Method, system and device for monitoring and predicting changes of water body and vegetation in natural reserve, medium and product

Abstract

The invention discloses a water body and vegetation change monitoring and predicting method, system and device of a natural reserve, a medium and a product, and relates to the technical field of natural resource change monitoring. The method comprises the following steps: respectively determining a water body / non-water body binary image map and a vegetation / non-vegetation binary image map at each to-be-measured moment based on radar remote sensing image data at each to-be-measured moment, and respectively carrying out vector extraction to obtain a water body vector map and a vegetation vector map at each to-be-measured moment; performing superposition operation on the water body / non-water body binary image maps and the vegetation / non-vegetation binary image maps at all to-be-measured moments to obtain a water body submerging frequency map and a vegetation coverage frequency map; and respectively inputting the prediction parameters at each to-be-measured moment into the water body submerging area prediction model and the vegetation coverage area prediction model to obtain a prediction value of the water body submerging area and a prediction value of the vegetation coverage area at each to-be-measured moment. According to the invention, monitoring and prediction of water and vegetation changes in the natural reserve are realized.

Description

Technical Field

[0001] The present application relates to the technical field of natural resource change monitoring, and in particular to a method, system, device, medium and product for monitoring and predicting water and vegetation changes in nature reserves. Background Art

[0002] Nature reserves are vital ecosystems on Earth, and changes in their water bodies and vegetation are directly related to the health and stability of these ecosystems. In recent years, with the increasing global climate change and human activities, accurate and timely monitoring and prediction of changes in water bodies and vegetation in nature reserves has become increasingly important. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, device, medium and product for monitoring and predicting changes in water bodies and vegetation in nature reserves, so as to realize the monitoring and prediction of changes in water bodies and vegetation in nature reserves.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve, comprising:

[0006] Obtain radar remote sensing image data and prediction parameters at multiple test moments during a test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature;

[0007] Determine the water body / non-water body binary image map and the vegetation / non-vegetation binary image map at each time point based on the radar remote sensing image data at each time point;

[0008] Perform vector extraction on the water body / non-water body binary image at each time to be measured, and obtain the water body vector diagram at each time to be measured;

[0009] Performing a superposition operation on the water body / non-water body binary image maps at all the time points to be measured to obtain a water body flooding frequency map of the nature reserve during the time period to be measured;

[0010] Perform vector extraction on the vegetation / non-vegetation binary image at each time to be measured, and obtain the vegetation vector map at each time to be measured;

[0011] Performing a superposition operation on the vegetation / non-vegetation binary image maps at all the test moments to obtain a vegetation coverage frequency map of the nature reserve during the test period;

[0012] The prediction parameters of each time moment to be measured are respectively input into the water body inundation area prediction model and the vegetation coverage area prediction model to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time moment to be measured; the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation coverage area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation coverage area.

[0013] Optionally, determining a water body / non-water body binary image map and a vegetation / non-vegetation binary image map at each time to be measured based on the radar remote sensing image data at each time to be measured, including:

[0014] Performing filtering on the radar remote sensing image data at each time to be measured, respectively, to obtain the radar remote sensing image data after filtering at each time to be measured;

[0015] Determine the dual-polarization water index map and the ratio vegetation index map at each time point according to the filtered radar remote sensing image data at each time point;

[0016] The OTSU algorithm is used to determine the water / non-water binary image at each testing moment based on the dual-polarization water index map at each testing moment, and the vegetation / non-vegetation binary image at each testing moment is determined based on the ratio vegetation index map at each testing moment.

[0017] Optionally, determining the dual-polarization water index map and the ratio vegetation index map at each time to be measured based on the filtered radar remote sensing image data at each time to be measured, includes:

[0018] Determine any moment to be measured as the current moment;

[0019] Calculate the dual-polarization water index and ratio vegetation index of each pixel point at the current moment according to the backscatter coefficient of each pixel point in the radar remote sensing image data after filtering at the current moment;

[0020] Determine a dual-polarization water index map at the current moment based on the dual-polarization water indexes of all pixels at the current moment;

[0021] Based on the ratio vegetation indexes of all pixel points at the current moment, a ratio vegetation index map at the current moment is determined.

[0022] Optionally, a calculation formula for the dual-polarization water index of any pixel point includes:

[0023] SDWI=ln(VV×VH×10)-8;

[0024] Wherein, SDWI is the dual-polarization water index of the pixel point; VV is the backscatter coefficient of the pixel point when vertical transmission and vertical reception are performed; VH is the backscatter coefficient of the pixel point when vertical transmission and horizontal reception are performed.

[0025] Optionally, the calculation formula for the ratio vegetation index of any pixel point includes:

[0026]

[0027] Among them, RVI is the ratio vegetation index of the pixel point.

[0028] Optionally, the process of determining the water body inundation area prediction model and the vegetation cover area prediction model includes:

[0029] Obtain radar remote sensing image data and prediction parameters for multiple historical moments in a preset historical period of the nature reserve;

[0030] Performing filtering processing on the radar remote sensing image data at each historical moment to obtain the radar remote sensing image data after filtering processing at each historical moment;

[0031] Based on the filtered radar remote sensing image data at each historical moment, determine the dual-polarization water index map and ratio vegetation index map at each historical moment;

[0032] Using the OTSU algorithm, the water / non-water binary image maps at each historical moment were determined based on the dual-polarization water index map at each historical moment, and the vegetation / non-vegetation binary image maps at each historical moment were determined based on the ratio vegetation index map at each historical moment.

[0033] Vector extraction is performed on the binary image of water bodies and non-water bodies at each historical moment to obtain the vector map of water bodies at each historical moment, and the actual value of the water body submerged area at each historical moment is determined based on the vector map of water bodies at each historical moment;

[0034] Vector extraction is performed on the vegetation / non-vegetation binary image maps at each historical moment to obtain the vegetation vector map at each historical moment, and the actual value of the vegetation coverage area at each historical moment is determined based on the vegetation vector map at each historical moment;

[0035] The prediction model of water body inundation area is obtained by fitting the prediction parameters at each historical moment with the actual value of water body inundation area.

[0036] The vegetation coverage area prediction model is obtained by fitting the prediction parameters at each historical moment and the actual value of the vegetation coverage area.

[0037] In a second aspect, the present application provides a system for monitoring and predicting water and vegetation changes in a nature reserve, including:

[0038] A data acquisition module is used to obtain radar remote sensing image data and prediction parameters at multiple test moments during a test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature;

[0039] A segmentation module is used to determine a water body / non-water body binary image map and a vegetation / non-vegetation binary image map at each time to be measured based on the radar remote sensing image data at each time to be measured;

[0040] The first vector extraction module is used to extract vectors from the water body / non-water body binary image at each time to be measured, so as to obtain a water body vector diagram at each time to be measured;

[0041] The first superposition module is used to perform superposition operation on the water body / non-water body binary image maps at all the time points to be measured, so as to obtain the water body flooding frequency map of the nature reserve during the time period to be measured;

[0042] The second vector extraction module is used to extract vectors from the vegetation / non-vegetation binary image at each time to be measured, so as to obtain the vegetation vector map at each time to be measured;

[0043] The second superposition module is used to perform superposition operation on the vegetation / non-vegetation binary image maps at all the time points to be measured, so as to obtain the vegetation coverage frequency map of the nature reserve during the time period to be measured;

[0044] The prediction module is used to input the prediction parameters of each time moment to be measured into the water body inundation area prediction model and the vegetation coverage area prediction model respectively, to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time moment to be measured; the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation coverage area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation coverage area.

[0045] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and runnable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for monitoring and predicting water and vegetation changes in nature reserves.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for monitoring and predicting water and vegetation changes in a nature reserve as described in any one of the above items.

[0047] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the water body and vegetation change monitoring and prediction method in a nature reserve as described in any of the above items.

[0048] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0049] The present application discloses a method, system, device, medium and product for monitoring and predicting water and vegetation changes in a nature reserve. First, radar remote sensing image data and prediction parameters are obtained at multiple test moments in a test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature; secondly, based on the radar remote sensing image data at each test moment, a water body / non-water body binary image map and a vegetation / non-vegetation binary image map at each test moment are determined; then, vector extraction is performed on the water body / non-water body binary image map at each test moment to obtain a water body vector map at each test moment; the water body / non-water body binary image map at all test moments is superimposed to obtain the water body inundation frequency of the nature reserve in the test period. Figure; again, vector extraction is performed on the vegetation / non-vegetation binary image map at each time to be measured, and the vegetation vector map at each time to be measured is obtained; the vegetation / non-vegetation binary image map at all times to be measured is superimposed to obtain the vegetation coverage frequency map of the nature reserve during the time period to be measured; finally, the prediction parameters at each time to be measured are input into the water body inundation area prediction model and the vegetation coverage area prediction model, respectively, to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time to be measured; the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation coverage area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation coverage area. This application uses radar remote sensing image data to determine the water body vector map, water body inundation frequency map, vegetation vector map and vegetation coverage frequency map, and uses precipitation values, water level observation values, runoff, sediment volume and temperature to predict the water body inundation area and vegetation coverage area. The monitoring and prediction of water and vegetation changes in nature reserves have been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1A flow chart of a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve provided in one embodiment of the present application;

[0052] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The purpose of this application is to provide a method, system, device, medium and product for monitoring and predicting changes in water bodies and vegetation in nature reserves, aiming to realize the monitoring and prediction of changes in water bodies and vegetation in nature reserves.

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0056] In an exemplary embodiment, Figure 1 As shown, a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve is provided, including:

[0057] Step 1: Obtain radar remote sensing image data and prediction parameters at multiple test moments during the test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature.

[0058] Step 2: Based on the radar remote sensing image data at each time to be measured, determine the water body / non-water body binary image map and vegetation / non-vegetation binary image map at each time to be measured.

[0059] As an optional implementation, step 2 includes:

[0060] Step 21: performing filtering processing on the radar remote sensing image data at each time to be measured, and obtaining the radar remote sensing image data after filtering processing at each time to be measured.

[0061] Specifically, the GEE platform is used to perform filtering processing on the radar remote sensing image data at each time to be measured, so as to obtain the radar remote sensing image data after filtering processing at each time to be measured.

[0062] Step 22: Determine the dual-polarization water index map and the ratio vegetation index map at each time point according to the filtered radar remote sensing image data at each time point.

[0063] As an optional implementation, step 22 includes:

[0064] Step 221: Determine any time to be measured as the current time.

[0065] Step 222: Calculate the dual-polarization water index and ratio vegetation index of each pixel at the current moment based on the backscatter coefficient of each pixel in the filtered radar remote sensing image data at the current moment.

[0066] As an optional implementation, in step 222, the calculation formula for the dual-polarization water index of any pixel point includes:

[0067] SDWI=ln(VV×VH×10)-8.

[0068] Wherein, SDWI is the dual-polarization water index of the pixel point; VV is the backscatter coefficient of the pixel point when vertical transmission and vertical reception are performed; VH is the backscatter coefficient of the pixel point when vertical transmission and horizontal reception are performed.

[0069] Specifically, the GEE platform is used to calculate the dual-polarization water index of each pixel.

[0070] As an optional implementation, in step 222, the calculation formula of the ratio vegetation index of any pixel point includes:

[0071]

[0072] Among them, RVI is the ratio vegetation index of the pixel point.

[0073] Specifically, the GEE platform is used to calculate the ratio vegetation index of each pixel.

[0074] Step 223: Determine a dual-polarization water index map at the current moment based on the dual-polarization water indexes of all pixels at the current moment.

[0075] Step 224: Determine a ratio vegetation index map at the current moment based on the ratio vegetation indexes of all pixel points at the current moment.

[0076] Step 23: Using the OTSU algorithm, determine the water / non-water binary image map at each time point based on the dual-polarization water index map at each time point, and determine the vegetation / non-vegetation binary image map at each time point based on the ratio vegetation index map at each time point.

[0077] Specifically, any pixel point in the water body / non-water body binary image is a water body or a non-water body, and any pixel point in the vegetation / non-vegetation binary image is a vegetation or a non-vegetation.

[0078] Step 3: Perform vector extraction on the water body / non-water body binary image at each time to be measured to obtain the water body vector map at each time to be measured.

[0079] Specifically, in Arcgis 10.8 software, the reclassification function is used to extract vectors from the water body / non-water body binary image maps at each time to be measured, and the water body vector map at each time to be measured is obtained. Furthermore, based on the water body vector map at each time to be measured, a long-term series of water body change monitoring data for the protected area is constructed.

[0080] Step 4: Perform overlay operations on the water body / non-water body binary image maps at all the test moments to obtain the water body flooding frequency map of the nature reserve during the test period.

[0081] Specifically, in Arcgis 10.8 software, the overlay analysis function was used to perform overlay operations on the water body / non-water body binary image maps at all the test times to obtain the water body flooding frequency map of the nature reserve during the test period, to find the spatial distribution pattern of water bodies in the nature reserve, to monitor the flooding of sub-lakes in the nature reserve, and to generate spatially distributed protection zone monitoring data.

[0082] Step 5: Perform vector extraction on the vegetation / non-vegetation binary image at each time to be measured to obtain the vegetation vector map at each time to be measured.

[0083] Specifically, in Arcgis 10.8 software, the reclassification function is used to extract vectors from the vegetation / non-vegetation binary image at each time to be measured, and the vegetation vector map at each time to be measured is obtained.

[0084] Step 6: Perform overlay operations on the vegetation / non-vegetation binary image maps at all the test moments to obtain the vegetation coverage frequency map of the nature reserve during the test period.

[0085] Specifically, in Arcgis 10.8 software, the overlay analysis function was used to perform overlay operations on the vegetation / non-vegetation binary image maps at all the test times to obtain the vegetation coverage frequency map of the nature reserve during the test period.

[0086] Step 7: Input the prediction parameters at each time to be measured into the water body inundation area prediction model and the vegetation coverage area prediction model respectively to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time to be measured.

[0087] Among them, the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation cover area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation cover area.

[0088] As an optional implementation, in step 7, the process of determining the water body submerged area prediction model and the vegetation cover area prediction model includes:

[0089] Step 71: Obtain radar remote sensing image data and prediction parameters at multiple historical moments in a preset historical period of the nature reserve.

[0090] Step 72: Filter the radar remote sensing image data at each historical moment to obtain the filtered radar remote sensing image data at each historical moment.

[0091] Step 73: Based on the filtered radar remote sensing image data at each historical moment, determine the dual-polarization water index map and the ratio vegetation index map at each historical moment.

[0092] Step 74: Using the OTSU algorithm, determine the water / non-water binary image map at each historical moment based on the dual-polarization water index map at each historical moment, and determine the vegetation / non-vegetation binary image map at each historical moment based on the ratio vegetation index map at each historical moment.

[0093] Step 75: Perform vector extraction on the water body / non-water body binary image at each historical moment to obtain the water body vector map at each historical moment, and determine the actual value of the water body submerged area at each historical moment based on the water body vector map at each historical moment.

[0094] Step 76: Perform vector extraction on the vegetation / non-vegetation binary image at each historical moment to obtain the vegetation vector map at each historical moment, and determine the actual value of the vegetation coverage area at each historical moment based on the vegetation vector map at each historical moment.

[0095] Step 77: Fitting is performed based on the prediction parameters at each historical moment and the actual value of the water body inundation area to obtain a water body inundation area prediction model.

[0096] Step 78: Fitting is performed based on the prediction parameters at each historical moment and the actual value of the vegetation coverage area to obtain a vegetation coverage area prediction model.

[0097] In an exemplary embodiment, a system for monitoring and predicting changes in water bodies and vegetation in a nature reserve is provided, comprising:

[0098] The data acquisition module is used to obtain radar remote sensing image data and prediction parameters at multiple test moments during the test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature.

[0099] The segmentation module is used to determine the water body / non-water body binary image map and the vegetation / non-vegetation binary image map at each time to be measured based on the radar remote sensing image data at each time to be measured.

[0100] The first vector extraction module is used to perform vector extraction on the water body / non-water body binary image at each time to be measured, so as to obtain the water body vector map at each time to be measured.

[0101] The first superposition module is used to perform superposition operation on the water body / non-water body binary image maps at all the time points to be measured, so as to obtain the water body flooding frequency map of the nature reserve during the time period to be measured.

[0102] The second vector extraction module is used to extract vectors from the vegetation / non-vegetation binary image at each time to be measured, so as to obtain the vegetation vector map at each time to be measured.

[0103] The second superposition module is used to perform superposition operations on the vegetation / non-vegetation binary image maps at all the time points to be measured, so as to obtain the vegetation coverage frequency map of the nature reserve during the time period to be measured.

[0104] The prediction module is used to input the prediction parameters of each time moment to be measured into the water body inundation area prediction model and the vegetation coverage area prediction model respectively, so as to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time moment to be measured; the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation coverage area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation coverage area.

[0105] In an exemplary embodiment, a computer device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve.

[0106] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve is implemented.

[0107] In an exemplary embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve.

[0108] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 2As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for monitoring and predicting changes in water bodies and vegetation in a nature reserve is implemented.

[0109] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0110] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0111] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0112] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0113] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for monitoring and predicting changes in water bodies and vegetation in a nature reserve, characterized in that: The method for monitoring and predicting changes in water bodies and vegetation in the nature reserve includes: Obtain radar remote sensing image data and prediction parameters at multiple test moments during a test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature; Determine the water body / non-water body binary image map and the vegetation / non-vegetation binary image map at each time point based on the radar remote sensing image data at each time point; Perform vector extraction on the water body / non-water body binary image at each time to be measured, and obtain the water body vector diagram at each time to be measured; Performing a superposition operation on the water body / non-water body binary image maps at all the time points to be measured to obtain a water body flooding frequency map of the nature reserve during the time period to be measured; Perform vector extraction on the vegetation / non-vegetation binary image at each time to be measured, and obtain the vegetation vector map at each time to be measured; Performing a superposition operation on the vegetation / non-vegetation binary image maps at all the test moments to obtain a vegetation coverage frequency map of the nature reserve during the test period; The prediction parameters of each time moment to be measured are respectively input into the water body inundation area prediction model and the vegetation coverage area prediction model to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time moment to be measured; the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation coverage area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation coverage area.

2. The method for monitoring and predicting changes in water bodies and vegetation in nature reserves according to claim 1, characterized in that: Based on the radar remote sensing image data at each time to be measured, the water body / non-water body binary image map and the vegetation / non-vegetation binary image map at each time to be measured are determined, including: Performing filtering on the radar remote sensing image data at each time to be measured, respectively, to obtain the radar remote sensing image data after filtering at each time to be measured; Determine the dual-polarization water index map and the ratio vegetation index map at each time point according to the filtered radar remote sensing image data at each time point; The OTSU algorithm is used to determine the water / non-water binary image at each testing moment based on the dual-polarization water index map at each testing moment, and the vegetation / non-vegetation binary image at each testing moment is determined based on the ratio vegetation index map at each testing moment.

3. The method for monitoring and predicting changes in water bodies and vegetation in nature reserves according to claim 2, characterized in that: Determine the dual-polarization water index map and the ratio vegetation index map at each time point based on the filtered radar remote sensing image data at each time point, including: Determine any moment to be measured as the current moment; Calculate the dual-polarization water index and ratio vegetation index of each pixel point at the current moment according to the backscatter coefficient of each pixel point in the radar remote sensing image data after filtering at the current moment; Determine a dual-polarization water index map at the current moment based on the dual-polarization water indexes of all pixels at the current moment; Based on the ratio vegetation indexes of all pixel points at the current moment, a ratio vegetation index map at the current moment is determined.

4. The method for monitoring and predicting changes in water bodies and vegetation in nature reserves according to claim 3, characterized in that: The calculation formula of the dual-polarization water index of any pixel point includes: SDWI=ln(VV×VH×10)-8; Wherein, SDWI is the dual-polarization water index of the pixel point; VV is the backscatter coefficient of the pixel point when vertical transmission and vertical reception are performed; VH is the backscatter coefficient of the pixel point when vertical transmission and horizontal reception are performed.

5. The method for monitoring and predicting changes in water bodies and vegetation in nature reserves according to claim 4, characterized in that: The calculation formula of the ratio vegetation index of any pixel point includes: Among them, RVI is the ratio vegetation index of the pixel point.

6. The method for monitoring and predicting changes in water bodies and vegetation in nature reserves according to claim 1, characterized in that: The process of determining the water body inundation area prediction model and the vegetation cover area prediction model includes: Obtain radar remote sensing image data and prediction parameters for multiple historical moments in a preset historical period of the nature reserve; Performing filtering processing on the radar remote sensing image data at each historical moment to obtain the radar remote sensing image data after filtering processing at each historical moment; Based on the filtered radar remote sensing image data at each historical moment, determine the dual-polarization water index map and ratio vegetation index map at each historical moment; Using the OTSU algorithm, the water / non-water binary image maps at each historical moment were determined based on the dual-polarization water index map at each historical moment, and the vegetation / non-vegetation binary image maps at each historical moment were determined based on the ratio vegetation index map at each historical moment. Vector extraction is performed on the binary image of water bodies and non-water bodies at each historical moment to obtain the vector map of water bodies at each historical moment, and the actual value of the water body submerged area at each historical moment is determined based on the vector map of water bodies at each historical moment; Vector extraction is performed on the vegetation / non-vegetation binary image maps at each historical moment to obtain the vegetation vector map at each historical moment, and the actual value of the vegetation coverage area at each historical moment is determined based on the vegetation vector map at each historical moment; The prediction model of water body inundation area is obtained by fitting the prediction parameters at each historical moment with the actual value of water body inundation area. The vegetation coverage area prediction model is obtained by fitting the prediction parameters at each historical moment and the actual value of the vegetation coverage area.

7. A water and vegetation change monitoring and prediction system for a nature reserve, characterized by: The water and vegetation change monitoring and prediction system of the nature reserve includes: A data acquisition module is used to obtain radar remote sensing image data and prediction parameters at multiple test moments during a test period of the nature reserve; the prediction parameters include: precipitation value, water level observation value, runoff, sediment volume and temperature; A segmentation module is used to determine a water body / non-water body binary image map and a vegetation / non-vegetation binary image map at each time to be measured based on the radar remote sensing image data at each time to be measured; The first vector extraction module is used to extract vectors from the water body / non-water body binary image at each time to be measured, so as to obtain a water body vector diagram at each time to be measured; The first superposition module is used to perform superposition operation on the water body / non-water body binary image maps at all the time points to be measured, so as to obtain the water body flooding frequency map of the nature reserve during the time period to be measured; The second vector extraction module is used to extract vectors from the vegetation / non-vegetation binary image at each time to be measured, so as to obtain the vegetation vector map at each time to be measured; The second superposition module is used to perform superposition operation on the vegetation / non-vegetation binary image maps at all the time points to be measured, so as to obtain the vegetation coverage frequency map of the nature reserve during the time period to be measured; The prediction module is used to input the prediction parameters of each time moment to be measured into the water body inundation area prediction model and the vegetation coverage area prediction model respectively, to obtain the predicted value of the water body inundation area and the predicted value of the vegetation coverage area at each time moment to be measured; the water body inundation area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the water body inundation area, and the vegetation coverage area prediction model is obtained by fitting the prediction parameters of multiple historical moments and the actual values of the vegetation coverage area.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor executes the computer program to implement the method for monitoring and predicting changes in water bodies and vegetation in a nature reserve as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for monitoring and predicting changes in water bodies and vegetation in nature reserves as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for monitoring and predicting changes in water bodies and vegetation in nature reserves as described in any one of claims 1 to 6 is implemented.

Images