CN116106265A - Small-river-basin agricultural non-point source pollution intelligent monitoring and early warning method and system based on hyperspectral remote sensing - Google Patents
Small-river-basin agricultural non-point source pollution intelligent monitoring and early warning method and system based on hyperspectral remote sensing Download PDFInfo
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Abstract
The invention relates to a hyperspectral remote sensing-based intelligent monitoring and early warning method and system for small-river-basin agricultural non-point source pollution, which solve the defect that the intelligent monitoring and early warning for small-river-basin agricultural non-point source pollution is difficult to perform in real time compared with the prior art. The invention comprises the following steps: monitoring and partitioning agricultural non-point source pollution and layout of control sections; investigation and monitoring of agricultural non-point source pollution background of small watershed water environment; constructing a hyperspectral remote sensing background database of a small-river basin water environment; intelligent monitoring and early warning for agricultural non-point source pollution in small watershed. The method is used for monitoring the pollution conditions and the change trend of agricultural pollution sources and receiving water bodies, knowing the space-time evolution rule of the agricultural non-point source pollution of the small watershed, realizing the real-time dynamic monitoring and early warning of the influence on the quality of the water environment of the regional agricultural non-point source pollution, and providing a basic support for the agricultural non-point source pollution treatment.
Description
Technical Field
The invention relates to the technical field of water quality monitoring and early warning, in particular to a small-river-basin agricultural non-point source pollution intelligent monitoring and early warning method and system based on hyperspectral remote sensing.
Background
The agricultural non-point source pollution refers to pollution to ecological environment caused by unreasonable use of chemical inputs such as chemical fertilizers, pesticides and mulching films, untimely or improper treatment of livestock and poultry aquaculture wastes, crop straws and the like, and generated nutrient substances such as nitrogen, phosphorus and organic matters under the common drive of rainfall and topography, taking surface, underground runoff and soil erosion as carriers, and excessively accumulating or entering a receiving water body in the soil. The prevention and control of agricultural non-point source pollution is a prominent difficulty in the current ecological environment protection work due to the self obvious characteristics.
The method monitors the types of pollutants in agricultural pollution sources and receiving water bodies, the concentration and the change trend of various pollutants, calculates and evaluates the load of the agricultural pollutants in the water bodies, grasps the time-space evolution rule of agricultural non-point source pollution, realizes the dynamic evaluation of the influence on the environmental quality of the agricultural non-point source pollution, provides basic support for the agricultural non-point source pollution treatment, and is very important in the agricultural non-point source pollution control.
The current environmental protection department knows river water quality condition mainly depends on traditional laboratory test and automatic water quality monitor mode two modes after manual sampling: the water quality monitoring device can accurately monitor water quality at a river monitoring position, but has high labor cost, long detection time and limited monitoring range, and cannot monitor a large-area water area with surface dimensions; the water quality monitoring system can automatically and continuously monitor the water body, has lower monitoring precision and higher cost, can only monitor the water quality at a certain point, can only know the water quality condition on a monitoring section, has only local and typical representative significance, can not reflect the overall space-time variation of the ecological environment of the whole water body, and lacks the macroscopic monitoring capability of a large coverage range. Meanwhile, the conventional method cannot realize real-time monitoring.
The hyperspectral remote sensing technology has the advantages of rapidness, macroscopicity and low cost, can make up for the defects of the complex and time-consuming traditional detection method, and is increasingly applied to the field of water quality monitoring. The satellite remote sensing technology is adopted, so that certain limitations of the traditional ground monitoring means can be overcome, the cost is low, dynamic, rapid and large-scale monitoring is realized, the distribution trend of polluted water bodies can be revealed, and the satellite remote sensing technology plays an increasingly important role in water body monitoring. However, for watershed water quality monitoring, satellite remote sensing has the defects that revisiting period and spatial resolution are difficult to be complete, and the satellite remote sensing is easily affected by weather. The aerial remote sensing technology has the advantages of maneuver, flexibility, high spatial resolution and the like.
The patent discloses a medium and small water quality type identification method (application number 201910664888.1) based on unmanned aerial vehicle imaging spectrum, which is based on hyperspectral data acquired by unmanned aerial vehicle, and identifies the water quality type through a support vector machine model. The patent discloses a hyperspectral remote sensing black and odorous water body classification method (application number 202011637628.4) based on a semi-supervised learning strategy, which is based on inversion of dissolved oxygen, redox potential, ammonia nitrogen and turbidity of CASI hyperspectral images and divides the water body into clean water body, light black and odorous water body and heavy black and odorous water body according to pollutant content. The patent discloses a method (202010298497.5) for acquiring urban black and odorous river water quality parameters by using an unmanned aerial vehicle, wherein multispectral images are acquired by using the unmanned aerial vehicle, a supervision image classification method is used for extracting a water body range, and a black and odorous water body spectrum index model is provided for dividing a river into a general water body, a light black and odorous water body and a heavy black and odorous water body. The river basin aerial remote sensing monitoring system (201320060363.5) based on the video processor DM6467 performs high-definition video acquisition on the river basin through a hyperspectral camera, and finds out the most suitable spectral reflectivity inversion model according to spectral feature analysis and modeling of measured water on hyperspectral aerial remote sensing data, so as to realize water quality parameter analysis based on machine vision. The utility model provides a device (202220654231.4) of online full spectrum remote sensing monitoring surface water quality that unmanned aerial vehicle carried carries out water quality testing to the waters that keeps away from the bank with hyperspectral quality of water multiparameter monitor on unmanned aerial vehicle, in the lower environment of visibility, through with the light passageway that provides, can gather quality of water data in the position higher from the surface of water. According to the river water quality rapid monitoring system (application number 201911305055.2) based on the unmanned aerial vehicle hyperspectral image, the river water hyperspectral image is obtained through aerial photography of a miniature hyperspectral meter carried by the unmanned aerial vehicle, and the river water hyperspectral image is processed into a water body reflectivity image through an image preprocessing system; and (3) introducing the water body reflectivity image of the unmanned aerial vehicle into a water quality inversion model, and calculating to obtain a water quality index concentration distribution map so as to realize rapid monitoring of the surface dimension of river water quality. After hyperspectral image data information of a water area to be monitored is acquired through an unmanned aerial vehicle monitoring module, the hyperspectral image data information is sent to a background management control center, and the background management control center outputs corresponding water pollution degree according to the hyperspectral image data information, so that a monitoring result of the water area to be monitored is output.
The method comprises the steps of carrying out video acquisition on a surface water body in a flow field through a hyperspectral camera, obtaining hyperspectral images, carrying out spectral feature analysis and modeling on the hyperspectral images according to measured water, finding out the most suitable spectral reflectivity inversion model, and carrying out inversion according to the spectral reflectivity inversion model to realize monitoring on water quality parameters. The system can not utilize artificial intelligence algorithm adaptability to determine the surface water quality, the intelligent degree is lower, the accuracy of the water quality determination result is poor, the environmental error is large, and the timeliness and reliability of surface water pollution early warning are affected.
Therefore, how to provide a small-river-basin agricultural non-point source pollution intelligent monitoring and early-warning device and system based on hyperspectral remote sensing, and through the intelligent monitoring and early-warning device and system, the real-time monitoring and early-warning of the small-river-basin agricultural non-point source pollution is realized by means of hyperspectral remote sensing, so that the device and the system become the technical problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a hyperspectral remote sensing-based intelligent monitoring and early warning method and system for small-river-basin agricultural non-point source pollution, which are used for solving the problems of difficulty in intelligent monitoring and early warning of small-river-basin agricultural non-point source pollution in real time.
In order to achieve the above object, the technical scheme of the present invention is as follows:
a hyperspectral remote sensing-based intelligent monitoring and early warning method for small-river-basin agricultural non-point source pollution comprises the following steps:
monitoring partition of agricultural non-point source pollution and layout of control sections: carrying out monitoring subareas of agricultural non-point source pollution and layout of control sections of the agricultural non-point source pollution subareas;
investigation and monitoring of agricultural non-point source pollution background of small watershed water environment;
constructing a hyperspectral remote sensing background database of a small-river basin water environment;
intelligent monitoring and early warning of small-river-basin agricultural non-point source pollution: based on the comparison of hyperspectral remote sensing data and a background database, intelligent monitoring and early warning of small-river-basin agricultural non-point source pollution are carried out.
The arrangement of the monitoring subareas and the control sections of the agricultural non-point source pollution comprises the following steps:
constructing a basic database, wherein the content of the database is the catchment range corresponding to the water quality monitoring section of the combined surface water, and comprehensively considering the topography, soil type, land utilization mode and agricultural production activity; the database comprises land utilization, water system vectors, DEM (digital elevation model) and catchment ranges of areas, and different agricultural non-point source pollution sources and water quality monitoring sections in province or city control;
carrying out monitoring subareas based on a catchment range and agricultural non-point source pollution emission load, namely monitoring subareas of agricultural non-point source pollution, and respectively arranging agricultural non-point source pollution monitoring control sections at the positions of the upper, middle and downstream parts and branch flow diversion or confluence parts of the river stem of the small river basin by adopting spatial information superposition analysis;
in the monitoring subarea, the ground site investigation, the agricultural non-point source pollution characteristic analysis and the surface water distribution of the pit and pond ditch and the gathering state of residents in villages and towns are combined, and the agricultural non-point source pollution monitoring control points of the monitoring subarea are arranged based on the water collecting condition.
The investigation and monitoring of the agricultural non-point source pollution background of the small watershed water environment comprises the following steps:
investigation and understanding of the current situation level of non-point source pollution of regional planting industry, breeding industry and rural sewage and garbage agriculture;
acquiring agricultural non-point source pollution background data of a small watershed water environment:
collecting water samples from agricultural non-point source pollution monitoring control sections and partition monitoring control points, performing water quality analysis, wherein monitoring indexes comprise chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen, and performing water pollutant component analysis by adopting a chromatographic technology;
and acquiring pollutants and change conditions in the water environment of the current area by combining current investigation of the current situation of regional planting industry, aquaculture industry and rural sewage and garbage agricultural pollution.
The construction of the hyperspectral remote sensing background database of the small watershed water environment comprises the following steps:
the method comprises the steps that when water samples are collected from agricultural non-point source pollution monitoring control sections and partition monitoring control points, a ground object spectrometer is utilized to carry out on-site spectrum measurement to obtain ground measurement spectrum data, and an unmanned aerial vehicle is carried with a micro hyperspectral spectrometer to obtain hyperspectral remote sensing data of a small-basin water environment;
according to the analysis result of the water pollutant components, the spectrum database is searched to obtain the spectrum data of pollutants in the regional water environment, and the data analysis of the agricultural non-point source pollution monitoring control section and the regional monitoring control point surface water spectrum is carried out:
by adopting a machine learning technology, by means of water sample component chemical analysis data and ground spectrum data as basic data and combining with laboratory configured simulated water sample spectrum data, constructing correlation functions of hyperspectral full-band reflectivity and water quality monitoring indexes such as chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen by means of chemometric software, and constructing a water quality parameter inversion model suitable for a target area;
preprocessing hyperspectral remote sensing data of a small-river-basin water environment, which are acquired by an unmanned aerial vehicle, and enhancing spectral reflection characteristics of a water body by utilizing a spectral differentiation technology;
then, performing contrast analysis with the ground measurement spectrum data, and correcting unmanned hyperspectral remote sensing data by using the ground measurement spectrum data;
and applying the water quality parameter inversion model to the processed hyperspectral remote sensing data of the small-river basin water environment to obtain the spatial distribution of the water quality monitoring index in the regional surface water body and the spatial distribution of the concentration of pollutants in the regional water environment, and establishing a hyperspectral remote sensing background database of the small-river basin water environment.
The intelligent monitoring and early warning of the agricultural non-point source pollution of the small watershed comprises the following steps:
in the supervision work of small-river-basin agricultural non-point source pollution, according to the supervision standard of regional agricultural non-point source pollution, carrying an unmanned aerial vehicle of a miniature hyperspectral meter, wherein the spectrum range of the unmanned aerial vehicle is 400-1000nm, and acquiring regional surface water hyperspectral data through a pre-planned and set route;
the data automatic transmission, storage and intelligent analysis system carries out pretreatment and correction on hyperspectral remote sensing data, and outputs the spatial distribution of water quality monitoring indexes in regional surface water bodies and the spatial distribution of pollutant concentration in regional water environments through a water quality parameter inversion model;
comparing the water environment hyperspectral remote sensing background database of the small watershed with the water environment hyperspectral remote sensing background database of the small watershed, displaying the pollution condition and the change trend of the water body received by the agricultural non-point source pollution of the area, and the time-space evolution condition of the agricultural non-point source pollution, thereby realizing the intelligent monitoring of the agricultural non-point source pollution.
The intelligent monitoring and early warning of the agricultural non-point source pollution in the small watershed is agricultural non-point source pollution syn-source and early warning, and comprises the following steps:
comparing the regional surface water hyperspectral data of the agricultural non-point source pollution monitoring control section and the regional surface water hyperspectral remote sensing background database of the regional surface water environment of the small river basin;
acquiring regional agricultural non-point source pollution risk conditions in real time through characteristic spectrum increment or abnormal change;
and comparing the hyperspectral remote sensing data of the main river channel, the tributaries, the ditches and the pit water body with background data to obtain the space-time evolution and the spatial distribution of the agricultural non-point source pollution, and realizing the plastic source pollution and the early warning of the agricultural non-point source pollution.
The intelligent monitoring and early warning of the agricultural non-point source pollution in the small watershed is to display the spatial distribution, evolution condition and pollution synoptic source of the exceeding pollution in real time: when the water quality index of the water quality monitoring section is abnormal or exceeds standard, the regional surface water hyperspectral data is compared with a small watershed water environment hyperspectral remote sensing background database, and the spatial distribution, evolution condition and pollution synoptic source of the exceeding standard pollution are displayed in real time.
The hyperspectral remote sensing-based intelligent monitoring and early warning system for small-river-basin agricultural non-point source pollution comprises an unmanned aerial vehicle hyperspectral data acquisition system and a data automatic transmission, storage and intelligent analysis system; the unmanned aerial vehicle hyperspectral data acquisition system is a miniature hyperspectral instrument carried by an unmanned aerial vehicle, the spectrum range of the miniature hyperspectral instrument is 400-1000nm, and hyperspectral data of the surface water body of the area are acquired through a preset route; the data automatic transmission, storage and intelligent analysis system comprises a hyperspectral remote sensing data processing module, an regional surface water pollution monitoring module and an agricultural non-point source pollution intelligent early warning module, and an algorithm and system software supporting hyperspectral remote sensing data processing, analysis, water quality index display and flow and standardization processing of a small-basin surface water pollution situation map are used for realizing dynamic monitoring and early warning of the influence of agricultural non-point source pollution environment quality.
Advantageous effects
Compared with the prior art, the hyperspectral remote sensing-based intelligent monitoring and early warning method and system for small-river-basin agricultural non-point source pollution are used for monitoring pollution conditions and change trends of agricultural pollution sources and receiving water bodies, knowing the space-time evolution rule of the small-river-basin agricultural non-point source pollution, realizing real-time dynamic monitoring and early warning on the influence of regional agricultural non-point source pollution water environment quality, and providing a basic support for agricultural non-point source pollution treatment. According to the invention, the hyperspectral image can be obtained by adopting the unmanned aerial vehicle and the spectrometer which are available in the market, so that the hyperspectral image acquisition method is easy to realize, low in monitoring cost and high in intelligent degree.
The invention also includes the following advantages:
1. through carrying out the scientific division of agricultural non-point source pollution monitoring, set up monitoring control section, be convenient for subregion management, adapt to subsequent water quality testing and data processing. When the water quality problem occurs, the water source can be quickly positioned to a corresponding area and polluted.
2. The current levels of agricultural pollution sources such as regional planting industry, aquaculture industry and the like are investigated and known, control points are used for sampling and analyzing, agricultural non-point source pollution background data of the small-river basin water environment are obtained, the data comprise water quality indexes and main pollutants and change conditions in the regional water environment, a small-river basin water environment agricultural non-point source pollution background database is constructed, and basic data support is provided for subsequent spectral water quality parameter inversion modeling and small-river basin water environment agricultural non-point source pollution monitoring.
3. The method comprises the steps of obtaining monitoring control section spectrum data by using a ground object spectrometer, adopting a machine learning technology, relying on water sample component chemical analysis data and ground spectrum data as raw data, combining laboratory configured simulated water sample spectrum data, constructing a correlation function of hyperspectral full-band reflectivity and water quality monitoring indexes by using chemometric software, constructing a water quality inversion model, and ensuring the reliability of water quality inversion results.
4. The unmanned aerial vehicle is carried with a micro hyperspectral instrument to obtain hyperspectral remote sensing data of the small-river basin water environment, and the hyperspectral remote sensing data of the unmanned aerial vehicle is corrected by using the ground measurement spectrum data. The constructed water quality parameter inversion model is used for obtaining the spatial distribution of water quality parameters in regional surface water bodies and the spatial distribution of main pollutant concentration in regional water environments, constructing a hyperspectral remote sensing background database of the small-river basin water environments, and providing data support for intelligent monitoring and early warning of regional agricultural non-point source pollution.
5. In the small-river-basin agricultural non-point source pollution supervision work, according to supervision requirements, an unmanned aerial vehicle carrying a miniature hyperspectral instrument acquires regional surface water hyperspectral data through a pre-planned and set route. And acquiring the space-time evolution and the spatial distribution of the agricultural non-point source pollution by using the constructed water quality parameter inversion model and the hyperspectral remote sensing background database of the small-river basin water environment, displaying the spatial distribution, the evolution condition and the pollution synthon of the pollution in real time, and providing quick response for supervision work. The intelligent degree of the system is improved, the timeliness of agricultural non-point source pollution monitoring and early warning is enhanced, and related departments can know the overall water quality condition and pollution distribution of a small river basin.
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Detailed Description
For a further understanding and appreciation of the structural features and advantages achieved by the present invention, the following description is provided in connection with the accompanying drawings, which are presently preferred embodiments and are incorporated in the accompanying drawings, in which:
the non-point source pollution is also called non-point source pollution, and the agricultural non-point source pollution has the following characteristics: firstly, the fixed pollution sources are generally provided with definite coordinates and drain ports, while the agricultural non-point source pollution sources are dispersed and diversified, and have no definite drain ports, so that geographical boundaries and positions are difficult to identify and determine, and effective monitoring is difficult to implement; secondly, uncertainty, namely the emission of the fixed source pollutants usually has a definite time rule, the emission amount and components are easy to determine, and the occurrence of agricultural non-point source pollution is influenced by factors such as natural geographic conditions, hydrologic climate characteristics and the like, and the pollutants show time randomness and space uncertainty in the process of moving to soil and a receiving water body; thirdly, hysteresis is carried out, a fixed pollution source enters the environment through a discharge port, the environment quality can be directly influenced, the agricultural non-point source pollution is influenced by the joint effect of the bio-geochemical conversion and the hydrologic transmission process, and nutrient elements such as nitrogen and phosphorus remained in agricultural production are generally accumulated in soil and slowly released to the external environment, so that hysteresis exists on the influence of the environment quality of a receiving water body; fourthly, the components of the fixed source pollutant are complex, harmful substances such as heavy metals, persistent organic pollutants and the like are often contained, the harmful substances are often seriously damaged to human bodies and the environment, the agricultural non-point source pollutant mainly contains nitrogen and phosphorus nutrient substances, is a resource for agricultural production, and is only a pollutant when entering a receiving water body or being excessively accumulated in soil.
Aiming at the characteristics of agricultural non-point source pollution, the hyperspectral remote sensing-based intelligent monitoring and early warning method for small-basin agricultural non-point source pollution is used for monitoring and zoning agricultural non-point source pollution and distributing agricultural non-point source pollution zone control sections through investigation and monitoring of the small-basin water environment agricultural non-point source pollution background, so that effective monitoring of pollutants entering a receiving water body in runoff under the common driving of rainfall and topography is realized.
As shown in FIG. 1, the invention discloses an intelligent monitoring and early warning method for small-river-basin agricultural non-point source pollution based on hyperspectral remote sensing, which comprises the following steps:
firstly, monitoring and partitioning agricultural non-point source pollution and distributing control sections: and carrying out the arrangement of the monitoring subareas of the agricultural non-point source pollution and the control sections of the agricultural non-point source pollution subareas.
(1) Constructing a basic database, wherein the content of the database is the catchment range corresponding to the water quality monitoring section of the combined surface water, and comprehensively considering the topography, soil type, land utilization mode and agricultural production activity; the database comprises land utilization, water system vectors, DEM, catchment ranges of areas, different agricultural non-point source pollution sources (paddy field crop areas, dry field crop areas, large-scale economic crop areas, large-scale cultivation areas (aquatic products and livestock), distributed cultivation areas (aquatic products and livestock), irregular garbage stacking areas and the like), and provincial or municipal water quality monitoring sections.
(2) And (3) carrying out monitoring subareas based on the catchment range and the agricultural non-point source pollution emission load (planting and raising intensity) by adopting spatial information superposition analysis, namely, monitoring subareas of agricultural non-point source pollution, and respectively arranging agricultural non-point source pollution monitoring control sections at the positions of the upper, middle and downstream parts and the branch or confluence parts of the river stem of the small river basin.
(3) In the monitoring subarea, the ground site investigation, the agricultural non-point source pollution characteristic analysis and the surface water distribution of the pit and pond ditch and the gathering state of residents in villages and towns are combined, and the agricultural non-point source pollution monitoring control points of the monitoring subarea are arranged based on the water collecting condition.
And secondly, investigation and monitoring of agricultural non-point source pollution background of the water environment in the small river basin.
(1) The current state level of non-point source pollution of regional planting industry, breeding industry and rural sewage and garbage agriculture is investigated and known.
(2) Acquiring agricultural non-point source pollution background data of a small watershed water environment:
water samples are collected at agricultural non-point source pollution monitoring control sections and partition monitoring control points, water quality analysis is carried out, monitoring indexes comprise chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen, and a chromatographic technique is adopted for water pollutant component analysis.
(3) And acquiring pollutants and change conditions in the water environment of the current area by combining current investigation of the current situation of regional planting industry, aquaculture industry and rural sewage and garbage agricultural pollution.
And thirdly, constructing a hyperspectral remote sensing background database of the small-river basin water environment.
The hyperspectral remote sensing water quality monitoring is different from the chemical analysis method, and is an indirect analysis technology based on a near infrared spectrum analysis technology. The near infrared spectrum measurement is mainly frequency multiplication and frequency combination absorption of vibration of the hydrogen-containing group X-H (x= C, N, O), which contains information on the composition and molecular structure of most types of organic compounds, and if the composition of the samples is the same, the spectrum is the same, and vice versa. The near infrared spectrum analysis technology is an indirect relative analysis, necessary data is measured through collecting a large number of representative samples (commonly known as training sets) and through strict and detailed chemical analysis, a mathematical model is built through a computer so as to reflect the normal distribution rule of the tested sample group to the maximum extent, and then the required data of an unknown sample is predicted through the mathematical model. The correction method used for establishing the model is different according to the difference of the property relation between the spectrum of the sample and the to-be-analyzed, and commonly used methods include multiple linear regression, principal component regression, partial least squares, artificial neural networks, topological methods and the like. The method has the remarkable advantages of no pretreatment, no pollution, convenience and rapidness, direct detection, no need of any chemical reagent, multi-component simultaneous detection, good analysis reproducibility and low cost. The inherent disadvantages are that an indirect measurement means needs to acquire a certain amount of sample data by a reference method (generally a chemical analysis method), so that the measurement accuracy can never reach the measurement accuracy of the reference method, the test sensitivity is low, the relative error is large, and a certain amount and representativeness are needed for the samples used for modeling.
For monitoring pollutants from agricultural production activities, which are driven by rainfall and topography and enter a receiving water body in runoff, the water quality indexes such as chemical oxygen demand, total nitrogen, total phosphorus and the like obtained by a conventional chemical analysis method indicate the amount of certain pollutants. The hyperspectral remote sensing data are obtained by frequency multiplication and frequency combination absorption of vibration of hydrogen-containing groups X-H (X= C, N, O) in a water body, and the information indicates the composition of various pollutants. Therefore, the training set required by modeling the water quality parameter inversion model is huge, and the test sample is required to be covered by the training set to have a good effect. In actual work, the application cost is overlarge due to a huge training set; secondly, an 'abnormal' water quality sample is difficult to obtain during modeling, so that the water quality of the polluted water body is difficult to adaptively measure, the accuracy of the obtained result is poor, and the timeliness and reliability of surface water body pollution early warning are affected.
In practical application, a reality (fact) is found, hyperspectral remote sensing data are obtained by frequency multiplication and frequency combination absorption of vibration of hydrogen-containing groups X-H (X= C, N, O) in a water body, and the hyperspectral remote sensing data carry composition information of various pollutants entering the water body to be detected, and the information is required by agricultural non-point source pollution monitoring and early warning.
Aiming at the characteristics of the hyperspectral remote sensing technology and the actual requirements of agricultural non-point source pollution monitoring and early warning, the intelligent monitoring and early warning method for small-river basin agricultural non-point source pollution based on hyperspectral remote sensing adopts spatial information superposition analysis to monitor and partition based on the water collecting range and the agricultural non-point source pollution emission load, and lays the agricultural non-point source pollution monitoring control points of the monitoring partition based on the water collecting condition. And acquiring the agricultural non-point source pollution background data of the water environment of the small river basin based on investigation and monitoring of the agricultural non-point source pollution background of the water environment of the small river basin, and constructing a hyperspectral remote sensing background database of the water environment of the small river basin. By means of chemometric software, a correlation function of hyperspectral full-band reflectivity and water quality monitoring indexes such as chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen is built, and a water quality parameter inversion model suitable for a target area is built. Although the number of samples used to build the model is very limited, the model obtained by chemometric processing is highly representative.
The significance of constructing the regional water environment hyperspectral remote sensing background database is that although the information of the polluted water body sample is complex, the spectrum peaks of various groups in the near infrared spectrum region are overlapped, and the information analysis is difficult, by comparing the regional water environment hyperspectral remote sensing background database with the background database and combining the agricultural non-point source pollution characteristics of the monitoring region and the relevant monitoring control point space-time information, the real-time response, the accurate monitoring early warning and the pollution synoptic source of the agricultural non-point source pollution monitoring can be realized by means of the spatial information superposition analysis.
(1) And (3) collecting water samples from the agricultural non-point source pollution monitoring control section and the regional monitoring control point, and simultaneously, performing on-site spectrum measurement by using a ground object spectrometer to obtain ground measurement spectrum data, and carrying a micro hyperspectral spectrometer on an unmanned aerial vehicle to obtain hyperspectral remote sensing data of the small-river basin water environment.
(2) According to the analysis result of the water pollutant components, the spectrum database is searched to obtain the spectrum data of pollutants in the regional water environment, and the data analysis of the agricultural non-point source pollution monitoring control section and the regional monitoring control point surface water spectrum is carried out:
by means of machine learning technology, by means of water sample component chemical analysis data and ground spectrum data as basic data and combining with laboratory configured simulated water sample spectrum data, a correlation function of hyperspectral full-band reflectivity and water quality monitoring indexes such as chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen is built by means of chemometric software, and a water quality parameter inversion model suitable for a target area is built.
(3) Preprocessing hyperspectral remote sensing data of a small-river-basin water environment, which are acquired by an unmanned aerial vehicle, and enhancing spectral reflection characteristics of a water body by utilizing a spectral differentiation technology;
and then comparing and analyzing the data with the ground measurement spectrum data, and correcting unmanned hyperspectral remote sensing data by using the ground measurement spectrum data.
(4) And applying the water quality parameter inversion model to the processed hyperspectral remote sensing data of the small-river basin water environment to obtain the spatial distribution of the water quality monitoring index in the regional surface water body and the spatial distribution of the concentration of pollutants in the regional water environment, and establishing a hyperspectral remote sensing background database of the small-river basin water environment.
Fourth, intelligent monitoring and early warning of small-river-basin agricultural non-point source pollution: based on the comparison of hyperspectral remote sensing data and a background database, intelligent monitoring and early warning of small-river-basin agricultural non-point source pollution are carried out.
As a first implementation mode for realizing intelligent monitoring and early warning of agricultural non-point source pollution in a small watershed, namely intelligent monitoring of agricultural non-point source pollution is realized, the method comprises the following steps:
(1) In the supervision work of small-river-basin agricultural non-point source pollution, according to the supervision requirement of regional agricultural non-point source pollution, an unmanned plane carrying a miniature hyperspectral meter is provided, the spectrum range of the unmanned plane is 400-1000nm, and hyperspectral data of regional surface water bodies are acquired through a pre-planned and set route.
(2) The data automatic transmission, storage and intelligent analysis system carries out pretreatment and correction on the hyperspectral remote sensing data, and outputs the spatial distribution of water quality monitoring indexes in regional surface water bodies and the spatial distribution of pollutant concentration in regional water environments through a water quality parameter inversion model.
(3) Comparing the water environment hyperspectral remote sensing background database of the small watershed with the water environment hyperspectral remote sensing background database of the small watershed, displaying the pollution condition and the change trend of the water body received by the agricultural non-point source pollution of the area, and the time-space evolution condition of the agricultural non-point source pollution, thereby realizing the intelligent monitoring of the agricultural non-point source pollution.
As a second implementation mode for realizing intelligent monitoring and early warning of agricultural non-point source pollution in a small watershed, namely realizing agricultural non-point source pollution synoptic source and early warning, the intelligent monitoring and early warning method comprises the following steps:
(1) Comparing the regional surface water hyperspectral data of the agricultural non-point source pollution monitoring control section and the regional surface water hyperspectral remote sensing background database of the regional surface water environment of the small river basin;
(2) Acquiring regional agricultural non-point source pollution risk conditions in real time through characteristic spectrum increment or abnormal change;
(3) And comparing the hyperspectral remote sensing data of the main river channel, the tributaries, the ditches and the pit water body with background data to obtain the space-time evolution and the spatial distribution of the agricultural non-point source pollution, and realizing the plastic source pollution and the early warning of the agricultural non-point source pollution.
As a third implementation mode for realizing intelligent monitoring and early warning of agricultural non-point source pollution in small watershed, the method can display the spatial distribution, evolution condition and pollution plastic source of out-of-standard pollution in real time: when the water quality index of the water quality monitoring section is abnormal or exceeds standard, the regional surface water hyperspectral data is compared with a small watershed water environment hyperspectral remote sensing background database, and the spatial distribution, evolution condition and pollution synoptic source of the exceeding standard pollution are displayed in real time.
Here, still provide a little basin agricultural non point source pollution intelligent monitoring early warning system based on hyperspectral remote sensing, its characterized in that: the system comprises an unmanned aerial vehicle hyperspectral data acquisition system and a data automatic transmission, storage and intelligent analysis system; the unmanned aerial vehicle hyperspectral data acquisition system is a miniature hyperspectral instrument carried by an unmanned aerial vehicle, the spectrum range of the miniature hyperspectral instrument is 400-1000nm, and hyperspectral data of the surface water body of the area are acquired through a preset route; the data automatic transmission, storage and intelligent analysis system comprises a hyperspectral remote sensing data processing module, an regional surface water pollution monitoring module (water quality and pollutant conditions), an agricultural non-point source pollution intelligent early warning module (the pollution condition and change trend of a receiving water body, the space-time evolution condition of agricultural non-point source pollution, the real-time source pollution and early warning), algorithms and system software for supporting hyperspectral remote sensing data processing, analysis, water quality index display and flow and standardization processing of a small-basin surface water pollution situation map, and realizes dynamic monitoring and early warning of the influence of the environmental quality of agricultural non-point source pollution.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The intelligent monitoring and early warning method for the agricultural non-point source pollution in the small watershed based on hyperspectral remote sensing is characterized by comprising the following steps:
11 Monitoring partition of agricultural non-point source pollution and layout of control sections: carrying out monitoring subareas of agricultural non-point source pollution and layout of control sections of the agricultural non-point source pollution subareas;
12 Investigation and monitoring of agricultural non-point source pollution background of small watershed water environment;
13 Constructing a hyperspectral remote sensing background database of the small watershed water environment;
14 Intelligent monitoring and early warning of agricultural non-point source pollution in small watershed: based on the comparison of hyperspectral remote sensing data and a background database, intelligent monitoring and early warning of small-river-basin agricultural non-point source pollution are carried out.
2. The hyperspectral remote sensing data-based intelligent monitoring and early warning method for small-river basin agricultural non-point source pollution is characterized in that the arrangement of the monitoring subareas and the control sections of the agricultural non-point source pollution comprises the following steps:
21 A basic database is constructed, the content of the database is the catchment range corresponding to the water quality monitoring section of the combined surface water, and the topography, the soil type, the land utilization mode and the agricultural production activity are comprehensively considered; the database comprises land utilization, water system vectors, DEM (digital elevation model) and catchment ranges of areas, and different agricultural non-point source pollution sources and water quality monitoring sections in province or city control;
22 Carrying out monitoring subareas based on a catchment range and agricultural non-point source pollution emission load by adopting spatial information superposition analysis, namely, monitoring subareas of agricultural non-point source pollution, and respectively arranging agricultural non-point source pollution monitoring control sections at the positions of the river stem, the middle and downstream of the small river basin and the branch flow diversion or confluence position;
23 In the monitoring subarea, combining ground on-site investigation, agricultural non-point source pollution characteristic analysis and surface water distribution of pit ditches and village and town resident gathering state of the monitoring subarea, and laying agricultural non-point source pollution monitoring control points of the monitoring subarea based on water collecting conditions.
3. The hyperspectral remote sensing-based intelligent monitoring and early warning method for small-river-basin agricultural non-point source pollution is characterized in that the investigation and monitoring of the small-river-basin water environment agricultural non-point source pollution background comprises the following steps:
31 Investigation and understanding of the current situation level of non-point source pollution of regional planting industry, breeding industry and rural sewage and garbage agriculture;
32 Acquiring agricultural non-point source pollution background data of a small watershed water environment:
collecting water samples from agricultural non-point source pollution monitoring control sections and partition monitoring control points, performing water quality analysis, wherein monitoring indexes comprise chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen, and performing water pollutant component analysis by adopting a chromatographic technology;
33 And (3) acquiring pollutants and change conditions in the water environment of the current area by combining current investigation of the current situation of the agricultural pollution of the regional planting industry, the cultivation industry and rural sewage and garbage.
4. The hyperspectral remote sensing-based small-river-basin agricultural non-point source pollution intelligent monitoring and early warning method according to claim 1, wherein the construction of the small-river-basin water environment hyperspectral remote sensing background database comprises the following steps:
41 The method comprises the steps of) collecting water samples from agricultural non-point source pollution monitoring control sections and regional monitoring control points, and simultaneously, performing on-site spectrum measurement by using a ground object spectrometer to obtain ground measurement spectrum data, wherein an unmanned aerial vehicle is carried with a micro hyperspectral meter to obtain hyperspectral remote sensing data of a small-basin water environment;
42 According to the analysis result of the water pollutant components, searching a spectrum database to obtain spectrum data of pollutants in regional water environment, and analyzing the data of the surface water spectrum of the agricultural non-point source pollution monitoring control section and the regional monitoring control point:
by adopting a machine learning technology, by means of water sample component chemical analysis data and ground spectrum data as basic data and combining with laboratory configured simulated water sample spectrum data, constructing correlation functions of hyperspectral full-band reflectivity and water quality monitoring indexes such as chemical oxygen demand, total nitrogen, ammonia nitrogen, total phosphorus, turbidity and dissolved oxygen by means of chemometric software, and constructing a water quality parameter inversion model suitable for a target area;
43 Preprocessing the hyperspectral remote sensing data of the small-river basin water environment obtained by the unmanned aerial vehicle, and enhancing the spectral reflection characteristics of the water body by utilizing a spectral differentiation technology;
then, performing contrast analysis with the ground measurement spectrum data, and correcting unmanned hyperspectral remote sensing data by using the ground measurement spectrum data;
44 The water quality parameter inversion model is applied to the processed hyperspectral remote sensing data of the small-river basin water environment, the spatial distribution of water quality monitoring indexes in the regional surface water body is obtained, the spatial distribution of the concentration of pollutants in the regional water environment is obtained, and a hyperspectral remote sensing background database of the small-river basin water environment is established.
5. The hyperspectral remote sensing-based intelligent monitoring and early warning method for small-river-basin agricultural non-point source pollution is characterized by comprising the following steps of:
51 In the supervision work of small-river-basin agricultural non-point source pollution, according to the supervision standard of regional agricultural non-point source pollution, carrying an unmanned aerial vehicle of a miniature hyperspectral meter, wherein the spectrum range of the unmanned aerial vehicle is 400-1000nm, and acquiring regional surface water hyperspectral data through a pre-planned and set route;
52 The data automatic transmission, storage and intelligent analysis system carries out pretreatment and correction on hyperspectral remote sensing data, and outputs the spatial distribution of water quality monitoring indexes in regional surface water bodies and the spatial distribution of pollutant concentration in regional water environments through a water quality parameter inversion model;
53 Comparing the agricultural non-point source pollution with a hyperspectral remote sensing background database of a small watershed water environment, and displaying the pollution condition and the change trend of the receiving water body polluted by the agricultural non-point source in the area, so as to realize the intelligent monitoring of the agricultural non-point source pollution.
6. The hyperspectral remote sensing-based intelligent monitoring and early warning method for small-river-basin agricultural non-point source pollution is characterized in that the intelligent monitoring and early warning for small-river-basin agricultural non-point source pollution is agricultural non-point source pollution and early warning, and comprises the following steps:
61 Comparing the regional surface water hyperspectral data of the agricultural non-point source pollution monitoring control section and the regional surface water hyperspectral remote sensing background database of the regional surface water hyperspectral data of the regional monitoring control point;
62 Acquiring regional agricultural non-point source pollution risk conditions in real time through characteristic spectrum increment or abnormal change;
63 And then comparing the hyperspectral remote sensing data of the main river channel, the tributaries, the ditches and the pit water body with background data to obtain the space-time evolution and the spatial distribution of the agricultural non-point source pollution, and realizing the plastic source pollution and the early warning of the agricultural non-point source pollution.
7. The hyperspectral remote sensing-based intelligent monitoring and early warning method for small-river-basin agricultural non-point source pollution is characterized in that the intelligent monitoring and early warning for small-river-basin agricultural non-point source pollution is to display spatial distribution, evolution condition and pollution synthon of out-of-standard pollution in real time: when the water quality index of the water quality monitoring section is abnormal or exceeds standard, the regional surface water hyperspectral data is compared with a small watershed water environment hyperspectral remote sensing background database, and the spatial distribution, evolution condition and pollution synoptic source of the exceeding standard pollution are displayed in real time.
8. The utility model provides a little river basin agricultural non-point source pollution intelligent monitoring early warning system based on hyperspectral remote sensing which characterized in that: the system comprises an unmanned aerial vehicle hyperspectral data acquisition system and a data automatic transmission, storage and intelligent analysis system; the unmanned aerial vehicle hyperspectral data acquisition system is a miniature hyperspectral instrument carried by an unmanned aerial vehicle, the spectrum range of the miniature hyperspectral instrument is 400-1000nm, and hyperspectral data of the surface water body of the area are acquired through a preset route; the data automatic transmission, storage and intelligent analysis system comprises a hyperspectral remote sensing data processing module, an regional surface water pollution monitoring module and an agricultural non-point source pollution intelligent early warning module, and an algorithm and system software supporting hyperspectral remote sensing data processing, analysis, water quality index display and flow and standardization processing of a small-basin surface water pollution situation map are used for realizing dynamic monitoring and early warning of the influence of agricultural non-point source pollution environment quality.
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| CN116882638A (en) * | 2023-09-07 | 2023-10-13 | 北京建工环境修复股份有限公司 | Management method and system of perfluoro compound pollution monitoring equipment |
| CN117558107A (en) * | 2024-01-12 | 2024-02-13 | 济南天楚科技有限公司 | Agricultural environment monitoring system based on Internet of things |
| CN117909913B (en) * | 2024-03-20 | 2024-05-14 | 江苏省环境监测中心 | Agricultural non-point source pollution data management method, system, equipment and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116882638A (en) * | 2023-09-07 | 2023-10-13 | 北京建工环境修复股份有限公司 | Management method and system of perfluoro compound pollution monitoring equipment |
| CN116882638B (en) * | 2023-09-07 | 2023-12-01 | 北京建工环境修复股份有限公司 | Management method and system of perfluoro compound pollution monitoring equipment |
| CN117558107A (en) * | 2024-01-12 | 2024-02-13 | 济南天楚科技有限公司 | Agricultural environment monitoring system based on Internet of things |
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