15. For example, the positioning module 15a may be provided on the upper part of the tail body 3 in order to easily acquire signals from GNSS satellites.

[0048] The positioning module 15a receives GNSS signals transmitted from GNSS satellites and performs positioning based on the GNSS signals. 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 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).

[0049] The acceleration sensor 15b is a three-axis acceleration sensor that detects acceleration in each of the X-axis, Y-axis, and Z-axis directions. If the acceleration sensor 15b is a six-axis acceleration sensor, it can further detect the roll acceleration, pitch angular velocity, and yaw acceleration of the unmanned helicopter 1. The acceleration sensor 15b may have a plurality of one-axis or two-axis acceleration sensors, and may be configured to detect each of the directions of the coordinate system XYZ using these one-axis or two-axis acceleration sensors. The air pressure sensor 15c detects air pressure. The current altitude can be known from the detected air pressure. Since the relationship between air pressure and altitude is publicly known, the description will be omitted in this specification. The geomagnetic sensor 15d detects the current direction of the unmanned helicopter 1. The ultrasonic sensor 15e is used to detect the 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 each of the acceleration sensor 15b and the geomagnetic sensor 15d. The flight data and the aircraft data are provided to the signal processing circuit 15g.

[0050] The communication circuit 15f has a communication circuit that performs wireless communication conforming to the Bluetooth (registered trademark) and / or Wi-Fi (registered trademark) standards. The communication circuit 15f may further perform wireless communication using a mobile phone line or a line via an artificial satellite. The communication circuit 15f receives flight route data before flight, and performs necessary communication with the ground by wireless during flight. The flight route data includes each data of the coordinates of the route that the unmanned helicopter 1 should fly and the absolute altitude.

[0051] The storage device 15j stores a computer program that controls the operation of the signal processing circuit 15g. The storage device 15j may store a computer program for causing the signal processing circuit 15g to execute control of the flight of the unmanned helicopter 1 and control of 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 telecommunication line (e.g., the Internet). The computer program may be provided to the unmanned helicopter 1 by wireless communication. Such a computer program may be sold as commercial software.

[0052] The signal processing circuit 15g executes a control program stored in the storage device 15j to fly the unmanned helicopter 1. More specifically, the signal processing circuit 15g flies the unmanned helicopter 1 along a flight route prepared in advance while monitoring the above-mentioned flight data, aircraft data, and operating condition data such as engine RPM and throttle opening.

[0053] 4, the flight control box 15 is connected to the LiDAR sensor 20. The LiDAR sensor 20 outputs a scan result (a set of time data, direction data, distance data, etc.) to the flight control box 15. Using the position data indicating the flight position of the unmanned helicopter 1 output from the positioning module 15a and the scan result output from the LiDAR sensor 20, the position of the measurement target expressed in, for example, a geographic coordinate system can be calculated.

[0054] The LiDAR sensor 20 does not have to be connected to the flight control box 15. In this case, the scan result of the LiDAR sensor 20 may be stored in a storage device in the LiDAR sensor 20. The scan result of the LiDAR sensor 20 may be output from the LiDAR sensor 20 to the outside by wireless communication. The flight control box 15 and the LiDAR sensor 20 may share a power source.

[0055] Also, the positioning module 15a may be provided in the unmanned helicopter 1 independently of the flight control box 15, and position data may be output from the positioning module 15a to each of the flight control box 15 and the LiDAR sensor 20. In this case, the scan result of the LiDAR sensor 20 may be stored in a storage device in the LiDAR sensor 20. Also, when a processor in the LiDAR sensor 20 calculates the position of the measurement target using the position data output by the positioning module 15a and the scan result of the LiDAR sensor 20, the calculation result may be stored in a storage device in the LiDAR sensor 20.

[0056] 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 checking the flight conditions. A remote control receiving antenna 13 that receives command signals from a remote control aircraft is provided at the rear end of the tail body 3 (Fig. 2).

[0057] 2 and 3, the LiDAR sensor 20 is an optical device that emits (radiates) a near-infrared laser pulse 22, for example, and detects the reflected light of the laser pulse 22 to measure the distance to a reflection point.

[0058] In an exemplary embodiment, the LiDAR sensor 20 is a mechanical rotation type, and the head 23 that emits the laser pulse 22 and detects the reflected light of the laser pulse 22 rotates around the rotation axis 21. The rotation of the head 23 allows scanning in all directions of 360 degrees. In this embodiment, the range that is blocked by the body 4 of the unmanned helicopter 1, etc., of the scannable range of the LiDAR sensor 20 is not reflected in the measurement result. For convenience of description, FIG. 3 shows only a part of the laser pulse 22 emitted in all directions of 360 degrees. In this specification, the rotation of the head 23 of the LiDAR sensor 20 may be expressed as "rotation of the LiDAR sensor 20".

[0059] The head 23 of the LiDAR sensor 20 changes the direction of the emission port by rotating, and emits multiple laser pulses 22 simultaneously at every predetermined angle pitch α (rad). In FIG. 2, as an example, N laser pulses 22 are shown emitted simultaneously along a certain plane through which the rotation axis 21 of the LiDAR sensor 20 passes. For convenience of description, the laser pulses 22 are described in a beam shape rather than a pulse shape. Note that "simultaneous" does not necessarily mean exactly the same time, and also includes approximately the same time. The value of N is arbitrary, for example, 12, 16, 32, or 64, but N is not limited to these values. For example, N laser light sources are arranged in the head 23 of the LiDAR sensor 20, and the laser pulses 22 are emitted from the emission ports of the N laser pulses 22. The laser light source is, for example, a laser diode, but is not limited to this.

[0060] With reference to Fig. 3, the 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 the laser pulse 22 at every 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 every predetermined angular pitch α. The predetermined angular pitch α may be a fixed value or a variable value.

[0061] The LiDAR sensor 20 may be a LiDAR sensor that oscillates the emission direction of the laser pulse as described above and scans a measurement target within a predetermined angle range centered on the oscillation axis. In this specification, the "rotation" and "rotation axis" of the LiDAR sensor 20 include such "oscillation" and "oscillation axis." The rotation angle range when the emission direction of the laser pulse is oscillated is, for example, 180 degrees or less, but is not limited thereto.

[0062] In this embodiment, the LiDAR sensor 20 is mounted so that its rotation axis 21 faces in the forward / rearward 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.

[0063] FIG. 5 is a diagram showing an unmanned helicopter 1 performing forest measurement. In the example shown in FIG. 5, a forest 54 spreads on a slope 52. The forest 54 spreading on the slope 52 can be irradiated with the laser pulse 22 not only from an obliquely upward direction, but also from a lateral direction or from an obliquely downward direction. When the laser pulse 22 is irradiated from a lateral direction, the laser pulse 22 can easily pass between the leaves that basically grow horizontally to receive sunlight. The laser pulse 22 reaches and is reflected not only by the crown 56a of the tree 56 but also by the trunk 56b of the tree 56, the understory vegetation 58, and the ground surface 59. This makes it possible to obtain 3D point cloud data including not only the crown point cloud but also the trunk point cloud, the understory vegetation point cloud, and the ground surface point cloud.

[0064] (Forest condition estimation system) Next, a forest condition estimation system 100 according to this embodiment will be described. Fig. 6 is a functional block diagram showing the forest condition estimation system 100 in functional block units. Forest condition estimation system 100 includes a data acquisition unit 101, a number acquisition unit 102, an estimation unit 103, a storage unit 104, and a model generation unit 105.

[0065] The data acquisition unit 101 acquires three-dimensional point cloud data obtained by forest measurement from an external device. The external device is, for example, an unmanned helicopter 1 equipped with a LiDAR sensor 20, or any device that stores data acquired from the unmanned helicopter 1. The three-dimensional point cloud data is acquired, for example, by forest measurement performed by flying an unmanned helicopter 1 equipped with a LiDAR sensor 20. The three-dimensional point cloud data includes a tree crown point cloud, a trunk point cloud, an understory vegetation point cloud, and a ground surface point cloud.

[0066] The number acquisition unit 102 determines the altitude of each of the multiple points included in the three-dimensional 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 number acquisition unit 102 generates a DTM (Digital Terrain Model: digital elevation model) from the three-dimensional point cloud data, and determines the absolute altitude of each of the multiple points using data obtained by subtracting the DTM from the three-dimensional point cloud data. Note that a DSM (Digital Surface Model: digital surface model) and the DTM may be generated from the three-dimensional 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).

[0067] The number acquiring unit 102 divides the multiple points for which absolute altitudes have been determined into predetermined altitude intervals, and acquires the number of points for each predetermined interval.

[0068] The estimation unit 103 estimates the state of the forest based on the number of points for each predetermined width. For example, the estimation unit 103 estimates whether the forest is an overcrowded stand. Based on the estimation result, the user can decide whether thinning is necessary.

[0069] The storage unit 104 stores the number of points for each predetermined width and the state of the forest as a data set. For example, the data acquisition unit 101 acquires from an external device 3D point cloud data obtained by forest measurement and forest state information indicating the state of the forest that is the measurement target. The forest state information includes, for example, information on the state of the forest determined by a human being, and is used as training data for machine learning, which will be described later.

[0070] As described above, the number acquiring unit 102 acquires the number of points for each predetermined width using the 3D point cloud data. The number acquiring unit 102 stores the number of points for each predetermined width and the forest condition information as a data set in the storage unit 104.

[0071] The data acquisition unit 101 acquires 3D point cloud data and forest condition information for each of the multiple forests from an external device. The number acquisition unit 102 acquires the number of points for each predetermined width for each of the multiple forests, and stores a data set for each of the multiple forests in the storage unit 104. The storage unit 104 stores the multiple data sets.

[0072] The model generation unit 105 uses multiple data sets stored in the storage unit 104 as teacher data, and generates an estimation model by machine learning, with the input being the number of points per predetermined width and the output being the state of the forest. A known supervised learning method can be used as a method for generating the estimation model. The model generation unit 105 stores the generated estimation model in the storage unit 104.

[0073] The estimation unit 103 can estimate the state of the forest from the number of points per predetermined width using an estimation model read from the storage unit 104. The estimation unit 103 may estimate the state of the forest using a method other than a method using an estimation model generated by machine learning. For example, the state of the forest may be estimated based on a judgment criterion set in advance by a human being. For example, information on such judgment criteria is stored in advance in the storage unit 104, and the estimation unit 103 may estimate the state of the forest based on the judgment criterion read from the storage unit 104.

[0074] In addition to the dataset and the estimation model, the memory unit 104 may store information necessary for processing by the forest condition estimation system 100.

[0075] The forest condition estimation system 100 may be, for example, a user terminal device or a server computer (hereinafter referred to as a "server"). The user terminal device is, for example, a personal computer (PC), a tablet computer, a smartphone, or a PDA (Personal Digital Assistant). The server is, for example, a cloud server or an edge server. The forest condition estimation system 100 may be a dedicated device that functions as a support tool for supporting forest measurement.

[0076] 7 is a block diagram showing an example hardware configuration of forest condition estimation system 100. Forest condition estimation system 100 includes an input device 110, a display device 120, a communication IF 130, a storage device 140, a processor 150, a ROM 160, and a RAM 170. These components are connected via a bus 180 so as to be able to communicate with each other.

[0077] The input device 110 is a device for converting instructions from a user into data and inputting the data to a computer. The input device 110 is, for example, a keyboard, a mouse, a touch panel, a microphone, or a combination thereof. The display device 120 is, for example, a liquid crystal display or an OLED (Organic Light-Emitting Diode) display. The display device 120 can display the results of forest measurement, etc.

[0078] The communication IF 130 is a communication module for performing data communication between the forest condition estimation system 100 and the outside. The communication IF 130 can perform wired communication and / or wireless communication. The communication IF 130 can perform wired communication conforming to a communication standard such as USB, IEEE1394 (registered trademark), or Ethernet (registered trademark). The communication IF 130 can perform wireless communication conforming to the Bluetooth (registered trademark) standard and / or the Wi-Fi (registered trademark) standard. The communication IF 130 may be a communication module capable of performing wireless communication conforming to a communication method such as BLE (Bluetooth Low Energy) or LPWA (Low Power Wide Area). By using a communication method such as BLE or LPWA, long-distance and wide-range communication can be realized with low power consumption. The communication IF 130 may perform wireless communication using a mobile phone line or a line via an artificial satellite.

[0079] The storage device 140 mainly functions as a database storage. The storage device 140 is, for example, a magnetic storage device, an optical storage device, a semiconductor storage device, or a combination thereof. An example of an optical storage device is an optical disk drive or a magneto-optical disk (MD) drive. An example of a magnetic storage device is a hard disk drive (HDD). An example of a semiconductor storage device is a solid-state drive (SSD). The storage device 140 may be a cloud storage.

[0080] The processor 150 is a semiconductor integrated circuit and includes, for example, a central processing unit (CPU). The processor 150 may be realized by a microprocessor or a microcontroller. The processor 150 sequentially executes a computer program stored in a ROM 160, which describes a group of instructions for executing various processes, to realize a desired process.

[0081] Processor 150 may be 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.

[0082] ROM 160 is, for example, a writable memory (e.g., PROM), a rewritable memory (e.g., flash memory), or a read-only memory. ROM 160 stores a computer program that controls the operation of processor 150. ROM 160 does not need to be a single recording medium, and may be a collection of multiple recording media. Part of the collection of multiple recording media may be a removable memory.

[0083] The RAM 170 provides a working area for temporarily loading, at boot time, the computer programs stored in the ROM 160. The RAM 170 does not have to be a single recording medium, and may be a collection of multiple recording media.

[0084] The processing of each functional block shown in Fig. 6 is written in a computer program on a software module basis. However, when using an FPGA or the like, all or part of these functional blocks may be implemented as a hardware accelerator.

[0085] A computer program including a group of instructions describing the processing to be performed by the forest condition estimation system 100 may be provided to the forest condition estimation system 100 via a storage medium (e.g., a semiconductor memory or an optical disk) or an electric communication line (e.g., the Internet). Such a computer program may be sold as commercial software.

[0086] (Process to estimate forest conditions) Fig. 8 is a flowchart showing an example of a process for estimating the state of a forest. The process shown in Fig. 8 is a process for estimating the state of a forest by using an already generated estimation model or a preset judgment criterion.

[0087] The communication IF 130 of the forest condition estimation system 100 receives the three-dimensional point cloud data obtained by forest measurement from an external device (step S10). The processor 150 stores the three-dimensional point cloud data received by the communication IF 130 in the storage device 140, for example.

[0088] The processor 150 reads the three-dimensional point cloud data from the storage device 140 and determines the absolute elevation of each of the multiple points included in the three-dimensional point cloud data. As described above, the processor 150 generates, for example, a DTM (digital elevation model) from the three-dimensional point cloud data and subtracts the DTM from the three-dimensional point cloud data to determine the absolute elevation of each of the multiple points.

[0089] 9 and 10 are diagrams showing examples of 3D point cloud data. The ground surface where forests spread generally includes slopes. By subtracting the DTM from the 3D point cloud data, 3D point cloud data in which the ground surface is flattened can be obtained.

[0090] By using a DTM determined from the latest 3D point cloud data, rather than using existing map data or DTM, highly accurate values ​​for the absolute altitude of each of multiple points can be obtained.

[0091] The processor 150 divides the points for which the absolute altitudes have been determined into predetermined altitude intervals (step S11). The altitude intervals may be any interval, for example, but are not limited to, 0.2 m to 1.0 m.

[0092] Next, the processor 150 obtains the number of points for each predetermined range of altitude (step S12). Figures 11 and 12 show altitude histograms showing examples of the number of points for each predetermined range.

[0093] The lower altitude histograms in each of Figures 11 and 12 show the number of points as real numbers. The upper altitude histograms in each of Figures 11 and 12 show the number of points as logarithms. The vertical axis of each altitude histogram shown in Figures 11 and 12 is the number of points, and the horizontal axis is the altitude.

[0094] The processor 150 estimates the state of the forest based on the number of points for each predetermined width (step S13). In the forest state estimation process, the processor 150 divides the points divided by the predetermined altitude interval into a plurality of groups with different altitudes. For example, the processor 150 divides the points into a plurality of groups including an understory vegetation group G1, a trunk group G2, and a crown group G3.

[0095] The altitude ranges of the understory vegetation group G1, the trunk group G2, and the crown group G3 may be set according to the type of plants that live in the forest. The altitude ranges of each group may be set according to the density of standing trees and the forest maintenance status such as thinning. In the example shown in FIG. 11, the altitude range of the understory vegetation group G1 is 0 m or more and less than 1.5 m, the altitude range of the trunk group G2 is 1.5 m or more and less than 21 m, and the altitude range of the crown group G3 is 21 m or more and less than 35 m. In the example shown in FIG. 12, the altitude range of the understory vegetation group G1 is 0 m or more and less than 1.5 m, the altitude range of the trunk group G2 is 1.5 m or more and less than 13 m, and the altitude range of the crown group G3 is 13 m or more and less than 23 m. These numerical values ​​are examples, and the embodiment of the present invention is not limited to these numerical values.

[0096] The processor 150 can estimate the state of the forest based on the number of points included in each of the multiple groups G1, G2, and G3. The number of points included in each of the multiple groups G1, G2, and G3 reflects vegetation such as the presence of understory vegetation and trunks, and the amount of branches and leaves, and therefore can estimate the type, amount, form, etc. of plants living in the forest.

[0097] The processor 150 may estimate the state of the forest based on the ratio of the number of points among the multiple groups G1, G2, and G3. The ratio of the number of points among the multiple groups G1, G2, and G3 includes a characteristic pattern that indicates the state of the forest, and the degree of tree density, the proportion of inferior trees, and the like can be estimated from the pattern.

[0098] The 3D point cloud data shown in Fig. 9 and the altitude histogram shown in Fig. 11 show data for a healthy forest that is not an overcrowded forest stand. The 3D point cloud data shown in Fig. 10 and the altitude histogram shown in Fig. 12 show data for an overcrowded forest stand. Compared with the altitude histogram of the healthy forest (Fig. 11), the altitude histogram of the overcrowded forest (Fig. 12) shows that the ratio of the number of points included in the understory vegetation group G1 and the stem group G2 to the number of points included in the canopy group G3 is small.

[0099] For example, when the ratio of the number of points included in the understory vegetation group G1 and the trunk group G2 to the number of points included in the crown group G3 is equal to or less than a predetermined value, the processor 150 estimates that the forest is an overcrowded forest stand. For example, when the number of points included in the understory vegetation group G1 and the trunk group G2 is equal to or less than 5 percent of the number of points included in the crown group G3, the processor 150 estimates that the forest is an overcrowded forest stand. The value of 5 percent is an example and is not limited thereto. Note that such a ratio may be calculated by considering only the first pulses reflected from the crowns, trunks, understory vegetation, etc.

[0100] If the ratio of understory vegetation and trunks to the crown is below a certain level, the forest can be estimated to be overcrowded, and it can be determined that thinning of the forest is desirable.

[0101] The condition of the forest may also be estimated according to the degree of variation in the number of points per given width in the trunk group G2. In an overcrowded forest stand, low trees with poor growth conditions are scattered, and the variation in the number of points at a lower altitude than in the crown group G3 may be large. Compared to the altitude histogram of a healthy forest (FIG. 11), the altitude histogram of an overcrowded forest (FIG. 12) shows that the variation in the number of points per given width in the trunk group G2 is large. For example, the condition of the forest may be estimated by focusing on the degree of variation in the number of points per given width in the trunk group G2.

[0102] The condition of the forest may also be estimated based on the ratio of the number of points included in the understory vegetation group G1 to the number of points included in the crown group G3 and the trunk group G2. When the amount of understory vegetation is equal to or greater than a certain level, fertile soil suitable for tree growth may be formed. By focusing on the ratio of the number of points included in the understory vegetation group G1, the condition of the forest can be estimated.

[0103] Processor 150 stores information indicating the estimation result of the forest condition in storage device 140. Forest condition estimation system 100 may output the information indicating the estimation result of the forest condition to the outside, or may display it on display device 120. By displaying the estimation result of the forest condition on display device 120, the user can understand the estimation result of the forest condition.

[0104] The processor 150 may divide the forest measurement target into a plurality of areas and acquire the number of points per predetermined width for each area. By acquiring the altitude histogram of the point cloud as shown in FIG. 11 and FIG. 12 for each area, it is possible to grasp the state of the forest, which may differ depending on the location. The three-dimensional point cloud data includes position information indicating the geographic coordinates of the measurement target. The processor 150 can divide the three-dimensional point cloud data into a plurality of areas using the position information and acquire the number of points per predetermined width for each area.

[0105] (Process for generating an estimated model) FIG. 13 is a flowchart illustrating an example of a process for generating an estimation model by machine learning.

[0106] The communication IF 130 of the forest condition estimation system 100 acquires from an external device the 3D point cloud data obtained by forest measurement and forest condition information indicating the condition of the measurement target forest (step S20). The processor 150 stores the 3D point cloud data and forest condition information received by the communication IF 130 in, for example, the storage device 140.

[0107] The processor 150 reads the three-dimensional point cloud data from the storage device 140 and determines the absolute elevation of each of the multiple points included in the three-dimensional point cloud data. As described above, the processor 150 generates, for example, a DTM (digital elevation model) from the three-dimensional point cloud data and subtracts the DTM from the three-dimensional point cloud data to determine the absolute elevation of each of the multiple points.

[0108] As in the process of step S11 shown in Fig. 8, processor 150 divides the multiple points for which absolute altitudes have been determined into predetermined altitude intervals (step S21). As in the process of step S12 shown in Fig. 8, processor 150 obtains the number of points for each predetermined interval (step S22).

[0109] The processor 150 stores the number of points for each predetermined width and the forest condition information as a data set in the storage unit 104 (step S23).

[0110] The forest condition estimation system 100 acquires 3D point cloud data and forest condition information for each of a plurality of forests from an external device. The processor 150 acquires the number of points for each predetermined width for each of the plurality of forests, and stores a data set for each of the plurality of forests in the storage unit 104. The storage unit 104 stores a plurality of data sets.

[0111] The processor 150 uses multiple data sets stored in the storage unit 104 as teacher data and generates an estimation model by machine learning, with the input being the number of points per predetermined width and the output being the state of the forest (step S24). A known supervised learning method (such as binary classification or multi-class classification) can be used to generate the estimation model. For example, the estimation model is generated from the distribution pattern of the number of points in multiple types of healthy forests and the distribution pattern of the number of points in multiple types of overcrowded forests. The model generation unit 105 stores the generated estimation model in the storage unit 104.

[0112] In the above-mentioned process of estimating the state of the forest, the processor 150 can estimate the state of the forest from the number of points per predetermined width using an estimation model read from the storage unit 104. By using an estimation model generated by machine learning, it becomes possible to utilize correlations between the state of the forest and features hidden in the distribution pattern of the number of points per predetermined width (height histogram of the point cloud).

[0113] Next, another example of the hardware configuration of the forest condition estimation system 100 will be described.

[0114] FIG. 14 is a diagram showing another example of the hardware configuration of forest condition estimation system 100. In the example shown in FIG. 14, forest condition estimation system 100 includes a cloud server 201 and one or more user terminal devices 202. Each of cloud server 201 and user terminal devices 202 has the hardware configuration shown in FIG. 7, for example. A plurality of user terminal devices 202 may be connected via a local area network (LAN). Cloud server 201 and each user terminal device 202 may be communicatively connected to each other via network 210. Network 210 is, for example, the Internet, but is not limited thereto.

[0115] The above-mentioned "process of estimating a forest state" and "process of generating an estimation model" may be performed in cooperation between the cloud server 201 and the user terminal device 202. Furthermore, these processes may be performed in cooperation between a plurality of user terminal devices 202.

[0116] (Estimation of the number of recessive trees) Next, a method for estimating the number of recessive trees in a forest will be described.

[0117] When an unmanned helicopter 1 equipped with a LiDAR sensor 20 is flown to perform forest measurement from a relatively high position in the sky above the forest, the laser pulse 22 is mainly reflected by the treetops, and 3D point cloud data including a treetop point cloud is obtained. By calculating the number of treetops using the 3D point cloud data, information on the number of trees in the measurement area can be obtained.

[0118] On the other hand, the recessive trees 561 are shorter than the healthy trees 560, and when viewing the forest from above, they are often hidden under the canopies of the healthy trees 560, making it difficult to confirm the presence of the recessive trees 561. When the recessive trees 561 are hidden under the canopies of the healthy trees 560, the number of trees calculated from the number of tree canopies does not include the number of recessive trees 561.

[0119] As described above, by flying the unmanned helicopter 1 equipped with the LiDAR sensor 20 at a relatively low altitude, the laser pulses emitted from the LiDAR sensor 20 can reach the trunks of trees, and 3D point cloud data including a trunk point cloud can be obtained. This trunk point cloud includes point clouds of both the trunks of the healthy tree 560 and the trunks of the recessive tree 561, and the number of trunks can be calculated from the trunk point cloud.

[0120] The calculated number of trunks includes the number of trunks of the recessive trees 561. On the other hand, if the recessive trees 561 are hidden under the canopies of the healthy trees 560, the calculated number of canopies does not include the number of canopies of the recessive trees 561. For this reason, the number of recessive trees 561 in the measurement area can be estimated by subtracting the "calculated number of canopies" from the "calculated number of trunks."

[0121] For example, the processor 150 (FIG. 7) calculates the number of tree crowns and the number of trunks from the 3D point cloud data. The processor 150 can calculate the number of recessive trees 561 in the measurement area by subtracting the "calculated number of tree crowns" from the "calculated number of trunks". Based on the information on the number of recessive trees 561, the condition of the forest can be estimated.

[0122] Exemplary embodiments of the present invention have been described above.

[0123] A forest condition estimation system 100 according to one embodiment of the present invention comprises a data acquisition unit 101 that acquires three-dimensional point cloud data obtained by forest measurement, a number acquisition unit 102 that determines the altitude of each of a plurality of points included in the three-dimensional point cloud data, divides the plurality of points into predetermined altitude increments, and acquires the number of points for each predetermined altitude increment, and an estimation unit 103 that estimates the forest condition based on the number of points for each predetermined increment.

[0124] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest.

[0125] In one embodiment, the three-dimensional point cloud data may be three-dimensional point cloud data obtained by forest measurement performed by flying an unmanned aerial vehicle equipped with a LiDAR sensor.

[0126] 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), the laser pulse emitted from the lidar sensor reaches and is reflected not only by the tree crowns but also by the tree trunks and understory vegetation, etc. This makes it possible to obtain 3D point cloud data including not only the tree crown point cloud but also the trunk point cloud and understory vegetation point cloud.

[0127] In one embodiment, the estimation unit 103 may determine a digital elevation model from the three-dimensional point cloud data, and use the digital elevation model to calculate the elevation of each of the multiple points.

[0128] By using a digital elevation model determined from the latest 3D point cloud data, rather than using existing map data or digital elevation models, highly accurate values ​​for the elevation of each of multiple points can be obtained.

[0129] In one embodiment, the number density of the multiple points on a horizontal plane when each of the multiple points included in the three-dimensional point cloud data is projected onto the horizontal plane is 100 points / m 2 It may be more than that.

[0130] By increasing the density of the point cloud, it is possible to increase the amount of information that can be obtained from below the canopy, enabling a more detailed understanding of the condition of the forest.

[0131] In one embodiment, the number acquiring unit 102 may divide the forest measurement target into a plurality of areas and acquire the number of points per predetermined width for each area.

[0132] By acquiring altitude histograms of point clouds on an area-by-area basis, it is possible to understand the condition of the forest, which may differ depending on the location.

[0133] In one embodiment, the estimation unit 103 may group a plurality of points divided at a predetermined altitude increment into a plurality of groups having different altitudes, including an understory vegetation group G1, a trunk group G2, and a canopy group G3, and estimate the condition of the forest based on the number of points included in each of the plurality of groups.

[0134] The number of points included in each of the multiple groups reflects vegetation such as the presence of understory vegetation and trunks, and the amount of branches and leaves, and therefore makes it possible to estimate the type, amount, form, etc. of plants inhabiting the forest.

[0135] In an embodiment, the estimation unit 103 may estimate the state of the forest based on the ratio of the number of points among a plurality of groups.

[0136] The ratio of the number of points contained in each of the multiple groups contains a characteristic pattern that indicates the condition of the forest, and from this pattern it is possible to estimate the degree of tree density and the proportion of inferior trees, etc.

[0137] In one embodiment, the estimation unit 103 may estimate that the forest is an overcrowded stand if the ratio of the number of points included in the understory vegetation group G1 and the trunk group G2 to the number of points included in the crown group G3 is below a predetermined value.

[0138] If the ratio of understory vegetation and trunks to the crown is equal to or less than a predetermined value, the forest can be estimated to be an overcrowded stand, and it can be determined that thinning of the forest is desirable.

[0139] In one embodiment, the forest condition estimation system 100 further includes a memory unit 104 that stores the number of points per predetermined width and the state of the forest as a dataset, and a model generation unit 105 that uses the multiple datasets stored in the memory unit 104 as training data and generates an estimation model through machine learning, with the input being the number of points per predetermined width and the output being the state of the forest, and the estimation unit 103 may use the estimation model to estimate the state of the forest from the number of points per predetermined width.

[0140] By using an estimation model generated by machine learning, it is possible to utilize correlations between features hidden in the distribution pattern of the number of points per specified width (point cloud altitude histogram) and the condition of the forest.

[0141] A forest condition estimation system 100 according to one embodiment of the present invention is a system that estimates the condition of a forest using three-dimensional point cloud data obtained by forest measurement, and includes a processor 150 and a storage device 140 that stores a program that controls the operation of the processor 150. In accordance with the program, the processor 150 performs the following operations: acquires the three-dimensional point cloud data obtained by forest measurement, determines the altitude of each of multiple points included in the three-dimensional point cloud data, divides the multiple points into predetermined altitude increments, acquires the number of points for each predetermined altitude increment, and estimates the forest condition based on the number of points for each predetermined increment.

[0142] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest.

[0143] A forest condition estimation method according to one embodiment of the present invention is a method for estimating the condition of a forest using three-dimensional point cloud data obtained by forest measurement, and includes the steps of acquiring three-dimensional point cloud data obtained by forest measurement, determining the altitude of each of a plurality of points contained in the three-dimensional point cloud data, dividing the plurality of points into predetermined altitude increments, acquiring the number of points for each predetermined altitude increment, and estimating the forest condition based on the number of points for each predetermined increment.

[0144] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest.

[0145] A computer program according to one embodiment of the present invention is a computer program that causes a computer to estimate the condition of a forest using three-dimensional point cloud data obtained by forest measurement, and the computer program causes a computer to acquire the three-dimensional point cloud data obtained by forest measurement, determine the altitude of each of a plurality of points contained in the three-dimensional point cloud data, divide the plurality of points into predetermined altitude increments, acquire the number of points for each predetermined altitude increment, and estimate the condition of the forest based on the number of points for each predetermined increment.

[0146] According to an embodiment of the present invention, a plurality of points included in 3D point cloud data obtained by forest measurement are divided into predetermined altitude intervals, and the condition of the forest is estimated based on the number of points per predetermined interval. The number of points per predetermined interval (altitude histogram of the point cloud) contains information indicating characteristics such as the distribution of inferior trees in the forest. Therefore, by estimating the condition of the forest from the number of points per predetermined interval, it becomes possible to monitor the health and other conditions of the forest. [Industrial Applicability]

[0147] According to an embodiment of the present invention, a technique is provided that makes it possible to easily grasp the condition of a forest. [Explanation of symbols]

[0148] 1: unmanned helicopter, 2: main body, 3: tail body, 4: fuselage, 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: air pressure sensor, 15d: geomagnetic sensor, 15e: ultrasonic sensor, 15f: communication circuit, 15g: signal processing circuit, 15j: storage device, 15k: internal bus, 20: LiDAR sensor, 21: rotation axis, 22: laser pulse, 23: head, 25: bracket, 50: measurement target area, 52: slope, 54: forest, 56: tree, 56a: tree crown, 56b: trunk, 58: Understory vegetation, 59: Ground surface, 100: Forest condition estimation system, 101: Data acquisition unit, 102: Number acquisition unit, 103: Estimation unit, 104: Memory unit, 105: Model generation unit, 110: Input device, 120: Display device, 130: Communication IF, 140: Storage device, 150: Processor, 160: ROM, 170: RAM, 180: Bus, 201: Cloud server, 202: User terminal device, 210: Network, G1: Understory vegetation group, G2: Trunk group, G3: Crown group, 560: Healthy trees, 561: Recessive trees

Claims

1. a data acquisition unit for acquiring three-dimensional point cloud data obtained by forest measurement; a number acquisition unit that determines an altitude of each of a plurality of points included in the three-dimensional point cloud data, divides the plurality of points into a predetermined altitude interval, and acquires the number of points for each predetermined interval of the altitude; an estimation unit that estimates a forest condition based on the number of points for each predetermined width; Equipped with The estimation unit is The plurality of points divided by the predetermined elevation interval are grouped into a plurality of groups having different elevations, the groups including an understory vegetation group, a trunk group, and a crown group; estimating a state of the forest based on the number of points included in each of the plurality of groups; A forest condition estimation system that estimates that the forest is an overcrowded stand if the ratio of the number of points included in the understory vegetation group and the trunk group to the number of points included in the crown group is below a predetermined value.

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

3. The forest condition estimation system according to claim 1 , wherein the number acquisition unit determines a digital elevation model from the three-dimensional point cloud data, and calculates an elevation of each of the plurality of points using the digital elevation model.

4. When each of the plurality of points included in the three-dimensional point cloud data is projected onto a horizontal plane, the number density of the plurality of points on the horizontal plane is 100 points / m ２ The forest condition estimation system according to any one of claims 1 to 3.

5. The forest condition estimation system according to claim 1 , wherein the number acquisition unit divides a forest measurement target into a plurality of areas and acquires the number of points for each of the predetermined widths for each area.

6. The forest condition estimation system according to claim 1 , wherein the estimation unit estimates the forest condition based on a ratio of the number of points among the plurality of groups.

7. a storage unit that stores the number of points for each predetermined width and the state of the forest as a data set; a model generation unit that uses the plurality of data sets stored in the storage unit as training data to generate an estimation model by machine learning, the input of which is the number of points for each predetermined width, and the output of which is the state of the forest; Further equipped with The forest condition estimation system according to claim 1 , wherein the estimation unit estimates the state of the forest from the number of points within each of the predetermined widths using the estimation model.

8. A forest condition estimation system that estimates a forest condition using three-dimensional point cloud data obtained by forest measurement, comprising: A processor; a storage device that stores a program for controlling the operation of the processor; Equipped with The processor, according to the program, Acquiring three-dimensional point cloud data obtained by forest measurement; determining an elevation of each of a plurality of points included in the three-dimensional point cloud data, dividing the plurality of points into predetermined elevation intervals, and acquiring the number of points for each predetermined elevation interval; estimating a forest condition based on the number of points per predetermined width; Grouping the plurality of points divided by the predetermined elevation interval into a plurality of groups having different elevations, the groups including an understory vegetation group, a trunk group, and a tree crown group; estimating a state of the forest based on the number of points included in each of the plurality of groups; When a ratio of the number of points included in the understory vegetation group and the trunk group to the number of points included in the crown group is equal to or less than a predetermined value, the forest is estimated to be an overcrowded stand; A forest condition estimation system that performs the above.

9. A method for estimating forest conditions using three-dimensional point cloud data obtained by forest measurement, comprising the steps of: Acquiring three-dimensional point cloud data obtained by forest measurement; determining an elevation of each of a plurality of points included in the three-dimensional point cloud data, dividing the plurality of points into predetermined elevation intervals, and acquiring the number of points for each predetermined elevation interval; estimating a forest condition based on the number of points per predetermined width; Grouping the plurality of points divided by the predetermined elevation interval into a plurality of groups having different elevations, the groups including an understory vegetation group, a trunk group, and a tree crown group; estimating a state of the forest based on the number of points included in each of the plurality of groups; When a ratio of the number of points included in the understory vegetation group and the trunk group to the number of points included in the crown group is equal to or less than a predetermined value, the forest is estimated to be an overcrowded stand; A method for performing.

10. A computer program for causing a computer to execute an estimation of a forest condition using three-dimensional point cloud data obtained by forest measurement, comprising: The computer program comprises: Acquiring three-dimensional point cloud data obtained by forest measurement; determining an elevation of each of a plurality of points included in the three-dimensional point cloud data, dividing the plurality of points into predetermined elevation intervals, and acquiring the number of points for each predetermined elevation interval; estimating a forest condition based on the number of points per predetermined width; Grouping the plurality of points divided by the predetermined elevation interval into a plurality of groups having different elevations, the groups including an understory vegetation group, a trunk group, and a tree crown group; estimating a state of the forest based on the number of points included in each of the plurality of groups; When a ratio of the number of points included in the understory vegetation group and the trunk group to the number of points included in the crown group is equal to or less than a predetermined value, the forest is estimated to be an overcrowded stand; A computer program for causing the computer to execute the above.

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