Program, information processing device, method, and system

Abstract

Achieve greater efficiency and sophistication in forest management operations. [Solution] A program to be executed by a computer having a processor, which causes the processor to perform the following steps: acquiring status information indicating the state of the forest at a specified time from multiple information sources; organizing the acquired status information in chronological order using identification information that uniquely identifies specific areas within the forest geographically as a common key, and storing it in a database as historical information; analyzing time-series changes in the satellite data included in the historical information, and extracting areas of change where vegetation in the forest has changed; and comparing the extracted changed areas with ground record data included in the historical information, and calculating the priority of visiting the forest for on-site inspection based on whether or not there is ground record data corresponding to the changed areas.

Description

[Technical Field]

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

[0002] Japan is one of the world's leading forest countries, but the number of forestry workers who support its forests has declined significantly over the past few decades and is aging. With limited personnel to manage the vast forest area, there is an urgent need to improve the productivity of forest management operations in order to maintain the multiple functions of forests and achieve sustainable forest management.

[0003] Furthermore, the multifaceted functions of forests are not limited to timber production, but also extend to watershed conservation, disaster prevention, biodiversity conservation, and even cultural aspects. Therefore, efficient and sustainable forest management is an extremely important issue not only from an economic perspective, but also in terms of protecting the nation and society as a whole.

[0004] In the field of forest management, a technique for displaying forest information (for example, forest registers or forest land ledgers) on a map using a geographic information system (GIS) has been known. In addition, attempts have been made to grasp the status of forests over a wide area using satellite images or aerial photographs using remote sensing technology.

[0005] For example, Patent Document 1 discloses a technology that extracts feature information from two orthoimage data sets taken at different times and determines whether there is a change in forest type. This technology detects areas where changes have occurred in the forest by comparing image data. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2018-066943 Summary of the Invention [Problem to be solved by the invention]

[0007] However, in the current state of forest management, various types of information such as forest ledgers, subsidy application documents, landslide area ledgers, and field survey records are managed in different formats, such as paper, Excel®, or individual GIS data, by each department or person in charge. This makes it difficult to centrally and chronologically understand the history of past operations or disasters related to a specific forest plot.

[0008] Furthermore, while the life cycle of a forest can span more than 50 years, from planting to harvesting, local government or forestry association personnel are often transferred every few years. As a result, there is a problem that management information or know-how is not smoothly transferred between personnel, and management tends to become personalized, relying on the memories or experience of specific personnel.

[0009] Furthermore, the technology disclosed in Patent Document 1 is limited to detecting changes by comparing image data. Therefore, in order to determine whether a detected change is due to "legitimate logging—when, by whom, and for what reason" or whether it is a "sign of a natural disaster" or "illegal logging," it is necessary to manually compare the data with separately managed ledgers or on-site records. Therefore, the technology disclosed in Patent Document 1 has limitations in efficiently managing vast forests.

[0010] The present disclosure has been made in consideration of the problems with the conventional technology, and its purpose is to realize more efficient and advanced forest management operations. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems, one aspect of the program of the present disclosure is a program to be executed by a computer having a processor, and causes the processor to perform the following steps: acquiring status information indicating the state of the forest at a predetermined time from multiple information sources; organizing the acquired status information in chronological order using identification information for geographically uniquely identifying a specific area within the forest as a common key, and storing it in a database as historical information; analyzing time-series changes in the satellite data included in the historical information and extracting areas of change where vegetation in the forest has changed; and comparing the extracted changed areas with ground record data included in the historical information, and calculating the priority of visiting the forest to conduct on-site inspections based on whether or not there is ground record data corresponding to the changed areas. [Effects of the Invention]

[0012] According to the present disclosure, it is possible to achieve more efficient and sophisticated forest management operations. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram illustrating an example of an overall configuration of a system according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram illustrating an example hardware configuration of a user terminal according to an embodiment of the present disclosure. [Figure 3] FIG. 2 is a block diagram showing functional units realized by a control unit of the user terminal described above. [Figure 4] FIG. 2 is a block diagram illustrating an example hardware configuration of a server according to an embodiment of the present disclosure. [Figure 5] FIG. 2 is a block diagram showing functional units realized by a control unit of the server described above. [Figure 6] FIG. 10 is a diagram illustrating an example of a data structure of a forest partition table according to an embodiment of the present disclosure. [Figure 7] FIG. 10 is a diagram illustrating an example of a data structure of an event history table according to an embodiment of the present disclosure. [Figure 8] 10 is a flowchart illustrating an example of the operation of the server described above. [Figure 9] FIG. 2 is a schematic diagram showing an example of a map display screen displayed on the user terminal. [Figure 10] FIG. 10 is a schematic diagram illustrating an example of a feature information input screen displayed on a user terminal according to a modified example of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated explanations will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0015] In the following description, a "processor" refers to one or more processors. A processor may be expressed as, for example, processing circuitry. The at least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may also be another type of processor such as a GPU (Graphics Processing Unit). The at least one processor may be single-core or multi-core. The at least one processor may also be a general-purpose processor or a special-purpose processor.

[0016] Furthermore, the at least one processor may be a processor in the broad sense, such as a hardware circuit (for example, a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) that performs part or all of the processing.

[0017] In the following explanation, information that produces an output for an input may be described using expressions such as "xxx table," but this information may be data of any structure, or may be a learning model such as a neural network that produces an output for an input. Therefore, an "xxx table" may be referred to as "xxx information."

[0018] Furthermore, in the following description, the configuration of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0019] The program may be pre-installed in the information processing device described below, or may be stored on a recording medium (e.g., non-transitory) that can be read by the information processing device and then installed in the information processing device. The program may also be transmitted from a program distribution server to the information processing device and installed. In the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0020] In the following description, various types of identification information are used, but the identification information may be any information that indicates a specific object, and the specific data is not limited to those described in the embodiments. The identification information may be an identification number or an identifier including letters and symbols.

[0021] [Embodiment] <Summary> The system according to this embodiment is provided as a cloud-based service aimed at enabling users, such as employees of local governments or forestry associations, to streamline and eliminate the dependency on individuals in the management of vast forests. The system according to this embodiment acquires forest status information from multiple sources, organizes the acquired status information in chronological order using a common key, which uniquely identifies specific areas within the forest. The system then analyzes the time-series changes in satellite data included in the historical information, automatically extracts areas of vegetation change, and compares these areas with the ground-recorded data included in the historical information. Based on the comparison results, the system according to this embodiment calculates a priority for the user's need to visit the site and presents it to the user. This allows the user to efficiently identify areas that need to be inspected, eliminating the dependency on individuals in forest management and ensuring systematic information inheritance.

[0022] <System configuration> FIG. 1 is a block diagram showing an example of the overall configuration of a system 1 according to this embodiment. The system 1 provides a cloud-based platform service that centrally manages a wide variety of information related to forest management in chronological order, centered on location, and combines remote sensing technology with on-the-ground records to improve the efficiency and sophistication of forest management operations. The system 1 includes a user terminal 10, a server 20, a satellite data provision system 50, and a public data system 60. The server 20 is communicatively connected to the user terminal 10, the satellite data provision system 50, and the public data system 60 via a network 80. The network 80 may be the Internet, a local area network (LAN), a mobile communication network, or the like, and enables communication between the various devices.

[0023] The user terminal 10 is a client terminal used by users who perform forest management work (e.g., local government officials, forestry association members, forest owners, etc.). The user terminal 10 is, for example, an information terminal such as a personal computer (PC), tablet, or smartphone, and uses functions provided by the server 20 via a web browser or the like. The user uses the user terminal 10 to view various types of forest-related information on a map, record events observed on-site (input ground record data), check alerts notified by the server 20, and so on.

[0024] The server 20 is an information processing device that forms the core of the system 1 and is an example of a computer and information processing device according to one aspect of the present disclosure. The server 20 is typically a cloud server configured with one or more computers, and executes various processes according to this embodiment. The server 20 has a function of acquiring, collecting, and storing data from external systems, etc., a function of performing time series analysis or priority calculation of satellite data, and a function of providing data or analysis results in response to a request from the user terminal 10.

[0025] The satellite data providing system 50 is a system operated by the European Space Agency (ESA), the Japan Aerospace Exploration Agency (JAXA), or a private company, and provides satellite data observed by optical satellites such as Sentinel-2 or SAR (Synthetic Aperture Radar) satellites via an API (Application Programming Interface), etc. The server 20 uses this satellite data providing system 50 as one of multiple information sources.

[0026] The public data system 60 is a system managed by public institutions such as the Legal Affairs Bureau, the Forestry Agency, prefectures, and municipalities, and provides public data such as registry map data, forest land registers, and forest ledgers. The server 20 also uses this public data system 60 as one of multiple information sources.

[0027] 1 shows one user terminal 10 and one server 20, but there may be multiple of these. When the server 20 is configured as a collection of multiple devices, the way in which the multiple functions required to realize the server 20 are distributed among the multiple devices can be determined appropriately depending on the processing capacity of each device and / or the specifications required for the server 20. Furthermore, the information sources used by the server 20 are not limited to the satellite data providing system 50 and the public data system 60.

[0028] <Hardware configuration of user terminal> Fig. 2 is a block diagram showing an example of the hardware configuration of a user terminal 10 according to this embodiment. As shown in Fig. 2, the user terminal 10 includes a control unit 101, a storage unit 102, a communication unit 103, an input unit 104, an output unit 105, a camera 106, a position sensor 107, and an acceleration sensor 108. The blocks included in the user terminal 10 are electrically connected by, for example, a bus or the like.

[0029] The control unit 101 executes various programs stored in the storage unit 102 to perform various processes. The control unit 101 is, for example, a processor such as a CPU. The processor is hardware for executing an instruction set written in a program. The processor is composed of an arithmetic unit, a register, a peripheral circuit, etc.

[0030] The storage unit 102 includes a main storage device and an auxiliary storage device. The storage unit 102 stores various programs and various information. The storage unit 102 stores, for example, an application program 120. The application program 120 includes, for example, a programming language executed on a web browser application (not shown) stored in the storage unit 102.

[0031] The communication unit 103 performs processes such as modulation and demodulation for the user terminal 10 to communicate with an external device (e.g., server 20). The communication unit 103 performs transmission processing on the signal generated by the control unit 101 and transmits it to the external device. The communication unit 103 performs reception processing on the signal received from the external device and outputs it to the control unit 101.

[0032] The input unit 104 accepts instructions or information input by the user. The input unit 104 may be realized, for example, by a touch-sensitive device in which instructions, etc. are input by touching the operation surface. If the user terminal 10 is a PC or the like, the input unit 104 may be realized by a reader, keyboard, mouse, etc. The input unit 104 converts instructions, etc. input by the user into electrical signals and outputs them to the control unit 101. Note that the input unit 104 may include, for example, a receiving port that accepts electrical signals input from an external input device. The input unit 104 may also include a microphone that accepts audio input.

[0033] The output unit 105 presents information to the user. The output unit 105 is realized by, for example, a display. The display displays various information according to the control of the control unit 101. The display is realized by, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display. The output unit 105 may include, for example, an output port that outputs an electrical signal to an external output device. The output unit 105 may include a speaker that outputs sound. In other words, presentation includes display on the display unit and output to a speaker or other output device.

[0034] The camera 106 is an imaging device that captures images using visible light. That is, the camera 106 is a device that receives visible light with a light receiving element and outputs image data as a capture signal.

[0035] The position sensor 107 is a sensor that detects the position of the user terminal 10 and is generally a GNSS device, such as a GPS module. The GPS module is a receiving device used in a satellite positioning system. The satellite positioning system receives signals from at least three or four satellites and detects the current position of the user terminal 10 equipped with the GPS module as coordinate values ​​based on the received signals. The position sensor 107 may detect the current position of the user terminal 10 from the position of a wireless base station to which the user terminal 10 is connected via the communication unit 103.

[0036] The acceleration sensor 108 is a sensor that detects the acceleration applied to the user terminal 10. Preferably, the acceleration sensor 108 has a function of detecting tilt around each axis (X-axis, Y-axis, Z-axis) of a three-dimensional coordinate system with the position of the user terminal 10 as the origin. The acceleration sensor 108 having such a function can detect the attitude of the user terminal 10, that is, the direction with respect to the X-axis, Y-axis, and Z-axis, by detecting the gravitational acceleration of the earth.

[0037] <User terminal functional configuration> Fig. 3 is a block diagram showing functional units realized by the control unit 101. As shown in Fig. 3, the control unit 101 includes, as functional units, an operation reception unit 131, a transmission / reception unit 132, and a presentation control unit 133. Specifically, the control unit 101 reads an application program 120 stored in the storage unit 102 and executes instructions included in the application program 120 to realize each functional unit.

[0038] The operation reception unit 131 performs processing for receiving instructions or information input from the input unit 104. Specifically, for example, the operation reception unit 131 receives text input or photo selection operations when inputting ground record data. The transmission / reception unit 132 performs processing for the user terminal 10 to transmit and receive data to and from an external device in accordance with a communication protocol. Specifically, for example, the transmission / reception unit 132 transmits the received ground record data or location information acquired by the position sensor 107 to the server 20. Furthermore, for example, the transmission / reception unit 132 receives data for a map display screen 900 or an alert notification transmitted from the server 20. The presentation control unit 133 controls the output unit 105 to present various information to the user. Specifically, for example, the presentation control unit 133 displays the map display screen 900 or various input screens on the display. Details of the map display screen 900 and the alert notification will be described later.

[0039] <Server hardware configuration> Fig. 4 is a block diagram showing an example of the hardware configuration of the server 20 according to this embodiment. As shown in Fig. 4, the server 20 includes a control unit 201, a storage unit 202, a communication unit 203, and an input / output IF 204. The control unit 201 executes various programs stored in the storage unit 202 to perform various processes. The control unit 201 is, for example, a processor such as a CPU.

[0040] The storage unit 202 includes a main storage device and an auxiliary storage device. The storage unit 202 stores various programs and various information. In this embodiment, the storage unit 202 stores, for example, an application program 220, a forest partition table 401, and an event history table 402. In other words, the storage unit 202 stores a database 400 constructed from these two tables.

[0041] The application program 220 is application software for executing and managing services provided by the system 1. The application program 220 includes, for example, a programming language such as JavaScript (registered trademark) that runs on a web browser application stored in the user terminal 10. The forest partition table 401 is a table that stores information about partitions of forests that are managed by the server 20. A partition is a specific area within a forest. The event history table 402 is a table that stores information about events that have occurred in partitions of forests that are managed by the server 20. Hereinafter, a forest that is managed by the server 20 will be referred to as a "target forest," and a partition that is managed by the server 20 will be referred to as a "target partition."

[0042] The communication unit 203 performs processes such as modulation and demodulation for communication between the server 20 and an external device (for example, the user terminal 10). The input / output IF 204 functions as an interface for an input device (not shown) for receiving input operations from the manager / operator of the server 20 and an output device (not shown) for outputting information to the manager / operator.

[0043] <Server functional configuration> 5 is a block diagram showing functional units realized by the control unit 201. The control unit 201 includes, as functional units, a communication control unit 211, an information management unit 212, an analysis processing unit 213, and an information presentation unit 214. Specifically, the control unit 201 realizes each functional unit by reading a program (including an application program 220) stored in the storage unit 202 and executing instructions included in the program.

[0044] The communication control unit 211 controls data communication between the user terminal 10, the satellite data providing system 50, and the public data system 60. Specifically, it receives requests from the web browser of the user terminal 10 using a protocol such as HTTPS, and transmits the processing results as a response. The information management unit 212 centrally manages the database 400 constructed in the storage unit 202. As described above, the database 400 includes a forest partition table 401 and an event history table 402.

[0045] The analysis processing unit 213 is a functional unit that executes core processing of the server 20. Specifically, it executes the following processes.

[0046] That is, the analysis processing unit 213 acquires status information indicating the state of the target forest at a given time from multiple information sources. The "predetermined time" refers to any time when the state indicated by the status information was observed or recorded. For example, this corresponds to the date and time of observation by a satellite in the case of satellite data, the date and time of ledger creation or update in the case of public data, and the date of operation or disaster discovery in the case of ground record data. The "state of the target forest" is information that captures multiple aspects of the target forest according to the characteristics of each information source. Specifically, the "state of the target forest" is a concept that encompasses the state of vegetation obtained from satellite images, the status of various rights contained in public data, and the physical state of operations, disasters, etc. contained in ground record data.

[0047] The status information is composed of information indicating the status of all the plots that make up the target forest, including the target plot. In this embodiment, the status information is a concept that encompasses multiple pieces of information of different natures, and includes at least satellite data, public data, and ground record data.

[0048] Satellite data is objective observation information obtained from artificial satellites, and includes, for example, optical satellite images or SAR images. The analysis processing unit 213 periodically accesses the satellite data providing system 50 to obtain the latest satellite data regarding the target forest.

[0049] Official data is ledger information obtained from the systems of public institutions, and includes, for example, map data provided by the registry office, forest land registers, forest registers, etc. This data includes information on land parcel numbers or forest compartments / subcompartments, which serve as identifying information (described below), as well as information on the owners of the target forests.

[0050] Ground record data broadly refers to electronic data acquired or recorded by human work or observations on-site in the target forest. Specifically, ground record data includes, but is not limited to, records of operations (e.g., work details such as felling, thinning, or reforestation, or work dates, workers, or various notification documents), records from on-site investigations (e.g., reports of disaster detection, reports on disease or animal damage, on-site image data, user comments or notes), and data acquired by a drone operated by a user. The analysis processing unit 213 accepts these ground record data transmitted from the user terminal 10 at any time.

[0051] Furthermore, the analysis processing unit 213 organizes the acquired various types of state information in chronological order using the identification information as a common key, and stores the information in the database 400 as history information.

[0052] Identification information is information that uniquely identifies a specific area within the target forest geographically. For example, a parcel number included in public data, or information on forest compartments or forest subcompartments managed by the Forestry Agency, is used as identification information. The parcel number or forest subcompartment functions as a time-invariant location ID that indicates a specific forest plot. Various pieces of information (events) with recorded dates and times of occurrence are linked in chronological order to this unchanging location ID.

[0053] Historical information is a collection of status information organized chronologically using identification information as a key. This allows a series of events from the past to the present (e.g., "May 10, 2023: Thinning carried out," "June 15, 2024: Changes in vegetation index observed using satellite data") to be constructed as a history for a specific section of the target forest (e.g., "123, Town B, City A").

[0054] The analysis processing unit 213 also extracts satellite data from the historical information stored in the database 400 and analyzes time-series changes to extract areas where vegetation has changed in the target forest. Vegetation is a group of plants growing in a certain location. In the context of remote sensing, vegetation is often understood as the density or activity of plants. Specific examples of vegetation include artificial forests such as cedar or cypress, natural forests such as beech or oak, or bamboo grasslands or grasslands.

[0055] An example of an analysis method used by the analysis processing unit 213 is differential analysis using the Normalized Difference Vegetation Index (NDVI). NDVI is an index that indicates the level of plant activity, and is calculated from the reflectance of the near-infrared band and red band of an optical satellite image. The analysis processing unit 213 compares satellite data from different periods (e.g., one year ago and the present) for the same area, and extracts areas where the NDVI value has decreased by more than a predetermined threshold as areas of change that may be due to felling or collapse. In addition, various known remote sensing techniques can be applied, such as an analysis method that uses changes in backscattering coefficients calculated based on SAR (synthetic aperture radar) satellite data.

[0056] The analysis processing unit 213 also compares the extracted changed area with the ground record data in the history information stored in the database 400. Then, the analysis processing unit 213 calculates the priority of visiting the target forest for on-site inspection based on the comparison result, i.e., the presence or absence of ground record data corresponding to the changed area. There are no particular limitations on the way the priority is presented, and the priority may be presented as a rank such as "high, medium, low" or "A, B, C," or as a number such as "1, 2, 3." Alternatively, the target section or the entire target forest may be presented on the map data in a color-coded manner according to priority.

[0057] For example, for a certain change area, if the history information contains ground record data such as a felling notification, weather information, or work record that corresponds to the time and place where the change was observed, the analysis processing unit 213 determines that the change is due to legitimate operations and sets the priority to "low." On the other hand, if there is no corresponding ground record data, the analysis processing unit 213 determines that there is a possibility of unauthorized felling or an unexpected disaster and sets the priority to "high."

[0058] The information presentation unit 214 performs processing for presenting the analysis results by the analysis processing unit 213 to the user terminal 10. Specifically, for example, the information presentation unit 214 generates screen data for displaying forest sections on a map in different colors according to priority, and transmits the screen data to the user terminal 10.

[0059] <Data Structure> 6 and 7 are diagrams showing examples of the data structure of tables stored in the storage unit 202. The tables shown in FIGS. 6 and 7 refer to a relational database, which is used to manage data sets called tables in a tabular format structurally defined by rows and columns in association with each other. In a database, a table is called a table, a column in a table is called a column, and a row in a table is called a record. In a relational database, relationships between tables can be set and associated.

[0060] Typically, each table has a column set as a primary key for uniquely identifying a record, but setting a primary key to a column is not essential. The control unit 201 can cause the processor to add, delete, or update records in a specific table stored in the storage unit 202 according to various programs.

[0061] 6 and 7 are merely examples and do not exclude data that is not listed. Furthermore, even if data is listed in the same table, it may be stored in separate storage areas in the storage unit 202.

[0062] 6 is a diagram showing an example of the data structure of the forest parcel table 401. The forest parcel table 401 manages information about the target parcel and corresponds to basic layer information in a forest GIS. This table includes columns such as parcel ID, lot number, forest parcel number, owner information, area, tree species, forest age, timber volume per hectare, and geographic information.

[0063] The "Parcel ID" column is a primary key for uniquely identifying each parcel within the forest and stores identification information for uniquely identifying each parcel. The "Lot Number" and "Forest Parcel Number" columns function as "identification information" in this embodiment and store the parcel number included in the registration information obtained from the public data system 60 and the forest parcel number included in the forest register information. The "Owner Information" column may store the name and contact information of the owner of each parcel, or may store an external key to a separately provided owner master table (not shown). The "Area" column stores the area (e.g., hectares) of each parcel. The "Tree Species," "Stand Age," and "Volume per Hectare" columns store resource information (tree species, stand age, and volume per hectare) included in the forest register information obtained from the public data system 60. This resource information can also be used for calculating carbon dioxide absorption, as described below. The "Geographic Information" column stores geographic information, such as polygon data depicting the boundaries of each parcel within the forest on a map. The polygon data may be in a data format such as GeoJSON or Well-Known Text (WKT). To handle geospatial information such as polygon data, database 400 may be constructed using a spatial database extension function such as PostGIS.

[0064] 7 is a diagram showing an example of the data structure of the event history table 402. The event history table 402 is a table that records events that occurred in each section in chronological order, and is a specific storage location for history information in this embodiment. This table includes columns such as event ID, section ID, event date and time, event type, data source, and detailed information.

[0065] The "Event ID" column is a primary key for uniquely identifying each event record and stores identification information for uniquely identifying each event record. The "Parcel ID" column is a foreign key to the forest parcel table 401 and defines a relationship indicating which parcel an event occurred in. The "Event Date and Time" column stores the date and time the event occurred (such as the date and time of satellite data observation, the date of work, or the date of discovery) in, for example, a timestamp format. The "Event Type" column stores the type of event, such as satellite data observation, logging, thinning, disaster detection, or animal damage detection. An event is a unit of information that records an occurrence or observed condition that occurred at a certain point in time in a specific parcel within the forest. In other words, events for the entire target forest constitute status information. The "Data Source" column stores information indicating the source of the event information. The "Detailed Information" column stores the specific details of the event in a format such as JSON or JSONB, which can maintain flexible data structures. The information stored in the "Detailed Information" column varies depending on the event type or data source. For example, if the event type is satellite data observation, the analysis results (e.g., NDVI value) are stored in the "detailed information" column, and if the event type is felling, the work history of the worker, location information of the felling location, photo file path, application / notification information, etc. Also, for example, the "detailed information" column stores information about various subsidy applications.

[0066] <Operation> An example of the operation of the server 20 will be described below with reference to the flowchart of Fig. 8. Fig. 8 is a flowchart showing the flow of the basic operation of the server 20.

[0067] First, in step S801 (acquisition step), the analysis processing unit 213 acquires status information from multiple information sources. Specifically, the analysis processing unit 213 periodically acquires satellite data by calling the API of the satellite data providing system 50 based on a preset schedule. This acquisition process may be executed within the server 20 as a cron job, for example, or may be executed by a timer trigger in a serverless architecture. A cron job is a function in a UNIX (registered trademark)-based OS that automatically executes a command or script at a specified time. A timer trigger in a serverless architecture is a function that automatically executes processing at regular intervals in a cloud computing format that eliminates the need for management of the server 20. For public data, the analysis processing unit 213 periodically acquires and synchronizes differential or all data from the public data system 60 via file transfer using SFTP (SSH File Transfer Protocol) or API integration. SFTP is a protocol for transferring files via an encrypted, secure path. For ground record data, the analysis processing unit 213 accepts HTTP requests (e.g., POST requests to a RESTful API) from the user terminal 10 in real time.

[0068] Next, in step S802 (storing step), the analysis processing unit 213 organizes the acquired status information in chronological order using the identification information as a key, and stores the information as history information in the database 400 (specifically, the event history table 402). During this process, for information that does not directly include a parcel address or forest compartment number, such as ground record data, the analysis processing unit 213 uses the latitude and longitude information assigned to the ground record data to perform a spatial search between the information and the geographic information stored in the forest compartment table 401. Specifically, for example, the analysis processing unit 213 uses the ST_Contains function of PostGIS to identify the polygon of the compartment that contains the latitude and longitude, and associates it with the corresponding compartment ID. The ST_Contains function of PostGIS is a function provided by the spatial database extension function PostGIS that determines whether one geometry completely contains another geometry. After associating all the state information with the partition ID, the information management unit 212 adds information such as the partition ID, event date and time, and event type to the event history table 402 as a new record under transaction management of the database 400.

[0069] Next, in step S803 (extraction step), the analysis processing unit 213 periodically (for example, as a weekly nighttime batch process) analyzes time-series changes in the satellite data included in the history information and extracts changed areas. This process specifically includes the following substeps.

[0070] First, the analysis processing unit 213 retrieves the latest satellite image of the target plot and a past satellite image (e.g., from the same month one year ago) for comparison from the event history table 402. Next, the analysis processing unit 213 performs preprocessing, such as cloud masking, to remove pixels containing clouds or their shadows as invalid data. Next, the analysis processing unit 213 calculates the Normalized Difference Vegetation Index (NDVI) for each pixel of each satellite image using reflectance values ​​in the near-infrared (NIR) band and red band, using a common calculation formula. Next, the analysis processing unit 213 collects the calculated NDVI values ​​of all pixels to generate image data (NDVI image). Next, the analysis processing unit 213 calculates the difference between the past NDVI image and the current NDVI image to generate a difference image. Finally, the analysis processing unit 213 extracts, from the calculated difference image, a group of pixels whose NDVI value is below a predetermined negative threshold (e.g., −0.3), which indicates a clear decrease in vegetation, and polygonizes clusters with a certain area or greater as changed areas.

[0071] Next, in step S804 (calculating step), the analysis processing unit 213 checks whether or not there is corresponding ground record data for each extracted changed area, and calculates the priority of visiting the target forest for on-site inspection. This process specifically includes the following substeps.

[0072] First, the analysis processing unit 213 defines conditions for searching the database 400 based on the extracted polygon of the changed area and the observation period of the satellite data. These conditions include, for example, (i) the location of the event indicated by the location information stored in the event history table 402 spatially intersects with the polygon of the changed area, (ii) the date and time of the event is within the observation period of the satellite data, and (iii) the event type is one that can reasonably explain the vegetation change, such as "tree cutting," "tree thinning," or "disaster report." Next, the analysis processing unit 213 searches the event history table using the defined conditions. If the search results in one or more records matching the conditions, the analysis processing unit 213 determines that the change in the changed area is due to a recorded event and sets the priority to "low." On the other hand, if no matching record is found, the analysis processing unit 213 determines that the change in the changed area is likely due to an unreported event, such as unauthorized tree cutting or an unknown disaster, and sets the priority to "high." The priority is not limited to the binary values ​​of "high" and "low", but may be a three-level value of "high", "medium", and "low", or may be a numerical score with more levels.

[0073] Finally, in step S805, the information presenting unit 214 causes the user terminal 10 to present the analysis results including the calculated priorities. Specifically, for example, the information presenting unit 214 generates polygon data of the changed area and information indicating the priorities associated with the polygon data as data in GeoJSON format. Then, the information presenting unit 214 transmits the generated GeoJSON data to the user terminal 10. A map library (e.g., Mapbox GL JS or Leaflet.js) running on the web browser on the user terminal 10 side interprets the received GeoJSON data and displays it on the map display screen 900 in different colors according to the priorities.

[0074] <Screen example> 9 is a schematic diagram showing an example of a map display screen 900 displayed on the display of the user terminal 10. The map display screen 900 displays a boundary line 902 of a target section as a polygon on map data 901 of the target forest.

[0075] As a result of the analysis by the analysis processing unit 213, sections determined to have high priority are displayed in red (high-priority section 903), and sections determined to have medium priority are displayed in yellow (medium-priority section 904). In the example of FIG. 9, the high-priority section 903 and the medium-priority section 904 are displayed in different shades of gray. In reality, however, the sections are displayed in different colors according to the degree of priority. This allows the user to grasp at a glance which locations in the vast target forest should be prioritized for inspection. The map display screen 900 may, for example, display the priority of forest roads (e.g., forest road 906 in the figure) on the map data 901. Specifically, the map display screen 900 may display the priority in different colors according to the condition of the forest road. This is because, because a road closure due to a collapse or fallen tree on the forest road makes it impossible to access the section beyond and causes a significant delay in the entire forest management operation, understanding the condition of the forest road is as important as understanding the target section.

[0076] When the operation reception unit 131 receives a user's selection of a specific section on the map display screen 900, a pop-up 905 is displayed that displays detailed information about the section. In addition to basic information such as the parcel number, owner, and area, the pop-up 905 displays a chronological order of past event history acquired from the event history table 402 (display of history information). This allows the user to check in detail why the section was determined to have a high priority (for example, a significant decrease in vegetation for which there is no corresponding ground record data) and what the past events were before heading to the site.

[0077] <Summary> As described above, this embodiment centrally manages a wide variety of information related to forest management in chronological order, centered on location. Therefore, even if a user (person in charge) is transferred or retires, anyone can easily understand what has happened in a particular forest in the past. This facilitates smooth handover of tasks and enables the organization to continue systematic forest management. Furthermore, by automating the monitoring of satellite data or the reconciliation of ledgers, it is possible to efficiently identify areas that need to be checked within the vast target forest, allowing users to focus on more important tasks, such as on-site inspections or coordination with stakeholders. Thus, this embodiment improves the quality of management by enhancing the inheritance of information and promotes optimal allocation of human resources, thereby achieving more efficient and sophisticated forest management operations.

[0078] [Modification] The above-described embodiment is merely an example of the present disclosure, and various modifications are possible without departing from the gist of the invention.

[0079] <First Modification> In the first modified example, the server 20 may compare the calculated priority with a predetermined threshold value (which can be set arbitrarily) and, if the priority exceeds the predetermined threshold value, cause the server 20 to notify the user terminal 10 of an alert. Specifically, for example, if the calculated priority exceeds the predetermined threshold value, the information presenter 214 generates notification information for notifying the user terminal 10 of an alert and transmits the notification information to the user terminal 10. Upon receiving the notification information, the presentation controller 133 controls the output unit 105 to notify the alert from the speaker.

[0080] The technical significance of the predetermined threshold is that it allows the administrator to flexibly adjust the alert notification sensitivity based on their own or the user's management policy or the characteristics of the target forest. For example, the administrator may use the management screen of server 20 to set a priority level (e.g., "4 or higher") for which notifications should be sent (e.g., "4 or higher") among five levels (e.g., "1" to "5"). Different predetermined thresholds may also be set for different areas of the target forest. For example, a lower threshold of "3 or higher" could be set for forests designated as landslide-prone areas, while a higher threshold of "5" could be set for forests on stable, flat land, so that only extremely unusual events are notified. This allows for both improved operational efficiency and risk management by notifying users of only truly important information, preventing alert fatigue and reducing the risk of overlooking serious events.

[0081] The alert in the first modified example encompasses any notification means for attracting the user's attention. Examples of alerts include a pop-up notification displayed on the screen of the user terminal 10, a push notification, an email, an SMS (Short Message Service), a warning message within an application, and the output of a warning sound. The information presenter 214 may cooperate with a known push notification service such as Firebase Cloud Messaging or Apple Push Notification Service to send a notification to an application on the user terminal 10. The alert may include information that allows the user to quickly grasp the situation, such as the location information of the changed area, the calculated priority value, and a link to a comparative display of related satellite images.

[0082] In this way, by only sending an alert when an event that requires a particularly high level of on-site inspection occurs, users can focus on other tasks and only respond in emergencies. Unlike a configuration that simply sends a notification every time a change area is detected, this system excludes less urgent notifications, such as legitimate operations, so users are not bothered by unnecessary notifications and the importance of alerts is maintained. This prevents the overlooking of urgent issues such as unauthorized logging or signs of disaster, enables a rapid initial response, and improves overall work efficiency.

[0083] <Second Modification> In a second modified example, the server 20 may calculate the amount of carbon dioxide absorbed by the forest based on the history information stored in the database 400, and generate a report to be used for applying for the J-Credit Scheme.

[0084] The J-Credit Scheme is a system in which the government certifies the amount of greenhouse gas emissions reduced or absorbed through the introduction of energy-saving equipment or forest management as credits. In the case of forest management projects, the amount of carbon dioxide absorbed that has increased through appropriate forest management (e.g., afforestation and thinning) can be sold as credits and revenue can be earned.

[0085] Specifically, for example, the analysis processing unit 213 references the operation history of forestry operations, such as afforestation, thinning, or felling, recorded in the event history table 402. Then, the analysis processing unit 213 calculates the carbon dioxide absorption amount based on the calculation methodology established by the J-Credit Scheme. That is, the analysis processing unit 213 uses the operation history as a basis for judgment or input information for the calculation methodology to calculate the carbon dioxide absorption amount. More specifically, the analysis processing unit 213 combines the operation history (e.g., tree species, planting date, thinning area) with information on the forest age and tree species stored in the forest plot table 401, as well as a pre-stored publicly known yield forecast table or growth model such as the Richards formula, to estimate the change in forest biomass of the target forest over a specific period. The yield forecast table is statistical data indicating the standard stem volume for each forest age for a specific tree species and region. The analysis processing unit 213 then converts the estimated change amount into carbon dioxide absorption by multiplying it by coefficients designated by the J-Credit Scheme, such as the biomass expansion coefficient, aboveground / underground ratio, and carbon content.

[0086] In the report generation process, the information presentation unit 214 first aggregates the calculated total amount of carbon dioxide absorption and its breakdown (e.g., by section and type of operation). Next, the information presentation unit 214 organizes the individual operation records (e.g., operation date and time, location information, operation content, and photos taken before and after the operation) extracted from the operation history in the event history table 402, which are the basis for this numerical data, as evidence. Next, the information presentation unit 214 incorporates the aggregated numerical data and evidence into a template (e.g., HTML format or XML format) conforming to the format of the J-Credit Scheme project plan or monitoring report. Next, the information presentation unit 214 generates a PDF report file from this template using a publicly known library such as WeasyPrint or Puppeteer. Finally, the information presentation unit 214 controls the presentation control unit 133 to present the generated report file on the display of the user terminal 10 in a format that the user can download. The report clearly states the calculation results of the carbon dioxide absorption along with the operation history that served as the basis for the calculation. The highly reliable historical information centrally managed by the server 20 serves as a strong evidence base for objectively proving the amount of credit created, thereby enabling users to significantly reduce the time and effort required to prepare complicated application documents.

[0087] <Third Modification> In a third modification, the server 20 may refer to meteorological data and topographical data acquired from an external system in addition to the history information, and calculate the priority using a machine learning model. Here, "referencing" refers to utilizing the history information, meteorological data, and topographical data as input data or information for the machine learning model to calculate the priority.

[0088] The weather data may be, for example, the amount of rainfall, snowfall, temperature, or wind speed in a specific region. The server 20 can acquire this data from a database provided by the Japan Meteorological Agency or a private weather company as an external system via API integration or the like. The topographical data may be, for example, the elevation, slope angle, slope direction, or catchment area of ​​each point calculated from a digital elevation model (DEM) provided by the Geospatial Information Authority of Japan as an external system.

[0089] The server 20 acquires these data from an external system and formats the data corresponding to each change region into a format that is easy for the machine learning model to analyze. That is, the server 20 formats the acquired weather data into weather mesh data and the acquired topography data into topography raster data. The server 20 then stores the formatted data in the storage unit 202.

[0090] The machine learning model is a supervised learning model that has been trained using past events such as landslides or illegal logging as ground truth data. The server 20 inputs the following features into the machine learning model: (i) the amount of change in satellite data in the change area (e.g., the magnitude of NDVI decrease), (ii) the presence or absence of corresponding ground record data (a flag of "0" or "1"), (iii) meteorological data (e.g., the cumulative rainfall over the 72 hours prior to the change observation), and (iv) topographical data (e.g., the average slope angle). Based on this input data, the machine learning model outputs the priority of on-site inspection as a numerical score (e.g., "1" to "5"). Known classification and regression models, such as gradient boosting decision trees (e.g., XGBoost or LightGBM), random forests, or neural networks, can be used as machine learning models. The machine learning model may be stored in the storage unit 202 or installed in an external AI system.

[0091] Specifically, for example, after extracting a change area, the analysis processing unit 213 uses the polygon data of the change area to extract values ​​for the corresponding area from the meteorological mesh data and terrain raster data stored in the storage unit 202. For example, the analysis processing unit 213 extracts the "72-hour cumulative rainfall amount before change observation" for the change area from the meteorological mesh data and the "average slope angle within the change area" for the change area from the terrain raster data. Next, the analysis processing unit 213 combines these extracted values, the average NDVI decrease obtained from the satellite data, and a recording flag indicating the presence or absence of ground-recorded data (e.g., "0" for "present" and "1" for "absent") into a single feature vector. The analysis processing unit 213 then inputs this feature vector into a machine learning model. The machine learning model performs calculations based on internal weighting parameters and ultimately calculates a priority score (e.g., "4.8") indicating the risk level of the change area. For example, vegetation changes in a change area with steep terrain and heavy rainfall can be determined to be an extremely high risk of landslides. In this way, according to the third modified example, by evaluating multiple factors in a composite manner, it becomes possible to calculate a more accurate and objective priority that takes into account disaster risk (for example, collapse potential evaluation) in particular.

[0092] <Fourth Modification> The fourth modification relates to the input of ground record data. That is, the fourth modification employs a configuration in which the user inputs, from the user terminal 10, feature information about features in the target forest that the user has discovered on-site.

[0093] A feature refers to a specific object or phenomenon that exists in a forest, and includes, for example, a specific tree, a fallen tree, evidence of animal damage, or a collapsed part of a forest road. Feature information is information that indicates the state of the feature, and includes text comments, still images taken by the camera 106, video images, and voice memos.

[0094] 10 is a schematic diagram showing an example of a feature information input screen 1000 according to the fourth modified example. When a user discovers a feature in the field, the user uses this screen to record the feature information in the server 20. As shown in FIG. 10, the feature information input screen 1000 is provided with a category selection button 1001, an image upload button 1002, and a comment input field 1003.

[0095] The category selection button 1001 is a UI (user interface) for selecting the type of feature to record in the server 20. When the user taps this display area, a list of feature types such as "wildlife damage," "disaster detection," and "illegal dumping" is displayed in a drop-down format or on a separate screen. The user selects the appropriate type from the displayed list.

[0096] The image upload button 1002 is a UI for recording a still image or video as feature information in the server 20. When the user taps this display area, operation options such as "Take a photo with the camera" or "Select from library" are displayed. The user can select one of the options to start the camera 106 and take a photo, or to read a captured still image or video stored in the storage unit 102.

[0097] The comment input field 1003 is a UI for the user to enter detailed information about the feature they have discovered in text. When the user taps this display area, a software keyboard is displayed, allowing the user to enter information in free text format.

[0098] When the user specifies a desired still image or video and inputs text, the user terminal 10 acquires current location information from the position sensor 107 via a Geolocation API implemented in the browser. The user terminal 10 then transmits the input feature information (still image or video, text) and the automatically acquired location information as a set to the server 20.

[0099] The information management unit 212 associates the feature information and location information received from the user terminal 10 and stores them in the event history table 402. The "location information" here refers to the location information of the user terminal 10 at the time the feature information was input. This makes it possible to accurately record where a reported event occurred without the user having to manually specify a location on a map, thereby improving work efficiency and data reliability.

[0100] In the fourth modification, the server 20 may store input rules for controlling the input of feature information and accept the input of feature information based on the input rules. That is, the server 20 may control the input of feature information based on the input rules. "Controlling the input of feature information based on the input rules" specifically refers to a series of processes such as dynamically changing the input screen of the user terminal 10 and verifying the input content based on the input rules defined by the server 20. This makes it possible to standardize the quality of the recorded information and prevent omissions in input. The input rules define the items to be input depending on the type of feature information, and are stored and managed in the storage unit 202.

[0101] Specifically, for example, the storage unit 202 stores an input rule in a format such as JSON schema, such as, "If the feature type is "disaster detection," an image is required as feature information." When a user selects "disaster detection" from the list of feature types, the transceiver 132 transmits the received selection information to the server 20. The information presentation unit 214 reads the above-mentioned input rule from the storage unit 202 based on the received selection information and transmits it to the user terminal 10. The web application of the user terminal 10 interprets the received input rule and dynamically renders the image upload button 1002 on the feature information input screen 1000 as a required field (e.g., displays an asterisk). If a user attempts to submit feature information without attaching an image related to the disaster detection, validation is performed on the client side, and an error message is displayed on the display of the user terminal 10. The information management unit 212 verifies that the feature information transmitted from the user terminal 10 complies with the input rule, and then officially accepts the feature information.

[0102] In this way, the operation of inputting feature information itself is performed on the user terminal 10, but the entire data generation process is controlled based on input rules managed by the server 20, which ultimately accepts the data. This makes it possible to standardize the quality of the information input and prevent omissions in records.

[0103] <Fifth Modification> As a fifth modification, the server 20 may evaluate the multiple functions of forests other than carbon dioxide absorption and generate a report that quantitatively calculates their value. The multiple functions of forests include watershed conservation, soil erosion prevention, and biodiversity conservation.

[0104] Specifically, for example, the analysis processing unit 213 refers to history information (e.g., changes in forest area, tree species, and forest age structure) and topographical data (slope, geology) stored in the event history table 402. Next, the analysis processing unit 213 quantitatively calculates the value of each function using evaluation models or conversion coefficients published by the Forestry Agency or various research institutions. In the case of a water source conservation function, the analysis processing unit 213 estimates the annual water storage volume from the forest area or tree species, and converts the estimated value into a water rate, dam construction costs, or the like to calculate monetary value. In the case of a sediment runoff prevention function, the analysis processing unit 213 estimates the amount of sediment runoff suppression from the topographical slope or geology and forest age, and converts the estimated value into a construction cost of a sabo dam, or the like to calculate monetary value. Finally, the information presentation unit 214 generates an evaluation report summarizing these calculation results and provides the report to the user via the user terminal 10. This allows users to grasp the diverse values ​​of forests in objective numerical form, and can be used as evidence for accountability to local residents or for obtaining budgets.

[0105] Among the multiple information sources in this embodiment and each of the modified examples, the information source for ground record data is positioned as the user terminal 10 that digitizes user observations and transmits the data to the server 20. One of the features of the present disclosure is that information from this user terminal 10 is integrated with information from a satellite or a system of a public institution.

[0106] [Note] The matters described in the above embodiments will be supplemented below.

[0107] <Appendix 1> A program to be executed by a computer having a processor, causing the processor to execute the following steps: acquiring status information indicating the state of a forest at a specified time from multiple information sources; organizing the acquired status information in chronological order using identification information that uniquely identifies a specific area within the forest as a common key, and storing it in a database as historical information; analyzing time-series changes in satellite data included in the historical information and extracting areas of change in vegetation in the forest; and comparing the extracted changed areas with ground record data included in the historical information, and calculating the priority of visiting the forest to conduct on-site inspections based on whether or not there is ground record data corresponding to the changed areas.

[0108] <Appendix 2> The program according to claim 1, further causing the processor to execute a step of notifying an alert to the user terminal when the calculated priority exceeds a predetermined threshold.

[0109] <Appendix 3> A program described in (Appendix 1) or (Appendix 2) that causes the processor to further execute a step of calculating the amount of carbon dioxide absorbed by forests based on historical information and generating a report to be used in applying for the J-Credit Scheme.

[0110] <Appendix 4> A program described in any one of (Appendix 1) to (Appendix 3), wherein in the calculating step, the priority is calculated using a machine learning model by further referring to meteorological data and topographical data.

[0111] <Appendix 5> The ground recording data includes feature information regarding features within the forest, and the program described in any of (Appendix 1) to (Appendix 4) further causes the processor to execute a step of acquiring location information of the user terminal that has accepted the feature information input by the user, and storing the acquired location information in a database in association with the feature information.

[0112] <Appendix 6> The program according to claim 5, further comprising the step of storing input rules for controlling input of feature information, and causing the processor to execute a step of accepting input of feature information based on the input rules.

[0113] <Appendix 7> An information processing device including a processor, the processor executing all steps in the program according to any one of (Supplementary Note 1) to (Supplementary Note 6).

[0114] <Appendix 8> A method executed by a computer having a processor, wherein the processor executes all steps in the program described in any one of (Appendix 1) to (Appendix 6).

[0115] <Appendix 9> A system comprising one or more processors that execute all steps in the program described in any one of (Appendix 1) to (Appendix 6). [Explanation of symbols]

[0116] 1. System 10...User terminal 20...Server 50...Satellite data provision system 60...Public Data System 80…Network 101, 201...Control section 102, 202...Storage section 103, 203…Communications Department 107...Position sensor 131...Operation reception section 132...Transmitter / receiver 133...Presentation control unit 211...Communication control unit 212…Information Management Department 213...Analysis processing unit 214…Information presentation section 400...Database 401...Forest plot table 402...Event History Table 900...Map display screen 1000...Feature information input screen

Claims

1. A program to be executed by a computer having a processor, the processor, obtaining state information from a plurality of sources that indicates the state of the forest at a given time; organizing the acquired status information in chronological order using identification information for uniquely identifying a specific area within the forest as a common key, and storing the information in a database as history information; analyzing time-series changes in the satellite data included in the history information and extracting areas where vegetation in the forest has changed; a step of comparing the extracted changed area with ground record data included in the history information, and calculating a priority of visiting the forest for on-site inspection based on whether or not there is ground record data corresponding to the changed area; A program that executes the following.

2. 2. The program according to claim 1, further causing the processor to execute a step of notifying an alert to a user terminal when the calculated priority exceeds a predetermined threshold.

3. The program according to claim 1, further causing the processor to execute a step of calculating the amount of carbon dioxide absorbed by forests based on the historical information and generating a report to be used in applying for the J-Credit Scheme.

4. The program according to claim 1 , wherein the calculating step further refers to meteorological data and topographical data and calculates the priority using a machine learning model.

5. the ground record data includes feature information regarding features within the forest; The program according to claim 1, further causing the processor to execute a step of acquiring location information of the user terminal sent from the user terminal that accepted the user's input of the feature information, and storing the acquired location information in the database in association with the feature information.

6. 6. The program according to claim 5, further causing the processor to execute a step of storing input rules for controlling input of the feature information, and accepting input of the feature information based on the input rules.

7. 7. An information processing apparatus comprising a processor, the processor executing all steps of the program according to claim 1.

8. A method executed by a computer having a processor, the method comprising the steps of: executing all the steps of the program according to any one of claims 1 to 6.

9. A system comprising one or more processors that execute all steps in the program according to any one of claims 1 to 6.

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