CN111816263A - Method for qualitatively identifying salinity water area mineralization quantity based on ETM + remote sensing data - Google Patents
Method for qualitatively identifying salinity water area mineralization quantity based on ETM + remote sensing data Download PDFInfo
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- 229910052500 inorganic mineral Inorganic materials 0.000 claims abstract description 19
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- 238000002834 transmittance Methods 0.000 claims abstract description 13
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- 150000003839 salts Chemical class 0.000 description 8
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- 235000013628 Lantana involucrata Nutrition 0.000 description 3
- 235000006677 Monarda citriodora ssp. austromontana Nutrition 0.000 description 3
- 240000007673 Origanum vulgare Species 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000012267 brine Substances 0.000 description 2
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- HPALAKNZSZLMCH-UHFFFAOYSA-M sodium;chloride;hydrate Chemical compound O.[Na+].[Cl-] HPALAKNZSZLMCH-UHFFFAOYSA-M 0.000 description 2
- 239000003643 water by type Substances 0.000 description 2
- ZOXJGFHDIHLPTG-UHFFFAOYSA-N Boron Chemical compound [B] ZOXJGFHDIHLPTG-UHFFFAOYSA-N 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- 229910052770 Uranium Inorganic materials 0.000 description 1
- 229910052796 boron Inorganic materials 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- 239000013505 freshwater Substances 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
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- 229910052749 magnesium Inorganic materials 0.000 description 1
- 239000002366 mineral element Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
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Abstract
The invention belongs to the technical field of extraction of salt lake water body information, and relates to a method for qualitatively identifying the mineralization quantity of a salt lake water body based on ETM + remote sensing data, which comprises the following steps: ETM + remote sensing data acquisition; II, secondly: preprocessing ETM + remote sensing data; thirdly, the method comprises the following steps: extracting a salt lake water area boundary line based on the ETM + remote sensing data in the seventh wave band; fourthly, the method comprises the following steps: identifying the salt lake water area based on the ETM + remote sensing data in the sixth wave band; fifthly: estimating the pixel brightness temperature value of the sixth wave band of ETM + remote sensing data of the salt lake water area; sixthly, the method comprises the following steps: estimating the average atmospheric acting temperature of the salt lake region; seventhly, the method comprises the following steps: estimating the atmospheric transmittance of the salt lake region; eighthly: extracting the temperature information of the salt lake water area based on the ETM + remote sensing data in the sixth wave band; nine: and qualitatively identifying the mineralization quantity of the salt lake water area based on ETM + remote sensing data. The method can quickly and qualitatively identify the mineral-bearing quantity of the salt lake water area, and has very important significance for reducing the cost of artificial sampling and chemical analysis of the salt lake and improving the efficiency of investigation and development of salt lake minerals.
Description
Technical Field
The invention belongs to the technical field of extraction of water body information of salt lakes, and particularly relates to a method for qualitatively identifying the mineralization quantity of a water area of salt lakes based on ETM + remote sensing data.
Background
China is one of the countries with the most distributed salt lakes in the world, and is famous for all types, large quantity, rich resources and rich rare mineral elements. A large number of salt lakes are distributed in western regions such as Xinjiang, Qinghai, inner Mongolia and Tibet, salt lake surface brine and intercrystalline brine have high salinity, and abundant mineral resources such as potassium, lithium, magnesium, boron, uranium and the like are stored, so that the potential development and utilization values are extremely high. Generally, fresh water lakes and salt water lakes are not mineralized, not all salt lakes are mineralized, only salt lake water bodies formed under specific geological environments are mineralized, and the mineralized amount of different salt lake water bodies is obviously different. Therefore, the qualitative identification of the mineral-bearing amount of different water areas of the salt lake has important significance for analyzing the formation and evolution of the salt lake and identifying the mineral resource enrichment part of the salt lake.
The traditional acquisition mode of salt lake water oregano volume information is mostly to carry out the regional manual sampling to full salt lake waters, then send to the chemical analysis laboratory and carry out the oregano volume analysis, and the high waters of delineating salt lake oregano volume are developed. Because the number of salt lakes in China is large, and the traffic conditions of most salt lake regions are very inconvenient, the regional artificial water sample collection is difficult to finish in a short time. Therefore, the traditional salt lake water area mineralization quantity identification method is long in period and high in cost, and is not beneficial to rapid evaluation of a plurality of salt lake mineralization quantities.
The remote sensing technology is used as an excellent high-tech survey means, has the characteristics of synthesis, macroscopicity, dynamic state and rapidness, achieves good application effects in the aspects of mineral resource exploration and development, land illegal land occupation treatment, environment monitoring and global climate research, and also provides a new detection technical method for the research of related scientific problems of salt lake water areas. With the increasing innovation of remote sensing information extraction technical means, the research on salt lakes by applying remote sensing technology is receiving more and more attention. However, most of the existing researches aim at the identification of factors such as salinity of salt lake, and qualitative identification and evaluation of mineralization quantities of different salt lake water areas are not carried out. Therefore, it is necessary to develop a method for qualitatively identifying the mineral bearing capacity of the salt lake water area based on ETM + remote sensing data, and the method has very important significance for qualitatively evaluating the relative high and low mineral bearing capacity of different water areas of the salt lake, quickly positioning the rich water area, and reducing the investigation and analysis cost of the mineral bearing capacity of the salt lake.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for qualitatively identifying the mineral bearing capacity of a salt lake water area based on ETM + remote sensing data, which can effectively reduce the exploration and chemical analysis cost of qualitatively identifying the mineral bearing capacity of the salt lake water area.
In order to solve the technical problem, the invention discloses a method for qualitatively identifying the mineralization quantity of a salt lake water area based on ETM + remote sensing data, which sequentially comprises the following steps:
step one, ETM + remote sensing data acquisition. Selecting ETM + satellite remote sensing short wave infrared and thermal infrared data which are suitable for data acquisition time phase and high in quality and cover a certain salt lake water area;
step two, preprocessing ETM + remote sensing data. Carrying out radiation correction and geometric correction pretreatment on the salt lake water area ETM + remote sensing data short wave infrared seventh waveband and thermal infrared sixth waveband data obtained in the step one to obtain pretreated ETM + remote sensing data;
and step three, extracting the boundary line of the salt lake water area based on the ETM + remote sensing data in the seventh wave band. Extracting salt lake water area boundary line information of the seventh wave band of the ETM + remote sensing data obtained in the step two to obtain a salt lake water area boundary line of the seventh wave band of the ETM + remote sensing data;
and step four, identifying the salt lake water area based on the ETM + remote sensing data in the sixth wave band. Taking the seventh wave band salt lake water area boundary line of the ETM + remote sensing data obtained in the third step as a mask layer, and carrying out mask processing on the sixth wave band of the ETM + remote sensing data to obtain a sixth wave band image of the ETM + remote sensing data of the salt lake water area;
and step five, estimating the pixel brightness temperature value of the sixth wave band of the ETM + remote sensing data of the salt lake water area. Based on the approximate Planck formula TETM+6＝k2/ln(1+k1/R), estimating pixel brightness temperature value T for sixth waveband image of ETM + remote sensing data of salt lake water area obtained in the fourth stepETM+6；
And step six, estimating the average atmospheric acting temperature of the salt lake region. Estimating the average atmospheric temperature T based on the temperature data of the salt lake region during the acquisition of ETM + remote sensing dataair；
And seventhly, estimating the atmospheric transmittance of the salt lake region. Estimating the atmospheric transmittance tau based on the relative humidity data of the salt lake region during ETM + remote sensing data acquisition;
and step eight, extracting the temperature information of the salt lake water area based on the ETM + remote sensing data in the sixth wave band. Based on the formula Tlake＝{-67.36×(1-A-B)+[0.46×(1-A-B)+A+B]×TETM+6-B×TairCalculating and extracting the temperature information of the water area of the salt lake from the sixth wave band of the ETM + remote sensing data, and acquiring the temperature information of the water area of the salt lake ETM + remote sensing image;
and step nine, qualitatively identifying the mineralization quantity of the salt lake water area based on the ETM and the remote sensing data. And D, performing density segmentation of five levels on the temperature information ETM + remote sensing image of the salt lake water area obtained in the step eight according to a certain value interval, respectively assigning colors from deep to light to the five levels with the segmented brightness values from high to low, wherein five gray levels from deep to light represent five levels of the salt lake water area from low to high, namely the salt lake water area assigned ore amount qualitative identification graph.
In the first step, the proper data acquisition time phase means that the acquisition time of the ETM + satellite remote sensing data is noon in winter, and the high quality means that the atmospheric condition when the ETM + satellite remote sensing data is acquired is clear, cloudless, windless or breeze; the ETM + satellite remote sensing short-wave infrared and thermal infrared data refer to ETM + a seventh wave band (the wavelength range is 2.09-2.35 mu m) and a sixth wave band (the wavelength range is 10.40-12.50 mu m);
in the second step, the radiation correction refers to the radiation correction of the seventh waveband data of the ETM + remote sensing data based on a radiation regression analysis method; based on the formula R ═ 3.2+0.037 XDNETM+6Completing radiation correction of the sixth waveband of the ETM + remote sensing data, wherein R refers to the spectral radiance of the sixth waveband of the ETM + remote sensing data and has the unit of W/(m)2·sr·μm)，DNETM+6The brightness value (DN is more than or equal to 0) of the pixel of the sixth wave band of ETM + remote sensing dataETM+6<255)；
In the second step, the geometric correction means that a collinear equation correction method is adopted to complete geometric position deviation correction of a seventh wave band and a sixth wave band of the ETM + remote sensing data after radiation correction;
in the third step, the extraction method of the boundary line information of the salt lake water area is a minimum distance supervision and classification method;
in the fifth step, TETM+6Indicating ETM + remote sensing data sixth wave band pixel brightness temperature value, unit: k, K1＝666.09m·W·cm-2·sr-1·μm-1，k21282.71K, ln means 1+ K1The natural logarithm of/R, wherein R refers to the spectral radiance of the sixth waveband of ETM + remote sensing data;
in the sixth step, the temperature data of the salt lake area can be inquired from the weather record data of the area where the salt lake water area is; the estimation formula of the average acting temperature of the atmosphere is Tair＝0.91×Tland+19.27, wherein, TairMeans the average temperature of action of the atmosphere, unit: k, TlandThe temperature of the salt lake region is expressed by unit: k;
in the seventh step, the relative humidity data of the salt lake area can be inquired from the weather record data of the area where the salt lake water area is; the atmospheric transmittance estimation formula is that tau is 0.98-0.097 Xh, wherein tau refers to atmospheric transmittance and h refers to relative humidity;
in the eighth step, TlakeThe temperature value of the water area of the salt lake is expressed as unit: k, a ═ τ, B ═ 1- τ) × [1+ (1-) τ]Wherein 0.98, τ is the atmospheric transmittance obtained in step seven, TETM+6Indicating the pixel brightness temperature value T of the sixth wave band of ETM + remote sensing data of the salt lake water area obtained in the step fiveairThe average atmospheric temperature obtained in the sixth step;
in the ninth step, the certain value interval refers to 5 intervals of the temperature information ETM + remote sensing image value of the salt lake water area from low to high averagely.
The invention has the beneficial technical effects that: the method can quickly and qualitatively identify the relative level of the mineral-bearing capacity of the salt lake water area, greatly reduces the investigation and analysis cost of the mineral-bearing capacity of the salt lake water area, and has important significance for analyzing the evolution of the salt lake and the development of mineral products.
Drawings
FIG. 1 is a qualitative identification diagram of the mineralization quantity of a water area of a certain salt lake based on ETM + remote sensing data;
wherein, the color codes 1-5 represent that the ore-adding amount is from low to high.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a method for qualitatively identifying the mineralization quantity of a salt lake water area based on ETM + remote sensing data, which comprises the following steps:
step one, ETM + remote sensing data acquisition. Selecting ETM + satellite remote sensing short-wave infrared seventh band (wavelength range: 2.09-2.35 mu m) and thermal infrared sixth band (wavelength range: 10.40-12.50 mu m) data covering a certain salt lake water area, wherein the data acquisition time is at noon in winter, and the atmospheric conditions are clear, cloudless, windless or breezy;
step two, preprocessing ETM + remote sensing data. Finishing radiation correction of the salt lake water area ETM + remote sensing data short wave infrared seventh wave band obtained in the step one based on a radiation regression analysis method; based on the formula R ═ 3.2+0.037 XDNETM+6Completing radiation correction of the thermal infrared sixth waveband of the ETM + remote sensing data, wherein R refers to the spectral radiance of the sixth waveband of the ETM + remote sensing data and has the unit of W/(m)2·sr·μm)，DNETM+6The brightness value (DN is more than or equal to 0) of the pixel of the sixth wave band of ETM + remote sensing dataETM+6<255) (ii) a Completing geometric position deviation correction of the seventh wave band and the sixth wave band of the ETM + remote sensing data after radiation correction by adopting a collinear equation correction method; acquiring the preprocessed ETM + remote sensing data;
and step three, extracting the boundary line of the salt lake water area based on the ETM + remote sensing data in the seventh wave band. Extracting the boundary line information of the salt lake water area of the seventh wave band of the ETM + remote sensing data obtained in the step two by adopting a minimum distance supervision classification method to obtain the boundary line of the salt lake water area of the seventh wave band of the ETM + remote sensing data;
and step four, identifying the salt lake water area based on the ETM + remote sensing data in the sixth wave band. Taking the seventh wave band salt lake water area boundary line of the ETM + remote sensing data obtained in the step three as a mask layer, and carrying out mask processing on the sixth wave band of the ETM + remote sensing data to obtain a sixth wave band image of the ETM + remote sensing data of the salt lake water area;
and step five, estimating the pixel brightness temperature value of the sixth wave band of the ETM + remote sensing data of the salt lake water area. Based on the approximate Planck formula TETM+6＝k2/ln(1+k1/R), estimating pixel brightness temperature value T for the sixth waveband image of the ETM + remote sensing data of the salt lake water area obtained in the step fourETM+6(ii) a In the formula, TETM+6Indicating ETM + remote sensing data sixth wave band pixel brightness temperature value (unit: K), K1＝666.09m·W·cm-2·sr-1·μm-1，k21282.71K, ln means 1+ K1The natural logarithm of/R, wherein R refers to the spectral radiance of the sixth waveband of the ETM + remote sensing data obtained in the second step;
and step six, estimating the average atmospheric acting temperature of the salt lake region. Based on the formula Tair＝0.91×Tland+19.27 estimate the mean atmospheric temperature in the salt lake region, where TairMeans the mean temperature of action of the atmosphere (unit: K), TlandIndicating the temperature data (unit: K) of the salt lake region when ETM + remote sensing data is acquired;
and seventhly, estimating the atmospheric transmittance of the salt lake region. Estimating the atmospheric permeability of the salt lake region based on a formula tau of 0.98-0.097 × h, wherein tau refers to the atmospheric permeability, and h refers to the relative humidity data of the salt lake region during ETM + remote sensing data acquisition;
and step eight, extracting the temperature information of the salt lake water area based on the ETM + remote sensing data in the sixth wave band. Based on the formula Tlake＝{-67.36×(1-A-B)+[0.46×(1-A-B)+A+B]×TETM+6-B×TairCalculating and extracting the temperature information of the water area of the salt lake from the sixth wave band of the ETM + remote sensing data, and acquiring the temperature information of the water area of the salt lake ETM + remote sensing image; in the formula, TlakeThe temperature value (unit: K) of the water area of the salt lake, TETM+6Indicating the brightness temperature value T of the image pixel of the sixth waveband of the ETM + remote sensing data of the salt lake water area obtained in the step fiveairThe average action temperature of the atmosphere obtained in the step six is shown, wherein A is tau, B is (1-tau) x [1+ (1-) tau]Wherein, 0.98, τ refers to the atmospheric transmittance obtained in step seven;
and step nine, qualitatively identifying the mineralization quantity of the salt lake water area based on the ETM and the remote sensing data. Dividing the temperature information ETM + remote sensing image of the salt lake water area obtained in the step eight into 5 intervals according to the average image value from low to high, performing density segmentation, respectively assigning colors from deep to light to five levels of the segmented brightness value from high to low, wherein five gray levels from deep to light represent five levels of the salt lake water area assigned ore amount from low to high, and the five levels are the qualitative identification graph of the salt lake water area assigned ore amount, as shown in fig. 1.
By combining the analysis, the level of the mineral-bearing amount in the salt lake water area can be rapidly and qualitatively identified by the method, the investigation and analysis cost of the mineral-bearing amount in the salt lake water area is greatly reduced, the investigation efficiency of the salt lake mineral-bearing water area is improved, and the method has important significance for analyzing the formation and evolution of the salt lake and identifying the mineral resource enrichment part.
While the embodiments of the present invention have been described in detail, the above embodiments are merely preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (9)
1. A method for qualitatively identifying the mineralization quantity of a salt lake water area based on ETM + remote sensing data is characterized by comprising the following steps:
step one, obtaining ETM + remote sensing data; selecting ETM + satellite remote sensing short wave infrared and thermal infrared data which are suitable for data acquisition time phase and high in quality and cover a certain salt lake water area;
step two, preprocessing ETM + remote sensing data; carrying out radiation correction and geometric correction pretreatment on the salt lake water area ETM + remote sensing data short wave infrared seventh waveband and thermal infrared sixth waveband data obtained in the step one to obtain pretreated ETM + remote sensing data;
extracting a salt lake water area boundary line based on the ETM + remote sensing data in the seventh wave band; extracting salt lake water area boundary line information of the seventh wave band of the ETM + remote sensing data obtained in the step two to obtain a salt lake water area boundary line of the seventh wave band of the ETM + remote sensing data;
identifying the salt lake water area based on the ETM + remote sensing data in the sixth wave band; taking the seventh wave band salt lake water area boundary line of the ETM + remote sensing data obtained in the third step as a mask layer, and carrying out mask processing on the sixth wave band of the ETM + remote sensing data to obtain a sixth wave band image of the ETM + remote sensing data of the salt lake water area;
estimating the pixel brightness temperature value of the sixth waveband of the ETM + remote sensing data of the salt lake water area; based on the approximate Planck formula TETM+6＝k2/ln(1+k1/R), estimating pixel brightness temperature value T for sixth waveband image of ETM + remote sensing data of salt lake water area obtained in the fourth stepETM+6；
Estimating the average atmospheric acting temperature of the salt lake region; estimating the average atmospheric temperature T based on the temperature data of the salt lake region during the acquisition of ETM + remote sensing dataair；
Step seven, estimating the atmospheric transmittance of the salt lake region; estimating the atmospheric transmittance tau based on the relative humidity data of the salt lake region during ETM + remote sensing data acquisition;
step eight, extracting the temperature information of the salt lake water area based on the ETM + remote sensing data in the sixth wave band; based on the formula Tlake＝{-67.36×(1-A-B)+[0.46×(1-A-B)+A+B]×TETM+6-B×TairCalculating and extracting the temperature information of the water area of the salt lake from the sixth wave band of the ETM + remote sensing data, and acquiring the temperature information of the water area of the salt lake ETM + remote sensing image;
step nine, qualitatively identifying the mineralization quantity of the salt lake water area based on ETM + remote sensing data; and D, performing density segmentation of five levels on the temperature information ETM + remote sensing image of the salt lake water area obtained in the step eight according to a certain value interval, respectively assigning colors from deep to light to the five levels with the segmented brightness values from high to low, wherein five gray levels from deep to light represent five levels of the salt lake water area from low to high, namely the salt lake water area assigned ore amount qualitative identification graph.
2. The method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the first step, the proper data acquisition time phase means that the acquisition time of the ETM + satellite remote sensing data is noon in winter, and the high quality means that the atmospheric condition when the ETM + satellite remote sensing data is acquired is clear, cloudless, windless or breeze; the ETM + satellite remote sensing short wave infrared and thermal infrared data refer to ETM + seventh waveband, wavelength range: 2.09-2.35 μm and a sixth wavelength band, the wavelength range: 10.40 to 12.50 μm.
3. The method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the second step, the radiation correction refers to the radiation correction of the seventh waveband data of the ETM + remote sensing data based on a radiation regression analysis method; based on the formula R ═ 3.2+0.037 XDNETM+6Completing radiation correction of the sixth waveband of the ETM + remote sensing data, wherein R refers to the spectral radiance of the sixth waveband of the ETM + remote sensing data and has the unit of W/(m)2·sr·μm)，DNETM+6The brightness value of pixel of the sixth wave band of ETM + remote sensing data is more than or equal to 0 DNETM+6<255; and the geometric correction means that the geometric position deviation correction of the seventh wave band and the sixth wave band of the ETM + remote sensing data after radiation correction is finished by adopting a collinear equation correction method.
4. The method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the third step, the extraction method of the boundary line information of the salt lake water area is a minimum distance supervision and classification method.
5. The method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the fifth step, TETM+6Indicating ETM + remote sensing data sixth wave band pixel brightness temperature value, unit: k; k is a radical of1＝666.09m·W·cm-2·sr-1·μm-1，k21282.71K, ln means 1+ K1The natural logarithm of/R, wherein R refers to the spectral radiance of the sixth waveband of the ETM + remote sensing data.
6. The ETM + remote sensing data-based data set of claim 1The method for qualitatively identifying the mineral content of the salt lake water area is characterized by comprising the following steps of: in the sixth step, the temperature data of the salt lake area can be inquired from the weather record data of the area where the salt lake water area is; the estimation formula of the average acting temperature of the atmosphere is Tair＝0.91×Tland+19.27, wherein, TairMeans the average temperature of action of the atmosphere, unit: k, TlandThe temperature of the salt lake region is expressed by unit: K.
7. the method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the seventh step, the relative humidity data of the salt lake area can be inquired from the weather record data of the area where the salt lake water area is; the atmospheric transmittance is estimated by the formula tau of 0.98-0.097 × h, wherein tau is atmospheric transmittance and h is relative humidity.
8. The method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the eighth step, TlakeThe temperature value of the water area of the salt lake is expressed as unit: k, a ═ τ, B ═ 1- τ) × [1+ (1-) τ]Wherein 0.98, τ is the atmospheric transmittance obtained in step seven, TETM+6Indicating the pixel brightness temperature value T of the sixth wave band of ETM + remote sensing data of the salt lake water area obtained in the step fiveairAnd 6, the average action temperature of the atmosphere obtained in the step six.
9. The method for qualitatively identifying the mineralization quantity in a salt lake water area based on ETM + remote sensing data as claimed in claim 1, wherein: in the ninth step, the certain value interval refers to 5 intervals of the temperature information ETM + remote sensing image value of the salt lake water area from low to high averagely.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103454693A (en) * | 2013-09-12 | 2013-12-18 | 核工业北京地质研究院 | Method for identifying ore-forming element remote sensing map feature for alaskite type uranium mine exploration |
| CN103512661A (en) * | 2012-12-07 | 2014-01-15 | 核工业北京地质研究院 | Method for quickly identifying buried fault structure of salt lake water areas based on remote-sensing images |
| CN103852807A (en) * | 2014-03-20 | 2014-06-11 | 青海省地质矿产研究所 | High-altitude permafrost region porphyry type copper polymetallic mine exploration technology combination method |
| CN104537375A (en) * | 2015-01-22 | 2015-04-22 | 西安煤航卫星数据应用有限公司 | Brown iron mineralization information extracting method based on satellite remote sensing data |
| CN104614782A (en) * | 2015-01-29 | 2015-05-13 | 青海中航资源有限公司 | Salt lake brine detection method |
| CN107561024A (en) * | 2017-07-17 | 2018-01-09 | 核工业北京地质研究院 | A kind of high-spectrum remote-sensing recognition methods suitable for salt lake richness uranium water body |
| CN109269987A (en) * | 2018-10-11 | 2019-01-25 | 核工业北京地质研究院 | A kind of qualitative estimating and measuring method of salt lake water body salinity based on SPOT5 remotely-sensed data |
| CN109406405A (en) * | 2018-10-11 | 2019-03-01 | 核工业北京地质研究院 | A kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity |
| US20190331831A1 (en) * | 2018-04-26 | 2019-10-31 | Jiangsu Meteorological Observatory | Sea fog monitoring method based on multi-source satellite remote sensing data |
| CN110806605A (en) * | 2019-11-15 | 2020-02-18 | 中国地质科学院矿产综合利用研究所 | Remote sensing ore searching method for rare earth-uranium ore in high-latitude and high-cold area |
-
2020
- 2020-06-30 CN CN202010622326.3A patent/CN111816263B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103512661A (en) * | 2012-12-07 | 2014-01-15 | 核工业北京地质研究院 | Method for quickly identifying buried fault structure of salt lake water areas based on remote-sensing images |
| CN103454693A (en) * | 2013-09-12 | 2013-12-18 | 核工业北京地质研究院 | Method for identifying ore-forming element remote sensing map feature for alaskite type uranium mine exploration |
| CN103852807A (en) * | 2014-03-20 | 2014-06-11 | 青海省地质矿产研究所 | High-altitude permafrost region porphyry type copper polymetallic mine exploration technology combination method |
| CN104537375A (en) * | 2015-01-22 | 2015-04-22 | 西安煤航卫星数据应用有限公司 | Brown iron mineralization information extracting method based on satellite remote sensing data |
| CN104614782A (en) * | 2015-01-29 | 2015-05-13 | 青海中航资源有限公司 | Salt lake brine detection method |
| CN107561024A (en) * | 2017-07-17 | 2018-01-09 | 核工业北京地质研究院 | A kind of high-spectrum remote-sensing recognition methods suitable for salt lake richness uranium water body |
| US20190331831A1 (en) * | 2018-04-26 | 2019-10-31 | Jiangsu Meteorological Observatory | Sea fog monitoring method based on multi-source satellite remote sensing data |
| CN109269987A (en) * | 2018-10-11 | 2019-01-25 | 核工业北京地质研究院 | A kind of qualitative estimating and measuring method of salt lake water body salinity based on SPOT5 remotely-sensed data |
| CN109406405A (en) * | 2018-10-11 | 2019-03-01 | 核工业北京地质研究院 | A kind of high-definition remote sensing quantitative estimation method suitable for salt lake water body salinity |
| CN110806605A (en) * | 2019-11-15 | 2020-02-18 | 中国地质科学院矿产综合利用研究所 | Remote sensing ore searching method for rare earth-uranium ore in high-latitude and high-cold area |
Non-Patent Citations (4)
| Title |
|---|
| M.A. ISLAM等: "High mobility and transparent ZTO ETM prepared by RF reactive co-sputtering for perovskite solar cell application", RESULTS IN PHYSICS, vol. 14, pages 1 - 7 * |
| 周立国;冯学智;肖鹏峰;谢顺平;王得玉;: "盐湖遥感研究的进展与展望", 地球科学进展, no. 02, pages 35 - 43 * |
| 张佩民等: "罗布泊地区钾盐矿产遥感地质应用研究", 矿产勘查, vol. 01, no. 02, pages 161 - 164 * |
| 邓国武: "热红外航空高光谱蚀变信息找矿应用研究", 矿产勘查, vol. 11, no. 02, pages 344 - 351 * |
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