Information processing device, information processing method, and information processing program

Abstract

To provide appropriate support for forest management. [Solution] The information processing device includes a receiving unit, a prediction unit, and an output control unit. The receiving unit receives a designation of a range for a forest displayed on map information. The prediction unit predicts output information for the forest included in the range based on statistical information generated from forest information about the forest. The output control unit controls the output information to be output to the user.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Conventionally, techniques relating to the use of forest data have been proposed. For example, Patent Document 1 discloses a method for quantitatively evaluating the value of forests. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-197084 Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned conventional technology, a quantitative evaluation result of forest value is calculated based on a learning model that is trained using predicted values ​​based on forest conditions, meteorological conditions, topographical conditions, human conditions, economic conditions, institutional conditions, and forest spatial sensing data for each area on a map, and actual measured values ​​of the quantitative value of forests in each area as learning data. Even if the above-mentioned conventional technology can use the evaluation results in forest management consulting work, there is room for improvement in terms of appropriate support for forest management.

[0005] Therefore, the above-mentioned conventional techniques may not necessarily be able to provide appropriate support for forest management.

[0006] The present invention has been made in view of the above, and provides an information processing device, an information processing method, and an information processing program that can appropriately support forest management. [Means for solving the problem]

[0007] In order to solve the above problem, one form of information processing device of the present invention comprises a reception unit that receives a specification of a range for a forest displayed on map information, a prediction unit that predicts output information for the forest included in the range based on statistical information generated based on forest information about the forest, and an output control unit that controls the output information to be output to a user. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an information processing system according to the first embodiment. [Figure 2] FIG. 2 shows an example of forest analysis using airborne laser surveying. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of a server device according to the first embodiment. [Figure 4] FIG. 4 is a flowchart showing the procedure of information processing according to the first embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the accuracy estimation process. [Figure 6] FIG. 6 is a diagram illustrating an example of the generation process. [Figure 7] FIG. 7 is a diagram showing a business model for a forest management business. [Figure 8] Figure 8 shows an overview of the forest management system. [Figure 9] FIG. 9 is a diagram illustrating an example of the configuration of an information processing system according to the second embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the configuration of a server device according to the second embodiment. [Figure 11] FIG. 11 is a diagram showing an outline of a method for generating the simulation model M. [Figure 12] FIG. 12 is a diagram showing an example of a method for generating the growth prediction simulation model M1. [Figure 13] FIG. 13 is a diagram showing how the range RA is specified. [Figure 14] FIG. 14 is a diagram showing elements necessary for generating the simulation model M11. [Figure 15] FIG. 15 is a diagram showing an example of a forest landscape map based on the simulation results. [Figure 16] FIG. 16 is a diagram showing a display example of the guide information RE. [Figure 17] FIG. 17 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the server device. DETAILED DESCRIPTION OF THE INVENTION

[0009] [Embodiment] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant explanations will be omitted.

[0010] One or more embodiments (including examples, modifications, and application examples) described below can be implemented independently. However, at least a portion of the embodiments described below may be implemented in appropriate combination with at least a portion of another embodiment. These embodiments may include novel features that are different from each other. Therefore, these embodiments may contribute to solving different purposes or problems and may produce different effects from each other.

[0011] First Embodiment 1. Introduction In Japan, where approximately 67% of the country is covered by forests, the importance of forest management as a means of reducing greenhouse gas emissions is clear. However, it is difficult for companies to directly and continuously manage forests and carry out afforestation in terms of profitability. For this reason, the use of J Credits (registered trademark) and other carbon credits is an effective means.

[0012] Meanwhile, proper forest management is an important issue for local governments and forest managers, who are the main players in forest management. Here, proper forest management does not simply mean increasing profits by selling cut timber, but also includes elements such as reducing costs through efficient resource management and operation (improving cost efficiency), quantitatively understanding and making transparent management activities and results (quantitative visualization), and maximizing the forest's CO2 absorption capacity to increase its environmental value (maximizing its role as a CO2 sink). Therefore, combining these elements is expected to lead to sustainable forest management.

[0013] J Credit is a system certified by the government that allows forest owners or businesses entrusted with forest management to sell the amount of CO2 absorbed through forest growth as "credits" by conducting appropriate forest management.

[0014] Furthermore, efforts are being promoted for forest owners and managers to generate and utilize forest-derived J-Credits in order to achieve the goal of achieving carbon neutrality. In generating J-Credits, it is conceivable that support will be provided for solving issues related to forest development and management, such as clarifying forest boundaries and promoting forest management systems, by utilizing airborne laser measurements, satellite monitoring, forest resource analysis technology, and spatial information processing technology.

[0015] For example, proper forest management requires a means of investigating the current state of tree species, age, and height, as well as changes over time, and highly accurate measurement technology is essential.Appropriate thinning, final cutting, and afforestation activities are carried out at regular intervals, and the results are verified through measurements.

[0016] The candidate measurement methods, in order of increasing accuracy, are visual inspection, UAV laser surveying, airborne laser surveying, and satellite surveying. UAV laser surveying, airborne laser surveying, and satellite surveying are all types of 3D surveying techniques, but UAV laser surveying and airborne laser surveying belong to aerial surveying techniques. On the other hand, satellite surveying belongs to laser surveying techniques using artificial satellites.

[0017] UAV laser surveying, airborne laser surveying, and satellite surveying differ in point density (elevation point density) and scope of application (scale of survey area). Specifically, point density increases and scope of application increases in the order of UAV laser surveying, airborne laser surveying, and satellite surveying. For this reason, for example, the area that can be measured in one day increases in the order of UAV laser surveying, airborne laser surveying, and satellite surveying, and the cost per unit area decreases in inverse proportion.

[0018] For expensive methods, the frequency of measurements has to be reduced. In tree height measurements for J-Credit and other forest resource surveys, the accuracy of other methods is compared on the assumption that data obtained by visual inspection is the most accurate data, and while a certain level of accuracy has been recognized for airborne laser surveys, satellite surveys have not been certified as official measurements due to issues with measurement accuracy.

[0019] For example, J-Credit has certified forest analysis methods that use data obtained by airborne laser surveying (airborne laser surveying data), but has not certified forest analysis methods that use data obtained by satellite surveying (satellite data).However, in order to conduct continuous measurements over a wide area at low cost, multifaceted analysis methods should be devised, including the use of satellite data.

[0020] [2. Overview of proposed technology] In view of the above problems, Proposed Technology 1 of the present invention (hereinafter referred to as "Proposed Technology 1") calculates a first amount of change, which is the amount of change in forest conditions in a specified area, by comparing time series information of airborne laser survey data (an example of first survey data) that has been certified by J Credit, and calculates a second amount of change, which is the amount of change in forest conditions in a specified area, by comparing time series information of satellite data (an example of second survey data) that has not been certified by J Credit. Then, Proposed Technology 1 estimates the accuracy (reliability) of the satellite data related to J Credit based on the difference between the first amount of change and the second amount of change.

[0021] Proposed Technology 1 allows for an appropriate evaluation of whether satellite data is reliable enough for J-Credit, a service that utilizes forest resources as a financial resource. Furthermore, if a certain level of reliability can be achieved in satellite data, it will become possible to utilize satellite surveying in environments and periods where airborne laser surveying is not possible, potentially making satellite surveying a complete substitute for airborne laser surveying. As mentioned above, satellite surveying is characterized by a higher point density and wider application range than airborne laser surveying. Therefore, by using satellite surveying instead of airborne laser surveying, it will be possible to collect the data required for forest analysis more efficiently, in larger quantities, and at lower cost.

[0022] [3. System Configuration] Fig. 1 is a diagram showing an example of the configuration of an information processing system according to the first embodiment. Fig. 1 shows an information processing system 1 as an example of the information processing system according to the first embodiment. Information processing according to the first embodiment (i.e., Proposed Technology 1) is realized in the information processing system 1.

[0023] As shown in Fig. 1, the information processing system 1 includes a user device 10, an external device 80, and a server device SV. Note that the information processing system 1 may include a plurality of user devices 10, a plurality of external devices 80, and a plurality of server devices SV. Fig. 1 shows a server device 100 as an example of the server device SV. The server device 100 is the server device SV according to the first embodiment.

[0024] The user device 10 is an example of an information processing terminal used by a user U. The user device 10 may be a smartphone, a wearable device, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. For example, an application for transmitting and receiving information to and from the server device 100 may be installed in the user device 10. Such an application may be a general-purpose application such as a web browser, or may be a dedicated application newly implemented in accordance with the present invention.

[0025] The user U here may be any of the forest owner, the local government, or the forest manager.

[0026] The external device 80 may be an information processing device that manages the first survey data OIF1 (airborne laser survey data), the second survey data OIF2 (satellite data), etc. Therefore, the server device 100 acquires these data from the external device 80 and stores them in the storage unit 120.

[0027] The first survey data OIF1 and the second survey data OIF2 may be 3D (three-dimensional) data. For example, the first survey data OIF1 and the second survey data OIF2 may be 3D point cloud data or 3D images generated based on the 3D point cloud data. The first survey data OIF1 may be an orthoimage generated from airborne laser survey data, and the second survey data OIF2 may be an orthoimage generated from satellite data.

[0028] The server device 100 is a central information processing device that performs information processing according to the first embodiment, and has the function of executing various forest analyses. According to the forest analysis function, the server device 100 can acquire tree information TIF1 for each forest area AR that is the measurement target, for example, based on forest analysis technology (tree vertex extraction method or laser forest type map) using aerial laser surveying. For example, the server device 100 can calculate, as the tree information TIF1, the tree species in the forest area AR, the number of trees in the forest area AR for each tree species, the height of each tree for each tree species, the breast height diameter of each tree, etc.

[0029] FIG. 1 shows an example in which the server device 100 acquires tree information TIF11 corresponding to one area AR1 of the forest area AR through forest analysis using aerial laser surveying. The tree information TIF11 includes calculation results such as the number of trees and tree height for each tree species. FIG. 1 also shows an example in which the server device 100 acquires tree information TIF12 corresponding to one area AR2 of the forest area AR through forest analysis using aerial laser surveying. The tree information TIF12 includes calculation results such as the number of trees and tree height for each tree species.

[0030] The server device 100 can also apply forest analysis techniques (tree vertex extraction techniques and laser forest type maps) to satellite surveying, thereby acquiring tree information TIF2 for each forest area AR being measured, just as with aerial laser surveying. For example, the server device 100 can calculate, as the tree information TIF2, the tree species in the forest area AR, the number of trees in the forest area AR for each tree species, the height of each tree for each tree species, the breast height diameter of each tree, etc.

[0031] FIG. 1 shows an example in which the server device 100 acquires tree information TIF21 corresponding to one area AR1 of the forest area AR through forest analysis using satellite surveying. The tree information TIF21 includes calculation results such as the number of trees and tree height for each tree species. FIG. 1 also shows an example in which the server device 100 acquires tree information TIF22 corresponding to one area AR2 of the forest area AR through forest analysis using satellite surveying. The tree information TIF22 includes calculation results such as the number of trees and tree height for each tree species.

[0032] Here, an overview of forest analysis using airborne laser surveying will be explained using Figure 2. Figure 2 is a diagram showing an example of forest analysis using airborne laser surveying. Figure 2 also shows the logic for calculating tree height using airborne laser surveying.

[0033] Airborne laser surveying is a surveying method using an aircraft equipped with a laser scanner. Unlike UAVs (unmanned aerial vehicles), airborne laser surveying uses manned aircraft such as helicopters and Cessnas, and the measurement range of a UAV such as a drone is about 100 meters above the ground, while a manned aircraft can measure at heights of more than 100 meters above the ground.

[0034] In airborne laser surveying, data is acquired by linking information from an aircraft-mounted laser scanner, GNSS receiver, and IMU. As shown in Figure 2, airborne laser surveying can generate 3D point cloud data based on the reflection (reflection intensity, reflection time) of a laser (electromagnetic wave such as a pulse signal). For example, the apex of a tree can be extracted from the shape of the tree surface shown in the 3D point cloud data, and the height of each tree can be calculated from the distance between the apex and the ground.

[0035] Although not shown in Figure 2, satellite surveying involves acquiring digital data using sensors mounted on artificial satellites, processing it into orthoimages, and then utilizing them for 3D surveying. Satellite data acquired from artificial satellites differs from airborne laser surveying in that it can be analyzed by dividing it into wavelength bands such as visible light and near-infrared light. Also, while airborne laser surveying observes from an altitude of around 300 to 3,000 meters above ground, satellite surveying takes images from an altitude of over 600 kilometers, which has the advantage of being able to observe a wider area at once compared to airborne laser surveying.

[0036] 4. Server Device Configuration The server device 100 according to the first embodiment will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the server device 100 according to the first embodiment. As shown in Fig. 3, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0037] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. For example, the communication unit 110 transmits and receives information to and from the user device 10 and the external device 80.

[0038] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or optical disk. The storage unit 120 may store, for example, data and programs related to the information processing according to the first embodiment. The storage unit 120 may also store first survey data OIF1 (airborne laser survey data) and second survey data OIF2 (satellite data).

[0039] (control unit 130) The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs (for example, the information processing program according to the first embodiment) stored in a storage device inside the server device 100 using RAM as a work area. The control unit 130 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0040] As shown in Fig. 3, control unit 130 has an acquisition unit 131, a calculation unit 132, an estimation unit 133, a determination unit 134, a storage unit 135, a generation unit 136, and a provision unit 137, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of control unit 130 is not limited to the configuration shown in Fig. 3, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between each processing unit included in control unit 130 is not limited to the connection relationship shown in Fig. 3, and may be other connection relationships.

[0041] (Acquisition part 131) The acquisition unit 131 acquires first survey data OIF1 (e.g., aerial radar survey data) obtained by surveying a specified area on the ground (forest area AR to be measured) using aerial survey technology (e.g., aerial radar survey) approved for use in service SA (i.e., J Credit) for utilizing forest resources as a financial resource.

[0042] In addition, the acquisition unit 131 acquires second survey data OIF2 (e.g., satellite data) obtained by surveying a specified area on the ground (the forest area AR to be measured) using satellite surveying technology (e.g., laser surveying by artificial satellite) that has not been approved for use in the service SA (i.e., J Credit) for utilizing forest resources as a financial resource.

[0043] (Calculation unit 132) The calculation unit 132 may be an example of a forest analysis function. For example, the calculation unit 132 calculates a first change amount ΔX, which is the amount of change in the condition of a predetermined area, by comparing multiple first survey data OIF1 with different time series. For example, the calculation unit 132 calculates the first change amount ΔX, which indicates the change in the first forest condition, by comparing the first forest condition, which is the forest condition in a predetermined area, measured for each first survey data OIF1 among the multiple first survey data OIF1. The first forest condition here may be an index indicating a change in the forest or an index indicating the growth of trees, such as tree species, tree age, tree height, and breast height diameter. Therefore, the first change amount ΔX may be, for example, a numerical value indicating the amount of change over time in tree species, tree age, tree height, breast height diameter, etc.

[0044] The calculation unit 132 also calculates a second change amount ΔY, which is the amount of change in the condition of a predetermined area, by comparing multiple sets of second survey data OIF2 with different time series. For example, the calculation unit 132 calculates the second change amount ΔY, which indicates the change in the condition of the second forest condition, by comparing the second forest condition, which is the forest condition in a predetermined area, measured for each set of second survey data OIF2 among the multiple sets of second survey data OIF2. The second forest condition here may be an index indicating a change in the forest or an index indicating the growth of trees, such as tree species, tree age, tree height, and breast height diameter. Therefore, the second change amount ΔY may be, for example, a numerical value that indicates the amount of change over time in tree species, tree age, tree height, breast height diameter, etc.

[0045] (Estimation part 133) The estimation unit 133 estimates the accuracy of the second survey data OIF2 related to the service SA (J credit) based on the difference D between the first change amount ΔX and the second change amount ΔY. For example, the estimation unit 133 estimates the measurement accuracy of the second forest condition as the accuracy of the second survey data OIF2 based on the difference D between the first change amount ΔX and the second change amount ΔY. For example, when a forest analysis is performed using satellite data obtained by satellite surveying, the estimation unit 133 estimates the degree of reliability of the measurement accuracy of the forest analysis compared to the measurement accuracy of a forest analysis using airborne radar surveying obtained by airborne radar surveying, based on the difference D between the first change amount ΔX and the second change amount ΔY. Note that the estimation unit 133 may estimate the accuracy for all accumulated second survey data OIF2.

[0046] (Judgment unit 134) The determination unit 134 determines whether the second survey data OIF2 can be used in the service SA based on the accuracy of the second survey data OIF2. For example, the determination unit 134 may determine that the second survey data OIF2 with an accuracy equal to or greater than a predetermined value can be used in the service SA, and may determine that the second survey data OIF2 with an accuracy less than the predetermined value cannot be used in the service SA.

[0047] (Storage unit 135) The accumulation unit 135 accumulates the second survey data OIF2 according to the usability determination result as big data, for example, in the memory unit 120. For example, the accumulation unit 135 may accumulate the second survey data OIF2 that is determined to be usable as big data.

[0048] (Generation unit 136) The generation unit 136 generates new second survey data OIF2 that can be used in the service SA based on second survey data OIF2 that has been determined to be unusable in the service SA because it does not have an accuracy equal to or greater than a predetermined value, and the first change amount ΔX corresponding to the second survey data OIF2. For example, the generation unit 136 combines second survey data OIF2 that has been used to calculate the second change amount ΔY between the second survey data OIF2 that has been determined to be unusable in the service SA and the first change amount ΔX to generate new second survey data OIF2 that can be used in the service SA.

[0049] This processing by the generation unit 136 is a process of replacing the second survey data OIF2 that has been determined to be unusable with newly generated second survey data OIF2, and can be said to be a process of correcting the second survey data OIF2 that has been determined to be unusable to a usable state.

[0050] The estimation unit 133 may estimate the accuracy so as to determine whether the second survey data OIF2 generated by the generation unit 136 can actually be used in the service SA. Furthermore, when the determination unit 134 determines based on the accuracy of the second survey data OIF2 generated by the generation unit 136 that the data can be used in the service SA, the accumulation unit 135 may accumulate the second survey data OIF2 generated by the generation unit 136 as big data.

[0051] (Providing Department 137) The providing unit 137 may provide the second survey data OIF2 accumulated as big data to a user, for example, as reliable survey data. The user here may be, for example, a local government that manages forests. The second survey data OIF2 accumulated as big data may be used in information processing according to a second embodiment, which will be described later.

[0052] [5. Example of Server Device Operation] From here, an operation procedure by the server device 100 for realizing information processing according to the first embodiment will be described. FIG. 4 is a flowchart showing the procedure of information processing according to the first embodiment. While FIG. 4 shows a method of estimating accuracy using tree height, accuracy estimation may also be performed using, for example, tree species, tree age, and breast height diameter following this method. Also, FIG. 4 shows an example of accuracy estimation based on observation of area AR1 among the forest areas AR to be measured, but similar processing may also be performed for other forest areas AR.

[0053] The acquisition unit 131 acquires first survey data OIF1_TM1 and first survey data OIF1_TM2 as first survey data OIF1 obtained by observing area AR1 (step S401). The first survey data OIF1_TM1 and the first survey data OIF1_TM2 have different observation times TM. For example, the first survey data OIF1_TM1 is data from 10 years ago, and the first survey data OIF1_TM2 is data from 5 years ago. For this reason, the first survey data OIF1_TM1 and the first survey data OIF1_TM2 are in different time series.

[0054] The acquisition unit 131 also acquires second survey data OIF2_TM1 and second survey data OIF2_TM2 as second survey data OIF2 obtained by observing area AR1 (step S402). The second survey data OIF2_TM1 and second survey data OIF2_TM2 have different observation times TM. For example, the second survey data OIF2_TM1 is data from 10 years ago, and the second survey data OIF2_TM2 is data from 5 years ago. For this reason, the second survey data OIF2_TM1 and second survey data OIF2_TM2 are in different time series.

[0055] Since it takes a long period of time to capture changes in forests over time, the information processing according to the first embodiment compares data from 10 years ago with data from 5 years ago. However, the timing TM at which the data obtained are used for comparison is not limited to this example.

[0056] The calculation unit 132 measures the tree height Th for each tree species N through forest analysis of the first survey data OIF1 (step S403a), and measures the tree height Th for each tree species N through forest analysis of the second survey data OIF2 (step S403b).

[0057] The calculation unit 132 calculates the tree height change ΔX (an example of the first change ΔX) by comparing the tree height Th at timing TM1 with the tree height Th at timing TM2 (step S404a). Specifically, the calculation unit 132 calculates the tree height change ΔX over five years by comparing the tree height Th, which is the result of the forest analysis of the first survey data OIF1_TM1, with the tree height Th, which is the result of the forest analysis of the first survey data OIF1_TM2.

[0058] The calculation unit 132 also calculates the tree height change ΔY (an example of the second change ΔY) by comparing the tree height Th at timing TM1 with the tree height Th at timing TM2 (step S404b). Specifically, the calculation unit 132 calculates the tree height change ΔY over five years by comparing the tree height Th, which is the result of the forest analysis of the second survey data OIF2_TM1, with the tree height Th, which is the result of the forest analysis of the second survey data OIF2_TM2.

[0059] The calculation unit 132 then calculates the difference D between the tree height change amount ΔX and the tree height change amount ΔY (step S405). The estimation unit 133 estimates an index value V indicating the accuracy of the second survey data OIF2 based on this difference D (step S406). For example, the estimation unit 133 may estimate the value of the difference D itself as the index value V. This allows the estimation unit 133 to estimate that the smaller the difference D, the closer the second survey data OIF2 is to the first survey data OIF1 and the higher its accuracy. The index value V can also be interpreted as the reliability of the data.

[0060] The determination unit 134 determines whether the second survey data OIF2 can be used in service SA (J credit) based on the index value V (step S407). For example, the determination unit 134 may determine whether the second survey data OIF2 can be used in service SA based on whether the index value V exceeds a predetermined threshold.

[0061] If it is determined that the second survey data OIF2 is usable in the service SA (step S407; Yes), the storage unit 135 stores the second survey data OIF2 that is determined to be usable as big data (step S408).

[0062] On the other hand, if the generation unit 136 determines that the second survey data OIF2 is unusable in the service SA (step S407; No), it generates new second survey data OIF2 that is estimated to be usable in the service SA based on the second survey data OIF2 that was determined to be unusable and the tree height change amount ΔX corresponding to the second survey data OIF2 (step S409).

[0063] [6. Specific methods for accuracy estimation processing] FIG. 4 shows an overall view of information processing according to the first embodiment. From here, a specific method of accuracy estimation in information processing according to the first embodiment will be described. FIG. 5 is a diagram showing an example of accuracy estimation processing. FIG. 5 shows a scene in which accuracy estimation is performed using first survey data OIF1 as a 3D image obtained by observing area AR1 by airborne radar surveying, and second survey data OIF2 as a 3D image obtained by observing area AR1 by satellite surveying, and corresponds to the example of FIG. 4.

[0064] 5(a) shows the first survey data OIF1_TM1 based on an aerial radar survey at time TM1, "May 2014." An example is shown in which the forest analysis in step S403a using the first survey data OIF1_TM1 yields a measurement result of "Th11" for the tree height of tree species N1.

[0065] 5(b) shows the first survey data OIF1_TM2 based on an aerial radar survey at the timing TM2 of "May 2019." An example is shown in which the forest analysis in step S403a using the first survey data OIF1_TM2 yields a measurement result of "Th12" for the tree height of tree species N1.

[0066] In this situation, in step S404a, the calculation unit 132 calculates the tree height change ΔX over a five-year period by comparing the tree height Th11, which is the result of forest analysis for the first survey data OIF1_TM1, with the tree height Th12, which is the result of forest analysis for the first survey data OIF1_TM2.

[0067] For ease of explanation, Figures 5(a) and 5(b) show an example in which a part of a forest in area AR1 has grown rapidly over a five-year period. Figure 5(c) shows an example in which the amount of tree height change ΔX due to rapid growth in this rapidly growing part is calculated using "tree height Th12 - tree height Th11".

[0068] Next, Figure 5(d) shows the second survey data OIF2_TM1 based on satellite surveying at time TM1, "May 2014." An example is shown in which the forest analysis in step S403b using the second survey data OIF2_TM1 yields a measurement result of "T211" for the tree height of tree species N1.

[0069] 5(e) shows second survey data OIF2_TM2 based on satellite surveying at timing TM2, "May 2019." An example is shown in which the forest analysis in step S403b using the second survey data OIF2_TM2 yields a measurement result of "Th22" for the tree height of tree species N1.

[0070] In this situation, in step S404b, the calculation unit 132 calculates the tree height change ΔY over a five-year period by comparing the tree height Th21, which is the result of forest analysis for the second survey data OIF2_TM1, with the tree height Th22, which is the result of forest analysis for the second survey data OIF2_TM2.

[0071] Figures 5(d) and 5(e) also show an example of rapid growth in a portion of the forest in area AR1 over a five-year period. Figure 5(f) shows an example of the amount of tree height change ΔY due to rapid growth in this rapidly growing portion calculated using "tree height Th22 - tree height Th21."

[0072] Therefore, in step S405, the calculation unit 132 calculates the difference D between the tree height change amount ΔX and the tree height change amount ΔY. In addition, in step S406, the estimation unit 133 estimates an index value V indicating the accuracy of the second survey data OIF2 based on this difference D.

[0073] Furthermore, if the index value V exceeds a predetermined threshold, the determining unit 134 may determine that both the second survey data OIF2_TM1 and the second survey data OIF2_TM2 are usable in the service SA.

[0074] Also, assume that a past accuracy estimation process has determined that the second survey data OIF2_TM1, for example, can be used in service SA. In this way, when the second survey data OIF2_TM1 is used as the comparison standard, the determination unit 134 may determine that only the second survey data OIF2_TM2 can be used in service SA.

[0075] Here, the artificial satellite orbits the Earth in a specific orbit and can observe the same point on the Earth's surface, for example, every 14 days. According to this example, the server device 100 can acquire second survey data OIF2 in which area AR1 is observed, every 14 days. According to this example, the server device 100 may accumulate second survey data OIF2 at 14-day intervals for the period from May 2014 to September 2014, for example. In this situation, if the server device 100 determines that second survey data OIF2_TM1 and second survey data OIF2_TM2 are available for use in service SA, the server device 100 may also determine that other accumulated second survey data OIF2 are available for use in service SA.

[0076] On the other hand, the server device 100 may also perform the accuracy estimation process on the other second survey data OIF2 in the same manner and determine whether each of them can be used in the service SA.

[0077] [7. Specific generation processing method] Next, a specific method of generating new usable second survey data OIF2 when it is determined that the second survey data OIF2 is unavailable in the service SA will be described. Fig. 6 is a diagram showing an example of the generation process. Fig. 6 shows an example of the generation process when the second survey data OIF2_TM1 in Fig. 5 is determined to be available in the service SA, but the second survey data OIF2_TM2 is determined to be unavailable in the service SA.

[0078] According to the example of Figure 6, the generation unit 136 may combine the second survey data OIF2_TM1 used to calculate the tree height change ΔY between the second survey data OIF2_TM2 and the tree height change ΔX used to calculate the difference D from the tree height change ΔY to generate new second survey data OIF2 that can be used in the service SA.

[0079] The second survey data OIF2_TM1 and the second survey data OIF1 each include location information. Therefore, the generation unit 136 may generate new second survey data OIF2 that is estimated to be usable for the service SA by applying the tree height change ΔX to the position on the second survey data OIF2_TM1 side that corresponds to the position where the tree height change ΔX was calculated, as shown in Figure 6.

[0080] Second Embodiment 1. Introduction Next, we will explain the second embodiment. For example, for proper forest management, in addition to highly accurate measurement technology, sophisticated simulation technology is also required. By optimally selecting thinning, final harvesting, and afforestation activities, CO2 absorption can be maximized. Furthermore, in today's world where climate change is occurring on a global scale, future climate change will also be an important factor in forest management. It will be necessary to predict changes in temperature and sunlight, select appropriate measures, and even change tree species depending on the predictions. Simulations that satisfy these factors are not available, and the problem is that forest management, which is essentially an effort that looks decades into the future, must be managed without any guidance.

[0081] It is also said that approximately 20% of the country's land area is unowned. In many cases, forests are unowned or unmanaged, so identifying the owner and guiding them to smoothly relinquish their rights when there is no intention to manage them can be said to be the foundation of forest management. Approximately 67% of unowned land is said to arise from inheritance, and in some cases local governments will send postcards to the person they consider to be the right holder. Land that has been left abandoned for many years can become overgrown beyond the landowner's imagination, but if the landowner continues to own it without properly understanding its current state, it can end up unused.

[0082] [2. Overview of proposed technology] In consideration of the above issues, Proposed Technology 2 of the present invention (hereinafter referred to as "Proposed Technology 2") analyzes optimal thinning, final cutting, and afforestation actions for each climate and tree species based on past forest history information, and predicts the future based on the analysis results. For example, Proposed Technology 2 outputs multiple solution patterns based on a simulation spanning several decades into the future that takes climate change into account, showing how CO2 absorption will change in relation to forest management costs if certain actions are taken at certain times. Proposed Technology 2 then selects several solutions that are considered close to the optimal solution and provides a service in the form of forest management consulting.

[0083] Proposed Technology 2 allows users to inherit appropriate support for forest management, making it easier to see the future of forest management and enabling them to compare costs and benefits and select appropriate measures. Proposed Technology 2 also makes it possible to compile forest management data between regions, which has never been shared before, into big data in one place, making it possible to use this data as a shared asset for optimizing future forest management in each region.

[0084] In addition, in consideration of the above issues, proposed technology 2 performs a cost simulation of forest management for unknown forests, forests whose owners or managers are unknown, or forests that the government wishes to encourage to be abandoned.

[0085] Proposed Technology 2 will enable users to inherit appropriate support for forest management, allowing them to correctly understand the current state of the forest and accurately recognize the costs required for its maintenance and utilization. Furthermore, with Proposed Technology 2, for example, if some (ideally as much as possible) of land that is difficult to utilize is abandoned and placed under the management of local governments, it can be expected that it will ultimately become part of the target of appropriate forest management.

[0086] [3. Business Model] Here, we will explain the business model for forest management proposed in Proposed Technology 2. Figure 7 is a diagram showing a business model for forest management business. For example, it is considered necessary to have forest management that can simultaneously realize the growth of forests as an industry and properly manage forest resources.

[0087] Therefore, the business model for Proposed Technology 2 is a service that visualizes forest resources by combining surveying data, AI simulation results (future predictions), and forest management know-how. As shown in Figure 7, the forest resource visualization service displays forest boundary information, tree information, information on the terrain where forests are formed, and guidance information to guide users U to abandon unknown forests.

[0088] Furthermore, forest boundary information may include information such as owners, forest compartments, and forest maps. Tree information may include information such as the number of trees, tree species, tree height, material quality, breast height diameter, and timber price. Topographic information may include microtopographic maps, contour maps, and slope classification maps. Guidance information may include unknown forest management costs, which are costs required for managing unknown forests in the future, and response costs, which are costs required to respond to disasters in the event of a disaster occurring in unknown forests or untended, overgrown forests.

[0089] Furthermore, the forest resource visualization service may provide future forest conditions predicted by AI simulation using survey data. The survey data here may be the first survey data OIF1 described in the first embodiment, or the second survey data OIF2 determined to be usable in the service SA by the information processing according to the first embodiment. By using the second survey data OIF2 determined to be usable in the service SA, it is believed that it will be possible to efficiently provide simulation results for forests in the area desired by the user U.

[0090] Furthermore, as mentioned above, even though many forests in Japan are approaching the full-scale harvesting period, there are issues such as forest management (logging and reforestation) not being carried out appropriately due to a decline in forest owners' motivation to manage their forests, as well as issues such as unknown owners and unclear boundaries, which require a lot of effort in forest management.Therefore, as shown in Figure 8, there is a forest management system in place in which user U1 (forest owner) entrusts forest management to user U2 (local government), and forests suitable for forestry management are then sub-entrusted to user U3 (private business operator).

[0091] Since the creation of J-Credits is also important in the forest management system, as shown in FIG. 8, the server device SV may provide the user U with survey data (first survey data OIF1 or second survey data OIF2). The server device SV may also perform information processing to support forest management based on the survey data (first survey data OIF1 or second survey data OIF2). For this reason, the server device SV may perform information processing to realize a forest resource visualization service. Below, information processing related to the second embodiment will be described as information processing to realize the forest resource visualization service.

[0092] [4. System Configuration] Fig. 9 is a diagram showing an example of the configuration of an information processing system according to the second embodiment. Fig. 9 shows an information processing system 2 as an example of the information processing system according to the second embodiment. Information processing according to the second embodiment (i.e., Proposed Technology 2) is realized in the information processing system 2.

[0093] As shown in Fig. 9, the information processing system 2 includes a user device 10, an external device 80, and a server device SV. Note that the information processing system 2 may include a plurality of user devices 10, a plurality of external devices 80, and a plurality of server devices SV. Fig. 9 shows a server device 200 as an example of the server device SV. The server device 200 is a server device SV according to the second embodiment.

[0094] The user device 10 is as shown in Figure 2, so a detailed description will be omitted. The external device 80 may manage not only the first survey data OIF1 (airborne laser survey data) and the second survey data OIF2 (satellite data), but also forest information FrIF related to forests. Therefore, the server device 200 acquires the forest information FrIF from the external device 80 and stores it in the memory unit 220.

[0095] The server device 200 is a central information processing device that performs information processing according to the second embodiment, and has a function for realizing the forest resource visualization service described in Fig. 7. The server device 200 may be configured by adding a function for realizing the forest resource visualization service to the server device 100 according to the first embodiment.

[0096] As shown in Figure 9, in addition to tree information TIF1 obtained by forest analysis using aerial laser surveying, the server device 200 may also have data linked to the first survey data OIF1, such as CO2 information KIF1, disaster information DIF1, topographical information LIF1, weather information WIF1, and unknown forest information NIF1.

[0097] The CO2 information KIF1 is information on the amount of CO2 absorbed by the forest calculated based on the first survey data OIF1 corresponding to the forest area AR being measured. The disaster information DIF1 is information indicating disasters that have occurred in the forest area AR in the past. The topography information LIF1 is information indicating the topography of the forest area AR. The weather information WIF1 is information regarding weather that has occurred in the forest area AR in the past.

[0098] The unknown forest information NIF1 is information that indicates the location of unknown forests, which are forests whose owners or managers are unknown. Furthermore, as shown in Figure 9, the unknown forest information NIF1 may include cost information CIF1 that indicates management costs or response costs. Management costs are unknown forest management costs, which are costs required for managing the unknown forest in the future, and response costs are compensation, which are costs required for responding to disasters if they occur in unknown forests or untended, overgrown forests.

[0099] In addition to tree information TIF2 obtained by forest analysis using satellite surveying, the server device 200 may also have data linked to the second surveying data OIF2, such as CO2 information KIF2, disaster information DIF2, topographical information LIF2, weather information WIF2, and unknown forest information NIF2.

[0100] The CO2 information KIF2 is information on the amount of CO2 absorbed by the forest calculated based on the second survey data OIF2 corresponding to the forest area AR being measured. The disaster information DIF2 is information indicating disasters that have occurred in the forest area AR in the past. The topography information LIF2 is information indicating the topography of the forest area AR. The weather information WIF2 is information on weather that has occurred in the forest area AR in the past.

[0101] The unknown forest information NIF2 is information that indicates the location of unknown forests, which are forests whose owners or managers are unknown. Furthermore, as shown in Figure 9, the unknown forest information NIF2 may include cost information CIF2 that indicates management costs or response costs. Management costs are unknown forest management costs, which are costs required for managing the unknown forest in the future, and response costs are compensation, which are costs required for responding to disasters in the event of a disaster occurring in an unknown forest or an untended, overgrown forest.

[0102] Note that the forest information FrIF is not limited to the example of Fig. 11 and may further include information on the hours of sunshine in the forest area AR, the amount of rainfall in the forest area AR, and the temperature in the forest area AR. The forest information FrIF may also further include information on final cutting, thinning, or reforestation in the forest area AR.

[0103] The server device 200 may also have forest planning data PLDA. The forest planning data PLDA may include a forest planning map and a forest register.

[0104] 5. Server Device Configuration The server device 200 according to the second embodiment will be described with reference to Fig. 10. Fig. 10 is a diagram showing an example of the configuration of the server device 200 according to the second embodiment. As shown in Fig. 10, the server device 200 includes a communication unit 210, a storage unit 220, and a control unit 230.

[0105] (Communication unit 210) The communication unit 210 is realized by, for example, a NIC etc. For example, the communication unit 210 transmits and receives information to and from the user device 10 and the external device 80.

[0106] (Storage unit 220) The storage unit 120 is realized by, for example, a semiconductor memory element such as RAM or flash memory, or a storage device such as a hard disk or optical disk. The storage unit 220 may store, for example, data and programs related to the information processing according to the second embodiment. The storage unit 220 may also store forest information FrIF.

[0107] (control unit 230) The control unit 230 is realized by a CPU, an MPU, or the like executing various programs (for example, the information processing program according to the second embodiment) stored in a storage device inside the server device 200 using the RAM as a work area. The control unit 230 is also realized by an integrated circuit such as an ASIC or an FPGA.

[0108] 10, the control unit 230 has an analysis unit 231, a generation unit 232, a reception unit 233, a prediction unit 234, and an output control unit 235, and realizes or executes the functions and actions of the information processing described below. Note that the internal configuration of the control unit 230 is not limited to the configuration shown in FIG. 10, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between the processing units included in the control unit 230 is not limited to the connection relationship shown in FIG. 10, and may be other connection relationships.

[0109] The control unit 230 corresponds to the control unit 130 of the server device 100, and may also include an acquisition unit 131, a calculation unit 132, an estimation unit 133, a determination unit 134, a storage unit 135, a generation unit 136, and a provision unit 137, although not shown.

[0110] (Analysis Department 231) The analysis unit 231 may acquire the forest information FrIF by analyzing the observation data. For example, the analysis unit 231 may acquire the forest information FrIF required to generate a simulation model by analyzing the first survey data OIF1. The analysis unit 231 may also acquire the forest information FrIF required to generate a simulation model by analyzing the second survey data OIF2.

[0111] (Generation unit 232) The generation unit 232 generates a simulation model M (an example of statistical information) by recursively analyzing the forest information FrIF. Here, an example of generation of the simulation model M (regression model) by the generation unit 232 will be described with reference to FIG.

[0112] Fig. 11 is a diagram showing an overview of a method for generating a simulation model M. First, Fig. 11(a) shows an example of generating a growth prediction simulation model M1. The generation unit 232 may perform multiple regression analysis by trying combinations of forest information FrIF obtained by analyzing the first survey data OIF1 as an explanatory variable and tree information TIF measured in a field survey as a target variable, and select the optimal regression equation with the highest degree of fit as the growth prediction simulation model M1, which is a prediction equation for predicting changes in forest conditions.

[0113] In addition, the forest information FrIF may use tree species, tree height, breast height diameter, etc. as indicators indicating changes in the forest or indicators indicating tree growth, and in such an example, the generation unit 232 can generate a tree species prediction formula, tree height prediction formula, breast height diameter prediction formula, etc. as the growth prediction simulation model M1.

[0114] The server device 100 can predict the changes over time (growth amount) of the forest within the range RA, such as how it will change in the future, by inputting forest information FrIF of the forest in the range RA specified for map information MP (e.g., an orthoimage) generated based on the first survey data OIF1 into the growth prediction simulation model M1.

[0115] 11(b) shows an example of generating a CO2 absorption prediction simulation model M2. The generation unit 232 may perform a multiple regression analysis by trying combinations of forest information FrIF obtained by analyzing the first survey data OIF1 as an explanatory variable and CO2 emission KIF calculated based on the actual measured annual stem growth volume per hectare of forest as a target variable, and select the optimal regression equation with the highest degree of fit as the CO2 absorption prediction simulation model M2, which is a prediction equation for predicting CO2 emissions due to changes in forest conditions. Note that the generation unit 232 may also use information on sunshine hours, rainfall, weather, or temperature as the forest information FrIF as an explanatory variable.

[0116] The server device 100 can predict the CO2 absorption amount according to the changes (growth amount) over time of the forest within the range RA by inputting forest information FrIF of the forest in the range RA specified for the map information MP (e.g., orthoimage) generated based on the first survey data OIF1 into a CO2 absorption amount prediction simulation model M2.

[0117] 11(c) shows an example of generating a disaster prediction simulation model M3. The generation unit 232 may perform multiple regression analysis by trying combinations of forest information FrIF obtained by analyzing the first survey data OIF1 as an explanatory variable and disaster information DIF indicating disasters that have actually occurred in the forest as a target variable, and select the optimal regression equation with the highest degree of fit as the disaster prediction simulation model M3, which is a prediction equation for predicting the risk of disaster occurrence according to the forest condition.

[0118] The server device 100 inputs forest information FrIF of the forest in the range RA specified for the map information MP (e.g., orthoimage) generated based on the first survey data OIF1 into the disaster prediction simulation model M3, thereby being able to predict when, where, and what type of disaster will occur depending on the forest conditions of the forest within the range RA.

[0119] 11(d) shows an example of generating a cost prediction simulation model M4. The generating unit 232 may perform a multiple regression analysis by trying combinations of unknown forest information NIF from the forest information FrIF obtained by analyzing the first survey data OIF1 as an explanatory variable and cost information CIF of the costs (management costs or countermeasure costs) actually required depending on the forest condition as a target variable, and select the optimal regression equation with the highest degree of fit as the cost prediction simulation model M4, which is a prediction equation for predicting costs associated with changes in the forest condition.

[0120] The server device 100 can predict costs according to the changes over time (growth) of the forest within the range RA by inputting forest information FrIF of the forest in the range RA specified for map information MP (e.g., an orthoimage) generated based on the first survey data OIF1 into a cost prediction simulation model M4.

[0121] Next, a more specific example of generating the growth prediction simulation model M1 shown in Fig. 11(a) will be described. Fig. 12 is a diagram showing an example of a method for generating the growth prediction simulation model M1. Fig. 12 shows a scene in which a prediction formula for predicting diameter at breast height is generated as the growth prediction simulation model M1.

[0122] According to the example of Figure 12, the analysis unit 231 may extract items such as crown surface area, crown volume, crown length, crown length ratio, tree height, etc. based on the crown height data obtained by analyzing the first survey data OIF1.

[0123] In such an example, the generation unit 232 may perform a multiple regression analysis by trying combinations of the breast height diameter (e.g., average breast height diameter) included in the tree information TIF measured in a field survey as the objective variable and the extracted items as the explanatory variables, and select the optimal regression equation with the highest degree of fit as the growth prediction simulation model M1, which is a prediction equation for predicting the breast height diameter.

[0124] The analysis unit 231 can generate a crown projection polygon based on the tree vertex data extracted from the first survey data OIF1 and the tree sight data, as shown in Fig. 12, and calculate the crown length and crown length ratio based on the crown projection polygon. The analysis unit 231 can also calculate the crown surface area and crown volume by assuming that the crown is conical.

[0125] (Reception Department 233) Returning to the explanation of Figure 10, the reception unit 233 receives various information from the user U. For example, the reception unit 233 may receive a designation of an area RA for a forest displayed in the map information MP. The reception unit 233 may also receive a designation of an area RA for an unknown forest displayed in the map information MP. The map information MP may be an orthoimage generated based on survey data (for example, the first survey data OIF1 or the second survey data OIF2), or may be a 3D image generated based on a forest planning map.

[0126] Furthermore, the output control unit 235 (described later) may control the output so that the map information MP is displayed on the user device 10, and the reception unit 233 may receive from the user U a designation of the range RA for the map information MP.

[0127] (Prediction unit 234) The prediction unit 234 predicts output information about the forest included in the range RA based on statistical information generated based on the forest information FrIF about the forest.

[0128] For example, the prediction unit 234 predicts the future forest condition of the forest included in the range RA as output information based on statistical information obtained by statistically analyzing changes in the forest condition indicated by the forest information FrIF. For example, the prediction unit 234 may input the forest information FrIF of the forest in the range RA specified for the map information MP into a growth prediction simulation model M1 to predict secular changes (growth amount) of how the forest within the range RA will change in the future.

[0129] Furthermore, the prediction unit 234 predicts the future CO2 absorption amount by the forests included in the range RA as output information based on statistical information obtained by statistically analyzing changes in CO2 absorption amount by the forests in accordance with changes in the forest conditions indicated by the forest information FrIF. For example, the prediction unit 234 may predict the CO2 absorption amount according to changes over time (growth amount) of the forests in the range RA by inputting the forest information FrIF of the forests in the range RA specified in the map information MP into a CO2 absorption amount prediction simulation model M2. Note that the prediction unit 234 may predict the CO2 absorption amount based on statistical information obtained by statistically analyzing changes in CO2 absorption amount according to the hours of sunshine, rainfall, or temperature included in the forest information FrIF.

[0130] Furthermore, the prediction unit 234 predicts the risk of future disasters occurring in the forests included in the range RA as output information based on statistical information obtained by statistically analyzing the relationship between the forest conditions indicated by the forest information FrIF and disasters that have occurred in the forests. For example, the prediction unit 234 may input the forest information FrIF of the forests in the range RA specified in the map information MP into the disaster prediction simulation model M3, thereby predicting when, where, and what type of disaster will occur according to the forest conditions of the forests within the range RA.

[0131] Furthermore, the prediction unit 234 predicts output information regarding the unknown forest included in the range RA based on statistical information generated based on the unknown forest information NIF1 (or the unknown forest information NIF2) regarding the unknown forest.

[0132] As an example, the prediction unit 234 predicts, as output information, the unknown forest management costs required for future management of the unknown forest included in the range RA, based on statistical information obtained by statistically analyzing the relationship between changes in the forest condition of the unknown forest indicated by the unknown forest information NIF1 and the management costs required for managing the unknown forest in accordance with changes in the forest condition of the unknown forest.

[0133] As another example, the prediction unit 234 predicts, as output information, the response costs required if a disaster occurs in an unknown forest included in the range RA, based on statistical information obtained by statistically analyzing the relationship between a disaster that occurs in the unknown forest indicated by the unknown forest information NIF1 and the response costs required to respond to the disaster, which are response costs according to the forest condition of the unknown forest.

[0134] For example, the prediction unit 234 may input unknown forest information NIF1 of unknown forests in the range RA specified for the map information MP into the cost prediction simulation model M4, and predict management costs and response costs as costs based on the changes over time (growth amount) of the unknown forests within the range RA.

[0135] 13 shows a scene in which the range RA is specified. FIG. 13 shows an example of map information MP displayed on the screen of the user device 10. In this state, the user U can specify a range RA of any shape at any position by, for example, performing a predetermined user operation on the screen of the user device 10 (for example, tracing the screen with a finger or a touch pen). The method for specifying the range RA is not limited to the above example. For example, the output control unit 235 may specify the range RA using a rectangular area by displaying a resizable rectangular area on the screen.

[0136] (output control unit 235) The output control unit 235 is responsible for the visualization part of the forest resource visualization service. For example, the output control unit 235 controls so that output information is output to user U. As an example, the output control unit 235 may control so that guidance information RE that guides user U2 (local government) to abandon the unknown forest included in the range RA is output to user U2. Furthermore, when user U1 (owner) is known, the output control unit 235 may control so that guidance information RE is output to user U1. Furthermore, when user U1 (owner) is unknown but user U3 (administrator) is known, the output control unit 235 may control so that guidance information is output to user U3.

[0137] [6. Other functions] The server device 200 may also perform information processing to support artificial forestry planning. For example, in sustainable forest management, appropriate artificial forestry planning is necessary to generate J-Credits and improve CO2 absorption capacity. For this reason, the forest resource visualization service also includes a service that supports artificial forestry planning by simulating how forests change when tree species or topography are changed.

[0138] Therefore, as a function of supporting the artificial tree plantation plan, the reception unit 233 may receive forest conditions in the artificial tree plantation plan as predetermined conditions related to the forest included in the range RA. In this case, the prediction unit 234 may predict the future forest condition of the forest included in the range RA as output information, which is the forest condition according to the forest conditions, based on statistical information obtained by statistically analyzing changes in the forest condition.

[0139] Here, the generation unit 232 may generate a simulation model M11 that predicts how a forest will change by changing the tree species or topography, based on the growth prediction simulation model M1. Figure 14 shows the elements required to generate the simulation model M11.

[0140] For example, because forests are dynamic systems in which processes such as birth, growth, reproduction, and death occur, it is necessary to incorporate elements that can reconstruct the forest system into the growth prediction simulation model M1. Also, individual trees have various characteristics, such as being of different species, sizes, or genotypes. Also, seeds are dispersed around the parent tree, and genes are exchanged between nearby individuals of the same species via pollen.

[0141] In other words, the processes of tree growth, seed production, death, etc. depend on the tree species, size, light environment, genotype, etc. The brighter the light, the better the tree will grow, the less likely it will die, and the more seeds it will produce. Furthermore, the competitive relationships between trees are not simply determined by the tendency that, for example, tree species A is stronger than tree species B, but rather their behavior is determined by the light environment.

[0142] Based on the above, the generation unit 232 may input tree species, size, age, location, genotype, amount of light received, breeding resources, flowering amount, fruiting amount, etc. as explanatory variables into the growth prediction simulation model M1, as shown in Figure 14.

[0143] Furthermore, the generating unit 232 may input, as explanatory variables, for example, growth characteristics, morphological characteristics, mortality characteristics, flowering and fruiting characteristics, seed dispersal characteristics, germination and establishment characteristics, etc., regarding the seeds into the growth prediction simulation model M1.

[0144] Furthermore, although not shown, the generation unit 232 may also input information on pollinators (e.g., insects) and seed predators (e.g., animals), as well as information on forest fires, typhoons, landslides, disease, and the like, into the growth prediction simulation model M1. The information on pollinators may include, for example, the tree species that transmits pollen, the movement pattern of the pollinator, and the pollination distance. The information on seed predators may include the tree species that are preyed upon, the preying locations, and the movement pattern of the pollinator. The growth prediction simulation model M1 may also be a model with a spatial structure. The generation unit 232 may also input information on final cutting, thinning, and reforestation, which is included in the forest information FrIF, into the growth prediction simulation model M1. In other words, the generation unit 232 may also use the information on final cutting, thinning, and reforestation as explanatory variables.

[0145] [7. Example of simulation result output] The output control unit 235 may perform output control so that a 3D graphic image obtained by generating an image of a forest landscape based on the prediction result by the prediction unit 234 is displayed on the user device 10. Fig. 15 is a diagram showing an example of a forest landscape map based on the simulation result.

[0146] Figure 15 shows a scene in which a forest landscape is virtually reproduced based on the predicted forest growth rate 10, 20, and 30 years from now and is displayed on the user device 10 as a forest landscape map C1.

[0147] 15, the output control unit 235 may predict how the landscape within the range RA will change 10 years from now based on the number of trees, tree height, breast height diameter, etc. predicted for the target tree species in the forest within the range RA. The output control unit 235 may then virtually reproduce the predicted landscape results. The output control unit 235 may also generate a forest landscape map C1 by performing similar processing for 20 years and 30 years from now.

[0148] The prediction unit 234 may also predict the average tree height and the number of trees per hectare, and the output control unit 235 may include these prediction results in the forest landscape map C1.

[0149] [8. Example of guidance information output] 15 shows an example in which the output control unit 235 displays a 3D graphic image obtained by generating an image of a forest landscape based on the prediction result by the prediction unit 234. Therefore, the output control unit 235 may perform output control so that guidance information RE, which guides the user to abandon the unknown forest included in the range RA, is displayed on the user device 10 together with the 3D graphic image. Fig. 16 is a diagram showing a display example of the guidance information RE.

[0150] Figure 16 shows an example in which the output control unit 235 outputs the predicted management costs CIF and guidance information RE based on the predicted management costs CIF, along with the forest landscape map C1 described in Figure 15. In the example of Figure 16, the predicted management costs CIF are the management costs required to manage the unknown forest in accordance with changes in the forest conditions of the unknown forest, and are management costs predicted for 10, 20, and 30 years from now using the cost prediction simulation model M4.

[0151] Figure 16 shows an example in which management costs are predicted to be 100,000 / ha in the forest condition 10 years from now, 1 million / ha in the forest condition 20 years from now, and 1.5 million / ha in the forest condition 30 years from now.

[0152] The output control unit 235 may also determine whether the owner of the unknown forest should abandon the unknown forest based on the amount of each predicted management cost CIF, the rate of increase of the predicted management cost CIF, etc. For example, the output control unit 235 may evaluate the degree of recommendation that suggests the owner of the unknown forest abandon the unknown forest based on the amount of each predicted management cost CIF, the rate of increase of the predicted management cost CIF, etc. Figure 16 shows an example in which information indicating this recommendation degree, indicated by the number of star marks, is displayed as guidance information RE on the forest landscape map C1.

[0153] 16, the output control unit 235 may control so that guidance information RE guiding the user U2 (local government) to abandon the unknown forest included in the range RA is output to the user U2 (local government). Furthermore, the output control unit 235 may control so that guidance information RE is output to the user U1 when the user U1 (owner) is known. Furthermore, the output control unit 235 may control so that guidance information is output to the user U3 when the user U1 (owner) is unknown but the user U3 (administrator) is known.

[0154] 16, in response to access from user U2, who is a local government, the output control unit 235 causes the user device 10 of user U2 to display guidance information RE. However, if the user U1 (owner) or user U3 (administrator) is identified, the output control unit 235 may further display information urging the user U1 or user U3 to notify the guidance information RE.

[0155] The output control unit 235 may further display a comment according to the number of star marks. For example, if abandoning the unknown forest is recommended according to the number of star marks, the output control unit 235 may display a comment such as "It seems that managing the forest would be a high financial burden. Please consider giving it up."

[0156] Although FIG. 16 shows an example of outputting the guide information RE associated with the management cost, following this example, it is possible to output the guide information RE associated with the handling cost (for example, compensation).

[0157] <Hardware configuration> The above-mentioned server device SV may be realized, for example, by a computer 1000 configured as shown in Fig. 17. Fig. 17 is a hardware configuration diagram showing an example of a computer that realizes the functions of the server device SV. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0158] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0159] The HDD 1400 stores programs executed by the CPU 1100, data used by these programs, etc. The communication interface 1500 receives data from other devices via a predetermined communication network and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0160] The CPU 1100 controls an output device such as a display and an input device such as a keyboard via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. The CPU 1100 also outputs generated data to the output device via the input / output interface 1600.

[0161] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program or data from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0162] For example, when the computer 1000 functions as the server device 100 according to the first embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to implement the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0163] Furthermore, when the computer 1000 functions as the server device 200 according to the second embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200, thereby realizing the functions of the control unit 230. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0164] <Other> Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0165] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0166] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0167] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the aspects described in the "present invention" section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0168] SV server device 1. Information Processing Systems 2. Information Processing Systems 10 User Device 80 External equipment 100 Server device 120 Storage section 130 control section 131 Acquisition Department 132 Calculation Unit 133 Estimation Department 134 Judgment section 135 Storage Unit 136 Generation part 137 Provision Department 200 Server device 220 Storage section 230 Control Unit 231 Analysis Department 232 Generation part 233 Reception Department 234 Prediction Department 235 Output control section

Claims

1. a reception unit that receives a designation of a range of a forest displayed on the map information; a prediction unit that predicts output information about the forest included in the range based on statistical information generated based on forest information about the forest; an output control unit that controls the output information to be output to a user; Equipped with The receiving unit receives a designation of an area of ​​an unknown forest, which is a forest whose owner is unknown and is displayed on the map information, The prediction unit predicts the output information regarding the unknown forest included in the range based on the statistical information generated based on the forest information regarding the unknown forest, The output control unit controls so that guidance information generated based on the output information and guiding the person to abandon the unknown forest included in the range is output to the person identified as the owner. Information processing device.

2. the prediction unit predicts, as the output information, a future forest condition of the forest included in the range based on the statistical information obtained by statistically analyzing changes in the forest condition indicated by the forest information; The information processing device according to claim 1 .

3. the receiving unit receives forest conditions in an artificial forestry plan as predetermined conditions related to the forest included in the range; the prediction unit predicts, as the output information, the future forest status of the forest included in the range, based on the statistical information obtained by statistically analyzing changes in the forest status, and the forest status according to the forest conditions. The information processing device according to claim 2 .

4. the prediction unit predicts, as the output information, a future amount of carbon dioxide absorption by the forest included in the range based on the statistical information obtained by statistically analyzing changes in the amount of carbon dioxide absorption by the forest in accordance with changes in the forest condition. The information processing device according to claim 2 .

5. the prediction unit predicts, as the output information, a risk of a future disaster occurring in the forest included in the range based on the statistical information obtained by statistically analyzing the relationship between the forest condition indicated by the forest information and the disaster that has occurred in the forest; The information processing device according to claim 1 .

6. The prediction unit predicts, as the output information, unknown forest management costs that will be required for future management of the unknown forest included in the range, based on the statistical information obtained by statistically analyzing the relationship between changes in the forest condition of the unknown forest indicated by the forest information and management costs that will be required for managing the unknown forest in accordance with changes in the forest condition of the unknown forest. The information processing device according to claim 1 .

7. The prediction unit predicts, as the output information, the response costs required in the event of a disaster occurring in the unknown forest included in the range, based on the statistical information obtained by statistically analyzing the relationship between a disaster that occurred in the unknown forest indicated by the forest information and the response costs required to respond to the disaster, which response costs depend on the forest condition of the unknown forest. The information processing device according to claim 1 .

8. The forest information includes information on sunshine hours, rainfall, weather, temperature, final cutting, thinning, or reforestation related to the forest. The information processing device according to claim 1 .

9. An information processing method executed by an information processing device, a receiving step of receiving a designation of the range of the forest displayed on the map information; a prediction step of predicting output information about the forest included in the range based on statistical information generated based on forest information about the forest; an output control step of controlling the output information to be output to a user; Including, The receiving step includes receiving a designation of an area of ​​an unknown forest, which is a forest whose owner is unknown and is displayed on the map information; The prediction step predicts the output information regarding the unknown forest included in the range based on the statistical information generated based on the forest information regarding the unknown forest, The output control step controls so that guidance information generated based on the output information and guiding the person to abandon the unknown forest included in the range is output to the person identified as the owner. Information processing methods.

10. A procedure for accepting the designation of the range of the forest displayed on the map information; a prediction step of predicting output information about forests included in the range based on statistical information generated from forest information about the forests; an output control procedure for controlling the output information to be output to a user; on the computer, The receiving step includes receiving a designation of an area of ​​an unknown forest, which is a forest whose owner is unknown and is displayed on the map information; The prediction step predicts the output information regarding the unknown forest included in the range based on the statistical information generated based on the forest information regarding the unknown forest, The output control procedure controls so that guidance information generated based on the output information and guiding the person to abandon the unknown forest included in the range is output to the person identified as the owner. Information processing program.

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