CN109297968A - A kind of method of generation face domain water quality monitoring result - Google Patents
A kind of method of generation face domain water quality monitoring result Download PDFInfo
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- CN109297968A CN109297968A CN201811394024.4A CN201811394024A CN109297968A CN 109297968 A CN109297968 A CN 109297968A CN 201811394024 A CN201811394024 A CN 201811394024A CN 109297968 A CN109297968 A CN 109297968A
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Abstract
The invention discloses a kind of methods of generation face domain water quality monitoring result, comprising the following steps: determines detected regional scope；It is passed by situation according to the satellite in the region, determines the time of water quality sampling；Water sample is acquired, and measures the position coordinate of sampled point；Water sample is chemically examined, the relevant parameter for obtaining sampling water body obtains the satellite-remote-sensing image on the region date；Remote sensing image is pre-processed；The boundary for being detected waters is extracted, the remote sensing image in monitoring region is cut out；The Remote Sensing Reflectance numerical value of the point is extracted by the coordinate of sampled point；Establish sampled point reflectivity data library；Analyze the correlation of each band combination with reflectivity data library；With the strong band combination of correlation, regression model library is established；With the model in regression model library, the face domain monitoring figure of the various water quality monitoring factors is generated；Each water quality monitoring parameter is normalized, in conjunction with the weight of each water quality parameter, generates comprehensive face domain water quality monitoring result；Report is finally write, achievement is submitted.
Description
Technical field
The invention belongs to the interleaving techniques fields of water quality monitoring technology and remote sensing technology, specifically, being related to a kind of generation
The method of face domain water quality monitoring result.
Background technique
With becoming increasingly conspicuous for environmental problem, the monitoring means of all kinds of environmental factors is constantly updated, wherein traditional water quality
Monitoring technology is all based on dotted sampled point and carries out water body acquisition, and the water quality information of sampled point is then obtained by chemical examination.It passes
System method takes time and effort, and its end result is to obtain the water quality parameter data of sampled point, these data can only represent sampling
The water quality situation of point, is not easy to the water quality situation of whole control area to be monitored.
Some manufacturing enterprises for causing water pollution can avoid Water-quality Monitoring Points, keep traditional water quality monitoring quasi-
The integral status of water quality is really held, therefore, the water quality monitoring result for how obtaining face domain becomes very crucial.
Summary of the invention
It is an object of the invention to propose a kind of method of generation face domain water quality monitoring result.This method is led to too small amount of
Eyeball water quality monitoring information generates the water quality monitoring information in face domain in conjunction with corresponding remote sensing image, helps environmentally friendly administrative staff
It is whole effectively to differentiate water pollution situation.
Its technical solution is as follows:
A kind of method of generation face domain water quality monitoring result, comprising the following steps:
1. determining detected regional scope；2. passing by situation according to the satellite in the region, the time of water quality sampling is determined；
3. acquiring water sample, and measure the position coordinate of sampled point；4. chemically examining water sample, (chlorophyll contains the relevant parameter of acquisition sampling water body
Amount, total nitrogen, total phosphorus etc.)；5. obtaining the satellite-remote-sensing image on the region date；6. a pair remote sensing image is pre-processed (radiation
Calibration, atmospheric correction, just penetrating correction etc.)；7. extracting the boundary for being detected waters, the remote sensing image in monitoring region is cut out；8.
The Remote Sensing Reflectance numerical value of the point is extracted by the coordinate of sampled point；9. establishing the reflectivity of each wave band of sampled point and band combination
The corresponding relationship of database and actual measurement water quality data library；10. analyzing reflectivity data library and surveying the correlation in water quality data library；
11. establishing regression model library with the strong band combination of correlation；12. generating various water quality with the model in regression model library
The face domain of Monitoring factors monitors figure；13. pair each water quality monitoring parameter is normalized, in conjunction with the power of each water quality parameter
Weight generates comprehensive face domain water quality monitoring result；14. writing report, achievement is submitted.
Further, when determining in 2 sampling time of above-mentioned steps, water sampling should be carried out at the time of satellite passes by as far as possible,
If condition does not allow, also should satellite pass by the same day complete acquisition；During step 3 acquires water sample, sampled point should be made as far as possible
It is evenly distributed in entire waters, to keep the data of sampled point more representative；In the process of data preprocessing of step 6,
Absolute radiation calibration and absolute atmospheric correction must be used, otherwise inversion result can be made relatively large deviation occur；In step 10
During analysed for relevance, the numerical value of correlation is not only considered, also to take into account otherness and significantly examine numerical value (Sig)；
The regression model library of step 11 will be according to the power of correlation, it is determined whether use multivariate regression models, if water quality parameter with
A certain wave band or band combination correlation are stronger, then the regression model of formula (1):
Y=f (X1) (1)
Wherein Y is water quality parameter vector, X1For the stronger wave band of correlation or band combination vector, if water quality parameter to
Amount is not strong with the correlation of all wave bands or band combination vector, then uses the relatively stronger wave band of correlation or band combination
Vector carries out multiple regression, and regression model is as follows:
Y=f (X1)+f(X2)+f(X3)+…+f(Xn) (2)
Wherein Y is water quality parameter vector, X1、X2、X3…XnFor the stronger wave band of correlation or band combination vector；Step
Water quality parameter weight in 13 will according to different waters and season, by related fields expert be according to the actual situation each factor into
Row marking, finally counts the marking situation of each expert, calculates the weight of each factor.
The invention has the benefit that
1. realizing the method for by marginally surface sample point, generating face domain water quality monitoring result；
2. the importance of each parameter can be comprehensively considered according to the weight of different parameters, comprehensive face domain water quality monitoring is generated
As a result, the discovery water pollution source of decision-making section much sooner can be made.
Detailed description of the invention
Fig. 1 is the flow chart of water quality monitoring result method in generation face domain of the present invention；
Fig. 2 is chlorophyll-a concentration inversion result figure and data statistics histogram, wherein Fig. 2A is inversion result figure, Fig. 2 B
For histogram；
Fig. 3 is permanganate index inversion result figure and data statistics histogram, wherein Fig. 3 A is inversion result figure, figure
3B is histogram；
Fig. 4 is ammonia nitrogen concentration inversion result figure and data statistics histogram, wherein Fig. 4 A is inversion result figure, and Fig. 4 B is
Histogram；
Fig. 5 is total nitrogen concentration inversion result figure and data statistics histogram, wherein Fig. 5 A is inversion result figure, and Fig. 5 B is
Histogram；
Fig. 6 is composite water quality inversion result figure.
Specific embodiment
Technical solution of the present invention is described in more detail with reference to the accompanying drawings and detailed description.
It is overall technology process of the invention shown in Fig. 1, which is totally divided into three parts, and left side top half is
Traditional water quality test part, the water quality parameter that the present invention only needs a small amount of ground monitoring point that large area can be completed generate work；
Right part is that satellite remote sensing date handles part, and main includes radiation calibration, atmospheric correction, the ortho-rectification etc. of remotely-sensed data
It pre-processes content and remote sensing image information extracts work；Left side lower half portion is water quality data in conjunction with remotely-sensed data, is carried out
Data regression and inverting work.
Fig. 2-Fig. 5 respectively indicates chlorophyll-a concentration inversion result, permanganate index inversion result, ammonia nitrogen concentration inverting
As a result with total nitrogen concentration inversion result, wherein each inversion result figure has the data distribution histogram of each inverting index below,
Whole water quality condition can also be judged with aid decision personnel.Water quality parameter selected by this mainly reflects water eutrophication journey
Degree, should be the result is that the conclusion obtained based on the authoritative water monitoring data that environmental protection department provides.This experiment is with 5 monitorings
Point carried out data regression and inverting, the deeper region of color shows that concentration is lower in inversion result figure, it is on the contrary then show concentration compared with
It is high.
Fig. 6 is to weight generation according to a certain percentage by above-mentioned each monomial factor, can preferably reflect the rich battalion in the waters
Feedingization degree.Due to the waters entirety water (flow) direction from north orientation south, from West to East, subsequently into water-drawing channel, so northern
Eutrophic extent is lower, and with river water movement, eutrophic extent is further decreased near flume, and the waters is overall rich
Nutrient laden degree is not high, meets drinking water standard.
The present invention, in conjunction with satellite remote sensing images, generates face domain water quality monitoring knot on the basis of dotted water quality parameter data
Fruit.Water quality monitoring result in face domain can be more clear with aid decision making personnel, intuitively understand the water quality situation in entire waters, thus
It was found that water pollution source, carrying out has the fwaater resources protection of emphasis to work.
The foregoing is only a preferred embodiment of the present invention, the scope of protection of the present invention is not limited to this, it is any ripe
Know those skilled in the art within the technical scope of the present disclosure, the letter for the technical solution that can be become apparent to
Altered or equivalence replacement are fallen within the protection scope of the present invention.
Claims (6)
1. a kind of method of generation face domain water quality monitoring result, which comprises the following steps:
Step 1. determines detected regional scope；Step 2. is passed by situation according to the satellite in the region, determines water quality sampling
Time；Step 3. acquires water sample, and measures the position coordinate of sampled point；Step 4. chemically examines water sample, obtains the correlation of sampling water body
Parameter；The satellite-remote-sensing image on the step 5. acquisition region date；Step 6. pre-processes remote sensing image；Step 7. mentions
The boundary for taking detected waters cuts out the remote sensing image in monitoring region；Step 8. extracts the point by the coordinate of sampled point
Remote Sensing Reflectance numerical value；Step 9. establishes sampled point reflectivity data library；Step 10. analyzes each band combination and reflectivity data
The correlation in library；Step 11. band combination strong with correlation, establishes regression model library；Step 12. uses regression model library
Model, generate the face domain monitoring figure of the various water quality monitoring factors；Place is normalized to each water quality monitoring parameter in step 13.
Reason generates comprehensive face domain water quality monitoring result in conjunction with the weight of each water quality parameter；Step 14. writes report, submits achievement.
2. the method for water quality monitoring result in generation face domain according to claim 1, which is characterized in that adopted in above-mentioned steps 2
When the sample time determines, water sampling should be carried out at the time of satellite passes by as far as possible, if condition does not allow, should also be passed by satellite
Same day acquisition is completed.
3. the method for water quality monitoring result in generation face domain according to claim 1, which is characterized in that step 3 acquires water sample
In the process, sampled point should be made to be evenly distributed in entire waters as far as possible, to keep the data of sampled point more representative.
4. the method for water quality monitoring result in generation face domain according to claim 1, which is characterized in that in the data of step 6
In preprocessing process, it is necessary to use absolute radiation calibration and absolute atmospheric correction, inversion result can otherwise occurred larger
Deviation.
5. the method for water quality monitoring result in generation face domain according to claim 1, which is characterized in that analyze phase in step 10
During closing property, not only to consider the numerical value of correlation, also to take into account otherness and significantly examine numerical value Sig.
6. the method for water quality monitoring result in generation face domain according to claim 1, which is characterized in that the recurrence mould of step 11
It type library will be according to the power of correlation, it is determined whether multivariate regression models is used, if water quality parameter and a certain wave band or wave band
Combined relevance is stronger, then the regression model of formula (1):
Y=f (X1) (1)
Wherein Y is water quality parameter vector, X1For the stronger wave band of correlation or band combination vector, if water quality parameter vector with
The correlation of all wave bands or band combination vector is not strong, then uses the relatively stronger wave band of correlation or band combination vector
Multiple regression is carried out, regression model is as follows:
Y=f (X1)+f(X2)+f(X3)+…+f(Xn) (2)
Wherein Y is water quality parameter vector, X1、X2、X3…XnFor the stronger wave band of correlation or band combination vector；
Water quality parameter weight in step 13 will be every by related fields expert according to different waters and season according to the actual situation
A factor is given a mark, and is finally counted the marking situation of each expert, is calculated the weight of each factor.
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Cited By (8)
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| CN111220786A (en) * | 2020-03-09 | 2020-06-02 | 生态环境部华南环境科学研究所 | Method for rapidly monitoring organic pollution of deep water sediments |
| CN111220410A (en) * | 2020-03-09 | 2020-06-02 | 生态环境部华南环境科学研究所 | Deep water sediment sampling system capable of rapidly sampling |
| CN111735503A (en) * | 2020-07-26 | 2020-10-02 | 榆林学院 | Water resource monitoring system based on big data and monitoring method thereof |
| CN113447623A (en) * | 2021-09-02 | 2021-09-28 | 航天宏图信息技术股份有限公司 | Atmospheric environment monitoring method and system |
| CN113533672A (en) * | 2021-09-15 | 2021-10-22 | 深圳市迈珂斯环保科技有限公司 | Water quality on-line monitoring method and device |
| CN113686788A (en) * | 2021-09-18 | 2021-11-23 | 重庆星视空间科技有限公司 | Conventional water quality monitoring system and method based on remote sensing wave band combination |
| CN113928516A (en) * | 2021-10-28 | 2022-01-14 | 中国水利水电科学研究院 | Underwater robot and method for monitoring anoxic zone of lake reservoir |
| CN116451481A (en) * | 2023-04-19 | 2023-07-18 | 北京首创大气环境科技股份有限公司 | Multi-parameter rapid water quality inversion method based on GEE cloud platform and Sentinel-2 image |
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| CN113533672A (en) * | 2021-09-15 | 2021-10-22 | 深圳市迈珂斯环保科技有限公司 | Water quality on-line monitoring method and device |
| CN113686788A (en) * | 2021-09-18 | 2021-11-23 | 重庆星视空间科技有限公司 | Conventional water quality monitoring system and method based on remote sensing wave band combination |
| CN113928516A (en) * | 2021-10-28 | 2022-01-14 | 中国水利水电科学研究院 | Underwater robot and method for monitoring anoxic zone of lake reservoir |
| CN116451481A (en) * | 2023-04-19 | 2023-07-18 | 北京首创大气环境科技股份有限公司 | Multi-parameter rapid water quality inversion method based on GEE cloud platform and Sentinel-2 image |
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