Mangrove forest wetland identification method and system

Abstract

The invention discloses a mangrove forest wetland identification method and system, belongs to the technical field of image processing, and can improve the efficiency and precision of mangrove forest wetland remote sensing identification. Comprising the following steps: acquiring a multispectral remote sensing image; preprocessing the multispectral remote sensing image; identifying a first mangrove forest wetland potential distribution area and a second mangrove forest wetland potential distribution area in the preprocessed multispectral remote sensing image; the point file is adopted to calibrate a potential distribution area of the first mangrove forest wetland, the surface file is adopted to calibrate a potential distribution area of the second mangrove forest wetland, and the range of the second mangrove forest wetland area is larger than that of the first mangrove forest wetland area; drawing a buffer area according to the point file and the surface file; cutting the preprocessed multispectral remote sensing image according to the buffer area to obtain a cut image; and extracting a mangrove forest wetland distribution area in the cut image, and generating a mangrove forest wetland spatial distribution map.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a mangrove wetland identification method and system. Background Art

[0002] Mangroves grow and distribute in tropical and subtropical coastal zones or within 3 to 5 km of estuaries. They have important functions such as preventing wind and sand, protecting the coast, purifying water quality, improving and maintaining ecological stability, and are an important part of coastal wetland ecosystems. As an area with active human production activities, mangroves and their ecosystems in coastal areas have been severely damaged by human interference, showing a variety of problems such as a sharp decrease in the area of ​​mangrove wetlands, degradation of mangrove wetlands, destruction of biodiversity, and gradual loss of ecological service functions. It is urgent to protect and restore mangroves. Rapidly and accurately identifying the distribution range of mangrove wetlands can provide important data support for mangrove monitoring, protection and management.

[0003] Remote sensing technology has been widely used in the classification and extraction of mangrove wetlands and is currently the most mainstream method. However, there are two difficult problems to solve in remote sensing interpretation. On the one hand, mangrove wetlands are only distributed in coastal tidal flats, but due to the range of remote sensing images, a large number of non-coastal areas are included in the range, resulting in a large workload for interpretation. On the other hand, the spectra of mangroves and terrestrial vegetation are similar, and remote sensing interpretation cannot effectively distinguish between mangrove vegetation and terrestrial forest vegetation. These two aspects seriously affect the efficiency and accuracy of mangrove wetland identification, especially for mangrove wetland identification in larger areas or scales. Therefore, how to provide a high-efficiency and high-precision remote sensing identification method for mangrove wetlands is one of the technical problems that need to be solved in this field.

[0004] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of the present application. Summary of the invention

[0005] The present application provides a mangrove wetland identification method and system, which can improve the efficiency and accuracy of remote sensing identification of mangrove wetlands.

[0006] To achieve the above objectives, the present application discloses the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for identifying a mangrove wetland, comprising the following steps:

[0008] Acquire multispectral remote sensing images;

[0009] Preprocess multispectral remote sensing images;

[0010] Identify the potential distribution area of ​​the first mangrove wetland and the potential distribution area of ​​the second mangrove wetland in the pre-processed multispectral remote sensing image;

[0011] The potential distribution area of ​​the first mangrove wetland is calibrated using a point file, and the potential distribution area of ​​the second mangrove wetland is calibrated using a surface file, wherein the scope of the second mangrove wetland area is larger than that of the first mangrove wetland area;

[0012] Draw buffer according to point file and surface file;

[0013] The pre-processed multispectral remote sensing image is cropped according to the buffer zone to obtain a cropped image;

[0014] The distribution area of ​​mangrove wetlands in the cropped image was extracted to generate a spatial distribution map of mangrove wetlands.

[0015] In an embodiment of the present application, the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area are calibrated respectively by point files and surface files, and a buffer zone is drawn according to the point files and surface files. This step ensures the effective identification of the distribution range of mangroves and reduces the processing of non-mangrove areas, that is, reduces the scope of remote sensing identification and interpretation, reduces workload, improves interpretation efficiency, and effectively reduces the interference of terrestrial vegetation during identification and interpretation, thereby improving the identification accuracy of mangrove wetlands.

[0016] In some possible implementations of the first aspect, the spatial resolution of the multispectral remote sensing image is greater than or equal to 30m, and the multispectral remote sensing image selects multispectral remote sensing images in cloudless and low tide periods. Among them, images with high spatial resolution (greater than or equal to 30 meters) can capture more ground details, and can more clearly show the boundary between mangroves and the surrounding environment compared to low-resolution images, thereby significantly improving the accuracy of mangrove identification and avoiding confusion and misjudgment caused by insufficient resolution. Selecting multispectral remote sensing images in cloudless and low tide periods can reduce the impact of cloud cover on ground information acquisition, ensure the clarity and availability of images, and thus improve the accuracy of analysis. During low tide, the sea water recedes, the roots and ground parts of the mangroves are more obvious, and the boundaries are clear, which helps to more accurately identify the distribution range of mangroves, especially in areas where mangroves border water bodies, where boundary identification will be clearer. In this way, it helps to improve the efficiency and accuracy of mangrove wetland identification.

[0017] In some possible implementations of the first aspect, preprocessing includes radiometric calibration, atmospheric correction, geometric correction and image fusion. In this way, the physical and spatial accuracy of multispectral remote sensing images can be improved, atmospheric interference can be reduced, the amount of image information can be enhanced, and the classification and recognition effect can be improved, thereby improving the efficiency and accuracy of the mangrove wetland identification method.

[0018] In some possible implementations of the first aspect, the width range of the buffer zone is greater than or equal to 3 km and less than or equal to 5 km. In this way, the width range of the buffer zone is more in line with the natural distribution characteristics of mangroves within 3 to 5 km of tropical and subtropical coastal zones or estuaries. In this way, on the one hand, data processing can be concentrated on areas where mangroves may be distributed, avoiding the processing of a large number of non-mangrove areas, which is conducive to avoiding the problem of misclassification and improving the accuracy of mangrove wetland identification; on the other hand, it can reduce the total amount of data to be processed and improve the efficiency of mangrove wetland identification.

[0019] In some possible implementations of the first aspect, the cropped image is a remote sensing image within the buffer zone. In this way, the amount of data to be processed can be further reduced (i.e., the scope of interpretation can be further narrowed), thereby further improving the efficiency and accuracy of mangrove wetland identification.

[0020] In some possible implementations of the first aspect, the step of extracting the mangrove wetland distribution area in the cropped image and generating a mangrove wetland spatial distribution map includes:

[0021] Extract the mangrove wetland distribution area in the cropped image;

[0022] The distribution area of ​​mangrove wetlands is corrected by identifying and deleting fragmented patches with an area less than or equal to a first preset threshold and patches that are scattered and misclassified as mangroves;

[0023] According to the revised mangrove wetland distribution area, a mangrove wetland spatial distribution map is obtained, so that the accuracy of mangrove wetland identification can be improved, thereby improving the quality of generating a mangrove wetland spatial distribution map.

[0024] In a second aspect, the embodiment of the present application provides a mangrove wetland identification system, including:

[0025] The first acquisition module is used to acquire multispectral remote sensing images;

[0026] Preprocessing module, used to preprocess multispectral remote sensing images;

[0027] A first identification module is used to identify a first potential distribution area of ​​a mangrove wetland and a second potential distribution area of ​​a mangrove wetland in the pre-processed multispectral remote sensing image;

[0028] A first calibration module is used to calibrate a potential distribution area of ​​a first mangrove wetland using a point file, and to calibrate a potential distribution area of ​​a second mangrove wetland using a surface file, wherein the range of the second mangrove wetland area is greater than the range of the first mangrove wetland area;

[0029] A first drawing module, used for drawing a buffer according to a point file and a surface file;

[0030] The first cropping module is used to crop the pre-processed multispectral remote sensing image according to the buffer to obtain a cropped image;

[0031] The first generation module is used to extract the mangrove wetland distribution area in the cropped image and generate a mangrove wetland spatial distribution map.

[0032] In some possible implementations of the second aspect, the spatial resolution of the multispectral remote sensing image is greater than or equal to 30 m, and the multispectral remote sensing image is selected from multispectral remote sensing images during cloudless and low tide periods.

[0033] In some possible implementations of the second aspect, preprocessing includes radiation calibration, atmospheric correction, geometric correction and image fusion.

[0034] In some possible implementations of the second aspect, the width of the buffer zone ranges from greater than or equal to 3 km to less than or equal to 5 km.

[0035] In some possible implementations of the second aspect, the cropped image is a remote sensing image within a buffer zone.

[0036] In some possible implementations of the second aspect, the first generating module is specifically used to:

[0037] Extract the mangrove wetland distribution area in the cropped image;

[0038] The distribution area of ​​mangrove wetlands is corrected by identifying and deleting fragmented patches with an area less than or equal to a first preset threshold and patches that are scattered and misclassified as mangroves;

[0039] Based on the revised distribution area of ​​mangrove wetlands, the spatial distribution map of mangrove wetlands was obtained.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, comprising one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any technical solution of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any technical solution of the first aspect is implemented.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any technical solution of the first aspect.

[0043] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different design methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0045] Figure 1 A schematic diagram of a process for identifying a mangrove wetland provided in some embodiments of the present application;

[0046] Figure 2 A schematic diagram of the potential distribution area of ​​mangrove wetlands provided in some embodiments of the present application;

[0047] Figure 3 A schematic diagram of a mangrove wetland buffer zone provided for some embodiments of the present application;

[0048] Figure 4 A schematic diagram of a region of a cropped image provided in some embodiments of the present application;

[0049] Figure 5 A schematic diagram of the structure of a mangrove wetland identification system provided in some embodiments of the present application;

[0050] Figure 6 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0051] Specific embodiments of the present invention will now be mentioned in detail. Although the present invention is described in conjunction with these specific embodiments, it should be appreciated that it is not intended to limit the present invention to these specific embodiments. On the contrary, these embodiments are intended to cover substitutions, changes or equivalent embodiments that may be included in the spirit and scope of the invention defined by the claims. In the following description, a large number of specific details are set forth in order to provide a comprehensive understanding of the present invention. The present invention may be implemented without some or all of these specific details.

[0052] When used in conjunction with "including," "methods comprising," or similar language in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] Application Overview:

[0054] Mangroves grow and distribute in tropical and subtropical coastal zones or within 3 to 5 km of estuaries. They have important functions such as preventing wind and sand, protecting the coast, purifying water quality, improving and maintaining ecological stability, and are an important part of coastal wetland ecosystems. As an area with active human production activities, mangroves and their ecosystems in coastal areas have been severely damaged by human interference, showing a variety of problems such as a sharp decrease in the area of ​​mangrove wetlands, degradation of mangrove wetlands, destruction of biodiversity, and gradual loss of ecological service functions. It is urgent to protect and restore mangroves. Rapidly and accurately identifying the distribution range of mangrove wetlands can provide important data support for mangrove monitoring, protection and management.

[0055] Remote sensing technology has been widely used in the classification and extraction of mangrove wetlands and is currently the most mainstream method. However, there are two difficult problems to solve in remote sensing interpretation. On the one hand, mangrove wetlands are only distributed in coastal tidal flats, but due to the range of remote sensing images, a large number of non-coastal areas are included in the range, resulting in a large workload for interpretation. On the other hand, the spectra of mangroves and terrestrial vegetation are similar, and remote sensing interpretation cannot effectively distinguish between mangrove vegetation and terrestrial forest vegetation. These two aspects seriously affect the efficiency and accuracy of mangrove wetland identification, especially for mangrove wetland identification in larger areas or scales. Therefore, how to provide a high-efficiency and high-precision remote sensing identification method for mangrove wetlands is one of the technical problems that need to be solved in this field.

[0056] In response to the above technical problems, the overall idea of ​​the technical solution provided by the present application is as follows: An embodiment of the present application provides a method for identifying mangrove wetlands, comprising the following steps: obtaining a multispectral remote sensing image; preprocessing the multispectral remote sensing image; identifying a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the preprocessed multispectral remote sensing image; using a point file to calibrate the first mangrove wetland potential distribution area, and using a surface file to calibrate the second mangrove wetland potential distribution area, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area; drawing a buffer zone according to the point file and the surface file; cropping the preprocessed multispectral remote sensing image according to the buffer zone to obtain a cropped image; extracting the mangrove wetland distribution area in the cropped image to generate a mangrove wetland spatial distribution map.

[0057] In an embodiment of the present application, the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area are calibrated respectively by point files and surface files, and a buffer zone is drawn according to the point files and surface files. This step ensures the effective identification of the distribution range of mangroves and reduces the processing of non-mangrove areas, that is, reduces the scope of remote sensing identification and interpretation, reduces workload, improves interpretation efficiency, and effectively reduces the interference of terrestrial vegetation during identification and interpretation, thereby improving the identification accuracy of mangrove wetlands.

[0058] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0059] See also Figure 1-4 The present application embodiment provides a method for identifying a mangrove wetland, comprising the following steps:

[0060] S101: Acquire multispectral remote sensing images;

[0061] Specifically, in some embodiments, the spatial resolution of the multispectral remote sensing image is greater than or equal to 30m, and the multispectral remote sensing image selects the multispectral remote sensing image in the cloudless and low tide period. Among them, the image with high spatial resolution (greater than or equal to 30 meters) can capture more ground details, and can more clearly show the boundary between mangroves and the surrounding environment compared with low-resolution images, thereby significantly improving the accuracy of mangrove identification and avoiding confusion and misjudgment caused by insufficient resolution. Selecting multispectral remote sensing images in cloudless and low tide periods can reduce the impact of cloud cover on ground information acquisition, ensure the clarity and availability of the image, and thus improve the analysis accuracy. During low tide, the sea water recedes, the roots and ground parts of the mangroves are more obvious, and the boundaries are clear, which helps to more accurately identify the distribution range of mangroves, especially in areas where mangroves border water bodies, where boundary identification will be clearer. In this way, it helps to improve the efficiency and accuracy of mangrove wetland identification.

[0062] S102: Preprocess the multispectral remote sensing image.

[0063] Specifically, in some embodiments, preprocessing includes but is not limited to radiometric calibration, atmospheric correction, geometric correction and image fusion. In this way, the physical and spatial accuracy of multispectral remote sensing images can be improved, atmospheric interference can be reduced, the image information volume can be enhanced, and the classification and recognition effect can be improved, thereby improving the efficiency and accuracy of the mangrove wetland identification method.

[0064] S103: Identify the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area in the pre-processed multispectral remote sensing image.

[0065] Specifically, in some embodiments, the first potential distribution area of ​​mangrove wetlands and the second potential distribution area of ​​mangrove wetlands can be identified by visually judging the coastline in the multispectral remote sensing image based on the growth distribution characteristics of mangroves.

[0066] S104: Using a point file to calibrate the potential distribution area of ​​the first mangrove wetland, and using a surface file to calibrate the potential distribution area of ​​the second mangrove wetland, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area, as shown in the following example: Figure 2 Specifically, the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area can be calibrated using ArcGIS10.5 software.

[0067] S105: Draw the buffer according to the point file and the surface file, as follows: Figure 3 As shown;

[0068] Specifically, in some embodiments, the drawing tool in ArcGIS 10.5 software can be used to connect the point file and the surface file to draw the buffer zone.

[0069] Furthermore, in some embodiments, the width range of the buffer zone is greater than or equal to 3 km and less than or equal to 5 km. In this way, the width range of the buffer zone is more consistent with the natural distribution characteristics of mangroves in tropical and subtropical coastal zones or estuaries within 3 to 5 km. In this way, on the one hand, data processing can be concentrated in areas where mangroves may be distributed, avoiding the processing of a large number of non-mangrove areas, which is conducive to avoiding the problem of misclassification and improving the accuracy of mangrove wetland identification; on the other hand, it can reduce the total amount of data to be processed and improve the efficiency of mangrove wetland identification.

[0070] S106: Crop the preprocessed multispectral remote sensing image according to the buffer to obtain a cropped image. Figure 4 shown.

[0071] Preferably, the cropped image is a remote sensing image within the buffer zone. In this way, the amount of data to be processed can be further reduced (i.e., the scope of interpretation can be further narrowed), thereby further improving the efficiency and accuracy of mangrove wetland identification. Exemplarily, the image capture tool in ArcGIS10.5 software can be used to cut the remote sensing image using the generated buffer zone, and only the remote sensing image of the buffer zone is retained.

[0072] Of course, the present application is not limited thereto. In other embodiments, the cropped image may also be cut based on a dividing line offset outwards by a preset distance from the boundary line of the buffer zone, and only the remote sensing image within the dividing line is retained.

[0073] S107: Extract the mangrove wetland distribution area in the cropped image to generate a mangrove wetland spatial distribution map.

[0074] Preferably, the step of extracting the mangrove wetland distribution area in the cropped image and generating a mangrove wetland spatial distribution map comprises:

[0075] The first step is to extract the distribution area of ​​mangrove wetlands in the cropped image;

[0076] Specifically, visual interpretation and supervised classification methods can be used to extract the distribution range of mangrove wetlands.

[0077] In the second step, the distribution area of ​​mangrove wetlands was corrected by identifying and deleting fragmented patches with an area less than or equal to the first preset threshold and patches that were misclassified as mangroves;

[0078] Specifically, patches misclassified as mangroves can be identified and deleted through visual judgment and manual deletion.

[0079] The third step is to obtain the mangrove wetland spatial distribution map based on the revised mangrove wetland distribution area. In this way, the accuracy of mangrove wetland identification can be improved, thereby improving the quality of generating the mangrove wetland spatial distribution map.

[0080] See also Figure 5 Based on the same inventive concept as the mangrove wetland identification method in the aforementioned embodiment, the present application embodiment provides a mangrove wetland identification system, including:

[0081] The first acquisition module 201 is used to acquire multispectral remote sensing images;

[0082] A preprocessing module 202 is used to preprocess the multispectral remote sensing image;

[0083] The first identification module 203 is used to identify the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area in the pre-processed multispectral remote sensing image;

[0084] A first calibration module 204 is used to calibrate the potential distribution area of ​​the first mangrove wetland using a point file, and calibrate the potential distribution area of ​​the second mangrove wetland using a surface file, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area;

[0085] A first drawing module 205, used for drawing a buffer according to a point file and a surface file;

[0086] A first cropping module 206 is used to crop the pre-processed multispectral remote sensing image according to the buffer to obtain a cropped image;

[0087] The first generating module 207 is used to extract the mangrove wetland distribution area in the cropped image and generate a mangrove wetland spatial distribution map.

[0088] In some embodiments, the spatial resolution of the multispectral remote sensing image is greater than or equal to 30 m, and the multispectral remote sensing image is selected from multispectral remote sensing images in cloudless and low tide periods.

[0089] In some embodiments, pre-processing includes radiometric calibration, atmospheric correction, geometric correction, and image fusion.

[0090] In some embodiments, the width of the buffer zone ranges from greater than or equal to 3 km to less than or equal to 5 km.

[0091] In some embodiments, the cropped image is a remote sensing image in a buffer.

[0092] In some embodiments, the first generation module 207 is specifically used to: extract the mangrove wetland distribution area in the cropped image; correct the mangrove wetland distribution area by identifying and deleting scattered patches with an area less than or equal to a first preset threshold and patches misclassified as mangroves; and obtain a mangrove wetland spatial distribution map based on the corrected mangrove wetland distribution area.

[0093] It is understandable that the modules recorded in the mangrove wetland identification system are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the mangrove wetland identification system and the modules contained therein, and will not be described in detail here.

[0094] See also Figure 6Based on the inventive concept of a mangrove wetland identification method in the aforementioned embodiment, an embodiment of the present application provides an electronic device. The electronic device may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device includes a processing device 301 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a ROM 302 (read-only memory) or a program loaded from a storage device 308 to a RAM 303 (random access memory). In RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output interface (i.e., an I / O interface 305) is also connected to the bus 304.

[0095] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0096] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0097] It should be noted that the computer-readable medium recorded in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0098] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0099] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains multispectral remote sensing images; preprocesses the multispectral remote sensing images; identifies the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area in the preprocessed multispectral remote sensing images; calibrates the first mangrove wetland potential distribution area using a point file, and calibrates the second mangrove wetland potential distribution area using a surface file, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area; draws a buffer zone according to the point file and the surface file; crops the preprocessed multispectral remote sensing image according to the buffer zone to obtain a cropped image; extracts the mangrove wetland distribution area in the cropped image, and generates a mangrove wetland spatial distribution map.

[0100] The computer program code for performing the operation of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0101] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The modules described in some embodiments of the present application may be implemented by software or by hardware. The modules described may also be set in a processor. For example, it may be described as follows: a processor includes a first acquisition module, a preprocessing module, a first recognition module, a first calibration module, a first drawing module, a first clipping module, and a first generation module, wherein the names of these modules do not constitute a limitation on the modules themselves in certain cases, for example, the first acquisition module may also be described as a "module for acquiring multispectral remote sensing images".

[0103] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0104] Some embodiments of the present application also provide a computer program product, including a computer program, which implements any of the above-mentioned mangrove wetland identification methods when executed by a processor.

[0105] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for identifying mangrove wetlands, characterized in that: The following steps are involved: Acquire multispectral remote sensing images; Preprocessing the multispectral remote sensing image; Identify a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the pre-processed multispectral remote sensing image; The potential distribution area of ​​the first mangrove wetland is calibrated using a point file, and the potential distribution area of ​​the second mangrove wetland is calibrated using a surface file, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area; Draw a buffer according to the point file and the surface file; Cropping the preprocessed multispectral remote sensing image according to the buffer to obtain a cropped image; The mangrove wetland distribution area in the cropped image is extracted to generate a mangrove wetland spatial distribution map.

2. The method for identifying mangrove wetlands according to claim 1, characterized in that: The spatial resolution of the multispectral remote sensing image is greater than or equal to 30m, and the multispectral remote sensing image is selected from multispectral remote sensing images in cloudless and low tide periods.

3. The mangrove wetland identification method according to claim 2, characterized in that: The preprocessing includes radiation calibration, atmospheric correction, geometric correction and image fusion.

4. The method for identifying mangrove wetlands according to claim 1, characterized in that: The width of the buffer zone is greater than or equal to 3 km and less than or equal to 5 km.

5. The method for identifying mangrove wetlands according to claim 1, characterized in that: The cropped image is a remote sensing image in the buffer zone.

6. The method for identifying mangrove wetlands according to any one of claims 1 to 5, characterized in that: The steps of extracting the mangrove wetland distribution area in the cropped image and generating a mangrove wetland spatial distribution map include: Extracting the mangrove wetland distribution area in the cropped image; The distribution area of ​​the mangrove wetland is corrected by identifying and deleting scattered patches with an area less than or equal to a first preset threshold and patches misclassified as mangroves; Based on the revised distribution area of ​​the mangrove wetlands, a spatial distribution map of the mangrove wetlands is obtained.

7. A mangrove wetland identification system, characterized in that: include: The first acquisition module is used to acquire multispectral remote sensing images; A preprocessing module, used for preprocessing the multispectral remote sensing image; A first identification module is used to identify a first potential distribution area of ​​mangrove wetlands and a second potential distribution area of ​​mangrove wetlands in the pre-processed multispectral remote sensing image; A first calibration module, used to calibrate the potential distribution area of ​​the first mangrove wetland using a point file, and calibrate the potential distribution area of ​​the second mangrove wetland using a surface file, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area; A first drawing module, used for drawing a buffer according to the point file and the surface file; A first cropping module, used for cropping the pre-processed multispectral remote sensing image according to the buffer to obtain a cropped image; The first generating module is used to extract the mangrove wetland distribution area in the cropped image and generate a mangrove wetland spatial distribution map.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processing device, the method according to 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 processing device, the method according to any one of claims 1 to 6 is implemented.

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