Sediment-laden river surface water body classification and dynamic monitoring method

Abstract

The invention discloses a silt-laden river surface water body classification and dynamic monitoring method, which comprises the following steps: calculating suspended sediment concentration by adopting spectral measurement, dividing silt-laden river water bodies into clear water, light turbid water and medium turbid water according to the suspended sediment concentration, and respectively selecting optimal water body indexes for water body identification for each water body type by adopting a grading classification concept. Fusing OpenStreetMap data, a lake data set, a reservoir data set, a land utilization product and national drainage basin water system data, making a surface water body classification template, and determining a water system grade according to an area and confluence relationship; according to the method, a silt-laden river surface water body classification and dynamic monitoring method is established on the basis of a classification concept, a surface water body classification data set is made year by year on the basis of a template matching method, spatial and temporal change characteristics of each type of surface water body are analyzed by adopting a trend analysis method, and a surface water body dynamic monitoring system is researched and developed on the basis of a webGIS platform.

Description

Technical Field

[0001] This invention relates to the field of surface water classification technology, and in particular to a method for classifying and dynamically monitoring surface water bodies in rivers with high sediment content. Background Technology

[0002] Water classification in sediment-rich rivers takes into account changes in sediment concentration. The size, shape, and settling rate of sediment particles directly affect water stratification. Without effective classification during dynamic monitoring, accurate responses to water changes may be missed, especially in areas with high sediment concentrations. High sediment concentrations in dynamic monitoring can increase errors in optical and remote sensing technologies. Sediment can alter the reflectivity of remote sensing images, causing water classification results to differ from reality, thus affecting the accuracy and reliability of monitoring results. Water bodies with different sediment contents exhibit varying flow and sedimentation characteristics. Failure to fully consider the impact of sediment on hydrodynamics during dynamic monitoring may reduce the adaptability of hydrological models, thereby affecting the ability to predict future water changes. Sediment-rich rivers often possess complex ecological characteristics; changes in sediment concentration affect the habitats of aquatic plants and animals. Therefore, dynamic ecological monitoring requires different monitoring strategies based on sediment classification to better reflect the health status of the water body.

[0003] In Chinese Patent 202410138313.7, the image to be processed is obtained through a trained surface water body classification model, which cannot classify and grade different rivers, reservoirs, lakes, etc. Therefore, this invention proposes a method for classifying and dynamically monitoring surface water bodies in rivers with high sediment content to solve the problems existing in the prior art. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to propose a method for classifying and dynamically monitoring surface water bodies in rivers with high sediment content. This method establishes a hierarchical classification approach for extracting surface water bodies in rivers with high sediment content, taking into account the concentration of suspended particulate matter. Based on the data, a classification template for surface water bodies in rivers with high sediment content is created. Based on the template matching principle, a long-term time-series classification dataset for surface water bodies in rivers with high sediment content is automatically and quickly generated. A dynamic monitoring system for surface water bodies in rivers with high sediment content is developed, revealing the spatiotemporal variation patterns of suspended sediment concentration in rivers with high sediment content.

[0005] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a method for classifying and dynamically monitoring surface water bodies in rivers with high sediment content, comprising the following steps;

[0006] Step 1: First, collect river sediment data, use spectral measurement to obtain suspended sediment concentration, and classify river water with high sediment content into clear water, slightly turbid water and moderately turbid water according to suspended sediment concentration;

[0007] Step 2: Using a hierarchical classification approach, after classification, select the optimal water body index for each type of water body to identify the water body.

[0008] Step 3: After classification, integrate OpenStreetMap data, lake datasets, reservoir datasets, land use products, and national watershed and river system data to create a surface water body classification template;

[0009] Step 4: Determine the water system level based on area and runoff relationship, and create a surface water body classification dataset year by year based on template matching method;

[0010] Step 5: Finally, using trend analysis, we analyze the spatiotemporal variation characteristics of each type of surface water body, and develop a dynamic monitoring system for surface water bodies based on the webGIS platform.

[0011] Further improvements are made in the following steps: First, the location of the sampling point is determined, the collected water sample is filtered and dried, the volume of the water sample before and after filtration and the mass of sediment after drying are recorded, and the sediment-rich river water is classified by spectral measurement range.

[0012] A further improvement is made in step two, after the classification is completed, by analyzing the sample data and conducting experimental verification, the water index that performs best within the spectral measurement range is determined.

[0013] A further improvement is that, in step two, the indices reflecting the concentration of suspended sediment in the water body include turbidity, transparency, reflectance, and spectral slope, which are obtained by inversion from on-site satellite remote sensing data.

[0014] Further improvements are made in step three by integrating OpenStreetMap data, lake datasets, reservoir datasets, land use products, and national watershed data to create a surface water classification template. Model analysis is performed based on the geographical characteristics of sediment-laden rivers, and flow rate, velocity, and slope are sampled. The long-term trend and seasonal variation of suspended sediment concentration are analyzed through the dataset, and statistical methods are used for verification. The collected data is then input into the water classification template.

[0015] Further improvements are made in the following aspects: In step four, rivers, lakes, and reservoirs are classified into main streams and first-order tributaries based on their area and confluence relationship. The main stream is the main channel of the river, and the first-order tributaries flow directly into the main stream. In the classification of lakes, they are classified into large, medium, and small according to their area size. In the classification of reservoirs, reservoirs are classified into large, medium, and small according to their capacity, as semi-artificial water bodies.

[0016] Further improvements are made in step four, where, after classification, GIS tools are used to perform spatiotemporal analysis to create a time series map of suspended sediment concentration, identify changes in suspended sediment concentration over different time periods, and identify potential sediment deposition areas by analyzing the spatial distribution of suspended sediment concentration and sediment transport pathways, thus creating a surface water body classification dataset year by year.

[0017] Further improvements are made in step five, which involves multi-scale monitoring of the surface of rivers with high sediment content, including monitoring at the watershed, river section, and local scales. The monitoring data is integrated into a webGIS platform, which is used for spatial analysis and visualization of the data. In-depth quantitative analysis is conducted on the analyzed dataset, and the results are applied to water resource management and ecological protection through statistical charts and quantitative indicators.

[0018] The beneficial effects of this invention are as follows: Based on the concept of hierarchical classification, this invention establishes a method for extracting sediment from surface water bodies in rivers with high sediment content. Through dynamic monitoring of sediment content, it can more accurately reflect changes in water bodies. In particular, sediment changes in water bodies exhibit significant spatiotemporal differences under different seasons and climatic conditions. In traditional monitoring, areas with high sediment concentrations are often difficult to accurately capture using traditional monitoring methods. However, the monitoring method for sediment-laden rivers can overcome this problem and improve the accuracy of monitoring data. At the same time, a dynamic monitoring system for surface water bodies in rivers with high sediment content has been developed, revealing the spatiotemporal variation patterns of suspended sediment concentration in rivers with high sediment content. This provides important information for the study of sediment transport and riverbed evolution. Analyzing the spatiotemporal changes in suspended sediment concentration helps water resource managers formulate more reasonable water resource allocation and sediment management strategies. The dynamic changes in sediment content not only affect water quality but may also affect the diffusion and deposition of pollutants. By monitoring sediment changes, it is possible to accurately assess water pollution sources, the migration paths of pollutants in water bodies, and the potential long-term impacts, thereby improving water resource utilization efficiency. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In document 202410138313.7, a trained surface water classification model is used to obtain the category of pixels in the image to be processed. Based on the category of pixels in the image to be processed, the water coverage of the corresponding pixels at different times is determined, and the surface water changes in the area to be processed are analyzed based on the water coverage. This obtains remote sensing data on human-caused changes in rivers and lakes, improving the accuracy of data acquisition on changes in rivers and lakes and enabling precise evidence collection of river and lake encroachment events. However, it cannot analyze the spatiotemporal variation characteristics of different types of surface water bodies in rivers with high sediment content. In this application, based on the concept of classification and grading, surface water bodies are divided into clean water bodies, slightly turbid water bodies, moderately turbid water bodies, and heavily turbid water bodies according to the concentration (or turbidity) of suspended particulate matter. For each type of water body, the best water body index is selected for water body extraction. Multivariate data are integrated to create a water body classification template, and the template matching method is used to further classify surface water bodies.

[0023] according to Figure 1 As shown, this embodiment provides a method for classifying and dynamically monitoring surface water bodies in rivers with high sediment content, including the following steps;

[0024] Step 1: First, collect river sediment data, use spectral measurement to obtain suspended sediment concentration, and classify river water with high sediment content into clear water, slightly turbid water and moderately turbid water according to suspended sediment concentration;

[0025] Step 2: Using a hierarchical classification approach, after classification, select the optimal water body index for each type of water body to identify the water body.

[0026] Step 3: After classification, integrate OpenStreetMap data, lake datasets, reservoir datasets, land use products, and national watershed and river system data to create a surface water body classification template;

[0027] Step 4: Determine the water system level based on area and runoff relationship, and create a surface water body classification dataset year by year based on template matching method;

[0028] Step 5: Finally, using trend analysis, we analyze the spatiotemporal variation characteristics of each type of surface water body, and develop a dynamic monitoring system for surface water bodies based on the webGIS platform.

[0029] In step one, the location of the sampling points is first determined, including upstream, midstream and downstream. The collected water samples are brought back to the laboratory for filtration and drying to separate suspended sediment. The volume of the water samples before and after filtration and the mass of the sediment after drying are recorded. Rivers with high sediment content are rich in sediment, and the suspended sediment concentration is closely related to the spectral characteristics of the water body. Rivers with high sediment content are classified by spectral measurement range.

[0030] In step two, the water body index that performs best within the spectral measurement range was determined through analysis and experimental verification of the sample data in the classification and grading strategy. The water body index performs best when the spectral measurement range is low, while it can more accurately capture the water body boundary when the spectral measurement range increases to a certain interval. The adaptive method of dynamically selecting the optimal index based on the actual spectral measurement range fully considers the spectral heterogeneity of rivers with high sediment content, significantly improves the targeting and accuracy of water body identification, and effectively avoids the limitations of traditional global water body indices in different turbidity regions.

[0031] In step two, indices reflecting the concentration of suspended sediment in the water body include turbidity, transparency, reflectance, and spectral slope. These indices are obtained by inverting on-site satellite remote sensing data. By analyzing the distribution of different water body categories, the river water quality is assessed.

[0032] In step three, a surface water classification template is created by integrating OpenStreetMap data, lake datasets, reservoir datasets, land use products, and national watershed data. Model analysis is performed based on the geographical characteristics of sediment-laden rivers, and flow rate, velocity, and slope are sampled to assess the accuracy of the classification. Based on the collected data and selected features, a classification template is designed. The classification template is typically composed of spectral information of different water body types. The classification template is created based on factors such as sediment content, flow velocity, and seasonal variations. The long-term trend and seasonal variation of suspended sediment concentration are analyzed using datasets, and statistical methods are used for verification. The collected data is then input into the water body classification template for validation and calibration.

[0033] In step four, regarding the classification of rivers, lakes, and reservoirs, they are divided into main streams and first-order tributaries based on area and confluence. The main stream serves as the primary channel of a river, with first-order tributaries flowing directly into it. For lakes, they are classified by size into large, medium, and small. Lake area is an important indicator of its ecological function and resource value. Large lakes typically have significant water storage and ecological capacity, playing a crucial role in regulating regional climate and maintaining biodiversity. Medium-sized lakes also have a certain influence on regional water resource allocation and ecological balance. Small lakes play a unique role in the local ecosystem and water resource utilization. Classification allows for a more intuitive assessment of the status and role of lakes of different sizes in the ecology and economic development of sediment-laden rivers. Regarding reservoir classification, as semi-artificial water bodies, reservoirs are categorized into large, medium, and small based on their capacity. Reservoir capacity reflects a reservoir's water storage and supply potential, which is crucial for water resource management and allocation. Large reservoirs typically undertake important tasks such as flood control, irrigation, and power generation, playing a central role in regional water resource allocation. Medium and small reservoirs, on the other hand, are significant in meeting local water demand and regulating runoff. This classification method helps to comprehensively understand the functions and roles of reservoirs, providing a scientific basis for the rational planning and management of reservoir resources.

[0034] In step four, as the tributary level gradually decreases, its scale and influence relatively diminish. Simultaneously, a webGIS platform is used for spatiotemporal analysis to create a time-series map of suspended sediment concentration, identifying changes in suspended sediment concentration over different time periods. By analyzing the spatial distribution of suspended sediment concentration and sediment transport pathways, a surface water body classification dataset is produced year by year. The dataset typically includes multispectral reflectance data, with each water body type having its unique spectral reflectance pattern, helping to identify different water body types. Clear water areas usually have lower reflectance, while turbid water areas have higher turbidity and exhibit stronger reflectance. The classification method based on confluence relationships clearly demonstrates the hierarchical structure of the river system, which is helpful for in-depth research on river dynamics, water resource allocation, and ecological environment changes.

[0035] In step five, trend analysis is used to analyze the changing trend of suspended sediment concentration over time. The monitoring data is integrated into a webGIS platform, which is used for spatial analysis and visualization of the data. Through the dataset, in-depth quantitative analysis is conducted, and the results are presented through statistical charts and quantitative indicators. The annual total water surface area, permanent water surface area, and seasonal water surface area of ​​the main stream of rivers, tributaries at all levels, lakes of different sizes, and reservoirs of different sizes are generated and analyzed. The original unclassified annual water body distribution map is transformed into a series of annual time series datasets classified by category. Through the dataset, in-depth quantitative analysis is conducted, and the results are applied to water resource management and ecological protection through statistical charts and quantitative indicators.

[0036] This method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers involves collecting necessary hydrological data, sediment sample data, and remote sensing image data. Field sampling is conducted at selected sampling points to collect water samples, which are then analyzed in the laboratory to determine suspended sediment concentration. The collected data is cleaned, corrected, and standardized. Based on suspended sediment concentration, water bodies are classified into rivers, lakes, and reservoirs. A mathematical model describing the relationship between suspended sediment concentration and hydrological and topographic factors is established. Statistical models between suspended sediment concentration and hydrological and topographic factors are built using statistical methods. Based on the geographical characteristics and hydrological conditions of sediment-laden rivers, stratified random sampling is conducted, and field monitoring is carried out at selected sample points to collect data. The collected data is input into the model for validation, and the accuracy of the model is evaluated. The validated data is integrated into a webGIS platform for spatiotemporal distribution analysis of suspended sediment concentration, tracking sediment transport paths, identifying depositional areas, and formulating water resource management strategies based on the analysis results. Finally, the analysis results are applied to water resource management and ecological protection.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for classifying and dynamically monitoring surface water bodies in rivers with high sediment content, characterized in that, Includes the following steps; Step 1: First, collect river sediment data, use spectral measurement to obtain suspended sediment concentration, and classify river water with high sediment content into clear water, slightly turbid water and moderately turbid water according to suspended sediment concentration; Step 2: Using a hierarchical classification approach, after classification, select the optimal water body index for each type of water body to identify the water body. Step 3: Integrate OpenStreetMap data, lake datasets, reservoir datasets, land use products, and national watershed and river system data to create a surface water body classification template; Step 4: Determine the water system level based on area and runoff relationship; and create a surface water body classification dataset annually based on the annual surface water body identification results using the template matching method. Step 5: Finally, using trend analysis, we analyze the spatiotemporal variation characteristics of each type of surface water body, and develop a dynamic monitoring system for surface water bodies based on the webGIS platform.

2. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step one, the location of the sampling point is first determined, the collected water sample is filtered and dried, the volume of the water sample before and after filtration and the mass of sediment after drying are recorded, and the river water with high sediment content is classified by spectral measurement range.

3. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step two, after classification, the water index that performs best within the spectral measurement range is determined through analysis of sample data and experimental verification.

4. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step two, the indices reflecting the concentration of suspended sediment in the water body include turbidity, transparency, reflectance, and suspended particulate matter concentration. These indices are obtained by inversion from on-site satellite remote sensing data.

5. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step three, a surface water classification template is created by integrating OpenStreetMap data, lake datasets, reservoir datasets, land use products, and national watershed data. Model analysis is performed based on the geographical characteristics of sediment-laden rivers, and flow rate, velocity, and slope are sampled. The long-term trend and seasonal variation of suspended sediment concentration are analyzed through dataset analysis, and statistical methods are used for verification. The collected data is then input into the water classification template.

6. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step four, regarding the classification of rivers, lakes, and reservoirs, they are divided into main streams and first-order tributaries based on their area and confluence relationship. The main stream is the main channel of the river, and the first-order tributaries flow directly into the main stream. In terms of lake classification, they are divided into large, medium, and small according to their area size. In terms of reservoir classification, reservoirs, as semi-artificial water bodies, are divided into large, medium, and small according to their storage capacity.

7. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step four, after classification, GIS tools are used to perform spatiotemporal analysis, create a time series map of suspended sediment concentration, identify changes in suspended sediment concentration over different time periods, and identify potential sediment deposition areas by analyzing the spatial distribution of suspended sediment concentration and sediment transport paths, thus producing a surface water body classification dataset year by year.

8. The method for classifying and dynamically monitoring surface water bodies in sediment-laden rivers according to claim 1, characterized in that: In step five, multi-scale monitoring is carried out on the surface of rivers with high sediment content, including monitoring at the watershed, river section, and local scales. The monitoring data is integrated into a webGIS platform, and spatial analysis and visualization of the data are performed using the webGIS platform. In-depth quantitative analysis is conducted through the analyzed dataset, and the analysis results are applied to water resource management and ecological protection through statistical charts and quantitative indicators.

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