River section extraction method and system, electronic device and storage medium

Abstract

The invention relates to a river section extraction method and system, an electronic device and a storage medium, and belongs to the field of hydrological remote sensing, and the method comprises the steps: obtaining satellite data, and obtaining a water level-river width original data set and a historical river width data set of a river section; screening the water level-river width original data set to obtain a water level-river width data set; obtaining a median river width by using the historical river width data set, and obtaining a median section area by combining the median river width with the existing median runoff and gradient; using the median river width to obtain a median water level from the water level-river width data set; obtaining a low water level control point according to the median river width, the median section area and the median water level; obtaining a high water level control point according to the water level-river width data set; and according to the water level-river width data set, the low water level control point and the high water level control point, generating a water level-river width hydrographic curve of the river section, and completing the extraction of the river section.

Description

Technical Field

[0001] The invention relates to a river section extraction method, system, electronic device and storage medium, belonging to the field of hydrological remote sensing. Background Art

[0002] Rivers are an important part of the global water cycle, and river runoff estimation plays an important role in preventing floods and protecting biodiversity. Traditional hydrological stations have a limited range of runoff monitoring, but remote sensing technology, thanks to its large-scale continuous observation characteristics, makes it possible to obtain spatiotemporal continuous runoff estimates on a large scale. Using satellite remote sensing to obtain water levels or river widths and then inverting runoff is one of the widely used runoff estimation methods. The accuracy of this method depends on river section information. However, most existing studies obtain high-precision river section information through local hydrological stations or drones, which limits the application of remote sensing runoff estimation in data-deficient areas such as western my country.

[0003] In recent years, with the development of remote sensing technology and the improvement of computing power, researchers have been able to obtain long-term water level and river width data with high spatial resolution on a global scale. In this context, some researchers have proposed to rely only on multi-source satellite remote sensing, couple water level, river width and other observation elements, and construct water level-river width hydrological curves by solving or interpolating all water level-river width data pairs to achieve river section reconstruction and estimate runoff based on this. However, remote sensing data has high uncertainty, and the sections above the water surface that can be directly observed are often limited in range. Directly using remote sensing data without quality control and using mathematical methods to extrapolate underwater sections may produce large deviations.

[0004] In view of the problems that remote sensing cannot observe underwater topography, the quality of remote sensing data is uneven, and the uncertainty of directly extrapolating underwater sections is large, the present invention proposes a river section extraction method that combines remote sensing height measurement and prior knowledge, performs a series of quality controls on remote sensing data, and introduces prior knowledge to constrain uncertainty in the process of constructing the water level-river width hydrological curve, which is of great significance for improving the accuracy of remote sensing runoff estimation in practice.

[0005] Chinese invention patent CN112729258B discloses a method for continuous measurement of river flow based on satellite big data: The first step is to select a flow test section and select a gradient section at a certain distance upstream and downstream.

[0006] The second step is to establish a water level-time process line diagram of the flow test section based on the altimetry satellite or three-dimensional imaging mapping satellite, and to establish a river width-time process line diagram of the flow test section based on the orthophoto remote sensing image; interpolate the river width when the satellite observes the water level in the water level-time process line diagram, and interpolate the water level when the satellite observes the river width in the river width-time process line diagram; establish the water level-river width relationship function of the flow test section based on all the interpolated water level-river width data points; similarly, obtain the water level-river width relationship function of the gradient section.

[0007] The third step is to measure the river width data of the flow test section and the upper and lower gradient sections through real-time data from orthophoto remote sensing satellite images, and calculate the real-time water level based on the water level-river width relationship; and obtain the roughness coefficient by referring to the natural river roughness table in the hydraulics textbook.

[0008] The fourth step is to obtain the gradient by dividing the water level difference between the upper and lower gradient sections by the distance; based on the water level-river width relationship function of the flow test section, use triangular similarity and other methods to extrapolate the lowest point water level (that is, use triangles to approximate the unobservable underwater terrain); based on the complete water level-river width relationship function, obtain the water-passing cross-sectional area and hydraulic radius at any water level; calculate the flow corresponding to the real-time water level of the flow test section according to the hydraulic Manning formula.

[0009] However, this Chinese invention patent has the following disadvantages: First, there is a lack of necessary quality control of remote sensing data. Water level and river width data obtained from satellite remote sensing may have large measurement errors and need to be screened before use, but existing technologies ignore this point.

[0010] Second, the extrapolation of underwater terrain that cannot be observed is too simple. The existing technology only relies on the lowest two water level-river width observation data points and uses the triangular similarity method to extrapolate the lowest point of the surface, ignoring the continuity of the above-water and underwater parts of the cross-section.

[0011] Third, the prior art does not explain how to obtain a continuous water level-river width hydrological curve from discrete water level-river width data points. Due to data quality issues, water level-river width data often have consistency issues, that is, the abnormal situation of river width increasing but water level decreasing may occur. Therefore, it is not reasonable to directly perform linear interpolation on all data points.

[0012] In view of the above shortcomings, the present invention first proposes a series of quality control methods for remote sensing data, striving to screen out accurate and reliable data. Secondly, for underwater terrain that cannot be observed, the present invention introduces prior knowledge obtained from multi-source geographic data to construct two high and low control points, thereby constraining the uncertainty of cross-section extraction. Finally, through nonlinear regression fitting of data points, a continuous water level-river width hydrological curve is obtained. Summary of the invention

[0013] In view of the limitation that it is difficult to directly observe underwater terrain using remote sensing, this paper proposes a river section extraction method that combines remote sensing height observation and prior knowledge to characterize the shape characteristics of the river section. Its characteristics are that the coupled remote sensing observed water level-river width data points are extrapolated to the underwater area that cannot be directly observed, and by controlling the quality of remote sensing data and introducing prior knowledge to constrain uncertainty, it provides scientific and technological support for further improving the accuracy of remote sensing runoff estimation results in areas with insufficient data.

[0014] A first aspect of the present invention provides a river section extraction method, comprising: Acquire satellite data to obtain a water level-river width original data set and a historical river width data set of the river section; Screening the water level-river width original data set to obtain a water level-river width data set; The median river width is obtained by using the historical river width dataset, and the median cross-sectional area is obtained by combining the current median runoff and gradient; Using the median river width, obtaining a median water level from the water level-river width dataset; Obtaining a low water level control point according to the median river width, the median cross-sectional area and the median water level; Obtaining a high water level control point according to the water level-river width data set; A water level-river width hydrological curve of the river section is generated according to the water level-river width data set, the low water level control point and the high water level control point, thereby completing the extraction of the river section.

[0015] Furthermore, the screening includes screening according to uncertainty, screening according to inverse relationship and screening according to outliers.

[0016] Furthermore, the historical river width dataset is used to determine the river section extraction range, and the median river width is further obtained.

[0017] Furthermore, the historical river width data set is used to calculate the river width corresponding to the high water level control point and the river width corresponding to the low water level control point, and the river section extraction range is determined based on the two.

[0018] Furthermore, the median runoff is obtained through the existing global runoff simulation data set, the gradient is obtained through the existing global river attribute data set, and the median cross-sectional area is obtained through the Manning formula.

[0019] Furthermore, the water level of the high water level control point is the highest water level in the water level-river width data set plus a set height difference.

[0020] Furthermore, the water level-river width hydrological curve is fitted by a power function.

[0021] A second aspect of the present invention provides a river section extraction system, comprising: A data acquisition module is used to acquire satellite data to obtain a water level-river width original data set and a historical river width data set of the river section; A data screening module, used for screening the water level-river width original data set to obtain a water level-river width data set; A parameter calculation module is used to obtain the median river width using the historical river width data set, and to obtain the median cross-sectional area by combining the existing median runoff and gradient; and to obtain the median water level from the water level-river width data set using the median river width; A control point calculation module, used to obtain a low water level control point according to the median river width, the median cross-sectional area and the median water level; and obtain a high water level control point according to the water level-river width data set; The river section extraction module is used to generate the water level-river width hydrological curve of the river section according to the water level-river width data set, the low water level control point and the high water level control point, so as to complete the river section extraction.

[0022] A third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program includes instructions for executing the above method.

[0023] A fourth aspect of the present invention provides a storage medium storing a computer program, wherein the computer program implements the above method when executed by a computer.

[0024] The beneficial effects of the present invention are as follows: 1. The prior art lacks the necessary quality control of remote sensing data. The present invention proposes a quality control method for water level-river width data to prevent unreliable data from affecting subsequent extraction sections.

[0025] 2. The prior art is too simple to extrapolate the underwater terrain that cannot be observed. The present invention uses a variety of prior knowledge to propose a method for calculating high and low water level control points, which effectively constrains the uncertainty of the water level-river width hydrological curve.

[0026] 3. The prior art does not explain how to obtain a continuous water level-river width hydrological curve from discrete water level-river width data points. The present invention proposes to fit the water level-river width hydrological curve using weighted nonlinear regression to ensure the monotonicity of the curve, while adding constraints of high and low water level control points to ensure that the hydrological curve extrapolated from the water level-river width sample data is more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of an embodiment of the present invention.

[0028] Figure 2 A schematic diagram of a historical river width distribution histogram calculated according to an embodiment of the present invention.

[0029] Figure 3 This is a comparison chart of the water level-river width hydrological curve calculated by an embodiment of the present invention and the measured results.

[0030] Figure 4 It is a framework diagram of the river section extraction system in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present invention is further described in detail below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention but not to limit the scope of the present invention.

[0032] The embodiment of the present invention provides a method for extracting river sections, the specific process is as follows: Figure 1 shown.

[0033] Step 1: Obtain and extract the original data of the river section based on remote sensing satellite observation data.

[0034] 1.1 Select the river section to be studied, determine the node closest to the river section in space among the static river nodes pre-defined by the Surface Water and Ocean Topography Satellite (SWOT), and obtain the temporally coupled water level data from this node. With river width data , and the corresponding uncertainty estimates, the original data set of water level-river width of the river section is obtained.

[0035] 1.2 Obtain orthophoto remote sensing images from 1984 to 2023 from Landsat, and extract water body pixels through water body classification algorithm; based on the existing river centerline data, construct a buffer zone with a size of twice the average river width at the section within an interval of 300 meters upstream and downstream of the river section, and divide the water area in the buffer zone by the 600-meter buffer zone length to obtain the historical river width dataset of the river section.

[0036] Step 2: Obtain river channel prior knowledge based on multi-source geographic data.

[0037] 2.1 Using the historical river width dataset obtained in step 1.2, construct a river width distribution histogram with 20 groups, and take the upper boundary of the last group with a frequency greater than 3% as the high water level river width The lower boundary of the first group with a frequency greater than 3% is taken as the low water level river width The frequency is the percentage of river width observations in the data to the total number of observations. The river width at low water level The interval between the two is used as the river section extraction range, such as Figure 2 As shown in Figure 2, the median river width of the river section is calculated using the historical river width dataset. This step will limit the study area to the common width of the river.

[0038] 2.2 Using the existing global runoff simulation dataset, calculate the median runoff ; Use the existing global river attribute dataset to obtain the gradient of the river section ; Manning roughness coefficient According to experience, it is set to 0.035.

[0039] Step 3 performs quality control on the original water level-river width dataset.

[0040] 3.1 Based on the uncertainty estimate obtained in step 1.1, remove the water level-river width data with relative river width uncertainty greater than 0.4 or water level uncertainty greater than 0.1 m from the original water level-river width data set obtained in step 1.1. The purpose of this step is to remove water level-river width data with excessive measurement uncertainty.

[0041] 3.2 For any two water level-river width data i and j in the water level-river width data screened in step 3.1, if and ,or and , then the relationship between the two data is defined as a "reverse" relationship. After traversing and calculating the pairwise relationships of all water level-river width data, remove the water level-river width data with the most reverse relationships. Repeat step 3.2 until the reverse ratio of each water level-river width data in the relationship with all other data is less than 50%, where the reverse ratio is the ratio of the number of reverse relationships between a data and all other data to the total number of relationships between the data and all other data. The purpose of this step is to remove water level-river width data that are inconsistent with the relative size relationship of water level-river width of most data and are more likely to have deviations.

[0042] 3.3 Calculate the sample median water level of the water level-river width data filtered in step 3.2 , remove outliers whose water level deviates from the sample median water level by more than twice the sample water level standard deviation, and whose river width is less than the low water level river width Or greater than the river width at high water level The purpose of this step is to remove outliers in height and width. Through steps 3.1, 3.2 and 3.3, the screened water level-river width data set is obtained, which achieves the purpose of removing data with large deviations and low quality, thereby optimizing the water level-river width data and completing quality control.

[0043] Step 4: Calculate the key control points of the water level-river width hydrological curve.

[0044] 4.1 The hydraulic radius can be approximately replaced by the river width, according to the hydraulic Manning formula. , solve the equation to get the median cross-sectional area .

[0045] 4.2 From the water level-river width dataset filtered in step 3.3, select the river width with the median The 5 water levels with the closest width to the river width data are linearly regressed and the median river width is used Water level prediction in linear regression as median water level .

[0046] 4.3 Water level-river width curve below the median water level using power function Approximately, is the lowest water level, is the coefficient, is an exponent. The power function satisfies ,and ,in Set to 1, representing the triangular cross-section assumption. and By solving the above two equations simultaneously, we get:

[0047] 4.4 Calculation of low water level river width The corresponding water level on this power function , as a low-water level control point to constrain underwater section characteristics.

[0048] 4.5 The river bank is used as the high water level control point, representing the outer boundary of the section, and the river width is the high water level width. , water level Set as the highest water level in the filtered water level-river width dataset Add a specific height difference based on . Set to 0.2 times the water level range in the filtered water level-river width dataset.

[0049] Step 5: Calculate the water level-river width hydrological curve.

[0050] 5.1 Based on the filtered water level-river width data set obtained in step 3 and the high and low water level control points obtained in step 4, the water level-river width hydrological curve is obtained by weighted nonlinear regression and power function fitting. The regression weights of the high and low water level control points are . Weight Set to 0.5.

[0051] Taking the Nenjiang Dalai hydrological station as an example, all the water level-river width measurement data in the 2020 measured flow results table of the hydrological station recorded in the hydrological yearbook are taken as the measured results. The river width at the high water level control point calculated by the method of the present invention is 424.7 meters, the water level is 126.7 meters, the river width at the low water level control point is 197.6 meters, the water level is 123.1 meters, and the calculated water level-river width hydrological curve expression is: Since the measured and calculated elevation benchmarks are different and cannot be directly compared, we first determine the water level-river width data point where the water level is the median in the measured results, and adjust the elevation benchmark of the calculated water level-river width hydrological curve to coincide with this point. The water level-river width hydrological curve after the elevation benchmark adjustment is expressed as , the calculated results are compared with the measured results. Figure 3 As shown. Figure 3 It can be seen that for a river about 300 meters wide, the error between the calculated curve and 87.5% of the measured water level-river width data is within ±0.6 meters, which proves the effectiveness of this method.

[0052] The embodiment of the present invention also provides a river section system, the structure of which is as follows: Figure 4 As shown, it is used to implement the above method.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. A person skilled in the art may modify or make equivalent substitutions for the technical solutions of the present invention without departing from the spirit and scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. A method for extracting a river section, comprising: Acquire satellite data to obtain a water level-river width original data set and a historical river width data set of the river section; Screening the water level-river width original data set to obtain a water level-river width data set; The median river width is obtained by using the historical river width dataset, and the median cross-sectional area is obtained by combining the current median runoff and gradient; Using the median river width, obtaining a median water level from the water level-river width dataset; Obtaining a low water level control point according to the median river width, the median cross-sectional area and the median water level; Obtaining a high water level control point according to the water level-river width data set; A water level-river width hydrological curve of the river section is generated according to the water level-river width data set, the low water level control point and the high water level control point, thereby completing the extraction of the river section.

2. The method according to claim 1, characterized in that The screening includes screening according to uncertainty, screening according to inverse relationship and screening according to outliers.

3. The method according to claim 1, characterized in that The historical river width dataset is used to determine the river section extraction range, and further obtain the median river width.

4. The method according to claim 3, characterized in that The historical river width data set is used to calculate the river width corresponding to the high water level control point and the river width corresponding to the low water level control point, and the river section extraction range is determined based on the two.

5. The method according to claim 1, characterized in that The median runoff is obtained through the existing global runoff simulation data set, the gradient is obtained through the existing global river attribute data set, and the median cross-sectional area is obtained through the Manning formula.

6. The method according to claim 1, characterized in that The water level of the high water level control point is the highest water level in the water level-river width data set plus a set height difference.

7. The method according to claim 1, characterized in that The water level-river width hydrological curve is fitted by a power function.

8. A river section extraction system, comprising: A data acquisition module is used to acquire satellite data to obtain a water level-river width original data set and a historical river width data set of the river section; A data screening module, used for screening the water level-river width original data set to obtain a water level-river width data set; A parameter calculation module is used to obtain the median river width using the historical river width data set, and to obtain the median cross-sectional area by combining the existing median runoff and gradient; and to obtain the median water level from the water level-river width data set using the median river width; A control point calculation module, used to obtain a low water level control point according to the median river width, the median cross-sectional area and the median water level; and obtain a high water level control point according to the water level-river width data set; The river section extraction module is used to generate the water level-river width hydrological curve of the river section according to the water level-river width data set, the low water level control point and the high water level control point, so as to complete the river section extraction.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program includes instructions for executing the method according to any one of claims 1 to 7.

10. A storage medium storing a computer program, wherein when the computer program is executed by a computer, the method according to any one of claims 1 to 7 is implemented.

Images