KR20190011619A - Apparatus and method for generating greenhouse gas distribution data - Google Patents

Apparatus and method for generating greenhouse gas distribution data Download PDF

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KR20190011619A
KR20190011619A KR1020170094368A KR20170094368A KR20190011619A KR 20190011619 A KR20190011619 A KR 20190011619A KR 1020170094368 A KR1020170094368 A KR 1020170094368A KR 20170094368 A KR20170094368 A KR 20170094368A KR 20190011619 A KR20190011619 A KR 20190011619A
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greenhouse gas
gas concentration
target area
concentration
point
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KR1020170094368A
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Korean (ko)
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엄정섭
황영석
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경북대학교 산학협력단
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Publication of KR20190011619A publication Critical patent/KR20190011619A/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing

Abstract

An apparatus for generating greenhouse gas distribution data is disclosed. A greenhouse gas distribution data generation apparatus includes a spatial distribution analysis unit for analyzing a spatial distribution of greenhouse gas concentration based on a greenhouse gas concentration and a sample point location information of a plurality of sample points in a target region, A zone concentration calculating unit for calculating a greenhouse gas concentration of a zone within the target zone based on at least one of a concentration estimating unit for estimating a greenhouse gas concentration at another point, And a data generating unit for generating greenhouse gas concentration data on land cover based on the greenhouse gas concentration of the target area and the land cover of the target area.

Description

[0001] APPARATUS AND METHOD FOR GENERATING GREENHOUSE GAS DISTRIBUTION DATA [0002]

The present invention relates to an apparatus and method for generating greenhouse gas distribution data, and more particularly to an apparatus and method for generating greenhouse gas distribution data for generating greenhouse gas concentration data according to land cover.

The right to discharge greenhouse gases such as carbon dioxide, methane, nitrous oxide, hydrogen fluoride, perfluorocarbon, and sulfur hexafluoride, which are the main causes of global warming, for a certain period of time is called carbon emission rights. According to the Kyoto Protocol, compulsory Parties should reduce carbon dioxide emissions by an average of 5% from 2008 to 2012, based on 1990 emissions. Countries or entities that fail to implement them should purchase their carbon credits elsewhere and fulfill their obligations.

To trade carbon credits, the emissions of greenhouse gases must be accurately estimated. The method of estimating the emission of greenhouse gases, in particular, carbon dioxide, has been proposed as a method of indirect measurement based on the direct measurement method and the fuel usage amount. Currently, most CO2 emissions estimates are based on indirect estimations based on energy use or input, which limits accuracy.

Conventionally, the method of measuring CO2 emissions using GOSAT satellite images has been used to analyze CO2 emissions at the global level, continents such as Asia and Africa due to the resolution of GOSAT satellite images, It was difficult to measure the carbon dioxide emissions.

Therefore, the conventional carbon dioxide emission measuring method has a problem that it is difficult to grasp the distribution of the greenhouse gas concentration change depending on the land characteristics.

It is an object of the present invention to provide an apparatus and a method for generating greenhouse gas distribution data for generating greenhouse gas concentration data according to land cover.

According to an aspect of the present invention, there is provided a spatial distribution analyzer for analyzing a spatial distribution of a greenhouse gas concentration based on a greenhouse gas concentration of a plurality of sample points in a target area and position information of the sample point, A concentration estimating unit for estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution, a zone concentration calculating unit for calculating a greenhouse gas concentration of the zone within the target zone based on at least one of the sample point and the other point A land cover classifier for classifying the target area into one of a plurality of land covers using the land cover image of the target area and a land cover classifier for classifying the land cover based on the greenhouse gas concentration of the target area and the land cover of the target area, And a data generator for generating greenhouse gas concentration data corresponding to the greenhouse gas concentration data.

Here, the greenhouse gas concentration of the sample point may include an average concentration of the atmospheric carbon dioxide of the sample point obtained from the OCO-2 (Orbiting Carbon Observatory-2) satellite image of the target region.

The land cover classification unit may measure the albedo of the target area using the land cover image of the target area and classify the target area into one of the plurality of land covers based on the measured albedo.

Here, the plurality of land coverings may comprise evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built- ).

In addition, the spatial distribution analyzer may calculate a spatial variability of a greenhouse gas concentration according to a location of the sample point in the target area.

Here, the spatial distribution analyzing unit may set an effective concentration range based on an actual value of the greenhouse gas concentration measured at the reference point in the target area, and determine, from the plurality of sample points, a sample in which the greenhouse gas concentration is out of the effective concentration range Point can be excluded from the calculation of the spatial variability.

The concentration estimator may determine a weight for each sample point on the basis of a variogram model corresponding to the calculated spatial variability and calculate a weight value for each of the calculated sample points based on the weight and the greenhouse gas concentration of the sample point The greenhouse gas concentration at the other point can be calculated according to a kriging algorithm corresponding to spatial variability.

Here, the concentration estimator may determine a weight for each sample point based on a spherical variogram model, and calculate a concentration based on the weight and the greenhouse gas concentration of the sample point according to a universal kriging algorithm The greenhouse gas concentration at the other point can be calculated.

Meanwhile, a method for generating greenhouse gas distribution data according to an embodiment of the present invention includes analyzing a spatial distribution of a greenhouse gas concentration based on a greenhouse gas concentration of a plurality of sample points in a target region and position information of the sample point, Estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution, calculating a greenhouse gas concentration of the target area within the target area based on at least one of the sample point and the other point, Classifying the target area into one of a plurality of land covers using the land cover image of the area and generating the greenhouse gas concentration data according to the land cover based on the greenhouse gas concentration and the land cover of the target area .

Here, the greenhouse gas concentration of the sample point may include an average concentration of the atmospheric carbon dioxide of the sample point obtained from the OCO-2 (Orbiting Carbon Observatory-2) satellite image of the target region.

The classifying step may measure the albedo of the target area using the land cover image of the target area, and classify the target area into one of the plurality of land covers based on the measured albedo.

Here, the plurality of land coverings may comprise evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built- ).

In addition, the analyzing step may include calculating a spatial variability of the greenhouse gas concentration according to the position of the sample point in the target area.

Here, the analyzing step may include setting an effective concentration range based on an actual value of the greenhouse gas concentration measured at the reference point in the target area, and setting the effective concentration range of the sample in which the greenhouse gas concentration is out of the effective concentration range And excluding the point from the calculation of the spatial variability.

The estimating may further include: determining a weight for each sample point based on a variogram model corresponding to the calculated spatial variability, and determining a weight for each sample point based on the weight and the greenhouse gas concentration of the sample point And calculating the greenhouse gas concentration at the other point according to a kriging algorithm corresponding to the calculated spatial variability.

The step of determining the weights may include the steps of: determining a weight for each sample point based on a spherical variogram model; and calculating the greenhouse gas concentration at the other point, The greenhouse gas concentration at the other point can be calculated according to a universal kriging algorithm based on the greenhouse gas concentration of the greenhouse gas.

Meanwhile, the method for generating greenhouse gas distribution data according to an embodiment of the present invention may be implemented by a computer-readable program and recorded on a computer-readable recording medium.

As described above, according to various embodiments of the present invention, it is possible to accurately calculate the greenhouse gas emission amount of a specific area and to easily generate the greenhouse gas concentration data according to the land cover.

1 is an exemplary block diagram of a greenhouse gas distribution data generating apparatus according to an embodiment of the present invention.
2 is an exemplary diagram illustrating a greenhouse gas concentration distribution of a subject area in accordance with an embodiment of the present invention.
3 is an exemplary diagram illustrating a land cover image of an object area according to an embodiment of the present invention.
4 is an exemplary diagram showing albedo data for each of a plurality of land coverings according to an embodiment of the present invention.
5 is an exemplary diagram illustrating a greenhouse gas concentration distribution for each of a plurality of land coverings according to an embodiment of the present invention.
6 is an exemplary diagram illustrating a greenhouse gas concentration distribution calculated according to a kriging algorithm according to an embodiment of the present invention.
FIG. 7 is an exemplary diagram showing the area determination coefficients for a plurality of land coverings according to an embodiment of the present invention.
8 is a flowchart illustrating a method for generating greenhouse gas distribution data according to an embodiment of the present invention.

Other advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims.

Unless defined otherwise, all terms (including technical or scientific terms) used herein have the same meaning as commonly accepted by the generic art in the prior art to which this invention belongs. Terms defined by generic dictionaries may be interpreted to have the same meaning as in the related art and / or in the text of this application, and may be conceptualized or overly formalized, even if not expressly defined herein I will not.

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. As used herein, the terms' comprise 'and / or various forms of use of the verb include, for example,' including, '' including, '' including, '' including, Steps, operations, and / or elements do not preclude the presence or addition of one or more other compositions, components, components, steps, operations, and / or components. The term 'and / or' as used herein refers to each of the listed configurations or various combinations thereof.

It should be noted that the terms such as '~', '~ period', '~ block', 'module', etc. used in the entire specification may mean a unit for processing at least one function or operation. For example, a hardware component, such as a software, FPGA, or ASIC. However, '~ part', '~ period', '~ block', '~ module' are not meant to be limited to software or hardware. Modules may be configured to be addressable storage media and may be configured to play one or more processors. ≪ RTI ID = 0.0 >

Thus, by way of example, the terms 'to', 'to', 'to block', 'to module' refer to components such as software components, object oriented software components, class components and task components Microcode, circuitry, data, databases, data structures, tables, arrays, and the like, as well as components, Variables. The functions provided in the components and in the sections ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ' , '~', '~', '~', '~', And '~' modules with additional components.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings attached hereto.

1 is an exemplary block diagram of a greenhouse gas distribution data generating apparatus according to an embodiment of the present invention.

1, the greenhouse gas distribution data generation apparatus 10 includes a spatial distribution analysis unit 121, a concentration estimation unit 122, a zone concentration calculation unit 123, a land cover classification unit 124, (125).

According to an embodiment of the present invention, the greenhouse gas distribution data generation apparatus 10 may include an input unit 110, a processing unit 120, a storage unit 130, and an output unit 140, 120 may include a spatial distribution analysis unit 121, a concentration estimation unit 122, a zone concentration calculation unit 123, a land cover classification unit 124, and a data generation unit 125. In addition, the processing unit 120 may be implemented as a processor capable of processing and calculating data.

The spatial distribution analyzer 121 can analyze the spatial distribution of the greenhouse gas concentration based on the greenhouse gas concentration of the plurality of sample points in the object area and the position information of the sample point. Specifically, the spatial distribution analyzer 121 calculates the spatial variability of the greenhouse gas concentration according to the position of the sample point in the target area and calculates the measured value of the greenhouse gas concentration measured at the reference point in the target area as It is possible to set the effective concentration range as a basis and to exclude the sample point where the greenhouse gas concentration out of the effective concentration range from among the plurality of sample points is calculated from the calculation of the spatial variability. Here, the greenhouse gas concentration at the sample point may include the average concentration of the atmospheric carbon dioxide of the sample point obtained from the OCO-2 (Orbiting Carbon Observatory-2) satellite image of the target area.

The concentration estimating unit 122 may estimate a greenhouse gas concentration at another point in the target region based on the spatial distribution. Specifically, the concentration estimating unit 122 determines a weight for each sample point based on a variogram model corresponding to the calculated spatial variability, and calculates a weight based on the weight and the greenhouse gas concentration of the sample point The greenhouse gas concentration at another point can be calculated according to a kriging algorithm corresponding to spatial variability. Here, the variogram is a measure of the similarity of data at a certain distance, and can be calculated as an expected value of squaring the difference between data separated by a certain distance h.

The concentration estimating unit 122 determines the weights for each sample point based on the spherical variogram model and calculates the concentration based on the weight and the greenhouse gas concentration at the sample point based on the universal kriging algorithm, Can be calculated. The spherical variogram model is expressed as a third order polynomial, and the variogram value at the correlation distance may match the threshold value.

The zone concentration calculation unit 123 may calculate the greenhouse gas concentration of the zone within the target zone based on at least one of the sample point and the other point. For example, the zone concentration calculation unit 123 obtains the boundary of the previously designated zone from the map data of the target zone, calculates the average value of the greenhouse gas concentration of at least one of the sample point and the other point located in the boundary, Of the greenhouse gas concentration.

The land cover classification unit 124 may classify the target area into one of a plurality of land cover using the land cover image of the target area. Specifically, the land cover classification unit 124 measures the albedo of the target area using the land cover image of the target area, and classifies the target area into one of the plurality of land cover based on the measured albedo. Herein, the plurality of land coverings are evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built-up forests, . ≪ / RTI >

The data generating unit 125 can generate the greenhouse gas concentration data according to the land cover based on the greenhouse gas concentration of the target area and the land cover of the target area.

The input unit 110 may receive data from a user. The input unit 110 is an input device such as a keyboard, a mouse, a touch pad, a touch screen, or the like, and can input user input necessary for generating greenhouse gas distribution data according to an embodiment of the present invention.

Also, the storage unit 130 may store data or programs necessary for generating greenhouse gas distribution data according to an embodiment of the present invention. The storage unit 130 may include a mass storage device such as an HDD, an SSD, or the like, but may include a high-capacity storage device such as a RAM, a ROM, a cache, a register,

In addition, the greenhouse gas distribution data according to the embodiment of the present invention may be outputted 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 or the like, and may display the greenhouse gas concentration data corresponding to the land cover on the screen and provide the same to the user.

According to the embodiment of the present invention, the greenhouse gas concentration of the sample point used as the basic data for generating the greenhouse gas distribution data for the target area by the greenhouse gas distribution data generation apparatus 10 is set to OCO-2 (Orbiting Carbon Observatory-2) < / RTI > satellite image. Here, the atmospheric carbon dioxide average concentration at the sample point may be a value obtained by removing the noise through filtering the image of the object region photographed by the OCO-2 (Orbiting Carbon Observatory-2) satellite.

2 is an exemplary diagram illustrating a greenhouse gas concentration distribution of a subject area in accordance with an embodiment of the present invention. For example, the distribution of greenhouse gas concentrations in India, which is located at 68-97 degrees east and 8-38 degrees north, can be calculated. In the Indian region, precipitation is concentrated by the summer monsoon in June-August and the monsoon in September-November, so that the distortion of the greenhouse gas concentration due to precipitation appears strongly during this period. In addition, since dietary habits are not active in December and February, the difference in greenhouse gas emission concentration by land cover is the lowest. Therefore, it is possible to use the measured XCO2 data from March to May when the precipitation is small and the atmosphere is stabilized and the temperature rises and the dietary life becomes active. The greenhouse gas concentrations of the sample points of FIG. 2 (circles (a, b, c, d, e, f in the map) in the map are XCO2 measured from OCO-2 satellite images from March 1 to May 31, 2015 Data. Hereinafter, the process of generating the greenhouse gas concentration data according to the land coverage of the target area using the XCO2 data will be described.

3, the land cover classification unit 124 measures the albedo of a target area (for example, points a, b, c, d, e, and f in FIG. 3) using the land cover image, Based on albedo, the area of interest is divided into evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built-up forests. It can be classified as any one land cover. For example, as shown in FIG. 4, according to the measured albedo, point a is evergreen broadleaf land cover, point b is evergreen coniferous forest cover, point c is mixed forest land cover, point d is farmland land cover, point e is wetland land cover , and point f can be classified as urban land cover.

5 is a diagram showing XCO2 measurement values of the target area (points a, b, c, d, e, and f in FIG. 2). 5, the greenhouse gas concentrations at points a, b, c, d, e, and f are calculated based on the XCO2 values at points a, b, c, d, e, and f measured from the OCO- Can be calculated. For example, as shown in FIG. 5, the average XCO2 concentrations at points d, e, and f are relatively high at 402.8 ppm, 401.8 ppm, and 401.3 ppm, respectively. , It can be judged that the greenhouse gas concentration is high in the city.

6 is a graph showing a result of applying a general kriging algorithm and a spherical variogram based on an XCO2 value of a target region. In order to calculate the greenhouse gas concentration of the target region, various methods such as equal interval, standard deviation, average value, isometric sequence and isometric sequence can be applied. For example, as shown in FIG. 6, (385.8 ppm, 393.3 ppm, 397.9 ppm, 400.8 ppm, 405.5 ppm, 413.5 ppm) can be applied.

As shown in FIG. 7, in order to calculate the XCO2 value and the spatial pattern of the land cover, the XCO2 value and the Global Moran's 1 index of each land cover (a, b, c, d, Index analysis can be performed. Here, the global peer 1 index analysis is a data pattern indicating whether the value of the measured data at one point is related to the measured data value in the neighboring area based on the distance between the data value and the data, that is, calculating the spatial autocorrelation . For example, the local determination factor (local R2) of farmland is the highest with a distribution of 0.000-0.847, and the regional decision coefficient of evergreen broadleaf is the lowest with a distribution of 0.000-0.039.

8 is a flowchart illustrating a method for generating greenhouse gas distribution data according to an embodiment of the present invention.

Referring to FIG. 8, the spatial distribution of the greenhouse gas concentration is analyzed based on the greenhouse gas concentration of the plurality of sample points in the target region and the position information of the sample point (S810).

Next, the greenhouse gas concentration at another point in the target area is estimated based on the spatial distribution (S820), and the greenhouse gas concentration in the target area is calculated based on at least one of the sample point and other points (S830).

Subsequently, the target area is classified into one of a plurality of land covers using the land cover image of the target area (S840). Specifically, the albedo of the target area may be measured using the land cover image of the target area, and the target area may be classified into any one of the plurality of land covers based on the measured albedo. Here, a plurality of land coverings can be used for evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built-up .

Then, the greenhouse gas concentration data according to the land cover is generated based on the greenhouse gas concentration of the target area and the land cover of the target area (S850).

As described above, according to various embodiments of the present invention, it is possible to accurately calculate the greenhouse gas emission amount of a specific area and to easily generate the greenhouse gas concentration data according to the land cover.

The method for generating greenhouse gas distribution data according to an embodiment of the present invention may be stored in a computer-readable recording medium that is manufactured as a program to be executed by a computer. The computer-readable recording medium includes all kinds of storage devices in which data that can be read by a computer system is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. Also, the greenhouse gas distribution data generation method 20 may be implemented as a computer program stored in a medium for execution in association with the computer.

While the present invention has been described with reference to the exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. Those skilled in the art will appreciate that various modifications may be made to the embodiments described above. The scope of the present invention is defined only by the interpretation of the appended claims.

110: input unit 120:
121: Spatial distribution analyzing unit 122: Concentration estimating unit
123: Zone concentration calculation unit 124: Land cover classification unit
125: data generation unit 130:
140:

Claims (17)

A spatial distribution analyzer for analyzing a spatial distribution of greenhouse gas concentrations based on greenhouse gas concentrations of a plurality of sample points in the target area and positional information of the sample points;
A concentration estimator for estimating a greenhouse gas concentration at another point in the target region based on the spatial distribution;
A zone concentration calculating unit for calculating a greenhouse gas concentration of the zone within the target zone based on at least one of the sample point and the other point;
A land cover classifying unit classifying the target area into one of a plurality of land cover using the land cover image of the target area; And
And a data generator for generating greenhouse gas concentration data according to the land cover based on the greenhouse gas concentration of the target area and the land cover of the target area. The method according to claim 1,
The greenhouse gas concentration of the sample point
And an average concentration of atmospheric carbon dioxide of the sample point obtained from an OCO-2 (Orbiting Carbon Observatory-2) satellite image of the target region. The method according to claim 1,
The land cover classification unit includes:
Measuring the albedo of the target area using the land cover image of the target area, and classifying the target area into one of a plurality of land covers based on the measured albedo. The method of claim 3,
Wherein the plurality of land covers comprise:
Greenhouse gas distribution data including evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built-up Device. The method according to claim 1,
Wherein the spatial distribution analyzer comprises:
Wherein the spatial variability of the greenhouse gas concentration according to the position of the sample point in the target area is calculated. 6. The method of claim 5,
Wherein the spatial distribution analyzer comprises:
Setting an effective concentration range based on an actual value of the greenhouse gas concentration measured at a reference point in the target area and calculating a sample point at which the greenhouse gas concentration out of the effective concentration range among the plurality of sample points is calculated from the calculation of the spatial variability A greenhouse gas distribution data generator for excluding greenhouse gases. 6. The method of claim 5,
The concentration estimating unit may calculate,
Determining a weight for each sample point based on a variogram model corresponding to the calculated spatial variability,
And calculates a greenhouse gas concentration of the other point according to a kriging algorithm corresponding to the calculated spatial variability based on the weight and the greenhouse gas concentration of the sample point. 8. The method of claim 7,
The concentration estimating unit may calculate,
Based on the Spherical Variogram model, weights are determined for each sample point,
And a greenhouse gas concentration data generator for calculating a greenhouse gas concentration of the other point according to a universal kriging algorithm based on the weight and the greenhouse gas concentration of the sample point. Analyzing a spatial distribution of the greenhouse gas concentration based on the greenhouse gas concentration of the plurality of sample points in the target area and the location information of the sample points;
Estimating a greenhouse gas concentration at another point in the target area based on the spatial distribution;
Calculating a greenhouse gas concentration of the zone within the subject area based on at least one of the sample point and the other point;
Classifying the target area into one of a plurality of land cover using the land cover image of the target area; And
And generating greenhouse gas concentration data according to the land cover based on the greenhouse gas concentration of the target area and the land cover of the target area. 10. The method of claim 9,
The greenhouse gas concentration of the sample point
And an average concentration of atmospheric carbon dioxide of the sample point obtained from an OCO-2 (Orbiting Carbon Observatory-2) satellite image of the target region. 10. The method of claim 9,
Wherein said classifying comprises:
Measuring the albedo of the target area using the land cover image of the target area, and classifying the target area into one of a plurality of land covers based on the measured albedo. 12. The method of claim 11,
Wherein the plurality of land covers comprise:
Greenhouse gas distribution data including evergreen needleleaf forests, evergreen broadleaf forests, mixed forests, croplands, permanent wetlands and urban and built-up Way. 10. The method of claim 9,
Wherein the analyzing comprises:
And calculating a spatial variability of a greenhouse gas concentration according to a location of the sample point in the target area. 14. The method of claim 13,
Wherein the analyzing comprises:
Setting an effective concentration range based on an actual value of the greenhouse gas concentration measured at a reference point in the target area and calculating a sample point at which the greenhouse gas concentration out of the effective concentration range among the plurality of sample points is calculated from the calculation of the spatial variability The method comprising the steps of: 14. The method of claim 13,
Wherein the estimating step comprises:
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 the other point according to a kriging algorithm corresponding to the calculated spatial variability based on the weight and the greenhouse gas concentration of the sample point. 16. The method of claim 15,
The step of determining the weight includes:
Based on the Spherical Variogram model, weights are determined for each sample point,
Wherein the step of calculating the greenhouse gas concentration at the other point comprises:
And calculating the greenhouse gas concentration at the other point according to a universal kriging algorithm based on the weight and the greenhouse gas concentration of the sample point. A computer-readable recording medium having recorded thereon a program for executing a method for generating greenhouse gas distribution data according to any one of claims 9 to 16 by a computer.

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20210032808A (en) * 2019-09-17 2021-03-25 한국과학기술연구원 Diurnal Pattern Automated Analysis Method From Air Quality Measurement Data
CN116429938A (en) * 2023-04-12 2023-07-14 中国科学院地理科学与资源研究所 Land N based on remote sensing image 2 O emission measuring and calculating method and system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20210032808A (en) * 2019-09-17 2021-03-25 한국과학기술연구원 Diurnal Pattern Automated Analysis Method From Air Quality Measurement Data
CN116429938A (en) * 2023-04-12 2023-07-14 中国科学院地理科学与资源研究所 Land N based on remote sensing image 2 O emission measuring and calculating method and system
CN116429938B (en) * 2023-04-12 2024-01-02 中国科学院地理科学与资源研究所 Land N based on remote sensing image 2 O emission measuring and calculating method and system

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