JP2022092730A - Information processing device, tree growth rate prediction system, tree growth rate prediction method, and program - Google Patents

Abstract

To predict the growth rates of trees in correspondence to various densities in number of trees.SOLUTION: A management device comprises: an acquisition unit for acquiring an average tree age, an average thickness, and a density in number of trees, with regards to a plurality of trees planted in a prescribed area; a selection unit for selecting a density level curve, of which the distance from the average thickness and coordinates of the density in number in the prescribed area is shortest, from among a first, a second and a third density level curve; a first calculation unit for calculating, on the basis of the average tree age, a first component value that indicates a first component of the average growth rate, assuming the prescribed area as a forest corresponding to the selected density level curve; a second calculation unit for calculating, on the basis of a relationship corresponding to the selected density level curve, a second component value that indicates a second component of the average growth rate; and a prediction unit for adding up the first and the second component values and thereby predicting the average growth rate of the plurality of trees.SELECTED DRAWING: Figure 3

Description

The present invention relates to a technique for predicting the growth rate of a tree that can cope with various number densities.

Predicting tree growth in forestry is important for predicting profits. The conventional tree growth rate prediction can be applied only to forests in which the number of trees per unit area (hereinafter referred to as the number density) is managed in a narrow width.
On the other hand, at present, forests of various densities such as abandoned and overcrowded forests due to the deterioration of profitability of forestry as an industry, and low-density forests that aim to shift to environmental forests by inducing natural trees. It is necessary to manage and monetize.
Therefore, there has been a demand for a method for predicting the growth rate of trees that can handle these various tree density densities. Number density

In Patent Document 1, as a related technique, a means for collecting current information collects the diameter and height of a tree in a local area in a plurality of candidate plantations input from a plantation terminal, and the collected information is used as a candidate for tree planting. The current amount of carbon dioxide absorbed in the land is obtained, the plantation site determination means determines a candidate site whose carbon dioxide absorption amount is smaller than the first threshold value as the plantation site, and the planning means plantes in the plantation site. Calculate the profits when changing the tree species and the tree species recorded in the tree species database, select the tree species and planting area that can obtain the most profit, and select the tree species / planting area and tree growth. A technique has been proposed in which the predicted value of change can be approximated to the Mitcherrich equation, which is one of the growth curves, and recorded in the planning database as tree planting project planning information.
Further, Patent Document 2 measures the electromagnetic wave energy reflected from the measurement target from the sky by using a camera mounted on a satellite or an aircraft, and shows the state of vegetation based on the spectral characteristics shown by the measurement result. Techniques for calculating indicators are disclosed.

International Publication No. WO2004 / 046986 Japanese Unexamined Patent Publication No. 2008-79549

However, in the technique described in Patent Document 1, it is known that the density control of stands affects the diameter growth. When the density is high, the diameter becomes small, and when the density is low, the diameter becomes large. Since it may be assumed that the tree grows almost according to the diameter growth curve described above, it is sufficient to have a table corresponding to the forest age and the density of each tree species, and the growth rate of the tree corresponding to various number densities is predicted. There was the problem that it was difficult.
Further, with the technique described in Patent Document 2, even if the state of vegetation can be known, it is not possible to predict the growth rate of trees according to the number density.

An object of the present invention is to provide a technique for predicting the growth rate of a tree that can cope with various number densities.

The management device of the present invention is
For a plurality of trees planted in a predetermined area, an acquisition unit for acquiring the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point.
In the graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the average thickness of the first density level curve, the second density level curve, and the third density level curve. And a selection unit that selects the density level curve with the shortest distance from the coordinates of the number density in the predetermined region.
The predetermined region is assumed as a forest corresponding to the density level curve selected by the selection unit, and the first component value indicating the first component among the average growth rates of the plurality of trees is set based on the average age of the trees. The first calculation unit to calculate and
A second calculation unit that calculates a second component value indicating the second component of the average growth rates of the plurality of trees based on the relationship corresponding to the density level curve selected by the selection unit.
By summing the first component value and the second component value, the average thickness of the plurality of trees at the second time point after a predetermined period has passed from the first time point with respect to the average thickness at the first time point. It is equipped with a prediction unit that predicts the average growth rate of the plurality of trees, which is a ratio.
The number density of the forest corresponding to the first density level curve is set to be larger than the number density of the forest corresponding to the second density level curve and smaller than the number density of the forest corresponding to the third density level curve. ..

Further, the tree growth rate prediction system of the present invention is used.
It is a tree growth rate prediction system equipped with a management device, a user terminal, and an information collection device.
The user terminal transmits designated information for designating the predetermined area to the management device, and the user terminal transmits the designated information to the management device.
The information collecting device transmits the imaging data obtained by capturing the predetermined area from the sky and the position data indicating the position of the information collecting device at the time of imaging to the management device.
The management device is
A specific unit that specifies the predetermined area based on the designated information, and
It includes a generation unit that generates the number density of the predetermined region based on the image pickup data and the position data.

Further, the method for predicting the growth rate of trees of the present invention is:
For a plurality of trees planted in a predetermined area, the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point are obtained.
In the graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the average thickness of the first density level curve, the second density level curve, and the third density level curve. And select the density level curve with the shortest distance from the coordinates of the number density in the predetermined area.
The predetermined region is assumed as a forest corresponding to the selected density level curve, and the first component value indicating the first component among the average growth rates of the plurality of trees is calculated based on the average age.
Based on the relationship corresponding to the selected density level curve, the second component value indicating the second component of the average growth rate of the plurality of trees was calculated.
By summing the first component value and the second component value, the average thickness of the plurality of trees at the second time point after a predetermined period has passed from the first time point with respect to the average thickness at the first time point. Predict the average growth rate of the multiple trees, which is a ratio,
The number density of the forest corresponding to the first density level curve is set to be larger than the number density of the forest corresponding to the second density level curve and smaller than the number density of the forest corresponding to the third density level curve. ..

In addition, the tree growth rate prediction program of the present invention is
Information processing equipment,
For a plurality of trees planted in a predetermined area, an acquisition unit for acquiring the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point.
In the graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the average thickness of the first density level curve, the second density level curve, and the third density level curve. And a selection unit that selects the density level curve with the shortest distance from the coordinates of the number density in the predetermined region.
The predetermined region is assumed as a forest corresponding to the density level curve selected by the selection unit, and the first component value indicating the first component among the average growth rates of the plurality of trees is set based on the average age of the trees. The first calculation unit to calculate and
A second calculation unit that calculates a second component value indicating the second component of the average growth rates of the plurality of trees based on the relationship corresponding to the density level curve selected by the selection unit.
By summing the first component value and the second component value, the average thickness of the plurality of trees at the second time point after a predetermined period has passed from the first time point with respect to the average thickness at the first time point. It functions as a predictor that predicts the average growth rate of the plurality of trees, which is a ratio.
The number density of the forest corresponding to the first density level curve is set to be larger than the number density of the forest corresponding to the second density level curve and smaller than the number density of the forest corresponding to the third density level curve. ..

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to provide a technique for predicting the growth rate of a tree that can cope with various number densities.

It is a figure which shows the structure of the tree growth rate prediction system which concerns on 1st Embodiment of this invention. It is a block diagram of the user terminal which comprises the tree growth rate prediction system which concerns on this embodiment. It is a block diagram of the server which constitutes the tree growth rate prediction system which concerns on the same embodiment. It is a block diagram which shows an example of the storage contents of the database which concerns on the same embodiment. It is a graph which shows an example of a growth rate curve. It is a graph which shows an example of a density level curve. It is a graph which shows an example of the distribution of a tree. It is a graph which shows the change of the tree height by thinning. It is a figure which shows the state which the position of a tree is seen from directly above. It is a graph which shows an example of a tree height curve. It is a block diagram of the server which concerns on 2nd Embodiment of this invention. It is a flowchart explaining the flow of the process which provides the quantitative value of the forest based on the learning model of the tree growth rate prediction system which concerns on this embodiment. It is a figure which shows the output example in the tree growth rate prediction system which concerns on the same embodiment. It is a conceptual diagram explaining an example of the tree growth rate prediction system which concerns on this embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

1. 1. First Embodiment 1-1: Overall Configuration The configuration of the tree growth rate prediction system 1 according to the first embodiment of the present invention will be described with reference to FIG.

As shown in FIG. 1, the tree growth rate prediction system 1 includes one or more user terminals 100, one or more information collecting devices 200, and a server 300A.
Each is connected via a predetermined communication network such as the Internet. The predetermined communication network may be a LAN (Local Area Network) or the like, and may be wired or wireless.

The user terminal 100 is an information processing device that processes various types of information. The user terminal 100 includes storage means such as a CPU (Central Processing Unite), ROM (Read Only Memory), RAM (Random Access Memory), and input / output means. The control program is stored in the ROM. The CPU reads a control program from the ROM and executes the read control program to function as a control center of the user terminal 100. The input / output means has a function of accepting a user's operation and a function of presenting information to the user. The user terminal 100 is, for example, a smartphone, a small personal computer, a mobile terminal, or the like. The user terminal 100 may be able to use the service (software) provided by the server 300A by the cloud (SaaS / ASP).

The information collecting device 200 is a device provided with a sensor (camera, voice recorder, etc.) for sensing, and is, for example, a drone, a space satellite, a forestry machine attached to a sensor, or the like. The information collecting device 200 transmits the collected information to the user terminal 100 and the server 300A. The information collecting device 200 can collect information by receiving an instruction command from the user terminal 100 or the server 300A and operating according to the instruction / command. A device ID for uniquely identifying the device is assigned to one or more information collecting devices 200, and each information collecting device 200 stores the device ID.

The server 300A is an information processing device and has a function of predicting the growth rate of trees. The server 300A is an example of a management device. The server 300A is, for example, a workstation, a high-performance personal computer, or the like.

Each device constituting the tree growth rate prediction system 1 is mainly an information processing device, and various known techniques may be applied to these detailed configurations, and description other than the above will be omitted here. The tree growth rate prediction system 1 manages a region on the earth for each of a plurality of individual regions divided in a grid pattern. In the following explanation, management in Japan will be explained as an example, but it goes without saying that it may be applied to other countries. The size of the individual area is arbitrarily set, but for example, one individual area has a range of 100 m in length and 100 m in width, and the area of the individual area is 1 ha. The whole of Japan is divided into a plurality of individual areas, and an area ID for identifying the individual area is assigned to each of the plurality of individual areas. The individual areas may be 10 m in length and 10 m in width to 50 m in length and 50 m in width.

1-2: User terminal FIG. 2 is a block diagram showing the functions of the user terminal 100.
Referring to FIG. 2, the user terminal 100 includes a communication unit 110, an input unit 120, an output unit 130, a storage unit 140, a sensor unit 150, and a control unit 160. Each part may be realized by a known input / output I / F, a CPU, or the like, or by an HDD or the like.

The communication unit 110 has a function of communicating with each device connected to a predetermined communication network.

The input unit 120 has a function of receiving an input based on a user operation. The input unit 120 is composed of, for example, a touch panel, a microphone, or the like. Instead of the sensing data detected by the sensor unit 150 described later, the user may input the sensing data using the input unit 120. For example, the user can specify a specific point on the map by checking the map information displayed on the touch panel and tapping an arbitrary point on the map.

Here, the display of the map is executed hierarchically. For example, a map of Japan is displayed first, and when the user taps an arbitrary point on the map of Japan, a map in which the tapped point is enlarged is displayed. Furthermore, when the user taps the enlarged map, a more detailed map is displayed.
By repeating this operation, the user terminal 100 displays a detailed map of the area desired by the user. In this state, when the user taps a point on the map after pressing the inquiry button, the input unit 120 generates designated information indicating the latitude and longitude of the tapped point. The designated information is transmitted to the server 300A via the communication unit 110. A table for storing latitude and longitude and an area ID in association with each other is stored in the storage unit 140, and the input unit 120 generates an area ID by referring to the table, and the generated area ID. May be transmitted to the server 300A.
The input unit 120 may generate designated information by selecting a specific area from a pull-down list or the like.

The output unit 130 has a function of outputting various data such as image data and audio data by, for example, a display mechanism such as a display or an audio output mechanism such as a speaker.

The storage unit 140 has a function of storing a program for operating the information processing apparatus and various data.

The sensor unit 150 may acquire the location position of the user terminal 100, which is various devices for detecting various states of the user terminal 100, by the GPS function.

The control unit 160 has a function of executing various types of information processing. Data transmission / reception processing with the server device 300 is executed.

1-3: Server FIG. 3 is a block diagram showing the configuration of the server 300A. The server device 300 includes a processing device 10, a storage device 20, a communication device 30, an input device 40, and an output device 50.
The processing device 10 is a processor that controls the entire server 300A, and is composed of, for example, a single or a plurality of chips. The processing device 10 is composed of, for example, a central processing unit (CPU: Central Processing Unit) including an interface with peripheral devices, an arithmetic unit, registers, and the like.

The storage device 20 is a recording medium that can be read by the processing device 10, and stores a plurality of programs executed by the processing device 10 and various data used by the processing device 10. The plurality of programs include the prediction program PR. Further, various data include a learning data LD, a first table TBL1, a second table TBL2, and a database DB. The storage device 20 includes, for example, a non-volatile storage circuit such as a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), or an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), and a RAM (Random Access Memory). It is composed of.

In the first table TBL1, the area ID of the individual area, the latitude range of the individual area, and the longitude range of the individual area are associated and stored. By referring to the first table TBL1, it is possible to know the area ID corresponding to the individual area to which the point designated by the set of latitude and longitude belongs.

In the second table TBL2, the device ID that identifies the information collecting device 200 and the angle of view data indicating the angle of view of the camera mounted on the information collecting device 200 are stored in association with each other. By referring to the second table TBL2, it is possible to know the angle of view of the camera used by the information collecting device 200 designated by the device ID when imaging the ground.

A plurality of records are stored in the database DB. FIG. 4 shows an example of the stored contents of the database DB. The records stored in the database DB include the area ID, the average age of multiple trees planted in individual areas, the average thickness of multiple trees planted in individual areas, the density of individual areas, and the individual areas. Corresponds the tree species of multiple trees planted in and the update date of the record. The thickness of a tree is the diameter of the tree at a predetermined height from the surface of the earth. The thickness of the tree may be, for example, the diameter at breast height, which is the diameter of the tree at a height of about 1.3 m from the ground surface. That is, the average thickness may be, for example, the average breast height diameter. The number density is the number of trees per unit area for a plurality of trees planted in individual areas. Any area can be set as the unit area, but in this example it is 1 ha. Further, since the area of the individual area in this example is 1 ha, the number density and the number of trees planted in the individual area match. The database DB shown in FIG. 4 shows that 950 cedar trees are vegetated in the individual area corresponding to the area ID "0001", the average age is 10 years, and the average thickness is 15 cm.

The records stored in the database DB were measured based on the data obtained by the worker measuring a plurality of trees planted in individual areas and the imaged data captured by the information collecting device 200. Data is stored. The record update date is the date when the state of the trees in the individual area was measured. In addition, even in the same individual area, the record is updated when the state of the tree is newly measured. By making the stored contents of the database DB updatable, it is possible to improve the prediction accuracy when predicting the growth rate of trees in individual areas.

The communication device 30 communicates with another device via a mobile communication network or a communication network such as the Internet. The communication device 30 communicates with, for example, the user terminal 100 and the information collecting device 200. The input device 40 generates an operation signal according to the user's operation, and outputs the operation signal to the processing device 10. The input device 40 includes, for example, a keyboard, a touch panel, a potting device, a camera, a microphone, and the like. The output device 50 provides information to the user. The output device 50 corresponds to, for example, a display that provides image information and a speaker that provides audio information.
The communication device 30 receives from the user terminal 100 an inquiry or the like that specifies a point on the map to be evaluated for the quantitative value of the forest.

The processing device 10 reads the prediction program PR from the storage device 20 and executes the prediction program PR to execute the prediction program PR, thereby specifying the specific unit 301, the generation unit 302, the acquisition unit 303, the selection unit 304, the first calculation unit 305, and the second calculation unit. It functions as 306, a prediction unit 307, and an evaluation result providing unit 308.

The designated information transmitted from the user terminal 100 is supplied to the specific unit 301 via the communication device 30. The designated information specifies an individual area in which the user wants to know information about the tree. The designation information designates a point designated by the user, for example, by latitude and longitude. As mentioned above, the Japanese map is divided into a plurality of individual areas. Of the plurality of individual areas, the individual area designated by the user is referred to as a predetermined area. Since the designated information is information for designating an individual area in which the user wants to know information about the tree, it is information for designating a predetermined area. The specifying unit 301 specifies a predetermined area based on the designated information. Specifically, the specifying unit 301 specifies a predetermined area by referring to the first table TBL and acquiring the area ID corresponding to the designated information.

The generation unit 302 receives measurement data from the information collection device 200 via the communication device 30. The measurement data includes an apparatus ID for identifying the information collecting device 200, imaging data captured by the information collecting device 200, position data indicating the position of the information collecting device 200 at the time of imaging, and time data indicating the time of imaging. In the second table TBL2, the device ID that identifies the information collecting device 200 and the angle of view data indicating the angle of view of the camera mounted on the information collecting device 200 are stored in association with each other. The generation unit 302 refers to the second table TBL2 and acquires the angle of view data associated with the device ID.

The location data indicates latitude, longitude, and altitude. The generation unit 302 identifies which range on the ground the image pickup data has imaged based on the position data and the angle of view data. By analyzing the imaging data, the generation unit 302 generates tree data that specifies the position of the tree, the thickness of the tree, and the tree species that is the type of the tree for each one or a plurality of trees existing in the imaging range. .. The generation unit 302 generates a record in the database DB based on the tree data, and stores the generated record in the database DB. Further, the tree data may be stored in the database DB in association with the area ID and the tree ID for identifying the tree. Even if the server 300A acquires planting history data owned by the forest owner, data such as administration or forest book, etc. via the communication device 30, and specifies the age of the tree based on the acquired data. good. In this case, the tree data may include the age of the tree. In addition, tree data is recorded in advance in the database DB based on the planting history data owned by the forest owner, data such as administration or forest book, and based on the imaging data obtained by the information collecting device 200. , The thickness and species of trees contained in the tree data may be updated.

The generation unit 302 extracts an image of each tree from the captured image indicated by the captured data, and identifies the tree species based on the extracted image. The generator 302 further identifies the canopy based on the extracted image and estimates the thickness of the tree based on the identified canopy. In this estimation, the generation unit 302 uses, for example, a regression equation. Further, by selecting the regression equation according to the tree species, the generation unit 302 can improve the estimation accuracy of the thickness of the tree as compared with the case where one regression equation is used.

The generation unit 302 identifies an individual area to which the tree corresponding to the tree data belongs, based on the position indicated by the tree data. The generation unit 302 calculates the number density by accumulating the number of trees belonging to the individual area for each individual area. Further, the generation unit 302 calculates the average thickness by averaging the thicknesses of the trees belonging to the individual region.

The acquisition unit 303 acquires the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point for the plurality of trees planted in the predetermined area. Specifically, the acquisition unit 303 reads the record corresponding to the area ID of the predetermined area specified by the specific unit 301 from the database DB. Here, assuming that the first time point is the present, the acquisition unit 303 calculates the difference between the update date and the current date included in the record. The acquisition unit 303 adds the difference to the average tree age included in the record, and acquires the addition result as the average tree age at the first time point. Further, the acquisition unit 303 is planted in a predetermined area by operating the selection unit 304, the first calculation unit 305, the second calculation unit 306, and the prediction unit 307, which will be described later, with the update date as the current time. The average growth rate R of the tree may be predicted, and the average thickness at the first time point may be calculated based on the predicted average growth rate R, the difference, and the average thickness at the update date.

The server 300A uses the selection unit 304, the first calculation unit 305, the second calculation unit 306, and the prediction unit 307 to calculate the average growth rate R for a plurality of trees belonging to a predetermined region.
Here, the average growth rate R is given by the following formula 1.
R = _{DN + 1} / _DN ... Equation 1
However, _DN is the average thickness at the first time point, and _{DN + 1} is the average thickness after a predetermined period has elapsed from the first time point. The predetermined period can be set as appropriate, but in the following description, the predetermined period is one year. That is, the average growth rate is that for a plurality of trees belonging to a predetermined region, a plurality of trees one year later (a second time point after a predetermined period has passed from the first time point) with respect to the current (first time point) average thickness _DN . Is the ratio of the average thickness _{DN + 1} .

Further, the average growth rate R is given by the following formula 2.
R = R1 + R2 ... Equation 2
Here, R1 is a first component value indicating the first component of the average growth rate R, and R2 is a second component value indicating the second component of the average growth rate R. The first component is a component of the growth rate with respect to the age of the tree. On the other hand, the second component is a component of the growth rate related to the growing environment such as the number density and the thickness of the tree.

Next, the growth rate of the tree will be described separately for the first component and the second component. The first component is a component of the growth rate with respect to the tree age, and the first component value R1 is given by a function F (t) with the tree age t as a variable. Trees grow less when they are older than when they are younger (younger), and their growth rate declines. In addition, the growth rate of trees changes depending on the growing environment of trees.

FIG. 5 shows a growth rate curve defined by R1 = F (t), which is the first component.
The growth rate curve g1 shows the relationship between the first component value R1 and the age t in a forest in which the number density is controlled in a very narrow width. The growth rate curve g1 is a typical example of a forest to which traditional number density management is applied. The growth rate curve g1 is given by the following equation.
F (t) = P1 · e- ^K1t ... Equation 3
However, P1 and K1 are constants determined according to the number density of the forest.

The growth rate curve g2 shows the relationship between the first component and the age t in a forest in which the trees in the forest are vacant (the number density is low). In such a forest, the growth conditions such as sunlight are good, so the growth rate shifts to the upper right of the growth rate curve g1. The growth rate curve g2 corresponds to the upper limit density of trees that cannot promote the growth of trees in the forest even if the density of trees in the forest is reduced. The growth rate curve g2 is given by the following equation.
F (t) = P2 ・ e- ^K2t ... Equation 4
However, P2 and K2 are constants determined according to the density of the number of forests.

The growth rate curve g3 shows the relationship between the first component and the age t in a forest in a state where the trees in the forest are crowded (the number density is high). In such a forest, the growth rate curve g3 shifts to the lower left of the growth rate curve g1 due to poor growth conditions such as sunlight. The growth rate curve g3 corresponds to the limit number density at which natural mortality occurs when the average thickness increases as a plurality of trees planted in the forest grow further. The growth rate curve g3 is given by the following equation.
F (t) = P3 ・ e- ^K3t ... Equation 5
However, P3 and K3 are constants determined according to the number density of the forest.
As is clear from FIG. 5, the first component value R1, which is a component of the growth rate with respect to the age t, changes according to the number density in the forest.

Next, the second component will be described. The second component is a component of the growth rate related to the growing environment of trees, and the second component value R2 has the average thickness D of a plurality of trees planted in the forest and the density N of the number of forests as variables. Given by the function B (N, D).
R2 = B (N, D) =-bLogN-logD + C ... Equation 6
However, b and C are constants determined according to the environment of the forest.

Here, the relationship between the average thickness D and the number density N in the forest will be described for a plurality of trees planted in the forest. Hereinafter, a graph showing the relationship between the two when the vertical axis is logD and the horizontal axis is logN is referred to as a density level curve.

The density level curve G1 shown in FIG. 6A corresponds to the growth rate curve g1 shown in FIG. That is, the density level curve G1 targets a forest where the number density is traditionally managed with a very narrow width. The density level curve G1 is given by the following equation 7.
logD = -b1LogN + C1 ... Equation 7
However, b1 and C1 are constants determined according to the forest environment.

The points X having the number density Nx and the thickness Dx shown in FIG. 6A exist on the density level curve G1. In this case, b1 and C1 are set so that B (Nx, Dx) = 0. That is, the following equation 8 holds.
B (Nx, Dx) = −b1LogNx-logDx + C1 = 0 ... Equation 8
Therefore, in the forest corresponding to the density level curve G1, the average growth rate R of the trees is R = R1 + R2 = R1 = F (t) = P1 · e ^−K1t .

On the other hand, the point Y having the number density Ny and the thickness Dy is located below the density level curve G1.
The environment at the point Y (Ny, Dy) is lower in density than the forest corresponding to the density level curve G1 even though the average thickness of the trees is thin, and is an environment in which the growth conditions such as vacant state and sunlight are good. ..
In this case, R2 = B (Ny, Dy)> 0. Therefore, the second component value R2 contributes to the average growth rate R of the tree, and R> R1.

On the other hand, the point Z having the number density Nz and the thickness Dz is located above the density level curve G1.
The environment at the point Y (Nz, Dz) is higher in density than the forest corresponding to the density level curve G1 despite the thick average thickness of the trees, and is an environment in which the growth conditions such as crowded state and sunlight are poor. ..
In this case, R2 = B (Nz, Dz) <0. Therefore, the second component value R2 does not contribute to the average growth rate R of the tree, and R <R1.

Next, FIG. 6B shows the density level curve G1 plus the density level curve G2 and the density level curve G3.
The density level curve G2 corresponds to the growth rate curve g2 in FIG. Even if the trees in the forest are vacant (the density of trees is low), when a certain vacancy is reached, the influence of competition for sunlight will be reduced, and the effect of boosting the growth rate of trees will level off. The density level curve G2 is determined in consideration of. The density level curve G2 is a density level curve showing the relationship between the number density and the average thickness, which cannot promote the growth of a plurality of trees planted in the forest when the density of the number of trees in the forest is reduced. The density level curve G2 has the relationship shown in Equation 9.
logD = -b2LogN + C2 ... Equation 9
Further, the second component value R2 of the forest in the vicinity of the density level curve G2 is given by the following equation 10.
B (N, D) =-b2LogN-logD + C2 ... Equation 10

The density level curve G3 corresponds to the growth rate curve g3 in FIG. The density level curve G3 is a density level curve showing the relationship between the number density at which natural death occurs and the average thickness when the average thickness of a plurality of trees planted in the forest is increased. As multiple trees planted in a forest grow, the sun gradually gets worse. As a result, spontaneous death occurs. That is, there is a density level curve corresponding to the boundary between the state in which natural death does not occur and the state in which natural death occurs. The density level curve G3 is a density level curve corresponding to the boundary. The density level curve G3 has the relationship shown in Equation 11.
logD = -b2LogN + C2 ... Equation 11
Further, the second component value R2 of the forest in the vicinity of the density level curve G3 is given by the following equation 12.
B (N, D) =-b3LogN-logD + C3 ... Equation 12

Next, with reference to FIG. 6 (c), correspondence to the management of various number densities using the above three density level curves G1, G2, and G3 will be described.
For example, when the relationship between the density of the number of trees in a predetermined region and the average thickness is obtained as the density level curve Gα, the second component value R2 (= B (Nα, Dα)) corresponding to the density level curve Gα is obtained. I wish I could. In this case, it is necessary that the constants bα and Cα corresponding to the density level curve Gα are known.

However, it is not easy to set a constant in advance for the relationship between the density of all trees and the average thickness. Therefore, in the present embodiment, the forest environment is approximated by using three density level curves G1, G2, and G3.

Specifically, the coordinates defined by the density of the number of trees and the average thickness and the distances between the three density level curves G1, G2, and G3 are calculated, respectively, and the shortest density level curve is obtained. Select and select the constant corresponding to the selected density level curve.
For example, given the point α (Nα, Dα) shown in FIG. 6 (c), the density level curve with the shortest distance from the point α is the density level curve G3. Therefore, when the number density Nα in the predetermined region and the average thickness Dα, the second component value R2 is calculated by the following equation 13.
R2 = B (Nα, Dα) = −b3LogNα-logDα + C3 ... Equation 13
Similarly, for each density level curve according to various number densities (number densities), which of the three density level curves G1, G2, and G3 is located is determined, and the corresponding one is selected and adopted as a criterion. Will be done. Hereinafter, the selection unit 304, the first calculation unit 305, the second calculation unit 306, and the prediction unit 307 will be described.

The selection unit 304 shown in FIG. 3 is a graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the first density level curve, the second density level curve, and the third density. Among the level curves, the density level curve having the shortest distance from the coordinates of the average thickness of the predetermined area and the number density of the predetermined area is selected. Here, the number density of the forest corresponding to the first density level curve is larger than the number density of the forest corresponding to the second density level curve, and smaller than the number density of the forest corresponding to the third density level curve. That is, the three density level curves correspond to forests having different number densities.

In addition, the second density level curve shows the relationship between the number density and the average thickness that cannot promote the growth of a plurality of trees planted in the forest when the number density of the forest corresponding to the second density level curve is reduced. It is preferably a density level curve. That is, the second density level curve is preferably the above-mentioned density level curve G2.

Further, the third density level curve is preferably a density level curve showing the relationship between the number density at which natural death occurs and the average thickness when the average thickness of a plurality of trees planted in the forest is increased. .. As multiple trees planted in a forest grow, the sun gradually gets worse. As a result, spontaneous death occurs. That is, there is a density level curve corresponding to the boundary between the state in which natural death does not occur and the state in which natural death occurs. The third density level curve is a density level curve corresponding to the boundary. That is, the third density level curve is preferably the density level curve G3 described above.
Further, the first density level curve is preferably the above-mentioned density level curve G1.

The first calculation unit 305 assumes that the predetermined area is a forest corresponding to the density level curve selected by the selection unit 304, and based on the average age, the average growth rate R of a plurality of trees planted in the predetermined area. Of these, the first component value R1 indicating the first component is calculated.
Specifically, the first calculation unit 305 calculates the first component value R1 according to the following formula.
R1 = P ・ e- ^Kt
However, t is the average age of a plurality of trees planted in a predetermined area at the first time point, and P and K are positive constants assuming a forest corresponding to the density level curve selected by the selection unit 304. ..

The storage device 20 stores three constants P1, P2, and P3 for the constant P, and three constants K1, K2, and K3 for the constant K. The first calculation unit 305 reads the constant P1 and the constant K1 from the storage device 20 when the first density level curve is selected by the selection unit 304, and reads the constant P1 and the constant K1 from the storage device 20 when the second density level curve is selected. The constant P2 and the constant K2 are read, and when the third density level curve is selected, the constant P3 and the constant K3 are read from the storage device 20. The first calculation unit 305 calculates the first component value R1 by applying the set of read constants to the above equation.

The second calculation unit 306 is a plurality of plants planted in a predetermined area based on the relationship between the average value of the thicknesses of the plurality of trees in the forest corresponding to the density level curve selected by the selection unit 304 and the number density. The second component value R2 indicating the second component of the average growth rate R of the tree is calculated.
Specifically, the second calculation unit 306 calculates the second component value R2 according to the formula shown below.
R2 = B (N, D) =-bLogN-logD + C
However, N is the density of the number of trees in the predetermined area, D is the average thickness of a plurality of trees planted in the predetermined area, and b and C are determined according to the forest corresponding to the density level curve selected by the selection unit 304. It is a constant.

The storage device 20 stores three constants b1, b2, and b3 for the constant b, and three constants C1, C2, and C3 for the constant C. The second calculation unit 306 reads the constant b1 and the constant C1 from the storage device 20 when the first density level curve is selected by the selection unit 304, and reads the constant b1 and the constant C1 from the storage device 20 when the second density level curve is selected. The constant b2 and the constant C2 are read, and when the third density level curve is selected, the constant b3 and the constant C3 are read from the storage device 20. The second calculation unit 306 calculates the second component value R2 by applying the set of read constants to the above equation.

The prediction unit 307 calculates the sum of the first component value R1 and the second component value R2, and sets the calculation result as the average growth rate R of a plurality of trees planted in a predetermined area.

As mentioned above, the second component is a component of the growth rate of trees with respect to the environment of the forest. In this case, it is assumed that the forest environment does not change between the first time point and the second time point when the predetermined period elapses. However, thinning may be scheduled for a predetermined period. If appropriate thinning is carried out, the growing conditions such as sunlight will improve, and the growth rate of trees will tend to increase. Therefore, the prediction unit 307 may calculate the average growth rate R by the formula shown below.
R = R1 + ρR2
However, ρ is a correction coefficient and is determined according to the tree species and the thinning pattern scheduled to be carried out in a predetermined period.

More specifically, the correction factor ρ changes depending on the tree species (positive tree / shade tree) whether or not it requires more sunlight. A sun tree is a tree that grows in a place where the sunlight is sufficient, and includes pine trees and oak trees. Shade trees are trees with relatively low demand for sunlight, such as sugi and beech.
For example, even if the trees in the forest become crowded and the sunlight gets worse, the canopy of the shade tree is less likely to die than the canopy of the sun tree. ). Therefore, when the trees in the forest become vacant and the sun becomes sunny, the shade canopy is exposed to the sun, which contributes to the growth. Therefore, the average growth rate R of the shade tree may be larger than the average growth rate R of the positive tree. Therefore, the correction coefficient ρ may be larger (higher sensitivity) in the case of shade trees than in the case of positive trees. However, not only the distinction between positive tree and shade tree, but also the correction coefficient ρ differs depending on the tree species, so it is not always the case that ρ is positive tree <shade tree.

Next, it will be described that the correction coefficient ρ changes depending on the thinning (thinning) pattern based on FIG. 7.
FIG. 7 shows the vertical axis N: number density, the horizontal axis D: thickness, and the graph shows the distribution of trees.
Here, the line (a) means a case where many thin trees are thinned out. Many thin trees are also low in height and do not originally press other trees, so the correction coefficient ρ (for example, 1.6) is larger than 0, but relatively smaller than the line (b) below. (The effect of the correction is small).

On the other hand, the line (b) means that the thickness is thinned out evenly, and some of the trees in the high position of the forest are also cut down, which improves the sunlight of the trees in the low position and promotes the growth of the trees. , The correction coefficient ρ (for example, 2.0) is larger than that of the line (a) (the effect of the correction is relatively large).
On the other hand, the line (c) means a case where many thick trees are thinned out.

A correction table associated with a tree species, a thinning (thinning) pattern, and a correction coefficient ρ may be stored in the storage device 20, and the prediction unit 307 may acquire the correction coefficient ρ by referring to the correction table.
In this way, the prediction unit 307 can generate the second component value R2 by considering the influence of the tree species, the thinning pattern, and the like by the correction coefficient ρ. As a result, the prediction accuracy of the average growth rate R is improved.

FIG. 8 is a graph showing changes in tree height due to thinning. The vertical axis is H: height (tree height), the horizontal axis is t: tree age, and the average tree height is increased by thinning (in the case of line (a) in FIG. 7). This situation may be taken into consideration when calculating the forest age, average thickness, and the like.

Further, the prediction unit 307 predicts the region growth amount Q. The area growth amount Q is the growth amount of the entire plurality of trees planted in a predetermined area.
In order to predict the region growth amount Q, the prediction unit 307 calculates the average growth amount q of a plurality of trees planted in the predetermined region in a predetermined period according to the formula shown below.
q = (R-1) ・ D
However, R is the average growth rate of the plurality of trees, and D is the average thickness of the plurality of trees at the first time point.

Further, the prediction unit 307 predicts the region growth amount Q according to the formula shown below.
Q = n ・ q
However, n is the number of a plurality of trees planted in a predetermined area. That is, the prediction unit 307 calculates the region growth amount Q by multiplying the average growth amount q by the number n of a plurality of trees.

Next, the evaluation result providing unit 308 has a function of calculating and providing the result of quantitative value evaluation by numerical values such as the economic value of the forest. Specifically, the evaluation result providing unit 308 generates various image data. The various image data include image data showing the area growth amount Q at a position corresponding to a predetermined area of the map, and image data showing an average growth rate R at a position corresponding to a predetermined area of the map.
The evaluation result providing unit 308 provides the generated image data to the user terminal 100 via the communication device in response to the inquiry from the user terminal 100.

1-4: Modification example of the first embodiment The above-mentioned first embodiment can be modified as follows.
(1) In the first embodiment, the average growth rate R and the area growth amount Q of a plurality of trees planted in the predetermined area are predicted based on the average thickness, the average age, and the number density in the predetermined area. Of course, the growth rate and the amount of growth may be predicted for one tree. In this case, the average thickness may be replaced with the thickness of one tree, and the average age may be replaced with the age of one tree. Further, the number density may be the number density of the individual region to which one tree belongs.
In this case, the server 300A (an example of the management device) has the age of one of the plurality of trees planted in the predetermined area at the first time point, the thickness of the one tree at the first time point, and the first. In the graph showing the relationship between the acquisition unit for acquiring the number density of the predetermined area at one time point and the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the first density level curve and the second density. Among the level curve and the third density level curve, the selection unit for selecting the density level curve having the shortest distance from the coordinates of the thickness of the one tree and the number density of the predetermined region, and the predetermined region are described. Assuming a forest corresponding to the density level curve selected by the selection unit, the first calculation unit that calculates the first component value indicating the first component of the growth rate of the one tree based on the age of the tree, and the first calculation unit. Based on the relationship corresponding to the density level curve selected by the selection unit, the second calculation unit for calculating the second component value indicating the second component of the growth rate of the one tree, and the first component. By summing the value and the second component value, the ratio of the thickness of the one tree at the second time point after a predetermined period has passed from the first time point to the thickness of the one tree at the first time point. A prediction unit for predicting the growth rate of a certain tree is provided, and the number density of forests corresponding to the first density level curve is larger than the number density of forests corresponding to the second density level curve. It is smaller than the number density of the forest corresponding to the third density level curve.
(2) In the first embodiment, as shown in FIG. 6B and the like, the relationship between the average thickness D and the number density N in the forest for a plurality of trees planted in the forest is a density level curve. However, the degree of congestion of the forest may be evaluated by other indicators.
For example, as an index of the degree of congestion, even if the yield ratio (RY): the one showing the relative congestion when the maximum density (the upper limit density at a certain tree height) is set to 1. good. As an example, the density level curve G3 shown in FIG. 6B can be expressed as the case of RY1.
For example, if the yield ratio (RY) is 0.8 or more, it is too crowded, and when it reaches 0.8, it may be controlled to automatically output a logging alert.
With further development, it will be possible to output a logging plan with the maximum profit in total, and to calculate and output how much profit (profit amount) will be obtained by logging for each minute area.

(3) The age of the tree may be estimated based on a predetermined database that stores historical data at the time of tree planting using the subsidy. In addition, known tree age estimation techniques such as estimating tree age from tree species, tree thickness, height, etc. in consideration of forest conditions, ground conditions, etc. can be applied.

(4) FIG. 9 is a diagram showing a state in which the positions of trees are viewed from directly above. The position of the tree may be represented as a standing tree position map, and the position of each tree may be displayed at a point (latitude and longitude information) on the map as location information. It is output to the server 300A, the user terminal 100, or the like.
Based on the average growth rate R calculated by the prediction unit 307, the growth amount of each tree is predicted. Large trees grow large, and small trees grow small.
For example, the thickness after 5 years can be calculated by multiplying R to the 5th power.
For example, as shown in FIG. 9, the current thickness is 10 cm or 11 cm, and the evaluation result is provided to the server 300A, the user terminal 100, etc. as shown by the broken line showing the average thickness of the future predicted value after the growth of the tree. It may be output via the unit 308.
In the past, analysis was performed by average and variance in units of the entire forest such as 1ha, but it is possible to analyze how much it grows spatially by incorporating it into the growth of each tree. Furthermore, by aggregating the growth amount of each of these trees in individual regions (average, etc.), it is possible to connect to predictions for each individual region.

As shown in the tree height curve of FIG. 10, D: thickness can be converted to H: height (tree height). In general, there is a correlation between diameter and tree height, and it is statistically and empirically known that thick trees are high (thin trees are low). In addition, as the forest age increases, the slope becomes gentle while shifting upward.
It is also possible to analyze from the viewpoint of reducing the risk of mountain disasters (disaster prevention) based on the tension of the roots of large trees.

2. 2. 2nd Embodiment 2-1: Overall Configuration The tree growth rate prediction system 1 according to the 2nd embodiment has the same configuration as the 1st embodiment except that the server 300B is used instead of the server 300A. The differences will be described below. The server 300B is an example of a management device.

2-2: Server FIG. 11 is a block diagram showing the configuration of the server 300B. The server 300B according to the second embodiment is different from the server 300A shown in FIG. 3 in the following points. By reading the prediction program PR from the storage device 20 and executing the processing device 10, the specifying unit 301, the generation unit 302, the acquisition unit 303, the selection unit 304, the first calculation unit 305, the second calculation unit 306, and the prediction unit In addition to 307 and the evaluation result providing unit 308, it functions as a learning data storage unit 309 and a model generation unit 310.
Further, the learning data LD and the learning model M are stored in the storage device 20.

The learning data storage unit 309 generates a learning data LD (teacher data), and stores the generated learning data LD in the storage device 20. There are two modes of learning data LD. In the first aspect, the learning data LD is composed of the predicted data such as the average growth rate R predicted by the prediction unit 307 and the actually measured actual data acquired by the acquisition unit 303. In the second aspect, the learning data LD is the actual data actually measured acquired by the acquisition unit 303, which is the actual data of the parameter portion of the calculation, and the result of the calculation (quantitative value of the forest). It is composed of actual data.

The model generation unit 310 generates and stores a learning model M that learns the relationship between the prediction data and the actually measured actual data acquired by the acquisition unit 303 based on the learning data LD by the artificial intelligence (AI) module. It is stored in the device 20. Specifically, the model generation unit 310 machine-learns the relationship between the predicted data and the actual data by regression analysis or the like. As a method of regression, a known method such as linear regression, polynomial regression or logistic regression can be used.
Further, the model generation unit 310 is the actual data actually measured by the acquisition unit 303 based on the learning data LD (teacher data), and is the actual data of the parameter portion of the calculation, and the calculation result ( It may be possible to generate a learning model that learns the relationship with the actual data (quantitative value of the forest) and store it in the storage device 20. Specifically, the model generation unit 310 may execute machine learning by deep learning (deep learning) using a multi-layered neural communication network based on a large amount of learning data LD.
Further, the model generation unit 310 may generate a learning model M by other known machine learning or the like.

The evaluation result providing unit 308 corrects the prediction data predicted by the prediction unit 307 by the learning model M based on the area ID corresponding to the specific point on the map inquired by the user terminal 100, and evaluates the evaluation result data (correction). It is provided as a later estimated value / predicted value). The predicted value (before correction) such as the growth rate of the tree calculated by the prediction unit 307 may be provided without correction as it is.
Further, the evaluation result providing unit 308 is actually measured data to be evaluated by the acquisition unit 303 based on the area ID corresponding to the specific point to be evaluated on the map inquired by the user terminal 100. Therefore, evaluation result data (estimated value / predicted value) calculated by a learning model or the like by deep learning using a neural communication network may be provided based on the actual data of the parameter portion of the calculation.

Next, FIG. 12 (FIG. 12) shows two basic (general-purpose) processing operations when providing a quantitative value of a forest based on the learning model M of the tree growth rate prediction system 1 according to the present embodiment. It will be described with reference to the flowcharts of a) and (b).

First, referring to FIG. 12A, first, the communication device 30 of the server 300B receives an inquiry of a predetermined value of the forest at a specific point from the user terminal 100 (step S301). It may be possible to specify a specific point on the map by tapping the screen from any point on the Japanese map displayed on the user terminal 100. Of course, it may be specified by selecting a specific area from a pull-down list or the like.

Next, the evaluation result providing unit 308 causes the prediction unit 307 to solve the evaluation problem of the value of the forest (the type of value specified by the user) in the area ID corresponding to the point designated by the user, and before the correction. (Step S302).

Next, the evaluation result providing unit 308 corrects the prediction data by the learning model M generated by the model generation unit 310 (step S303).

Then, the evaluation result providing unit 308 provides the corrected value as the evaluation result data (corrected estimated value / predicted value) of the value of the forest (step S304).

Next, referring to FIG. 12B, as in step S301 above, first, the communication device 30 of the server 300B receives an inquiry about the predetermined value of the forest at a specific point from the user terminal 100 (step S301). ´).

Next, the evaluation result providing unit 308 solves the evaluation problem of the forest value (value of the kind specified by the user) in the area ID corresponding to the point designated by the user, and the parameter part of the calculation acquired by the acquisition unit 303. Based on the actual data of the above, the estimated value is calculated by solving with the learning model M or the like by deep learning using the neural communication network generated by the model generation unit 310 (step S302').

Then, the evaluation result providing unit 308 provides the calculated value as the evaluation result data (estimated value / predicted value) of the value of the forest (step S303').
The two basic (general-purpose) processing operations related to the learning model for providing the quantitative value of the forest have been described above. Even if it is not explicitly stated, the processing operation of the learning model can be applied in parallel or in an alternative manner in either of the two, and therefore the repeated description will be omitted in individual parts.

2-3: Database Next, the details of the database DB will be described. In order to make the UI (user interface) based on the map of the whole of Japan, the forests of the whole of Japan are divided into areas of "10m x 10m" to "50m x 50m" in a mesh shape, and 100 million to 2.5 billion points are individually specified. Positioned as a collection of regions, it forms a database platform for big data.

Map information is composed of mesh units separated into rectangles at regular latitude / longitude intervals. Further, each mesh is composed of a plurality of layers having different scales separated by a predetermined unit. In the case of Japan, the mesh may be, for example, adopting the standard of the standard area mesh defined by the Ministry of Internal Affairs and Communications. The standard area mesh is composed of a primary mesh, a secondary mesh, and a tertiary mesh in the order of about 1/10 of the area ratio. Further, as the mesh, a divided area mesh subdivided from the primary to tertiary mesh may be adopted as the mesh unit. When the map information is divided into mesh units, it has an area ID and corresponding latitude and longitude information.

Area IDs for identifying individual areas as each area are assigned to the rows of the table stored in the database DB.
The data stored in the columns of the table of the database DB is used to quantitatively evaluate the value of the forest, that is, there is some correlation with the value of the forest in view of common technical knowledge, and the value of the forest It is data that can be used as parameters for quantitative evaluation of value. For example, forest conditions (tree species, number (density), thickness (trunk diameter), height (tree height), tree age) in each region. , Tree position, etc.), Meteorological conditions (monthly temperature / precipitation / sunshine time / snowfall, wind speed / wind direction / maximum instantaneous wind speed, etc.), Geological conditions (latitude / longitude, altitude, terrain slope, position, curvature, valley) Density, water collection area estimation, disaster history (mountain collapse history), etc.), artificial conditions (childcare / thinning system, cutting period (period until cutting), thinning frequency, thinning rate, machines used, etc.), economic conditions (wood) Market price, wages, shipping costs, interest rates, etc.), system conditions (subsidies, tax rates, planning systems, etc.), forest space sensing data (outer shape of animals and plants, ridge sounds, wind sounds, animal calls, human voices, etc. The shape, smell, taste, etc. of the tree), and are stored in association with each area ID. In addition, it is used to quantitatively evaluate the value of forests, that is, there is some correlation with the value of forests in view of common technical knowledge, and parameters etc. are used when quantitatively evaluating the value of forests. The data available as may be associated with each area ID and stored in a column of the table. It is thought that specifications such as resolution of space satellites (observation satellites) will be updated, and include observation data from far outside the forest, such as irradiating laser light from high altitude to high density. Further, the meaning of each data of forest condition, meteorological condition, geological condition, man-made condition, economic condition, institutional condition and forest space sensing data in this embodiment is defined as a concept including the above examples.

As the initial data stored in the database DB, data already acquired throughout Japan (generally private) may be used. Since the foundation of the database DB has already been constructed so far, these data can be utilized.

The additional data added to the database DB includes actual data observed by the user (number, thickness, height of trees) and big data of the information collection device 200 (drone, space satellite, forestry machine with sensor, etc.). There is actual data (number of trees, thickness, height) collected as.
Further, as the additional data added to the database DB, there is actual data (profit amount, etc.) as a result of the actual execution by the user based on the estimated data (profit amount, etc.) by AI.
Furthermore, as additional data to be added to the database DB, results and information based on the results of big data analysis by AI will be added.
The basis is to link these information with the area ID as a key and store them in the database DB. It should be noted that the area ID may be used as a key to cooperate with various DBs.
Further, the database DB may be made into a cloud to improve usability.

2-4: UI / Output Example Based on the above database DB, a UI (user interface) based on a map of the whole of Japan as shown in FIG. 10 is used. The screen may be displayed on a display mechanism such as a display as the output unit 130 of the user terminal 100.
At the individual territory, basin, municipality, prefecture, national level, and in some cases the individual tree level, it is possible to accommodate various scales, and the wider layer is composed of a stack of lower layers on a scale. And the continuity of information between scales is guaranteed by using known techniques.

In other words, if you stack from a small scale to a large scale and focus on a certain point, you can grasp the point finely, while if you draw a viewpoint, you can understand a wide area well.
Here, the area ID is used as a key, and it is configured by using a known technique so that there is no contradiction in the total value of each layer, and there is also a contradiction between grasping the current situation between layers, prediction, optimization, and the like. It is configured so that it does not exist.

Figure 10 shows the individual areas (future) that can be profitable by logging according to the fluctuations in the profit amount (income-cost = profit) and the timber price (timber price) for each individual area based on the map of the whole of Japan. Profit depends on the timber price at the present time, but it is an example of visualizing and outputting (output) for each individual area that is still profitable even if there is a predetermined fluctuation based on the timber price at the present time.

FIG. 13 shows raster data or a vector as an aggregate of individual regions, such as a digital elevation model (DEM), by extracting individual regions that are economically viable (sustainable) in the long term according to fluctuations in material prices. This is an example shown in a map covering the entire area of Japan with one or both of the data. For example, the quantitative output of tree height, thickness, and number density is based on the type of tree, etc., for the set individual area, and the current state and future growth amount (trees are cut down at some point). It is necessary to include the viewpoint of sustainability instead of maximizing the profit at one point at any timing.) Is calculated and generated based on the above-mentioned tree growth rate prediction, and is newly generated from the present. The height, thickness, number density, etc. of trees for the future are generated and stored in the database. At that time, for example, a self-thinning rate (natural death rate) may be set, and a method for estimating the number density in consideration of future natural death may be used.

Then, for the set individual area, the timber profit amount from the present to the future is newly generated and stored in the database DB. For example, an interest rate of 1% may be set as a discount rate, and a valuation method based on the present value in consideration of future interest rates may be used. Based on the current subsidy rate (subsidy), based on the mode of annual market conditions (there is a market for material prices), the slope of the terrain (mountain disaster risk, sunlight), position (land productivity / fertility) This is an example that considers only the amount of water and nutrients (mostly near rivers). Even if the output takes into consideration other items (deer distribution / cost to drive away), it will output information for each individual area and expand it over a wide area while preserving detailed information from stands and basins to the entire area of Japan. It is also possible.

2-5: Software cloud service The server 300B provides at least a part of the functions of the server 300B shown in the above functional block by software, and the service (software) provided by the server 300A by the cloud (SaaS / ASP). Is used by the user from the user terminal 100 to access the database of the server 300A and exchange input / output information. To contribute.
With the above software, the database DB of the server 300B can be accessed from all over Japan, and can be linked with input / output data via the software of the user terminal 100 of users in all areas of the forest that occupy 70% of the country, and big data. It leads to the collection of.

2-6: AI analysis In the AI analysis server 300B, for example, the AI analysis generates a learning model from the obtained big data to determine how much profit the decision to cut in the forest should be made. To analyze.
The model generation unit 310 records and inputs, for example, the calculated estimated profit amount (= timber price, type, thickness, height, etc. of the profit item-topography around the cost item, wages, shipping cost, etc.). It may be to generate a learning model that learns the relationship with the actually measured (observed) profit amount obtained by doing so.

The output as a quantitative evaluation result by AI analysis is the profit amount (corrected estimated value / predicted value) itself as the evaluation result data of the forest value, but it can be further expanded to, for example, the expected profit. It may be an estimated value or a predicted value such as the range of the forest to be cut, the amount of timber production, the required labor amount, etc., which is calculated based on the amount. The output to be generated may be a real number such as wood production or a ratio such as the utilization rate of labor.
For example, the output of wood production is calculated based on the accumulation of the current situation and the future growth amount based on the tree growth rate prediction based on the type of tree etc. for each of multiple individual areas corresponding to the points on the map. The amount of wood production from the present to the future is generated and stored in the database. For example, a certain discount rate (interest rate), profit amount or profit rate is set, and profitability is taken in consideration of future interest rate and profit amount. You may count and output only the timber production that can be obtained, or you may use the logic to calculate the age of felling trees that maximizes profits.

Further, the model generation unit 310 autonomously performs the training data LD (the wood production amount of the above estimated data (corrected) and the wood production amount actually observed or sensed as the actual data input from the user terminal or the like. ), If there is an error above a predetermined threshold between the estimated value and the observed value, a new training model is generated by machine learning so as to minimize the error and improve the accuracy, and the training model. It may be corrected by the above to provide an output value such as a tree growth rate or a wood production amount. As a result, it becomes possible to efficiently automate the improvement of the database and software by new knowledge and feedback from big data, and the simulation, optimization, planning, etc. will be monitored afterwards, taking into account the linkage with the wide area communication network system. It is automatically corrected by the observed value of the real-time input information obtained by, and a system that does not become obsolete over time is realized. The learning model (predictable technique) may be expressed by a mathematical formula or the like and incorporated into software by adding it to the source code of the program.

Also, in AI analysis, we will recognize the rules without deciding the category by ourselves, or let us judge what data is missing and what data is needed for better performance. By automating a system that acquires big data by AI itself, it is possible to establish a general-purpose AI that enables autonomous improvement in a wide area.
As a specific algorithm, for example, the accuracy of the result may be verified. Conventionally, since only high-frequency data is collected in a large amount, the accuracy of the part with a small amount of data becomes poor and the error becomes large. Considering the reliability of the data used for prediction, if the required estimation (simulation) reliability is not satisfied, AI collects the data by itself via the communication network.

As shown in FIG. 14, on the horizontal axis (forest age) of the graph, there are many forests with a forest age of 40 years in the whole of Japan, and therefore, on the vertical axis (number of data), a large amount of data is collected and observed values (square marks). The deviation between the estimated value (triangle mark) is small, that is, the accuracy is high and the error is small. On the other hand, the number of 100-year-old forests is small, the amount of data is small, the difference between the observed value and the estimated value is large, and the reliability of the required estimation (simulation) may not be satisfied.

Therefore, if it is determined that the reliability of estimation (simulation) required by AI analysis is not satisfied (the difference between the observed value and the estimated value exceeds a predetermined threshold value), for example, flight by a drone as an information collecting device 200 Make a plan and output a plan such as what altitude and range to fly in order to collect the data according to the data required for prediction (estimation). Then, the drone is skipped so as to collect the data of the 100-year-old forest intensively, the data is collected, the estimated value is improved, and the difference between the observed value and the estimated value is within the allowable range (within the threshold value). It can be made smaller.

2-7: Spatial axis / time axis "Spatial axis": At the level of the whole of Japan, trees grow due to various factors such as temperature, precipitation, sunshine duration, status, etc., and Japan has four seasons and individual regional characteristics. ..
"Time axis": Consider not only the current time but also the time axis from the viewpoint of tree growth. For example, estimated data such as yearly, monthly, daily, etc. are calculated and stored in the database DB. The multifaceted functions of forests, described below, depend on changes in forest conditions. Therefore, by estimating the change in the state of the forest as long-term continuous data by predicting the growth rate of trees, it is possible to perform a simulation extended in the time direction exceeding 100 years. It is possible to make long-term simulations, optimizations, logging projects, etc. in the time direction through tree growth forecasts.

Specific examples of simulation methods in the time direction include the following.
As a premise, the type of the tree is determined based on the existing image data and the image data collected from the user terminal 100 and the information collecting device 200 (drone or the like). Specifically, the determination is made based on the feature amount of the tree such as the tree shape and the shape / color of the leaves. For this determination, the relationship between the feature quantity and the tree type can be determined by using a high-quality trained model generated by the model generation unit 310 by deep learning using a multi-layered neural network. good. The appearance of trees changes depending on the season. In addition, the appearance of trees changes depending on the altitude and latitude / longitude. Therefore, the type of the tree may be determined by including these elements in the feature amount.

Next, we predict the future of forests. There is growth according to the type of tree and the environment.
Environmental parameters for tree growth include, for example, temperature in each month, precipitation in each month, sunshine hours in each month, and number of trees per unit area.
The model generation unit 310 is a machine based on the learning data LD (for example, the growth rate predicted by the prediction unit 307 and the actually measured growth rate acquired by the acquisition unit 303 obtained by recording and inputting by the user). A learning model may be generated by learning, and the evaluation result providing unit 308 may provide the growth rate of the tree as evaluation result data (corrected estimated value / predicted value) after being corrected by the learning model. As a prediction result of the growth rate of trees, data on the size (thickness, height) of one tree is stored in the database for each of a plurality of individual areas (area IDs) corresponding to points on the map. The quantitative output generated as a result of predicting the growth rate of trees is the yield ratio (1 is 100%) even if it is a real number such as stand accumulation (volume m ³ of forests planted all at once). The number density is preferably 0.6 to 0.7, and if it is high, self-thinning starts). Further, for example, future climate change may be predicted from past climate change and reflected in the prediction result of the growth rate of trees.

2-8: Acquisition of physicality of wide-ranging AI Diverse forests throughout Japan with four seasons are vastly distributed. Until now, AI with physicality applicable to diverse and vast forests throughout Japan has not been realized.
As shown in Fig. 1, sensing by a large amount of information gathering device 200 by a national level communication network via a system targeting forests throughout Japan, for example, automatic forest photography by a drone or space satellite camera mechanism or laser irradiation. (Tree position, thickness, height, outline of animals and plants, etc.), automatic recording by voice recorder (ridges in the forest, wind, animal calls, human voices, etc.), sensing by sensor-attached forestry machines, etc. (The shape of the tree to be cut (straight is suitable for wood), thickness, length, etc.) is automatically acquired.

AI will acquire "physicality (five senses)" consisting of "eyes (visual = camera mechanism)", "ears (hearing = voice recorder)", "hands (tactile = sensors of forestry machines, etc.)" .. It may be provided with a "nose (smell)" odor sensor, a "tongue (taste)" taste sensor, and the like.
It should be noted that the harmonious control of a large number of drones may be performed. Harmonic control of a large number of drones refers to self-sacrificing overall optimization control that is difficult for humans (for example, in field observations in an emergency such as a forest fire, a safe route to the observation target site with the preceding drone group. It is a control such as exploring on the premise of self-damage, following the route of the returned drone, and conducting a full-scale exploration by the subsequent drone.)

As a utilization of the above sensing results,
(1) It can be used to calculate wood production, biodiversity, CO2 absorption in forests, and risk of mountain disasters by taking pictures in the forest.
For example, based on the sensing results of tree height, thickness, and number density obtained from observations taken in the forest for each of multiple individual areas corresponding to points on the map, the above-mentioned tree growth is based on the type of tree, etc. The rate can be calculated.
(2) It can be used for evaluation of biodiversity by voice recording or the like.
For example, the output of biodiversity in forests is based on data such as the types of birds obtained by discriminating from observations by voice recording in forests for each of multiple individual areas corresponding to points on the map. Deep learning (deep layer) using a multi-layered neural communication network from the generation of sex and the deforestation (the diversity decreases due to deforestation, but the diversity may increase in the entire forest even if partly deforested). (Learning), using the high-quality trained model generated by the model generation unit 310, the newly calculated biodiversity from the present to the future is generated and stored in the database. For example, the concept of Shannon's diversity index or indicator species may be set, and an evaluation method based on observation of a specific rare species may be used. The output to be generated may be a real number such as the number of species or a relativized index such as biodiversity.
(3) It can be used for evaluation such as evaluation of efficiency and profit amount of wood production based on the sensing result by the forestry machine attached to the sensor.
The model generator has a multi-layered structure based on the learning data LD (travel time calculated by the difference based on observations of forestry machines with sensors between two points on the map (cutting costs decrease in a short time)). Even if it is possible to provide output such as efficiency of wood production by using a high quality trained model generated by the model generator 310 by deep learning using the neural communication network of. good.

2-9: Example The system in this embodiment can be used as the basic material for accountability and consensus building of the government that has invested a large amount of tax by AI analysis, etc., as follows. Is possible. A high quality trained model generated by the model generation unit 310 may be used.
(1) If you want to minimize the risk of disaster prevention based on quantitative output, you can also determine the logging field and the time and space of logging timing when logging and replanting. For example, logging is linked to the installation of road networks (forest roads, work roads, etc.). Since the installation of the road network increases the risk of disaster prevention due to the weakening of the ground and the outflow of earth and sand, the area where the risk of disaster prevention is potentially high controls the collapse of the mountains by refraining from installing the road network and cutting down. It is possible to perform optimization calculations such as As the tree grows, the depth of the rhizome (the function of deepening the rhizome as it grows and rooting in cracks in the basement layer to prevent erosion and collapse of the ground), which affects disaster prevention risk, also changes, so it becomes huge over time. A time axis may be added to the estimation by forecasting the forest.
(2) Predict the risk of mountain disasters. Predict the risk of mountain disasters based on the type of trees, age of trees, and slope of terrain. The risk of mountain disasters may be represented by contour density per unit area and mapped. For example, the quantitative output of mountain disaster risk is to calculate the current risk and future risk based on the type of tree, for example, set random numbers and scenarios, and rain due to future guerrilla rainstorms. An evaluation method based on uncertainty or probability theory that considers events may be used. The output to be generated may be a real number such as the amount of timber damage expected due to a mountain disaster, or may be a ratio such as the probability of occurrence of a disaster (steep slope, based on the complexity of the terrain).
(3) For negotiations with individual forest owners, city planning for each municipality, consulting, etc., such as setting areas where logging is not profitable and the frequency of disasters is low as target areas for solar power generation. It will also be possible to utilize it. For example, the quantitative output may be a real number such as a profit amount or a ratio such as a disaster occurrence probability. Furthermore, it is possible to evaluate more advantageous land use options by comparing with the profitability of solar power generation.
(4) Predict the amount of CO2 absorbed (the amount of carbon dioxide absorbed). For example, the amount of CO2 absorbed per year is predicted based on the type, age, location, topography, etc. of the tree. The parameters of CO2 absorption are the frequency of thinning (frequency of thinning, 20% of area x 2 times, 40% of area x 1 time, balance with cost, balance with surrounding forests, at the timing when it becomes cheaper in total). And so on, the CO2 absorption amount is calculated by executing the calculation of the number density, the age of the tree, the position and the like. For example, the quantitative output of calculating the amount of CO2 absorbed by trees is to calculate the current status and future growth rate for the set individual areas based on the types of trees obtained from observations by flux towers in the forest. Then, the CO2 absorption amount and the like are newly generated based on the growth rate from the present to the future and the CO2 conversion rate set for each tree species. At that time, for example, a biomass expansion coefficient may be set, and an evaluation method based on the amount of CO2 absorbed in consideration of branches and leaves and rhizomes from the stem volume may be used.

According to the above-described embodiment, it is possible to support various number density management by using three density level curves. These can reflect the amount of growth of tree thickness, and the effects of artificial work such as thinning can be taken into consideration in detail.
In addition, as shown in FIG. 11, if there is insufficient data in estimating each parameter related to the growth rate of the tree, rare data is actively searched, Big Data is collected, and the parameter is corrected again from the collected data. However, by improving the prediction formula, repeating this process, and comparing the observed value with the estimated value by the prediction formula, an AI algorithm for further automation of accuracy improvement can be applied.
In addition, if the growth of each tree is predicted based on the above-mentioned tree growth rate prediction and the growth of each of these trees is aggregated in individual areas, it will lead to predictions in each layer such as for each individual area. You can also.
Further, by being provided as an application for a smartphone as the user terminal 100, the user can also confirm the growth prediction of the tree with a 3D image or the like.

Furthermore, according to the above-mentioned embodiment, the value such as the economic value of the forest including the viewpoint of tree growth is numerically calculated by utilizing the AI applicable to various and vast forests throughout Japan. It is possible to realize a technology for using forest data that can be evaluated quantitatively. And, as long as the above effect is obtained, it is needless to say that some of the processing steps may be changed in order, may be processed in parallel, or may be omitted, which is not essential.

It should be noted that the above-described embodiment is a preferred embodiment of the present invention, and various modifications can be made without departing from the gist of the present invention. For example, a process for realizing the function of each device / system may be performed by reading and executing a program for realizing the function of each device / system in each device / system. Further, the program is transmitted to another computer system by a transmission wave via a computer-readable recording medium such as a CD-ROM or a magneto-optical disk, or via a transmission medium such as the Internet or a telephone line. May be good. Further, some systems may be realized through human movement.
In addition, specific examples of the above-mentioned processes may be carried out in combination, and the known data and the like used at that time may be acquired from the communication network in cooperation with a known database or the like, or may be acquired thereof. The data may be stored in the database of this system and used. That is, the description of details that can be understood and realized by those skilled in the art from common general knowledge may be omitted as appropriate.

1 Tree growth rate prediction system 100 User terminal 200 Information collection device 300A, 300B server

As mentioned above, the second component is a component of the growth rate of trees with respect to the environment of the forest. In this case, it is assumed that the forest environment does not change between the first time point and the second time point when the predetermined period elapses. However, thinning may be scheduled for a predetermined period. If appropriate thinning is carried out, the growing conditions such as sunlight will improve, and the growth rate of trees will tend to increase. Therefore, the prediction unit 307 may calculate the average growth rate R by the formula shown below.
R = R1 + δ R2
However, δ is a correction coefficient, which is determined according to the tree species and the thinning pattern scheduled to be implemented in a predetermined period.

More specifically, the correction factor δ changes depending on the tree species (positive tree / shade tree) whether or not it requires more sunlight. A sun tree is a tree that grows in a place where the sunlight is sufficient, and includes pine trees and oak trees. Shade trees are trees with relatively low demand for sunlight, such as sugi and beech.
For example, even if the trees in the forest become crowded and the sunlight gets worse, the canopy of the shade tree is less likely to die than the canopy of the sun tree. ). Therefore, when the trees in the forest become vacant and the sun becomes sunny, the shade canopy is exposed to the sun, which contributes to the growth. Therefore, the average growth rate R of the shade tree may be larger than the average growth rate R of the positive tree. Therefore, the correction coefficient δ may be larger (higher sensitivity) in the case of shade trees than in the case of positive trees. However, not only the distinction between positive and negative trees, but also

Next, it will be described that the correction coefficient δ changes depending on the thinning (thinning) pattern based on FIG. 7.
FIG. 7 shows the vertical axis N: number density, the horizontal axis D: thickness, and the graph shows the distribution of trees.
Here, the line (a) means a case where many thin trees are thinned out. Many thin trees are also low in height and do not originally press other trees, so the correction coefficient δ (for example, 1.6) is larger than 0, but relatively smaller than the line (b) below. (The effect of the correction is small).

On the other hand, the line (b) means that the thickness is thinned out evenly, and some of the trees in the high position of the forest are also cut down, which improves the sunlight of the trees in the low position and promotes the growth of the trees. , The correction coefficient δ (for example, 2.0) is larger than that of the line (a) (the effect of the correction is relatively large).
On the other hand, the line (c) means a case where many thick trees are thinned out.

A correction table associated with a tree species, a thinning (thinning) pattern, and a correction coefficient δ may be stored in the storage device 20, and the prediction unit 307 may acquire the correction coefficient δ by referring to the correction table.
In this way, the prediction unit 307 can generate the second component value R2 by considering the influence of the tree species, the thinning pattern, and the like by the correction coefficient δ . As a result, the prediction accuracy of the average growth rate R is improved.

Claims (12)

For a plurality of trees planted in a predetermined area, an acquisition unit for acquiring the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point.
In the graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the average thickness of the first density level curve, the second density level curve, and the third density level curve. And a selection unit that selects the density level curve with the shortest distance from the coordinates of the number density in the predetermined region.
The predetermined region is assumed as a forest corresponding to the density level curve selected by the selection unit, and the first component value indicating the first component among the average growth rates of the plurality of trees is set based on the average age of the trees. The first calculation unit to calculate and
A second calculation unit that calculates a second component value indicating the second component of the average growth rates of the plurality of trees based on the relationship corresponding to the density level curve selected by the selection unit.
By summing the first component value and the second component value, the average thickness of the plurality of trees at the second time point after a predetermined period has elapsed from the first time point with respect to the average thickness at the first time point. A prediction unit that predicts the average growth rate of the plurality of trees, which is the ratio of
The number density of the forest corresponding to the first density level curve is larger than the number density of the forest corresponding to the second density level curve and smaller than the number density of the forest corresponding to the third density level curve.
A management device characterized by that. The second density level curve shows the relationship between the number density and the average thickness that cannot promote the growth of a plurality of trees planted in the forest when the number density of the forest corresponding to the second density level curve is reduced. The management device according to claim 1, wherein the control device is a density level curve. The third density level curve shows the relationship between the number density at which spontaneous death occurs and the average thickness when the average thickness of a plurality of trees planted in the forest corresponding to the third density level curve is increased. The management device according to claim 1 or 2, characterized in that it is a density level curve. In the first calculation unit, P and K are positive constants assuming a forest corresponding to the density level curve selected by the selection unit, the first component value is R1, and the plurality of units at the first time point. Assuming that the average age of the tree is t, the first component value is calculated by the following formula.
R1 = P ・ e- ^Kt
The management device according to any one of claims 1 to 3. In the second calculation unit, b and C are constants determined according to the forest corresponding to the density level curve selected by the selection unit, the second component value is R2, and the density level curve selected by the selection unit is used. When the average value of the thicknesses of a plurality of trees in the corresponding forest is D and the number density in the forest is N, the second component value is calculated by the following formula.
R2 = -blogN-logD + C
The management device according to any one of claims 1 to 4. The prediction unit
The correction coefficient is determined according to the tree species of the plurality of trees and the thinning pattern scheduled to be carried out during the predetermined period.
Instead of the sum of the first component value and the second component value, the sum of the multiplication result obtained by multiplying the second component value by the correction coefficient and the first component value is used as the average growth rate. Predict,
The management device according to any one of claims 1 to 5. The prediction unit
When the average growth rate of the plurality of trees is R, the average growth amount of the plurality of trees in the predetermined period is q, and the average thickness of the plurality of trees at the first time point is D, the following formula is followed. Predict the average growth of the multiple trees
q = (R-1) ・ D
Further, by multiplying the average growth amount by the number of the plurality of trees, the region growth amount, which is the growth amount of the entire plurality of trees planted in the predetermined region, is predicted.
The management device according to any one of claims 1 to 6. The management device according to claim 7, further comprising an evaluation result providing unit that generates image data indicating the amount of growth in the region at a position corresponding to the predetermined region on the map. A tree growth rate prediction system including the management device according to any one of claims 1 to 8, a user terminal, and an information collecting device.
The user terminal transmits designated information for designating the predetermined area to the management device, and the user terminal transmits the designated information to the management device.
The information collecting device transmits the imaging data obtained by capturing the predetermined area from the sky and the position data indicating the position of the information collecting device at the time of imaging to the management device.
The management device is
A specific unit that specifies the predetermined area based on the designated information, and
It is provided with a generation unit that generates the number density of the predetermined region based on the image pickup data and the position data.
A tree growth rate prediction system characterized by this. A tree growth rate prediction system including the management device according to claim 8 and a user terminal.
The user terminal transmits designated information for designating the predetermined area to the management device, and the user terminal transmits the designated information to the management device.
The management device is
A specific unit for specifying the predetermined area based on the designated information is provided.
By transmitting the image data to the user terminal, a map showing the amount of growth of the area is displayed on the user terminal at a position corresponding to the predetermined area.
A tree growth rate prediction system characterized by this. For a plurality of trees planted in a predetermined area, the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point are obtained.
In the graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the average thickness of the first density level curve, the second density level curve, and the third density level curve. And select the density level curve with the shortest distance from the coordinates of the number density in the predetermined area.
The predetermined region is assumed as a forest corresponding to the selected density level curve, and the first component value indicating the first component among the average growth rates of the plurality of trees is calculated based on the average age.
Based on the relationship corresponding to the selected density level curve, the second component value indicating the second component of the average growth rate of the plurality of trees was calculated.
By summing the first component value and the second component value, the average thickness of the plurality of trees at the second time point after a predetermined period has passed from the first time point with respect to the average thickness at the first time point. Predict the average growth rate of the multiple trees, which is a ratio,
The number density of the forest corresponding to the first density level curve is larger than the number density of the forest corresponding to the second density level curve and smaller than the number density of the forest corresponding to the third density level curve.
A method for predicting the growth rate of trees. Information processing equipment,
For a plurality of trees planted in a predetermined area, an acquisition unit for acquiring the average age at the first time point, the average thickness at the first time point, and the number density of the predetermined area at the first time point.
In the graph showing the relationship between the average value of the thicknesses of a plurality of trees in the forest and the number density in the forest, the average thickness of the first density level curve, the second density level curve, and the third density level curve. And a selection unit that selects the density level curve with the shortest distance from the coordinates of the number density in the predetermined region.
The predetermined region is assumed as a forest corresponding to the density level curve selected by the selection unit, and the first component value indicating the first component among the average growth rates of the plurality of trees is set based on the average age of the trees. The first calculation unit to calculate and
A second calculation unit that calculates a second component value indicating the second component of the average growth rates of the plurality of trees based on the relationship corresponding to the density level curve selected by the selection unit.
By summing the first component value and the second component value, the average thickness of the plurality of trees at the second time point after a predetermined period has passed from the first time point with respect to the average thickness at the first time point. It functions as a predictor that predicts the average growth rate of the plurality of trees, which is a ratio.
The number density of the forest corresponding to the first density level curve is larger than the number density of the forest corresponding to the second density level curve and smaller than the number density of the forest corresponding to the third density level curve.
A tree growth rate prediction program characterized by this.

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