Method and device for producing ground vegetation input data for executing global climate change prediction models

Abstract

Disclosed is an input data generating device for generating forced data used as input data for a climate change prediction model. The device includes: a memory storing commands; and a processor executing the commands to collect new land type data from LUH (Land-Use Harmonization) data restored by HYDE (History Database of the Global Environment) and provided by CMIP (Coupled Model Inter-comparison Project), collect existing land type data calculated by existing models in previous stages of CMIP, give priority to the new land type data, perform data aggregation on the new land type data and the existing land type data to generate aggregated land type data, perform distortion correction for data distortion occurring during the data aggregation process, and generate forced data from the aggregated land type data.

Description

Technical Field

[0001] The present invention relates to the production of ground vegetation input data for executing global climate change prediction models. More specifically, the present invention relates to the process of generating forcing data from LUH (Land-Use Harmonization) data provided by the international climate research program CMIP (Coupled Model Inter-comparison Project). Background Art

[0002] Vegetation forcing data, used as input to global climate change prediction models, can be used to calculate vegetation-related numerical values ​​within the prediction models. Climate change prediction models used by organizations like the Korea Meteorological Administration can use a variety of vegetation types, and different numerical calculation modules are applied depending on the vegetation type. Therefore, it is crucial to correctly classify vegetation types and generate forcing data.

[0003] Meanwhile, the Korea Meteorological Administration and its related agencies are using data from MODIS (Moderate Resolution Imaging Spectroradiometer) satellites and land type data from the International Geosphere-Biosphere Program (IGBP) in climate change prediction models and other numerical models. However, these data are incompatible with climate change prediction models that classify land vegetation types, leading to problems such as user subjective judgments being incorporated or failure to account for changes in land vegetation types over time. In other words, these existing general-purpose data may contain categories that are unsuitable for climate change prediction models or fail to reflect temporal trends in climate simulations.

[0004] To address the above issues, a technology is required to generate forcing data, which, as input data to climate change prediction models, reflects two points: high compatibility with the ground vegetation types used by the prediction models and that the types of ground vegetation can change over time. Summary of the Invention

[0005] Technical issues

[0006] The technical problem to be solved by the present invention is to propose a method for generating forced data, wherein the forced data reflects the two points that the ground vegetation types used in the climate change prediction model are highly compatible and the ground vegetation types can be changed at any time over time, thereby solving the problems caused by the original general data.

[0007] Technical Solution

[0008] As a solution to the technical problems described above, according to one aspect of the present invention, an input data generation device for generating forcing data used as input data for a climate change prediction model includes: a memory that stores commands; and a processor that executes the commands, thereby collecting new ground type data from LUH (Land-Use Harmonization) data restored by HYDE (History Database of the Global Environment) and provided by CMIP (Coupled Model Inter-comparison Project), collecting original ground type data calculated by original models in previous stages of the CMIP, giving priority to the new ground type data, performing data aggregation of the new ground type data and the original ground type data to generate aggregated ground type data, performing distortion correction for data distortion occurring during the data aggregation process, and generating the forcing data from the aggregated ground type data.

[0009] According to another aspect of the present invention, a method for generating input data forcing data used as input data for a climate change prediction model, executed by a processor running commands stored in a memory, includes: a step of collecting new ground type data from LUH (Land-Use Harmonization) data restored by HYDE (History Database of the Global Environment) and provided by CMIP (Coupled Model Inter-comparison Project); a step of collecting original ground type data calculated by an original model in a previous stage of the CMIP; a step of giving priority to the new ground type data and performing data aggregation of the new ground type data and the original ground type data to generate aggregated ground type data; and a step of performing distortion correction for data distortion occurring during the data aggregation process to generate the forcing data from the aggregated ground type data.

[0010] Effects of the Invention

[0011] According to the input data generation device and method of the present invention, it is possible to generate forcing data composed of ground vegetation types used in climate change prediction models, thereby providing highly reliable input data that can be used in many aspects such as global-scale numerical models used by the Korea Meteorological Administration and others.

[0012] In addition, in the process of generating forced data, new land type data collected from LUH data can be aggregated in priority over the original land type data based on the original CMIP model. Therefore, even if the type of ground vegetation changes over time, the corresponding changes can be appropriately reflected in the forced data, thereby further improving the accuracy and reliability of the forced data. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a diagram for explaining a system for generating forced data according to one embodiment.

[0014] Figure 2 This is a block diagram showing elements constituting an input data generating device according to some embodiments.

[0015] Figure 3 This is a diagram for explaining a specific process of generating forced data according to some embodiments.

[0016] Figure 4 is a flowchart illustrating steps constituting a method for generating input data according to some embodiments. DETAILED DESCRIPTION

[0017] The following describes embodiments of the present invention in detail with reference to the accompanying drawings. The following description is intended only to illustrate the embodiments and is not intended to limit or define the scope of the present invention. Anything that can be easily deduced by a person skilled in the art from the present disclosure and embodiments should be construed as falling within the scope of the present invention.

[0018] While the terms used in this invention are generally used in the technical fields relevant to this invention, their meanings may vary depending on the intentions of those skilled in the relevant field, the emergence of new technologies, examination standards, and precedents. Some terms may be arbitrarily selected by the applicant, in which case the meanings of the arbitrarily selected terms will be explained in detail. The terms used in this invention should not be interpreted solely in terms of dictionary meanings but rather as reflecting the overall context of the specification.

[0019] Terms such as "compose" or "include" used in the present invention should not be interpreted as necessarily including all the constituent elements or steps recorded in the specification. The situation where some constituent elements or steps are not included or the situation where additional constituent elements or steps are included should also be interpreted as being derived from the intention of the corresponding terms.

[0020] Terms including ordinal numbers, such as "first" or "second," used in the present invention may be used to describe various components or steps, but the corresponding components or steps should not be limited by the corresponding ordinal numbers. Terms including ordinal numbers should only be interpreted as being used to distinguish one component or step from other components or steps.

[0021] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Detailed descriptions of matters known to those skilled in the art will be omitted.

[0022] Figure 1 This is a diagram for explaining a system for generating forced data according to one embodiment.

[0023] If reference Figure 1 The system 10 for generating the forced data 300 may be composed of the LUH data 110, the CMIP original model 120, the input data generating device 200, and the forced data 300. In the system 10, the input data generating device 200 may generate the forced data 300 from the LUH data 110 and the CMIP original model 120.

[0024] LUH (Land-Use Harmonization) data 110, which is data restored using the HYDE (History Database of the Global Environment) database, can be provided by the Coupled Model Inter-comparison Project (CMIP). As described later, LUH data 110 can be data that only considers C3 and C4 vegetation types and urban non-vegetation types.

[0025] LUH data 110 only considers C3, C4, and urban types, which can be used to study the impact of human activities such as agriculture and urbanization. While LUH data 110 can account for changes in ground vegetation caused by human activities and over time, it only reflects three types, requiring refinement of the remaining types.

[0026] The CMIP legacy model 120 may refer to a prediction model that participated in a phase prior to the CMIP (Coupled Model Inter-comparison Project), an international climate research program. For example, the CMIP legacy model 120 may refer to a prediction model from the recently concluded CMIP Phase 5, or may refer to a phase after Phase 5 as the program progresses.

[0027] The CMIP original model 120 can provide the multiple types of ground vegetation that make up the forcing data 300. However, the CMIP original model 120 is based on a previously completed model. Therefore, even if the ground vegetation types have changed due to human activities or the passage of time since its completion, these changes will not be reflected.

[0028] The input data generation device 200 can generate the forced data 300 based on the LUH data 110 and the CMIP legacy model 120. LUH data 110 is prioritized for the C3, C4, and urban types. This allows for the reflection of changes in ground vegetation types over time. Furthermore, types that cannot be collected using the LUH data 110 can be collected using the CMIP legacy model 120. Furthermore, the input data generation device 200 can correct for distortions that may occur during the aggregation process between the LUH data 110 and the CMIP legacy model 120.

[0029] The forced data 300 can be generated by the input data generation device 200 through aggregation and distortion correction of the LUH data 110 and the CMIP legacy model 120. For some types, the LUH data 110 can reflect more accurate data. To ensure compatibility with climate change prediction models used by organizations such as the Korea Meteorological Administration, specifically to improve ground vegetation types, the CMIP legacy model 120 can be reflected. Consequently, the system 10 can generate forcing data 300 that is universally usable and highly reliable.

[0030] Figure 2 This is a block diagram showing elements constituting an input data generating device according to some embodiments.

[0031] If reference Figure 2 The input data generating device 200 for generating the forced data used as the input data for the climate change prediction model may include a memory 210 and a processor 220. However, this is not limited to this. Figure 2 In addition to the elements shown, the input data generating device 200 may further include other common elements.

[0032] The input data generation device 200 may be a computing device for generating the forcing data 300 based on the LUH data 110 and the original CMIP model 120. The data input and output and processing performed by the input data generation device 200 may be implemented as a mobile / web application or computer program. For example, the input data generation device 200 may be implemented in a form factor such as a PC, smartphone, or tablet. However, this is not a limitation and the input data generation device 200 may be implemented in a variety of electronic devices with processing capabilities.

[0033] The input data generating device 200 may include a memory 210 as a device for storing various data, commands, at least one program or software, and may include a processor 220 as a device for executing commands and at least one program to perform processing on various data.

[0034] The memory 210 may store various commands for generating the forced data 300. For example, the memory 210 may store commands constituting software such as a computer program or a mobile / web application. In addition, the memory 210 may store various data required for the application or program to run.

[0035] The memory 210 may be implemented as a non-volatile memory such as ROM (read-only memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), flash memory, PRAM (phase change random access memory), MRAM (magnetic random access memory), RRAM (resistive random access memory), FRAM (ferroelectric random access memory), etc., or may be implemented as a volatile memory such as DRAM (dynamic random access memory), SRAM (static random access memory), SDRAM (synchronous dynamic random access memory), PRAM (phase change random access memory), RRAM (resistive random access memory), FeRAM (ferroelectric random access memory), etc. In addition, the memory 210 may also be implemented as an HDD (hard disk drive), SSD (solid state drive), SD (memory card), Micro-SD (micro memory card), etc.

[0036] The processor 220 executes the commands stored in the memory 210, thereby executing a series of processes for generating the forced data 300. In addition, the processor 220 can execute comprehensive functions for controlling the input data generating device 200 and process various operations within the input data generating device 200.

[0037] The processor 220 can be implemented as an array of multiple logic gates or a general-purpose microprocessor. The processor 220 can be composed of a single processor or multiple processors. The processor 220 can also be integrated with the memory 210, rather than being independent of the memory 210 that stores commands. For example, the processor 220 can be implemented in the form of at least one of a CPU (central processing unit), a GPU (graphics processing unit), and an AP (application processor) equipped in the input data generating device 200.

[0038] The processor 220 can execute commands stored in the memory 210, thereby collecting new land type data from the LUH (Land-Use Harmonization) data 110 restored through HYDE (History Database of the Global Environment) and provided by CMIP (Coupled Model Inter-comparison Project).

[0039] New ground type data is collected through the processing of the LUH data 110. For example, the new ground type data can be collected based on grid transformation, aggregation of grassland data such as pasture and natural, and collection of urban type non-vegetation data.

[0040] When collecting new land type data, processor 220 may perform grid conversion on LUH data 110 having a resolution of 0.25°*0.25°, and collect new land type data having a resolution of 1.875°*1.25°. Specifically, the grid size may be converted to a format compatible with climate change prediction models used by organizations such as the Korea Meteorological Administration.

[0041] The processor 220 executes the commands stored in the memory 210, thereby collecting the original ground type data calculated by the original model in the previous stage of CMIP.

[0042] Data on older ground vegetation types can be obtained as original ground type data from an older CMIP phase, namely, the CMIP original model 120. For example, to collect data on types other than the C3, C4, and urban types collected based on the LUH data 110, CMIP Phase 5 or the like can be used as the CMIP original model 120.

[0043] The processor 220 executes the commands stored in the memory 210, thereby giving priority to the new ground type data and performing data aggregation on the new ground type data and the original ground type data, thereby generating aggregated ground type data.

[0044] The original ground type data generated by the original CMIP model 120 is an old version of the data. In contrast, the new ground type data based on the LUH data 110 reflects changes caused by human activities or the passage of time. Therefore, for the parts where the new ground type data and the original ground type data overlap, the new ground type data can be reflected first and data aggregation can be performed, thereby generating aggregated ground type data.

[0045] On the other hand, the forced data 300 can be nine grid array data, with one of nine types arranged in each grid, including five vegetation types consisting of broadleaf tree type, needleleaf tree type, C3 type, C4 type, and shrub type, and four non-vegetation types consisting of urban type, inland-water type, bare-soil type, and ice type. The new ground type data can be composed of vegetation types of C3 and C4 types and non-vegetation types of urban type.

[0046] As described above, the forced data 300 can be composed of nine types, allowing it to be used as input data for a climate change prediction model. Furthermore, the nine types comprising the forced data 300 can be composed of five vegetation types and four non-vegetation types. In this case, the new land type data generated from the LUH data 110 can be composed of three types: C3 type, C4 type, and urban type.

[0047] During the data aggregation process, related to the ground vegetation type, when generating aggregated ground type data, the processor 220 can use new ground type data for C3 type, C4 type and urban type, and use the original ground type data for the remaining 6 types to perform data aggregation.

[0048] As mentioned above, for C3, C4 and urban types, the new land type data amount to further updated information and are therefore reflected. For the remaining six of the nine types, namely, broadleaf type, coniferous type, shrub type, inland water type, bare ground type and ice type, the original land type data can be used.

[0049] On the other hand, regarding the collection of vegetation types of new ground type data, namely C3 type and C4 type, when collecting new ground type data, the processor 220 can collect C3 type and C4 type data based on the pasture and natural grassland data in the detailed items of the LUH data 110.

[0050] Specifically, the LUH data 110 may include C3 pasture, C4 pasture, and C4 nature data as grassland data. By identifying, verifying, and collecting these data, C3 and C4 data can be collected from the LUH data 110. Similarly, urban non-vegetation data or new landform data can be collected from the data included in the LUH data 110.

[0051] On the other hand, regarding the data form of the forcing data 300 , the forcing data 300 may include area ratios of 9 types calculated based on the areas of the 9 types of grids.

[0052] For example, the forcing data 300 may be grid data in which each grid point of a grid having a resolution of 1.875° by 1.25° is assigned to one of nine types. In this case, the ratio of the number of grid points assigned to each of the nine types relative to the total number of grid points in the forcing data 300, that is, relative to the total number of grid points formed in the target area of ​​the forcing data 300, can be calculated as a value between 0 and 1, with the sum of all nine area ratios being 1.

[0053] The processor 220 executes the commands stored in the memory 210 , thereby performing distortion correction for data distortion occurring during the data aggregation process, and generating the forced data 300 from the aggregated ground type data.

[0054] Because the new and existing terrain type data were collected from different sources, some distortion may occur in the aggregated terrain type data when they are aggregated. For example, this distortion may occur, such as the mesh area ratio of some types exceeding the specified area ratio, leading to concerns about reduced accuracy.

[0055] Regarding the distortion correction performed on the aggregated ground type data to prevent low accuracy as described above, when the processor 220 generates the forcing data 300, when an error occurs due to data aggregation, it can save new ground type data and adjust the original ground type data to perform distortion correction.

[0056] As previously mentioned, the original land type data is an outdated version of the data, while the new land type data is newly updated. Therefore, when there is a conflict between the two, the new land type data is prioritized over the original land type data. Therefore, changes in vegetation types over time and due to human activities can be more appropriately reflected in the forcing data 300.

[0057] On the other hand, regarding the specific method of distortion correction, when performing distortion correction, the processor 200 can adjust the grid area of ​​the bare-soil type in the original ground type data. More specifically, when the grid area of ​​the C3 type and the grid area of ​​the C4 type exceed the overall grid area of ​​the vegetation type, the grid area of ​​the bare-soil type can be removed according to the excess range.

[0058] That is, as a result of data aggregation, when an error exceeding the grid area ratio, etc. occurs according to the data of C3 type, C4 type or urban type, for example, when the sum of the grid area ratio of C3 type and the grid area ratio of C4 type exceeds the area ratio of the entire vegetation area ratio, the corresponding excess part is subtracted from the bare land type, and distortion correction can be performed in this way.

[0059] Alternatively, distortion correction can be performed to reflect the characteristics of the target location of the forcing data 300. For example, when the target location of the forcing data 300 is a polar region such as the Antarctic, the grid area ratio can be adjusted based on the ice type. In this case, it is preferable to adjust only the land portion of the polar region to the ice type. For grids that overlap with the ocean, it is preferable to remove the grid value so that the grid is classified as ocean.

[0060] Figure 3 This is a diagram for explaining a specific process of generating forced data according to some embodiments.

[0061] If reference Figure 3 , showing a process 310 for collecting new ground type data, processes 320 and 330 for calculating vegetation type areas and non-vegetation type areas by data aggregation, and a process 340 for performing distortion correction.

[0062] In process 310, new land type data can be collected from the raw data of the LUH data 110. During this process, the raw LUH data can be gridded, and the grid resolution can be changed to 1.875° by 1.25°, which is suitable for climate change prediction models. After the gridding is changed, grassland data such as pasture and natural areas can be aggregated, and urban land type data can be collected, resulting in new land type data consisting of C3, C4, and urban land types.

[0063] In process 320, after collecting the original land type data from the original CMIP model 120, the non-vegetated area can be calculated through a data aggregation process. The grid areas of four non-vegetated classes (urban, inland water, bare ground, and ice) can be calculated from the original land type data. The urban class can be excluded to reflect the new land type data.

[0064] In process 330, after collecting existing land type data from the original CMIP model 120, vegetation type areas can be calculated through a data aggregation process. The grid areas of five vegetation types (broadleaf, conifer, C3, C4, and shrub) can be calculated from the existing land type data. C3 and C4 types can be excluded to reflect the new land type data.

[0065] In process 340, distortion correction can be performed on the aggregated land type data to generate forced data 300. Distortion correction can be performed based on the area ratio of vegetation and non-vegetation types. For example, if the sum of the area ratio of C3 and C4 types is greater than the total area ratio of all vegetation types, resulting in an error (case N), to reflect the new data in LUH data 110, C3 and C4 types can be retained and removed from the bare-soil type based on the difference. A similar process can be performed for urban types, resulting in the generation of forced data 300.

[0066] Figure 4 is a flowchart illustrating steps constituting a method for generating input data according to some embodiments.

[0067] If reference Figure 4 , the input data generation method may include steps 410 to 440. However, it is not limited thereto, except Figure 4 In addition to the steps shown, the input data generation method may also include other general steps.

[0068] Figure 4 The input data generation method can be done by Figures 1 to 3 The input data generating device 200 described above is configured as a step executed in a time series. Therefore, even if the content is omitted below, the content described above for the input data generating device 200 is also applicable to the input data generating method.

[0069] In step 410 , the input data generating device 200 may collect new land type data from the LUH (Land-Use Harmonization) data 110 restored by HYDE (History Database of the Global Environment) and provided by CMIP (Coupled Model Inter-comparison Project).

[0070] When collecting new ground type data, the input data generating device 200 may perform grid conversion on the LUH data 110 with a resolution of 0.25°*0.25°, and collect new ground type data with a resolution of 1.875°*1.25°.

[0071] When collecting the land type data, the input data generating device 200 may collect C3 type and C4 type data based on the pasture and natural grassland data in the detailed items of the LUH data 110 .

[0072] In step 420 , the input data generating device 200 may collect the original ground type data calculated by the original model 120 in the previous stage of CMIP.

[0073] The forced data 300 can be nine grid array data, with one of nine types arranged in each grid, including five vegetation types consisting of broadleaf tree type, needleleaf tree type, C3 type, C4 type, and shrub type, and four non-vegetation types consisting of urban type, inland-water type, bare-soil type, and ice type. The new ground type data can be composed of C3 and C4 vegetation types and urban non-vegetation types.

[0074] In step 430 , the input data generating device 200 may give priority to the new ground type data and perform data aggregation on the new ground type data and the original ground type data, thereby generating aggregated ground type data.

[0075] When the input data generating device 200 generates aggregated ground type data, it can use new ground type data for C3 type, C4 type and city type, and use original ground type data for the remaining 6 types to perform data aggregation.

[0076] The forcing data 300 may include area ratios of 9 types calculated based on the areas of the 9 types of meshes.

[0077] In step 440 , the input data generating device 200 may perform distortion correction for data distortion occurring during the data aggregation process, and generate the forcing data 300 from the aggregated ground type data.

[0078] When the input data generating device 200 generates the forced data 300, if an error occurs due to data aggregation, the new ground type data can be saved and the original ground type data can be adjusted to perform distortion correction.

[0079] For example, the format of the forcing data 300 may be a NetCDF file, a PP file, or a FF file, but is not limited thereto.

[0080] When the input data generating device 200 performs distortion correction, if the grid area of ​​the C3 type and the grid area of ​​the C4 type exceed the grid area of ​​the entire vegetation type, the grid area of ​​the bare-soil type may be removed according to the excess range.

[0081] on the other hand, Figure 4 The input data generating method may be recorded in a computer-readable recording medium in the form of at least one program or software including a command for executing the method.

[0082] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs (Compact Disc Read Only Drives) and DVDs (Digital Versatile Discs); magneto-optical media such as floppy disks; and hardware devices specifically configured to store and execute program commands, such as read-only memories (ROMs), random access memories (RAMs), and flash memories. Examples of program commands include not only machine language codes generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like.

[0083] The embodiments of the present invention are described in detail above, but the scope of the present invention is not limited thereto. Various modifications and improvements of the basic concepts of the present invention described in the following claims by technicians in the relevant field should also be interpreted as being included in the scope of the present invention.

Claims

1. An input data generating device for generating forcing data used as input data for a climate change prediction model, comprising: a memory storing commands; as well as a processor that executes the command, thereby, New land type data were collected from land use harmonization data restored through the Global Environmental History Database and provided by the Coupled Model Intercomparison Project, collecting legacy ground type data calculated from legacy models in previous phases of the coupled model comparison project, Prioritizing the new ground type data, performing data aggregation on the new ground type data and the original ground type data, thereby generating aggregated ground type data, and performing distortion correction for data distortion occurring during said data aggregation, generating said forcing data from said aggregated ground type data, The forced data is nine grid array data in which one of nine types of vegetation types, namely, five vegetation types, namely, broadleaf tree type, coniferous tree type, C3 type, C4 type, and shrub type, and four non-vegetation types, namely, urban type, inland water type, bare land type, and ice type, are arranged in each grid; the new land type data is composed of the vegetation types of the C3 type and the C4 type, and the non-vegetation type of the urban type; and When generating the aggregated ground type data, the processor uses the new ground type data for the C3 type, the C4 type and the city type, and uses the original ground type data for the remaining six types to perform the data aggregation.

2. The input data generating device according to claim 1, wherein: When collecting the new land type data, the processor performs grid conversion on the land use coordination data having a resolution of 0.25°*0.25° and collects the new land type data having a resolution of 1.875°*1.25°.

3. The input data generating device according to claim 1, wherein: When collecting the new land type data, the processor collects the C3 type data and the C4 type data based on the basic data of pasture and nature in the detailed items of the land use coordination data.

4. The input data generating device according to claim 1, wherein: The forcing data includes area ratios of the nine types, calculated based on mesh areas of the nine types.

5. The input data generating device according to claim 1, wherein: When the processor generates the forced data, if an error occurs due to the data aggregation, it saves the new ground type data, adjusts the original ground type data, and performs the distortion correction.

6. The input data generating device according to claim 5, wherein: When the processor performs the distortion correction, when the grid area of ​​the C3 type and the grid area of ​​the C4 type exceed the grid area of ​​the entire vegetation type calculated based on the original ground type data, the grid area of ​​the bare land type is removed according to the exceeding range.

7. A method for generating input data, the method being executed by a processor running instructions stored in a memory, for generating forcing data for use as input data for a climate change prediction model, comprising: Steps to collect new land type data from land use harmonized data restored through the Global Environmental History Database and provided by the Coupled Model Intercomparison Project; The step of collecting legacy ground type data calculated by legacy models in a previous phase of said coupled model comparison project; giving priority to the new ground type data, performing data aggregation on the new ground type data and the original ground type data, thereby generating aggregated ground type data; as well as performing distortion correction for data distortion occurring during said data aggregation, the step of generating said forcing data from said aggregated ground type data, The forced data is nine grid array data in which one of nine types of vegetation types, namely, five vegetation types, namely, broadleaf tree type, coniferous tree type, C3 type, C4 type, and shrub type, and four non-vegetation types, namely, urban type, inland water type, bare land type, and ice type, are arranged in each grid; the new land type data is composed of the vegetation types of the C3 type and the C4 type, and the non-vegetation type of the urban type; and The generating of the aggregated ground type data further includes: using the new ground type data for the C3 type, the C4 type and the city type, and using the original ground type data for the remaining six types to perform the data aggregation.

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