Flood inundation area simulation method based on reconstructed lake basin DEM

Abstract

The invention discloses a flood inundation area simulation method based on a reconstructed lake basin DEM. The method comprises the steps that a remote sensing image in a target area within a preset time period and water level data of hydrometric stations distributed in the target area within the preset time period are acquired; determining shooting time of the remote sensing image, and obtaining a water submerging range of the remote sensing image; the shooting time of the remote sensing image is matched with the measuring time of the water level data, and lake water submerging areas corresponding to different water level moments are obtained; determining the responsible range of each hydrometric station in the target area; overlapping the lake water submerging areas corresponding to different water level moments, determining the lowest submerging water level, and reconstructing a lake basin DEM; and according to the water level of the hydrological station during the flood period, obtaining the range of the flood inundation area at the flood moment, and realizing the flood inundation area simulation based on the reconstructed lake basin DEM. According to the method, the limitation of a satellite revisit period is broken through, and the hour-level simulation response of flood routing is realized only by relying on the water level under the condition that remote sensing data is not needed.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood monitoring, and in particular relates to a flood inundation area simulation method based on reconstructing a lake basin DEM. Background Art

[0002] Flood disasters have wide-ranging impacts and are highly destructive, posing a serious threat to ecological security. Current monitoring of flood-inundated areas relies heavily on satellite remote sensing data, but the temporal resolution of existing satellite systems is insufficient to meet the demands of dynamic flood monitoring. This is particularly true when responding to sudden flood events, such as rapid water level rises caused by short bursts of heavy rainfall, which require hourly responses. Traditional satellite observations typically have data acquisition intervals of 6 to 24 hours, making it difficult to monitor rapid changes in flood processes. Real-time simulation of flood-inundated areas can also be achieved using hydrodynamic models, but this method relies on high-precision DEM data. Currently available publicly available DEM data generally suffer from insufficient spatial resolution (>30m) and large vertical errors (>2m), making them insufficient for accurate simulation of flood-inundated areas. Summary of the Invention

[0003] Purpose of the Invention: This invention provides a flood inundation simulation method based on a reconstructed lake basin DEM. This method overcomes the limitations of satellite revisit cycles and, without remote sensing data input, achieves hourly flood simulation responses solely based on water level input. This method also avoids the complex conversion between water level and elevation, resulting in significant theoretical and technical significance.

[0004] Technical solution: The present invention provides a flood inundation area simulation method based on a reconstructed lake basin DEM. The method constructs a lake basin DEM when the lake basin topography is unknown, and uses the reconstructed lake basin DEM to simulate the flood inundation area. The method comprises the following steps:

[0005] Step 1: Acquire remote sensing images of the target area within a preset time period and water level data of hydrological stations distributed within the target area within the preset time period, and then proceed to step 2;

[0006] Step 2: Determine the shooting time of the remote sensing image, obtain the water inundation range of the remote sensing image, and then proceed to step 3;

[0007] Step 3: Determine the scope of responsibility of each hydrological station in the target area, and then proceed to Step 4;

[0008] Step 4: Match the shooting time of the remote sensing image with the measurement time of the water level data to obtain the lake water body inundation area corresponding to different water level moments, and then proceed to step 5;

[0009] Step 5: Superimpose the lake water body inundation areas corresponding to different water level moments to determine the lowest inundation water level, reconstruct the lake basin DEM, and then proceed to step 6;

[0010] Step 6: According to the water level of the hydrological station during the flood period, the scope of the inundated area at the time of the flood is obtained, and the flood inundation area simulation based on the reconstructed lake basin DEM is realized.

[0011] Furthermore, in step 1, the remote sensing image includes Sentinel-1 remote sensing image and Gaofen-3 remote sensing image; the remote sensing image includes two bands: VV polarization and VH polarization.

[0012] Furthermore, in step 2, obtaining the water body submergence range of the remote sensing image specifically includes the following steps:

[0013] Step 2.1: Calculate the Sentinel-1 Dual Polarization Water Index (SDWI). The specific calculation method is:

[0014] SDWI=log(10×VH×VV)-8

[0015] Where VV and VH represent the backscattering rates of the ground objects in the remote sensing image under VV and VH polarizations respectively;

[0016] Step 2.2: Based on the calculated SDWI index, the maximum inter-class variance method (OTSU) is used to determine the optimal segmentation threshold for water bodies. The inter-class variance σ 2 The specific calculation formula of (t) is:

[0017] σ 2 (t)=ω(t)×ω′(t)×[μ(t)-μ′(t)] 2

[0018] Where ω(t) and ω′(t) represent the pixel ratios of water and non-water bodies under the threshold t, respectively; μ(t) and μ′(t) represent the average pixel values ​​of water and non-water bodies under the threshold t, respectively;

[0019] Step 2.3: After traversing all thresholds t, select the inter-class variance σ 2 The t when (t) is the maximum is used as the segmentation threshold, and the area where SDWI is greater than the threshold t is determined to be a water body, and the area where SDWI is less than the threshold t is determined to be a non-water body.

[0020] Furthermore, in step 3, determining the scope of responsibility of each hydrological station in the target area specifically includes the following steps:

[0021] Step 3.1: Calculate the distance from any point in the target area to each hydrological station. The specific calculation method is:

[0022]

[0023] Where x, x 'Represent the longitude of any point in the target area and the longitude of the hydrological station respectively; y, y ' represent the longitude of any point in the target area and the longitude of the hydrological station respectively;

[0024] Step 3.2: Repeat step 3.1 until the distances between the point and all hydrological stations are calculated and the point is determined to be under the responsibility of the hydrological station closest to it.

[0025] Step 3.3: Repeat steps 3.1 and 3.2 until the responsible hydrological stations for all points in the target area are determined.

[0026] Furthermore, in step 4, the shooting time of the remote sensing image and the measurement time of the water level data are matched to obtain the lake water body inundation area corresponding to different water level moments, which specifically includes the following steps:

[0027] Step 4.1: For a remote sensing image, extract its actual shooting time (usually available from image metadata or file name);

[0028] Step 4.2: Based on the actual shooting time of the remote sensing image, determine the water level observation time at the hydrological station that is closest to the shooting time;

[0029] Step 4.3: The water level of the hydrological station at the observation time is used as the water level of the flooded area of ​​the remote sensing image.

[0030] Furthermore, in step 5, determining the lowest flooding level and reconstructing the lake basin DEM specifically includes the following steps:

[0031] Step 5.1: Select any point in the target area, determine the hydrological station responsible for the point, and arrange the water levels of the hydrological stations in reverse order within the preset time period;

[0032] Step 5.2: Determine the lowest flooding level at the point based on the water body flooding situation in the remote sensing image that matches the water level;

[0033] Step 5.3: Repeat steps 5.1 and 5.2 until the lowest flooding water level of all points in the target area is determined.

[0034] Furthermore, in step 5.2, the method of determining the lowest flooding water level at the point based on the flooding situation of the remote sensing image matching the water level specifically includes the following steps:

[0035] Step 5.2.1: Determine whether the point was not submerged during the five periods of highest water levels. If so, use the highest water level of the hydrological station responsible for the point during the preset time period as the minimum submerged water level for the point. If not, proceed to Step 5.2.2.

[0036] Step 5.2.2: Determine whether the point has not been flooded for five consecutive flooding times since the last time it was flooded. If so, use the water level at the last time the point was flooded as the lowest flooding level of the point. If not, proceed to step 5.2.3.

[0037] Step 5.2.3: The last flooded water level at this point is taken as the lowest flooded water level.

[0038] Furthermore, in step 6, the flood inundation area simulation based on the reconstructed lake basin DEM specifically includes the following steps:

[0039] Step 6.1, obtaining the measured water level data at the flood time of the hydrological stations around the target area;

[0040] Step 6.2: Select any point in the study area and determine the hydrological station responsible for that point;

[0041] Step 6.3: After determining the hydrological station responsible for the point, obtain the lowest flooding water level H1 and the flood moment water level H2 of the point;

[0042] Step 6.4: Determine whether H2 is greater than H1. If so, the point is determined to be flooded; otherwise, the point is determined to be not flooded.

[0043] Step 6.5: Repeat steps 5.2 to 5.4 until all points in the study area have been determined to be flooded, completing the flood inundation area simulation.

[0044] The present invention further discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0045] The present invention further discloses a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the method of the present invention when the computer program / instruction is executed by a processor.

[0046] Beneficial Effects: Compared with existing technologies, this invention offers the following significant advantages: It can reconstruct a DEM for unknown lake basins, overcome the limitations of satellite revisit cycles, and achieve hourly simulation responses to flood evolution using only water levels, without remote sensing data input. Simulation accuracy is high, with the highest accuracy achieved during flood peaks. This method effectively addresses the technical challenge of existing satellite systems' inability to meet the demands of high-frequency dynamic flood monitoring. By replacing elevation with water levels, this method avoids the complex conversion between water levels and elevations used in traditional DEM simulations of flood evolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the flood inundation area simulation method based on the reconstruction of the lake basin DEM of the present invention;

[0048] Figure 2 This is a schematic diagram of the areas responsible for each hydrological station in the target area of ​​the present invention;

[0049] Figure 3 This is the result of reconstructing the lake basin DEM of the present invention;

[0050] Figure 4 This is a water level information diagram of a hydrological station for a flood case of the present invention;

[0051] Figure 5 The present invention is based on the simulated flood inundation area distribution map of individual cases; (a) is the simulated flood inundation area distribution map on June 15, 2024, (b) is the simulated flood inundation area distribution map on July 2, 2024, (c) is the simulated flood inundation area distribution map on July 5, 2024, and (d) is the simulated flood inundation area distribution map on July 10, 2024;

[0052] Figure 6 It is a flood area simulation accuracy map based on individual case simulation of flood inundation areas of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0054] like Figure 1 As shown, the process of the present invention is as follows:

[0055] Step 1: Acquire Sentinel-1 remote sensing images of the target area within a preset time period and water level data of hydrological stations distributed within the target area within the preset time period; then proceed to Step 2;

[0056] Specifically, in step 1, the Sentinel-1 remote sensing image includes two bands: VV polarization and VH polarization;

[0057] Step 2: For the acquired Sentinel-1 remote sensing image, determine the image capture time and obtain the water inundation range of the remote sensing image; then proceed to Step 3;

[0058] Specifically, in step 2, the water inundation range of the remote sensing image is obtained according to the following steps:

[0059] Step 2.1: Calculate the Sentinel-1 dual-polarization water index (SDWI). The specific calculation method is:

[0060] SDWI=log(10×VH×VV)-8

[0061] Where VV and VH represent the backscattering rates of the ground objects in the Sentinel-1 image at VV and VH polarizations, respectively;

[0062] Step 2.2: Based on the calculated SDWI index, the maximum inter-class variance method is used to determine the optimal segmentation threshold for water bodies. The inter-class variance σ 2 The specific calculation formula of (t) is:

[0063] σ 2 (t)=ω(t)×ω′(t)×[μ(t)-μ′(t)] 2

[0064] Where ω(t) and ω′(t) represent the pixel ratios of water and non-water bodies under threshold t, respectively; μ(t) and μ′(t) represent the average pixel values ​​of water and non-water bodies under threshold t, respectively;

[0065] Step 2.3: After traversing all thresholds t, select the inter-class variance σ 2 (t) The maximum t is used as the segmentation threshold. The area where SDWI is greater than the threshold t is considered to be a water body, and the area where SDWI is less than the threshold t is considered to be a non-water body.

[0066] Step 3: Determine the scope of responsibility of each hydrological station in the target area; then proceed to Step 4;

[0067] Specifically, in step 3, the scope of responsibility of each hydrological station in the target area is determined according to the following steps:

[0068] Step 3.1: Calculate the distance from any point in the target area to each hydrological station. The specific calculation method is:

[0069]

[0070] Where dis is the distance from any point in the target area to a specific hydrological station; x, x ' Represent any point in the target area and a specific longitude respectively; y, y ' Represents any point and a specific longitude in the target area respectively;

[0071] Step 3.2: Repeat step 3.1 until the distances between the point and all eight hydrological stations are calculated, and the point is determined to be under the responsibility of the hydrological station closest to it;

[0072] Step 3.3: Repeat steps 3.1 and 3.2 until all points in the target area are determined to be responsible for one hydrological station.

[0073] In step 4, the shooting time of the remote sensing image and the measurement time of the water level data are matched to obtain the lake water body inundation area corresponding to different water level moments; then proceed to step 5;

[0074] Specifically, in step 4, the lake water body submerged area corresponding to different water level moments is obtained according to the following steps:

[0075] Step 4.1: For a remote sensing image, extract its actual shooting time (usually available from image metadata or file name);

[0076] Step 4.2: Based on the actual shooting time of the remote sensing image, determine the water level observation time at the hydrological station that is closest to the shooting time;

[0077] Step 4.3: The water level of the hydrological station at the observation time is used as the water level of the flooded area of ​​the remote sensing image.

[0078] Step 5: Superimpose the lake water body inundation areas corresponding to different water level moments, determine the lowest inundation water level, and reconstruct the lake basin DEM; then proceed to step 6;

[0079] Specifically, in step 5, the lowest flooding level is determined according to the following steps to reconstruct the lake basin DEM:

[0080] Step 5.1: Select any point in the target area, determine the hydrological station responsible for that point, and arrange the water levels of the hydrological stations in reverse order within the preset time period;

[0081] Step 5.2: Determine the lowest flooding level at the point based on the water body inundation conditions in the remote sensing image that matches the water level;

[0082] Step 5.3: Repeat steps 5.1 and 5.2 until the lowest flooding level of all points in the target area is determined.

[0083] Furthermore, in step 5.2, the lowest flooding water level of the selected point is obtained according to the following steps:

[0084] Step 5.2.1: Determine whether the point has not been flooded for five consecutive flooding times since the last time it was flooded. If so, use the water level at the last time the point was flooded as the lowest flooding level of the point. If not, proceed to step 5.2.2.

[0085] Step 5.2.2: Determine whether the point was not submerged during the five periods of highest water levels. If so, use the highest water level of the hydrological station responsible for the point during the preset time period as the minimum submerged water level for the point. If not, proceed to Step 5.2.3.

[0086] Step 5.2.3: The last flooded water level of the point is taken as the lowest flooded water level;

[0087] Step 6: According to the water level of the hydrological station during the flood period, the scope of the inundated area at the time of the flood is obtained, and the flood inundation area simulation based on the reconstructed lake basin DEM is realized.

[0088] Furthermore, in step 6, the simulation of the flood inundation area based on the reconstructed lake basin DEM comprises the following specific steps:

[0089] Step 6.1: Obtain the measured water level data at the flood time of the hydrological stations around the target area;

[0090] Step 6.2: Select any point in the study area and determine which hydrological station is responsible for the point according to step 3.3 of claim 9;

[0091] Step 6.3: After determining the hydrological station responsible for the point, obtain the lowest flooding water level H1 and the flood moment water level H2 of the point according to step 5.2 and claim 11 respectively;

[0092] Step 6.4: Determine whether H2 is greater than H1. If so, the point is considered to be flooded; otherwise, the point is considered not to be flooded.

[0093] Step 6.5: Repeat steps 6.2 to 6.4 until all points in the study area have been determined to be flooded, completing the flood inundation area simulation.

[0094] Example

[0095] In one embodiment, first, according to step 1, taking Hunan as an example, 199 Sentinel-1 remote sensing images of the target area from 2017 to 2024 are collected, as well as water level data from eight hydrological stations in the target area, including Chenglingji (Qilishan), Lujiao (II), Yingtian, Yangliutan, Yuanjiang (II), Xiaohezui, Nanzui, and Dongnanhu, from 2017 to 2024. Then, step 2 is performed.

[0096] According to step 2, for the remote sensing image of the target area, the SDWI value of each pixel in the image is calculated according to SDWI = log (10 × VH × VV)-8, and the SDWI value of each pixel in the image is calculated by σ 2 (t)=ω(t)×ω′(t)×[μ(t)-μ′(t)] 2 Calculate the inter-class variance with different SDWI as threshold t, the inter-class variance σ 2 The t when (t) is the maximum is used as the segmentation threshold. The area where SDWI is greater than or equal to the threshold t is considered to be a water body, and the area where SDWI is less than the threshold t is considered to be a non-water body. Taking the Sentinel-1 remote sensing image on August 30, 2021 as an example, σ is calculated. 2 (t) When t is the maximum, it is 0.08. Taking 0.08 as the threshold, in this remote sensing image, pixels with SDWI greater than or equal to 0.08 are water bodies, and pixels with SDWI less than 0.08 are non-water bodies.

[0097] For the 199 collected Sentinel-1 remote sensing images, all water bodies were extracted according to the above operations. After obtaining the water body range, step 3 was entered to determine the responsibility range of each hydrological station in the target area.

[0098] According to step 3, according to Determine the scope of responsibility of each hydrological station in the target area. Specifically, the longitude and latitude of Chenglingji (Qilishan) hydrological station are 113.124°E, 29.414°N; the longitude and latitude of Lujiao (II) hydrological station are 113.003°E, 29.16°N; the longitude and latitude of Yingtian hydrological station are 112.902°E, 28.843°N; the longitude and latitude of Yangliutan hydrological station are 112.617°E, 28.783°N; the longitude and latitude of Yuanjiang (II) hydrological station are 112.374°E, 28.858°N; the longitude and latitude of Dongnanhu hydrological station are 112.352°E, 28.932°N; the longitude and latitude of Xiaohezui hydrological station are 112.317°E, 28.85°N; and the longitude and latitude of Nanzui hydrological station are 112.283°E, 29.067°N. Take a point in the target area as an example, assuming that the latitude and longitude of the point are 112.622°E, 28.988°N. The distances between this point and the eight hydrological stations are: Chenglingji (Qilishan) 0.658°, Lujiao (II) 0.418°, Yingtian 0.315°, Yangliutan 0.205°, Yuanjiang (II) 0.280°, Dongnanhu 0.276°, Xiaohezui 0.335°, and Nanzui 0.348°. Yangliutan is the closest of the eight hydrological stations to this point, so it is designated as the station responsible for this point.

[0099] Similarly, the scope of responsibility of each hydrological station in the target area is determined. For the target area, the scope of responsibility of each hydrological station in the target area determined in step 3 is as follows: Figure 2 As shown, go to step 4.

[0100] According to step 4, match the shooting time of the Sentinel-1 image with the water level measurement time. Taking the Sentinel-1 remote sensing image of August 30, 2021 as an example, first obtain the shooting time of the remote sensing image as 18:35 on August 30, 2021, and select the water level measured at the measurement time closest to this time among the eight hydrological stations as the matching water level of the remote sensing image.

[0101] Taking Chenglingji (Qilishan) as an example, the observation time closest to 18:35 is 19:00, and the water level is 30.34 meters. Therefore, it is believed that the water level of the Chenglingji (Qilishan) hydrological station that matches the water body inundation range of the remote sensing image is 30.34 meters.

[0102] By analogy, the water levels of the eight hydrological stations corresponding to the inundation range of the remote sensing image are: Chenglingji (Qilishan) 30.34 meters, Lujiao (II) 30.43 meters, Yingtian 30.444 meters, Yangliutan 30.616 meters, Yuanjiang (II) 31.356 meters, Xiaohezui 31.43 meters, Nanzui 31.98 meters, and Dongnanhu 31.64 meters, and then proceed to step 5.

[0103] According to step 5, the lake's inundation areas corresponding to different water levels are superimposed to determine the minimum inundation level and reconstruct the lake basin DEM. Assume there are five points a, b, c, d, and e in the target area, all of which are responsible for determining the minimum inundation level at the Chenglingji (Qilishan) hydrological station. In step 5.1, assume that the inundation status of points a, b, c, d, and e at the Chenglingji (Qilishan) hydrological station, arranged in reverse order by water level, is as shown in Table 1: √ indicates that the point is inundated, and × indicates that the point is not inundated.

[0104] Table 1 Example of minimum flooding water level

[0105] Water level / meter Flooding at point a Flooding at point b Flooding at point C Flooding at point d Flooding at point e 34.47 √ √ √ × √ 32.65 √ √ √ × √ 31.19 √ √ × × × 30.13 × √ × × √ 29.23 √ √ × × × 28.54 √ √ × × √ 27.62 √ × × × √ 27.12 √ × × × √ 26.63 × × × × √ 26.12 × × × × √ 25.82 × × × × √ 25.42 × × √ × √ 25.06 × √ √ × √ 24.73 √ × × × √ 24.45 √ × × × √

[0106] According to steps 4.2.1 to 4.2.3, the lowest flooding water levels of the five points a, b, c, d, and e in the target area are 27.12 meters, 28.54 meters, 32.65 meters, 34.47 meters, and 24.45 meters respectively. Similarly, determine the lowest flooding water levels of all points in the target area and proceed to step 6. The reconstructed lake basin DEM of the target area is as follows: Figure 3 shown.

[0107] According to step 6, the water level at the hydrological station during the flood period is obtained to determine the extent of the inundation area at the time of the flood. Assume that there are five points a, b, c, d, and e in the target area. The Chenglingji (Qilishan) hydrological station is responsible for determining the lowest inundation water levels for these points. Their lowest inundation water levels are 27.12 meters, 28.54 meters, 32.65 meters, 34.47 meters, and 24.45 meters, respectively. Assuming that the water levels at the Chenglingji (Qilishan) hydrological station at 18:35 on June 15, 2024, 18:35 on June 15, 2024, 18:35 on June 15, 2024, and 18:35 on June 15, 2024, are 26.48 meters, 33.68 meters, 34.14 meters, and 32.44 meters, respectively, and that the lowest flooding level at point a is 27.12 meters, then point a would be flooded at 18:35 on June 15, 2024, at 07:06 on July 2, at 11:08 on July 5, and at 06:36 on July 10, 2024, respectively. The flooding conditions at points b, c, d, and e in these individual cases are shown in Table 2. By analogy, it is determined whether all points in the target area are flooded, and the flood inundation area simulation is completed. The flood inundation area distribution map of the flood inundation area simulated by the flood case is as follows: Figure 4 shown.

[0108] Table 2 Flood case hypothetical point inundation

[0109]

[0110] The method provided by the present invention extracts the water body inundation area through remote sensing data, combines the water level observation data at the time of remote sensing image shooting, reconstructs the lake basin DEM, and then simulates the flood inundation area based on the reconstructed lake basin DEM and the water level data observed by the hydrological station during the flood, thereby realizing the simulation of the flood inundation area without remote sensing image data. That is, the method provided by the present invention can break through the limitation of the satellite revisit cycle, and only rely on the input water level data to realize the hourly simulation response of the flood evolution in the absence of remote sensing data input. At the same time, it can avoid the complex conversion between water level and elevation, realize the high-efficiency simulation of the flood inundation area, and the simulation accuracy is high. The average accuracy (OA), precision (P), recall rate (R), F1-score, Kappa coefficient, and critical success index (CSI) at the peak of the flood can reach 0.979, 0.949, 0.969, 0.959, 0.945, and 0.921 respectively. The simulation accuracy of the flood inundation area is as follows: Figure 6 shown.

Claims

1. A flood inundation area simulation method based on reconstructed lake basin DEM, characterized in that: Constructing a lake basin DEM when the lake basin topography is unknown, and using the reconstructed lake basin DEM to simulate the flood inundation area, the method includes the following steps: Step 1: Acquire remote sensing images of the target area within a preset time period and water level data of hydrological stations distributed within the target area within the preset time period, and then proceed to step 2; Step 2: Determine the shooting time of the remote sensing image, obtain the water inundation range of the remote sensing image, and then proceed to step 3; Step 3: Determine the scope of responsibility of each hydrological station in the target area, and then proceed to Step 4; Step 4: Match the shooting time of the remote sensing image with the measurement time of the water level data to obtain the lake water body inundation area corresponding to different water level moments, and then proceed to step 5; Step 5: Superimpose the lake water body inundation areas corresponding to different water level moments to determine the lowest inundation water level, reconstruct the lake basin DEM, and then proceed to step 6; Step 6: According to the water level of the hydrological station during the flood period, the scope of the inundated area at the time of the flood is obtained, and the flood inundation area simulation based on the reconstructed lake basin DEM is realized.

2. The flood inundation area simulation method based on reconstructed lake basin DEM according to claim 1 is characterized in that: In step 1, the remote sensing images include Sentinel-1 remote sensing images and Gaofen-3 remote sensing images; the remote sensing images include two bands: VV polarization and VH polarization.

3. The flood inundation area simulation method based on reconstructed lake basin DEM according to claim 2 is characterized in that: In step 2, obtaining the water body submergence range of the remote sensing image specifically includes the following steps: Step 2.1: Calculate the Sentinel-1 Dual Polarization Water Index (SDWI). The specific calculation method is: SDWI=log(10×VH×VV)-8 Where VV and VH represent the backscattering rates of the ground objects in the remote sensing image under VV and VH polarizations respectively; Step 2.2: Based on the calculated SDWI index, the maximum inter-class variance method (OTSU) is used to determine the optimal segmentation threshold for water bodies. The inter-class variance σ 2 The specific calculation formula of (t) is: s 2 (t)=ω(t)×ω′(t)×[μ(t)-μ′(t)] 2 Where ω(t) and ω′(t) represent the pixel ratios of water and non-water bodies under the threshold t, respectively; μ(t) and μ′(t) represent the average pixel values ​​of water and non-water bodies under the threshold t, respectively; Step 2.3: After traversing all thresholds t, select the inter-class variance σ 2 The t when (t) is the maximum is used as the segmentation threshold, and the area where SDWI is greater than the threshold t is determined to be a water body, and the area where SDWI is less than the threshold t is determined to be a non-water body.

4. The flood inundation area simulation method based on reconstructed lake basin DEM according to claim 1 is characterized in that: In step 3, determining the scope of responsibility of each hydrological station in the target area specifically includes the following steps: Step 3.1: Calculate the distance from any point in the target area to each hydrological station. The specific calculation method is: Where x and x' represent the longitude of any point in the target area and the hydrological station respectively; y and y' represent the longitude of any point in the target area and the hydrological station respectively; Step 3.2: Repeat step 3.1 until the distances between the point and all hydrological stations are calculated and the point is determined to be under the responsibility of the hydrological station closest to it. Step 3.3: Repeat steps 3.1 and 3.2 until the responsible hydrological stations for all points in the target area are determined.

5. The flood inundation area simulation method based on reconstructed lake basin DEM according to claim 1 is characterized in that: In step 4, the capturing time of the remote sensing image and the measuring time of the water level data are matched to obtain the lake water body inundation area corresponding to different water level moments, which specifically includes the following steps: Step 4.1: For a remote sensing image, extract its actual shooting time from the image metadata or file name; Step 4.2: Based on the actual shooting time of the remote sensing image, determine the water level observation time at the hydrological station that is closest to the shooting time; Step 4.3: The water level of the hydrological station at the observation time is used as the water level of the flooded area of ​​the remote sensing image.

6. The method for simulating flood inundation areas based on reconstructed lake basin DEM according to claim 1, characterized in that: In step 5, determining the lowest flooding level and reconstructing the lake basin DEM specifically includes the following steps: Step 5.1: Select any point in the target area, determine the hydrological station responsible for the point, and arrange the water levels of the hydrological stations in reverse order within the preset time period; Step 5.2: Determine the lowest flooding level at the point based on the water body flooding situation in the remote sensing image that matches the water level; Step 5.3: Repeat steps 5.1 and 5.2 until the lowest flooding water level of all points in the target area is determined.

7. The method for simulating flood inundation areas based on reconstructed lake basin DEM according to claim 6, characterized in that: In step 5.2, the lowest flooding level at the point is determined based on the water body flooding situation in the remote sensing image that matches the water level. The specific steps include the following: Step 5.2.1: Determine whether the point was not submerged during the five periods of highest water levels. If so, use the highest water level of the hydrological station responsible for the point during the preset time period as the minimum submerged water level for the point. If not, proceed to Step 5.2.

2. Step 5.2.2: Determine whether the point has not been flooded for five consecutive time periods since the last time it was flooded. If so, use the water level at the last time the point was flooded as the lowest flooding level of the point. If not, proceed to step 5.2.

3. Step 5.2.3: The last flooded water level at this point is taken as the lowest flooded water level.

8. The method for simulating flood inundation areas based on reconstructed lake basin DEM according to claim 1, characterized in that: In step 6, the flood inundation area simulation based on the reconstructed lake basin DEM specifically includes the following steps: Step 6.1, obtaining the measured water level data at the flood time of the hydrological stations around the target area; Step 6.2: Select any point in the study area and determine the hydrological station responsible for that point; Step 6.3: After determining the hydrological station responsible for the point, obtain the lowest flooding water level H1 and the flood moment water level H2 of the point; Step 6.4: Determine whether H2 is greater than H1. If so, the point is determined to be flooded; otherwise, the point is determined to be not flooded. Step 6.5: Repeat steps 6.2 to 6.4 until all points in the study area have been determined to be flooded, completing the flood inundation area simulation.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to claim 1.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

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