CN108507949B - River water quality monitoring method based on high-resolution remote sensing satellite - Google Patents
River water quality monitoring method based on high-resolution remote sensing satellite Download PDFInfo
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Abstract
The invention relates to a river water quality monitoring method based on a high-resolution remote sensing satellite, which is characterized in that a high-resolution remote sensing image a of a river to be monitored is obtained from a ground station based on the high-resolution remote sensing satellite, and a digital topographic map of the area of the river to be monitored is obtained; the preprocessing module is used for preprocessing the high-resolution remote sensing image a to obtain n high-resolution remote sensing images c which are positioned in different wave bands and can display the complete river channel image to be monitored; the controller conducts regression analysis on the actual mass concentration of each reference position point and the reflectivity of each reference position point on each high-resolution remote sensing image c to obtain a corresponding regression equation, and then establishes a relation function between the actual mass concentration of the reference position point and the reflectivity of each reference position on the corresponding position of each high-resolution remote sensing image c based on the regression equation with the minimum discrete degree; the controller calculates the reflectivity of the monitoring point on the n high-resolution remote sensing images c, and calculates the mass concentration of the water quality parameter at the monitoring point according to the relation function.
Description
Technical Field
The invention relates to a riverway water quality monitoring method based on a high-resolution remote sensing satellite.
Background
River water quality monitoring is one of the main contents of environmental monitoring, accurately, timely and comprehensively reflects the current situation and development trend of water quality, provides scientific basis for water environment management, pollution source control, environmental planning and the like, and plays a vital role in the aspects of water environment protection, water pollution control and water environment health maintenance. At present, the river water quality monitoring mainly comprises the following two methods:
one, traditional physicochemical monitoring
After the monitoring target is investigated and researched, monitoring network points are determined, sampling time and sampling frequency are arranged, and physical and chemical analysis is performed by periodic sampling, so that the purpose of monitoring is achieved. This method has the following disadvantages:
1) the whole process has too many variable factors, and the monitoring result is distorted as long as errors occur at one place in the determination of the sampling frequency, the monitoring point and the analysis method;
2) monitoring can be carried out only on monitoring points, and overall monitoring cannot be achieved;
3) the number of monitoring points determines the monitoring cost, the number of the monitoring points is small, the monitoring effect is poor, the number of the monitoring points is large, the cost is high, and the cost is difficult to control when the monitoring is carried out on a wide river channel;
4) artificial errors and equipment faults in the monitoring process can affect monitoring data;
5) the monitoring can be carried out only periodically, and continuous long-term dynamic monitoring cannot be realized.
Second, biological monitoring
The change of aquatic organism community structure is used for monitoring. The water quality condition changes, and the structure of the aquatic organism community also changes correspondingly. This method has the following disadvantages:
1) the water pollution cannot be qualitatively and quantitatively measured;
2) the sensitivity and specificity of detection are not as good as those of physicochemical detection;
3) some biological tests require a long time.
In summary, the existing water quality monitoring mode has disadvantages, and the current monitoring needs cannot be met.
Disclosure of Invention
In order to overcome the defects in the background technology, the invention provides a river water quality monitoring method based on a high-resolution remote sensing satellite.
The technical scheme for solving the problems is as follows:
a river water quality monitoring method based on a high-resolution remote sensing satellite comprises the following steps:
1.1, a high-resolution remote sensing satellite acquires global remote sensing image data and transmits the global remote sensing image data to a ground station, an image acquisition module acquires a plurality of high-resolution remote sensing images a of a river channel to be monitored, wherein the high-resolution remote sensing images a are positioned in n wave bands, the high-resolution remote sensing images a positioned in the same wave band can be spliced into a complete image of the river channel to be monitored, n is greater than 0, and n is a natural number;
1.2, a map retrieving module retrieves a digital topographic map of an area where a river to be monitored is located from a network;
1.3, the preprocessing module preprocesses the high resolution remote sensing image a, and the preprocessing module comprises the following steps:
1.3.1 geometric correction and Gray resampling
The geometric correction module marks m control points with fixed positions in monitoring time on a digital topographic map along the length direction of a river channel to be monitored, and marks mapping points corresponding to the control points on a high-resolution remote sensing image a; establishing two different two-dimensional Cartesian coordinate systems on the digital topographic map and the high-resolution remote sensing image a respectively, and establishing a polynomial correction function according to the coordinates of the control points on the digital topographic map and the coordinates of the mapping points of the high-resolution remote sensing image a; then, geometric correction is carried out on pixels of the high-resolution remote sensing image a one by the aid of the polynomial correction function, gray resampling is carried out on the high-resolution remote sensing image a by a bilinear interpolation method while geometric correction is carried out, the high-resolution remote sensing image a is changed into a high-resolution remote sensing image b after geometric correction and gray resampling, m is greater than 20, and m is a natural number;
1.3.2 high resolution remote sensing image b splicing mosaic
The remote sensing image splicing and embedding module splices and inlays a plurality of high-resolution remote sensing images b positioned in the same wave band into a high-resolution remote sensing image c capable of displaying a complete river channel image to be monitored by a shift matching method, a histogram matching method and a seam line-based image embedding method respectively; obtaining a high-resolution remote sensing image c for each wave band;
1.3.3, stretching each remote sensing image c by a histogram correction method through a histogram correction module so as to enhance the definition of the remote sensing image c;
1.4, automatic recognition by a controller:
1.4.1, uniformly selecting k reference position points and s monitoring points on a digital topographic map along the length direction of a river channel to be monitored, wherein k is greater than 20, s is greater than 0, and both k and s are natural numbers;
1.4.2, collecting the actual mass concentration of a plurality of water quality parameters of each reference position point on the spot;
1.4.3, extracting the reflectivity of each reference position point at the corresponding position on each high-resolution remote sensing image c by the controller;
1.4.4, the controller makes regression analysis of the actual mass concentration of each reference position point and the reflectivity of each reference position point on each high-resolution remote sensing image c in a linear, exponential, logarithmic, polynomial and power manner to obtain a corresponding regression equation, and then establishes a relation function between the actual mass concentration of the reference position point and the reflectivity of each reference position on the corresponding position of each high-resolution remote sensing image c based on the regression equation with the minimum discrete degree;
1.4.5, the controller calculates the reflectivity of the monitoring points on the n high-resolution remote sensing images c, calculates the mass concentration of the water quality parameter at the monitoring points according to the relation function, and marks the concentration on the high-resolution remote sensing images c according to the color depth, so as to obtain a spatial distribution map of the mass concentration of the water quality parameter of the monitoring points in the river to be monitored;
1.5, artificial identification
Checking the spatial distribution map in real time to obtain a real-time monitoring conclusion of the water quality condition of the river to be monitored; and carrying out comparative analysis on the spatial distribution map of the river channel water quality parameters in each time period to obtain a dynamic monitoring conclusion of the water quality condition of the river channel to be monitored.
Further, the n bands are respectively a blue band, a green band, a red band and a near-infrared band.
Further, the control points include road intersections, bridges, and/or river corners.
Further, the water quality parameters include chlorophyll, suspended matter, total phosphorus, total nitrogen and/or total organic carbon.
Further, a specific process of splicing and embedding a high-resolution remote sensing image c capable of displaying a complete river channel image to be monitored by a shift matching method, a histogram matching method and a seam line-based image embedding method is as follows: moving one high-resolution remote sensing image a to another adjacent high-resolution remote sensing image a to enable the relative position of the two high-resolution remote sensing images a to be changed continuously, and completing matching of the two high-resolution remote sensing images a when the overlapped parts of the two high-resolution remote sensing images a are completely overlapped; then, adjusting the tone of the two high-resolution remote sensing images a to be consistent by adopting a histogram matching method; then, selecting a joint line of two high-resolution remote sensing images a according to a principle that the joint line does not intersect with the image of the river channel on the high-resolution remote sensing image a, splicing the two high-resolution remote sensing images a along the joint line, and performing feathering treatment on the joint line; and finally, inlaying the high-resolution remote sensing images a in the same wave band into a high-resolution remote sensing image c capable of displaying the whole river channel to be monitored.
Further, the specific process of performing gray resampling on the high-resolution remote sensing image a by using a bilinear interpolation method is as follows: and taking the gray value obtained by linearly interpolating the gray values of 4 adjacent pixels around each pixel on the high-resolution remote sensing image a in the x-axis direction and the y-axis direction as the gray value after the geometric correction of the pixel.
Further, the geometric correction comprises the following specific steps: respectively substituting different coordinates of each control point on a digital topographic map and the high-resolution remote sensing image a into a polynomial to establish an equation set for solving coefficients of a polynomial correction function, and obtaining the coefficients of the polynomial correction function by adopting a least square method; substituting the coefficient into a polynomial to obtain a polynomial correction function; and substituting the coordinates of the pixel of the high-resolution remote sensing image a into a polynomial correction function to obtain the pixel coordinates of the high-resolution remote sensing image a after geometric correction.
Principle of satellite remote sensing: according to the theory of electromagnetic wave, various sensing instruments are used to collect, process and finally image the information of the electromagnetic wave radiated and reflected by the distant target, thus detecting and identifying various scenery on the ground.
The method is based on the multi-source satellite remote sensing technology, on the basis of analyzing the water body condition monitoring capacity of different sensors, the comprehensive capacity of the satellite remote sensing monitoring water body condition is fully excavated by utilizing the complementarity of the multi-source sensors, a composite method and technology for monitoring the water body condition based on multi-band, multi-temporal and multi-type remote sensing data are established on a macro scale, a fast extraction method of the drainage water system water body is formed, a water quality parameter inversion model of a typical polluted area is established by utilizing the multi-source hyperspectral satellite remote sensing data, the technical scheme of fast positioning the water area and fast extracting the comprehensive water quality information of the water body is achieved, macro information under the large scale is provided, and the large-area water body monitoring and emergency assistant decision of low cost and wide.
The invention has the following beneficial effects:
1) the monitoring range is wide, the satellite remote sensing data belongs to global data, and global monitoring can be carried out.
2) The method has the advantages that the speed is high, after the template is established, data are automatically analyzed through a computer, the flow efficiency is improved, the cost is low, after the remote sensing data are obtained, the maintenance cost caused by increase of monitoring points and the like does not exist in the later period.
3) The remote monitoring system can be used for carrying out long-term dynamic monitoring, trend general survey information is mainly provided by satellite remote sensing, the pollution change trend of the river water quality in a target area is monitored and analyzed by taking seasons as periods, and directional assistance is provided for other monitoring operations of an application unit.
4) The error is small, and the problems of monitoring point omission and incomplete integration are avoided aiming at the global analysis technology in the range.
Detailed Description
A river water quality monitoring method based on a high-resolution remote sensing satellite comprises the following steps:
1.1, a high-resolution remote sensing satellite acquires global remote sensing image data and transmits the global remote sensing image data to a ground station, an image acquisition module acquires a plurality of high-resolution remote sensing images a of a river channel to be monitored, wherein the high-resolution remote sensing images a are positioned in n wave bands, the high-resolution remote sensing images a positioned in the same wave band can be spliced into a complete image of the river channel to be monitored, n is greater than 0, and n is a natural number;
1.2, a map retrieving module retrieves a digital topographic map of an area where a river to be monitored is located from a network;
1.3, the preprocessing module preprocesses the high resolution remote sensing image a, and the preprocessing module comprises the following steps:
1.3.1 geometric correction and Gray resampling
The geometric correction module marks m control points with fixed positions in monitoring time on a digital topographic map along the length direction of a river channel to be monitored, and marks mapping points corresponding to the control points on a high-resolution remote sensing image a; establishing two different two-dimensional Cartesian coordinate systems on the digital topographic map and the high-resolution remote sensing image a respectively, and establishing a polynomial correction function (namely a binary nth-degree polynomial correction function) by using the coordinates of the control points on the digital topographic map and the coordinates of the mapping points of the high-resolution remote sensing image a; then, geometric correction is carried out on pixels of the high-resolution remote sensing image a one by the aid of the polynomial correction function, gray resampling is carried out on the high-resolution remote sensing image a by a bilinear interpolation method while geometric correction is carried out, the high-resolution remote sensing image a is changed into a high-resolution remote sensing image b after geometric correction and gray resampling, m is greater than 20, and m is a natural number;
1.3.2 high resolution remote sensing image b splicing mosaic
The remote sensing image splicing and embedding module splices and inlays a plurality of high-resolution remote sensing images b positioned in the same wave band into a high-resolution remote sensing image c capable of displaying a complete river channel image to be monitored by a shift matching method, a histogram matching method and a seam line-based image embedding method respectively; obtaining a high-resolution remote sensing image c for each wave band;
1.3.3, stretching each remote sensing image c by a histogram correction method through a histogram correction module so as to enhance the definition of the remote sensing image c;
1.4, automatic recognition by a controller:
1.4.1, uniformly selecting k reference position points and s monitoring points on a digital topographic map along the length direction of a river channel to be monitored, wherein k is greater than 20, s is greater than 0, and both k and s are natural numbers;
1.4.2, collecting the actual mass concentration of a plurality of water quality parameters of each reference position point on the spot; the actual mass concentration of the water quality parameter is measured manually by using instruments and tools, such as chlorophyll mass concentration measurement by a leaf green measuring instrument, suspended matter mass concentration measurement by a suspended matter on-line measuring instrument, total phosphorus mass concentration measurement by a total phosphorus on-line analyzer, total nitrogen mass concentration measurement by a total nitrogen on-line analyzer, and total organic carbon mass concentration measurement by a total organic carbon detector, which are all commercially available.
1.4.3, extracting the reflectivity of each reference position point at the corresponding position on each high-resolution remote sensing image c by the controller;
1.4.4, the controller makes regression analysis of the actual mass concentration of each reference position point and the reflectivity of each reference position point on each high-resolution remote sensing image c in a linear, exponential, logarithmic, polynomial and power manner to obtain a corresponding regression equation, and then establishes a relation function between the actual mass concentration of the reference position point and the reflectivity of each reference position on the corresponding position of each high-resolution remote sensing image c based on the regression equation with the minimum discrete degree;
1.4.5, the controller calculates the reflectivity of the monitoring points on the n high-resolution remote sensing images c, calculates the mass concentration of the water quality parameter at the monitoring points according to the relation function, and marks the concentration on the high-resolution remote sensing images c according to the color depth, so as to obtain a spatial distribution map of the mass concentration of the water quality parameter of the monitoring points in the river to be monitored;
1.5, artificial identification
Checking the spatial distribution map in real time to obtain a real-time monitoring conclusion of the water quality condition of the river to be monitored; and carrying out comparative analysis on the spatial distribution map of the river channel water quality parameters in each time period to obtain a dynamic monitoring conclusion of the water quality condition of the river channel to be monitored.
Further, the n bands are respectively a blue band, a green band, a red band and a near-infrared band.
Further, the control points include road intersections, bridges, and/or river corners.
Further, the water quality parameters include chlorophyll, suspended matter, total phosphorus, total nitrogen and/or total organic carbon.
Further, a specific process of splicing and embedding a high-resolution remote sensing image c capable of displaying a complete river channel image to be monitored by a shift matching method, a histogram matching method and a seam line-based image embedding method is as follows: moving one high-resolution remote sensing image a to another adjacent high-resolution remote sensing image a to enable the relative position of the two high-resolution remote sensing images a to be changed continuously, and completing matching of the two high-resolution remote sensing images a when the overlapped parts of the two high-resolution remote sensing images a are completely overlapped; then, adjusting the tone of the two high-resolution remote sensing images a to be consistent by adopting a histogram matching method; then, selecting a joint line of two high-resolution remote sensing images a according to a principle that the joint line does not intersect with the image of the river channel on the high-resolution remote sensing image a, splicing the two high-resolution remote sensing images a along the joint line, and performing feathering treatment on the joint line; and finally, inlaying the high-resolution remote sensing images a in the same wave band into a high-resolution remote sensing image c capable of displaying the whole river channel to be monitored.
Further, the specific process of performing gray resampling on the high-resolution remote sensing image a by using a bilinear interpolation method is as follows: the gray value obtained by linearly interpolating the gray values of 4 adjacent pixels around each pixel on the high-resolution remote sensing image a in the x-axis direction and the y-axis direction (the x-axis direction and the y-axis direction on a two-dimensional Cartesian coordinate system) is used as the gray value after the geometric correction of the pixel.
Further, the geometric correction comprises the following specific steps: respectively substituting different coordinates of each control point on a digital topographic map and the high-resolution remote sensing image a into a polynomial to establish an equation set for solving coefficients of a polynomial correction function, and obtaining the coefficients of the polynomial correction function by adopting a least square method; substituting the coefficient into a polynomial to obtain a polynomial correction function; and substituting the coordinates of the pixel of the high-resolution remote sensing image a into a polynomial correction function to obtain the pixel coordinates of the high-resolution remote sensing image a after geometric correction.
The invention monitors water quality based on satellite remote sensing, has the characteristics of high visual point, wide visual field, fast data acquisition, repetition and continuous observation, obtains digitalized data, and can directly enter a computer image processing system of a user, and the scheme has the advantages which cannot be compared with the traditional monitoring method.
The invention solves the problems that the prior art cannot carry out overall and long-term trend monitoring, monitoring equipment is too much and dependent, monitoring timeliness is low, monitoring procedures are complicated, personnel accidents can be caused during manual sampling, maintenance cost is high and the like.
In the invention, the high-resolution remote sensing image a, the high-resolution remote sensing image b and the high-resolution remote sensing image c are all high-resolution remote sensing images, are named for the convenience of distinguishing the high-resolution remote sensing images in different steps, and have no meaning by letters.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but includes equivalent technical means as would be recognized by those skilled in the art based on the inventive concept.
Claims (7)
1. A river water quality monitoring method based on a high-resolution remote sensing satellite is characterized by comprising the following steps: the method comprises the following steps:
1.1, a high-resolution remote sensing satellite acquires global remote sensing image data and transmits the global remote sensing image data to a ground station, an image acquisition module acquires a plurality of high-resolution remote sensing images a of a river channel to be monitored, wherein the high-resolution remote sensing images a are positioned in n wave bands, the high-resolution remote sensing images a positioned in the same wave band can be spliced into a complete image of the river channel to be monitored, n is greater than 0, and n is a natural number;
1.2, a map retrieving module retrieves a digital topographic map of an area where a river to be monitored is located from a network;
1.3, the preprocessing module preprocesses the high resolution remote sensing image a, and the preprocessing module comprises the following steps:
1.3.1 geometric correction and Gray resampling
The geometric correction module marks m control points with fixed positions in monitoring time on a digital topographic map along the length direction of a river channel to be monitored, and marks mapping points corresponding to the control points on a high-resolution remote sensing image a; respectively establishing a two-dimensional Cartesian coordinate system on the digital topographic map and the high-resolution remote sensing image a, and establishing a polynomial correction function according to the coordinates of the control points on the digital topographic map and the coordinates of the mapping points of the high-resolution remote sensing image a; then, geometric correction is carried out on pixels of the high-resolution remote sensing image a one by the aid of the polynomial correction function, gray resampling is carried out on the high-resolution remote sensing image a by a bilinear interpolation method while geometric correction is carried out, the high-resolution remote sensing image a is changed into a high-resolution remote sensing image b after geometric correction and gray resampling, m is greater than 20, and m is a natural number;
1.3.2 high resolution remote sensing image b splicing mosaic
The remote sensing image splicing and embedding module splices and inlays a plurality of high-resolution remote sensing images b positioned in the same wave band into a high-resolution remote sensing image c capable of displaying a complete river channel image to be monitored by sequentially using a shift matching method, a histogram matching method and a seam line-based image embedding method; obtaining a high-resolution remote sensing image c for each wave band;
1.3.3, stretching each remote sensing image c by a histogram correction method through a histogram correction module so as to enhance the definition of the remote sensing image c;
1.4, automatic recognition by a controller:
1.4.1, uniformly selecting k reference position points and s monitoring points on a digital topographic map along the length direction of a river channel to be monitored, wherein k is greater than 20, s is greater than 0, and both k and s are natural numbers;
1.4.2, collecting the actual mass concentration of a plurality of water quality parameters of each reference position point on the spot;
1.4.3, extracting the reflectivity of each reference position point at the corresponding position on each high-resolution remote sensing image c by the controller;
1.4.4, the controller makes regression analysis of the actual mass concentration of each reference position point and the reflectivity of each reference position point on each high-resolution remote sensing image c in a linear, exponential, logarithmic, polynomial and power manner to obtain a corresponding regression equation, and then establishes a relation function between the actual mass concentration of the reference position point and the reflectivity of each reference position on the corresponding position of each high-resolution remote sensing image c based on the regression equation with the minimum discrete degree;
1.4.5, the controller calculates the reflectivity of the monitoring points on the n high-resolution remote sensing images c, calculates the mass concentration of the water quality parameter at the monitoring points according to the relation function, and marks the concentration on the high-resolution remote sensing images c according to the color depth, so as to obtain a spatial distribution map of the mass concentration of the water quality parameter of the monitoring points in the river to be monitored;
1.5, artificial identification
Checking the spatial distribution map in real time to obtain a real-time monitoring conclusion of the water quality condition of the river to be monitored; and carrying out comparative analysis on the spatial distribution map of the river channel water quality parameters in each time period to obtain a dynamic monitoring conclusion of the water quality condition of the river channel to be monitored.
2. The riverway water quality monitoring method based on the high-resolution remote sensing satellite according to claim 1, characterized in that: the n wave bands are respectively a blue wave band, a green wave band, a red wave band and a near infrared wave band.
3. The riverway water quality monitoring method based on the high-resolution remote sensing satellite as claimed in claim 2, characterized in that: the control points include road intersections, bridges, and/or river corners.
4. The riverway water quality monitoring method based on the high-resolution remote sensing satellite according to claim 3, characterized in that: the water quality parameters comprise chlorophyll, suspended matters, total phosphorus, total nitrogen and/or total organic carbon.
5. The riverway water quality monitoring method based on the high-resolution remote sensing satellite according to claim 4, characterized in that: the specific process of splicing and embedding a high-resolution remote sensing image c capable of displaying a complete river channel image to be monitored by a shift matching method, a histogram matching method and a seam line-based image embedding method is as follows: moving one high-resolution remote sensing image b to another adjacent high-resolution remote sensing image b to enable the relative position of the two high-resolution remote sensing images b to be changed continuously, and completing matching of the two high-resolution remote sensing images b when the overlapped parts of the two high-resolution remote sensing images b are completely overlapped; then, adjusting the tone of the two high-resolution remote sensing images b to be consistent by adopting a histogram matching method; then, selecting a joint line of the two high-resolution remote sensing images b according to a principle that the joint line does not intersect with the image of the river channel on the high-resolution remote sensing image b, splicing the two high-resolution remote sensing images b along the joint line, and performing feathering treatment on the joint line; and finally, inlaying the high-resolution remote sensing image b positioned in the same wave band into a high-resolution remote sensing image c capable of displaying the whole river channel to be monitored.
6. The riverway water quality monitoring method based on the high-resolution remote sensing satellite according to claim 5, characterized in that: the specific process of performing gray level resampling on the high-resolution remote sensing image a by adopting a bilinear interpolation method is as follows: and taking the gray value obtained by linearly interpolating the gray values of 4 adjacent pixels around each pixel on the high-resolution remote sensing image a in the x-axis direction and the y-axis direction as the gray value after the geometric correction of the pixel.
7. The riverway water quality monitoring method based on the high-resolution remote sensing satellite according to claim 6, characterized in that: the geometric correction comprises the following specific steps: respectively substituting different coordinates of each control point on a digital topographic map and the high-resolution remote sensing image a into a polynomial to establish an equation set for solving coefficients of a polynomial correction function, and obtaining the coefficients of the polynomial correction function by adopting a least square method; substituting the coefficient into a polynomial to obtain a polynomial correction function; and substituting the coordinates of the pixel of the high-resolution remote sensing image a into a polynomial correction function to obtain the pixel coordinates of the high-resolution remote sensing image a after geometric correction.
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