A mangrove wetland identification method and system

Abstract

The application discloses a mangrove wetland identification method and system, belongs to the technical field of image processing, and can improve the efficiency and precision of mangrove wetland remote sensing identification; the method comprises the following steps: acquiring a multispectral remote sensing image; pre-processing the multispectral remote sensing image; identifying a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the pre-processed 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 greater than the range of the first mangrove wetland area; drawing a buffer area according to the point file and the surface file; cutting the pre-processed multispectral remote sensing image according to the buffer area to obtain a cut image; extracting a mangrove wetland distribution area in the cut image to generate a mangrove wetland spatial distribution map.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and in particular to a mangrove wetland recognition method and system. BACKGROUND

[0002] Mangrove grows in the range of 3-5 km of the tropical and subtropical coastal zone or the estuary, and has important functions such as wind and sand prevention, coast protection, water quality purification, and ecological stability maintenance. As an important part of the coastal wetland ecosystem, mangrove and its ecosystem have been severely damaged under the interference of human activities in the active human production and living area. The mangrove wetland area is dramatically reduced, the mangrove wetland is degraded, the biodiversity is destroyed, and the ecological service function is gradually lost. Therefore, it is urgent to protect and restore the mangrove. Rapid and accurate recognition of the distribution range of the mangrove wetland can provide important data support for mangrove monitoring, protection and management.

[0003] Remote sensing technology has been widely used in mangrove wetland classification and extraction, and is currently the most popular method. However, there are two problems in remote sensing interpretation. On the one hand, mangrove wetlands are only distributed in coastal beaches, but due to the influence of remote sensing image map range, a large amount of non-coastal areas are included in the range, resulting in a large amount of interpretation work. On the other hand, the spectrum of mangrove is similar to that of terrestrial vegetation, and remote sensing interpretation cannot effectively distinguish mangrove vegetation and terrestrial forest vegetation. These two aspects seriously affect the efficiency and accuracy of mangrove wetland recognition, especially for large-scale mangrove wetland recognition.

[0004] The disclosure of the above background art content is only used to assist in understanding the concept and technical scheme of the present application, and it does not necessarily belong to the prior art of the present patent application. In the absence of explicit evidence that the above content has been disclosed on the filing date of the present patent application, the above background art should not be used to evaluate the novelty and inventiveness of the present application. SUMMARY

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

[0006] To achieve the above purpose, the embodiments of the present application disclose the following technical scheme:

[0007] In a first aspect, the embodiments of the present application provide a mangrove wetland recognition method, comprising the following steps:

[0008] obtaining a multispectral remote sensing image;

[0009] Preprocessing the multispectral remote sensing image;

[0010] Identifying a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the preprocessed multispectral remote sensing image;

[0011] Calibrating the first mangrove wetland potential distribution area by using a point file and calibrating the second mangrove wetland potential distribution area by using a surface file, wherein the range of the second mangrove wetland area is greater than the range of the first mangrove wetland area;

[0012] Drawing a buffer area according to the point file and the surface file;

[0013] Cutting the preprocessed multispectral remote sensing image according to the buffer area to obtain a cut image;

[0014] Extracting a mangrove wetland distribution area in the cut image to generate a mangrove wetland spatial distribution map.

[0015] In the embodiments of the present application, the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area are calibrated by using a point file and a surface file respectively, and a buffer area is drawn according to the point file and the surface file. This step ensures the effective identification of the distribution range of the mangrove forest, reduces the processing of non-mangrove forest areas, i.e. reduces the range of remote sensing identification and interpretation, reduces the workload, improves the interpretation efficiency, effectively reduces the interference of terrestrial vegetation in the identification and interpretation process, and improves the identification accuracy of the mangrove wetland.

[0016] In some possible implementations of the first 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 in a cloud-free and low tide period. The high spatial resolution (greater than or equal to 30 m) image can capture more ground details, and compared with low resolution images, it can more clearly show the boundary between the mangrove forest and the surrounding environment, thereby significantly improving the accuracy of mangrove forest identification and avoiding confusion and misjudgment due to insufficient resolution. Selecting multispectral remote sensing images in a cloud-free and low tide period can reduce the influence of cloud cover on ground information acquisition, ensure the clarity and usability of the images, and thus improve the analysis accuracy. In the low tide period, the sea recedes, the roots and ground parts of the mangrove forest are more obvious, and the boundary is clear, which helps to more accurately identify the distribution range of the mangrove forest, especially in the area where the mangrove forest borders with water, the boundary identification will be clearer. In this way, it helps to improve the efficiency and accuracy of mangrove wetland identification.

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

[0018] In some possible implementation manners of the first aspect, the width of the buffer region ranges from greater than or equal to 3 km to less than or equal to 5 km. In this way, the width of the buffer region is more in line with the natural distribution characteristics of the mangrove distributed in the 3-5 km range of the tropical, subtropical coastal belt or the estuary. In this way, on the one hand, the data processing can be concentrated in the area where the mangrove is possibly distributed, avoiding the processing of a large number of non-mangrove areas, which is beneficial to avoiding the problem of misclassification and improving the accuracy of mangrove wetland recognition. On the other hand, the total amount of data to be processed can be reduced, and the efficiency of mangrove wetland recognition can be improved.

[0019] In some possible implementation manners of the first aspect, the cropped image is a remote sensing image in the buffer region. In this way, the amount of data to be processed can be further reduced (that is, the translation range is further reduced), thereby further improving the efficiency and accuracy of mangrove wetland recognition.

[0020] In some possible implementation manners of the first aspect, the step of extracting the mangrove wetland distribution area in the cropped image to generate the mangrove wetland spatial distribution map includes:

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

[0022] correcting 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;

[0023] obtaining the mangrove wetland spatial distribution map according to the corrected mangrove wetland distribution area. In this way, the accuracy of mangrove wetland recognition can be improved, and thus the quality of the generated mangrove wetland spatial distribution map can be improved.

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

[0025] a first acquisition module configured to acquire a multispectral remote sensing image;

[0026] a preprocessing module configured to preprocess the multispectral remote sensing image;

[0027] a first identification module configured to identify a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the preprocessed multispectral remote sensing image;

[0028] The first calibration module is configured to calibrate a first mangrove wetland potential distribution region by using a point file and to calibrate a second mangrove wetland potential distribution region by using a surface file, wherein the second mangrove wetland region has a larger range than the first mangrove wetland region.

[0029] The first drawing module is configured to draw a buffer region according to the point file and the surface file.

[0030] The first clipping module is configured to clip the preprocessed multispectral remote sensing image to obtain a clipped image according to the buffer region.

[0031] The first generation module is configured to extract a mangrove wetland distribution region in the clipped image and to generate a mangrove wetland spatial distribution map.

[0032] In some possible implementation manners 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 in a cloud-free and low-tide period.

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

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

[0035] In some possible implementation manners of the second aspect, the clipped image is a remote sensing image in the buffer region.

[0036] In some possible implementation manners of the second aspect, the first generation module is specifically configured to:

[0037] extract a mangrove wetland distribution region in the clipped image;

[0038] correct the mangrove wetland distribution region by identifying and deleting scattered patches with an area less than or equal to a first preset threshold and patches misclassified as mangroves; and

[0039] obtain the mangrove wetland spatial distribution map according to the corrected mangrove wetland distribution region.

[0040] In a third aspect, an electronic device is provided, including one or more processors, a storage device having one or more programs stored thereon, and 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 of the technical solutions of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the method according to any one of the first aspect.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product, and the computer program product includes a computer program. The computer program is executed by a processor to implement the method according to any one of the first aspect.

[0043] The technical effects brought by any one of the second aspect to the fifth aspect can refer to the technical effects brought by different design manners in the first aspect, and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained from the provided drawings without creative labor.

[0045] Figure 1 A flowchart of a mangrove wetland identification method provided by some embodiments of the present application is shown in the figure;

[0046] Figure 2 A potential distribution area demarcation diagram of a mangrove wetland provided by some embodiments of the present application is shown in the figure;

[0047] Figure 3 A buffer area diagram of a mangrove wetland provided by some embodiments of the present application is shown in the figure;

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

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

[0050] Figure 6 A structure diagram of an electronic device suitable for implementing some embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0051] Specific embodiments of the invention will now be described in detail. Although the invention is described in conjunction with these specific embodiments, it should be understood that it is not intended to limit the invention to these specific embodiments. Rather, these embodiments are intended to cover alternative, modified, or equivalent embodiments that may be included within the spirit and scope of the invention as defined by the claims. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. The invention may be practiced without some or all of these specific details.

[0052] When used in conjunction with the terms "comprising," "method comprising," or similar language in this specification and appended claims, the singular forms "a," "some," and "the" include plural references unless the context clearly indicates otherwise. Unless otherwise defined, 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 pertains.

[0053] Application Overview:

[0054] Mangroves grow and are distributed within 3-5 km of tropical and subtropical coastlines or river estuaries. They play vital roles in windbreak and sand fixation, coastal protection, water purification, and improving and maintaining ecological stability, making them an important component of coastal wetland ecosystems. As coastal areas are heavily impacted by human activities, mangroves and their ecosystems have suffered severe damage, resulting in a dramatic reduction in mangrove wetland area, degradation, biodiversity loss, and gradual loss of ecosystem services. Therefore, the protection and restoration of mangroves is urgently needed. Rapidly and accurately identifying the distribution range of mangrove wetlands can provide crucial data support for mangrove monitoring, protection, and management.

[0055] Remote sensing technology has been widely applied to the classification and extraction of mangrove wetlands and is currently the most mainstream method. However, two major challenges exist in remote sensing interpretation. First, mangrove wetlands are only distributed along coastal mudflats, but the imagery area is limited by the range of remote sensing data, which includes a large number of non-coastal areas, resulting in a significant workload for interpretation. Second, mangroves and terrestrial vegetation have similar spectra, making it difficult for remote sensing interpretation to effectively distinguish between mangrove vegetation and terrestrial forest vegetation. These two factors severely impact the efficiency and accuracy of mangrove wetland identification, especially for larger-scale mangrove wetlands. Therefore, providing a highly efficient and accurate remote sensing method for mangrove wetland identification is one of the urgent technical problems to be solved in this field.

[0056] To address the aforementioned technical problems, the overall approach of the technical solution provided in this application is as follows: This application provides a method for identifying mangrove wetlands, comprising the following steps: acquiring multispectral remote sensing images; preprocessing the multispectral remote sensing images; identifying a first potential distribution area and a second potential distribution area of ​​mangrove wetlands in the preprocessed multispectral remote sensing images; labeling the first potential distribution area of ​​mangrove wetlands using point files and the second potential distribution area of ​​mangrove wetlands using polygon files, wherein the range of the second mangrove wetland area is larger than the range of the first mangrove wetland area; drawing a buffer zone based on the point files and polygon files; cropping the preprocessed multispectral remote sensing images based on the buffer zone to obtain a cropped image; extracting the mangrove wetland distribution areas from the cropped image to generate a spatial distribution map of mangrove wetlands.

[0057] In this embodiment, the potential distribution areas of the first and second mangrove wetlands are marked using point files and polygon files, respectively, and a buffer zone is drawn based on the point files and polygon files. This step ensures the effective identification of the distribution range of mangroves and reduces the processing of non-mangrove areas, thereby reducing the scope of remote sensing identification and interpretation, reducing workload, improving interpretation efficiency, and effectively reducing interference from terrestrial vegetation during identification and interpretation, thus improving the accuracy of mangrove wetland identification.

[0058] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0059] Please see Figures 1-4 This application provides a method for identifying mangrove wetlands, including the following steps:

[0060] S101: Acquire multispectral remote sensing images;

[0061] Specifically, in some embodiments, the spatial resolution of the multispectral remote sensing imagery is greater than or equal to 30m, and the multispectral remote sensing imagery selected is from cloudless and low tide periods. High spatial resolution (greater than or equal to 30 meters) images can capture more ground details and, compared to low-resolution images, more clearly show the boundary between mangroves and their surrounding environment, thus significantly improving the accuracy of mangrove identification and avoiding confusion and misjudgment due to insufficient resolution. Selecting multispectral remote sensing imagery from cloudless and low tide periods reduces the impact of cloud cover on ground information acquisition, ensuring image clarity and usability, thereby improving analysis accuracy. During low tide, the receding seawater makes the roots and ground parts of mangroves more visible, with clear boundaries, which helps to more accurately identify the distribution range of mangroves, especially in areas where mangroves meet water bodies, where boundary identification is even clearer. This contributes to improving the efficiency and accuracy of mangrove wetland identification.

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

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

[0064] S103: Identifying a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the preprocessed multispectral remote sensing image.

[0065] Specifically, in some embodiments, the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area can be identified according to the growth and distribution characteristics of mangroves, and the coastline in the multispectral remote sensing image is visually judged.

[0066] S104: Using a point file to demarcate the first mangrove wetland potential distribution area and using a surface file to demarcate the second mangrove wetland potential distribution area, wherein the range of the second mangrove wetland area is greater than the range of the first mangrove wetland area, as shown in Figure 2 Specifically, the first mangrove wetland potential distribution area and the second mangrove wetland potential distribution area can be demarcated by using the ArcGIS10.5 software.

[0067] S105: Drawing a buffer area according to the point file and the surface file, as shown in Figure 3

[0068] Specifically, in some embodiments, the point file and the surface file can be connected by using the drawing tool in the ArcGIS10.5 software to draw the buffer area.

[0069] Further, in some embodiments, the width range of the buffer area is greater than or equal to 3km and less than or equal to 5km. In this way, the width range of the buffer area is more in line with the natural distribution characteristics of mangroves distributed in the 3-5km range of the tropical, subtropical coastal belt or the estuary. In this way, on the one hand, the data processing can be concentrated in the area where mangroves may be distributed, avoiding processing of a large number of non-mangrove areas, which is conducive to avoiding misclassification and improving the accuracy of mangrove wetland recognition. On the other hand, the total amount of data to be processed can be reduced, thereby improving the efficiency of mangrove wetland recognition.

[0070] S106: Cropping the preprocessed multispectral remote sensing image according to the buffer area to obtain a cropped image, as shown in Figure 4

[0071] ​​Preferably, the cropped image is the remote sensing image within the buffer zone. In this way, the amount of data to be processed can be further reduced (i.e. the interpretation range is further narrowed), thereby further improving the efficiency and accuracy of mangrove wetland identification. For example, the image cutting tool in ArcGIS 10.5 software can be used to cut the remote sensing image using the generated buffer zone, and only the remote sensing image within the buffer zone is retained.

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

[0073] S107: Extracting 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 to generate a mangrove wetland spatial distribution map comprises:

[0075] First, extracting the mangrove wetland distribution area in the cropped image;

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

[0077] Second, correcting the mangrove wetland distribution area by identifying and deleting fragmented patches with an area less than or equal to a first preset threshold and sporadically distributed, and patches misclassified as mangroves;

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

[0079] Third, obtaining a mangrove wetland spatial distribution map according to the corrected mangrove wetland distribution area. In this way, the accuracy of mangrove wetland identification can be improved, thereby improving the quality of the generated mangrove wetland spatial distribution map.

[0080] Please refer to Figure 5 , based on the same inventive concept as the above-mentioned embodiment of a mangrove wetland identification method, the present application embodiment provides a mangrove wetland identification system, comprising:

[0081] A first acquisition module 201 is configured to acquire a multispectral remote sensing image.

[0082] A preprocessing module 202 is configured to preprocess the multispectral remote sensing image.

[0083] A first identification module 203 is configured to identify a first mangrove wetland potential distribution area and a second mangrove wetland potential distribution area in the preprocessed multispectral remote sensing image.

[0084] The first calibration module 204 is configured to calibrate a first mangrove wetland potential distribution region by using a point file and calibrate a second mangrove wetland potential distribution region by using a surface file, wherein the second mangrove wetland region has a larger range than the first mangrove wetland region.

[0085] The first drawing module 205 is configured to draw a buffer region according to the point file and the surface file.

[0086] The first clipping module 206 is configured to clip the preprocessed multi-spectral remote sensing image according to the buffer region to obtain a clipped image.

[0087] The first generation module 207 is configured to extract a mangrove wetland distribution region in the clipped image and generate a mangrove wetland spatial distribution map.

[0088] In some embodiments, the spatial resolution of the multi-spectral remote sensing image is greater than or equal to 30 m, and the multi-spectral remote sensing image is selected from multi-spectral remote sensing images in a cloud-free and low-tide period.

[0089] In some embodiments, the preprocessing includes radiation calibration, atmospheric correction, geometric correction, and image fusion.

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

[0091] In some embodiments, the clipped image is a remote sensing image within the buffer region.

[0092] In some embodiments, the first generation module 207 is specifically configured to: extract the mangrove wetland distribution region in the clipped image; correct the mangrove wetland distribution region 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 the mangrove wetland spatial distribution map according to the corrected mangrove wetland distribution region.

[0093] It can be understood that the modules described in the mangrove wetland identification system correspond to the respective steps in the method described above. Figure 1 The operations, features, and advantages described above for the method also apply to the mangrove wetland identification system and the modules contained therein, and will not be repeated here.

[0094] Please refer to Figure 6, based on the inventive concept of the mangrove wetland identification method in the foregoing embodiments, an electronic device is provided. The electronic device can include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device includes a processing device 301 (e.g., a central processing unit, a graphic processing unit, or the like) that can perform various appropriate actions and processes according to a program stored in a ROM 302 (Read Only Memory) or a program loaded into a RAM 303 (Random Access Memory) from a storage device 308. In the RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output interface (i.e., I / O interface 305) is also connected to the bus 304.

[0095] Generally, the following devices can 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, and the like; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; a storage device 308 including, for example, a magnetic tape, a hard disk, and the like; and a communication device 309. The communication device 309 can allow the electronic device to communicate wirelessly or wiredly with other devices to exchange data.

[0096] In particular, according to some embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product including a computer program carried on a computer readable medium, the computer program containing program codes for performing the methods shown in the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a 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-described functions defined in the methods of some embodiments of the present application are performed.

[0097] Note that the computer-readable medium in some embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program used by an instruction execution system, apparatus, or device and can be used by or in connection with that system, apparatus, or device. In some embodiments of the present application, the computer-readable signal medium can include a computer-readable program code that is carried in a baseband or as part of a carrier wave, in which the computer-readable program code can be transmitted by any suitable medium, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. The computer-readable signal medium can also be any computer-readable medium that can send, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including, but not limited to, wire, cable, RF, etc., or any suitable combination of the foregoing.

[0098] In some embodiments, the client, server, can communicate using any known or future developed network protocols, such as HTTP (HyperText Transfer Protocol), and can 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"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current known or future developed networks.

[0099] The computer readable medium can be included in the electronic device; or can exist independently of the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to acquire a multispectral remote sensing image, pre-process 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, demarcate the first mangrove wetland potential distribution area by using a point file and demarcate the second mangrove wetland potential distribution area by using a surface file, wherein the second mangrove wetland area is larger than the first mangrove wetland area, draw a buffer area according to the point file and the surface file, crop the pre-processed multispectral remote sensing image according to the buffer area to obtain a cropped image, and extract a mangrove wetland distribution area in the cropped image to generate a mangrove wetland spatial distribution map.

[0100] Computer program code for carrying out operations of some embodiments of the application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0101] The computer program code can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce the computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts and block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logic functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0102] The modules described in some embodiments of the present application can be implemented by means of software, or can be implemented by means of hardware. The modules described can also be arranged in a processor. For example, a processor can be described as comprising a first acquisition module, a preprocessing module, a first identification module, a first calibration module, a first drawing module, a first cropping module and a first generation module, wherein the names of these modules do not constitute a limitation of the modules themselves in some cases. For example, the first acquisition module can also be described as a module for acquiring multispectral remote sensing images.

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

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

[0105] Although the present application has been described in detail above with general and specific embodiments, it will be apparent to those skilled in the art that various modifications or improvements can be made to the present application without departing from the spirit thereof. Therefore, such modifications or improvements made on the basis of the present application, without departing from the spirit thereof, are within the scope of the present application.

Claims

1. A method for identifying mangrove wetlands, characterized in that, Includes the following steps: Acquire multispectral remote sensing images; The multispectral remote sensing image is preprocessed; Identify the potential distribution areas of the first and second mangrove wetlands in the preprocessed multispectral remote sensing images; The potential distribution area of ​​the first mangrove wetland is marked using a point file, and the potential distribution area of ​​the second mangrove wetland is marked using a polygon file, wherein the area of ​​the second mangrove wetland is larger than the area of ​​the first mangrove wetland. Draw a buffer based on the point file and the polygon file; The preprocessed multispectral remote sensing image is cropped according to the buffer to obtain a cropped image; Extracting the mangrove wetland distribution area from the cropped image to generate a mangrove wetland spatial distribution map specifically includes the following steps: Extract the mangrove wetland distribution area from the cropped image; The distribution area of ​​the mangrove wetland is corrected by identifying and deleting fragmented patches with an area less than or equal to a first preset threshold and scattered distribution, as well as patches that are misclassified as mangroves. Based on the revised distribution area of ​​mangrove wetlands, a spatial distribution map of mangrove wetlands is obtained.

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 cloudless and low tide periods.

3. The method for identifying mangrove wetlands according to claim 2, characterized in that, The preprocessing includes radiometric 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 within the buffer.

6. A mangrove wetland identification system, characterized in that, include: The first acquisition module is used to acquire multispectral remote sensing images; The preprocessing module is used to preprocess the multispectral remote sensing image; The first identification module is used to identify the first potential distribution area of ​​mangrove wetland and the second potential distribution area of ​​mangrove wetland in the preprocessed multispectral remote sensing image; The first calibration module is used to calibrate the potential distribution area of ​​the first mangrove wetland using a point file and to calibrate the potential distribution area of ​​the second mangrove wetland using a polygon file, wherein the area of ​​the second mangrove wetland is larger than the area of ​​the first mangrove wetland. The first drawing module is used to draw a buffer based on the point file and the polygon file; The first cropping module is used to crop the preprocessed multispectral remote sensing image according to the buffer to obtain a cropped image. The first generation module is used to extract the distribution area of ​​mangrove wetlands in the cropped image and generate a spatial distribution map of mangrove wetlands. The module performs the following steps: extracting the distribution area of ​​mangrove wetlands in the cropped image; The distribution area of ​​the mangrove wetland is corrected by identifying and deleting fragmented patches with an area less than or equal to a first preset threshold and scattered distribution, as well as patches that are misclassified as mangroves. Based on the revised distribution area of ​​mangrove wetlands, a spatial distribution map of mangrove wetlands is obtained.

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