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

Abstract

Embodiments of the present application provide a method, medium and electronic device for obtaining apparent water area of rivers and lakes, the method comprising: selecting satellite remote sensing data of corresponding resolution for a corresponding water area type according to the size of the area of different water areas; obtaining each water body identification result based on the satellite remote sensing data and a multi-index method; calculating the waterlogging frequency of each pixel within a monitoring time range through the multiple water body identification results, and obtaining the water body area of a monitoring area according to the waterlogging frequency, wherein the waterlogging frequency is used to represent the probability of each pixel being determined as a water body in the effective monitoring times. Embodiments of the present application can comprehensively utilize medium and high resolution remote sensing data, complete the rapid calculation of the apparent water area of rivers and lakes in a large scale range (such as a basin scale) and covering the entire hydrological period through a multi-index discrimination method and a waterlogging frequency calculation method, monitor the spatio-temporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in water ecological environment protection supervision.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of water area management, and in particular, embodiments of the present application relate to a method for obtaining apparent water area of rivers and lakes, a medium and an electronic device. BACKGROUND

[0002] Water resources are an important guarantee for human survival and economic development. Changes in water bodies such as rivers and lakes reflect 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 resources management, ecological protection, flood control and drought resistance, etc.

[0003] Traditional water resource monitoring relies on ground investigation and field measurement, which is time-consuming, limited in coverage, and difficult to achieve large-scale regional water body monitoring.

[0004] The existing water area acquisition method mainly has the following problems: spatial discontinuity: on a large scale, many existing methods have poor water body area identification effect, and it is difficult to achieve water body area monitoring of the entire water body. SUMMARY

[0005] Embodiments of the present application aim to provide a method for obtaining apparent water area of rivers and lakes, a medium and an electronic device. Embodiments of the present application provide a method for calculating apparent water area of rivers and lakes based on medium and high resolution satellite remote sensing data, which can comprehensively utilize existing medium and high resolution remote sensing data, complete rapid calculation of apparent water area of rivers and lakes in a large scale range (such as a watershed scale) and covering the entire hydrological period through a multi-index discrimination method and a water flooding frequency calculation method, monitor the spatio-temporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in water ecological environment protection supervision.

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

[0007] Embodiments of the present application complete rapid calculation of apparent water area of rivers and lakes in a large scale range (such as a watershed scale) and covering the entire hydrological period through medium and high resolution remote sensing data, a multi-index discrimination method and a water flooding frequency calculation method, monitor the spatio-temporal distribution of river and lake water bodies, and solve the problem of lack of effective data support in water ecological environment protection supervision.

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

[0009] Some embodiments of the present application use differentiated remote sensing data for water body area identification for different water area types, can comprehensively use existing medium and high resolution remote sensing data, complete fast calculation of river and lake apparent water area in a large scale range (for example, a river basin scale) and covering the entire hydrological period, and improve the accuracy of water body area calculation in a large area.

[0010] In some embodiments, the water body area of the monitoring area is obtained by calculating a water flooding frequency of each pixel in a monitoring time range based on the multiple water body identification results, and the water body area of the monitoring area is obtained based on the water flooding frequency, including: obtaining a ratio of a number of times each pixel in all to-be-identified remote sensing image data corresponding to the monitoring area is identified as a water body to the effective monitoring times in the monitoring time range, to obtain the water flooding frequency of each pixel; and regarding an area corresponding to all pixels with a water flooding frequency greater than a set value as the water body area of the monitoring area.

[0011] Embodiments of the present application can comprehensively use existing medium and high resolution remote sensing data, a multi-index discrimination method, and a water flooding frequency calculation method, complete fast calculation of river and lake apparent water area in a large scale range (for example, a river basin scale) and covering the entire hydrological period, monitor the spatio-temporal distribution of river and lake water bodies, and solve the problem of lack of effective data support for water ecological environment protection supervision.

[0012] In some embodiments, the selecting satellite remote sensing data of a corresponding resolution according to the area size of different water areas for the corresponding water area type comprises: acquiring Landsat 8 / 9 OLI data collected by a multispectral sensor carried on a running land observation satellite as first to-be-identified remote sensing image data corresponding to the lake; and the obtaining a water body identification result of each pixel in the first to-be-identified remote sensing image data corresponding to the lake according to the first multi-index water body detection rule comprises: calculating normalized difference vegetation index (NDVI), modified normalized water index (mNDWI), and enhanced vegetation index (EVI) of each pixel in the first to-be-identified remote sensing image data; and identifying water bodies included in the first to-be-identified remote sensing image data according to the normalized difference vegetation index (NDVI), the modified normalized water index (mNDWI), and the enhanced vegetation index (EVI) of the corresponding pixel to obtain the water body identification result of each pixel in the first to-be-identified remote sensing image data.

[0013] Some embodiments of the present application use Landsat 8 / 9 OLI data as remote sensing image data for lake water bodies and combine multi-index methods to determine the water body situation of the monitoring area, thereby improving the accuracy and objectivity of the water body determination result for lake water bodies.

[0014] In some embodiments, the identifying water bodies included in the first to-be-identified remote sensing image data according to the normalized difference vegetation index (NDVI), the modified normalized water index (mNDWI), and the enhanced vegetation index (EVI) of the corresponding pixel comprises: for an i-th pixel in the first to-be-identified remote sensing image data, if it is confirmed that the modified normalized water index (mNDWI) of the i-th pixel is greater than the enhanced vegetation index (EVI) 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, it is confirmed that a 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 to-be-identified remote sensing image data, if it is confirmed that the modified normalized water index (mNDWI) of the i-th pixel is greater than the normalized difference 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, 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.

[0015] Some embodiments of the present application provide two implementation algorithms corresponding to the first multi-index water body detection rule, thereby improving the universality of the technical solution and improving the accuracy of the obtained water body identification result.

[0016] In some embodiments, the selecting satellite remote sensing data with a corresponding resolution according to the area size of different water areas for the corresponding water area type comprises: acquiring Sentinel2 MSI data collected by a multispectral imager carried by a remote sensing satellite as second to-be-identified remote sensing image data corresponding to the river section with the first width; and the obtaining water body identification results of each pixel in the second to-be-identified remote sensing image data corresponding to the river section with the first width according to the second multi-index water body detection rule comprises: calculating normalized vegetation index NDVI, improved normalized water index mNDWI, and enhanced vegetation index EVI of each pixel in the second to-be-identified remote sensing image data; and identifying water bodies included in the second to-be-identified remote sensing image data according to the normalized vegetation index NDVI, the improved normalized water index mNDWI, and the enhanced vegetation index EVI of the corresponding pixel to obtain the water body identification results of 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 type water bodies with a first width and determine the water body situation of a monitoring area in combination with a multi-index method, thereby improving the accuracy and objectivity of the water body determination result for this type of water body.

[0018] In some embodiments, the identifying water bodies included in the second to-be-identified remote sensing image data according to the normalized vegetation index NDVI, the improved normalized water index mNDWI, and the enhanced vegetation index EVI of the corresponding pixel comprises: for a jth pixel in the second to-be-identified remote sensing image data, if it is confirmed that the improved normalized water index mNDWI of the jth pixel is greater than the enhanced vegetation index of the jth pixel, and it is confirmed that the enhanced vegetation index EVI of the jth pixel is less than a second threshold, it is confirmed that a ground area corresponding to the jth pixel is a water body, where j is an integer greater than or equal to 1; or, for a jth pixel in the second to-be-identified remote sensing image data, if it is confirmed that the improved normalized water index mNDWI of the jth pixel is greater than the normalized vegetation index NDVI of the jth pixel, and it is confirmed that the enhanced vegetation index EVI of the jth pixel is less than a second threshold, it is confirmed that a ground area corresponding to the jth pixel is a water body, where 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, thereby improving the universality of the technical solution and improving the accuracy of the obtained water body identification result.

[0020] In some embodiments, the selecting the satellite remote sensing data with the corresponding resolution according to the area size of the different water areas for the corresponding water area type comprises: obtaining GF 1 / 6 PMS data collected by a multispectral and panchromatic sensor carried on a running high-resolution remote sensing satellite as third to-be-identified remote sensing image data corresponding to the river section with the second width; and the obtaining the water body identification result of each pixel in the third to-be-identified remote sensing image data corresponding to the river section with the second width by comparing the normalized index value of each pixel in the third to-be-identified remote sensing image data with the target segmentation threshold value comprises: repeating the following process until a candidate segmentation threshold value that maximizes the inter-class variance and minimizes the intra-class variance is found, and taking the candidate segmentation threshold value as the target segmentation threshold value: using binary search to search for an nth candidate segmentation threshold value in the interval [-1, 1]; segmenting an image corresponding to sample remote sensing image data into water bodies and non-water bodies according to the nth candidate segmentation threshold value to obtain a decision result; and calculating the inter-class variance and the intra-class variance corresponding to the nth candidate segmentation threshold value according to the decision result and a variance calculation formula, where n is an integer greater than 1; if the normalized index value NDWI of a kth pixel on the third to-be-identified remote sensing image data is greater than the target segmentation threshold value, the ground area corresponding to the kth pixel is determined as a water body; and if the normalized index value NDWI of the kth pixel on the third to-be-identified remote sensing image data is less than or equal to the target segmentation threshold value, the ground area corresponding to the kth pixel is determined as a non-water body, where k is an integer greater than or equal to 1.

[0021] Some embodiments of the present application provide an algorithm for water body identification of a river section with a second width according to a determined threshold value, which improves the accuracy and objectivity of the water body identification result of such water bodies.

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

[0023] Some embodiments of the present application determine the optimal image segmentation threshold by inter-class variance and intra-class variance, and improve the accuracy of water body and non-water body identification.

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

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

[0026] In a fourth aspect, some embodiments of the present application provide a computer readable storage medium comprising computer program instructions, wherein the computer program instructions are read and run by a processor to execute the method 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 in the memory and executable on the processor, wherein the processor can implement the method in any one of the embodiments included in the first aspect or the third aspect when executing the program. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0029] Figure 1 One of the flowcharts of the method for obtaining apparent water area of rivers and lakes provided by the embodiments of the present application;

[0030] Figure 2 The second flowchart of the method for obtaining apparent water area of rivers and lakes provided by the embodiments of the present application;

[0031] Figure 3 FIG. 3 is a flowchart of a method for obtaining apparent water area of rivers and lakes according to an embodiment of the present application;

[0032] Figure 4 FIG. 4 is a block diagram of an apparatus for obtaining apparent water area of rivers and lakes according to an embodiment of the present application;

[0033] Figure 5 FIG. 5 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0035] It should be noted that similar reference numerals and letters refer to similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms “first”, “second”, and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0036] Sustainable management of water resources requires high-frequency, accurate monitoring data support. Therefore, the inventors of the present application found in 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 large-scale rivers and lakes based on medium-high resolution satellite data. The method for obtaining apparent water area of rivers and lakes provided by the embodiments of the present 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.

[0037] For example, the inventors of the present application found in research that remote sensing technology can be used as a new means for water body monitoring, especially with the continuous improvement of satellite data resolution, remote sensing technology can be used to efficiently and accurately monitor large-area water bodies such as rivers and lakes.

[0038] At least to solve the problem that the related art cannot 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 apparent water area of rivers and lakes, which uses medium-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), 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 water flooding frequency method, and represent the spatio-temporal distribution of river and lake water bodies.

[0039] That is, in some embodiments of the present application, satellite remote sensing data with appropriate resolution is selected according to the size characteristics of different water bodies; based on the multi-index method and the waterlogging frequency method, automatic, comprehensive and accurate extraction of various large-scale target river and lake water bodies is realized, so as to calculate the apparent water area of rivers and lakes and generate the spatio-temporal distribution of water bodies. The technical scheme based on the embodiments of the present application can provide effective data support for the supervision of water ecological environment of rivers and lakes in the whole country in the shortest time and at the lowest cost, and improve the technical effect of water management.

[0040] Please refer to Figure 1 , Figure 1 The method for obtaining the apparent water area of rivers and lakes provided by some embodiments of the present application comprises the following steps.

[0041] S110, selecting satellite remote sensing data with corresponding resolution for the corresponding water body type according to the size of different water bodies. That is, the embodiments of the present application will select corresponding to-be-identified remote sensing image data for different water bodies when obtaining the water area of the monitoring area.

[0042] For example, in some embodiments of the present application, the water body types include lakes, river sections with a first width, and river sections with a second width.

[0043] It should be noted that the resolutions of the to-be-identified remote sensing image data corresponding to different types of water bodies in the embodiments of the present application are different. The embodiments of the present application divide water bodies into different water body types according to the size of the water area. In some embodiments of the present application, the water body types 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 bodies or multiple types of water bodies according to the width of the river sections.

[0044] S120, obtaining each water body identification result based on the satellite remote sensing data and the multi-index method.

[0045] For example, in some embodiments of the present application, the S120 obtains a water body identification result for the monitoring area by repeating the following process:

[0046] S121, obtaining the water body identification result of each pixel in the to-be-identified remote sensing image data corresponding to the lake according to the first multi-index water body detection rule.

[0047] S122, obtaining the water body identification result of each pixel in the to-be-identified remote sensing image data corresponding to the river section with the first width according to the second multi-index water body detection rule.

[0048] S123, for the second width of the river segment, the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the second width of the river segment is obtained by comparing the normalized index value of each pixel in the remote sensing image data to be identified corresponding to the second width of the river segment with the target segmentation threshold. The target segmentation threshold is obtained by calculating the inter-class variance and intra-class variance corresponding to each candidate threshold. Each candidate threshold is obtained by statistically analyzing the normalized water body index value of each pixel in the sample remote sensing image data of the river segment with the second width.

[0049] For example, in some embodiments of this application, the second width is smaller than the first width.

[0050] S130: Calculate the flooding frequency of each pixel within the monitoring time range based on multiple water body identification results, and obtain the water area of ​​the monitoring area based on the flooding frequency.

[0051] For example, some embodiments of this application can obtain multiple water body identification results of the monitoring area within the monitoring period, thereby determining the final water body area and improving the accuracy of water body area identification.

[0052] For example, such as Figure 2 As shown, in some embodiments of this application, the process of S130, which calculates the flooding frequency of each pixel within the monitoring time range based on multiple water body identification results and obtains the water area of ​​the monitoring area based on the flooding frequency, includes, for example, S131, obtaining the proportion of times each pixel in all remote sensing image data to be identified as a water body to the number of valid observations within the monitoring time range (i.e., calculating the ratio of the number of times each pixel is identified as a water body to the number of valid monitoring observations), 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 area of ​​the monitoring area. The embodiments of this application can comprehensively utilize existing medium- and high-resolution remote sensing data, multi-index discrimination methods, and flooding frequency calculation methods to quickly calculate the apparent water area of ​​rivers and lakes on a large scale (e.g., watershed scale) and covering the entire hydrological cycle, monitor the spatiotemporal distribution of river and lake water bodies, and solve the problem of insufficient effective data support in water ecological environment protection and supervision.

[0053] It is easy to understand that some embodiments of this application use differentiated remote sensing data for different water bodies to identify water body areas. This can comprehensively utilize existing medium- and high-resolution remote sensing data to quickly calculate the apparent water area of ​​rivers and lakes on a large scale (e.g., 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.

[0054] The following illustrates the corresponding resolution of remote sensing data obtained for different water area types and the corresponding water body identification algorithm.

[0055] For example, in some embodiments of the present application, the process of selecting satellite remote sensing data of corresponding resolution for different water area types according to the area size of the water area, as described in S110, illustratively includes: obtaining Landsat 8 / 9 OLI data (as medium resolution data) collected by a multispectral sensor carried on a running land observation satellite as the first to-be-identified remote sensing image data corresponding to the lake. The process of obtaining each water body identification result based on the satellite remote sensing data and the multi-index method, as described in S120, illustratively includes: first, calculating the normalized difference vegetation index NDVI, the modified normalized water index mNDWI, and the enhanced vegetation index EVI of each pixel in the first to-be-identified remote sensing image data. Second, identifying the water body included in the first to-be-identified remote sensing image data according to the normalized difference vegetation index NDVI, the modified normalized water index mNDWI, and the enhanced vegetation index EVI of the corresponding pixel, to obtain the water body identification result of each pixel in the first to-be-identified remote sensing image data.

[0056] For example, in some embodiments of the present application, the process of identifying the water body included in the first to-be-identified remote sensing image data according to the normalized difference vegetation index NDVI, the modified normalized water index mNDWI, and the enhanced vegetation index EVI of the corresponding pixel, as described in the second step, illustratively includes: for the ith pixel in the first to-be-identified remote sensing image data, if it is confirmed that the modified normalized water index mNDWI of the ith pixel is greater than the enhanced vegetation index of the ith pixel, and it is confirmed that the enhanced vegetation index EVI of the ith pixel is less than a first threshold, it is confirmed that the ground area corresponding to the ith pixel is a water body, where i is an integer greater than or equal to 1; or, for the ith pixel in the first to-be-identified remote sensing image data, if it is confirmed that the modified normalized water index mNDWI of the ith pixel is greater than the normalized difference vegetation index NDVI of the ith pixel, and it is confirmed that the enhanced vegetation index EVI of the ith pixel is less than a first threshold, it is confirmed that the ground area corresponding to the ith 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 improves the universality of the technical solution and improves the accuracy of obtaining the water body identification result.

[0057] It is understandable that some embodiments of the present application use Landsat 8 / 9 OLI data as remote sensing image data for lake water bodies and combine multi-index methods to determine the water body situation of the monitoring area, which improves the accuracy and objectivity of the water body determination result for lake water bodies. It should be noted that the Landsat 8 / 9 OLI data is collected by the Earth observation satellite Landsat 8 and Landsat 9 launched by the National Aeronautics and Space Administration (NASA), and both Earth observation satellites Landsat 8 and Landsat 9 carry OLI (Operational Land Imager) as one of the sensors to collect observation data of the earth's surface.

[0058] For example, in some embodiments of the present application, the process of selecting satellite remote sensing data of corresponding resolution for the corresponding water area type according to the area size of different water areas in S110 exemplarily includes: acquiring Sentinel2 MSI data collected by a multispectral imager carried by a remote sensing satellite as second to-be-identified remote sensing image data corresponding to the river section with the first width. Correspondingly, the process of obtaining each water body identification result based on the satellite remote sensing data and the multi-index method in S120 exemplarily includes: first, calculating the normalized difference vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of each pixel in the second to-be-identified remote sensing image data. Second, according to the normalized difference vegetation index NDVI, the improved normalized water index mNDWI and the enhanced vegetation index EVI of the corresponding pixel, the water body included in the second to-be-identified remote sensing image data is identified, and the water body identification result of each pixel in the second remote sensing image data is obtained.

[0059] For example, in some embodiments of the present application, the process of identifying water bodies included in the second remote sensing image data according to the normalized difference vegetation index NDVI, the modified normalized difference water index mNDWI and the enhanced vegetation index EVI of the corresponding pixels in the second step exemplarily comprises: for the jth pixel in the second remote sensing image data, if it is confirmed that the modified normalized difference water index mNDWI of the jth pixel is greater than the enhanced vegetation index EVI of the jth pixel, and it is confirmed that the enhanced vegetation index EVI of the jth pixel is less than a second threshold, it is confirmed that the ground area corresponding to the jth pixel is a water body, where j is an integer greater than or equal to 1; or, for the jth pixel in the second remote sensing image data, if it is confirmed that the modified normalized difference water index mNDWI of the jth pixel is greater than the normalized difference vegetation index NDVI of the jth pixel, and it is confirmed that the enhanced vegetation index EVI of the jth pixel is less than a second threshold, it is confirmed that the ground area corresponding to the jth 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 improves the universality of the technical solution and improves the accuracy of the water body identification result.

[0060] It is not difficult to understand that some embodiments of the present application use Sentinel2 MSI data as remote sensing image data for river water bodies with a first width and combine multi-index methods to determine the water body situation of the monitoring area, which improves the accuracy and objectivity of the water body determination result for this type of water body. It should be noted that the Sentinel-2 MSI data is data obtained by a multi-spectral imager (MSI) carried by a Sentinel-2 satellite launched by the European Space Agency (ESA).

[0061] It should be noted that the first threshold and the second threshold have different sizes in some embodiments of the present application. In some other embodiments of the present application, the first threshold and the second threshold have the same size. Those skilled in the art can set the specific values of the first threshold and the second threshold according to actual needs.

[0062] For example, in some embodiments of the present application, the process of selecting satellite remote sensing data of a corresponding resolution for a corresponding water area type according to the area size of different water areas in S110 exemplarily comprises: acquiring GF 1 / 6 PMS data collected by a multi-spectral and panchromatic sensor carried on a running high-resolution remote sensing satellite as third to-be-identified remote sensing image data corresponding to the river section of the second width. Correspondingly, the process of obtaining each water body identification result based on the satellite remote sensing data and a multi-index method in S120 exemplarily comprises: repeating the following process until a candidate segmentation threshold value that maximizes the inter-class variance and minimizes the intra-class variance is confirmed, and taking the candidate segmentation threshold value as the target segmentation threshold value: using a bisection method to search for an nth candidate segmentation threshold value in the interval [-1, 1]; segmenting an image corresponding to sample remote sensing image data (the image data is also high-resolution GF 1 / 6 PMS data of a monitoring area collected by a multi-spectral and panchromatic sensor carried on a running high-resolution remote sensing satellite) according to the nth candidate segmentation threshold value to obtain a decision result, wherein the sample remote sensing image data is different from the third to-be-identified remote sensing image data; calculating inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold value according to the decision result and a variance calculation formula, wherein n is an integer greater than or equal to 1; if a normalized index value NDWI of a kth pixel on the third to-be-identified remote sensing image data is greater than the target segmentation threshold value, determining a ground area corresponding to the kth pixel as a water body; if the normalized index value NDWI of the kth pixel on the third to-be-identified remote sensing image data is less than or equal to the target segmentation threshold value, determining a ground area corresponding to the kth pixel as a non-water body, wherein k is an integer greater than or equal to 1. It should be noted that the GF 1 / 6 PMS data includes GF-1 and / or GF-6 PMS data, and the GF-1 and GF-6 PMS data are respectively from a panchromatic and multispectral sensor PMS (Panchromatic and Multispectral Sensor) carried by a high-resolution one satellite (GF-1) and a high-resolution six satellite (GF-6).

[0063] Some embodiments of the present application provide an algorithm for determining an image segmentation threshold value using a traversal method and provide an algorithm for water body identification of a river section of a second width according to the determined threshold value, thereby improving the accuracy and objectivity of the water body identification result for such water bodies.

[0064] The following describes some embodiments of the present application with reference to the accompanying drawings. Figure 3 The following describes some embodiments of the present application with reference to the accompanying drawings.

[0065] As Figure 3As shown, some embodiments of the present application provide a method for obtaining apparent water area of rivers and lakes, which is a remote sensing calculation method for apparent water area of rivers and lakes based on medium-high resolution satellite data, comprising the following steps:

[0066] S1: Remote sensing image acquisition and image data preprocessing, collating background geographic data such as water body distribution.

[0067] S1: Remote sensing image acquisition and image data preprocessing, collating background geographic data such as water body distribution.

[0068] For example, in some embodiments of the present application, S1 exemplarily comprises:

[0069] S1.1: Obtain remote sensing images of the monitoring target area and water body distribution background geographic data, wherein the remote sensing image data exemplarily comprises: Landsat 8 / 9 OLI, Sentinel2 MSI and GF 1 / 6 PMS.

[0070] S1.2: Preprocess the remote sensing images, wherein the preprocessing is selected from at least one of geometric correction, spatial cropping, radiation calibration and atmospheric correction, to obtain qualified ground reflectance data.

[0071] 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 is obtained, which provides remote sensing data support for subsequent extraction of water body according to different water area types using multi-index water body detection rules, so that different remote sensing data is used for different characteristics (mainly area characteristics) of the water body in the monitoring target area, thereby realizing the identification of each water body.

[0072] In addition, the water body distribution background geographic data is specifically the acquisition of river water body line vector, lake water area range face vector, water body name, and basic geographic attribute information such as the province and city to which it belongs in the monitoring target area, which is mainly used for correlating the apparent water body area result.

[0073] S2: Remove interference factors.

[0074] Some embodiments of the present application remove interference factors based on the image preprocessing results of S1. For example, in some embodiments of the present application, S2 exemplarily comprises S2.1: Based on the image data after S1 preprocessing, remove factors affecting the accuracy of water body identification, including removing cloud and fog influence and shadow influence.

[0075] Remove cloud and fog influence:

[0076] Clouds and fog will reduce the transmittance and reflectance of light, and the quality and clarity of the image of the image optical remote sensing. In the case of serious cloud and fog, the ground features will be blocked, resulting in the inability to obtain effective remote sensing information. Generally, the image is screened by the cloud content <20%, or the cloud and fog pixels are masked.

[0077] Removing the influence of shadows

[0078] Affected by the shadows formed by the terrain shielding or blocking, the surface features obtained by remote sensing images are severely damaged, making it difficult to accurately extract the ground information. Generally, combined with DEM data, satellite remote sensing image data is subjected to shadow detection or terrain radiation correction.

[0079] In step S2.1, by removing the influence of clouds and shadows, the remote sensing image data can better reflect the true appearance and geographical information of the monitoring target area water body, so as to more accurately and comprehensively measure the apparent area of the monitoring target area water body.

[0080] S3: Selecting remote sensing image data with appropriate resolution for different water body characteristics (i.e. for different water area types in the monitoring target area), and performing water body recognition.

[0081] In some embodiments of the present application, S3 further includes the following process:

[0082] S3.1: For large-area water body regions such as lakes, using the Landsat8 / 9 OLI data obtained by S2 processing, a multi-index water body detection rule (i.e. a first multi-index water body detection rule) is used to extract water bodies. Calculate normalized water index (NDWI), improved normalized 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 water body, otherwise it is non-water body, the first threshold Thresh is determined according to the situation of the vegetation on the shore, the general value range of the first threshold is [0, 0.1].

[0083] S3.2: For river sections with a water surface width of more than 100 meters in history (as an example of a river section with a first width), using the Sentinel2 MSI data obtained by S2 processing, a multi-index water body detection rule (i.e. a second multi-index water body detection rule) is used to extract water bodies. Calculate NDWI, mNDWI and EVI respectively, when the pixel satisfies the logical relationship [(mNDWI>EVI or mNDWI>NDVI) and (EVI<Thresh)], it is determined as water body, otherwise it is non-water body, the second threshold Thresh is determined according to the situation of the vegetation on the shore, the general value range is [0, 0.1].

[0084] S3.3: For the historical river sections with water surface width ≥20 meters and <100 meters (as an example of the river section with the second width), the GF1 / 6 PMS data processed by S2 is used to extract water bodies by using the normalized water index detection rule. The NDWI is calculated, and the image is segmented into water bodies and non-water bodies by using the bisection method to search for each possible threshold value in the interval [-1, 1] to find the optimal segmentation threshold value (i.e., the target segmentation threshold value) that maximizes the inter-class variance and minimizes the intra-class variance, so as to determine the NDWI segmentation threshold T. When a pixel satisfies the logical relationship NDWI > T, it is determined as a water body, otherwise as a non-water body.

[0085] For example, in some embodiments of the present application, according to the remote sensing surface reflectivity data, the water body pixel has the characteristics of strong reflection in the green light band (520-600 nm) and strong absorption in the red light band (630-690 nm), the near-infrared band (760-900 nm) and the short infrared band (1.57-1.65 μm). The green light band, red light band and near-infrared band of the data are selected for multi-index calculation, and the specific calculation formula is as follows:

[0086]

[0087] In the formula, -- normalized water index, -- green light band reflectivity, -- near-infrared light band reflectivity.

[0088]

[0089] In the formula, -- normalized vegetation index, -- near-infrared light band reflectivity, -- red light band reflectivity.

[0090]

[0091] In the formula, -- improved normalized water index, -- green light band reflectivity, -- short infrared light band reflectivity.

[0092]

[0093] In the formula, -- enhanced vegetation index, -- near-infrared band reflectivity, -- red light band reflectivity, -- blue light band reflectivity.​​​​

[0094] In the S3 step, some embodiments of the present application divide the water area types into lakes, river sections with a historical annual water surface width ≥ 100 meters (as an example of a first-width river section) and river sections with a historical annual water surface width ≥ 20 meters and < 100 meters (as an example of a second-width river section), and use different remote sensing image data to extract the water area types. The multi-index water body detection rule, i.e., the logical relationship described above, is used to accurately determine 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, and thus the water surface area can be accurately, comprehensively and automatically calculated on a large scale.

[0095] It should be noted that the "historical annual water surface width" can refer to the water surface width identified by remote sensing in the past 5 years or more. The "lake water body" described above also includes reservoirs and other water surface water bodies. For small water bodies with a historical annual water surface width < 20 meters, the water surface area is smaller than that of lakes and large rivers, and its contribution to the overall water surface area is relatively small. In the use of remote sensing satellite data for large-scale macro observation, such a small area usually has little effect on the overall water area result, and can be ignored.

[0096] S4: Calculate the apparent water area of rivers and lakes, i.e., calculate the apparent water area of rivers and lakes in the monitoring area.

[0097] For example, in some embodiments of the present application, the S4 further includes the following steps:

[0098] S4.1: Based on the data calculated in S3, calculate the water inundation frequency (WIF) of each pixel for different types of water bodies, i.e., the proportion of the number of times each pixel is identified as water by satellite data within the monitoring time range to the number of effective satellite observations.

[0099] S4.2: Within one year, the apparent river and lake water area is the water area corresponding to the WIF of different types of water bodies greater than a certain value.

[0100] Specifically, the water inundation frequency calculation method is as follows:

[0101] ,

[0102] In the formula, WIF Water inundation frequency, percentage; W Number of times identified as water by satellite remote sensing image within the monitoring time range; N Number of effective satellite observations within the monitoring time range.

[0103] In step S4, the water inundation frequency (WIF) of each pixel in different types of water area is considered, so that the apparent area of the water body in the monitoring target area can be calculated in a time range covering the entire hydrological period.

[0104] Please refer to Figure 4 , Figure 4 The device for obtaining the apparent water area of rivers and lakes is shown, and it should be understood that the device corresponds to the above-mentioned Figure 1 method embodiments, and can perform each step involved in the above-mentioned method embodiments. The specific functions of the device can be referred to the description in the foregoing, and the detailed description is appropriately omitted here to avoid repetition. The device includes at least one software function module stored in the form of software or firmware in the memory 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.

[0105] The remote sensing image data acquisition module is configured to select satellite remote sensing data of corresponding resolution for the corresponding water area type according to the area size of different water areas.

[0106] The pixel water body identification result acquisition module is configured to obtain each water body identification result based on the satellite remote sensing data and the multi-index method.

[0107] The water body area acquisition module is configured to calculate the water inundation frequency of each pixel in the monitoring time range through the multiple water body identification results, and obtain the water body area of the monitoring area according to the water inundation frequency, wherein the water inundation frequency is used to represent the probability that each pixel is determined as a water body in the effective monitoring times.

[0108] 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 foregoing method, and will not be described in more detail here.

[0109] Some embodiments of the present application provide a method for managing water areas, which includes: obtaining the water body area by using the method described in any one of the embodiments of the method for obtaining the apparent water area of rivers and lakes; and managing the corresponding water area through the water body area.

[0110] Some embodiments of the present application provide a computer readable storage medium including computer program instructions, which are read and run by a processor to execute the method described in any one of the embodiments of the method for obtaining the apparent water area of rivers and lakes or the method for managing water areas.

[0111] As Figure 5As shown, some embodiments of the present application provide an electronic device 500, comprising a memory 510, a processor 520, and a computer program stored in the memory 510 and capable of running on the processor 520, wherein the processor 520 reads programs and executes the programs through a bus 530, and can implement the method described in any one of the embodiments of the method for obtaining apparent water area of rivers and lakes or the method for managing water areas.

[0112] The processor 520 can process digital signals and can include various computing structures. For example, a complex instruction set computer structure, a reduced instruction set computer structure, or a structure implementing a combination of multiple instruction sets. In some examples, the processor 520 can be a microprocessor.

[0113] The memory 510 can be used to store instructions executed by the processor 520 or data related to the execution process of the instructions. These instructions and / or data can include code for implementing some or all of the functions of one or more modules described in the embodiments of the present application. The processor 520 of the embodiments of the present disclosure can be used to execute instructions in the memory 510 to implement the method shown in Figure 1 The memory 510 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memory well known to those skilled in the art.

[0114] 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 only schematic, for example, the flowcharts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders from that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0115] In addition, the functional modules in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0116] The functions described can be implemented in hardware, software, firmware or any combination thereof. If implemented in software, the functions can be stored or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium can be any available medium that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or other

[0117] The specific embodiments described hereinabove are illustrative only and not restrictive. Many variations and modifications of the application can become apparent to those skilled in the art in view of the foregoing description. The use of any and all examples, or exemplary language (e.g., "for example," "for instance," "as an example," "for example only," "e.g."), is intended merely to better illuminate the application and does not pose a limitation on the scope of the application. Numerous other embodiments of the application will be set forth in the following claims.

[0118] The specific embodiments described hereinabove are illustrative only and not restrictive. Many variations and modifications of the application can become apparent to those skilled in the art in view of the foregoing description. The use of any and all examples, or exemplary language (e.g., "for example," "for instance," "as an example," "for example only," "e.g."), is intended merely to better illuminate the application and does not pose a limitation on the scope of the application. Numerous other embodiments of the application will be set forth in the following claims.

[0119] It should be noted that, in the specification, relational terms such as first and second, and the like, can be used solely to distinguish one entity or action from another entity or action without necessarily implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

Claims

1. A method for obtaining the apparent water area of ​​rivers and lakes, characterized in that, The method includes: Select satellite remote sensing data of the corresponding resolution for the corresponding water body type based on the size of the water body area; The results of each water body identification were obtained 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 by multiple water body identification results, and the water area of ​​the monitoring area is obtained based on the flooding frequency. The flooding frequency is used to characterize the probability that each pixel is identified as a water body in the effective number of monitoring times. The water body type includes: a river segment with a second width; The selection of satellite remote sensing data with corresponding resolutions for different water body types based on their area size characteristics includes: Acquire GF-1 and / or GF-6 PMS data collected by multispectral and panchromatic sensors carried on a high-resolution remote sensing satellite as the third remote sensing image data to be identified corresponding to the river segment of the second width; The method of obtaining water body identification results based on the satellite remote sensing data and the multi-index method includes: for a river segment of the second width, obtaining the water body identification result for each pixel in the remote sensing image data to be identified corresponding to the second width river segment by comparing the normalized index value of each pixel in the remote sensing image data to be identified 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 statistically analyzing the normalized water body index value of each pixel in the sample remote sensing image data of the river segment with the second width; wherein... The water body identification result for each pixel in the remote sensing image data corresponding to the second width of the river segment is obtained by comparing the normalized index value of each pixel in the remote sensing image data to be identified with the target segmentation threshold. This includes: Repeat the following process until a candidate segmentation threshold that maximizes the inter-class variance and minimizes the intra-class variance is found, and use the candidate segmentation threshold as the target segmentation threshold: use a binary search to find 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 to obtain a decision result; calculate the inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold according to the decision 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 k-th pixel in the third remote sensing image data to be identified is greater than the target segmentation threshold, then the ground area corresponding to the k-th pixel is determined to be a water body; if the normalized index value (NDWI) of the k-th pixel in the third remote sensing image data to be identified is less than or equal to the target segmentation threshold, then the ground area corresponding to the k-th pixel is determined to be a non-water body, where k is an integer greater than or equal to 1.

2. The method as described in claim 1, characterized in that, The water body types include: lakes and river sections of the first width; The water body identification results obtained based on the satellite remote sensing data and the multi-index method include: For the lake, the water body identification results of each pixel in the remote sensing image data to be identified corresponding to the lake are obtained according to the first multi-index water body detection rule; For the river segment of the first width, the water body identification results of each pixel in the remote sensing image data to be identified corresponding to the river segment of the first width are obtained according to the second multi-index water body detection rule.

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

4. The method as described in claim 3, characterized in that, The selection of satellite remote sensing data with corresponding resolution based on the area size of different water bodies includes: Landsat 8 / 9OLI data acquired by the multispectral sensor onboard a land observation satellite is used as the first remote sensing image data to be identified corresponding to the lake. The step of obtaining the water body identification results for 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 includes: Calculate the Normalized Difference Vegetation Index (NDVI), Improved Normalized Water Index (mNDWI), and Enhanced Vegetation Index (EVI) for 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 a first threshold, then the ground area corresponding to the i-th pixel is confirmed as 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 a first threshold, then the ground area corresponding to the i-th pixel is confirmed as a water body, where i is an integer greater than or equal to 1.

5. The method as described in claim 3, characterized in that, The selection of satellite remote sensing data with corresponding resolution based on the area size of different water bodies includes: The Sentinel2 MSI data collected by the multispectral imager carried by the remote sensing satellite is used as the second remote sensing image data to be identified corresponding to the river segment with the first width. The water body identification results obtained according to the second multi-index water body detection rule for each pixel in the remote sensing image data to be identified, corresponding to the river segment of the first width, include: Calculate the Normalized Difference 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. 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 the ground area corresponding to the j-th pixel is confirmed as 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 the ground area corresponding to the j-th pixel is confirmed as a water body, where j is an integer greater than or equal to 1.

6. A device for obtaining the apparent water area of ​​rivers and lakes, characterized in that, The device includes: The remote sensing image data acquisition module is configured to select satellite remote sensing data of the corresponding resolution for the corresponding water body type based on the area size of different water bodies; The water body identification result acquisition module for each pixel is configured to obtain the water body identification results for each iteration 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 recognition results, and obtain the water area of ​​the monitoring area within the monitoring time period based on the flooding frequency, wherein the flooding frequency is used to characterize the probability that each pixel is identified as a water area in the effective monitoring count. The water body type includes: a river segment with a second width; The remote sensing image data acquisition module is further configured to: Acquire GF-1 and / or GF-6 PMS data collected by multispectral and panchromatic sensors carried on a high-resolution remote sensing satellite as the third remote sensing image data to be identified corresponding to the river segment of the second width; The pixel-level water body identification result acquisition module is further configured to: for the second width of the river segment, obtain the water body identification result of each pixel in the remote sensing image data to be identified corresponding to the second width of the river segment by comparing the normalized index value of each pixel in the remote sensing image data to be identified 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 statistically analyzing the normalized water body index value of each pixel in the sample remote sensing image data of the river segment with the second width; wherein... The water body identification result for each pixel in the remote sensing image data corresponding to the second width of the river segment is obtained by comparing the normalized index value of each pixel in the remote sensing image data to be identified with the target segmentation threshold. This includes: Repeat the following process until a candidate segmentation threshold that maximizes the inter-class variance and minimizes the intra-class variance is found, and use the candidate segmentation threshold as the target segmentation threshold: use a binary search to find 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 to obtain a decision result; calculate the inter-class variance and intra-class variance corresponding to the nth candidate segmentation threshold according to the decision 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 k-th pixel in the third remote sensing image data to be identified is greater than the target segmentation threshold, then the ground area corresponding to the k-th pixel is determined to be a water body; if the normalized index value (NDWI) of the k-th pixel in the third remote sensing image data to be identified is less than or equal to the target segmentation threshold, then the ground area corresponding to the k-th pixel is determined to be a non-water body, where k is an integer greater than or equal to 1.

7. A method for managing water areas, characterized in that, The method includes: The water area obtained by any one of claims 1-5; The corresponding water area is managed by the water body area.

8. A computer-readable storage medium, characterized in that, It includes computer program instructions that, when read and executed by a processor, perform the method as described in any one of claims 1-5 and claim 7.

Images