WO2017048002A1 - Apparatus and method for generating greenhouse gas distribution data on administrative district basis - Google Patents

Apparatus and method for generating greenhouse gas distribution data on administrative district basis Download PDF

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Publication number
WO2017048002A1
WO2017048002A1 PCT/KR2016/010218 KR2016010218W WO2017048002A1 WO 2017048002 A1 WO2017048002 A1 WO 2017048002A1 KR 2016010218 W KR2016010218 W KR 2016010218W WO 2017048002 A1 WO2017048002 A1 WO 2017048002A1
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greenhouse gas
concentration
gas concentration
point
sample point
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PCT/KR2016/010218
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French (fr)
Korean (ko)
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엄정섭
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경북대학교 산학협력단
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3504—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing gases, e.g. multi-gas analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Definitions

  • the present invention relates to an apparatus and method for generating greenhouse gas distribution data.
  • the right to emit greenhouse gases such as carbon dioxide, methane, nitrous oxide, hydrogen fluorocarbons, perfluorocarbons, and sulfur hexafluoride, which are the main culprit of global warming, is called carbon emission rights.
  • the Kyoto Protocol requires compulsory Parties to reduce their CO2 emissions on average by 5% from 2008 to 2012, based on their 1990 emissions. countries or companies that fail to do so must purchase carbon credits elsewhere to fulfill their obligations.
  • this indirect estimation method overlooks greenhouse gases emitted or absorbed from nature, and there is a problem in that it is impossible to compare and mutually evaluate each other because of different methods and standards for each industry.
  • An embodiment of the present invention is to provide an apparatus and method for generating greenhouse gas distribution data capable of accurately and reliably calculating greenhouse gas emissions over a wide area.
  • An embodiment of the present invention is to provide an apparatus and method for generating greenhouse gas distribution data capable of grasping the amount of greenhouse gas emissions by region, for example, by administrative region.
  • An apparatus for generating GHG distribution data may include a spatial distribution analyzer configured to analyze a spatial distribution of GHG concentrations based on GHG concentrations of a plurality of sample points in a target area and location information of the sample points. ; A concentration estimating unit estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution; And a zone concentration calculator configured to calculate a greenhouse gas concentration of the zone in the target area based on at least one of the sample point and the other point.
  • the greenhouse gas concentration of the sample point may include: an average concentration of atmospheric carbon dioxide of the sample point obtained from near-infrared satellite images of the target region.
  • the near-infrared satellite image of the target region may include a satellite image of the northern hemisphere temperate climate region taken from March to June.
  • the spatial distribution analyzer may calculate a spatial variability of the concentration of the greenhouse gas according to the position of the sample point in the target region.
  • the spatial distribution analyzer may be configured to: set an effective concentration range based on the measured value of the greenhouse gas concentration measured at the reference point in the target region, and select a sample point at which the greenhouse gas concentration is out of the effective concentration range among the plurality of sample points. It can be excluded from the calculation of the spatial variability.
  • the concentration estimating unit may determine a weight for each sample point based on a variogram model corresponding to the calculated spatial variability, and calculate the spatial variability based on the weight and the greenhouse gas concentration of the sample point.
  • the greenhouse gas concentration at the other point may be calculated according to a kriging algorithm corresponding to.
  • the concentration estimator may determine a weight for each sample point based on a spherical variogram model, and the other point according to a universal kriging algorithm based on the weight and the greenhouse gas concentration of the sample point. GHG concentration can be calculated.
  • the zone concentration calculator may include: obtaining a boundary of a predetermined administrative zone from map data of the target area, calculating an average value of at least one greenhouse gas concentration among the sample point and the other point located within the boundary, and calculating the average zone; Can be determined by the concentration of GHG.
  • the apparatus for generating GHG distribution data may further include a GHG distribution map generation unit that graphically shows a region surrounded by the boundary in the target region corresponding to a grade to which a GHG concentration of a corresponding administrative region belongs.
  • a method for generating greenhouse gas distribution data includes analyzing a spatial distribution of greenhouse gas concentrations based on greenhouse gas concentrations of a plurality of sample points in a target area and location information of the sample points; Estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution; And calculating a greenhouse gas concentration of a region in the target region based on at least one of the sample point and the other point.
  • the greenhouse gas concentration of the sample point may include: an average concentration of atmospheric carbon dioxide of the sample point obtained from near-infrared satellite images of the target region.
  • the near-infrared satellite image of the target region may include a satellite image of the northern hemisphere temperate climate region taken from March to June.
  • the analyzing of the spatial distribution of the greenhouse gas concentration may include calculating a spatial variability of the greenhouse gas concentration according to the position of the sample point in the target region.
  • the analyzing the spatial distribution of the greenhouse gas concentration may include: setting an effective concentration range based on the measured value of the greenhouse gas concentration measured at a reference point in the target region before calculating the spatial variability; And excluding a sample point at which a greenhouse gas concentration is out of the effective concentration range among the plurality of sample points.
  • the estimating the greenhouse gas concentration at another point may include: determining a weight for each sample point based on a variogram model corresponding to the calculated spatial variability; And calculating a greenhouse gas concentration at another point according to a kriging algorithm corresponding to the calculated spatial variability based on the weight and the greenhouse gas concentration at the sample point.
  • the determining of the weight may include: determining a weight for each sample point based on a spherical variogram model, and calculating the greenhouse gas concentration at the other point: the weight and the greenhouse at the sample point.
  • the method may include calculating a greenhouse gas concentration at another point based on a general kriging algorithm based on the gas concentration.
  • the calculating of the greenhouse gas concentration of the zone may include: obtaining a boundary of a predetermined administrative zone from map data of the target area; Calculating an average value of the greenhouse gas concentrations of at least one of the sample point and the other point located within the boundary; And determining the average value as the greenhouse gas concentration of the administrative region.
  • the method for generating GHG distribution data may further include displaying a region surrounded by the boundary in the target region in a graphic corresponding to a grade to which the greenhouse gas concentration of the administrative region belongs.
  • the method for generating GHG distribution data may be implemented as a computer-executable program and recorded on a computer-readable recording medium.
  • the method for generating greenhouse gas distribution data may be implemented by a computer program stored in a medium for execution in combination with a computer.
  • greenhouse gas emissions can be calculated accurately and reliably over a wide area.
  • FIG. 1 is an exemplary block diagram of an apparatus for generating greenhouse gas distribution data according to an embodiment of the present invention.
  • FIG. 2 is an exemplary diagram illustrating a target region to determine a greenhouse gas concentration distribution, sample points in the target region, and a carbon dioxide concentration according to an embodiment of the present invention.
  • FIG. 3 is an exemplary diagram illustrating a distribution of greenhouse gas concentrations in a target area calculated according to an embodiment of the present invention.
  • FIG. 4 is an exemplary diagram illustrating a distribution of GHG concentrations per administrative region in a target area according to an exemplary embodiment of the present invention.
  • FIG. 5 is an exemplary diagram illustrating a prediction error calculated based on the greenhouse gas concentration of another sample point with respect to the greenhouse gas concentration distribution of the target region calculated according to an embodiment of the present invention.
  • FIG. 6 is an exemplary flowchart of a method for generating greenhouse gas distribution data according to an embodiment of the present invention.
  • FIG. 7 is an exemplary flowchart for explaining a process of analyzing a spatial distribution of greenhouse gas concentrations according to an embodiment of the present invention.
  • FIG. 8 is an exemplary flowchart for explaining a process of calculating a greenhouse gas concentration at another point according to an embodiment of the present invention.
  • FIG. 9 is an exemplary flowchart for explaining a process of calculating a greenhouse gas concentration of a zone according to an embodiment of the present invention.
  • the terms ' ⁇ ', ' ⁇ ', ' ⁇ block', ' ⁇ module', etc. used throughout the present specification may mean a unit for processing at least one function or operation.
  • it can mean a hardware component such as software, FPGA, or ASIC.
  • ' ⁇ ', ' ⁇ ', ' ⁇ block', ' ⁇ module', etc. are not limited to software or hardware.
  • ' ⁇ ', ' ⁇ ', ' ⁇ ', ' ⁇ ' May be configured to reside in an addressable storage medium or may be configured to play one or more processors.
  • ' ⁇ ', ' ⁇ ', ' ⁇ block', ' ⁇ module' are components such as software components, object-oriented software components, class components, and task components. And processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and Contains variables
  • the components and the functions provided within ' ⁇ ', ' ⁇ ', ' ⁇ ', ' ⁇ ', ',' ⁇ Module 'or may be further separated into additional components and' ⁇ part ',' ⁇ group ',' ⁇ block ',' ⁇ module '.
  • FIG. 1 is an exemplary block diagram of a greenhouse gas distribution data generating apparatus 10 according to an embodiment of the present invention.
  • the GHG distribution data generating apparatus 10 includes a spatial distribution analyzer 121, a concentration estimator 122, and a zone concentration calculator 123.
  • the spatial distribution analyzer 121, the concentration estimator 122, and the zone concentration calculator 123 are included in the processor 120, and the processor 120 is a processor capable of processing and calculating data. For example, it may include a CPU.
  • the spatial distribution analyzer 121 may analyze the spatial distribution of the greenhouse gas concentration based on the greenhouse gas concentrations of the plurality of sample points in the target area and the location information of the sample points.
  • the concentration estimator 122 may estimate the greenhouse gas concentration at another point in the target area based on the spatial distribution.
  • the zone concentration calculator 123 may calculate the greenhouse gas concentration of the zone within the target area based on at least one of the sample point and the other point.
  • the input unit 110 may receive data from the user.
  • the input unit 110 is an input device such as a keyboard, a mouse, a touch pad, a touch screen, and the like, and receives a user input necessary for performing an embodiment of the present invention.
  • the data or program necessary for generating GHG distribution data may be stored in the storage 130 and called and used by the processing unit 120.
  • the storage unit 130 may include a mass storage device such as an HDD, SSD, etc., but is not limited thereto and may include a high speed small capacity storage device such as a RAM, a ROM, a cache, a register, and the like.
  • the greenhouse gas distribution data generated according to the embodiment of the present invention may be output through the output unit 140 and provided to the user.
  • the output unit 140 may include a display device such as an LCD.
  • the GHG concentration distribution map or the GHG concentration distribution map for each administrative region is generated as described below, the map is displayed on the screen. Can be displayed and provided to the user.
  • the greenhouse gas concentration of the sample point that the greenhouse gas distribution data generating apparatus 10 uses as basic data for generating the greenhouse gas distribution data for the target region is near-infrared ray of the target region. It may include an average concentration of atmospheric carbon dioxide at the sample point obtained from near-infrared satellite images.
  • the near-infrared satellite image is an image captured by the satellite according to the intensity of the near-infrared rays radiated from the atmospheric layer, and the greenhouse gas concentration of the sample point is obtained by filtering out noise from the satellite image. Represents the average concentration of carbon dioxide in the atmospheric layer.
  • FIG. 2 is an exemplary diagram illustrating a target region to determine a greenhouse gas concentration distribution, sample points in the target region, and a carbon dioxide concentration according to an embodiment of the present invention.
  • the near-infrared satellite image of the target region is a satellite image of a temperate climate region of the northern hemisphere, and may include satellite images taken in March to June.
  • Northeast Asia such as Korea, Japan, and China, belong to the northern hemisphere temperate climate zone, which is influenced by southeast winds in summer and northwestern winds in winter.
  • this climate zone is relatively less affected by monsoons in spring and autumn, reducing the tendency of GHG emissions from one point in the target area to move to another, but in the fall, the amount of GHG in the atmosphere is affected by clouds. Since it may not be reflected in the image, the embodiment of the present invention may utilize satellite images taken in March to June corresponding to the spring season.
  • the sample points of GOSAT (Greenhouse gases Observing Satellite) XCO 2 (column-averaged obtained from the satellite images taken in (map within the circle) March to June each year of carbon dioxide concentration is from 2009 to 2012 shown in Figure 2 CO 2 concentration) data.
  • GOSAT Greenhouse gases Observing Satellite
  • XCO 2 column-averaged obtained from the satellite images taken in (map within the circle) March to June each year of carbon dioxide concentration is from 2009 to 2012 shown in Figure 2 CO 2 concentration
  • the spatial distribution analyzer 121 may analyze the spatial distribution of the greenhouse gas concentration based on the greenhouse gas concentrations of the plurality of sample points in the target area and the location information of the sample points.
  • the spatial distribution analyzer 121 may analyze the spatial distribution pattern of the greenhouse gas concentration through exploratory spatial data analysis.
  • the spatial distribution analyzer 121 may calculate a spatial variability of the concentration of the greenhouse gas according to the location of the sample point in the target area based on geostatistics.
  • the spatial distribution analyzer 121 sets the effective concentration range based on the measured value of the greenhouse gas concentration measured at the reference point in the target area, and the greenhouse among the plurality of sample points. Sample points whose gas concentration is outside the effective concentration range may be excluded from the calculation of spatial variability.
  • the effective concentration range may be set at a predetermined margin with the lower limit and the upper limit before and after the measured value. According to an embodiment, the effective concentration range may be set to only one of a lower limit and an upper limit.
  • the embodiment of the present invention can obtain more objective and reliable greenhouse gas distribution data by reflecting not only the observation data obtained from the satellite image but also the measurement data at the reference point in calculating the greenhouse gas distribution over the wide area.
  • the concentration estimator 122 determines a weight for each sample point based on the calculated variogram model corresponding to the calculated spatial variability, and calculates the weight and the greenhouse gas concentration of the sample point. Based on the kriging algorithm corresponding to the calculated spatial variability, the greenhouse gas concentration at the other point may be calculated.
  • the variogram is a measure of the similarity between the data at a certain distance, and is calculated as below as the expected value of the square of the difference between the data separated by a certain distance h.
  • ⁇ (h) is the variogram function
  • Z ( ⁇ ) is the greenhouse gas concentration at the sample point
  • h is the distance between the sample points.
  • the concentration estimator 122 selects a spherical variogram model corresponding to the spatial variability calculated from the position of the sample point and the greenhouse gas concentration, and based on the spherical variogram model.
  • the weight for each sample point can be determined.
  • the spherical variogram model is expressed as a cubic polynomial and the variogram value may coincide with a threshold value at a correlation distance.
  • the concentration estimator 122 selects a universal kriging algorithm corresponding to the spatial variability calculated from the position of the sample point and the greenhouse gas concentration,
  • the greenhouse gas concentration of the other point in the target area may be calculated by inputting the weight and the greenhouse gas concentration of the sample point.
  • the general kriging algorithm is an algorithm that assumes that the local mean value of the region to be estimated is slowly changing in each region, and does not remove the spatial invariance of the data distribution when calculating the weight.
  • FIG. 3 is an exemplary diagram illustrating a distribution of greenhouse gas concentrations in a target area calculated according to an embodiment of the present invention.
  • a carbon dioxide concentration distribution over the entire target region may be obtained by estimating carbon dioxide concentrations of other points in the target region based on the carbon dioxide concentration of the sample point illustrated in FIG. 2.
  • the target region is divided into a total of four grades of carbon dioxide concentration range, but the number of grades used to represent carbon dioxide distribution is not limited thereto.
  • the accuracy of the GHG distribution data of the target region calculated as described above may be compared with that of another sample point.
  • FIG. 4 is an exemplary diagram illustrating a prediction error calculated based on the greenhouse gas concentration of another sample point with respect to the greenhouse gas concentration distribution of the target region calculated according to an embodiment of the present invention.
  • the prediction error of the greenhouse gas concentration distribution may be obtained by calculating a root mean square prediction error (RMSPE) for each other point.
  • RMSPE root mean square prediction error
  • the GHG concentration distribution calculated as shown in FIG. 3 has a prediction error over 0.001 to 2.9 ppm, and most of the target areas show a prediction error close to 0.001 ppm, which is a minimum error value.
  • the zone concentration calculator 123 may calculate the greenhouse gas concentration of the zone in the target area based on at least one of the greenhouse gas concentration of the sample point and the estimated greenhouse gas concentration of the other point.
  • the zone concentration calculator 123 obtains a boundary of a predetermined administrative zone from the map data of the target area, and at least one greenhouse gas among sample points and other points located within the boundary.
  • the average value of the concentration can be calculated to determine the greenhouse gas concentration in the administrative area.
  • the zone concentration calculation unit 123 extracts the administrative region boundary of Kyushu from the map data indicating the administrative region of the target region, and within the target region.
  • the average value of the concentration of the greenhouse gas may be calculated with respect to a point located within the extracted boundary among sample points and other points.
  • the average value of the calculated greenhouse gas concentration may be determined as the greenhouse gas concentration of the Kyushu, which is a corresponding administrative region.
  • the GHG distribution data generating device 10 may further include a GHG distribution map generator 124.
  • the GHG distribution map generation unit 124 may graphically indicate an area surrounded by the boundary of the administrative area in a target area corresponding to a grade to which the greenhouse gas concentration of the corresponding administrative area belongs.
  • FIG. 5 is an exemplary diagram illustrating a distribution of greenhouse gas concentrations per administrative region in a target area according to an exemplary embodiment of the present invention.
  • the GHG distribution map generation unit 124 determines the GHG concentration for each administrative region based on the GHG distribution data in the target region calculated as shown in FIG. 3, and then the GHG concentration of each administrative region.
  • a graphic eg, color
  • corresponding to the class to which it belongs may represent the area of each administrative area within the target area.
  • the concentrations of greenhouse gases in each administrative district are divided into four grades, but the number of grades is not limited thereto.
  • various expression methods may be applied in addition to the colors.
  • the above-described embodiment of the present invention enables more objective and reliable estimation of greenhouse gas emissions compared to conventional emission factor based greenhouse gas irradiation.
  • the wide-area distribution of GHGs can be taken into account, so that the regional distribution of GHGs (eg, by administrative district) can be compared objectively, providing a reliable basis for regional carbon credit trading.
  • FIG. 6 is an exemplary flowchart of a method 20 for generating greenhouse gas distribution data according to an embodiment of the present invention.
  • the method for generating GHG distribution data 20 may be performed by the GHG distribution data generating apparatus 10 according to the embodiment of the present invention described above.
  • the GHG distribution data generation method 20 may be produced by a computer executable program and stored in the storage unit 130, and the processing unit 120 will be described later by calling and executing a program from the storage unit 130.
  • the greenhouse gas distribution data generation method 20 may be performed.
  • the method 20 for generating GHG distribution data may include analyzing a spatial distribution of GHG concentrations based on GHG concentrations of a plurality of sample points in a target area and location information of the sample points ( S210), estimating a GHG concentration at another point in the target area based on the spatial distribution (S220), and calculating a GHG concentration in the area in the target area based on at least one of the sample point and the other point. It may include the step (S230).
  • the greenhouse gas concentration of the sample point may include an average concentration of atmospheric carbon dioxide at the sample point obtained from a near-infrared satellite image of the target region.
  • the near-infrared satellite image of the target region may include a satellite image of the northern hemisphere temperate climate region taken from March to June.
  • FIG. 7 is an exemplary flowchart for explaining a process (S210) of analyzing a spatial distribution of greenhouse gas concentrations according to an embodiment of the present invention.
  • the analyzing of the spatial distribution of the greenhouse gas concentration (S210) may include calculating the spatial variability of the greenhouse gas concentration according to the location of the sample point in the target region (S211). .
  • the effective concentration range based on the measured value of the greenhouse gas concentration measured at the reference point in the target area It may further comprise the step of setting (S201), and the step (S202) of excluding the sample point of the greenhouse gas concentration out of the effective concentration range of the plurality of sample points.
  • FIG. 8 is an exemplary flowchart for describing a process (S220) of calculating a greenhouse gas concentration at another point according to an embodiment of the present invention.
  • the estimating the greenhouse gas concentration of the other point may include determining a weight for each sample point based on the calculated variogram model corresponding to the calculated spatial variability (S221). And calculating a greenhouse gas concentration at another point according to the kriging algorithm corresponding to the calculated spatial variability based on the weight and the greenhouse gas concentration at the sample point (S222).
  • the determining of the weight (S221) may include determining the weight for each sample point based on the spherical variogram model.
  • the calculating of the greenhouse gas concentration at the other point may include calculating the greenhouse gas concentration at the other point according to a general kriging algorithm based on the weight and the greenhouse gas concentration of the sample point. have.
  • FIG. 9 is an exemplary flowchart for describing a process (S230) of calculating a greenhouse gas concentration of a zone according to an embodiment of the present invention.
  • the calculating of the greenhouse gas concentration of the zone may include obtaining a boundary of a predetermined administrative zone from map data of the target region (S231), a sample point located within the boundary, and the other. Computing an average value of at least one greenhouse gas concentration of the point (S232), and determining the average value as the greenhouse gas concentration of the administrative area (S233).
  • the area surrounded by the boundary of the administrative area in the target area is graphically represented corresponding to the grade to which the greenhouse gas concentration of the administrative area belongs (S240). It may further include.
  • the method for generating greenhouse gas distribution data 20 may be manufactured as a program for execution in a computer and stored in a computer-readable recording medium.
  • the computer readable recording medium includes all kinds of storage devices for storing data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like.
  • the method 20 for generating GHG distribution data may be implemented as a computer program stored in a medium for execution in combination with a computer.

Abstract

The present invention relates to an apparatus and a method for generating greenhouse gas distribution data. An apparatus for generating greenhouse gas distribution data according to one embodiment of the present invention may comprise: a spatial distribution analysis unit for analyzing the spatial distribution of greenhouse gas concentrations on the basis of the greenhouse gas concentrations at a plurality of sampling points within a target region and location information of the sampling points; a concentration estimation unit for estimating, on the basis of the spatial distribution, greenhouse gas concentrations at other points within the target region; and a district concentration calculation unit for calculating the greenhouse gas concentration of a district within the target region on the basis of at least one of the sampling points and the other points.

Description

행정구역별 온실가스 분포 데이터 생성 장치 및 방법Apparatus and Method for Generating Greenhouse Gas Distribution Data by Administrative District
본 발명은 온실가스 분포 데이터 생성 장치 및 방법에 관한 것이다.The present invention relates to an apparatus and method for generating greenhouse gas distribution data.
지구 온난화의 주범인 이산화탄소, 메탄, 아산화질소, 수소불화탄소, 과불화탄소, 육불화황 등 온실가스를 일정 기간 동안 배출할 수 있는 권리를 탄소배출권이라고 한다. 교토의정서에 따르면 의무 당사국들은 1990년 배출량을 기준으로 2008년에서 2012년까지 이산화탄소 배출량을 평균 5% 수준으로 감축해야 한다. 이를 이행하지 못하는 국가나 기업은 탄소배출권을 다른 곳에서 구입하여 의무 사항을 이행해야 한다.The right to emit greenhouse gases such as carbon dioxide, methane, nitrous oxide, hydrogen fluorocarbons, perfluorocarbons, and sulfur hexafluoride, which are the main culprit of global warming, is called carbon emission rights. The Kyoto Protocol requires compulsory Parties to reduce their CO2 emissions on average by 5% from 2008 to 2012, based on their 1990 emissions. Countries or companies that fail to do so must purchase carbon credits elsewhere to fulfill their obligations.
탄소배출권을 거래하기 위해서는 온실가스의 배출량이 정확하게 산정되어야 한다. 온실가스, 특히 이산화탄소의 배출량을 산정하는 방식은 크게 직접 측정에 의한 방식과 연료 사용량을 토대로 간접적으로 추정하는 방식이 제시되고 있다. 현재 대부분의 이산화탄소 배출량 산정은 에너지 사용량 또는 투입량에 기초한 간접 추계 방식으로 이루어져 왔기 때문에 정확성에 한계가 있다.In order to trade carbon credits, greenhouse gas emissions must be accurately estimated. The method of estimating the emission of greenhouse gas, especially carbon dioxide, has been proposed by the direct measurement method and the indirect estimation method based on the fuel consumption. Currently, most CO2 emissions estimates have been indirectly estimated based on energy use or input, which limits their accuracy.
특히, 이러한 간접 추계 방식은 자연에서 배출되거나 흡수되는 온실가스를 간과하고 있는 동시에 업종별로 적용 방법 및 기준이 상이해 상호간 비교 평가가 불가능하며 통일성이 없다는 문제가 있다.In particular, this indirect estimation method overlooks greenhouse gases emitted or absorbed from nature, and there is a problem in that it is impossible to compare and mutually evaluate each other because of different methods and standards for each industry.
우리나라의 경우 충남 태안군 안면도와 제주도에 지구 대기 감시 관측소를 운영하여 이산화탄소의 배출량을 지상에서 고정 관측하고 있으나, 이러한 상시 관측소는 전 세계에 약 120여 곳에 불과하므로 이러한 소수의 고정 관측 자료를 국가나 기업 간 탄소배출권 거래의 기초 데이터로 활용하는 데는 상당한 한계가 있다.In Korea, we operate a global atmospheric monitoring station in Anmyeon-do and Jeju-do, Taean-gun, Chungcheongnam-do, but we have fixed observations of carbon dioxide emissions from the ground. There are significant limitations in using it as the basis for inter-carbon credit trading.
본 발명의 실시예는 광역에 걸쳐 정확하고 신뢰성 있는 온실가스 배출량 산출이 가능한 온실가스 분포 데이터 생성 장치 및 방법을 제공하는 것을 목적으로 한다.An embodiment of the present invention is to provide an apparatus and method for generating greenhouse gas distribution data capable of accurately and reliably calculating greenhouse gas emissions over a wide area.
본 발명의 실시예는 지역별, 예컨대 행정 구역별로 온실가스의 배출 규모를 파악할 수 있는 온실가스 분포 데이터 생성 장치 및 방법을 제공하는 것을 목적으로 한다.An embodiment of the present invention is to provide an apparatus and method for generating greenhouse gas distribution data capable of grasping the amount of greenhouse gas emissions by region, for example, by administrative region.
본 발명의 일 실시예에 따른 온실가스 분포 데이터 생성 장치는, 대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석하는 공간 분포 분석부; 상기 공간 분포를 기반으로 상기 대상 영역 내 타 지점의 온실가스 농도를 추정하는 농도 추정부; 및 상기 샘플 지점 및 상기 타 지점 중 적어도 하나를 기반으로 상기 대상 영역 내 구역의 온실가스 농도를 산출하는 구역 농도 산출부;를 포함할 수 있다.An apparatus for generating GHG distribution data according to an embodiment of the present invention may include a spatial distribution analyzer configured to analyze a spatial distribution of GHG concentrations based on GHG concentrations of a plurality of sample points in a target area and location information of the sample points. ; A concentration estimating unit estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution; And a zone concentration calculator configured to calculate a greenhouse gas concentration of the zone in the target area based on at least one of the sample point and the other point.
상기 샘플 지점의 온실가스 농도는: 상기 대상 영역의 근-적외선 위성 영상으로부터 얻은 상기 샘플 지점의 대기층 이산화탄소의 평균 농도를 포함할 수 있다.The greenhouse gas concentration of the sample point may include: an average concentration of atmospheric carbon dioxide of the sample point obtained from near-infrared satellite images of the target region.
상기 대상 영역의 근-적외선 위성 영상은: 북반구 온대 기후 영역을 3월 내지 6월에 촬영한 위성 영상을 포함할 수 있다.The near-infrared satellite image of the target region may include a satellite image of the northern hemisphere temperate climate region taken from March to June.
상기 공간 분포 분석부는: 상기 대상 영역 내에서 상기 샘플 지점의 위치에 따른 온실가스 농도의 공간 변이성(spatial variability)을 산출할 수 있다.The spatial distribution analyzer may calculate a spatial variability of the concentration of the greenhouse gas according to the position of the sample point in the target region.
상기 공간 분포 분석부는: 상기 대상 영역 내 참조 지점에서 계측된 온실가스 농도의 실측값을 기초로 유효 농도 범위를 설정하고, 상기 복수의 샘플 지점 중에서 온실가스 농도가 상기 유효 농도 범위를 벗어나는 샘플 지점을 상기 공간 변이성의 산출에서 배제시킬 수 있다.The spatial distribution analyzer may be configured to: set an effective concentration range based on the measured value of the greenhouse gas concentration measured at the reference point in the target region, and select a sample point at which the greenhouse gas concentration is out of the effective concentration range among the plurality of sample points. It can be excluded from the calculation of the spatial variability.
상기 농도 추정부는: 상기 산출된 공간 변이성에 대응하는 베리오그램(variogram) 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하고, 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 상기 산출된 공간 변이성에 대응하는 크리깅(kriging) 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출할 수 있다.The concentration estimating unit may determine a weight for each sample point based on a variogram model corresponding to the calculated spatial variability, and calculate the spatial variability based on the weight and the greenhouse gas concentration of the sample point. The greenhouse gas concentration at the other point may be calculated according to a kriging algorithm corresponding to.
상기 농도 추정부는: 구형(spherical) 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하고, 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 일반 크리깅(universal kriging) 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출할 수 있다.The concentration estimator may determine a weight for each sample point based on a spherical variogram model, and the other point according to a universal kriging algorithm based on the weight and the greenhouse gas concentration of the sample point. GHG concentration can be calculated.
상기 구역 농도 산출부는: 상기 대상 영역의 지도 데이터로부터 기 지정된 행정 구역의 경계를 획득하고, 상기 경계 내에 위치하는 상기 샘플 지점 및 상기 타 지점 중 적어도 하나의 온실가스 농도의 평균값을 산출하여 상기 행정 구역의 온실가스 농도로 결정할 수 있다.The zone concentration calculator may include: obtaining a boundary of a predetermined administrative zone from map data of the target area, calculating an average value of at least one greenhouse gas concentration among the sample point and the other point located within the boundary, and calculating the average zone; Can be determined by the concentration of GHG.
상기 온실가스 분포 데이터 생성 장치는 상기 대상 영역에서 상기 경계로 둘러싸인 영역을 해당 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽으로 나타내는 온실가스 분포 지도 생성부를 더 포함할 수 있다.The apparatus for generating GHG distribution data may further include a GHG distribution map generation unit that graphically shows a region surrounded by the boundary in the target region corresponding to a grade to which a GHG concentration of a corresponding administrative region belongs.
본 발명의 일 실시예에 따른 온실가스 분포 데이터 생성 방법은, 대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석하는 단계; 상기 공간 분포를 기반으로 상기 대상 영역 내 타 지점의 온실가스 농도를 추정하는 단계; 및 상기 샘플 지점 및 상기 타 지점 중 적어도 하나를 기반으로 상기 대상 영역 내 구역의 온실가스 농도를 산출하는 단계;를 포함할 수 있다.A method for generating greenhouse gas distribution data according to an embodiment of the present invention includes analyzing a spatial distribution of greenhouse gas concentrations based on greenhouse gas concentrations of a plurality of sample points in a target area and location information of the sample points; Estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution; And calculating a greenhouse gas concentration of a region in the target region based on at least one of the sample point and the other point.
상기 샘플 지점의 온실가스 농도는: 상기 대상 영역의 근-적외선 위성 영상으로부터 얻은 상기 샘플 지점의 대기층 이산화탄소의 평균 농도를 포함할 수 있다.The greenhouse gas concentration of the sample point may include: an average concentration of atmospheric carbon dioxide of the sample point obtained from near-infrared satellite images of the target region.
상기 대상 영역의 근-적외선 위성 영상은: 북반구 온대 기후 영역을 3월 내지 6월에 촬영한 위성 영상을 포함할 수 있다.The near-infrared satellite image of the target region may include a satellite image of the northern hemisphere temperate climate region taken from March to June.
상기 온실가스 농도의 공간 분포를 분석하는 단계는: 상기 대상 영역 내에서 상기 샘플 지점의 위치에 따른 온실가스 농도의 공간 변이성을 산출하는 단계를 포함할 수 있다.The analyzing of the spatial distribution of the greenhouse gas concentration may include calculating a spatial variability of the greenhouse gas concentration according to the position of the sample point in the target region.
상기 온실가스 농도의 공간 분포를 분석하는 단계는: 상기 공간 변이성을 산출하는 단계 전에, 상기 대상 영역 내 참조 지점에서 계측된 온실가스 농도의 실측값을 기초로 유효 농도 범위를 설정하는 단계; 및 상기 복수의 샘플 지점 중에서 온실가스 농도가 상기 유효 농도 범위를 벗어나는 샘플 지점을 배제시키는 단계;를 더 포함할 수 있다.The analyzing the spatial distribution of the greenhouse gas concentration may include: setting an effective concentration range based on the measured value of the greenhouse gas concentration measured at a reference point in the target region before calculating the spatial variability; And excluding a sample point at which a greenhouse gas concentration is out of the effective concentration range among the plurality of sample points.
상기 타 지점의 온실가스 농도를 추정하는 단계는: 상기 산출된 공간 변이성에 대응하는 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하는 단계; 및 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 상기 산출된 공간 변이성에 대응하는 크리깅 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출하는 단계;를 포함할 수 있다.The estimating the greenhouse gas concentration at another point may include: determining a weight for each sample point based on a variogram model corresponding to the calculated spatial variability; And calculating a greenhouse gas concentration at another point according to a kriging algorithm corresponding to the calculated spatial variability based on the weight and the greenhouse gas concentration at the sample point.
상기 가중치를 결정하는 단계는: 구형 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하는 단계를 포함하고, 상기 타 지점의 온실가스 농도를 산출하는 단계는: 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 일반 크리깅 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출하는 단계를 포함할 수 있다.The determining of the weight may include: determining a weight for each sample point based on a spherical variogram model, and calculating the greenhouse gas concentration at the other point: the weight and the greenhouse at the sample point. The method may include calculating a greenhouse gas concentration at another point based on a general kriging algorithm based on the gas concentration.
상기 구역의 온실가스 농도를 산출하는 단계는: 상기 대상 영역의 지도 데이터로부터 기 지정된 행정 구역의 경계를 획득하는 단계; 상기 경계 내에 위치하는 상기 샘플 지점 및 상기 타 지점 중 적어도 하나의 온실가스 농도의 평균값을 산출하는 단계; 및 상기 평균값을 상기 행정 구역의 온실가스 농도로 결정하는 단계;를 포함할 수 있다.The calculating of the greenhouse gas concentration of the zone may include: obtaining a boundary of a predetermined administrative zone from map data of the target area; Calculating an average value of the greenhouse gas concentrations of at least one of the sample point and the other point located within the boundary; And determining the average value as the greenhouse gas concentration of the administrative region.
상기 온실가스 분포 데이터 생성 방법은 상기 대상 영역에서 상기 경계로 둘러싸인 영역을 상기 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽으로 나타내는 단계를 더 포함할 수 있다.The method for generating GHG distribution data may further include displaying a region surrounded by the boundary in the target region in a graphic corresponding to a grade to which the greenhouse gas concentration of the administrative region belongs.
본 발명의 실시예에 따른 온실가스 분포 데이터 생성 방법은 컴퓨터로 실행될 수 있는 프로그램으로 구현되어 컴퓨터로 읽을 수 있는 기록매체에 기록될 수 있다.The method for generating GHG distribution data according to an embodiment of the present invention may be implemented as a computer-executable program and recorded on a computer-readable recording medium.
본 발명의 실시예에 따른 온실가스 분포 데이터 생성 방법은 컴퓨터와 결합되어 실행하기 위하여 매체에 저장된 컴퓨터 프로그램으로 구현될 수 있다.The method for generating greenhouse gas distribution data according to an embodiment of the present invention may be implemented by a computer program stored in a medium for execution in combination with a computer.
본 발명의 실시예에 따르면, 광역에 걸쳐 정확하고 신뢰성 있게 온실가스 배출량을 산출할 수 있다.According to an embodiment of the present invention, greenhouse gas emissions can be calculated accurately and reliably over a wide area.
본 발명의 실시예에 따르면, 지역별, 예컨대 행정 구역별로 온실가스의 배출 규모를 파악할 수 있다.According to an embodiment of the present invention, it is possible to determine the amount of emission of greenhouse gases for each region, for example, for each administrative region.
도 1은 본 발명의 일 실시예에 따른 온실가스 분포 데이터 생성 장치의 예시적인 블록도이다.1 is an exemplary block diagram of an apparatus for generating greenhouse gas distribution data according to an embodiment of the present invention.
도 2는 본 발명의 일 실시예에 따라 온실가스 농도 분포를 파악할 대상 영역과 상기 대상 영역 내 샘플 지점들 및 그 이산화탄소 농도를 나타내는 예시적인 도면이다.FIG. 2 is an exemplary diagram illustrating a target region to determine a greenhouse gas concentration distribution, sample points in the target region, and a carbon dioxide concentration according to an embodiment of the present invention.
도 3은 본 발명의 일 실시예에 따라 산출된 대상 영역의 온실가스 농도 분포를 나타내는 예시적인 도면이다.3 is an exemplary diagram illustrating a distribution of greenhouse gas concentrations in a target area calculated according to an embodiment of the present invention.
도 4는 본 발명의 일 실시예에 따른 대상 영역 내 행정 구역별 온실가스 농도 분포를 나타내는 예시적인 도면이다.FIG. 4 is an exemplary diagram illustrating a distribution of GHG concentrations per administrative region in a target area according to an exemplary embodiment of the present invention.
도 5는 본 발명의 일 실시예에 따라 산출된 대상 영역의 온실가스 농도 분포에 대하여 또 다른 샘플 지점의 온실가스 농도에 기초하여 계산된 예측 에러를 나타내는 예시적인 도면이다.5 is an exemplary diagram illustrating a prediction error calculated based on the greenhouse gas concentration of another sample point with respect to the greenhouse gas concentration distribution of the target region calculated according to an embodiment of the present invention.
도 6은 본 발명의 일 실시예에 따른 온실가스 분포 데이터 생성 방법의 예시적인 흐름도이다.6 is an exemplary flowchart of a method for generating greenhouse gas distribution data according to an embodiment of the present invention.
도 7은 본 발명의 일 실시예에 따라 온실가스 농도의 공간 분포를 분석하는 과정을 설명하기 위한 예시적인 흐름도이다.7 is an exemplary flowchart for explaining a process of analyzing a spatial distribution of greenhouse gas concentrations according to an embodiment of the present invention.
도 8은 본 발명의 일 실시예에 따라 타 지점의 온실가스 농도를 산출하는 과정을 설명하기 위한 예시적인 흐름도이다.8 is an exemplary flowchart for explaining a process of calculating a greenhouse gas concentration at another point according to an embodiment of the present invention.
도 9는 본 발명의 일 실시예에 따라 구역의 온실가스 농도를 산출하는 과정을 설명하기 위한 예시적인 흐름도이다.9 is an exemplary flowchart for explaining a process of calculating a greenhouse gas concentration of a zone according to an embodiment of the present invention.
본 발명의 다른 이점 및 특징, 그리고 그것들을 달성하는 방법은 첨부되는 도면과 함께 상세하게 후술 되는 실시 예를 참조하면 명확해질 것이다. 그러나 본 발명은 이하에서 개시되는 실시 예에 한정되는 것이 아니라 서로 다른 다양한 형태로 구현될 수 있으며, 단지 본 실시 예는 본 발명의 개시가 완전하도록 하고, 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자에게 발명의 범주를 완전하게 알려주기 위해 제공되는 것이며, 본 발명은 청구항의 범주에 의해 정의될 뿐이다.Other advantages and features of the present invention, and a method of achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various forms, and only the present embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the art to which the present invention pertains. It is provided to fully convey the scope of the invention to those skilled in the art, and the present invention is defined only by the scope of the claims.
만일 정의되지 않더라도, 여기서 사용되는 모든 용어들(기술 혹은 과학 용어들을 포함)은 이 발명이 속한 종래 기술에서 보편적 기술에 의해 일반적으로 수용되는 것과 동일한 의미를 가진다. 일반적인 사전들에 의해 정의된 용어들은 관련된 기술 그리고/혹은 본 출원의 본문에 의미하는 것과 동일한 의미를 갖는 것으로 해석될 수 있고, 그리고 여기서 명확하게 정의된 표현이 아니더라도 개념화되거나 혹은 과도하게 형식적으로 해석되지 않을 것이다.If not defined, all terms used herein (including technical or scientific terms) have the same meaning as commonly accepted by universal techniques in the prior art to which this invention belongs. Terms defined by general dictionaries may be interpreted as having the same meaning as in the related description and / or text of the present application, and are not conceptualized or overly formal, even if not expressly defined herein. Will not.
본 명세서에서 사용된 용어는 실시 예들을 설명하기 위한 것이며 본 발명을 제한하고자 하는 것은 아니다. 본 명세서에서, 단수형은 문구에서 특별히 언급하지 않는 한 복수형도 포함한다. 명세서에서 사용되는 '포함한다' 및/또는 이 동사의 다양한 활용형들 예를 들어, '포함', '포함하는', '포함하고', '포함하며' 등은 언급된 조성, 성분, 구성요소, 단계, 동작 및/또는 소자는 하나 이상의 다른 조성, 성분, 구성요소, 단계, 동작 및/또는 소자의 존재 또는 추가를 배제하지 않는다. 본 명세서에서 '및/또는' 이라는 용어는 나열된 구성들 각각 또는 이들의 다양한 조합을 가리킨다.The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, the term "comprises" and / or the various forms of use of this verb, for example, "comprises," "comprising," "comprising," "comprising," and the like refer to compositions, ingredients, components, The steps, operations and / or elements do not exclude the presence or addition of one or more other compositions, components, components, steps, operations and / or elements. As used herein, the term 'and / or' refers to each of the listed configurations or various combinations thereof.
한편, 본 명세서 전체에서 사용되는 '~부', '~기', '~블록', '~모듈' 등의 용어는 적어도 하나의 기능이나 동작을 처리하는 단위를 의미할 수 있다. 예를 들어 소프트웨어, FPGA 또는 ASIC과 같은 하드웨어 구성요소를 의미할 수 있다. 그렇지만 '~부', '~기', '~블록', '~모듈' 등이 소프트웨어 또는 하드웨어에 한정되는 의미는 아니다. '~부', '~기', '~블록', '~모듈'은 어드레싱할 수 있는 저장 매체에 있도록 구성될 수도 있고 하나 또는 그 이상의 프로세서들을 재생시키도록 구성될 수도 있다.On the other hand, the terms '~', '~', '~ block', '~ module', etc. used throughout the present specification may mean a unit for processing at least one function or operation. For example, it can mean a hardware component such as software, FPGA, or ASIC. However, '~', '~', '~ block', '~ module', etc. are not limited to software or hardware. '~', '~', '~', '~' May be configured to reside in an addressable storage medium or may be configured to play one or more processors.
따라서, 일 예로서 '~부', '~기', '~블록', '~모듈'은 소프트웨어 구성요소들, 객체지향 소프트웨어 구성요소들, 클래스 구성요소들 및 태스크 구성요소들과 같은 구성요소들과, 프로세스들, 함수들, 속성들, 프로시저들, 서브루틴들, 프로그램 코드의 세그먼트들, 드라이버들, 펌웨어, 마이크로 코드, 회로, 데이터, 데이터베이스, 데이터 구조들, 테이블들, 어레이들 및 변수들을 포함한다. 구성요소들과 '~부', '~기', '~블록', '~모듈'들 안에서 제공되는 기능은 더 작은 수의 구성요소들 및 '~부', '~기', '~블록', '~모듈'들로 결합되거나 추가적인 구성요소들과 '~부', '~기', '~블록', '~모듈'들로 더 분리될 수 있다.Thus, as an example, '~', '~', '~ block', '~ module' are components such as software components, object-oriented software components, class components, and task components. And processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and Contains variables The components and the functions provided within '~', '~', '~', '~', ',' ~ Module 'or may be further separated into additional components and' ~ part ',' ~ group ',' ~ block ',' ~ module '.
이하, 본 명세서에 첨부된 도면을 참조하여 본 발명의 실시예를 상세하게 설명한다.Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
도 1은 본 발명의 일 실시예에 따른 온실가스 분포 데이터 생성 장치(10)의 예시적인 블록도이다.1 is an exemplary block diagram of a greenhouse gas distribution data generating apparatus 10 according to an embodiment of the present invention.
도 1을 참조하면, 상기 온실가스 분포 데이터 생성 장치(10)는 공간 분포 분석부(121), 농도 추정부(122) 및 구역 농도 산출부(123)를 포함한다. 상기 공간 분포 분석부(121), 상기 농도 추정부(122) 및 상기 구역 농도 산출부(123)는 처리부(120)에 포함되며, 상기 처리부(120)는 데이터를 처리 및 연산할 수 있는 프로세서로서 일 예로 CPU 등을 포함할 수 있다.Referring to FIG. 1, the GHG distribution data generating apparatus 10 includes a spatial distribution analyzer 121, a concentration estimator 122, and a zone concentration calculator 123. The spatial distribution analyzer 121, the concentration estimator 122, and the zone concentration calculator 123 are included in the processor 120, and the processor 120 is a processor capable of processing and calculating data. For example, it may include a CPU.
상기 공간 분포 분석부(121)는 대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석할 수 있다. 상기 농도 추정부(122)는 상기 공간 분포를 기반으로 상기 대상 영역 내 타 지점의 온실가스 농도를 추정할 수 있다. 상기 구역 농도 산출부(123)는 상기 샘플 지점 및 상기 타 지점 중 적어도 하나를 기반으로 상기 대상 영역 내 구역의 온실가스 농도를 산출할 수 있다.The spatial distribution analyzer 121 may analyze the spatial distribution of the greenhouse gas concentration based on the greenhouse gas concentrations of the plurality of sample points in the target area and the location information of the sample points. The concentration estimator 122 may estimate the greenhouse gas concentration at another point in the target area based on the spatial distribution. The zone concentration calculator 123 may calculate the greenhouse gas concentration of the zone within the target area based on at least one of the sample point and the other point.
본 발명의 실시예에 따라 온실가스 분포 데이터를 생성하는 과정에서 입력부(110)는 사용자로부터 데이터를 입력받을 수 있다. 상기 입력부(110)는 키보드, 마우스, 터치패드, 터치스크린 등과 같은 입력 장치로서, 본 발명의 실시예를 수행할 때 필요한 사용자 입력을 사용자로부터 입력받는다.In the process of generating the greenhouse gas distribution data according to an embodiment of the present invention, the input unit 110 may receive data from the user. The input unit 110 is an input device such as a keyboard, a mouse, a touch pad, a touch screen, and the like, and receives a user input necessary for performing an embodiment of the present invention.
본 발명의 실시예에 따른 온실가스 분포 데이터 생성 시 필요한 데이터 또는 프로그램은 저장부(130)에 저장되어 상기 처리부(120)에 의해 불려와 사용될 수 있다. 상기 저장부(130)는 HDD, SSD 등과 같은 대용량 저장 장치를 포함할 수 있으나, 이에 제한되지 않고 RAM, ROM, 캐쉬, 레지스터 등과 같은 고속의 소용량 저장 장치를 포함할 수도 있다.The data or program necessary for generating GHG distribution data according to an embodiment of the present invention may be stored in the storage 130 and called and used by the processing unit 120. The storage unit 130 may include a mass storage device such as an HDD, SSD, etc., but is not limited thereto and may include a high speed small capacity storage device such as a RAM, a ROM, a cache, a register, and the like.
또한, 본 발명의 실시예에 따라 생성된 온실가스 분포 데이터는 출력부(140)를 통해 출력되어 사용자에게 제공될 수 있다. 일 예로, 상기 출력부(140)는 LCD 등과 같은 표시 장치를 포함할 수 있으며, 후술하는 바와 같이 대상 영역의 온실가스 농도 분포 지도 또는 행정 구역별 온실가스 농도 분포 지도가 생성되면 그 지도를 화면에 표시하여 사용자에게 제공할 수 있다.In addition, the greenhouse gas distribution data generated according to the embodiment of the present invention may be output through the output unit 140 and provided to the user. For example, the output unit 140 may include a display device such as an LCD. When the GHG concentration distribution map or the GHG concentration distribution map for each administrative region is generated as described below, the map is displayed on the screen. Can be displayed and provided to the user.
본 발명의 실시예에 따르면, 상기 온실가스 분포 데이터 생성 장치(10)가 대상 영역에 대한 온실가스 분포 데이터를 생성하기 위한 기초 데이터로 사용하는 샘플 지점의 온실가스 농도는 상기 대상 영역의 근-적외선(near-infrared) 위성 영상으로부터 얻은 샘플 지점의 대기층 이산화탄소의 평균 농도를 포함할 수 있다.According to an embodiment of the present invention, the greenhouse gas concentration of the sample point that the greenhouse gas distribution data generating apparatus 10 uses as basic data for generating the greenhouse gas distribution data for the target region is near-infrared ray of the target region. It may include an average concentration of atmospheric carbon dioxide at the sample point obtained from near-infrared satellite images.
상기 근-적외선 위성 영상은 대기층으로부터 복사되는 근-적외선의 세기에 따라 위성에서 촬영되는 이미지로서, 상기 샘플 지점의 온실가스 농도는 이 위성 영상으로부터 필터링을 통해 노이즈를 제거하여 얻은 값으로 상기 샘플 지점의 대기층 이산화탄소의 평균 농도를 나타낸다.The near-infrared satellite image is an image captured by the satellite according to the intensity of the near-infrared rays radiated from the atmospheric layer, and the greenhouse gas concentration of the sample point is obtained by filtering out noise from the satellite image. Represents the average concentration of carbon dioxide in the atmospheric layer.
도 2는 본 발명의 일 실시예에 따라 온실가스 농도 분포를 파악할 대상 영역과 상기 대상 영역 내 샘플 지점들 및 그 이산화탄소 농도를 나타내는 예시적인 도면이다.FIG. 2 is an exemplary diagram illustrating a target region to determine a greenhouse gas concentration distribution, sample points in the target region, and a carbon dioxide concentration according to an embodiment of the present invention.
본 발명의 실시예에 따르면, 상기 대상 영역의 근-적외선 위성 영상은 북반구의 온대 기후 영역을 촬영한 위성 영상으로서, 3월 내지 6월에 촬영한 위성 영상을 포함할 수 있다.According to an embodiment of the present invention, the near-infrared satellite image of the target region is a satellite image of a temperate climate region of the northern hemisphere, and may include satellite images taken in March to June.
우리나라, 일본, 중국과 같은 동북아시아 권역은 북반구 온대 기후 영역에 속하며, 이 기후대는 여름에는 남동풍의 영향을 받고 겨울에는 북서풍의 영향을 받는다. 따라서, 이 기후대는 비교적 봄과 가을에 계절풍의 영향을 적게 받아 대상 영역 내 어느 한 지점에서 배출된 온실가스가 다른 지점으로 이동하는 경향이 감소하나, 가을에는 구름의 영향으로 대기권의 온실가스량이 위성 영상에 반영되지 않을 수 있으므로, 본 발명의 실시예는 봄철에 해당하는 3월 내지 6월에 촬영한 위성 영상을 활용할 수 있다.Northeast Asia, such as Korea, Japan, and China, belong to the northern hemisphere temperate climate zone, which is influenced by southeast winds in summer and northwestern winds in winter. Thus, this climate zone is relatively less affected by monsoons in spring and autumn, reducing the tendency of GHG emissions from one point in the target area to move to another, but in the fall, the amount of GHG in the atmosphere is affected by clouds. Since it may not be reflected in the image, the embodiment of the present invention may utilize satellite images taken in March to June corresponding to the spring season.
도 2에 도시된 샘플 지점(지도 내 동그라미)의 이산화탄소 농도는 2009년부터 2012년까지 각 연도의 3월 내지 6월에 촬영된 GOSAT(Greenhouse gases Observing Satellite) 위성 영상으로부터 얻은 XCO2(column-averaged CO2 concentration) 데이터이다. 이하에서는 이 XCO2 데이터를 이용하여 대상 영역의 이산화탄소 분포 데이터를 산출하는 과정을 설명하기로 한다.The sample points of GOSAT (Greenhouse gases Observing Satellite) XCO 2 (column-averaged obtained from the satellite images taken in (map within the circle) March to June each year of carbon dioxide concentration is from 2009 to 2012 shown in Figure 2 CO 2 concentration) data. Hereinafter, a process of calculating carbon dioxide distribution data of the target region using the XCO 2 data will be described.
상기 공간 분포 분석부(121)는 대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석할 수 있다.The spatial distribution analyzer 121 may analyze the spatial distribution of the greenhouse gas concentration based on the greenhouse gas concentrations of the plurality of sample points in the target area and the location information of the sample points.
일 실시예에 따르면, 상기 공간 분포 분석부(121)는 탐색적 공간 자료 분석(exploratory spatial data analysis)을 통해 온실가스 농도의 공간 분포 패턴을 분석할 수 있다.According to an embodiment, the spatial distribution analyzer 121 may analyze the spatial distribution pattern of the greenhouse gas concentration through exploratory spatial data analysis.
구체적으로, 상기 공간 분포 분석부(121)는 지구통계학에 기초하여 대상 영역 내에서 샘플 지점의 위치에 따른 온실가스 농도의 공간 변이성(spatial variability)을 산출할 수 있다.In detail, the spatial distribution analyzer 121 may calculate a spatial variability of the concentration of the greenhouse gas according to the location of the sample point in the target area based on geostatistics.
이 때, 본 발명의 실시예에 따르면, 상기 공간 분포 분석부(121)는 대상 영역 내 참조 지점에서 계측된 온실가스 농도의 실측값을 기초로 유효 농도 범위를 설정하고, 복수의 샘플 지점 중에서 온실가스 농도가 상기 유효 농도 범위를 벗어나는 샘플 지점을 공간 변이성의 산출 과정에서 배제시킬 수 있다.At this time, according to an embodiment of the present invention, the spatial distribution analyzer 121 sets the effective concentration range based on the measured value of the greenhouse gas concentration measured at the reference point in the target area, and the greenhouse among the plurality of sample points. Sample points whose gas concentration is outside the effective concentration range may be excluded from the calculation of spatial variability.
상기 유효 농도 범위는 그 하한값 및 상한값이 상기 실측값을 전후하여 소정의 여유치를 두고 설정될 수 있다. 실시예에 따라 상기 유효 농도 범위는 하한값과 상한값 중 어느 하나만으로 설정될 수도 있다.The effective concentration range may be set at a predetermined margin with the lower limit and the upper limit before and after the measured value. According to an embodiment, the effective concentration range may be set to only one of a lower limit and an upper limit.
이와 같이 본 발명의 실시예는 광역에 걸친 온실가스 분포 산출에 있어서 위성 영상으로부터 얻은 관측 자료뿐만 아니라 참조 지점에서의 실측 자료를 반영함으로써 보다 객관적이고 신뢰성 있는 온실가스 분포 데이터를 얻을 수 있다.As described above, the embodiment of the present invention can obtain more objective and reliable greenhouse gas distribution data by reflecting not only the observation data obtained from the satellite image but also the measurement data at the reference point in calculating the greenhouse gas distribution over the wide area.
그 다음으로, 상기 농도 추정부(122)는 상기 산출된 공간 변이성에 대응하는 베리오그램(variogram) 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하고, 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 상기 산출된 공간 변이성에 대응하는 크리깅(kriging) 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출할 수 있다.Next, the concentration estimator 122 determines a weight for each sample point based on the calculated variogram model corresponding to the calculated spatial variability, and calculates the weight and the greenhouse gas concentration of the sample point. Based on the kriging algorithm corresponding to the calculated spatial variability, the greenhouse gas concentration at the other point may be calculated.
여기서, 베리오그램이란 일정한 거리에 있는 데이터들의 유사성을 나타내는 척도로, 일정 거리 h만큼 떨어진 데이터들 간의 차이를 제곱한 것의 기대값으로 아래와 같이 계산된다.Here, the variogram is a measure of the similarity between the data at a certain distance, and is calculated as below as the expected value of the square of the difference between the data separated by a certain distance h.
2γ(h)=E[(Z(x)-Z(x+h))2]2 γ (h) = E [(Z (x) -Z (x + h)) 2 ]
여기서, 2γ(h)는 베리오그램 함수이고, Z(·)는 샘플 지점의 온실가스 농도이고, h는 샘플 지점들 사이의 거리이다.Where 2 γ (h) is the variogram function, Z (·) is the greenhouse gas concentration at the sample point, and h is the distance between the sample points.
본 발명의 실시예에 따르면, 상기 농도 추정부(122)는 샘플 지점의 위치 및 온실가스 농도로부터 산출된 공간 변이성에 대응하여 구형(spherical) 베리오그램 모델을 선택하고, 이 구형 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정할 수 있다. 상기 구형 베리오그램 모델은 3차 다항식으로 표현되며 상관 거리에서 베리오그램 값이 문턱값과 일치할 수 있다.According to an embodiment of the present invention, the concentration estimator 122 selects a spherical variogram model corresponding to the spatial variability calculated from the position of the sample point and the greenhouse gas concentration, and based on the spherical variogram model. The weight for each sample point can be determined. The spherical variogram model is expressed as a cubic polynomial and the variogram value may coincide with a threshold value at a correlation distance.
또한, 본 발명의 실시예에 따르면, 상기 농도 추정부(122)는 샘플 지점의 위치 및 온실가스 농도로부터 산출된 공간 변이성에 대응하여 일반 크리깅(universal kriging) 알고리즘을 선택하고, 이 일반 크리깅 알고리즘에 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 입력하여 대상 영역 내 타 지점의 온실가스 농도를 산출할 수 있다. 상기 일반 크리깅 알고리즘은 추정하고자 하는 지역의 국지적인 평균값이 각 지역 내에서 완만하게 변화하고 있음을 전제하는 알고리즘으로, 가중치를 계산할 때 자료 분포의 공간적 불변성을 제거하지 않는다.In addition, according to an embodiment of the present invention, the concentration estimator 122 selects a universal kriging algorithm corresponding to the spatial variability calculated from the position of the sample point and the greenhouse gas concentration, The greenhouse gas concentration of the other point in the target area may be calculated by inputting the weight and the greenhouse gas concentration of the sample point. The general kriging algorithm is an algorithm that assumes that the local mean value of the region to be estimated is slowly changing in each region, and does not remove the spatial invariance of the data distribution when calculating the weight.
도 3은 본 발명의 일 실시예에 따라 산출된 대상 영역의 온실가스 농도 분포를 나타내는 예시적인 도면이다.3 is an exemplary diagram illustrating a distribution of greenhouse gas concentrations in a target area calculated according to an embodiment of the present invention.
도 3을 참조하면, 도 2에 도시된 샘플 지점의 이산화탄소 농도를 기초로 대상 영역 내 타 지점들의 이산화탄소 농도를 추정하여 대상 영역 전체에 걸친 이산화탄소 농도 분포를 획득할 수 있다. 도 3에서 대상 영역은 총 네 등급의 이산화탄소 농도 범위로 구분되었으나, 이산화탄소 분포를 나타내기 위해 사용되는 등급의 개수는 이에 제한되지 않는다.Referring to FIG. 3, a carbon dioxide concentration distribution over the entire target region may be obtained by estimating carbon dioxide concentrations of other points in the target region based on the carbon dioxide concentration of the sample point illustrated in FIG. 2. In FIG. 3, the target region is divided into a total of four grades of carbon dioxide concentration range, but the number of grades used to represent carbon dioxide distribution is not limited thereto.
실시예에 따라, 위와 같이 산출된 대상 영역의 온실가스 분포 데이터는 또 다른 샘플 지점의 온실가스 농도와 비교하여 그 정확도가 검증될 수 있다.According to an embodiment, the accuracy of the GHG distribution data of the target region calculated as described above may be compared with that of another sample point.
도 4는 본 발명의 일 실시예에 따라 산출된 대상 영역의 온실가스 농도 분포에 대하여 또 다른 샘플 지점의 온실가스 농도에 기초하여 계산된 예측 에러를 나타내는 예시적인 도면이다.4 is an exemplary diagram illustrating a prediction error calculated based on the greenhouse gas concentration of another sample point with respect to the greenhouse gas concentration distribution of the target region calculated according to an embodiment of the present invention.
이 실시예에 따르면, 상기 온실가스 농도 분포의 예측 에러는 각 타 지점에 대하여 RMSPE(Root Mean Square Prediction Error)를 산출함으로써 얻을 수 있다.According to this embodiment, the prediction error of the greenhouse gas concentration distribution may be obtained by calculating a root mean square prediction error (RMSPE) for each other point.
도 4를 참조하면, 도 3과 같이 산출된 온실가스 농도 분포는 0.001 내지 2.9 ppm에 걸친 예측 에러를 가지며, 대상 영역 대부분이 최소 에러값인 0.001 ppm에 가까운 예측 에러를 나타냄을 알 수 있다.Referring to FIG. 4, it can be seen that the GHG concentration distribution calculated as shown in FIG. 3 has a prediction error over 0.001 to 2.9 ppm, and most of the target areas show a prediction error close to 0.001 ppm, which is a minimum error value.
상기 구역 농도 산출부(123)는 샘플 지점의 온실가스 농도 및 상기 추정된 타 지점의 온실가스 농도 중 적어도 하나를 기반으로 대상 영역 내 구역의 온실가스 농도를 산출할 수 있다.The zone concentration calculator 123 may calculate the greenhouse gas concentration of the zone in the target area based on at least one of the greenhouse gas concentration of the sample point and the estimated greenhouse gas concentration of the other point.
본 발명의 일 실시예에 따르면, 상기 구역 농도 산출부(123)는 대상 영역의 지도 데이터로부터 기 지정된 행정 구역의 경계를 획득하고, 상기 경계 내에 위치하는 샘플 지점 및 타 지점 중 적어도 하나의 온실가스 농도의 평균값을 산출하여 행정 구역의 온실가스 농도로 결정할 수 있다.According to an embodiment of the present invention, the zone concentration calculator 123 obtains a boundary of a predetermined administrative zone from the map data of the target area, and at least one greenhouse gas among sample points and other points located within the boundary. The average value of the concentration can be calculated to determine the greenhouse gas concentration in the administrative area.
예를 들어, 대상 영역에서 분석 대상 행정 구역으로 일본의 큐슈가 지정된 경우, 상기 구역 농도 산출부(123)는 대상 영역의 행정 구역을 나타내는 지도 데이터로부터 큐슈의 행정 구역 경계를 추출하고, 대상 영역 내 샘플 지점 및 타 지점 중에서 상기 추출된 경계 내에 위치하는 지점에 대하여 온실가스 농도의 평균값을 산출할 수 있다. 이와 같이 산출된 온실가스 농도의 평균값은 해당 행정 구역인 큐슈의 온실가스 농도로 결정될 수 있다.For example, when Kyushu in Japan is designated as the administrative region to be analyzed in the target region, the zone concentration calculation unit 123 extracts the administrative region boundary of Kyushu from the map data indicating the administrative region of the target region, and within the target region. The average value of the concentration of the greenhouse gas may be calculated with respect to a point located within the extracted boundary among sample points and other points. The average value of the calculated greenhouse gas concentration may be determined as the greenhouse gas concentration of the Kyushu, which is a corresponding administrative region.
나아가, 다시 도 1을 참조하면, 상기 온실가스 분포 데이터 생성 장치(10)는 온실가스 분포 지도 생성부(124)를 더 포함할 수 있다. 상기 온실가스 분포 지도 생성부(124)는 대상 영역에서 상기 행정 구역의 경계로 둘러싸인 영역을 해당 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽으로 나타낼 수 있다.Furthermore, referring again to FIG. 1, the GHG distribution data generating device 10 may further include a GHG distribution map generator 124. The GHG distribution map generation unit 124 may graphically indicate an area surrounded by the boundary of the administrative area in a target area corresponding to a grade to which the greenhouse gas concentration of the corresponding administrative area belongs.
도 5는 본 발명의 일 실시예에 따른 대상 영역 내 행정 구역별 온실가스 농도 분포를 나타내는 예시적인 도면이다.FIG. 5 is an exemplary diagram illustrating a distribution of greenhouse gas concentrations per administrative region in a target area according to an exemplary embodiment of the present invention.
도 5를 참조하면, 상기 온실가스 분포 지도 생성부(124)는 도 3과 같이 산출된 대상 영역 내 온실가스 분포 데이터를 기초로 행정 구역별 온실가스 농도를 결정한 뒤, 각 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽(예컨대, 색상)으로 대상 영역 내 각 행정 구역의 영역을 나타낼 수 있다.Referring to FIG. 5, the GHG distribution map generation unit 124 determines the GHG concentration for each administrative region based on the GHG distribution data in the target region calculated as shown in FIG. 3, and then the GHG concentration of each administrative region. A graphic (eg, color) corresponding to the class to which it belongs may represent the area of each administrative area within the target area.
도 5에서는 각 행정 구역의 온실가스 농도가 총 네 등급으로 구분되었으나 등급의 개수는 이에 제한되지 않으며, 등급을 구분하는 그래픽도 색상 외에 다양한 표현 방식이 적용될 수 있다.In FIG. 5, the concentrations of greenhouse gases in each administrative district are divided into four grades, but the number of grades is not limited thereto. In addition to the colors, various expression methods may be applied in addition to the colors.
전술한 본 발명의 실시예는 종래의 배출 계수 기반 온실가스 조사에 비해 보다 객관적이고 신뢰성 있는 온실가스 배출량 산정을 가능하게 한다. 또한, 온실가스의 광역에 걸친 분포를 고려할 수 있어 온실가스의 지역적 분포(예컨대, 행정 구역별 분포)를 객관적으로 비교할 수 있어 지역별 탄소배출권 거래를 위한 신뢰성 높은 기초 자료를 제공할 수 있을 것이다.The above-described embodiment of the present invention enables more objective and reliable estimation of greenhouse gas emissions compared to conventional emission factor based greenhouse gas irradiation. In addition, the wide-area distribution of GHGs can be taken into account, so that the regional distribution of GHGs (eg, by administrative district) can be compared objectively, providing a reliable basis for regional carbon credit trading.
도 6은 본 발명의 일 실시예에 따른 온실가스 분포 데이터 생성 방법(20)의 예시적인 흐름도이다.6 is an exemplary flowchart of a method 20 for generating greenhouse gas distribution data according to an embodiment of the present invention.
상기 온실가스 분포 데이터 생성 방법(20)은 전술한 본 발명의 실시예에 따른 온실가스 분포 데이터 생성 장치(10)에 의해 수행될 수 있다. 상기 온실가스 분포 데이터 생성 방법(20)은 컴퓨터로 실행될 수 있는 프로그램으로 제작되어 저장부(130)에 저장될 수 있으며, 처리부(120)는 상기 저장부(130)로부터 프로그램을 불러와 실행함으로써 후술하는 온실가스 분포 데이터 생성 방법(20)을 실시할 수 있다.The method for generating GHG distribution data 20 may be performed by the GHG distribution data generating apparatus 10 according to the embodiment of the present invention described above. The GHG distribution data generation method 20 may be produced by a computer executable program and stored in the storage unit 130, and the processing unit 120 will be described later by calling and executing a program from the storage unit 130. The greenhouse gas distribution data generation method 20 may be performed.
도 6을 참조하면, 상기 온실가스 분포 데이터 생성 방법(20)은, 대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석하는 단계(S210), 상기 공간 분포를 기반으로 대상 영역 내 타 지점의 온실가스 농도를 추정하는 단계(S220), 및 상기 샘플 지점 및 상기 타 지점 중 적어도 하나를 기반으로 대상 영역 내 구역의 온실가스 농도를 산출하는 단계(S230)를 포함할 수 있다.Referring to FIG. 6, the method 20 for generating GHG distribution data may include analyzing a spatial distribution of GHG concentrations based on GHG concentrations of a plurality of sample points in a target area and location information of the sample points ( S210), estimating a GHG concentration at another point in the target area based on the spatial distribution (S220), and calculating a GHG concentration in the area in the target area based on at least one of the sample point and the other point. It may include the step (S230).
본 발명의 일 실시예에 따르면, 상기 샘플 지점의 온실가스 농도는 대상 영역의 근-적외선 위성 영상으로부터 얻은 샘플 지점의 대기층 이산화탄소의 평균 농도를 포함할 수 있다.According to an embodiment of the present invention, the greenhouse gas concentration of the sample point may include an average concentration of atmospheric carbon dioxide at the sample point obtained from a near-infrared satellite image of the target region.
이 경우, 상기 대상 영역의 근-적외선 위성 영상은 북반구 온대 기후 영역을 3월 내지 6월에 촬영한 위성 영상을 포함할 수 있다.In this case, the near-infrared satellite image of the target region may include a satellite image of the northern hemisphere temperate climate region taken from March to June.
도 7은 본 발명의 일 실시예에 따라 온실가스 농도의 공간 분포를 분석하는 과정(S210)을 설명하기 위한 예시적인 흐름도이다.7 is an exemplary flowchart for explaining a process (S210) of analyzing a spatial distribution of greenhouse gas concentrations according to an embodiment of the present invention.
도 7을 참조하면, 상기 온실가스 농도의 공간 분포를 분석하는 단계(S210)는, 대상 영역 내에서 샘플 지점의 위치에 따른 온실가스 농도의 공간 변이성을 산출하는 단계(S211)를 포함할 수 있다.Referring to FIG. 7, the analyzing of the spatial distribution of the greenhouse gas concentration (S210) may include calculating the spatial variability of the greenhouse gas concentration according to the location of the sample point in the target region (S211). .
이 때, 상기 온실가스 농도의 공간 분포를 분석하는 단계(S210)는 공간 변이성을 산출하는 단계(S211) 전에, 대상 영역 내 참조 지점에서 계측된 온실가스 농도의 실측값을 기초로 유효 농도 범위를 설정하는 단계(S201), 및 복수의 샘플 지점 중에서 온실가스 농도가 상기 유효 농도 범위를 벗어나는 샘플 지점을 배제시키는 단계(S202)를 더 포함할 수 있다.At this time, analyzing the spatial distribution of the greenhouse gas concentration (S210) before the step of calculating the spatial variability (S211), the effective concentration range based on the measured value of the greenhouse gas concentration measured at the reference point in the target area It may further comprise the step of setting (S201), and the step (S202) of excluding the sample point of the greenhouse gas concentration out of the effective concentration range of the plurality of sample points.
도 8은 본 발명의 일 실시예에 따라 타 지점의 온실가스 농도를 산출하는 과정(S220)을 설명하기 위한 예시적인 흐름도이다.8 is an exemplary flowchart for describing a process (S220) of calculating a greenhouse gas concentration at another point according to an embodiment of the present invention.
도 8을 참조하면, 상기 타 지점의 온실가스 농도를 추정하는 단계(S220)는, 상기 산출된 공간 변이성에 대응하는 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하는 단계(S221), 및 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 상기 산출된 공간 변이성에 대응하는 크리깅 알고리즘에 따라 타 지점의 온실가스 농도를 산출하는 단계(S222)를 포함할 수 있다.Referring to FIG. 8, the estimating the greenhouse gas concentration of the other point (S220) may include determining a weight for each sample point based on the calculated variogram model corresponding to the calculated spatial variability (S221). And calculating a greenhouse gas concentration at another point according to the kriging algorithm corresponding to the calculated spatial variability based on the weight and the greenhouse gas concentration at the sample point (S222).
본 발명의 실시예에 따르면, 상기 가중치를 결정하는 단계(S221)는 구형 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하는 단계를 포함할 수 있다.According to an embodiment of the present invention, the determining of the weight (S221) may include determining the weight for each sample point based on the spherical variogram model.
그리고, 상기 타 지점의 온실가스 농도를 산출하는 단계(S222)는, 상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 일반 크리깅 알고리즘에 따라 타 지점의 온실가스 농도를 산출하는 단계를 포함할 수 있다.The calculating of the greenhouse gas concentration at the other point (S222) may include calculating the greenhouse gas concentration at the other point according to a general kriging algorithm based on the weight and the greenhouse gas concentration of the sample point. have.
도 9는 본 발명의 일 실시예에 따라 구역의 온실가스 농도를 산출하는 과정(S230)을 설명하기 위한 예시적인 흐름도이다.9 is an exemplary flowchart for describing a process (S230) of calculating a greenhouse gas concentration of a zone according to an embodiment of the present invention.
도 9를 참조하면, 상기 구역의 온실가스 농도를 산출하는 단계(S230)는, 대상 영역의 지도 데이터로부터 기 지정된 행정 구역의 경계를 획득하는 단계(S231), 상기 경계 내에 위치하는 샘플 지점 및 타 지점 중 적어도 하나의 온실가스 농도의 평균값을 산출하는 단계(S232), 및 상기 평균값을 상기 행정 구역의 온실가스 농도로 결정하는 단계(S233)를 포함할 수 있다.Referring to FIG. 9, the calculating of the greenhouse gas concentration of the zone (S230) may include obtaining a boundary of a predetermined administrative zone from map data of the target region (S231), a sample point located within the boundary, and the other. Computing an average value of at least one greenhouse gas concentration of the point (S232), and determining the average value as the greenhouse gas concentration of the administrative area (S233).
다시 도 1을 참조하면, 상기 온실가스 분포 데이터 생성 방법(20)은 대상 영역에서 상기 행정 구역의 경계로 둘러싸인 영역을 상기 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽으로 나타내는 단계(S240)를 더 포함할 수 있다.Referring back to FIG. 1, in the method 20 for generating GHG distribution data, the area surrounded by the boundary of the administrative area in the target area is graphically represented corresponding to the grade to which the greenhouse gas concentration of the administrative area belongs (S240). It may further include.
상기 온실가스 분포 데이터 생성 방법(20)은 컴퓨터에서 실행되기 위한 프로그램으로 제작되어 컴퓨터가 읽을 수 있는 기록매체에 저장될 수 있다. 상기 컴퓨터가 읽을 수 있는 기록매체는 컴퓨터 시스템에 의하여 읽혀질 수 있는 데이터가 저장되는 모든 종류의 저장장치를 포함한다. 컴퓨터가 읽을 수 있는 기록매체의 예로는 ROM, RAM, CD-ROM, 자기 테이프, 플로피디스크, 광 데이터 저장장치 등이 있다. 또한, 상기 온실가스 분포 데이터 생성 방법(20)은 컴퓨터와 결합되어 실행시키기 위하여 매체에 저장된 컴퓨터 프로그램으로 구현될 수 있다.The method for generating greenhouse gas distribution data 20 may be manufactured as a program for execution in a computer and stored in a computer-readable recording medium. The computer readable recording medium includes all kinds of storage devices for storing data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like. In addition, the method 20 for generating GHG distribution data may be implemented as a computer program stored in a medium for execution in combination with a computer.
이상에서 실시예를 통해 본 발명을 설명하였으나, 위 실시예는 단지 본 발명의 사상을 설명하기 위한 것으로 이에 한정되지 않는다. 통상의 기술자는 전술한 실시예에 다양한 변형이 가해질 수 있음을 이해할 것이다. 본 발명의 범위는 첨부된 특허청구범위의 해석을 통해서만 정해진다.The present invention has been described above by way of examples, but the above embodiments are only intended to illustrate the spirit of the present invention and are not limited thereto. Those skilled in the art will appreciate that various modifications may be made to the embodiments described above. The scope of the invention is defined only by the interpretation of the appended claims.

Claims (13)

  1. 대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석하는 공간 분포 분석부;A spatial distribution analyzer configured to analyze the spatial distribution of the greenhouse gas concentrations based on the greenhouse gas concentrations of the plurality of sample points in the target area and the location information of the sample points;
    상기 공간 분포를 기반으로 상기 대상 영역 내 타 지점의 온실가스 농도를 추정하는 농도 추정부; 및A concentration estimating unit estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution; And
    상기 샘플 지점 및 상기 타 지점 중 적어도 하나를 기반으로 상기 대상 영역 내 구역의 온실가스 농도를 산출하는 구역 농도 산출부;A zone concentration calculator configured to calculate a greenhouse gas concentration of a zone in the target area based on at least one of the sample point and the other point;
    를 포함하는 온실가스 분포 데이터 생성 장치.Greenhouse gas distribution data generation device comprising a.
  2. 제 1 항에 있어서,The method of claim 1,
    상기 샘플 지점의 온실가스 농도는:The greenhouse gas concentration at the sample point is:
    상기 대상 영역의 근-적외선 위성 영상으로부터 얻은 상기 샘플 지점의 대기층 이산화탄소의 평균 농도를 포함하는 온실가스 분포 데이터 생성 장치.And a mean concentration of atmospheric carbon dioxide at the sample point obtained from a near-infrared satellite image of the target region.
  3. 제 2 항에 있어서,The method of claim 2,
    상기 대상 영역의 근-적외선 위성 영상은:The near-infrared satellite image of the target region is:
    북반구 온대 기후 영역을 3월 내지 6월에 촬영한 위성 영상을 포함하는 온실가스 분포 데이터 생성 장치.Greenhouse gas distribution data generation device including satellite images of the northern hemisphere temperate climate region from March to June.
  4. 제 1 항에 있어서,The method of claim 1,
    상기 공간 분포 분석부는:The spatial distribution analysis unit:
    상기 대상 영역 내에서 상기 샘플 지점의 위치에 따른 온실가스 농도의 공간 변이성(spatial variability)을 산출하는 온실가스 분포 데이터 생성 장치.And a greenhouse gas distribution data generation device for calculating spatial variability of the greenhouse gas concentration according to the position of the sample point in the target region.
  5. 제 4 항에 있어서,The method of claim 4, wherein
    상기 공간 분포 분석부는:The spatial distribution analysis unit:
    상기 대상 영역 내 참조 지점에서 계측된 온실가스 농도의 실측값을 기초로 유효 농도 범위를 설정하고, 상기 복수의 샘플 지점 중에서 온실가스 농도가 상기 유효 농도 범위를 벗어나는 샘플 지점을 상기 공간 변이성의 산출에서 배제시키는 온실가스 분포 데이터 생성 장치.The effective concentration range is set based on the measured value of the greenhouse gas concentration measured at the reference point in the target region, and the sample point at which the greenhouse gas concentration is out of the effective concentration range is selected from the plurality of sample points. Exclusive greenhouse gas distribution data generator.
  6. 제 4 항에 있어서,The method of claim 4, wherein
    상기 농도 추정부는:The concentration estimating unit:
    상기 산출된 공간 변이성에 대응하는 베리오그램(variogram) 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하고,Determine a weight for each sample point based on a variogram model corresponding to the calculated spatial variability,
    상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 상기 산출된 공간 변이성에 대응하는 크리깅(kriging) 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출하는 온실가스 분포 데이터 생성 장치.And a GHG concentration data generation device for calculating the GHG concentration at the other point according to a kriging algorithm corresponding to the calculated spatial variability based on the weight and the GHG concentration at the sample point.
  7. 제 6 항에 있어서,The method of claim 6,
    상기 농도 추정부는:The concentration estimating unit:
    구형(spherical) 베리오그램 모델을 기반으로 각 샘플 지점에 대한 가중치를 결정하고,Determine the weight for each sample point based on the spherical variogram model,
    상기 가중치 및 상기 샘플 지점의 온실가스 농도를 기반으로 일반 크리깅(universal kriging) 알고리즘에 따라 상기 타 지점의 온실가스 농도를 산출하는 온실가스 분포 데이터 생성 장치.And a greenhouse gas distribution data generation device for calculating a greenhouse gas concentration at another point based on the weight and the greenhouse gas concentration at the sample point according to a universal kriging algorithm.
  8. 제 1 항에 있어서,The method of claim 1,
    상기 구역 농도 산출부는:The zone concentration calculation unit:
    상기 대상 영역의 지도 데이터로부터 기 지정된 행정 구역의 경계를 획득하고, 상기 경계 내에 위치하는 상기 샘플 지점 및 상기 타 지점 중 적어도 하나의 온실가스 농도의 평균값을 산출하여 상기 행정 구역의 온실가스 농도로 결정하는 온실가스 분포 데이터 생성 장치.A boundary of a predetermined administrative area is obtained from map data of the target area, and an average value of at least one greenhouse gas concentration among the sample point and the other point located within the boundary is calculated to determine the greenhouse gas concentration of the administrative area. GHG distribution data generator.
  9. 제 8 항에 있어서,The method of claim 8,
    상기 대상 영역에서 상기 경계로 둘러싸인 영역을 해당 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽으로 나타내는 온실가스 분포 지도 생성부를 더 포함하는 온실가스 분포 데이터 생성 장치.And a greenhouse gas distribution map generation unit for graphically representing a region surrounded by the boundary in the target region corresponding to a grade to which a greenhouse gas concentration of a corresponding administrative region belongs.
  10. 컴퓨팅 시스템에 의해 수행되는 온실가스 분포 데이터 생성 방법에 있어서,대상 영역 내 복수의 샘플 지점의 온실가스 농도 및 상기 샘플 지점의 위치 정보를 기반으로 온실가스 농도의 공간 분포를 분석하는 단계;A method for generating greenhouse gas distribution data performed by a computing system, the method comprising: analyzing a spatial distribution of greenhouse gas concentrations based on greenhouse gas concentrations of a plurality of sample points in a target area and location information of the sample points;
    상기 공간 분포를 기반으로 상기 대상 영역 내 타 지점의 온실가스 농도를 추정하는 단계; 및Estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution; And
    상기 샘플 지점 및 상기 타 지점 중 적어도 하나를 기반으로 상기 대상 영역 내 구역의 온실가스 농도를 산출하는 단계;Calculating a greenhouse gas concentration of a region in the target region based on at least one of the sample point and the other point;
    를 포함하는 온실가스 분포 데이터 생성 방법.Greenhouse gas distribution data generation method comprising a.
  11. 제 10 항에 있어서,The method of claim 10,
    상기 온실가스 농도를 산출하는 단계는,The step of calculating the greenhouse gas concentration,
    상기 대상 영역 내의 행정 구역을 나타내는 지도 데이터로부터 기 지정된 행정 구역의 경계를 획득하는 단계;Obtaining a boundary of a predetermined administrative zone from map data representing an administrative zone in the target area;
    상기 경계 내에 위치하는 상기 샘플 지점 및 상기 타 지점 중 적어도 하나의 온실가스 농도의 평균값을 산출하는 단계; 및Calculating an average value of the greenhouse gas concentrations of at least one of the sample point and the other point located within the boundary; And
    상기 평균값을 상기 기 지정된 행정 구역의 온실가스 농도로 결정하는 단계를 포함하는 온실가스 분포 데이터 생성 방법.And determining the average value as a greenhouse gas concentration of the predetermined administrative area.
  12. 제 11 항에 있어서,The method of claim 11,
    상기 대상 영역에서 상기 경계로 둘러싸인 영역을 해당 행정 구역의 온실가스 농도가 속하는 등급에 대응하는 그래픽으로 나타내는 단계를 더 포함하는 온실가스 분포 데이터 생성 방법.And displaying the region surrounded by the boundary in the target region in a graphic corresponding to a grade to which the greenhouse gas concentration of the corresponding administrative region belongs.
  13. 제 10 항 내지 제 12 항 중 어느 한 항에 따른 온실가스 분포 데이터 생성 방법을 컴퓨터로 실행하기 위한 프로그램이 기록된 컴퓨터 판독 가능 기록 매체.A computer-readable recording medium having recorded thereon a program for executing the method of generating greenhouse gas distribution data according to any one of claims 10 to 12 by a computer.
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