KR20110067964A - Method and apparatus for water quality monitoring using remote sensing technique - Google Patents

Abstract

PURPOSE: A method and apparatus for monitoring water quality are provided to enable the water quality assessment of the entire area of the river system by deriving and using the image band of remote exploration data and correlation between the actually surveyed water systems. CONSTITUTION: An image band extractor(10) extracts an image band from the image data acquired by the remote exploration. A correlation analyzer(20) compares the image data of the image band with the real data, and analyze the correlation. A TSI determination unit(30) determines the tropic state index(TSI) of the image data using the correlation. A water quality evaluator(40) evaluates the water quality of water system quantitatively by comparing a reference TSI with the TSI. A transmitter(50) transmits the water quality.

Description

Water quality monitoring method using remote sensing data and water quality monitoring device using the same {METHOD AND APPARATUS FOR WATER QUALITY MONITORING USING REMOTE SENSING TECHNIQUE}

The present invention relates to a water quality monitoring method and a water quality monitoring apparatus. Specifically, the present invention relates to a method and apparatus for monitoring water quality using remote sensing data.

The conventional water quality inspection method for the wide area water system is mainly evaluated from the water quality data measured for several main survey points due to the topography and the characteristics of the water system.

Such a water quality inspection method has a problem that it is difficult to grasp the overall water quality of the whole water system, and in order to grasp the comprehensive water quality, a water quality survey should be performed on the whole water system, which has a considerable cost and time.

Water quality monitoring method using the remote sensing data according to the present invention and the water quality monitoring apparatus using the same aims to solve the following problems.

First, it is intended to enable comprehensive water quality evaluation and monitoring of the entire water system using remote sensing data.

Second, I would like to be able to evaluate the whole water system by the factors of water quality evaluation.

Third, we want to enable constant water quality monitoring.

The solution to the problem of the present invention is not limited to those mentioned above, and other solutions not mentioned can be clearly understood by those skilled in the art from the following description.

The water quality monitoring method using the remote sensing data uses the image data obtained by the remote sensing and the remote sensing data using the actual measurement data of the water system to determine an image band from the image data obtained by the remote sensing. And S2 to analyze the correlation by comparing the image data and the measured data of the step S1, and the correlation analyzed in the step S2, and the image band passed through the step S1, the transparency of the undetected water system, the chlorophyll a concentration In step S3, at least one of the total phosphorus concentrations is obtained, and in step S4, the trophic state index is determined using the data obtained in step S3, and the eutrophication index determined in S4 is used. Step S5 to evaluate the water quality.

At this time, in step S2, the comparison factor when comparing the image data and the measured data is at least one of transparency, chlorophyll a concentration, total phosphorus (TP) concentration, or normalized water quality assessment index. It is preferable.

 In addition, in the step S2, when the comparison factor is the transparency, the correlation analysis is preferably performed using Equation 1 below.

[Equation 1]

SDT: Transparency of measured data

: 청색 가시광선 영역의 영상데이터 밴드값

: Image data band value of blue visible light region

: 적색 가시광선 영역의 영상데이터 밴드값

: Image data band value of red visible ray region

a0, a1, a2: population regression coefficient

In addition, in the step S2, when the factor to be compared is the concentration of chlorophyll a, it is preferable that the correlation analysis is performed using Equation 2 below.

[Equation 2]

Chl-a: Chlorophyll a concentration in actual data

: 녹색 가시광선 영역의 영상데이터 밴드값

: Image data band value of green visible light area

: 청색 가시광선 영역의 영상데이터 밴드값

: Image data band value of blue visible light region

: 근적외선 영역의 영상데이터 밴드값

: Image data band value of near infrared region

a0, a1, a2: population regression coefficient

In addition, in the step S2, when the comparison factor is the total phosphorus (T-P) concentration, the correlation analysis is preferably performed using the following equation (3).

&Quot; (3) "

TP: Total phosphorus concentration of measured data

: 적색 가시광선 영역의 영상데이터 밴드값

: Image data band value of red visible ray region

a0, a1: population regression coefficient

In addition, when the reference value is input in step S5, and compared to the eutrophication index determined in step S4, it is preferable that the eutrophication index is determined to be eutrophicated when the eutrophication index is greater than or equal to the reference value, and that the eutrophication index is lower than the reference value.

In addition, the step S5 preferably further includes a step S6 for transmitting the results of the eutrophication index determined in step S4 and the eutrophication index compared to the reference value in step S5.

Here, step S1 is a step S1a for geometrically correcting the image data obtained by remote sensing, and converts the digital number into a radiance from the image data passed through step S1a, and uses the converted radiance S1b and the S1b step to normalize the brightness value of the image band in the image data from the image data passed through the S1b step and the absolute atmospheric correction by converting it into reflectance S1d step of performing a radiation correction to reduce.

In addition, when the image data passing through the step S1b to the step S1b is single image data, it is preferable to normalize by adjusting the brightness value histogram between bands in the image data.

In addition, when the image data having passed through the S1b step S1b is multi-image data, it is preferable to select the pseudo-invariant features within the multi-image data to perform normalization between the multiple image data.

The method may further include a step S1e for extracting an aqueous region of the image data which has passed the step S1d.

In the step S1e, it is preferable to use a band of the near infrared region of the image data which has passed the step S1d.

The water quality monitoring apparatus using the remote sensing data includes an image band extracting unit which extracts an image band from the image data obtained by remote sensing by using image data obtained by remote sensing and actual measurement data of water systems; By using the correlation analysis unit comparing the image data extracted from the image band extracting unit and the measured data and analyzing the correlation, and using the correlation analyzed in the correlation analysis unit, the eutrophication index of the image data extracted from the image band ( a trophic index determining unit for determining trophic state index, a water quantitative evaluation unit for quantitatively evaluating and outputting water quality quantitatively by inputting a sub-film index value as a reference, and a nutrient nutrient index determined by the nutrient nutrient index determining unit; It includes a transmission unit for transmitting the water quality output from the water quality evaluation unit.

In addition, it is preferable that the comparison factor is at least one of transparency, chlorophyll a concentration or total phosphorus (T-P) concentration when comparing the image data and the measured data in the correlation analyzer.

 In the correlation analysis unit, when the comparison factor is transparency, the correlation analysis is preferably performed using Equation 4 below.

&Quot; (4) "

SDT: Transparency of measured data

: 청색 가시광선 영역의 영상데이터 밴드값

: Image data band value of blue visible light region

: 적색 가시광선 영역의 영상데이터 밴드값

: Image data band value of red visible ray region

a0, a1, a2: population regression coefficient

In the correlation analyzer, when the factor to be compared is chlorophyll a concentration, the correlation analysis is preferably performed using Equation 5 below.

[Equation 5]

Chl-a: Chlorophyll a concentration in actual data

: 녹색 가시광선 영역의 영상데이터 밴드값

: Image data band value of green visible light area

: 청색 가시광선 영역의 영상데이터 밴드값

: Image data band value of blue visible light region

: 근적외선 영역의 영상데이터 밴드값

: Image data band value of near infrared region

a0, a1, a2: population regression coefficient

Also, in the correlation analysis unit, when the comparison factor is the total phosphorus (T-P) concentration, the correlation analysis is preferably performed using Equation 6 below.

&Quot; (6) "

TP: Total phosphorus concentration of measured data

: 적색 가시광선 영역의 영상데이터 밴드값

: Image data band value of red visible ray region

a0, a1: population regression coefficient

Here, the image band extracting unit geometrically corrects image data obtained by remote sensing, and converts a digital number from the image data into radiance and reflects the converted luminance using the converted luminance. It is preferable to include an atmospheric correction processing unit for converting (reflectance) to absolute atmospheric correction, a normalization processing unit for normalizing the brightness value of the band in the image data from the image data, and a radiation correction processing unit for reducing the scattering influence of the atmosphere from the image data. Do.

The water quality monitoring method using the remote sensing data and the water quality monitoring apparatus using the same according to the present invention derive a correlation between the measured water system and the image band using the image band of the remote sensing data, and through the derived correlation, This has the effect of enabling water quality assessment in all areas.

In addition, there is an effect of enabling the overall water quality evaluation by the factors of the water quality evaluation, such as transparency, chlorophyll a, total phosphorus concentration, and the like.

In addition, it is possible to transmit the evaluated water quality information to the user has the effect of enabling constant water quality monitoring.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

Hereinafter, a water quality monitoring method using remote sensing data according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a flowchart of a method for monitoring water quality using remote sensing data according to an embodiment of the present invention, and FIG. 2 is a flowchart of a method for determining an image band from remote sensing data according to an embodiment of the present invention.

Step S1 of determining the image band of the visible light region and the near infrared region from the image data acquired by remote sensing according to the present invention determines the image band of the visible light region and the near infrared region from the image data obtained by remote sensing.

The image band determination method may be performed by various methods, but the image band determination method according to an embodiment of the present invention is performed according to the correction procedure shown in FIG.

First, the image data obtained by remote sensing is geometrically corrected (step S1a), and the brightness value (digital number) of the image data is converted into a radiance and then converted into a reflectance to absolute atmospheric correction (S1b). Brightness values in the band are normalized (step S1c), and radiation correction (S1d) is performed to determine image bands of the visible light region and the near infrared region.

Geometric correction (step S1a) is a correction that removes geometric distortions included in image data captured by satellites or aircraft, and selects 1: 5000 digital topographic maps by selecting clearly identified ground control points (GCPs). After acquiring three-dimensional coordinates through GPS surveying, geometric distortion of image data is removed through affine transformation.

Absolute atmospheric correction (step S1b) is a correction to remove the influence of the atmosphere included in the image data, and the radiance detected by the sensor from the digital number (DN) of each pixel of the remote sensing image data. Calculate and convert it back into the ratio surface reflectance for the water domain.

This luminance conversion is performed by Equation 1, and reflectance conversion is performed by Equation 2.

here

QCAL: Quantized calibrated pixel value in DN

QCAL _MAX : maximum quantized pixel value (DN = 225) corresponding to L _max )

L _max : Spectral radiance that is scaled to QCAL _MAX for the maximum measured value of the quantized pixel within the brightness value of each pixel

L _min : Spectral radiance that is scaled to QCAL _MIN for the minimum measurement of the quantized pixel within each pixel's brightness value

Means.

here,

θ: altitude of the sun at the time of obtaining remote data

ESUN: Average solar illuminance

D: normalized distance between sun and earth

Means.

In order to accurately extract the biological factors such as suspended solids, temperature, and biomass in the process of converting into reflections, the atmospheric conditions at the time of remote sensing data collection are collected in the field by means of a probe such as a spectroradiometer. Absolute atmospheric correction of the remote sensing data (step S1b) is performed using a radiation transfer model that estimates the absorption and scattering characteristics of the atmosphere from the sensing data.

Normalization (step S1c) is to correct the brightness value of the image band in the image data in the form of data acquired by the same atmospheric conditions or the same sensor.

When the image data is single image data, the atmospheric effect generated by increasing the minimum brightness value in the visible region due to the atmospheric scattering effect is normalized by adjusting the histogram of the brightness value between the image bands in the image data.

If the image data is multi-image data, select the reference image data and the appropriate pseudo-invariant features (PIF) in the image data, and then use different solar altitudes, atmospheric conditions, and surface coverage for the remote sensing data at multiple time periods. In order to minimize the effects due to the state, the spectral characteristics between the PIF of the reference image data and the PIF of the reference image data and other time-phase image data are normalized through linear regression analysis.

At this time, the correlation of the multi-image image data is calculated by calculating the pixel unit corresponding to each other or by calculating the average brightness value of the square multi-pixel grid (grid).

The coefficients of the linear regression model are estimated by the Least Square Method and the correlation coefficients are evaluated.

Residual error and excess error estimation by the least square method is performed by using a statistical standard that removes excessive error using standardized residuals.

Radiation correction (S1d step) is to correct the distortion of the image data caused by various influence factors such as the sun's altitude, terrain, fog, atmospheric conditions such as aerosol, sensor response.

Radiation correction (step S1d) of the present embodiment selects and extracts the same standard object (dark object such as a deep water area) from each image band with respect to the normalized remote sensing image data, and then, as shown in Equation 3 below, After analyzing the histogram, the minimum brightness value is determined and then subtracted from the original brightness value to reduce the distortion caused by the atmospheric effect by dark object subtraction (DOS) technique that reduces the effects of atmospheric scattering.

here,

BV _min : minimum brightness

BV _input : Brightness value of image data

BV _output : Brightness value of radiation-corrected image data

Means.

The step S1 of determining the image band of the visible light region and the near infrared region from the image data obtained by the remote sensing consisting of the above-described steps S1a to S1d may further include an S1e step of extracting an aqueous region of the image data. have.

In the present invention, step S1e is a step of extracting an aqueous region from the image data. Since the geographic data including the aqueous region is included in the image data, in order to improve the visual discrimination effect of the image and to perform the analysis more easily, It is preferable to extract only the data of the aqueous zone.

The step S1e may be performed in various ways, but the step S1e according to the present invention extracts an aqueous region using a band of the near infrared region of the image data which has undergone the step S1d.

In the image data, in the case of water and land, the contrast of the data of the band of the near infrared region of the image band is clear, so mask filtering and neighboring pixels are performed using the band data of the near infrared region. Convolution filtering is performed in consideration of the brightness value of the light source to extract the water domain from the image.

Image data that has passed the above-mentioned step S1 is performed by comparing the image data with the measured data to analyze the correlation between the two data.

In the present invention, the comparison between the image data and the measured data is performed by selecting at least one of comparison factors such as transparency, chlorophyll a concentration, or total phosphorus concentration included in each data.

When the comparison factor is transparency, the correlation analysis obtained by comparing the image data and the measured data is performed using Equation 4.

here

SDT: Transparency of measured data

: 청색 가시광선 영역의 영상데이터 밴드값

: Image data band value of blue visible light region

: 적색 가시광선 영역의 영상데이터 밴드값

: Image data band value of red visible ray region

a ₀ , a ₁ , a ₂ : population regression coefficient

Means.

By this equation 4, the regression coefficients a ₀ , a ₁ , a ₂ are determined.

If all the coefficient a _0, a _1, a ₂ are determined, Equation (4) Band being expressed as a function formula of the _Blue and Band _Red, the image band passed through the step S1 on the non-measured water system in step S3 Band _Blue and Band Substituting into _Red 's equation gives us the transparency of the unrealized water system.

When the factor to be compared is the concentration of chlorophyll a, correlation analysis obtained by comparing the image data and the measured data is performed using Equation 5.

here,

Chl-a: Chlorophyll a concentration in actual data

: 녹색 가시광선 영역의 영상데이터 밴드값

: Image data band value of green visible light area

: 청색 가시광선 영역의 영상데이터 밴드값

: Image data band value of blue visible light region

: 근적외선 영역의 영상데이터 밴드값

: Image data band value of near infrared region

a ₀ , a ₁ , a ₂ : population regression coefficient

Means.

The regression coefficients a ₀ , a ₁ , and a ₂ are determined by Equation 5.

When the regression coefficients a ₀ , a ₁ , and a ₂ are determined, Equation 5 is Band _Blue and Band _green. And Band _NIR , which is expressed as a function of Band _NIR , and has passed the S1 step of the water system not measured in step S3, Band _Blue and Band _green. By substituting the function of Band _NIR , it is possible to obtain the concentration of chlorophyll a in the unrealized water system.

In addition, when the comparison factor is the total phosphorus concentration, correlation analysis obtained by comparing the image data and the measured data is performed using Equation 6.

here,

TP: Total phosphorus concentration of measured data

: 적색 가시광선 영역의 영상데이터 밴드값

: Image data band value of red visible ray region

a ₀ , a ₁ : population regression coefficient

Means.

By the equation (6), the regression coefficients a ₀ and a ₁ are determined.

When the regression coefficients a ₀ and a ₁ are determined, Equation 6 is expressed as a function of Band _{red. If} the image band that passed the step S1 of the water system not measured in step S3 is substituted into the function of Band _red , The total phosphorus concentration in the water system can be obtained.

If data on transparency, chlorophyll a concentration, or total phosphorus concentration is determined through step S3, the eutrophication index of the image data may be determined through step S4 as follows.

Using the algebraic conversion equation of the Trophic State Index (TSI) proposed by Carlson or Aizaki, the transparency, chlorophyll a concentration, total phosphorus concentration, and total nitrogen concentration can be quantitatively determined according to the selected factors. have.

Once the eutrophication index of the water system is determined, it is possible to evaluate the water quality of the water system using the eutrophication index in step S5.

The assessment of water quality through the eutrophication index received the reference value, which is the boundary value between eutrophication and poor nutrientization in step S5, and compared with the eutrophication index determined in step S4, it is judged to be eutrophication when the eutrophication index is above the reference value, and the eutrophication index If is less than the reference value is carried out through the method to determine that the empty.

In this case, the reference value may represent one quantified value, but may be represented by a range having an upper limit and a lower limit.

Step S6 is a step of transmitting the results of the eutrophication index determined in step S4 and the eutrophication index compared to the reference value in step S5 to the user's terminal, the wireless terminal such as a user's computer or mobile phone, the eutrophication index determined in step S4 and S5 Send the results of the eutrophication index compared to the reference value in step.

The transmitted results allow the user to monitor the contamination of the water system.

In addition, the transmitted results can be visualized through the geospatial information system to monitor which areas of the water system are eutrophicated and which areas are vacant-nutrition.

3 is a block diagram of a water quality monitoring apparatus using remote sensing data according to an embodiment of the present invention, Figure 4 is an image band extraction unit 10 of the water quality monitoring apparatus using remote sensing data according to an embodiment of the present invention ) Is a configuration diagram.

Water quality monitoring apparatus using the remote sensing data according to the present invention is an image band extraction unit 10, correlation analysis unit 20, eutrophication index determination unit 30, water quality evaluation unit 40 and transmission unit 50 Is made of.

The image band extracting unit 10 according to the present invention functions to extract an image band from image data obtained by remote sensing.

The image band extracted by the image band extractor 10 extracts an image band in the visible light region and an image band in the near infrared region.

The image band extracting unit 10 performing such a function will be described in more detail with reference to FIG. 4 as follows.

The image band extracting unit 10 according to the present invention includes a geometric correction processing unit 11, an atmospheric correction processing unit 12, a normalization processing unit 13, and a radiation correction processing unit 14.

The geometric correction processing unit 11 according to the present invention performs geometric correction of image data obtained by remote sensing, and selects ground control points (GCPs) that are clearly identified from the image data. After 3D coordinates are acquired through GPS surveying, geometric distortion of image data is removed through affine transformation.

The atmospheric correction processing unit 12 according to the present invention converts a brightness value (digital number) from the image data into a radiance (radiance), and converts into a reflectance (reflectance) using the converted radiance to absolute atmospheric correction, Absolute atmospheric correction is performed from the image data by using Equations 1 and 2 described above.

The normalization processor 13 according to the present invention normalizes brightness values of bands in the image data from the image data.

If the image data processed by the normalization processor 13 is single image data, the atmospheric effect generated by increasing the minimum brightness value in the visible light region due to the atmospheric scattering effect is obtained from the histogram of the brightness value between the image bands in the image data. Normalize through adjustment.

On the other hand, if the image data processed by the normalization processor 13 is multi-image data, after selecting the reference image data and the appropriate pseudo-invariant features (PIF) in the image data, for each remote sensing data of the multi-time In order to minimize the effects of different sun altitudes, atmospheric conditions, and surface cover conditions, normalization is performed through linear regression analysis of the spectral characteristics between the PIF of the reference image data and the PIF of the reference image data and other time-phase image data. do.

At this time, the correlation of the multi-image image data is calculated by calculating the pixel unit corresponding to each other or by calculating the average brightness value of the square multi-pixel grid (grid).

The coefficients of the linear regression model are estimated by the Least Square Method and the correlation coefficients are evaluated. Residual error and excess error estimation by least squares method eliminates excessive error by using standardized residual.

The radiation correction processor 14 according to the present invention corrects the distortion of the image data caused by various influence factors such as the sun's altitude, terrain, fog, aerosol, air condition, sensor response, etc. A standard identical object (dark object such as a deep water area) is selected and extracted, and the distortion caused by the atmospheric effect is reduced by using Equation 3 described above.

The image band extracting unit 10 including the geometric correction processing unit 11, the atmospheric correction processing unit 12, the normalization processing unit 13, and the radiation correction processing unit 14 displays an image obtained by remote sensing in the visible light region. The bands and the image bands in the near infrared region are extracted.

The correlation analysis unit 20 performs a function of extracting a correlation between the data by analyzing the image obtained by remote sensing and the data of the actually measured water system.

The correlation analyzer 20 selects at least one of transparency, chlorophyll a concentration, and total phosphorus concentration from the image acquired by remote sensing and the data of the measured water system, and generates a correlation equation corresponding to the selected factor. Extract.

When transparency is selected as a factor, the correlation analyzer 20 analyzes the image obtained by the remote sensing and the data of the actually measured water system by using Equation 4 described above to extract a correlation between the two data. do.

When chlorophyll a is selected as a factor, the correlation analysis unit 20 extracts a correlation between the two data by analyzing the image acquired by the remote sensing and the data of the actually measured water system using Equation 5 described above. do.

When the total phosphorus concentration is selected as a factor, the correlation analyzer 20 extracts a correlation between the data obtained by analyzing the image obtained by the remote sensing and the data of the actually measured water system using the above-described Equation 6 do.

The eutrophication index determination unit 30 according to the present invention applies the image band of the image data to the correlation expression extracted by the correlation analysis unit 20, the transparency of the entire area including chlorophyll a, including the unrealized water system, chlorophyll a concentration Or calculate the total phosphorus concentration.

In this case, the calculated transparency, chlorophyll a concentration, or total phosphorus concentration correspond to a factor selected by the correlation analyzer 20. For example, when the transparency is selected as a factor, the concentration of the transparency is calculated. Done.

The calculated transparency, chlorophyll a concentration, or total phosphorus concentration is quantified by eutrophication index using the logarithmic conversion equation of the Trophic State Index (TSI) proposed by Carlson or Aizaki.

The water quality evaluation unit 40 according to the present invention receives a sub-film index value, which is a reference, and compares and outputs the water quality of the water system quantitatively in comparison with the eutrophication index quantified by the sub-nutrient quantification index determiner 30.

The input subfilm index value is a boundary between the hydrotrophic and empty nutrition of the water system, and the input eutrophic index value is compared with the eutrophication index quantified by the eutrophication index determination unit 30, and the quantified eutrophication index is inputted. If it is above the eutrophication index value, it is considered as eutrophication, and if it is less than the entered eutrophication index value, it is judged to be empty.

Since the determination in the water quality evaluation unit 40 is performed using the remote sensing data corresponding to the water system, the water quality of the entire area is evaluated and output including the water quality of the water system which is not actually measured.

The transmitter 50 according to the present invention transmits the nutrient quantification index and the water quality of the water system output from the water quality evaluation unit 40 to the wireless terminal such as a user's computer or a mobile phone.

Through the transmitted results, the user can monitor the pollution level of the water system and visualize the transmitted result through the geospatial information system to monitor which areas of the water system are eutrophicated and which areas are vacant-nutrified. have.

As described above, the transmitter 50 according to the present invention transmits quantitative eutrophication index and water quality information to an independent terminal, but does not necessarily transmit quantified eutrophication index and water quality information to an independent terminal. In addition, by installing a separate display unit in the water quality monitoring device using the remote sensing data, it is also possible to transmit the quantitative eutrophication index and water quality information of the water system.

1 is a flow chart of a water quality monitoring method using remote sensing data according to an embodiment of the present invention.

2 is a flowchart of a method for determining an image band using remote sensing data according to the present invention.

3 is a block diagram of a water quality monitoring apparatus using remote sensing data according to an embodiment of the present invention.

4 is a block diagram of an image band extraction unit of the water quality monitoring apparatus using remote sensing data according to an embodiment of the present invention.

<Explanation of symbols on main parts of the drawings>

10: image band extraction unit 11: geometric correction processing unit

12: atmospheric correction processing unit 13: normalization processing unit

14; Radiation Correction Processing Unit 20: Correlation Analysis Unit

30: eutrophication index determination unit 40: water quality evaluation unit

50: transmission unit

S1: Video band determination

S1a: Geometric correction S1b: Absolute atmospheric correction

S1c: Normalization S1d: Radiation Correction

S2: Correlation Analysis

S3: Acquisition of data of undiscovered water system

S4: Determination of eutrophication index

S5: Water Quality Assessment

S6: transmission

Claims (18)

A water quality monitoring method using remote sensing data using image data obtained by remote sensing and actual data of a water system, Determining an image band from the image data acquired by remote sensing; S2 step of analyzing the correlation by comparing the image data and the measured data that passed the step S1; Step S3 of obtaining data of at least one of transparency, chlorophyll a concentration and total phosphorus concentration of the undiscovered water system from the correlation analyzed in step S2 and the image band passed through step S1; Step S4 of determining a trophic state index using the data obtained in step S3; And Water quality monitoring method using the remote sensing data comprising the step S5 of evaluating the water quality of the water system using the eutrophication index determined in step S4. The method of claim 1, In the step S2, the comparison target factor when comparing the image data and the measured data is A method for monitoring water quality using remote sensing data, characterized in that at least one of transparency, chlorophyll a concentration, total phosphorus (T-P) concentration or normalized water quality assessment index. The method of claim 2, In the step S2, when the comparison factor is transparency, Correlation analysis is a water quality monitoring method using remote sensing data, characterized in that made using the following equation.

SDT: Transparency of measured data

: Image data band value of blue visible light region

: Image data band value of red visible ray region a ₀ , a ₁ , a ₂ : population regression coefficient The method of claim 2, In the step S2, when the comparison factor is chlorophyll a concentration, Correlation analysis is a water quality monitoring method using remote sensing data, characterized in that made using the following equation.

Chl-a: Chlorophyll a concentration in actual data

: Image data band value of green visible light area

: Image data band value of blue visible light region

: Image data band value of near infrared region a ₀ , a ₁ , a ₂ : population regression coefficient The method of claim 2, In the step S2, when the comparison factor is the total phosphorus (T-P) concentration, Correlation analysis is a water quality monitoring method using remote sensing data, characterized in that made using the following equation.

TP: Total phosphorus concentration of measured data

: Image data band value of red visible ray region a ₀ , a ₁ : population regression coefficient The method of claim 1, In step S5, the reference value is input, and when compared with the eutrophication index determined in step S4, the eutrophication index is determined to be eutrophicated when the reference value is greater than the reference value, and when the eutrophication index is less than the reference value, characterized in that it is determined to be empty. Water quality monitoring method using remote sensing data. 7. The method according to claim 1 or 6, In step S5 And a step S6 of transmitting the results of the eutrophication index determined in the step S4 and the eutrophication index compared with the reference value in the step S5. The method of claim 1, The step S1 Step S1a of geometrically correcting the image data obtained by remote sensing; A step S1b of converting a digital number into a luminance from the image data which has passed the step S1a and converting the digital number into a radiance and converting it into a reflectance using the converted radiance to absolute atmospheric correction; Step S1c of normalizing the brightness value of the image band in the image data from the image data which has passed the step S1b; And And a step S1d of performing radiation correction to reduce the scattering effect of the atmosphere from the image data passing through the step S1c. The method of claim 8, In step S1c When the image data that has passed the step S1b is single image data, Water quality monitoring method characterized in that the normalization by adjusting the histogram of the brightness value between the bands in the image data. The method of claim 8, In step S1c If the image data that has passed the step S1b is multi-image data, Water quality monitoring method characterized in that normalization between multiple image data is performed by selecting pseudo-invariant features within the multiple image data. The method of claim 8, The water quality monitoring method further comprises the step S1e of extracting the water region of the image data which has passed the step S1d. The method of claim 11, The step S1e is a water quality monitoring method, characterized in that using the band of the near infrared region of the image data passed through step S1d. A water quality monitoring apparatus using video data obtained by remote sensing and remote sensing data using actual data of a water system, An image band extracting unit extracting an image band from the image data obtained by remote sensing; A correlation analyzer for comparing correlations between the image data extracted from the image band extractor and the measured data; A eutrophication index determining unit for determining a trophic state index of the image data from which the image band is extracted using the correlations analyzed by the correlation analyzer; A water quality evaluation unit receiving a sub-film index value as a reference and quantitatively evaluating and outputting the water quality of the water system in comparison with the eutrophication index determined by the eutrophication index determination unit; And Water quality monitoring apparatus using the remote sensing data, characterized in that it comprises a transmission unit for transmitting the water quality output from the water quality evaluation unit. The method of claim 13, The comparison factor in comparing the image data and the measured data in the correlation analyzer is at least one of transparency, chlorophyll a concentration or total phosphorus (TP) concentration, water quality monitoring apparatus using remote sensing data . The method of claim 13, In the correlation analysis unit, when the comparison factor is transparency, Correlation analysis is a water quality monitoring apparatus using remote sensing data, characterized in that made using the following equation.

SDT: Transparency of measured data

: Image data band value of blue visible light region

: Image data band value of red visible ray region a ₀ , a ₁ , a ₂ : population regression coefficient The method of claim 13, In the correlation analysis unit, when the comparison factor is chlorophyll a concentration, Correlation analysis is a water quality monitoring apparatus using remote sensing data, characterized in that made using the following equation.

Chl-a: Chlorophyll a concentration in actual data

: Image data band value of green visible light area

: Image data band value of blue visible light region

: Image data band value of near infrared region a ₀ , a ₁ , a ₂ : population regression coefficient The method of claim 13, In the correlation analysis unit, when the comparison factor is the total phosphorus (T-P) concentration, Correlation analysis is a water quality monitoring apparatus using remote sensing data, characterized in that made using the following equation.

TP: Total phosphorus concentration of measured data

: Image data band value of red visible ray region a ₀ , a ₁ : population regression coefficient The method of claim 13, The image band extracting unit A geometric correction processor for geometrically correcting image data obtained by remote sensing; An atmospheric correction processor for converting a digital number from the image data into a radiance and converting the digital number into a radiance and converting the luminance number into a reflectance using the converted radiance; A normalization processor for normalizing brightness values of bands in the image data from the image data; And And a radiation correction processor for reducing the scattering effect of the atmosphere from the image data.

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