Method for acquiring apparent water area of river and lake, medium and electronic equipment

Abstract

The embodiment of the invention provides a method for acquiring the apparent water area of a river and a lake, a medium and electronic equipment. The method comprises the following steps: selecting satellite remote sensing data with corresponding resolution for corresponding water area types according to the area sizes of different water areas; obtaining each water body identification result based on the satellite remote sensing data and a multi-index method; the water logging frequency of each pixel in the monitoring time range is calculated according to multiple water body recognition results, the water body area of the monitoring area is obtained according to the water logging frequency, and the water logging frequency is used for representing the probability that each pixel is judged as the water body in the effective monitoring times. According to the embodiment of the invention, medium-high resolution remote sensing data can be comprehensively utilized, through a multi-index discrimination method and a flooding frequency calculation method, rapid calculation of the river and lake apparent water area covering the whole hydrological cycle in a large scale range (such as a watershed scale) is completed, and the spatial and temporal distribution condition of the river and lake water body is monitored. The problem that effective data support is lacked in the aspect of water ecological environment protection supervision is solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of water area management. Specifically, the embodiments of the present application relate to a method, medium and electronic device for obtaining the apparent water area of ​​rivers and lakes. Background Art

[0002] Water resources are an important guarantee for human survival and economic development. Changes in water bodies such as rivers and lakes reflect the impact of hydrological conditions, climate change and human activities on the environment. Therefore, timely and accurate acquisition of water area information is of great significance for water resource management, ecological protection, flood control and drought relief.

[0003] Traditional water resource monitoring relies on ground surveys and field measurements, which takes a long time and has limited coverage, making it difficult to achieve water monitoring in large areas.

[0004] The existing methods for obtaining water area mainly have the following problems: Spatial discontinuity: On a large scale, many existing methods have poor water area recognition effects, making it difficult to monitor the water area of ​​the entire water body. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method, medium and electronic device for obtaining the apparent water area of ​​rivers and lakes. The embodiments of the present application provide a method for calculating the apparent water area of ​​rivers and lakes based on medium to high resolution satellite remote sensing data in response to the deficiencies of the prior art. The method can comprehensively utilize the existing medium to high resolution remote sensing data to complete the rapid calculation of the apparent water area of ​​rivers and lakes on a large scale (such as a watershed scale) and covering the entire hydrological cycle through a multi-index discrimination method and a flooding frequency calculation method, monitor the spatiotemporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in the supervision of water ecological environment protection.

[0006] In a first aspect, some embodiments of the present application provide a method for obtaining the apparent water area of ​​rivers and lakes, the method comprising: selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas; obtaining water body identification results based on the satellite remote sensing data and a multi-index method; calculating the flooding frequency of each pixel within the monitoring time range through multiple water body identification results, and obtaining the water body area of ​​the monitoring area according to the flooding frequency, wherein the flooding frequency is used to characterize the probability of each pixel being judged as a water body in the effective monitoring times.

[0007] The embodiments of the present application use medium to high resolution remote sensing data, a multi-index discrimination method, and a flooding frequency calculation method to quickly calculate the apparent water area of ​​rivers and lakes on a large scale (such as a watershed scale) and covering the entire hydrological cycle, monitor the spatiotemporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in the supervision of water ecological environment protection.

[0008] In some embodiments, the water types include: lakes, river sections of a first width, and river sections of a second width, wherein the water body identification results obtained based on the satellite remote sensing data and the multi-index method include: for the lake, obtaining the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the lake according to the first multi-index water body detection rule; for the river section of the first width, obtaining the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the river section of the first width according to the second multi-index water body detection rule; for the river section of the second width, obtaining the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width with the target segmentation threshold, wherein the target segmentation threshold is obtained by calculating the inter-class variance and the intra-class variance corresponding to each candidate threshold, and each candidate threshold is obtained by counting the normalized water body index value of each pixel in the sample remote sensing image data of the river section with the second width.

[0009] Some embodiments of the present application use differentiated remote sensing data for different water types to identify water areas. They can comprehensively utilize existing medium and high-resolution remote sensing data to complete rapid calculation of the apparent water area of ​​rivers and lakes on a large scale (such as a river basin scale) and covering the entire hydrological cycle, thereby improving the accuracy of water area calculations over a large area.

[0010] In some embodiments, the flooding frequency of each pixel within the monitoring time range is calculated through multiple water body identification results, and the water body area of ​​the monitoring area is obtained according to the flooding frequency, including: obtaining the ratio of the number of times each pixel is identified as a water body in all remote sensing image data to be identified corresponding to the monitoring area within the monitoring time range to the effective monitoring number, to obtain the flooding frequency of each pixel; and taking the area corresponding to all pixels whose flooding frequency is greater than a set value as the water body area of ​​the monitoring area.

[0011] The embodiments of the present application can comprehensively utilize existing medium to high-resolution remote sensing data, multi-index discrimination methods and flooding frequency calculation methods to complete the rapid calculation of the apparent water area of ​​rivers and lakes on a large scale (such as a watershed scale) and covering the entire hydrological cycle, monitor the spatiotemporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in the supervision of water ecological environment protection.

[0012] In some embodiments, the selecting of satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas includes: obtaining Landsat8 / 9 OLI data collected by a multispectral sensor carried by a running land observation satellite as the first remote sensing image data to be identified corresponding to the lake; obtaining the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the lake according to the first multi-index water body detection rule, including: calculating the normalized vegetation index NDVI, improved normalized water index mNDWI and enhanced vegetation index EVI of each pixel in the first remote sensing image data to be identified; identifying the water body included in the first remote sensing image data to be identified according to the normalized vegetation index NDVI, improved normalized water index mNDWI and enhanced vegetation index EVI of the corresponding pixels, and obtaining the water body identification result of each pixel in the first remote sensing image data to be identified.

[0013] Some embodiments of the present application use Landsat 8 / 9 OLI data as remote sensing image data for lake water bodies and combine it with a multi-index method to determine the water condition of the monitoring area, thereby improving the accuracy and objectivity of the water body judgment results for lake water bodies.

[0014] In some embodiments, the identifying of the water body included in the first remote sensing image data to be identified based on the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel includes: for the i-th pixel in the first remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the i-th pixel is greater than the enhanced vegetation index of the i-th pixel, and it is confirmed that the enhanced vegetation index EVI of the i-th pixel is less than a first threshold, then it is confirmed that the ground area corresponding to the i-th pixel is a water body, wherein i is an integer greater than or equal to 1; or, for the i-th pixel in the first remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the i-th pixel is greater than the normalized vegetation index NDVI of the i-th pixel, and it is confirmed that the enhanced vegetation index EVI of the i-th pixel is less than the first threshold, then it is confirmed that the ground area corresponding to the i-th pixel is a water body, wherein i is an integer greater than or equal to 1.

[0015] Some embodiments of the present application provide two implementation algorithms corresponding to the first multi-index water body detection rule, which improve the versatility of the technical solution and the accuracy of the water body identification results.

[0016] In some embodiments, the selecting of satellite remote sensing data of corresponding resolution for corresponding water types according to the area sizes of different water areas includes: acquiring Sentinel2 MSI data collected by a multispectral imager carried by a remote sensing satellite as second remote sensing image data to be identified corresponding to the river section of the first width; obtaining a water body identification result for each pixel in the remote sensing image data to be identified corresponding to the river section of the first width according to a second multi-index water body detection rule, including: calculating the normalized vegetation index NDVI, improved normalized water index mNDWI and enhanced vegetation index EVI for each pixel in the second remote sensing image data to be identified; identifying the water body included in the second remote sensing image data to be identified according to the normalized vegetation index NDVI, improved normalized water index mNDWI and enhanced vegetation index EVI of the corresponding pixels, and obtaining the water body identification result for each pixel in the second remote sensing image data.

[0017] Some embodiments of the present application use Sentinel2 MSI data as remote sensing image data for river section water bodies with a first width and combine it with a multi-index method to determine the water condition of the monitoring area, thereby improving the accuracy and objectivity of the water body judgment results for this type of water body.

[0018] In some embodiments, the identifying of the water body included in the second remote sensing image data to be identified based on the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel includes: for the j-th pixel in the second remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the j-th pixel is greater than the enhanced vegetation index of the j-th pixel, and it is confirmed that the enhanced vegetation index EVI of the j-th pixel is less than a second threshold, then it is confirmed that the ground area corresponding to the j-th pixel is a water body, wherein j is an integer greater than or equal to 1; or, for the j-th pixel in the second remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the j-th pixel is greater than the normalized vegetation index NDVI of the j-th pixel, and it is confirmed that the enhanced vegetation index EVI of the j-th pixel is less than the second threshold, then it is confirmed that the ground area corresponding to the j-th pixel is a water body, wherein j is an integer greater than or equal to 1.

[0019] Some embodiments of the present application provide two implementation algorithms corresponding to the second multi-index water body detection rule, which improve the versatility of the technical solution and the accuracy of the water body identification results.

[0020] In some embodiments, the selecting of satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different waters includes: obtaining GF 1 / 6 PMS data collected by multispectral and panchromatic sensors carried by operating high-resolution remote sensing satellites as third remote sensing image data to be identified corresponding to the river section of the second width; obtaining water body identification results for each pixel in the remote sensing image data to be identified corresponding to the river section of the second width by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width with the target segmentation threshold, including: repeating the following process until a candidate segmentation threshold with the maximum inter-class variance and the minimum intra-class variance is found, and using the candidate segmentation threshold as the target segmentation threshold: using binary search for the nth candidate segmentation threshold in the interval [-1,1]; according to the candidate segmentation threshold, the nth candidate segmentation threshold is selected; and the target segmentation threshold is selected according to the target segmentation threshold. The image corresponding to the data is segmented into water bodies and non-water bodies to obtain a judgment result; the inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold are calculated according to the judgment result and the variance calculation formula, wherein n is an integer greater than 1; if the normalized difference index value NDWI value of the kth pixel on the third remote sensing image data to be identified is greater than the target segmentation threshold, the ground area corresponding to the kth pixel is determined to be a water body; if the normalized difference index value NDWI of the kth pixel on the third remote sensing image data to be identified is less than or equal to the target segmentation threshold, the ground area corresponding to the kth pixel is determined to be a non-water body, wherein k is an integer greater than or equal to 1.

[0021] Some embodiments of the present application provide an algorithm for identifying water bodies in a river section having a second width according to a determined threshold value, thereby improving the accuracy and objectivity of water body identification results for such water bodies.

[0022] In some embodiments, before obtaining the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width with the target segmentation threshold, the method also includes: repeating the following process until it is confirmed that a candidate segmentation threshold that maximizes the inter-class variance and minimizes the intra-class variance is found, and using the candidate segmentation threshold as the target segmentation threshold: using a binary search method to search for the nth candidate segmentation threshold in the interval [-1,1]; according to the nth candidate segmentation threshold, segmenting the image corresponding to the sample remote sensing image data into water bodies and non-water bodies to obtain a judgment result; calculating the inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold according to the judgment result and the variance calculation formula, wherein n is an integer greater than or equal to 1.

[0023] Some embodiments of the present application determine the optimal image segmentation threshold through inter-class variance and intra-class variance, thereby improving the accuracy of identifying water bodies and non-water bodies.

[0024] In a second aspect, some embodiments of the present application provide a device for obtaining the apparent water area of ​​rivers and lakes, the device comprising: a remote sensing image data acquisition module, configured to select satellite remote sensing data of corresponding resolution for corresponding water area types according to the area size of different water areas; a module for obtaining water body identification results for each pixel, configured to obtain each water body identification result based on the satellite remote sensing data and a multi-index method; a water body area acquisition module, configured to calculate the flooding frequency of each pixel through multiple water body identification results, and obtain the water body area of ​​the monitored area within the monitoring time period according to the flooding frequency, wherein the flooding frequency is used to characterize the probability of each pixel being judged as a water area in the effective monitoring times.

[0025] In a third aspect, some embodiments of the present application provide a method for managing water areas, the method comprising: obtaining a water area using the method described in any one of the embodiments included in the first aspect; and managing the corresponding water area using the water area.

[0026] In a fourth aspect, some embodiments of the present application provide a computer-readable storage medium, comprising computer program instructions, which, when read and executed by a processor, execute the method described in any one of the embodiments included in the first aspect or the third aspect.

[0027] In a fifth aspect, some embodiments of the present application provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of the embodiments included in the first aspect or the third aspect can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 One of the flow charts of the method for obtaining the apparent water area of ​​rivers and lakes provided in the embodiment of the present application; Figure 2 Flow chart 2 of the method for obtaining the apparent water area of ​​rivers and lakes provided in an embodiment of the present application; Figure 3Flow chart 3 of the method for obtaining the apparent water area of ​​rivers and lakes provided in the embodiment of the present application; Figure 4 A block diagram of the composition of the device for obtaining the apparent water area of ​​rivers and lakes provided in an embodiment of the present application; Figure 5 A schematic diagram of the composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0031] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0032] The sustainable management of water resources requires high-frequency and accurate monitoring data support. Therefore, the inventors of this application found in their research that it is particularly necessary to develop a remote sensing monitoring method that can automatically and accurately extract and calculate the water area of ​​rivers and lakes on a large scale based on medium and high-resolution satellite data. The method for obtaining the apparent water area of ​​rivers and lakes provided by the embodiments of this application makes water area monitoring more automated, continuous and high-precision, and can provide timely and reliable data support for the management, allocation and protection of water resources.

[0033] For example, the inventors of this application discovered in their research that remote sensing technology can be used as a new means of water body monitoring. In particular, as the resolution of satellite data continues to improve, remote sensing technology can be used to efficiently and accurately monitor large areas of water such as rivers and lakes.

[0034] At least in order to solve the problem that related technologies are unable to identify the water area of ​​a larger area and improve the accuracy of the obtained water area, some embodiments of the present application provide a method for obtaining the apparent water area of ​​rivers and lakes. The method uses medium and high resolution remote sensing data (for example, Landsat 8 / 9 OLI data and Sentinel2 MSI data as medium resolution data and GF 1 / 6 PMS data as high resolution data), and can comprehensively and accurately calculate the apparent water area of ​​rivers and lakes in a large-scale area (for example, a watershed scale) based on a multi-index method and a flooding frequency method, and represent the spatial and temporal distribution of river and lake water bodies.

[0035] That is to say, in some embodiments of this application, satellite remote sensing data of appropriate resolution is selected according to the area characteristics of different water bodies; based on the multi-index method and the flooding frequency method, automatic, comprehensive and accurate extraction of target river and lake water bodies of various large scales is achieved, thereby calculating the apparent water area of ​​rivers and lakes and the temporal and spatial distribution of generated water bodies. The technical solution based on the embodiments of this application can provide effective data support for the supervision of the water ecological environment of rivers and lakes across the country in the shortest time and at the lowest cost, and improve the technical effect of water area management.

[0036] Please see Figure 1 , Figure 1 A method for obtaining the apparent water area of ​​a river or lake provided in some embodiments of the present application comprises: S110, selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas. That is, when obtaining the water area of ​​the monitoring area, the embodiment of the present application selects corresponding remote sensing image data to be identified for different water areas.

[0037] For example, in some embodiments of the present application, the water types include: lakes, river sections of a first width, and river sections of a second width.

[0038] It should be noted that the resolution of the remote sensing image data to be identified corresponding to different types of water areas in the embodiments of the present application is different. The embodiments of the present application divide the water areas into different types of water areas according to the size of the water areas. In some embodiments of the present application, the water areas include: lakes and river sections with different water surface widths. For example, some embodiments of the present application divide all river sections into two types of water areas or multiple types of water bodies according to the different widths of the river sections.

[0039] S120, obtaining each water body identification result based on the satellite remote sensing data and the multi-index method.

[0040] For example, in some embodiments of the present application, S120 obtains a water body identification result within the monitoring area by repeating the following process: S121, for the lake, obtaining a water body recognition result for each pixel in the to-be-recognized remote sensing image data corresponding to the lake according to a first multi-index water body detection rule.

[0041] S122: For a river section of a first width, obtaining a water body recognition result for each pixel in the to-be-recognized remote sensing image data corresponding to the river section of the first width according to a second multi-index water body detection rule.

[0042] S123, for the river section of the second width, the water body recognition result of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width is obtained by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width with the target segmentation threshold, wherein the target segmentation threshold is obtained by calculating the inter-class variance and intra-class variance corresponding to each candidate threshold, and each candidate threshold is obtained by counting the normalized water body index value of each pixel in the sample remote sensing image data of the river section with the second width.

[0043] For example, in some embodiments of the present application, the second width is smaller than the first width.

[0044] S130, calculating the flooding frequency of each pixel within the monitoring time range through multiple water body identification results, and obtaining the water body area of ​​the monitoring area according to the flooding frequency.

[0045] For example, some embodiments of the present application can obtain multiple water body identification results in the monitoring area within the monitoring period, and then determine the final water body area, thereby improving the accuracy of water body area identification.

[0046] For example, Figure 2 As shown, in some embodiments of the present application, S130 calculates the flooding frequency of each pixel within the monitoring time range through multiple water body identification results and obtains the water body area of ​​the monitoring area according to the flooding frequency. The process exemplarily includes: S131, obtaining the ratio of the number of times each pixel is identified as a water body in all remote sensing image data to be identified corresponding to the monitoring area within the monitoring time range to the effective observation number (that is, calculating the ratio of the number of times each pixel is identified as a water body to the effective monitoring number), and obtaining the flooding frequency of each pixel; S132, taking the area corresponding to all pixels with a flooding frequency greater than a set value as the water body area of ​​the monitoring area. The embodiments of the present application can comprehensively utilize existing medium and high resolution remote sensing data, multi-index discrimination methods and flooding frequency calculation methods to complete the rapid calculation of the apparent water area of ​​rivers and lakes in a large scale (such as a watershed scale) and covering the entire hydrological cycle, monitor the spatiotemporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in the supervision of water ecological environment protection.

[0047] It is not difficult to understand that some embodiments of the present application use differentiated remote sensing data for different water bodies to identify water body areas, and can comprehensively utilize existing medium and high-resolution remote sensing data to complete rapid calculation of the apparent water area of ​​rivers and lakes on a large scale (such as a watershed scale) and covering the entire hydrological cycle (using multiple water body identification results to obtain the water body area), thereby improving the accuracy of water body area calculation over a large area.

[0048] The following is an illustrative description of remote sensing data of corresponding resolutions obtained for different water types and corresponding water body identification algorithms.

[0049] For example, in some embodiments of the present application, the process of selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas described in S110 exemplarily includes: obtaining Landsat 8 / 9 OLI data (as medium resolution data) collected by a multispectral sensor carried by an operating land observation satellite as the first remote sensing image data to be identified corresponding to the lake. The corresponding process of obtaining each water body identification result based on the satellite remote sensing data and the multi-index method described in S120 exemplarily includes: the first step is to calculate the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of each pixel in the first remote sensing image data to be identified. The second step is to identify the water body included in the first remote sensing image data to be identified according to the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel, and obtain the water body identification result of each pixel in the first remote sensing image data to be identified.

[0050] For example, in some embodiments of the present application, the process of identifying the water body included in the first remote sensing image data to be identified according to the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel in the second step exemplarily includes: for the i-th pixel in the first remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the i-th pixel is greater than the enhanced vegetation index of the i-th pixel, and it is confirmed that the enhanced vegetation index EVI of the i-th pixel is less than the first threshold, then it is confirmed that the ground area corresponding to the i-th pixel is a water body, where i is an integer greater than or equal to 1; or, for the i-th pixel in the first remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the i-th pixel is greater than the normalized vegetation index NDVI of the i-th pixel, and it is confirmed that the enhanced vegetation index EVI of the i-th pixel is less than the first threshold, then it is confirmed that the ground area corresponding to the i-th pixel is a water body, where i is an integer greater than or equal to 1. Some embodiments of the present application provide two implementation algorithms corresponding to the first multi-index water body detection rule, which improve the versatility of the technical solution and the accuracy of the water body identification results.

[0051] It is not difficult to understand that some embodiments of the present application use Landsat 8 / 9 OLI data as remote sensing image data for lake water bodies and combine it with a multi-index method to determine the water body conditions in the monitoring area, thereby improving the accuracy and objectivity of the water body judgment results for lake water bodies. It should be noted that Landsat 8 / 9 OLI data is data collected by the earth observation satellites Landsat 8 and Landsat 9 launched by the National Aeronautics and Space Administration (NASA). Both the earth observation satellites Landsat 8 and Landsat 9 carry OLI (Operational Land Imager) as one of the sensors for collecting observation data on the earth's surface.

[0052] For example, in some embodiments of the present application, the process of selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas in S110 exemplarily includes: obtaining Sentinel2 MSI data collected by a multispectral imager carried by a remote sensing satellite as the second remote sensing image data to be identified corresponding to the river section of the first width. The corresponding process of obtaining each water body identification result based on the satellite remote sensing data and the multi-index method in S120 exemplarily includes: the first step is to calculate the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of each pixel in the second remote sensing image data to be identified. The second step is to identify the water body included in the second remote sensing image data to be identified according to the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel, and obtain the water body identification result of each pixel in the second remote sensing image data.

[0053] For example, in some embodiments of the present application, the process of identifying the water body included in the second remote sensing image data to be identified according to the normalized vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel in the second step exemplarily includes: for the j-th pixel in the second remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the j-th pixel is greater than the enhanced vegetation index of the j-th pixel, and it is confirmed that the enhanced vegetation index EVI of the j-th pixel is less than the second threshold, then it is confirmed that the ground area corresponding to the j-th pixel is a water body, where j is an integer greater than or equal to 1; or, for the j-th pixel in the second remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the j-th pixel is greater than the normalized vegetation index NDVI of the j-th pixel, and it is confirmed that the enhanced vegetation index EVI of the j-th pixel is less than the second threshold, then it is confirmed that the ground area corresponding to the j-th pixel is a water body, where j is an integer greater than or equal to 1. Some embodiments of the present application provide two implementation algorithms corresponding to the second multi-index water body detection rule, which improve the versatility of the technical solution and the accuracy of the water body identification results.

[0054] It is not difficult to understand that some embodiments of the present application use Sentinel2 MSI data as remote sensing image data for river sections with a first width and combine it with a multi-index method to determine the water body conditions in the monitoring area, thereby improving the accuracy and objectivity of the water body judgment results for such water bodies. It should be noted that Sentinel-2 MSI data is data obtained by the multispectral imager (MSI) carried by the Sentinel-2 satellite launched by the European Space Agency (ESA).

[0055] It should be noted that in some embodiments of the present application, the first threshold and the second threshold are different in magnitude. In other embodiments of the present application, the first threshold and the second threshold are the same in magnitude. Those skilled in the art can set the specific values ​​of the first threshold and the second threshold according to actual needs.

[0056] For example, in some embodiments of the present application, the process of selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different waters described in S110 exemplarily includes: obtaining GF 1 / 6 PMS data collected by multispectral and panchromatic sensors carried by operating high-resolution remote sensing satellites as the third remote sensing image data to be identified corresponding to the river section of the second width. The corresponding process of obtaining each water body identification result based on the satellite remote sensing data and the multi-index method described in S120 exemplarily includes: repeating the following process until it is confirmed that a candidate segmentation threshold with the maximum inter-class variance and the minimum intra-class variance is found, and the candidate segmentation threshold is used as the target segmentation threshold: using the binary search method to search for the nth candidate segmentation threshold in the interval [-1,1]; according to the nth candidate segmentation threshold, the sample remote sensing image data (these image data are also collected by multispectral and panchromatic sensors carried by operating high-resolution remote sensing satellites) with high resolution GF 1 / 6 of the monitoring area The image corresponding to the third remote sensing image data to be identified is segmented into water bodies and non-water bodies to obtain a judgment result; the inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold are calculated according to the judgment result and the variance calculation formula, wherein n is an integer greater than or equal to 1; if the normalized difference index value NDWI value of the kth pixel on the third remote sensing image data to be identified is greater than the target segmentation threshold, the ground area corresponding to the kth pixel is determined to be a water body; if the normalized difference index value NDWI of the kth pixel on the third remote sensing image data to be identified is less than or equal to the target segmentation threshold, the ground area corresponding to the kth pixel is determined to be a non-water body, wherein k is an integer greater than or equal to 1. It should be noted that GF 1 / 6 PMS data includes GF-1 and / or GF-6 PMS data, and GF-1 and GF-6 PMS data come from the panchromatic and multispectral sensors PMS (Panchromatic and Multispectral Sensor) carried by the Gaofen-1 satellite (GF-1) and the Gaofen-6 satellite (GF-6), respectively.

[0057] Some embodiments of the present application provide an algorithm for determining an image segmentation threshold using a traversal method and provide an algorithm for identifying water bodies in a river section having a second width according to the determined threshold, thereby improving the accuracy and objectivity of water body identification results for such water bodies.

[0058] Combine the following Figure 3 The methods for obtaining the apparent water area of ​​rivers and lakes provided in some embodiments of the present application are illustrated by taking lakes, river sections with a historical water surface width of ≥100 meters as river sections of the first width, and river sections with a historical water surface width of ≥20 meters and <100 meters as river sections of the second width as examples.

[0059] like Figure 3As shown, some embodiments of the present application provide a method for obtaining the apparent water area of ​​rivers and lakes, which is a remote sensing calculation method for the apparent water area of ​​rivers and lakes based on medium and high resolution satellite data, comprising the following steps: S1: Remote sensing image acquisition and image data preprocessing, and collating background geographic information such as water body distribution.

[0060] Acquisition of medium to high resolution remote sensing images of the target area (i.e. monitoring area) and preprocessing of image data, and organization of background geographic information.

[0061] For example, in some embodiments of the present application, S1 exemplarily includes: S1.1: Obtain remote sensing images of the target area to be monitored and background geographical data on water distribution. Examples of the remote sensing image data include: Landsat 8 / 9 OLI, Sentinel2 MSI, and GF 1 / 6 PMS.

[0062] S1.2: Preprocessing the remote sensing image, wherein the preprocessing is selected from at least one of geometric correction, spatial clipping, radiation calibration and atmospheric correction to obtain surface reflectance data of qualified quality.

[0063] In the above step S1.1, the remote sensing image data of Landsat 8 / 9 OLI, Sentinel2 MSI and GF 1 / 6 PMS of the monitoring target area are obtained to provide remote sensing data support for the subsequent extraction of water bodies using multi-index water body detection rules according to different water types, so that different remote sensing data can be used according to the different characteristics of the water bodies in the monitoring target area (mainly area characteristics), thereby realizing the identification of each water body.

[0064] In addition, the water body distribution background geographic data specifically obtains basic geographic attribute information such as river water body line vectors, lake water area surface vectors, water body names, and provinces and cities to which they belong within the monitoring target area, which is mainly used to associate apparent water body area results.

[0065] S2: Remove interference factors.

[0066] Some embodiments of the present application remove interference factors based on the S1 image preprocessing results. For example, in some embodiments of the present application, S2 exemplarily includes S2.1: based on the image data preprocessed by S1, removing factors that affect the accuracy of water body recognition, including removing the influence of clouds and fog and shadows.

[0067] Remove the effect of clouds and fog: Clouds and fog will reduce the transmittance and reflectance of light, affecting the quality and clarity of optical remote sensing images. In severe cases of clouds and fog, ground features will be obscured, resulting in the inability to obtain effective remote sensing information. Generally, images are screened under the condition that the cloud cover is <20%, or masking processing is performed on cloud and fog pixels.

[0068] Removing the influence of shadows Affected by the shadows formed by terrain occlusion or obstruction, the surface features obtained from remote sensing images are severely damaged and difficult to be used for accurately extracting ground object information. Generally, combined with DEM data, shadow detection or terrain radiation correction is performed on satellite remote sensing image data.

[0069] In step S2.1, by removing the influence of clouds and fog and shadows, the remote sensing image data can better reflect the true appearance and geographical information of the water body in the monitoring target area, so as to more accurately and comprehensively measure the apparent area of the water body in the monitoring target area.

[0070] S3: For different water body characteristics (i.e., for different water area types in the monitoring target area), select remote sensing image data with appropriate resolution for water body identification.

[0071] Among them, in some embodiments of the present application, S3 further includes the following process: S3.1: For large-area water body areas such as lakes, use the Landsat8 / 9 OLI data processed by S2, and adopt a multi-index water body detection rule (i.e., the first multi-index water body detection rule) to extract the water body. Calculate the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (mNDWI), and Enhanced Vegetation Index (EVI) respectively. When the pixel satisfies the logical relationship [(mNDWI > EVI or mNDWI > NDVI) and (EVI < Thresh)], it is determined as a water body, otherwise it is a non-water body. The first threshold Thresh is determined according to the vegetation situation on the shore, and the general value range of this first threshold is [0, 0.1].

[0072] S3.2: For river sections with a historical water surface width ≥ 100 meters (as an example of a river section with a first width), use the Sentinel2 MSI data processed by S2, and adopt a multi-index water body detection rule (i.e., the second multi-index water body detection rule) to extract the water body. Calculate the NDWI, mNDWI, and EVI indexes respectively. When the pixel satisfies the logical relationship [(mNDWI > EVI or mNDWI > NDVI) and (EVI < Thresh)], it is determined as a water body, otherwise it is a non-water body. The second threshold Thresh is determined according to the vegetation situation on the shore, and the general value range is [0, 0.1].

[0073] S3.3: For river sections with a historical water surface width of ≥20 meters and <100 meters (as an example of a river section with the second width), the GF1 / 6 PMS data obtained by S2 is used to extract water bodies using the normalized water index detection rule. Calculate NDWI, use the binary search method to search for each possible threshold in the interval [-1,1], segment the image into water bodies and non-water bodies, and find the optimal segmentation threshold (i.e., the target segmentation threshold) that maximizes the inter-class variance and minimizes the intra-class variance, thereby determining the NDWI segmentation threshold T. When the pixel satisfies the logical relationship NDWI>T, it is judged as a water body, otherwise it is a non-water body.

[0074] For example, in some embodiments of the present application, based on the remote sensing surface reflectance data, water pixels have strong reflection in the green light band (520-600nm) and strong absorption in the red light band (630-690nm), near infrared band (760-900nm) and short infrared band (1.57-1.65μm), and the green light band, red light band and near infrared band of the data are selected for multi-index calculation. The specific calculation formula is as follows: , In the formula, -- Normalized Difference Water Index, -- Green light band reflectivity, -- Reflectivity in the near-infrared band.

[0075] , In the formula, -- Normalized Difference Vegetation Index, -- Reflectivity in the near-infrared band, -- Red light band reflectivity.

[0076] , In the formula, -- Improved Normalized Water Index, -- Green light band reflectivity, --Reflectivity in the short infrared band.

[0077] , In the formula, -- Enhanced Vegetation Index, -- Reflectivity in the near infrared band, -- Red light band reflectivity, -- Blue light band reflectivity.

[0078] In the above step S3, some embodiments of the present application divide the water types into: lakes, river sections with a historical water surface width of ≥100 meters for many years (as an example of a river section with a first width), and river sections with a historical water surface width of ≥20 meters and <100 meters for many years (as an example of a river section with a second width), and use different remote sensing image data to extract the above water types. Through the multi-index water body detection rule, that is, the above logical relationship, it is accurately determined whether each pixel on the remote sensing image of the monitoring area belongs to a water body, so that the water body can be accurately identified on a large scale, so that the surface area of ​​the water body can be accurately, comprehensively and automatically calculated on a large scale.

[0079] It should be noted that the "historical water surface width" may refer to the water surface width identified by remote sensing in the past 5 years or many years. The above-mentioned "lake water body" also includes surface water bodies such as reservoirs. For small water bodies with a historical water surface width of <20 meters, compared with the water surface area of ​​lakes and large rivers, their contribution to the overall water surface area is relatively small. In large-scale macro observations using remote sensing satellite data, this tiny area usually has a small impact on the overall water area result and can be ignored.

[0080] S4: Calculate the apparent water area of ​​rivers and lakes, that is, calculate the apparent water area of ​​rivers and lakes in the monitoring area.

[0081] For example, in some embodiments of the present application, the S4 further includes the following steps: S4.1: Based on the data calculated in S3, the water flooding frequency (WIF) of each pixel is calculated for different types of water bodies, that is, the proportion of the number of times each pixel is identified as water by satellite data to the number of effective satellite observations within the monitoring time range; S4.2: In a year, the apparent river and lake water area is the water area corresponding to the WIF of different types of water bodies when it is greater than a certain value.

[0082] Specifically, the flooding frequency calculation method is as follows: , In the formula, WIF -- Flooding frequency, percentage; W -- The number of times satellite remote sensing images identified water during the monitoring period; N -- The number of effective satellite remote sensing observations within the monitoring time range.

[0083] In the above step S4, the flooding frequency (WIF) of each pixel in different types of water bodies is taken into account, so that the apparent area of ​​the water body in the monitoring target area can be calculated within the time range covering the entire hydrological cycle.

[0084] Please refer to Figure 4 , Figure 4 The device for obtaining the apparent water area of ​​rivers and lakes provided in the embodiment of the present application is shown. It should be understood that the device is similar to the above-mentioned Figure 1 The method embodiment corresponds to the method embodiment and can execute each step involved in the above method embodiment. The specific functions of the device can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in the memory in the form of software or firmware or solidified in the operating system of the device. The device for obtaining the apparent water area of ​​rivers and lakes includes: a remote sensing image data acquisition module 410, a pixel water body identification result acquisition module 420, and a water body area acquisition module 430.

[0085] The remote sensing image data acquisition module is configured to select satellite remote sensing data of corresponding resolution for corresponding water area types according to the area size of different water areas.

[0086] The module for obtaining the water body identification result of each pixel is configured to obtain each water body identification result based on the satellite remote sensing data and the multi-index method.

[0087] The water area acquisition module is configured to calculate the flooding frequency of each pixel within the monitoring time range through multiple water body identification results, and obtain the water area of ​​the monitoring area according to the flooding frequency, wherein the flooding frequency is used to characterize the probability of each pixel being judged as a water body in the effective monitoring times.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0089] Some embodiments of the present application provide a method for managing water areas, the method comprising: obtaining a water area by using a method as described in any one of the embodiments included in the above-mentioned method for obtaining the apparent water area of ​​rivers and lakes; and managing the corresponding water area by using the water area.

[0090] Some embodiments of the present application provide a computer-readable storage medium including computer program instructions. When the computer program instructions are read and executed by a processor, the method described in any one of the embodiments including the method for obtaining the apparent water area of ​​rivers and lakes or the method for managing water areas is executed.

[0091] like Figure 5As shown, some embodiments of the present application provide an electronic device 500, including a memory 510, a processor 520, and a computer program stored in the memory 510 and executable on the processor 520, wherein when the processor 520 reads the program through a bus 530 and executes the program, a method described in any one of the embodiments including the method for obtaining the apparent water area of ​​rivers and lakes or the method for managing water areas can be implemented.

[0092] Processor 520 can process digital signals and can include various computing structures, such as complex instruction set computer structure, reduced instruction set computer structure, or a structure that implements a combination of multiple instruction sets. In some examples, processor 520 can be a microprocessor.

[0093] The memory 510 may be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include code to implement some or all functions of one or more modules described in the embodiments of the present application. The processor 520 of the present disclosure embodiment may be used to execute the instructions in the memory 510 to implement Figure 1 The memory 510 includes a dynamic random access memory, a static random access memory, a flash memory, an optical memory or other memory known to those skilled in the art.

[0094] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0095] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0096] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0097] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0099] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for obtaining the apparent water area of ​​rivers and lakes, characterized in that: The method comprises: Select satellite remote sensing data with corresponding resolution for the corresponding water types according to the size of different water areas; Obtaining each water body identification result based on the satellite remote sensing data and the multi-index method; The flooding frequency of each pixel within the monitoring time range is calculated through multiple water body identification results, and the water area of ​​the monitoring area is obtained according to the flooding frequency, wherein the flooding frequency is used to characterize the probability of each pixel being judged as a water body in the effective monitoring times.

2. The method according to claim 1, characterized in that The water types include: lakes, river sections of the first width, and river sections of the second width, wherein: The water body identification results obtained based on the satellite remote sensing data and the multi-index method include: For the lake, obtaining a water body recognition result of each pixel in the to-be-recognized remote sensing image data corresponding to the lake according to a first multi-index water body detection rule; For the river section of the first width, obtaining a water body recognition result of each pixel in the to-be-recognized remote sensing image data corresponding to the river section of the first width according to a second multi-index water body detection rule; For the river section of the second width, the water body recognition result of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width is obtained by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width with the target segmentation threshold, wherein the target segmentation threshold is obtained by calculating the inter-class variance and intra-class variance corresponding to each candidate threshold, and each candidate threshold is obtained by counting the normalized water body index value of each pixel on the sample remote sensing image data of the river section with the second width.

3. The method according to claim 2, characterized in that The method of calculating the flooding frequency of each pixel within the monitoring time range through multiple water body identification results, and obtaining the water area of ​​the monitoring area according to the flooding frequency, includes: Obtaining the ratio of the number of times each pixel in all the to-be-identified remote sensing image data corresponding to the monitoring area is identified as a water body to the number of effective monitoring times within the monitoring time range, and obtaining the flooding frequency of each pixel; The areas corresponding to all pixels whose flooding frequency is greater than a set value are taken as the water area of ​​the monitoring area.

4. The method according to claim 3, characterized in that The method of selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas includes: Acquire Landsat 8 / 9 OLI data collected by a multispectral sensor carried by an operating land observation satellite as first remote sensing image data to be identified corresponding to the lake; The step of obtaining the water body recognition result of each pixel in the to-be-recognized remote sensing image data corresponding to the lake according to the first multi-index water body detection rule includes: Calculating the Normalized Difference Vegetation Index NDVI, the Improved Normalized Difference Water Index mNDWI and the Enhanced Vegetation Index EVI of each pixel in the first remote sensing image data to be identified; For the i-th pixel in the first remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the i-th pixel is greater than the enhanced vegetation index of the i-th pixel, and it is confirmed that the enhanced vegetation index EVI of the i-th pixel is less than the first threshold, then it is confirmed that the ground area corresponding to the i-th pixel is a water body, where i is an integer greater than or equal to 1; or, for the i-th pixel in the first remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the i-th pixel is greater than the normalized vegetation index NDVI of the i-th pixel, and it is confirmed that the enhanced vegetation index EVI of the i-th pixel is less than the first threshold, then it is confirmed that the ground area corresponding to the i-th pixel is a water body, where i is an integer greater than or equal to 1.

5. The method according to claim 3, characterized in that The method of selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size of different water areas includes: Acquire Sentinel2 MSI data collected by a multispectral imager carried by a remote sensing satellite as second remote sensing image data to be identified corresponding to the river section of the first width; The step of obtaining the water body recognition result of each pixel in the to-be-recognized remote sensing image data corresponding to the river section of the first width according to the second multi-index water body detection rule includes: Calculating the Normalized Difference Vegetation Index NDVI, the Improved Normalized Difference Water Index mNDWI and the Enhanced Vegetation Index EVI of each pixel in the second remote sensing image data to be identified; For the j-th pixel in the second remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the j-th pixel is greater than the enhanced vegetation index of the j-th pixel, and it is confirmed that the enhanced vegetation index EVI of the j-th pixel is less than the second threshold, then it is confirmed that the ground area corresponding to the j-th pixel is a water body, where j is an integer greater than or equal to 1; or, for the j-th pixel in the second remote sensing image data to be identified, if it is confirmed that the improved normalized water index mNDWI of the j-th pixel is greater than the normalized vegetation index NDVI of the j-th pixel, and it is confirmed that the enhanced vegetation index EVI of the j-th pixel is less than the second threshold, then it is confirmed that the ground area corresponding to the j-th pixel is a water body, where j is an integer greater than or equal to 1.

6. The method according to any one of claims 1 to 5, characterized in that The method of selecting satellite remote sensing data of corresponding resolution for corresponding water types according to the area size characteristics of different water areas includes: Acquire GF 1 / 6 PMS data collected by a multispectral and panchromatic sensor carried by a high-resolution remote sensing satellite in operation as third remote sensing image data to be identified corresponding to the river section of the second width; The method of obtaining the water body recognition result of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the river section of the second width with the target segmentation threshold comprises: Repeat the following process until a candidate segmentation threshold with the maximum inter-class variance and the minimum intra-class variance is found, and use the candidate segmentation threshold as the target segmentation threshold: use the binary search method to search for the nth candidate segmentation threshold in the interval [-1,1]; segment the image corresponding to the sample remote sensing image data into water bodies and non-water bodies according to the nth candidate segmentation threshold, and obtain a judgment result; calculate the inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold according to the judgment result and the variance calculation formula, where n is an integer greater than or equal to 1; If the normalized index value NDWI of the kth pixel on the third remote sensing image data to be identified is greater than the target segmentation threshold, the ground area corresponding to the kth pixel is judged as a water body; if the normalized index value NDWI of the kth pixel on the third remote sensing image data to be identified is less than or equal to the target segmentation threshold, the ground area corresponding to the kth pixel is judged as a non-water body, where k is an integer greater than or equal to 1.

7. A device for obtaining the apparent water area of ​​rivers and lakes, characterized in that: The device comprises: The remote sensing image data acquisition module is configured to select satellite remote sensing data of corresponding resolution for corresponding water area types according to the area size of different water areas; A module for obtaining water body identification results of each pixel is configured to obtain each water body identification result based on the satellite remote sensing data and the multi-index method; The water area acquisition module is configured to calculate the flooding frequency of each pixel through multiple water body identification results, and obtain the water area of ​​the monitoring area within the monitoring time period according to the flooding frequency, wherein the flooding frequency is used to characterize the probability of each pixel being judged as a water area in the effective monitoring times.

8. A method for managing a water area, characterized in that: The method comprises: The area of ​​a body of water obtained by the method according to any one of claims 1 to 6; The corresponding water area is managed through the water area.

9. A computer-readable storage medium, characterized in that: The method comprises computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 6 and claim 8 is executed.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 and claim 8 can be implemented.

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