Flood process analysis method based on remote sensing

Abstract

The invention belongs to the technical field of hydrology, and provides a flood process analysis method and device based on remote sensing, electronic equipment and a storage medium, and the method comprises the steps: extracting the water surface area of each typical river reach from a current remote sensing image of a to-be-analyzed river reach when it is determined that a flood occurs in the to-be-analyzed river reach; obtaining topographic data of the to-be-analyzed river reach, and according to the water surface area and the topographic data of each typical river reach, determining the current flood water volume and the position of a flood peak of each unit river reach in the corresponding typical river reach; according to each current flood water volume, the position of the flood peak, the rainfall data and the air temperature data of the area of the to-be-analyzed river reach and the position of the barrier lake, the flood occurrence position of the to-be-analyzed river reach is identified, and the position of the barrier lake is obtained according to the current remote sensing image; based on a Muskingum method, generating a flood hydrograph of the to-be-analyzed river reach by utilizing the current flood water volume and the flood occurrence position; and determining the flood peak flow, the peak present time and the total flood amount based on the flood hydrograph. The flood process analysis is realized by using the remote sensing image.

Description

Technical Field

[0001] The present invention belongs to the field of hydrological technology, and in particular relates to a flood process analysis method, device, electronic equipment and storage medium based on remote sensing. Background Art

[0002] In the current context of global climate change and frequent extreme weather events, the frequency and intensity of floods have increased significantly. Accurate identification of flood processes is crucial to hydrological research and flood prevention and disaster reduction. Especially in mountainous canyon areas, the river channels are narrow, the slopes are steep, and the terrain is complex. These natural conditions lead to significant spatiotemporal changes in flood characteristics, which brings many challenges to traditional monitoring. Traditional flood monitoring mainly relies on data from ground hydrological stations, such as flow and water level information. However, the spatial distribution of hydrological stations is usually uneven, and it is difficult to fully cover the dynamics of the basin in mountainous canyon areas. Therefore, ground data is difficult to fully reflect the flood characteristics of the area. In addition, due to the sparse distribution of hydrological stations in areas with complex terrain, it is impossible to capture the spatial expansion and rapid changes of floods in real time.

[0003] With the rapid development of remote sensing technology, multi-temporal, high-resolution satellite images have provided new possibilities for flood monitoring. By extracting water boundary information in rivers through remote sensing images, the distribution and expansion range of water bodies can be monitored on a larger spatial scale. However, existing remote sensing methods usually focus on static water boundary extraction, which is difficult to adapt to the needs of dynamic changes in floods, especially in mountain rivers. The flood process in these areas is affected by multiple factors such as terrain slope and rainfall heterogeneity. Therefore, traditional static remote sensing methods are difficult to accurately capture the spatiotemporal evolution of floods and changes in flood intensity. Summary of the invention

[0004] To solve the above problems, the present disclosure provides a flood process analysis method, device, electronic device and storage medium based on remote sensing, the purpose of which is to analyze the flood process by using remote sensing images.

[0005] In order to achieve the above objectives, the present disclosure mainly provides the following technical solutions:

[0006] In a first aspect, the present disclosure provides a flood process analysis method based on remote sensing, comprising:

[0007] When it is determined that a flood occurs in the river section to be analyzed, extracting the water surface area of ​​each typical river section in the current remote sensing image of the river section to be analyzed;

[0008] Acquire the topographic data of the river section to be analyzed, and determine the current flood water volume and the location of the flood peak of each unit river section in the corresponding typical river section according to the water surface area of ​​each typical river section and the topographic data;

[0009] Identify the flood occurrence location of the river section to be analyzed based on the current flood water volume, the location of the flood peak, the rainfall data and temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake, wherein the location of the barrier lake is obtained based on the current remote sensing image;

[0010] Based on the Muskingum method, the flood process line of the river section to be analyzed is generated by using the current flood water volume and the flood occurrence location;

[0011] The flood peak flow, peak occurrence time and total flood volume are determined based on the flood process line.

[0012] In a second aspect, the present disclosure provides a flood process analysis device based on remote sensing, the device comprising:

[0013] An extraction unit is used to extract the water surface area of ​​each typical river section in the current remote sensing image of the river section to be analyzed when it is determined that a flood occurs in the river section to be analyzed;

[0014] A first determination unit is used to obtain the topographic data of the river section to be analyzed, and determine the current flood water volume and the location of the flood peak of each unit river section in the corresponding typical river section according to the water surface area of ​​each typical river section and the topographic data;

[0015] an identification unit, for identifying the flood occurrence location of the river section to be analyzed according to the current flood water volume, the location of the flood peak, the rainfall data and the temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake, wherein the location of the barrier lake is obtained according to the current remote sensing image;

[0016] A generating unit, used for generating a flood process line of the river section to be analyzed by using the current flood water volume and the flood occurrence location based on the Muskingum method;

[0017] The second determination unit is used to determine the flood peak flow, peak time, and total flood volume based on the flood process line.

[0018] On the other hand, the present disclosure further provides a storage medium, which is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method of the first aspect above.

[0019] On the other hand, the present disclosure also provides an electronic device, comprising at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; and the processor is used to call program instructions in the memory to execute the method of the first aspect as described above.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] When determining that a flood has occurred in a river section to be analyzed, the present disclosure extracts the water surface area of ​​each typical river section from the current remote sensing image of the river section to be analyzed, and uses the topographic data of the river section to be analyzed and the water surface area of ​​the typical river section to determine the current flood water volume of each unit river section in the corresponding typical river section. By using the current flood water volume of each unit river section, the rainfall data of the area where the river section to be analyzed is located, and the Muskingum method to calculate the entire process curve of the flood in the river section to be analyzed, the present disclosure uses remote sensing images to realize the analysis of the flood process.

[0022] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A schematic diagram of a process of flood process analysis based on remote sensing according to an embodiment of the present disclosure is shown;

[0025] Figure 2 A schematic diagram of a river section cross section showing changes in water volume according to an embodiment of the present disclosure;

[0026] Figure 3 A schematic diagram showing changes in water surface area of ​​a typical river section according to an embodiment of the present disclosure is shown;

[0027] Figure 4 A schematic diagram of another process of flood process analysis based on remote sensing according to an embodiment of the present disclosure is shown;

[0028] Figure 5 A schematic structural diagram of a flood process analysis device based on remote sensing according to an embodiment of the present disclosure is shown;

[0029] Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0031] Figure 1 A schematic diagram of a flood process analysis method based on remote sensing according to an embodiment of the present disclosure is shown. Figure 1 As shown, the flood process analysis method based on remote sensing in the embodiment of the present disclosure includes:

[0032] 101. When it is determined that a flood occurs in the river section to be analyzed, the water surface area of ​​each typical river section is extracted from the current remote sensing image of the river section to be analyzed.

[0033] The river section to be analyzed includes a plurality of typical river sections, and each typical river section is divided into a plurality of unit river sections.

[0034] In this step, the current remote sensing image of the river section to be analyzed and the monitoring data of the hydrological station can be combined to determine whether the current river section to be analyzed is flooding. If it is determined that the river section to be analyzed is flooding, the current remote sensing image of the river section to be analyzed will be obtained, and the water body boundary of the river section to be analyzed will be extracted from the current remote sensing image to obtain the water surface area of ​​the river section to be analyzed. Multiple typical river sections are determined in the river section to be analyzed, and based on the multiple typical river sections, the water surface area of ​​each typical river section is extracted from the water surface area of ​​the river section to be analyzed. Each typical river section in the embodiment of the present disclosure will be divided into multiple unit river sections, so as to analyze the entire process of this flood based on each unit river section.

[0035] In a feasible manner, the specific steps of extracting the water surface area of ​​each typical river section from the current remote sensing image of the river section to be analyzed are as follows:

[0036] Get the current remote sensing image of the river section to be analyzed, extract the water body boundary of the river section to be analyzed through NDWI, and combine the extraction results of typical river sections to be analyzed to generate the surface vector files of each typical river section. Based on this part of the vector file, calculate the water surface area of ​​each typical river section. NDWI distinguishes water bodies from other landforms through spectral information, and its formula is as follows:

[0037]

[0038] The specific method of identifying floods in the embodiment of the present disclosure is as follows:

[0039] 1011. Extract multiple typical river sections from the target river section.

[0040] The typical river section is the river section extracted from the isoflux river section of the target river section. The isoflux river section is the river section with a small change in runoff flux, for example, the change in the cumulative number of inlet confluence and the cumulative number of downstream outlet confluence of a river section with stable runoff flux is only 10%.

[0041] In this step, based on the digital elevation model (DEM) data of the area to be identified, firstly, unreasonable depressions can be eliminated by filling depressions to generate a depression-free elevation model; then, the D8 algorithm is used to calculate the water flow direction of the grid unit, and the upstream water flow is accumulated based on the flow direction data to generate a confluence accumulation grid map; the main water flow path is extracted according to the set confluence threshold, and the river network raster data is generated and vectorized and graded; finally, the watershed boundary and pouring point are determined by watershed analysis, and the small watershed is divided based on the pouring point, and a detailed analysis result including the distribution of the watershed and the river network is generated. The target river section in the embodiment of the present disclosure belongs to the main stream in the river network.

[0042] The isoflux reaches are determined as follows:

[0043] 1011a. Determine a plurality of reference points in the flow accumulation grid map.

[0044] The benchmark point is located in the river section. The benchmark point is a point on the river section in the confluence accumulation grid map, and each benchmark point corresponds to a grid.

[0045] In this step, multiple benchmark points are determined in the runoff accumulation grid map, and the equal flux river sections containing the benchmark points can be determined based on the benchmark points. The number and location of the benchmark points can be selected according to the actual scenario. For example, if the river section is long, the number of benchmark points can be large. It is also possible to determine each hydrological station or part of the hydrological stations on the river section in the area to be analyzed as benchmark points.

[0046] In one achievable manner, the specific implementation of determining the reference point is as follows:

[0047] Step 1: Use digital elevation model data to obtain the location information of each hydrological station in the river section.

[0048] In the disclosed embodiment, the digital elevation model data has all geographic information of the area to be analyzed, and correspondingly, the location information of each hydrological station monitoring the river section within the area to be analyzed is also recorded in the digital elevation model data.

[0049] Step 2: According to the location information, determine the corresponding reference point in the confluence accumulation grid map.

[0050] In this step, after obtaining the location information of all hydrological stations monitoring the river section based on the digital elevation model data, the grid corresponding to the hydrological station can be determined using the location information of the hydrological station and the correspondence between the location information and the corresponding grid in the confluence accumulation grid map. The position of the grid is the position of the reference point.

[0051] The disclosed embodiment determines each hydrological station in a river section as a reference point. Since the hydrological station itself monitors and records hydrological data, such as flow rate and other data, the hydrological data of the hydrological station itself can be conveniently used to verify the river section runoff flow monitoring data (remote sensing image) so as to further improve the accuracy of the river section runoff flow monitoring.

[0052] 1011b. Determine multiple equal flux river sections by using multiple benchmark points and the cumulative runoff of each grid in the runoff cumulative grid map.

[0053] Among them, the grid is the component unit of the runoff accumulation grid map, and all grids outline the entire river network in the area to be analyzed. Each grid has a runoff accumulation number. The runoff accumulation number is the accumulated amount of water flow. One benchmark point corresponds to an isoflux river section.

[0054] In this step, the D8 algorithm is used to calculate the water flow direction of the grid cells. The upstream grid numbers are accumulated based on the flow direction data, and the cumulative runoff of the entire river section can be statistically analyzed to determine the changing trend of the cumulative runoff in the river section. Based on the changing trend and the selected benchmark points, multiple equal flux river sections can be determined.

[0055] In one practicable manner, the specific implementation method of determining the isoflux river section is as follows:

[0056] Step 1: Use the cumulative runoff number and area of ​​each grid in the runoff accumulation grid map to obtain the runoff intensity of each grid.

[0057] Among them, the water catchment intensity is used to indicate the water catchment capacity of the corresponding grid.

[0058] In this step, the product of the accumulated runoff of the grid and the area of ​​the grid can be determined as the water collection intensity of the grid, or the product of the accumulated runoff of the grid, the area and the rainfall can be determined as the water collection intensity of the grid.

[0059] Exemplarily, the water harvesting intensity is determined as follows:

[0060] Step 1a: multiply the grid's cumulative flow accumulation by the grid's area to obtain the grid's flow accumulation area.

[0061] The formula for catchment area is as follows:

[0062] A = cumulative flow × grid area

[0063] Where A is the confluence area, m 2 .

[0064] Step 1b: Determine the catchment area as the catchment intensity of the grid.

[0065] Exemplarily, another way to determine the water-collection intensity is as follows:

[0066] Step 1a: multiply the accumulated flow of each grid by the corresponding area to obtain the flow area of ​​each grid.

[0067] Step 1b: Obtain rainfall data for the area to be analyzed.

[0068] In this step, satellite remote sensing rainfall data (such as TRMM or GPM) can be used to perform spatial interpolation of large-scale rainfall to obtain rainfall data for the area to be analyzed. And the satellite remote sensing rainfall data can be corrected by collecting rainfall station data in the area to be analyzed through the China Meteorological Network to ensure more accurate rainfall data.

[0069] Step 1c: Generate a rainfall distribution map based on rainfall data, use the rainfall distribution map to obtain the normalized rainfall weight of each grid, and calculate the initial flow of the corresponding grid based on the normalized rainfall weight.

[0070] The area of ​​each cell is the same, which is the unit area, and the unit is m 2 .

[0071] In this step, the rainfall data is the multi-year average rainfall, which is matched with the DEM data through spatial interpolation to ensure the consistency of spatial resolution. The obtained multi-year average rainfall distribution map needs to be normalized to serve as the weight file for the runoff accumulation calculation.

[0072] Specifically, the inverse distance weighted (IDW) method can be used to spread the point rainfall to the entire river network to generate a surface rainfall distribution map. The rainfall formula for the interpolation point in the surface rainfall distribution map is as follows:

[0073]

[0074] In the formula, (x,y) The rainfall at the point to be interpolated, mm, P i The rainfall at the i-th observation station, mm, d i The distance between the point to be interpolated and the i-th observation site, m, p represents the power of the distance, usually 2 represents inverse square weighting.

[0075] Generate rainfall distribution map: Save the rainfall data generated by IDW interpolation in raster format to generate a spatial distribution map of multi-year average rainfall.

[0076] Normalization of rainfall distribution map:

[0077] Purpose: To normalize the generated rainfall distribution map so that it can be used as a weight file for the runoff accumulation calculation, so that the rainfall data has a uniform dimension in spatial distribution. The normalized rainfall distribution map is used as a weight file for the runoff accumulation calculation.

[0078] Normalization of rainfall:

[0079]

[0080] Among them, W i，j is the normalized rainfall weight, G i，j The average annual rainfall of the grid, P min and P max They are the minimum and maximum rainfall in the rainfall distribution map, respectively.

[0081] 1. Calculate the initial flow of each grid

[0082] G per grid i The initial sink flow is:

[0083] P i，j =W i，j ×R×A i，j

[0084] Where P i，j G i，j Rainfall per cell, mm, W i，j is the corresponding normalized rainfall weight, mm, and R is the multi-year average maximum rainfall depth in the region (usually in millimeters or meters), which is used to convert the weight value into a specific rainfall depth. i，j is the area of ​​the grid, km 2 .

[0085] Step 1d: Perform slope analysis based on DEM data and generate a slope raster map.

[0086] Slope calculation: Perform slope analysis based on DEM data to generate slope raster data for simulating water flow paths and determining confluence relationships.

[0087] The DEM data is used to perform slope analysis to obtain the flow direction of each grid. The commonly used D8 algorithm (each grid flows to one of the eight adjacent grids around it) is used to calculate the slope and generate a slope grid map.

[0088] Step 1e: Calculate the total flow of the grid using the aspect grid map and the initial flow of the grid.

[0089] Confluence accumulation calculation: The flow is accumulated from the upstream grid to the downstream grid according to the aspect map.

[0090] The total flow rate of each grid is Q i，j Including its own rainfall sink P i，j And the flow rate transmitted from the upstream grid. The formula is as follows:

[0091]

[0092] In the formula, Q i，j is the grid G i，j Total sink flow; P i，j G i，j The initial flow rate of the grid; U i，j is the total grid G i，j The upstream grid collection of the confluence; Q m，n is the upstream grid G m,n The cumulative flow of m，n is the flow scale factor, representing the upstream grid G m，n The water reaches the downstream grid G i，j proportion.

[0093] Aspect weight f m，n :

[0094] Determine the flow ratio: If there are more than one downstream grids flowing from each upstream grid, you need to allocate the flow ratio. Assume that the flow is evenly distributed to all downstream grids:

[0095]

[0096] Step 1f: Determine the total catchment flow as the catchment intensity of the grid.

[0097] Step 2: Determine the grid where the reference point is located in the confluence accumulation grid map to obtain the first grid.

[0098] Step 3: Starting from the first grid, search for the second grid upstream of the first grid.

[0099] The variation range of the water collection intensity of the second grid is the same as that of the first grid and the preset range. For example, assuming the preset range is 5%, the water collection intensity of the first grid is 100, and the water collection intensity of the second grid is 95 or 105.

[0100] In this step, starting from the first grid, the second grid is searched along the upstream of the first grid, that is, along the upstream of the river section. For example, a grid with a water collection intensity change of 5% can be searched along the upstream direction of the first grid, and the grid is determined as the second grid.

[0101] Step 4: Starting from the first grid, search for the third grid downstream of the first grid.

[0102] The variation range of the water collection intensity of the third grid and the water collection intensity of the first grid is the same as the preset range.

[0103] Step 5: Use the position information of the second grid and the position information of the third grid to determine the isoflux river section corresponding to the reference point.

[0104] 1012. Obtain the current remote sensing image of the target river section.

[0105] In the embodiment of the present disclosure, fewer hydrological stations are set up in the target river section. In order to identify flood events more quickly, after the satellite captures the target river section, the current remote sensing image captured is used to identify whether a flood event has occurred.

[0106] 1013. Based on the baseline water surface area of ​​each unit river section in a typical river section and the current remote sensing image, identify whether a flood event has occurred in the target river section.

[0107] The typical river section is divided into a plurality of unit sections, and the lengths of the unit sections of the same typical river section are the same. The reference water surface area is obtained based on historical remote sensing images. The disclosed embodiment can obtain historical remote sensing images of the typical river section during the non-flood period, and extract the water surface area of ​​each unit section in the historical remote sensing images to obtain the reference water surface area of ​​each unit section.

[0108] In this step, the current water surface area of ​​each unit river section is extracted from the current remote sensing image, each current water surface area is compared with the corresponding reference water surface area one by one, and whether a flood event occurs in the target river section is identified based on the comparison result.

[0109] 102. Obtain the topographic data of the river section to be analyzed, and determine the current flood water volume and the location of the flood peak of each unit section in the corresponding typical river section based on the water surface area and topographic data of each typical river section.

[0110] The terrain data includes underwater terrain data and surface terrain data.

[0111] In this step, for each unit section in each typical river section, the area storage capacity curve of each unit section is constructed using the terrain data of the river section to be analyzed and the water surface set of the typical river section. And the water surface area of ​​each unit section is extracted from the water surface area of ​​the typical river section. For each unit section, the current flood water volume of the unit section is determined using the water surface area of ​​the unit section and the corresponding area storage capacity curve. Using the current flood water volume of each unit section, the location of the flood peak is identified by the flood volume change trend, that is, if the flood volume of the unit section continues to increase from downstream to upstream: the flood is in the process of rising, then extend to the entire equal flux section to search upstream, or find the flood peak through the remote sensing image at the next moment; if the flood volume of the unit section continues to decrease from downstream to upstream: the flood is in a receding state, then extend to the entire equal flux section to search downstream or find the flood peak through the remote sensing image at the previous moment. The flood volume of the unit section from downstream to upstream presents a small-large-small distribution: the current remote sensing image captures the flood process, and the flood peak is located in the unit section with the largest current flood volume.

[0112] Steps to construct river terrain:

[0113] 1. Extract waterside lines based on remote sensing images.

[0114] Optical remote sensing data is used to obtain river water morphology information under different water level conditions over the past 30 to 40 years, extract the NDWI of the water body, form a water body surface vector file, and obtain the water edge vector file.

[0115]

[0116] Wherein, NDWI is the normalized water index, Green is the green light band, and NIR is the near infrared band.

[0117] The water body boundary is extracted through NDWI to form a river water body vector file.

[0118] 2. Divide the oscillating river section and the stable river section

[0119] (1) Obtain the river centerline at different times.

[0120] (2) Calculate the mainstream line migration parameters.

[0121] According to the water body vector range and the river centerline at different periods, the 500m river section spacing is selected to obtain the water surface width, and the position of the intersection of the section and the centerline is obtained. The migration distance of the intersection at different periods is measured as s, and the migration speed v and acceleration a are obtained in turn.

[0122] In order to obtain the water surface width and migration distance s, the center lines of two river sections in different periods are merged, and then the migration distance is obtained by measurement. The migration speed is the ratio of the migration distance to the time, and the migration acceleration is the ratio of the migration speed to the time.

[0123] (3) Calculate the RSIRR-GG relative stability index.

[0124] The RSIRR-GG index can be regarded as a linear combination of the mainstream line migration distance s, velocity v, and acceleration a. The formula is as follows:

[0125] RSIRR-G=β1s+β2v+β3a

[0126] Among them, β1, β2, and β3 are undetermined coefficients, which contain the change information of the river during the time interval Δt.

[0127] The steps to determine the coefficient values ​​in the above formula using cluster analysis are as follows:

[0128] Step 1: Extract morphological indicators, including mainstream migration distance and channel width at the cross section (assuming the cross section is N), and calculate the values ​​of mainstream migration distance s, velocity v, and acceleration a.

[0129] Step 2: Cluster analysis. Since the number of groups g is unknown, the hierarchical clustering method is used to divide the above N sections into g groups based on three indicators (mainstream migration distance s, velocity v, and acceleration a), and the appropriate number of groups g is estimated. Then, the clustering results are calculated based on the similarity distance between the N sections and the natural grouping process.

[0130] Step 3: Calculate the F statistic for the above three indices. F is defined as the ratio of the sum of the variances to the sum of the between-group error variances.

[0131]

[0132]

[0133]

[0134] Where SS(TR) is the sum of squares between groups, SSE is the error sum of squares, g is the degrees of freedom, i.e. the number of classification groups; n i is the number of cross sections in the i-th group; x i and x are the average index values ​​of the cross sections of the i-th group and all groups, respectively; x ij is the index value of the jth segment in the i-th group.

[0135] The indicator F can be regarded as the weight of β1, β2, and β3, namely:

[0136] F1:F2:F3=β1:β2:β3

[0137] Step 4: Threshold determination. After determining the three coefficients, calculate RSIRR-G on each cross-section according to formula (4-7), and select the maximum RSIRR-G and the minimum RSIRR-G for each group max，i (i = 1, 2, …, g). Since the RSIRR-G threshold between two adjacent groups can be considered in the interval [RSIRR-G min，i , RSIRR-G max，I , if the interval is small, an approximate constant in this interval is given to replace the true value of the threshold. min，i+1

[0138] (4) Divide the river reach into stable river reaches and meandering river reaches.

[0139] Extend RSIRR-G from the cross-section scale to the river reach scale. The RSIRR at the river reach scale is equal to the average value of RSIRR-G on all cross-sections of the river reach.

[0140]

[0141] 1. Identify meandering river reaches and straight river reaches.

[0142] Based on the centerline of the river channel and the meandering degree of the river reach, determine whether the river reach is a straight river reach or a meandering river reach according to the size of the meandering degree.

[0143]

[0144] In the formula, s is the meandering degree of the river reach, L is the length of the river reach, m; l is the straight-line distance between the two ends of the river reach, m.

[0145] According to Rosge's meandering degree classification system, rivers can be classified into the following categories: rivers with a meandering degree (s) less than or equal to 1 are straight channels; rivers with 1 < s ≤ 1.2 are low-meandering channels; rivers with 1.2 < s ≤ 1.5 are medium-meandering channels; rivers with s greater than 1.5 are high-meandering channels.

[0146] 2. Data collection and spatio-temporal consistency calibration.

[0147] Collect basic data: Collect daily water level monitoring information of hydrological stations, and obtain Jason series or other high-resolution altimetry satellite data, and clarify the start time and time resolution of the data.

[0148] Spatio-temporal alignment: Calibrate remote sensing images and altimetry data according to the acquisition time and spatial position to ensure that the position of the river channel water body in the image is consistent with the elevation data provided by the altimetry satellite.

[0149] Table 1 Common parameters related to altimetry satellites

[0150]

[0151]

[0152] Table 2 Common optical satellite related parameters

[0153]

[0154]

[0155] Based on multi-source remote sensing images and altimetry satellite data, the waterside lines corresponding to the water levels of the river section in the driest and richest seasons can be accurately extracted, and the corresponding elevation information can be calculated based on the water level data.

[0156] 3. Assign water edge elevation information

[0157] The footprint of an altimeter satellite, that is, the coverage area of ​​the altimeter satellite, is generally large. Multiple points along the orbit direction can be formed within a footprint on a wide water surface. The linear equations fitted by these points are used to assign elevation values ​​to the intersection points with the water edge.

[0158] Straight river section processing: For relatively straight river sections, the elevation values ​​of the intersection points of the waterside lines are equal along the center line, so the elevation information provided by the altimetry satellite can be directly assigned. The points corresponding to the elevation values ​​are extracted by intersecting the centerline vertical line with the waterside line.

[0159] Curved river section processing: In a curved river section, the elevation of the waterside line and the center line may be different. In this case, the elevation values ​​of the altimetry satellite are used to perform linear fitting along the river channel to obtain continuous elevation changes. Elevation values ​​are assigned at the intersection points of the waterside lines through fitting, thereby more accurately reflecting the terrain changes of the curved river channel.

[0160] 4. Generate contour lines

[0161] (1) Interpolation calculation of waterside elevation points

[0162] Interpolation calculations are performed on the elevation points of the waterside to generate continuous elevation data along the river. Spline interpolation or Kriging interpolation methods are used to smoothly extend the discrete waterside elevation points to ensure the integrity of the elevation data in the river. The elevation surface generated in this way effectively fills the elevation gaps and is particularly suitable for curved river sections with significant terrain changes.

[0163] (2) Connect contour points to generate contour lines

[0164] On the interpolated elevation data, points of the same elevation (isoheight points) are identified along the waterline and these isoheight points are connected to generate contour lines.

[0165] Based on the waterside elevation data under different water level conditions, multiple contour lines are generated to further characterize the morphological changes of the river cross section at different water levels.

[0166] (3) Refinement and application of contour lines

[0167] The generated contour lines are smoothed to ensure the fluidity of the contour lines, which is convenient for subsequent applications in river section morphology recognition and flow estimation.

[0168] 5. River terrain construction

[0169] (1) Data Integration

[0170] Contour data: Use the contour lines generated in the previous step as the elevation basis of the three-dimensional terrain.

[0171] Minimum area water edge line: Extract the water edge line (minimum water level boundary) under the lowest water level conditions throughout the year as the reference boundary of the lowest water level of the river.

[0172] Maximum water edge: Extract the water edge (maximum water level boundary) under the highest water level conditions throughout the year and mark the maximum extension range of the river. (GF-7 stereo image pair)

[0173] (2) Underwater terrain construction

[0174] Generate underwater terrain based on minimum area water edges.

[0175] (3) Water terrain construction

[0176] Based on the maximum area water edge line, the water terrain is generated through the high-resolution 7 stereo image pair. The specific approach is: orthorectification and image fusion are performed to generate high-resolution orthophotos, and DSM and point cloud data are extracted based on the SuperView-1 stereo image pair data.

[0177] (4) Construction of 3D terrain model

[0178] Elevation interpolation: Based on the contour line and water edge data, elevation interpolation is performed along the river channel to generate elevation data from the lowest water level to the highest water level to form a continuous elevation surface.

[0179] Boundary filling: Fill the elevation data between the minimum and maximum water edges, and use interpolation methods (such as TIN interpolation or Kriging interpolation) to generate continuous elevation values ​​to ensure seamless connection of the 3D model.

[0180] (5) Combine underwater terrain, river terrain, and surface terrain

[0181] The underwater terrain and river elevation data generated by the minimum water level boundary line and the surface terrain generated by the maximum water level boundary line are used for three-dimensional splicing. First, the underwater, river and surface elevation data are seamlessly connected along the cross-sectional direction based on the centerline of the river channel. TIN interpolation or Kriging interpolation is used to smoothly transition between the surface and underwater elevations to ensure the continuity and smooth boundaries of the three-dimensional model.

[0182] (6) Generate DEM

[0183] DEM construction: The interpolated elevation data is converted into a digital elevation model (DEM) to ensure that the three-dimensional terrain model covers the entire spatial area of ​​the river, including the river bank, riverbed, and the impact of seasonal changes in water level.

[0184] 103. Identify the location of the flood in the river section to be analyzed based on the current flood water volume, the location of the flood peak, the rainfall data and temperature data in the area where the river section to be analyzed is located, and the location of the barrier lake.

[0185] The location of the barrier lake is obtained based on current remote sensing images.

[0186] In this step, the location of the flood peak is determined based on the current flood water volume. Based on the location of the flood peak, the watershed of the upstream area of ​​the river section to be analyzed is re-divided to obtain new watershed distribution information. The watershed distribution information and the rainfall data of the area where the river section to be analyzed is used to identify the flood convergence location based on the rainfall data, and use this location as the flood occurrence location. In addition, it is necessary to use the temperature data of the area where the river section to be analyzed is located and the estimated location of the barrier lake for auxiliary identification. The specific method of identifying the flood occurrence location based on temperature data or barrier lake is as follows.

[0187] Step 1: Data Collection

[0188] 1. Temperature data: Obtain rainfall information based on remote sensing technology, through remote sensing satellite data such as MODIS, Landsat series, Sentinel-3SLSTR, NOAA AVHRR, etc., to capture the increase in melt water caused by rising temperatures, thereby causing floods.

[0189] 2. Data sources for water body and barrier lake monitoring: Based on Sentinel-1 SAR and Landsat multispectral images, water body boundaries are extracted and the formation and breach of barrier lakes are monitored.

[0190] Step 2: Identify the location of the flood

[0191] 1. Temperature monitoring: Obtain daily surface temperature data through MODIS or Sentinel-3, combined with temperature changes in upstream snow-covered areas, to monitor potential meltwater. If the average daily temperature is close to or above a critical value (such as 0°C), it may cause a large amount of meltwater to enter the river, significantly increase runoff, and infer the location of floods.

[0192] 2. Identification of barrier lakes and early warning of breach risks

[0193] Identification of barrier lakes: Use Sentinel-1 SAR images to monitor the expansion of water bodies along the river and identify possible locations of barrier lakes. Use NDWI or MNDWI indices to extract water body boundaries and confirm the scope of newly formed or expanded water bodies.

[0194] Water level monitoring and burst warning of barrier lake: Combine DEM data and remote sensing images to monitor the rising trend of barrier lake water level and estimate its water storage capacity through the "water surface area-reservoir capacity" curve. If the water level of the barrier lake rises rapidly or the water surface area increases by more than 5%, it will be judged as a high-risk area for burst and used as a possible location for floods.

[0195] 104. Based on the Muskingum method, the flood process line of the river section to be analyzed is generated using the current flood water volume and flood location.

[0196] Among them, the Muskingum method is a river flow calculation algorithm based on the channel storage equation and the water balance equation.

[0197] Muskingum flow algorithm is a method that simplifies the Saint-Venant equations and uses the water balance principle and storage-discharge relationship of the river section to calculate the inflow process of the upstream section of a typical river section into the outflow process of the downstream section.

[0198] The Saint-Venant equations consist of the continuity equation for unsteady flow and the energy equation for unsteady, gradually varying flow:

[0199]

[0200] In the formula, A represents the cross-sectional area of ​​the water body, m 2 ; Q is the flow rate, m 3 / s; t is time, s; L is the length of a typical river section, m.

[0201]

[0202] Where V is the water velocity, m / s; g is the acceleration due to gravity, m / s 2 ; Z is the water surface elevation, m; t is the time, s; L is the length of the typical river section, m.

[0203] On this basis, the Muskingum method channel storage curve equation is obtained by simplifying the flood storage of the river channel. The water storage capacity S of the river section can be expressed as:

[0204] S = k[xI+(1-x)O]

[0205] In the formula, S is the water storage capacity of the river section; k is the water storage coefficient, which is physically equal to the propagation time of the flood in the river section; x is the flow weight factor; I is the inflow flow, which is initially 0; O is the outflow flow, which is initially 0; if the water surface is a straight line, there is l is the length of the unit river section; L is the length of the typical river section;

[0206] In this step, for each typical river section, the current flood water volume and flood location of each unit river section are used to obtain the time series flow data of each section during the flood propagation process of the typical river section based on the channel storage curve equation of the Muskingum method. This section is the outlet section of each unit river section. The flood process line of the river section to be analyzed is generated using the time series flow data of each typical river section.

[0207] 105. Determine the peak flow, peak time and total flood volume based on the flood process line.

[0208] Step 1: In the flood process line, determine the time point t when the flow reaches the peak p Record the flow value Q at this time p , which is the peak flow.

[0209] Step 2: Take the starting time t0 of the flood process line as the reference point and record the time t when the peak flow is reached. p , the peak time is the difference between the two, that is:

[0210] T p =t p -t0

[0211] Step 3: The total flood volume is the total cumulative flow volume of a unit river section during the flood process, and the flow volume is usually integrated over time. The specific calculation process is as follows:

[0212] The total amount of flood is expressed by the area under the flood process line (integral of flow and time), that is:

[0213]

[0214] Where V is the total flood volume m 3 ; t0 and t e are the start and end time of the flood process, s; Q(t) is the flow at any time point t, m 3 / s.

[0215] When determining that a flood has occurred in a river section to be analyzed, the disclosed embodiment extracts the water surface area of ​​each typical river section from the current remote sensing image of the river section to be analyzed, and uses the topographic data of the river section to be analyzed and the water surface area of ​​the typical river section to determine the current flood water volume of each unit river section in the corresponding typical river section. By using the current flood water volume of each unit river section, the rainfall data of the area where the river section to be analyzed is located, and the Muskingum method to calculate the entire process curve of the flood in the river section to be analyzed, the disclosed embodiment uses remote sensing images to realize the analysis of the flood process.

[0216] In order to explain in more detail a flood process analysis method based on remote sensing proposed in the present disclosure, the present disclosure proposes another embodiment of a flood process analysis method based on remote sensing. The specific implementation steps of the embodiment of the present disclosure are as follows: Figure 4 As shown, including:

[0217] When it is determined that a flood occurs in the river section to be analyzed, the water surface area of ​​each typical river section is extracted from the current remote sensing image of the river section to be analyzed.

[0218] This step is performed in the same way as Figure 1 The specific content of step 101 of the illustrated embodiment will not be repeated any more.

[0219] 202. For each typical river section, the water surface area of ​​each unit river section is extracted from the water surface area of ​​the typical river section to obtain the unit water surface area of ​​the corresponding typical river section.

[0220] 203. Based on the terrain data and the historical remote sensing images of the river section to be analyzed, the area storage capacity curve of each unit river section is constructed.

[0221] Among them, the area-storage capacity curve is used to indicate the correlation between the water surface area and water volume of the corresponding unit river section.

[0222] In this step, as shown above, the water surface set of each typical river section can be obtained based on the historical remote sensing images of the river section to be analyzed. Since there is a correlation between the water volume in a unit river section and the water surface area of ​​the unit river section, the topographic data of the river section to be analyzed and the water surface set of the typical river section can be used to construct the water surface area-reservoir capacity curve of each unit river section in the corresponding typical river section, that is, the area-reservoir capacity curve.

[0223] 204. For each unit river section, the current flood water volume of the corresponding unit river section is determined by using the unit water surface area and the corresponding area storage capacity curve.

[0224] In this step, for each typical river section, after obtaining the water surface area of ​​each unit river section, the area storage capacity curve of each unit river section is used to invert the current flood water volume of each unit river section. The current flood water volume includes the base flow and flood increment before the flood occurs.

[0225] 205. Based on the current flood water volume of each unit river section, the location of the flood peak is determined in each unit river section.

[0226] In one practicable manner, the specific steps for determining the location of the flood peak are as follows:

[0227] Based on the topographic data of the river section to be analyzed and the water surface area of ​​each unit section of the typical river section extracted from the current remote sensing image, as well as the base flow of each unit section (the average water volume in the non-flood season), the flood increment of each unit section is obtained.

[0228] The analysis is conducted from the downstream to the upstream of the river section to be analyzed. If the flood increment of the unit river section continues to increase, it means that the flood is in the process of rising. In this case, the river section to be analyzed should be searched upstream, or the flood peak can be found through the remote sensing image at the next moment to obtain the location of the flood peak.

[0229] If the flood increment of a unit section continues to decrease from the downstream to the upstream of the river section to be analyzed, it means that the flood is in a receding state. In this case, the flood peak should be found downstream of the river section to be analyzed or through the remote sensing image of the previous moment to obtain the location of the flood peak.

[0230] The flood increment in the unit section from the downstream to the upstream of the river section to be analyzed presents a small-large-small distribution, indicating that the current remote sensing image has captured the flood process, and the flood peak is located in the unit section with the largest current flood increment.

[0231] 206. Identify the flood occurrence location of the river section to be analyzed based on the location of the flood peak and the rainfall data in the area where the river section to be analyzed is located.

[0232] In one practicable manner, the specific steps for identifying the flood occurrence location of the river section to be analyzed are as follows:

[0233] Step 1: Set the flood peak location to the outlet basin.

[0234] After determining the location of the maximum flood peak, that is, the location of the flood peak, this location is defined as the exit point of the basin, and the basin of the upstream area of ​​the corresponding typical river section is re-divided based on this.

[0235] Step 2: Watershed division and identification of small watersheds.

[0236] Use DEM data and hydrological analysis tools to divide the watershed and generate the watershed boundary including the flood peak location. Divide the small watersheds to ensure that all small watersheds can reflect the runoff characteristics of different regions, so as to accurately match the rainfall and runoff data of each small watershed.

[0237] Step 3: Extract rainfall data of small watersheds.

[0238] Extract recent rainfall data within a small watershed from remote sensing rainfall data such as GPM or TRMM.

[0239] Step 4: Generate rainfall distribution map.

[0240] The rainfall distribution in the small watershed is calculated by the inverse proportional weight method, and the rainfall distribution map of the entire small watershed is generated to improve the spatial resolution and accuracy of the rainfall data. This step is consistent with the above rainfall data distribution method and will not be repeated here.

[0241] Step 5: Identify the flood gathering area as the flood occurrence site.

[0242] Based on the rainfall distribution map, combined with the rainfall time, it is determined which area the main flood comes from and what state the flood is in (the remote sensing image acquisition time is compared with the rainfall time. If the rainfall continues, the flood will continue to rise, and if the rainfall ends, the flood will evolve along the river channel). Combined with the cumulative flux data of the confluence of each small watershed, the location with a significant increase in flux is identified along the flood confluence path. If the cumulative flux of the confluence of a small watershed or unit river section increases by more than 5% compared with the baseline value, the location is marked as the beginning of the flood convergence, that is, it is determined as the initial occurrence point of the flood, thereby determining the location of the flood in the river section to be analyzed.

[0243] In one practicable manner, the specific steps for identifying the flood occurrence location of the river section to be analyzed are as follows:

[0244] Step 1: Use the DEM data containing the location of the flood peak to generate the target watershed information. The target watershed information is the information of each small watershed where the water flows into the flood peak location.

[0245] Step 2: Extract rainfall data of each small watershed from the rainfall data of the area where the river section to be analyzed is located, and generate a rainfall distribution map.

[0246] Step 3: Use the rainfall distribution map and the rainfall time corresponding to the rainfall distribution map to determine the location where the flood occurs in the river section to be analyzed.

[0247] 207. Use Manning's formula and terrain data to estimate the average flow velocity of each unit river section.

[0248] The average flow velocity of a unit river section based on Manning's formula is:

[0249]

[0250] Where n is the Manning roughness coefficient; R is the hydraulic radius; T is the river slope.

[0251]

[0252] Where A is the cross-sectional area of ​​the unit river section (m 2), and P is the wetted perimeter, which is the circumference of the water flow in contact with the riverbed (m). A larger hydraulic radius usually means less resistance to the water flow, resulting in a higher flow rate. The wetted perimeter can be obtained based on the base flow of a unit river section.

[0253] Based on DEM data, calculate the slope of a unit river section:

[0254]

[0255] Where ΔH is the elevation difference between two points in a unit river section, m; l is the length of the unit river section, m;

[0256] Based on the above formula, the average flow velocity of each unit river section is obtained

[0257] 208. Based on the Muskingum method, the flood process line of the river section to be analyzed is generated using the current flood water volume and average flow velocity of each unit river section, as well as the location of the flood.

[0258] Based on the above Muskingum method's tank storage curve equation and the non-steady flow continuity equation, the finite difference simplified form of the above equations can be solved to obtain the following equation system:

[0259] O n+1 =C0I n +C1I n+1 +C2O n

[0260] Where:

[0261]

[0262] C0+C1+C2=1

[0263] For each typical river section, the piecewise continuous routing method is used to route the inflow process to the outflow at the downstream outflow section.

[0264] Assume that a typical river section is divided into m sections, and the k of each unit section is l , x l are all equal. The upstream section of a typical river section has one unit inflow, and the rest of the time is zero. According to the Muskingum flow calculation formula, the outflow process of the downstream section of the river section after continuous calculation of m unit river sections can be obtained, that is, the Muskingum channel confluence coefficient:

[0265]

[0266] in:

[0267] A=C0+C1C2

[0268]

[0269] Where m is the number of unit river sections and n is the number of time periods.

[0270]

[0271] Where L is the length of a typical river section; l is the length of a unit river section; Δt is the time required from the flood occurrence location to the location of the maximum flood volume increment in the typical river section; C k is the average wave speed in this typical river section.

[0272]

[0273] Where L1 is the distance from the flood occurrence location to the location of the maximum flood volume increment in the typical river section, m; To calculate the average cross-sectional flow velocity of this typical river section according to the Manning formula.

[0274] For the calculation of parameter k, the average flow velocity of the section of the river is calculated according to the Manning formula and then the wave velocity coefficient is used to obtain:

[0275]

[0276] Where α is the wave velocity coefficient. Due to the different geometric shapes of the river section sections, the coefficients are different (triangle: 1.33; wide and shallow parabola: 1.44; wide and shallow rectangle: 1.67). is the average flow velocity, m / s; C k is the average wave speed in this typical river section, m / s;

[0277] For a unit river section, we have:

[0278]

[0279] After obtaining the confluence coefficient, the inflow of the upstream section in each period is multiplied by this confluence coefficient, and then linearly superimposed to obtain the calculated outflow process, thereby generating a flood process line to describe the flood peak value, flood peak propagation time and subsidence process.

[0280] In one achievable method, the specific steps for generating the flood process line of the river section to be analyzed are as follows:

[0281] Step 1: Based on the Muskingum method, the initial flood volume of the unit river section is reversely calculated using terrain data and the current flood volume of the unit river section.

[0282] Among them, the initial flood volume is used to indicate the inflow at the starting point of the flood in a unit river section.

[0283] Step 2: Based on the initial flood volume and current flood volume of the unit river section and the location of the flood, the flood process line of the unit river section is obtained.

[0284] Step 3: Based on the flood process line of each unit river section, the flood process line of the river section to be analyzed is obtained.

[0285] Based on the above method, the embodiment of the present disclosure provides a flood process analysis device based on remote sensing, which implements flood process analysis based on remote sensing images. The embodiment of the device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Specifically, Figure 5 As shown, the device comprises:

[0286] An extraction unit 31 is used to extract the water surface area of ​​each typical river section in the current remote sensing image of the river section to be analyzed when it is determined that a flood occurs in the river section to be analyzed;

[0287] The first determination unit 32 is used to obtain the topographic data of the river section to be analyzed, and determine the current flood water volume and the flood peak location of each unit river section in the corresponding typical river section according to the water surface area of ​​each typical river section and the topographic data;

[0288] The identification unit 33 is used to identify the flood occurrence position of the river section to be analyzed according to the current flood water volume, the location of the flood peak, the rainfall data and temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake, wherein the location of the barrier lake is obtained according to the current remote sensing image;

[0289] A generating unit 34 is used to generate a flood process line of the river section to be analyzed based on the Muskingum method using the current flood water volume and the flood occurrence location.

[0290] The second determination unit 35 is used to determine the flood peak flow, peak time, and total flood volume based on the flood process line.

[0291] Furthermore, the embodiment of the present disclosure also provides a processor, the processor is used to run a program, wherein the program executes the above Figure 1-4 The method described in .

[0292] Furthermore, the embodiment of the present disclosure also provides a storage medium, wherein the storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1-4 The method described in .

[0293] Furthermore, the present disclosure provides an electronic device 4, such as Figure 6As shown, the device includes at least one processor 41, at least one memory 42 and a bus 43 connected to the processor 41; the processor 41 and the memory 42 communicate with each other through the bus 43; the processor 41 is used to call the program instructions in the memory 42 to execute the above-mentioned flood process analysis method based on remote sensing. The device in this article can be a server, PC, PAD, mobile phone, etc.

[0294] Furthermore, the present disclosure also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the steps of the inspection method for network devices as described above.

[0295] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A flood process analysis method based on remote sensing, characterized in that: The method comprises: When it is determined that a flood occurs in the river section to be analyzed, extracting the water surface area of ​​each typical river section in the current remote sensing image of the river section to be analyzed; Acquire the topographic data of the river section to be analyzed, and determine the current flood water volume and the location of the flood peak of each unit river section in the corresponding typical river section according to the water surface area of ​​each typical river section and the topographic data; Identify the flood occurrence location of the river section to be analyzed based on the current flood water volume, the location of the flood peak, the rainfall data and temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake, wherein the location of the barrier lake is obtained based on the current remote sensing image; Based on the Muskingum method, the flood process line of the river section to be analyzed is generated by using the current flood water volume and the flood occurrence location; The flood peak flow, peak occurrence time and total flood volume are determined based on the flood process line.

2. The method according to claim 1, characterized in that According to the water surface area of ​​each typical river section and the terrain data, the current flood water volume and the location of the flood peak of each unit river section in the corresponding typical river section are determined, including: For each typical river section, the water surface area of ​​each unit river section is extracted from the water surface area of ​​the typical river section to obtain the unit water surface area of ​​the corresponding typical river section; According to the terrain data and the historical remote sensing images of the river section to be analyzed, constructing the area-storage capacity curve of each unit river section, wherein the area-storage capacity curve is used to indicate the correlation between the water surface area and the water volume of the corresponding unit river section; For each unit river section, the unit water surface area and the corresponding area storage capacity curve are used to determine the current flood water volume of the corresponding unit river section; The location of the flood peak is determined based on the current flood water volume of each unit river section.

3. The method according to claim 2, characterized in that The location of the flood peak is determined based on the current flood water volume of each unit river section, including: Based on the current flood water volume of each unit river section, determine the changing trend of the flood volume of each unit river section in the corresponding typical river section; The location of the flood peak of the river section to be analyzed is determined according to the change trend.

4. The method according to claim 2, characterized in that: Based on the Muskingum method, the flood process line of the river section to be analyzed is generated by using the current flood water volume and the flood occurrence location, including: The average flow velocity of each unit river section is estimated using the Manning formula and the terrain data; Based on the Muskingum method, the flood process line of the river section to be analyzed is generated by utilizing the current flood water volume and average flow velocity of each unit river section and the flood occurrence location.

5. The method according to claim 4, characterized in that Based on the Muskingum method, the flood process line of the river section to be analyzed is generated by using the current flood water volume and average flow velocity of each unit river section and the flood occurrence location, including: Based on the Muskingum method, the initial flood volume of the unit river section is reversely calculated by using the topographic data and the current flood volume of the unit river section, and the initial flood volume is used to indicate the inflow volume at the starting time of the flood of the unit river section; Obtaining a flood process line of the unit river section according to the initial flood volume and the current flood volume of the unit river section, as well as the location where the flood occurs; Based on the flood process lines of each unit river section, the flood process line of the river section to be analyzed is obtained.

6. The method according to claim 5, characterized in that According to the current flood water volume, the location of the flood peak, the rainfall data and temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake, the flood occurrence location of the river section to be analyzed is identified, including: Using the DEM data containing the location of the flood peak, generating target watershed information, the target watershed information is information of each small watershed where water flows into the location of the flood peak; Extracting rainfall data of each small watershed from rainfall data of the area where the river section to be analyzed is located, and generating a rainfall distribution map; The flood occurrence location of the river section to be analyzed is determined by using the rainfall distribution map, the rainfall time corresponding to the rainfall distribution map, the temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake.

7. A flood process analysis device based on remote sensing, characterized in that: The device comprises: An extraction unit is used to extract the water surface area of ​​each typical river section in the current remote sensing image of the river section to be analyzed when it is determined that a flood occurs in the river section to be analyzed; A first determination unit is used to obtain the topographic data of the river section to be analyzed, and determine the current flood water volume and the location of the flood peak of each unit river section in the corresponding typical river section according to the water surface area of ​​each typical river section and the topographic data; an identification unit, for identifying the flood occurrence location of the river section to be analyzed according to the current flood water volume, the location of the flood peak, the rainfall data and the temperature data of the area where the river section to be analyzed is located, and the location of the barrier lake, wherein the location of the barrier lake is obtained according to the current remote sensing image; A generating unit, used for generating a flood process line of the river section to be analyzed by using the current flood water volume and the flood occurrence location based on the Muskingum method; The second determination unit is used to determine the flood peak flow, peak time, and total flood volume based on the flood process line.

8. The device according to claim 7, characterized in that The first determining unit includes: An extraction module is used to extract the water surface area of ​​each unit river section from the water surface area of ​​the typical river section for each typical river section, so as to obtain the water surface area of ​​each unit of the corresponding typical river section; A construction module, used to construct an area-storage capacity curve of each unit river section according to the terrain data and the historical remote sensing image of the river section to be analyzed, wherein the area-storage capacity curve is used to indicate the correlation between the water surface area and the water volume of the corresponding unit river section; The determination module is used to determine the current flood water volume of each unit river section by using the unit water surface area and the corresponding area storage capacity curve for each unit river section.

9. An electronic device, characterized in that: The device includes at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; and the processor is used to call program instructions in the memory to execute the method described in any one of claims 1-6.

10. A computer storage medium, characterized in that: The storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

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