A surveying and mapping method based on remote sensing big data analysis

Abstract

The application discloses a surveying and mapping method based on remote sensing big data analysis, and relates to the technical field of remote sensing big data.The system runs, satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensors are used to collect images and spectral data of a target water body region, the collected remote sensing data is preprocessed, the spectral information in the remote sensing data is used to extract characteristic parameters of the water body through band combination or spectral line analysis, the on-site data is subjected to multidimensional analysis, and the water body turbidity coefficient Ct, the algal bloom coefficient Ca and the oil pollution coefficient Co are calculated and obtained.Combining the remote sensing data with geographic information, the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body is analyzed, the temporal and spatial variation of the water body pollution is analyzed, the trend of pollutant expansion or degradation is identified, the water quality grade early warning information is generated by comparing a comprehensive water quality index WQI with a preset threshold based on the results of the pollution assessment and the temporal and spatial analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing big data, in particular to a surveying and mapping method based on remote sensing big data analysis. BACKGROUND

[0002] Remote sensing big data technology is an important means of collecting and processing information related to the earth's surface and atmosphere through satellites, unmanned aerial vehicles, and ground sensors, etc. In recent years, remote sensing technology has been widely used in environmental monitoring due to its wide spatial coverage, high temporal resolution, and data acquisition automation. In water body monitoring, remote sensing data can achieve water quality monitoring of lakes, rivers, and oceans through optical, thermal infrared, and radar technology, covering comprehensive collection and analysis of parameters such as water color, optical reflectance, temperature, etc., to assess the pollution status of water bodies, such as the frequency of algal blooms, abnormal changes in turbidity, and the range and concentration of oil pollution. This technology has shown unique advantages in dynamic monitoring of water quality and pollution early warning.

[0003] The existing water quality monitoring method mainly relies on ground sampling and laboratory analysis, although the data accuracy is high, but it has the disadvantages of limited sampling point distribution, insufficient spatial coverage, poor dynamicity, etc. At the same time, due to the low efficiency of manual sampling and the difficulty in updating data in real time, it is not suitable for sudden pollution events or large-area water body monitoring. This limitation makes it difficult to discover and respond to abnormal situations of water pollution diffusion in a timely manner. For example, the outbreak of algal blooms often spreads rapidly in a short time due to excessive nitrogen and phosphorus concentrations, but traditional monitoring methods cannot capture the spread speed and coverage, thereby increasing the risk of water oxygen deficiency and ecological deterioration. Similarly, water quality changes caused by high turbidity or oil pollution incidents, if not assessed and warned in a timely manner, may cause irreversible damage to aquatic habitats and drinking water resources. These abnormal effects not only affect the balance of the ecological system, but also increase the difficulty and cost of pollution control, seriously restricting the sustainable use of water resources. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a surveying and mapping method based on remote sensing big data analysis, which solves the problems mentioned in the background art.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a surveying and mapping method based on remote sensing big data analysis, comprising the following steps,

[0006] Step 1: Collect image and spectral data of the target water body area through satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensor, including water color, temperature and optical reflectance data;

[0007] Step two: Perform radiation correction, atmospheric correction, and geometric correction on the collected remote sensing data, remove noise, and ensure data accuracy and consistency;

[0008] Step three: Extract the characteristic parameters of water bodies, including algae bloom, turbidity, and oil pollution concentration, using spectral information in remote sensing data through band combination or spectral analysis, extract, analyze, and model the characteristics of water bodies, and calculate the water turbidity coefficient Ct, algae bloom coefficient Ca, and oil pollution coefficient Co through multiple regression analysis or machine learning algorithms on the water quality parameters in the remote sensing image and multi-dimensional analysis of the field data;

[0009] Step four: Combine remote sensing data and geographic information, including watershed information and industrial discharge area, analyze the impact of potential pollution sources, agricultural runoff, and industrial wastewater discharge on water bodies, and identify the distribution of pollutants in water bodies, including detecting the diffusion of oil pollution and algae bloom pollutants, using change detection methods of remote sensing images;

[0010] Step five: Analyze the spatial and temporal changes of water pollution, identify the trend of pollution expansion or degradation, and the pollution changes under different seasons or weather conditions;

[0011] Step six: Based on the results of pollution assessment and spatio-temporal analysis, compare the comprehensive water quality index WQI with the preset threshold to generate water quality grade warning information, and provide data support for relevant management departments to develop pollution control or prevention measures.

[0012] Preferably, step one includes:

[0013] Collect image data of the target water body area through satellite remote sensing, unmanned aerial vehicle remote sensing, or ground sensor equipment, covering the color, texture, and spatial distribution information of the water body, providing visual features of the water body, and enabling preliminary identification of the appearance and pollution level of the water body;

[0014] Collect optical reflectance data of the water body through multispectral or hyperspectral remote sensing sensors, providing reflection characteristic information of the water surface and underlying water body materials, and key spectral bands include green light, red light, and near-infrared;

[0015] Collect temperature data of the water body through infrared remote sensing technology or water temperature sensors, and water temperature information has a direct impact on the identification of pollutants, oil pollution, and algae bloom, because different pollutants have different effects on water temperature, and changes in water temperature reflect potential pollution areas or the self-purification ability of the water body;

[0016] Chlorophyll-a concentration Chl-a, maximum concentration of chlorophyll-a in the reference water area Chl-amax, cyanobacterial biomass Cb, maximum cyanobacterial biomass in the reference water area Cbmax, optical reflectance Rλ;

[0017] Water turbidity Turbidity, standard turbidity of the reference area Turbidity ref , total suspended solids concentration TSS, and total suspended solids concentration TSSref in the reference water area are measured by water quality sensors, portable turbidity meters, and flow monitoring equipment of ground monitoring stations;

[0018] Spectral absorption characteristics are obtained using synthetic aperture radar (SAR) remote sensing data Sentinel-1 and multispectral data, and oil film coverage area A oil , maximum oil film coverage area A oilmax , reflection intensity I of the oil film at a specific spectral wavelength λoil , maximum reflection intensity I λmax of the reference area without oil film.

[0019] Water surface temperature WST and water surface temperature WST ref of an oil-free water area are obtained by thermal infrared remote sensing equipment and thermal infrared sensors carried on MODIS or unmanned aerial vehicles, and a thermometer is used for auxiliary collection and calibration.

[0020] Preferably, step two includes:

[0021] Radiation errors in the remote sensing image are corrected by eliminating errors caused by sensors, solar radiation, and terrain factors, ensuring that the radiation values of each pixel in the remote sensing image reflect the true features of the ground objects, improving the accuracy of the data, removing the effects of the atmosphere on the remote sensing image, including atmospheric scattering and absorption effects, ensuring that the spectral information of the data accurately reflects the spectral characteristics of the ground substances, and by geometric correction of the remote sensing data, geometric distortion caused by satellite or sensor position, tilt angle, and earth curvature factors is eliminated, and the geometric correction ensures that each pixel in the remote sensing image accurately corresponds to the geographic coordinate system.

[0022] Preferably, step three includes:

[0023] Key feature parameters of the water body are extracted using spectral information in the remote sensing data through band combination or spectral line analysis, including algal blooms, turbidity, and oil pollution concentration, and by analyzing spectral reflectance data of different bands, spectral features related to water pollutant concentration are extracted, and preliminary estimated values of water quality parameters are generated.

[0024] The spectral information in the remote sensing image is combined with the ground monitoring data using a multiple regression analysis method to establish an inversion model, and through the model, the spectral characteristics in the remote sensing data are converted into water quality parameters, and after calculation, the turbidity coefficient Ct of the water body, the algae bloom coefficient Ca, the oil pollution coefficient Co and the comprehensive water quality index WQI are obtained, which supplements the deficiency of the ground data.

[0025] Machine learning algorithms, including support vector machines, random forests and neural networks, are used to invert and correct the water quality parameters in the remote sensing image, and through the training data set, the machine learning model can automatically identify the pollutant distribution of the water body.

[0026] Preferably, step three further comprises:

[0027] The turbidity coefficient Ct of the water body is calculated by the following formula:

[0028]

[0029] In the formula, Turbidity represents the turbidity of the water body, Turbidity ref represents the standard turbidity of the reference area, TSS represents the total suspended solids concentration of the water body, TSS ref represents the total suspended solids concentration in the reference water area, x1 and x2 represent the weight values of the respective coefficients, β1 and β2 represent the fitting indexes, respectively, reflecting the influence of different types of suspended solids on turbidity.

[0030] Preferably, the algae bloom coefficient Ca is calculated by the following formula:

[0031]

[0032] In the formula, Chl-a represents the concentration of chlorophyll a in the water body, Chl-a max represents the maximum concentration of chlorophyll a in the reference water area, Cb represents the cyanobacterial biomass, Cb max represents the maximum value of cyanobacterial biomass in the reference water area, Rλ represents the optical reflectance, w1 and w2 represent the weight values of the respective parameters, α1 and α2 represent the spectral response indexes, respectively, adjusting the sensitivity of spectral reflectance characteristics to algae.

[0033] Preferably, the oil pollution coefficient Co is calculated by the following formula:

[0034]

[0035] In the formula, Aoil represents the area covered by the oil film, A oilmax represents the maximum coverage area of oil pollution in the reference water area, I λoil represents the reflectance intensity of the oil film at a specific spectral wavelength, Iλmax WST represents the temperature of the water surface, WST ref WST represents the surface temperature of the oil-free water area, v1, v2 and v3 represent the weight values of each parameter respectively, γ1, γ2 and γ3 are the exponential fitting coefficients, adjusting the sensitivity of each parameter to oil pollution;

[0036] The comprehensive water quality index WQI is calculated by the following formula:

[0037]

[0038] In the formula, Ct represents the turbidity coefficient of the water body, Ca represents the algal bloom coefficient, and Co represents the oil pollution coefficient, n1, n2 and n3 represent the weight coefficients of each coefficient.

[0039] Preferably, step four includes:

[0040] Combined with remote sensing data and geographic information system (GIS) technology, the location of potential pollution sources and their impact on water bodies are analyzed, and through the analysis of watershed information, industrial discharge area and agricultural runoff geographic factors, the pollution sources are identified and the impact on water pollution is evaluated;

[0041] Using remote sensing image change detection method, the spatial distribution of water pollutants is monitored and identified, and through the comparison of remote sensing images at different times, the diffusion and change trend of oil pollution and algal bloom pollutants are detected, providing support for pollutant control and management.

[0042] Preferably, step five includes:

[0043] Through the spatio-temporal sequence analysis of water pollution data, the trend of pollutant expansion or degradation is identified, and combined with historical data and real-time data, the dynamic change of pollutants in different seasons and different weather conditions is evaluated, revealing the time and space characteristics of water quality change;

[0044] Through the analysis of seasonal changes in water pollution, the water quality fluctuations caused by seasonal changes, including precipitation and temperature changes, are identified, and according to the seasonal change trend, targeted strategies for water quality management are provided to optimize pollution control measures;

[0045] Through the establishment of dynamic models of pollutants, combined with the physical processes of water flow, sedimentation and diffusion, the migration and transformation of pollutants in water bodies are simulated, through these models, the change of pollutants in water bodies is predicted in real time, and the future pollution trend is warned.

[0046] Preferably, step six includes:

[0047] By comparing the comprehensive water quality index WQI with the preset pollution threshold, the severity of water pollution is judged, and when the water quality index exceeds the preset threshold, the system will automatically generate water quality warning information to provide timely pollution warning for the management department;

[0048] The turbidity coefficient Ct of the water body is compared with the preset standard threshold U:

[0049] When the turbidity coefficient Ct of the water body is ≤ the preset standard threshold U, the water quality is good, the concentration of suspended solids in the water body is within the normal range, and the ecological system is not affected. Regularly monitor agricultural runoff, industrial wastewater discharge and surface erosion caused by rainfall in the watershed to avoid an increase in turbidity.

[0050] When the turbidity coefficient Ct of the water body is > the preset standard threshold U, the turbidity of the water body exceeds the preset threshold, indicating that the turbidity of the water body abnormally increases, and high-concentration suspended particles will cause ecological problems, including reducing the photosynthesis efficiency of aquatic plants and harming the habitat of fish. At the same time, it indicates that the surface runoff carries pollutants, including nutrients, pathogens or toxic substances. Control the amount and timing of fertilization, build rainwater collection and purification systems to reduce suspended particles brought into the water body during rainfall, and build sedimentation tanks or constructed wetlands in the water body to remove suspended particles through natural sedimentation and plant filtration.

[0051] The algae bloom coefficient Ca is compared with the preset standard threshold P:

[0052] When the algae bloom coefficient Ca is ≤ the preset standard threshold P, the water body does not have abnormal proliferation of algae, and the ecological system is in a healthy state. Reduce the use of fertilizers or build buffer zones, promote sewage treatment facilities in coastal areas to avoid direct discharge of domestic sewage into the water body, maintain the natural flow of the water body, increase the flow of the river, plant aquatic plants to competitively absorb nutrients, and reduce the opportunity for algae to reproduce.

[0053] When the algae bloom coefficient Ca is > the preset standard threshold P, the water body has abnormal proliferation of algae, leading to anoxic and fish death water body ecological crisis, and affecting the use of water body functions. Take measures to intercept nitrogen and phosphorus from agricultural non-point source pollution, introduce eco-friendly algae control agents or closed floating islands into the water body to limit the growth of algae, and use aeration devices to increase the dissolved oxygen concentration of the water body.

[0054] The oil pollution coefficient Co is compared with the preset standard threshold R:

[0055] When the oil pollution coefficient Co is ≤ the preset standard threshold R, the water body is not significantly contaminated by oil, and the aquatic ecosystem and water body use functions are not significantly affected. Install remote sensing monitoring equipment to regularly track the distribution of oil film, set up oil spill interception devices in coastal areas or waterways to reduce the spread of oil pollution.

[0056] When the oil pollution coefficient Co is greater than the preset standard threshold R, the surface of the water body is obviously covered with oil, which causes damage to the ecological system, suffocation of aquatic organisms or destruction of the ecological chain, and affects the use function of the water body, and the oil absorption device and the oil spill cleaning ship are used to clean the oil film on the surface of the water body in time, and the decomposing microorganisms or biological enzymes are put into the water body to accelerate the degradation of the oil pollution, and the water ecological system of the polluted area is repaired, including the reconstruction of wetlands, vegetation or the planting of water purification plants.

[0057] The application provides a surveying and mapping method based on remote sensing big data analysis, which has the following beneficial effects:

[0058] (1) When the system is running, the image and spectrum data of the target water body area are collected through satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensors, the collected remote sensing data are preprocessed, the characteristic parameters of the water body are extracted from the spectrum information in the remote sensing data through band combination or spectrum analysis, and the water body turbidity coefficient Ct, the algal bloom coefficient Ca and the oil pollution coefficient Co are calculated and obtained after multidimensional analysis of the field data; the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body is analyzed in combination with the remote sensing data and geographic information, the temporal and spatial variation of the water pollution is analyzed, the trend of pollution expansion or degradation is identified, the comprehensive water quality index WQI is compared with the preset threshold based on the results of the pollution assessment and the temporal and spatial analysis, and water quality grade early warning information is generated.

[0059] (2) The surveying and mapping method based on remote sensing big data analysis realizes comprehensive monitoring and evaluation of water pollution through a systematic process of six steps. In the first to third steps, the quantitative evaluation of key indicators such as water turbidity, algal bloom and oil pollution is completed through the collection, correction of multi-source data and the extraction of water quality parameters; in the fourth step, the source analysis of the pollution source and the monitoring of the pollutant diffusion are completed through the combination of remote sensing images and geographic information system (GIS); in the fifth and sixth steps, precise pollution early warning and governance decision support are provided on the basis of the temporal and spatial variation analysis and the water quality index calculation. This process not only completes the dynamic perception of water pollution, but also provides closed-loop management of the whole life cycle from data acquisition to decision support.

[0060] (3) Compared with traditional water quality monitoring techniques, this method has made significant improvements in spatial coverage, temporal resolution, and dynamic assessment. Traditional techniques mainly rely on ground sampling and laboratory analysis, although the data accuracy is high, but there are limited monitoring range, serious time lag, especially in dealing with large-scale and sudden water pollution, which is weak. This method realizes efficient processing from spatial data collection to pollution analysis through remote sensing equipment, optimizes the water quality parameter inversion accuracy using machine learning algorithms, and completes the comprehensive identification of pollution distribution and the prediction of change trend combined with GIS technology. The improved dynamic monitoring capability and data analysis automation greatly make up for the shortcomings of traditional methods.

[0061] (4) Through the synergistic optimization of the six steps, this method has achieved multi-faceted improvement in water pollution monitoring and treatment effect. First, in pollution identification, the accuracy and real-time performance are significantly enhanced, which can capture the dynamic changes of key issues such as algal blooms, turbidity, and oil pollution; second, in pollution assessment, the introduction of the comprehensive water quality index WQI provides a quantitative water quality health evaluation standard, providing a decision-making basis for water resource management departments; finally, in terms of treatment efficiency, through pollution diffusion trend prediction and precise early warning, early intervention and optimized allocation of treatment resources are realized. Overall, this method not only improves the scientificity and intelligent level of water quality monitoring, but also provides strong technical support for the sustainable management of water ecological systems. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 Figure 1 is a schematic diagram of the steps of the surveying and mapping method based on remote sensing big data analysis of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0064] Embodiment 1

[0065] The present application provides a surveying and mapping method based on remote sensing big data analysis, please refer to Figure 1 , comprising the following steps,

[0066] Step 1: Collect image and spectral data of the target water area through satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensor, including water color, temperature and optical reflectivity data;

[0067] Step two: Perform radiation correction, atmospheric correction, and geometric correction on the collected remote sensing data, remove noise, and ensure data accuracy and consistency.

[0068] Step three: Extract characteristic parameters of water bodies, including algae bloom, turbidity, and oil pollution concentration, using spectral information in remote sensing data through band combination or spectral line analysis. Extract, analyze, and model the characteristics of water bodies. Perform inversion and correction of water quality parameters in remote sensing images through multiple regression analysis or machine learning algorithms. Calculate water turbidity coefficient Ct, algae bloom coefficient Ca, and oil pollution coefficient Co after multi-dimensional analysis of field data.

[0069] Step four: Analyze the impact of potential pollution sources, agricultural runoff, and industrial wastewater discharge on water bodies by combining remote sensing data with geographic information, including watershed information and industrial discharge areas. Identify the distribution of pollutants in water bodies, including oil pollution and algae bloom, using change detection methods in remote sensing images.

[0070] Step five: Analyze the spatial and temporal changes of water pollution, identify the trend of pollutant expansion or degradation, and analyze the pollution changes under different seasons or weather conditions.

[0071] Step six: Based on the results of pollution assessment and spatial-temporal analysis, compare the comprehensive water quality index WQI with the preset threshold to generate water quality grade warning information and provide data support for relevant management departments to develop pollution control or prevention measures.

[0072] In this embodiment, satellite remote sensing, unmanned aerial vehicle remote sensing, or ground sensors are used to collect image and spectral data of the target water body area. The collected remote sensing data is preprocessed, and characteristic parameters of water bodies are extracted using spectral information in remote sensing data through band combination or spectral line analysis. After multi-dimensional analysis of field data, water turbidity coefficient Ct, algae bloom coefficient Ca, and oil pollution coefficient Co are calculated. The impact of potential pollution sources, agricultural runoff, and industrial wastewater discharge on water bodies is analyzed by combining remote sensing data with geographic information. Spatial and temporal changes of water pollution are analyzed, and the trend of pollutant expansion or degradation is identified under different seasons or weather conditions. Based on the results of pollution assessment and spatial-temporal analysis, the comprehensive water quality index WQI is compared with the preset threshold to generate water quality grade warning information and provide data support for relevant management departments to develop pollution control or prevention measures.

[0073] Embodiment 2

[0074] This embodiment is an explanation and description in Embodiment 1. Please refer to Figure 1 , specifically: Step one includes:

[0075] Through satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensor equipment to collect image data of the target water area, covering the color, texture and spatial distribution information of the water body, providing the visual features of the water body, which can preliminarily identify the appearance and pollution degree of the water body;

[0076] Through multispectral or hyperspectral remote sensing sensors to collect the optical reflectance data of the water body, providing the reflection characteristic information of the water body surface and the underlying water body, the key spectral bands including green light, red light and near infrared;

[0077] Through infrared remote sensing technology or water temperature sensor to collect the temperature data of the water body, the water temperature information has a direct influence on the identification of pollutants, oil pollution and algal blooms, because different pollutants have different influences on the water temperature, and the change of water temperature reflects the potential pollution area or the self-purification ability of the water body;

[0078] Through multispectral or hyperspectral remote sensing sensors to collect chlorophyll a concentration Chl-a, maximum concentration of chlorophyll a in reference water area Chl-amax, cyanobacterial biomass Cb, maximum value of cyanobacterial biomass in reference water area Cbmax, optical reflectance Rλ;

[0079] Through water quality sensor, portable turbidity meter and flow monitoring equipment of ground monitoring station to measure, obtain water turbidity Turbidity, standard turbidity of reference area Turbidity ref , total suspended solids concentration TSS and total suspended solids concentration TSSref in reference water area;

[0080] Using synthetic aperture radar SAR remote sensing data Sentinel-1 and multispectral data to obtain spectral absorption characteristics, ground oil film detector and infrared spectrometer to obtain oil film coverage area A oil , maximum coverage area of oil pollution in reference water area A oilmax , reflection intensity of oil film under specific spectral wavelength I λoil , maximum reflection intensity of reference area without oil film I λmax ;

[0081] Through thermal infrared remote sensing equipment and thermal infrared sensor carried on MODIS or unmanned aerial vehicle to obtain surface temperature of water body WST and surface temperature of oil-free water area WST ref , thermometer for auxiliary collection and calibration.

[0082] Step two includes:

[0083] The radiation error in the remote sensing image is corrected, the error caused by the sensor, solar radiation and terrain factors is eliminated, it is ensured that the radiation value of each pixel in the remote sensing image reflects the real feature of the ground object, the accuracy of the data is improved, the influence of the atmosphere on the remote sensing image is removed, including atmospheric scattering and absorption effect, it is ensured that the spectral information of the data can accurately reflect the spectral characteristics of the ground material, and the geometric correction is performed on the remote sensing data, and the geometric distortion caused by the position, inclination angle and earth curvature of the satellite or sensor is eliminated, and the geometric correction ensures that each pixel in the remote sensing image is accurately corresponding to the geographic coordinate system.

[0084] In the embodiment, by implementing steps one and two, the method significantly improves the comprehensiveness, accuracy and practicability of water body monitoring data. First, the multi-source data acquisition of step one ensures the acquisition of multi-dimensional information of the water body, including the color, texture, optical reflectivity, temperature and other parameters of the water body, which comprehensively covers the key features of water pollution. At the same time, through the multi-spectral and hyper-spectral remote sensing technology, combined with the monitoring of specific indicators such as chlorophyll-a concentration, turbidity and oil film coverage area, the distribution and concentration of algae bloom, suspended particles and oil pollution in the water body can be accurately identified. In addition, step two eliminates the radiation error, atmospheric interference and geometric distortion in the collected data through radiation correction, atmospheric correction and geometric correction, ensuring that the spectral characteristics of the remote sensing image truly reflect the ground information. The combination of the two steps not only improves the quality and credibility of the data, but also provides high-precision and high-resolution basic data for subsequent water quality analysis and pollution assessment, greatly improving the efficiency and scientificity of water quality monitoring and early warning.

[0085] Embodiment 3

[0086] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically: step three includes:

[0087] Through band combination or spectral line analysis, the key feature parameters of the water body are extracted from the spectral information in the remote sensing data, including algae bloom, turbidity and oil pollution concentration, the spectral reflectance data of different bands are analyzed, the spectral characteristics related to the concentration of water pollutants are extracted, and the preliminary estimated value of the water quality parameter is generated;

[0088] Using multiple regression analysis method, the spectral information in the remote sensing image is combined with the ground monitoring data to establish an inversion model, through the model, the spectral characteristics in the remote sensing data are converted into water quality parameters, and after calculation, the turbidity coefficient Ct of the water body, the algae bloom coefficient Ca, the oil pollution coefficient Co and the comprehensive water quality index WQI are obtained, which supplements the deficiency of the ground data;

[0089] Machine learning algorithms, including support vector machines, random forests, and neural networks, are used to invert and correct water quality parameters in remote sensing imagery. Through training datasets, machine learning models can automatically identify the distribution of pollutants in water bodies.

[0090] Step three also includes:

[0091] The turbidity coefficient Ct of the water body is calculated by the following formula:

[0092]

[0093] In the formula, Turbidity represents the turbidity of the water body, Turbidity ref represents the standard turbidity of the reference area, TSS represents the total suspended solids concentration of the water body, TSS ref represents the total suspended solids concentration in the reference water area, x1 and x2 represent the weight values of each coefficient, β1 and β2 represent the fitting exponents, reflecting the influence of different types of suspended solids on turbidity.

[0094] The algal bloom coefficient Ca is calculated by the following formula:

[0095]

[0096] In the formula, Chl-a represents the concentration of chlorophyll a in the water body, Chl-a max represents the maximum concentration of chlorophyll a in the reference water area, Cb represents the cyanobacterial biomass, Cb max represents the maximum value of cyanobacterial biomass in the reference water area, Rλ represents the optical reflectance, w1 and w2 represent the weight values of each parameter, α1 and α2 represent the spectral response exponents, adjusting the sensitivity of spectral reflectance characteristics to algae.

[0097] The oil pollution coefficient Co is calculated by the following formula:

[0098]

[0099] In the formula, Aoil represents the area covered by the oil film, A oilmax represents the maximum coverage area of oil pollution in the reference water area, I λoil represents the reflection intensity of the oil film at a specific spectral wavelength, I λmax represents the maximum reflection intensity of the reference area without the oil film, WST represents the surface temperature of the water body, WST ref represents the surface temperature of the water area without oil pollution, v1, v2, and v3 represent the weight values of each parameter, γ1, γ2, and γ3 are the fitting coefficients of the exponents, adjusting the sensitivity of each parameter to oil pollution.

[0100] The comprehensive water quality index WQI is calculated by the following formula:

[0101]

[0102] In the formula, Ct represents the turbidity coefficient of the water body, Ca represents the algal bloom coefficient, Co represents the oil pollution coefficient, and n1, n2 and n3 represent the weight coefficients of each coefficient.

[0103] In this embodiment, through the implementation of step three, the method effectively improves the quantification ability of water pollution characteristics and the accuracy of water quality analysis. First, by combining band combination or spectral line analysis to extract key spectral features, combined with multiple regression analysis method, the accurate conversion from remote sensing data to water quality parameters is realized, and the turbidity coefficient Ct, algal bloom coefficient Ca and oil pollution coefficient Co of the water body are generated, and the comprehensive water quality index WQI is further calculated, which provides multi-dimensional quantitative evaluation of the pollution status of the water body. At the same time, by using machine learning algorithms (such as support vector machine, random forest and neural network), through learning and optimization of training data, the water quality parameter inversion in remote sensing image is more efficient and intelligent, which ensures the accuracy of the identification of pollutant distribution, and the analysis process of water quality parameters is refined by using formula calculation, for example, the calculation of turbidity coefficient Ct and algal bloom coefficient Ca not only considers key indicators (such as turbidity, chlorophyll a concentration, blue-green algae biomass, etc.), but also combines spectral reflectance characteristics and weight distribution, fully reflecting the influence of different pollutants on water health. The oil pollution coefficient Co is calculated by integrating the oil film area, spectral absorption characteristics and water temperature and other multi-factor parameters, and comprehensively evaluating the coverage and pollution degree of oil pollution on water body. The quantification of these coefficients provides a scientific basis for the calculation of the comprehensive water quality index WQI.

[0104] Embodiment 4

[0105] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically: step four includes:

[0106] Combined with remote sensing data and geographic information system GIS technology, the location of potential pollution sources and their influence on water body are analyzed, and through analyzing the watershed information, industrial discharge area and agricultural runoff geographic factors, the pollution sources are identified and the influence degree of the pollution sources on water pollution is evaluated;

[0107] The spatial distribution of water pollutants is monitored and identified by using remote sensing image change detection method, and the diffusion situation and change trend of oil pollution and algal bloom pollutants are detected by comparing remote sensing images at different periods, which provides support for pollutant control and management.

[0108] Step five includes:

[0109] Through the spatio-temporal sequence analysis of water pollution data, the trend of pollution expansion or degradation is identified, and the dynamic changes of pollutants in different seasons and under different weather conditions are evaluated, revealing the time and space characteristics of water quality changes;

[0110] By analyzing the seasonal changes of water pollution, the fluctuations in water quality caused by seasonal changes, including precipitation and temperature changes, are identified, and targeted strategies for water quality management are provided based on seasonal trends, optimizing pollution control measures;

[0111] By establishing dynamic models of pollutants, combined with the physical processes of water flow, sedimentation and diffusion, the migration and transformation of pollutants in water bodies are simulated, and through these models, real-time prediction of changes in pollutants in water bodies and early warning of future pollution trends are achieved.

[0112] In this embodiment, by implementing steps four and five, the method significantly improves the spatial distribution analysis and dynamic change monitoring capability of water pollution. In step four, combined with remote sensing data and geographic information system (GIS) technology, the location of potential pollution sources and their impact on water bodies can be accurately located, especially for the analysis of industrial discharge areas, agricultural runoff and watershed information. Not only the types of pollution sources are identified, but also the contribution of each source to water bodies is quantified. At the same time, through remote sensing image change detection technology, the diffusion of pollutants such as oil pollution and algal blooms in different periods of image data is effectively monitored, providing scientific basis for the formulation of pollution control and management schemes. Step five further deepens the dimension of spatio-temporal dynamic analysis, by combining historical data with real-time data, the trend of pollution expansion or degradation is identified, especially in the influence of seasonal changes (such as precipitation, temperature changes, etc.) on water quality, revealing the change law of pollutants in time and space. Through the establishment of dynamic models of pollutants, combined with the physical processes of water flow, sedimentation and diffusion, the simulation of pollutant migration and the accurate early warning of future trends are achieved. This dynamic model not only can real-time predict the expansion range of pollution, but also provides forward-looking decision support for early management.

[0113] Embodiment 5

[0114] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically: step six includes:

[0115] By comparing the comprehensive water quality index WQI with the preset pollution threshold, the severity of water pollution is judged, and when the water quality index exceeds the preset threshold, the system will automatically generate water quality warning information to provide timely pollution warning for the management department;

[0116] The turbidity coefficient Ct of the water body is compared with the preset standard threshold U:

[0117] When the turbidity coefficient Ct of the water body is ≤ the preset standard threshold U, the water quality is good, the suspended matter concentration in the water body is within the normal range, and the ecosystem is not affected. Regular monitoring of agricultural runoff, industrial wastewater discharge, and surface erosion caused by rainfall in the watershed range is carried out to avoid an increase in turbidity.

[0118] When the turbidity coefficient Ct of the water body is > the preset standard threshold U, the turbidity of the water body exceeds the preset threshold, indicating that the turbidity of the water body abnormally increases, and a high concentration of suspended particles will cause ecological problems, including reducing the photosynthesis efficiency of aquatic plants and harming the habitat of fish. At the same time, it indicates that the surface runoff carries pollutants, including nutrients, pathogens, or toxic substances. Fertilizer application amount and timing should be controlled, rainwater collection and purification systems should be built, and the amount of suspended particles brought into the water body during rainfall should be reduced. Sedimentation tanks or constructed wetlands should be built in the water body to remove suspended particles through natural sedimentation and plant filtration.

[0119] The algal bloom coefficient Ca is compared with the preset standard threshold P:

[0120] When the algal bloom coefficient Ca is ≤ the preset standard threshold P, the water body does not have abnormal proliferation of algae, and the ecosystem is in a healthy state. Reducing the use of chemical fertilizers or building buffer zones, promoting sewage treatment facilities in coastal areas, avoiding direct discharge of domestic sewage into the water body, maintaining the natural flow of the water body, increasing the flow of the river, planting aquatic plants to competitively absorb nutrients, and reducing the chances of algae reproduction;

[0121] When the algal bloom coefficient Ca is > the preset standard threshold P, the water body has abnormal proliferation of algae, leading to an ecological crisis of oxygen deficiency and fish death in the water body, and affecting the use of water body functions. Measures should be taken to intercept nitrogen and phosphorus from agricultural non-point source pollution, introduce eco-friendly algae control agents or closed floating islands into the water body to limit the growth of algae, and use aeration devices to increase the dissolved oxygen concentration in the water body.

[0122] The oil pollution coefficient Co is compared with the preset standard threshold R:

[0123] When the oil pollution coefficient Co is ≤ the preset standard threshold R, the water body is not significantly contaminated by oil, and the aquatic ecosystem and water body use functions are not significantly affected. Install remote sensing monitoring equipment, regularly track the distribution of oil films, set up oil spill interception devices in coastal areas or waterways, and reduce the spread of oil pollution.

[0124] When the oil pollution coefficient Co is greater than the preset standard threshold R, the surface of the water body is obviously covered with oil, causing damage to the ecological system, suffocation of aquatic organisms or disruption of the ecological chain, and affecting the use function of the water body. The oil absorption device and the oil spill cleaning ship are used to clean the oil film on the surface of the water body in time, and the decomposing microorganisms or biological enzymes are put into the water body to accelerate the degradation of the oil pollution, so as to repair the water ecological system of the polluted area, including rebuilding the wetland, planting vegetation or putting the water purification plants.

[0125] In this embodiment, by implementing step six, the method realizes quantitative evaluation and intelligent early warning of water pollution, greatly improving the scientificity and timeliness of water pollution management. By comparing the comprehensive water quality index WQI with the preset threshold, the severity of water pollution can be accurately judged, and when the pollution exceeds the threshold, the system automatically generates early warning information, providing technical support for the rapid response of the management department. Further refining to the specific pollution type, the grading evaluation and comparison of the turbidity coefficient Ct, the algal bloom coefficient Ca and the oil pollution coefficient Co of the water body can identify the different influences of suspended particles, algal bloom and oil pollution respectively, and give targeted governance suggestions. When the pollution index is at a low risk level, the preventive measures proposed by the system (such as regular monitoring, optimization of agricultural and industrial discharge management, etc.) can effectively prevent water quality deterioration; while at a high risk level, the emergency response strategies for different pollution types (such as algae control measures, sedimentation tank or wetland construction, oil spill cleaning and ecological restoration, etc.) can quickly reduce the pollution level and alleviate the ecological crisis. Especially by combining remote sensing monitoring equipment and dynamic early warning function, the system has the ability to track the changes of pollution continuously, ensuring the rational allocation of governance resources.

[0126] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1.A surveying and mapping method based on remote sensing big data analysis, characterized in that: The method comprises the following steps: Step 1: Collect image and spectral data of the target water area by satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensor, including color, temperature and optical reflectivity data of the water body; Step 2: Perform radiation correction, atmospheric correction and geometric correction on the collected remote sensing data to remove noise and ensure the accuracy and consistency of the data; Step 3: Extract the characteristic parameters of the water body by band combination or spectral line analysis using the spectral information in the remote sensing data, including algae bloom, turbidity and oil pollution concentration, extract, analyze and model the characteristics of the water body, and calculate the water turbidity coefficient Ct, algae bloom coefficient Ca and oil pollution coefficient Co by multiple regression analysis or machine learning algorithm on the water quality parameters in the remote sensing image after multi-dimensional analysis of the field data; Step 4: Combine remote sensing data with geographic information, including watershed information and industrial discharge area, analyze the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body, and identify the distribution of pollutants in the water body by using change detection method of remote sensing image, including detecting the diffusion of oil pollution and algae bloom pollutants; Step 5: Analyze the spatial and temporal variation of water pollution, identify the trend of pollution expansion or degradation, and the pollution change under different seasons or weather conditions; Step 6: Based on the results of pollution assessment and spatio-temporal analysis, compare the comprehensive water quality index WQI with the preset threshold to generate water quality grade warning information, and provide data support for relevant management departments to develop pollution control or prevention measures; Step 3 further comprises: The turbidity coefficient Ct of the water body is calculated by the following formula: In the formula, Turbidity represents the turbidity of the water body, Turbidity ref represents the standard turbidity of the reference area, TSS represents the total suspended substance concentration of the water body, TSS ref represents the total suspended substance concentration in the reference water area, x1 and x2 represent the weight values of respective coefficients, β1 and β2 represent the fitting indexes, respectively, reflecting the influence of different types of suspended substances on turbidity; The algae bloom coefficient Ca is calculated by the following formula: where Chl-a represents the concentration of chlorophyll-a in the water body, Chl-a max represents the maximum concentration of chlorophyll-a in the reference water area, Cb represents the cyanobacterial biomass, Cb max represents the maximum value of cyanobacterial biomass in the reference water area, R represents the optical reflectance, w1 and w2 represent the weight values of the respective parameters, and a1 and a2 represent the spectral response indices, adjusting the sensitivity of the spectral reflectance characteristics to algae. The oil pollution coefficient Co is calculated by the following formula: wherein A oil represents the area covered by the oil film, A oilmax represents the maximum coverage area of oil slick in the reference water area, I λoil represents the reflection intensity of the oil film at the spectral wavelength, I λmax represents the maximum reflection intensity without the oil film in the reference area, WST represents the temperature of the water surface, WST ref represents the surface temperature of the water area without oil slick, v1, v2 and v3 represent the weight values of the respective parameters, γ1, γ2 and γ3 are the exponential fitting coefficients, adjusting the sensitivity of each parameter to the oil pollution; The comprehensive water quality index WQI is calculated by the following formula: In the formula, Ct represents the turbidity coefficient of the water body, Ca represents the algae bloom coefficient, Co represents the oil pollution coefficient, and n1, n2 and n3 represent the weight coefficients of each coefficient. 2.The surveying and mapping method based on remote sensing big data analysis according to claim 1, characterized in that: Step 1 comprises: Collect image data of the target water area by satellite remote sensing, unmanned aerial vehicle remote sensing or ground sensor equipment, covering color, texture and spatial distribution information of the water body, providing visual features of the water body, which can preliminarily identify the appearance and pollution degree of the water body; Collect optical reflectivity data of the water body by multispectral or hyperspectral remote sensing sensor, providing reflection characteristic information of the water surface and the underlying water body, key spectral bands including green light, red light and near-infrared; Collect temperature data of the water body by infrared remote sensing technology or water temperature sensor, water temperature information has a direct impact on the identification of pollutants, oil pollution and algae bloom, because different pollutants have different effects on water temperature, and the change of water temperature reflects the potential pollution area or the self-purification ability of the water body; Collect chlorophyll-a concentration Chl-a, maximum concentration of chlorophyll-a in reference water area Chl-amax, blue-green algae biomass Cb, maximum value of blue-green algae biomass in reference water area Cbmax, and optical reflectivity Rλ by multispectral or hyperspectral remote sensing sensor; The turbidity of the water body and the standard turbidity of the reference area were obtained by measuring the flow rate using water quality sensors, portable turbidity meters, and ground monitoring stations. ref Total suspended solids concentration (TSS) and total suspended solids concentration (TSSref) in the reference water body; SAR remote sensing data Sentinel-1 and multispectral data are combined to obtain spectral absorption characteristics, ground oil pollution detectors and infrared spectrometers are used to obtain oil film coverage area A oil , maximum coverage area A of oil pollution in the reference water area oilmax , reflection intensity I of the oil film at the spectral wavelength λoil , maximum reflection intensity I of the reference area without the oil film λmax ; The surface temperature of water bodies WST and the surface temperature of oil-free water areas WST are obtained by means of thermal infrared remote sensing equipment and thermal infrared sensors carried on MODIS or unmanned aerial vehicles ref , the thermometer is used for auxiliary acquisition and calibration. 3.The surveying and mapping method based on remote sensing big data analysis according to claim 1, characterized in that: Step 2 comprises: The radiation error in the remote sensing image is corrected, the error caused by the sensor, solar radiation and terrain factors is eliminated, it is ensured that the radiation value of each pixel in the remote sensing image reflects the real feature of the ground object, the accuracy of the data is improved, the influence of the atmosphere on the remote sensing image is removed, including atmospheric scattering and absorption effect, it is ensured that the spectral information of the data can accurately reflect the spectral characteristics of the ground material, the geometric correction is carried out on the remote sensing data, the geometric distortion caused by the position, inclination angle and earth curvature of the satellite or sensor is eliminated, and the geometric correction ensures that each pixel in the remote sensing image is accurately corresponding to the geographic coordinate system. 4.The surveying and mapping method based on remote sensing big data analysis according to claim 1, characterized in that: Step three includes: Through band combination or spectral line analysis, the key characteristic parameters of water body are extracted by using the spectral information in the remote sensing data, including algae bloom, turbidity and oil pollution concentration, the spectral characteristics related to the concentration of water pollutants are extracted by analyzing the spectral reflectance data of different bands, and the preliminary estimated value of the water quality parameter is generated; Using multiple regression analysis method, the spectral information in the remote sensing image is combined with the ground monitoring data to establish an inversion model, through the model, the spectral characteristics in the remote sensing data are converted into water quality parameters, and after calculation, the turbidity coefficient Ct of the water body, the algae bloom coefficient Ca, the oil pollution coefficient Co and the comprehensive water quality index WQI are obtained, and the shortage of ground data is supplemented; Machine learning algorithms, including support vector machine, random forest and neural network, are used to invert and correct the water quality parameters in the remote sensing image, and through the training data set, the machine learning model can automatically identify the distribution of water pollutants. 5.The surveying and mapping method based on remote sensing big data analysis according to claim 1, characterized in that: Step four includes: Combined with remote sensing data and geographic information system (GIS) technology, the location of potential pollution sources and its influence on water body are analyzed, the pollution sources are identified and the influence degree of the pollution sources on water pollution is evaluated by analyzing the basin information, industrial discharge area and agricultural runoff geographic factors; The spatial distribution of water pollutants is monitored and identified by using the change detection method of remote sensing image, the diffusion situation and change trend of oil pollution and algae bloom pollutants are detected by comparing the remote sensing images of different periods, and support is provided for pollution control and management. 6.The surveying and mapping method based on remote sensing big data analysis according to claim 1, characterized in that: Step five includes: Through the spatio-temporal sequence analysis of water pollution data, the trend of pollution expansion or degradation is identified, the dynamic change of pollutants in different seasons and different weather conditions is evaluated by combining historical data and real-time data, and the time and space characteristics of water quality change are revealed; By analyzing the seasonal change of water pollution, the water quality fluctuation caused by seasonal change, including precipitation and temperature change, is identified, and targeted strategies for water quality management are provided according to the seasonal change trend, and the pollution control measures are optimized; Through the establishment of dynamic model of pollutants, the migration and transformation of pollutants in water body are simulated by combining the physical processes of water flow, deposition and diffusion, through these models, the change of pollutants in water body is predicted in real time, and the future pollution trend is warned. 7.The surveying and mapping method based on remote sensing big data analysis according to claim 1, characterized in that: Step six includes: By comparing the comprehensive water quality index WQI with the preset pollution threshold, the severity of water pollution is judged, and when the water quality index exceeds the preset threshold, the system will automatically generate water quality warning information to provide timely pollution warning for the management department; The turbidity coefficient Ct of the water body is compared with the preset standard threshold U: When the turbidity coefficient Ct of the water body is ≤ the preset standard threshold U, the water quality is good, the concentration of suspended solids in the water body is within the normal range, and the ecological system is not affected. Regularly monitor the agricultural runoff, industrial wastewater discharge and rainfall-induced surface erosion in the watershed to avoid the increase of turbidity. When the turbidity coefficient Ct of the water body is > the preset standard threshold U, the turbidity of the water body exceeds the preset threshold, indicating that the turbidity of the water body abnormally increases, and high-concentration suspended particles will cause ecological problems, including reducing the photosynthesis efficiency of aquatic plants and harming the habitat of fish. At the same time, it indicates that the surface runoff carries pollutants, including nutrients, pathogens or toxic substances. Control the amount and timing of fertilization, build rainwater collection and purification systems to reduce the amount of suspended particles carried into the water body by rain, and build sedimentation tanks or constructed wetlands in the water body to remove suspended particles through natural sedimentation and plant filtration. The algae bloom coefficient Ca is compared with the preset standard threshold P: When the algae bloom coefficient Ca is ≤ the preset standard threshold P, the water body does not have abnormal proliferation of algae, and the ecological system is in a healthy state. Reduce the use of chemical fertilizers or build buffer zones, promote sewage treatment facilities in coastal areas, avoid direct discharge of domestic sewage into the water body, maintain the natural flow of the water body, increase the flow of the river, plant aquatic plants to competitively absorb nutrients and reduce the opportunity for algae to reproduce. When the algae bloom coefficient Ca is > the preset standard threshold P, the water body has abnormal proliferation of algae, leading to anoxic and fish death ecological crisis, and affecting the use of water body functions. Take measures to intercept nitrogen and phosphorus from agricultural non-point source pollution, introduce ecological-friendly algae control agents or closed floating islands into the water body to limit the growth of algae, and use aeration devices to increase the dissolved oxygen concentration of the water body. The oil pollution coefficient Co is compared with the preset standard threshold R: When the oil pollution coefficient Co is ≤ the preset standard threshold R, the water body is not significantly contaminated by oil, and the aquatic ecosystem and water body use function are not significantly affected. Install remote sensing monitoring equipment to regularly track the distribution of oil film, set up oil spill interception devices in coastal areas or waterways to reduce oil pollution diffusion; When the oil pollution coefficient Co is > the preset standard threshold R, the surface of the water body is significantly covered with oil, causing ecological damage, suffocation of aquatic organisms or disruption of the ecological chain, and affecting the use of the water body. Use oil absorption devices and oil spill cleaning ships to remove the oil film on the surface of the water body in a timely manner, and use decomposing microorganisms or biological enzymes to accelerate the degradation of oil pollution. Repair the aquatic ecosystem in the contaminated area, including rebuilding wetlands, vegetation or planting water purification plants.

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