CN114088723A - Water environment water quality tracking monitoring method - Google Patents
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 148
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- 238000002310 reflectometry Methods 0.000 claims abstract description 33
- 238000011160 research Methods 0.000 claims description 13
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- 238000013480 data collection Methods 0.000 claims description 8
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/94—Investigating contamination, e.g. dust
Abstract
The invention discloses a water environment water quality tracking and monitoring method, which comprises the following steps: s1: a plurality of image data acquisition points are arranged in a monitoring area of the water environment, and the image data acquisition points are uniformly distributed in the monitoring area; s2: obtaining a relation data set (f) of the multispectral reflectivity of the water area of the image data acquisition point and the water quality parameter Ab', A); s3: constructing a relation model of multispectral reflectivity and water quality parameters by using a plurality of relation data sets: ln (a) ═ m × ln (f)b') + q, wherein m is a relation coefficient and q is a deviation; s4: and acquiring the multispectral reflectivity of the water surface above the monitored water area by using the unmanned aerial vehicle, and substituting the multispectral reflectivity into the relation model to obtain the water quality parameter of the water area. The invention is helpful for acquiring and accumulating more accurate, higher space-time resolution and more complete rural water body pollution data sets, and is used for tracking the rural water body pollutionMore useful information is provided by the tracking monitoring and the prediction analysis of the water environment pollution degree and the pollution range.
Description
Technical Field
The invention relates to the technical field of water quality monitoring, in particular to a water environment water quality tracking and monitoring method.
Background
A great deal of research is carried out in all countries of the world to accurately quantify the influence of the surface water body and the underground water body on the pollution sources in the drainage basin of the rural area. The initial research stage is mainly influenced by high cost of basic equipment, experiments can be carried out only in an independent small-scale range, and European and American countries and a plurality of international organizations develop monitoring work in a large area and a large area until 90 years of the 20 th century so as to quantify the influence of agricultural activities on water environment, and evaluate the influence of various treatment measures which are already operated on regional agricultural sustainable development according to corresponding monitoring results.
According to different monitoring items, the monitoring points of the rural area are selected differently, such as punctiform monitoring objects of water outlets of a water collecting area, edge areas of farmland blocks, below a water level line, drainage water outlets of an irrigation field and the like, and corresponding monitoring means and technologies are developed according to different monitoring objects. With the continuous deepening of the basic research on the generating mechanism of the rural water environment pollution and the introduction of the automatic sensing technology and the remote sensing means, the rural water body tracking and monitoring technology is greatly improved in the aspects of scientificity and operability.
At present, in most developed countries in Europe and America, in a drainage basin with serious water eutrophication problem in villages, corresponding monitoring sites are already arranged to implement continuous data acquisition and tracking analysis and regularly release the results of monitoring data. However, most of the existing research on tracking and monitoring the water pollution in the rural area focuses on the research objects in a large scale range, and the monitoring precision is not high.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a water environment and water quality tracking and monitoring method with high monitoring precision.
In order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
the water environment water quality tracking and monitoring method comprises the following steps:
s1: a plurality of image data acquisition points are arranged in a monitoring area of the water environment, and the image data acquisition points are uniformly distributed in the monitoring area;
s2: shooting a multispectral image of a monitored water area right above an image data acquisition point by using a spectral camera carried by an unmanned aerial vehicle, and acquiring multispectral reflectivity f of the image data acquisition pointb'; and collecting the water quality parameter A at the position to obtain a relational data set (f) of the multispectral reflectivity of the water area and the water quality parameter A at the image data collection pointb'， A)；
S3: repeating the step S2, collecting the corresponding relation data sets of the other image data collection points, and constructing a relation model of the multispectral reflectivity and the water quality parameter by using a plurality of relation data sets: ln (a) ═ m × ln (f)b') + q, wherein m is a relation coefficient and q is a deviation;
s4: and acquiring the multispectral reflectivity of the water surface above the monitored water area by using the unmanned aerial vehicle, and substituting the multispectral reflectivity into the relation model to obtain the water quality parameter of the water area.
Further, step S2 includes:
s21: a spectrum camera carried by the unmanned aerial vehicle continuously shoots a plurality of multispectral images on an image data acquisition point;
s22: when the multispectral image is shot once, the unmanned aerial vehicle acquires the positioning coordinates of the shooting points once to obtain a plurality of positioning coordinates (x)1，y1，z1)，(x2，y2，z2)，···(xn，yn，zn)；
S23: calculating the center coordinates (x) when the unmanned plane collects a plurality of multispectral imagesa，ya，za) Wherein x isa＝(x1+x2···xn)/n，ya＝(y1+y2···yn)/n，za＝(z1+z2···zn)/n，xnIn order to shoot multispectral images, the x-direction coordinate and y-direction coordinate of the unmanned aerial vehicle in the plane of the monitored water areanFor capturing multi-spectral imagesThe time unmanned plane monitors the y-direction coordinate, z of the water area planenThe vertical height of the unmanned aerial vehicle from the water area plane when the multispectral image is shot is determined, and n is the number of the multispectral images collected on the data collection point;
s24: in the plane of the water area of the water environment, in terms of coordinates (x)a，ya，za) The position right below the determined point is used as a water quality data acquisition point, and a water quality parameter A at the water quality data acquisition point is acquired;
s25: combining a plurality of multispectral images to form a spectral research image;
s26: cutting out a water surface area in the spectral research image to obtain a water surface multispectral image;
s27: dividing the multispectral image on the water surface into a plurality of buffer windows with the size of S multiplied by S, and acquiring the multispectral reflectivity f of each buffer windowb；
S28: calculating the multispectral reflectance f of several buffer windowsbAverage value f ofb'：fb'＝(f1+f2+···+fb) /b；
S29: obtaining a relation data set (f) of the multispectral reflectivity of the water area of the data acquisition point and the water quality parameter Ab'， A)。
Further, step S25 includes:
s251: determining internal orientation elements, calibrating a multispectral camera, adding a TIFF image of a calibration reflector and TIFF images of four wave bands of a monitoring water area, wherein the four wave bands comprise a green wave band G, a Red wave band R, a Red edge Red _ edge and a near infrared wave band;
s252: searching and matching the homonymy points of a plurality of multispectral images by adopting an SIFT algorithm;
s253: carrying out block adjustment by utilizing the same name points and longitude and latitude information carried by a plurality of multispectral images, and restoring the positions and postures of the multispectral images;
s254: performing space-three encryption on the restored multispectral image by using an image control point to form an image point cloud;
s255: and generating a digital surface model by the image point cloud layer, and obtaining a digital orthophoto map and a spliced reflection map by using the digital surface model to form a spectrum research image.
Further, in step S1, the density of the image data acquisition points set in the monitored water area is 0.01-2.34 image data acquisition points/km2。
The invention has the beneficial effects that: the invention utilizes the unmanned aerial vehicle to carry out high multispectral image acquisition on the monitored water area, establishes a relation model of multispectral reflectivity of the water body and water quality parameters, and can directly obtain the water quality of the monitored water area by establishing the relation model only by shooting the multispectral image above the monitored water area by the unmanned aerial vehicle and acquiring the multispectral reflectivity of the water area in the process of monitoring the water quality.
Meanwhile, the system is favorable for timely and effectively monitoring rural water pollution and deeply knowing the water pollution condition, thereby having important significance for providing corresponding treatment measures and schemes. The method is favorable for acquiring and accumulating a more accurate, higher space-time resolution and more complete rural water body pollution data set, and provides more useful information for the tracking monitoring of rural water body pollution and the prediction analysis of the water environment pollution degree and the pollution range.
Drawings
Fig. 1 is a flow chart of a water environment water quality tracking and monitoring method.
FIG. 2 is a TIFF image of four bands of monitored waters.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in figure 1, the water environment water quality tracking and monitoring method comprises the following steps:
s1: a plurality of image data acquisition points are arranged in a monitoring area of the water environment, and the image data acquisition points are uniformly distributed in the monitoring area; the density of the image data acquisition points in the monitored water area is 0.01-2.34 image data acquisition points/km2。
S2: shooting a multispectral image of a monitored water area right above an image data acquisition point by using a spectral camera carried by an unmanned aerial vehicle, and acquiring multispectral reflectivity f of the image data acquisition pointb'; and collecting the water quality parameter A at the position to obtain a relational data set (f) of the multispectral reflectivity of the water area and the water quality parameter A at the image data collection pointb', A); different water qualities have different water quality parameters, and due to the difference of the water qualities, the higher the concentration of nutrient substances is, the more phytoplankton is, the transparency of the water body is reduced, and the reflected multispectral reflectivity is different. Meanwhile, the algae biomass and the water transparency can be roughly estimated by measuring the concentration of chlorophyll a of the water body and the turbidity of the water body respectively.
Step S2 includes:
s21: a spectrum camera carried by the unmanned aerial vehicle continuously shoots a plurality of multispectral images on an image data acquisition point;
s22: when the multispectral image is shot once, the unmanned aerial vehicle acquires the positioning coordinates of the shooting points once to obtain a plurality of positioning coordinates (x)1，y1，z1)，(x2，y2，z2)，···(xn，yn，zn)；
S23: calculating the center coordinate (x) when the unmanned plane collects a plurality of multispectral imagesa，ya，za) Wherein x isa＝(x1+x2···xn)/n，ya＝(y1+y2···yn)/n，za＝(z1+z2···zn)/n，xnIn order to shoot multispectral images, the x-direction coordinate and y-direction coordinate of the unmanned aerial vehicle in the plane of the monitored water areanIn order to shoot multispectral images, the y-direction coordinate, z of the unmanned aerial vehicle in the plane of the monitored water areanThe vertical height of the unmanned aerial vehicle from the water area plane when the multispectral image is shot is determined, and n is the number of the multispectral images collected on the data collection point;
because unmanned aerial vehicle is at the in-process of shooing, because influences such as aerial strong wind, air current, the great condition of rocking can appear, and its position of shooing multispectral image is not a fixed position, so, through the integration to rocking in-process image acquisition point, ask mid point coordinate, improve the precision.
S24: in the plane of the body of water in an aquatic environment, in coordinates (x)a，ya，za) The position right below the determined point is used as a water quality data acquisition point, and a water quality parameter A at the water quality data acquisition point is acquired;
s25: combining a plurality of multispectral images to form a spectral research image; the method comprises the following steps:
s251: determining internal orientation elements, calibrating a multispectral camera, adding a TIFF image of a calibration reflector and TIFF images of four wave bands of a monitoring water area, wherein the four wave bands comprise a green wave band G, a Red wave band R, a Red edge Red _ edge and a near infrared wave band; as shown in fig. 2.
S252: searching and matching the homonymy points of a plurality of multispectral images by adopting an SIFT algorithm;
s253: carrying out block adjustment by utilizing longitude and latitude information of the homonymy points and the multispectral images, and restoring the positions and the postures of the multispectral images;
s254: performing space-three encryption on the restored multispectral image by using an image control point to form an image point cloud;
s255: and generating a digital surface model by the image point cloud layer, and obtaining a digital orthophoto map and a spliced reflection map by using the digital surface model to form a spectrum research image.
S26: cutting out a water surface area in the spectral research image to obtain a water surface multispectral image;
s27: dividing the multispectral image on the water surface into a plurality of buffer windows with the size of S multiplied by S, and acquiring the multispectral reflectivity f of each buffer windowb(ii) a Is composed ofThe invention reduces the influence of water flow, mixed pixel spectrum, mirror reflection and the like on the multispectral reflectivity, and divides the multispectral image into a plurality of small buffer windows, and obtains the average value of each small buffer window to ensure that the multispectral reflectivity can reach the optimal matching value.
S28: calculating the multispectral reflectance f of several buffer windowsbAverage value f ofb'：fb'＝(f1+f2+···+fb) /b；
S29: obtaining a relation data set (f) of the multispectral reflectivity of the water area of the data acquisition point and the water quality parameter Ab'， A)。
S3: repeating the step S2, collecting the corresponding relation data sets of the other image data collection points, and constructing a relation model of the multispectral reflectivity and the water quality parameter by using a plurality of relation data sets: ln (a) ═ m × ln (f)b') + q, wherein m is a relation coefficient and q is a deviation;
s4: and acquiring the multispectral reflectivity of the water surface above the monitored water area by using the unmanned aerial vehicle, and substituting the multispectral reflectivity into the relation model to obtain the water quality parameter of the water area.
The invention utilizes the unmanned aerial vehicle to carry out high multispectral image acquisition on the monitored water area, establishes a relation model of multispectral reflectivity of the water body and water quality parameters, and can directly obtain the water quality of the monitored water area by establishing the relation model only by shooting the multispectral image above the monitored water area by the unmanned aerial vehicle and acquiring the multispectral reflectivity of the water area in the process of monitoring the water quality.
Meanwhile, the system is favorable for timely and effectively monitoring rural water pollution and deeply knowing the water pollution condition, thereby having important significance for providing corresponding treatment measures and schemes. The method is favorable for acquiring and accumulating a more accurate, higher space-time resolution and more complete rural water body pollution data set, and provides more useful information for the tracking monitoring of rural water body pollution and the prediction analysis of the water environment pollution degree and the pollution range.
Claims (4)
1. A water environment water quality tracking and monitoring method is characterized by comprising the following steps:
s1: a plurality of image data acquisition points are arranged in a monitoring area of the water environment, and the image data acquisition points are uniformly distributed in the monitoring area;
s2: shooting a multispectral image of a monitored water area right above an image data acquisition point by using a spectral camera carried by an unmanned aerial vehicle, and acquiring multispectral reflectivity f of the image data acquisition pointb'; and collecting the water quality parameter A to obtain a relation data set (f) of the multispectral reflectivity and the water quality parameter A of the water area at the image data collection pointb'，A)；
S3: repeating the step S2, collecting the corresponding relation data sets of the other image data collection points, and constructing a relation model of the multispectral reflectivity and the water quality parameter by using a plurality of relation data sets: ln (a) ═ m × ln (f)b') + q, wherein m is a relation coefficient and q is a deviation;
s4: and acquiring the multispectral reflectivity of the water surface above the monitored water area by using the unmanned aerial vehicle, and substituting the multispectral reflectivity into the relation model to obtain the water quality parameter of the water area.
2. The water environment water quality tracking and monitoring method according to claim 1, wherein the step S2 includes:
s21: continuously shooting a plurality of multispectral images on an image data acquisition point by a spectral camera carried by the unmanned aerial vehicle;
s22: when the multispectral image is shot once, the unmanned aerial vehicle acquires the positioning coordinates of the shooting points once to obtain a plurality of positioning coordinates (x)1，y1，z1)，(x2，y2，z2)，···(xn，yn，zn)；
S23: calculating the center coordinate (x) when the unmanned plane collects a plurality of multispectral imagesa，ya，za) Wherein x isa＝(x1+x2···xn)/n，ya＝(y1+y2···yn)/n，za＝(z1+z2···zn)/n，xnIn order to shoot multispectral images, the x-direction coordinate and y-direction coordinate of the unmanned aerial vehicle in the plane of the monitored water areanIn order to shoot multispectral images, the y-direction coordinate, z of the unmanned aerial vehicle in the plane of the monitored water areanThe vertical height of the unmanned aerial vehicle from the plane of the water area when the multispectral image is shot is determined, and n is the number of the multispectral images collected on the data collecting point;
s24: in the plane of the water area of the water environment, in terms of coordinates (x)a，ya，za) The position right below the determined point is used as a water quality data acquisition point, and a water quality parameter A at the water quality data acquisition point is acquired;
s25: combining a plurality of multispectral images to form a spectral study image;
s26: cutting out a water surface area in the spectral research image to obtain a water surface multispectral image;
s27: dividing the multispectral image on the water surface into a plurality of buffer windows with the size of S multiplied by S, and acquiring the multispectral reflectivity f of each buffer windowb；
S28: calculating the multispectral reflectivity f of several buffer windowsbAverage value f ofb'：fb'＝(f1+f2+···+fb)/b；
S29: obtaining a relation data set (f) of the multispectral reflectivity of the water area of the data acquisition point and the water quality parameter Ab'，A)。
3. The water environment water quality tracking and monitoring method according to claim 2, wherein the step S24 includes:
s251: determining internal orientation elements, calibrating a multispectral camera, adding a TIFF image of a calibration reflector and TIFF images of four wave bands of a monitoring water area, wherein the four wave bands comprise a green wave band G, a Red wave band R, a Red edge Red _ edge and a near infrared wave band;
s252: searching and matching the homonymy points of a plurality of multispectral images by adopting an SIFT algorithm;
s253: carrying out block adjustment by utilizing longitude and latitude information of the homonymy points and the multispectral images, and restoring the positions and the postures of the multispectral images;
s254: performing space-three encryption on the restored multispectral image by using an image control point to form an image point cloud;
s255: and generating a digital surface model by the image point cloud layer, and obtaining a digital orthophoto map and a spliced reflection map by using the digital surface model to form a spectrum research image.
4. The water environment water quality tracking and monitoring method according to claim 1, wherein the density of the image data acquisition points in the step S1 set in the monitored water area is 0.01-2.34 image data acquisition points/km2。
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