A method and system for quickly identifying the distribution range of shrubs in shrubby grassland

Abstract

The present invention discloses a method and system for rapidly identifying the distribution range of shrubs in a shrubbed grassland. The method comprises the following steps: obtaining a first grassland shrubbed region image within a first set time range of a specified area; considering an area with a pixel value greater than a set threshold value in the first grassland shrubbed region image as a grassland area where shrubs have not occurred; obtaining a second grassland shrubbed region image within a second set time range of the specified area; aligning the second grassland shrubbed region image with the first grassland shrubbed region image, removing the grassland area where shrubs have not occurred from the aligned second grassland shrubbed region image, marking the area with a pixel value greater than the set threshold value in the remaining area, and obtaining the distribution range of the shrubs.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method and a system for rapidly identifying the distribution range of shrubs in shrubby grasslands. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] When the density, coverage and biomass of shrubs increase in grassland ecosystems, they develop into shrub grasslands. The emergence of shrubs poses a threat to the sustainable development of both the ecosystem and the livestock industry. Once it begins, it is difficult to reverse. It is a common ecological problem faced by many arid and semi-arid regions.

[0004] The inventors have found that it is impossible to directly distinguish grassland and shrubs in shrubby grassland through vegetation index images such as NDVI and EVI obtained from multispectral remote sensing images through vegetation index values ​​or images. Summary of the invention

[0005] In order to solve the shortcomings of the prior art, the present invention provides a method and system for quickly identifying the distribution range of shrubs in shrubby grasslands; it can quickly identify the distribution range of shrubs, monitor the shrub expansion area, and reasonably utilize and protect the shrubby areas of grasslands, which has certain scientific value and production practice value.

[0006] In a first aspect, the present invention provides a method for quickly identifying the distribution range of shrubs in shrubby grasslands;

[0007] A method for quickly identifying the distribution range of shrubs in shrubby grasslands, comprising:

[0008] Acquire a first grassland shrubbery area image within a first set time range in a designated area;

[0009] From the first grassland shrubbery area image, the area with a pixel value greater than a set threshold is regarded as a grassland area where shrubbery has not occurred;

[0010] Acquire a second grassland shrubbery area image within a second set time range in the designated area;

[0011] The second grassland scrub area image is aligned with the first grassland scrub area image, and the grassland area without scrub is removed from the aligned second grassland scrub area image. The areas in the remaining areas with pixel values ​​greater than the set threshold are marked to obtain the distribution range of the scrub.

[0012] In a second aspect, the present invention provides a system for quickly identifying the distribution range of shrubs in shrubby grasslands;

[0013] A system for quickly identifying the distribution range of shrubs in shrubby grasslands, comprising:

[0014] A first acquisition module is configured to: acquire a first grassland shrubbery area image within a first set time range of a designated area;

[0015] A screening module is configured to: from the first grassland shrub area image, consider an area with a pixel value greater than a set threshold as a grassland area where shrubs have not occurred;

[0016] A second acquisition module is configured to: acquire an image of a second grassland shrubbery area within a second set time range of the designated area;

[0017] The output module is configured to: align the second grassland scrub area image with the first grassland scrub area image, remove the grassland area where scrub has not occurred from the aligned second grassland scrub area image, mark the area in the remaining area where the pixel value is greater than a set threshold, and obtain the distribution range of the scrub.

[0018] In a third aspect, the present invention further provides an electronic device, comprising:

[0019] a memory for non-transitory storage of computer-readable instructions; and

[0020] a processor for executing the computer readable instructions,

[0021] When the computer-readable instructions are executed by the processor, the method described in the first aspect is executed.

[0022] In a fourth aspect, the present invention further provides a storage medium that non-temporarily stores computer-readable instructions, wherein when the non-temporary computer-readable instructions are executed by a computer, the instructions of the method described in the first aspect are executed.

[0023] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, wherein the computer program is used to implement the method described in the first aspect when running on one or more processors.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] This method is easy to operate. Through programming and other technical means, it can automatically feedback the results after the spring growing season every year. The accumulation of data year by year can monitor the expansion and development of shrubs, and take reasonable measures to control the development of shrubs, thereby ensuring the sustainable development of ecology and production and life in grassland areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 This is a flow chart of the method of embodiment 1. DETAILED DESCRIPTION

[0028] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0029] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0031] In this embodiment, all data is obtained in compliance with laws and regulations and based on the user's consent, and is used legally.

[0032] Embodiment 1

[0033] This embodiment provides a method for quickly identifying the distribution range of shrubs in shrubby grasslands;

[0034] like Figure 1 As shown in the figure, a method for quickly identifying the distribution range of shrubs in shrubby grasslands includes:

[0035] S101: Acquire a first grassland shrubbery area image within a first set time range in a designated area;

[0036] S102: from the first grassland shrubbery region image, the region where the pixel value is greater than a set threshold is regarded as a grassland region where shrubbery has not occurred;

[0037] S103: Acquire a second grassland shrubbery area image within a second set time range in the designated area;

[0038] S104: Align the second grassland scrub area image with the first grassland scrub area image, remove grassland areas where scrub has not occurred from the aligned second grassland scrub area image, mark areas in the remaining areas where pixel values ​​are greater than a set threshold, and obtain the distribution range of the scrub.

[0039] Furthermore, the first grassland scrub area image refers to a MODIS (Moderate-resolution Imaging Spectroradiometer, abbreviated as MODIS, which means moderate-resolution imaging spectroradiometer in Chinese) remote sensing image of the grassland scrub area.

[0040] Furthermore, the designated area refers to a part or all of an area selected from the grassland.

[0041] Furthermore, the first set time range refers to mid-April to early May, for example, April 15 to May 1.

[0042] Preferably, the first set time range is replaced by a first time point; the first time point refers to April 20th.

[0043] Furthermore, the step S102: from the first grassland shrubbery region image, the region with a pixel value greater than a set threshold is regarded as a grassland region where shrubbery has not occurred; specifically refers to:

[0044] The NDVI (Normalized Difference Vegetation Index, abbreviated as NDVI, which is interpreted as the normalized vegetation index in Chinese) layer is extracted, and the areas whose pixel values ​​are greater than the set threshold are regarded as grassland areas without shrubbery; among them, the dynamic threshold is set to 0.1, which means that when the increase in the pixel NDVI value reaches 10% of its amplitude in the year, the area is identified as grassland.

[0045] Furthermore, the second set time range in S103 refers to the early May to early June, for example, May 2 to June 1.

[0046] Preferably, the second set time range is replaced by a second time point; the second time point refers to May 20th.

[0047] Furthermore, the S104: aligning the second grassland scrub region image with the first grassland scrub region image, using ORB (ORiented Brief) features to perform image alignment.

[0048] Furthermore, removing the grassland area where no bushification has occurred from the aligned second grassland bushification area image means: selecting the grassland area where no bushification has occurred from the aligned second grassland bushification area image, segmenting and removing the selected area or setting the pixel value of the selected area to zero.

[0049] Furthermore, marking the areas in the remaining area where the pixel value is greater than a set threshold to obtain the distribution range of the bush specifically includes: performing area statistics on the marked areas to obtain the distribution range of the bush.

[0050] This invention is based on the author's field observation data of the shrub and grassland greening period, combined with remote sensing data, to quickly identify the distribution range of shrubs. The specific method is as follows:

[0051] The earliest greening period of shrubs occurs in early May, while the surrounding grasslands all turn green by mid-April at the latest (see Fanet al., 2018). Therefore, MODIS remote sensing images of grassland shrub areas were obtained, and the greening period of each pixel was extracted using the dynamic threshold method with an NDVI starting threshold of 0.1.

[0052] The above results were segmented through the classification function of ArcGIS. The areas that turned green before May could be regarded as grassland areas without shrubbery, while the areas that turned green from May to June could be regarded as shrubs.

[0053] Embodiment 2

[0054] This embodiment provides a system for quickly identifying the distribution range of shrubs in shrubby grasslands;

[0055] A system for quickly identifying the distribution range of shrubs in shrubby grasslands, comprising:

[0056] A first acquisition module is configured to: acquire a first grassland shrubbery area image within a first set time range of a designated area;

[0057] A screening module is configured to: from the first grassland shrub area image, consider an area with a pixel value greater than a set threshold as a grassland area where shrubs have not occurred;

[0058] A second acquisition module is configured to: acquire an image of a second grassland shrubbery area within a second set time range of the designated area;

[0059] The output module is configured to: align the second grassland scrub area image with the first grassland scrub area image, remove the grassland area where scrub has not occurred from the aligned second grassland scrub area image, mark the area in the remaining area where the pixel value is greater than a set threshold, and obtain the distribution range of the scrub.

[0060] It should be noted that the first acquisition module, screening module, second acquisition module and output module described above correspond to steps S101 to S104 in Embodiment 1, and the examples and application scenarios implemented by the modules and corresponding steps are the same, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the modules described above as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0061] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0063] Embodiment 3

[0064] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory so that the electronic device executes the method described in the above embodiment one.

[0065] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0066] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0067] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0068] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0069] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0070] Embodiment 4

[0071] This embodiment further provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first embodiment is completed.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for quickly identifying the distribution range of shrubs in shrubby grasslands, characterized in that: include: Acquire a first grassland shrubbery area image within a first set time range in a designated area; From the first grassland shrubbery area image, the area with a pixel value greater than a set threshold is regarded as a grassland area where shrubbery has not occurred; Acquire a second grassland shrubbery area image within a second set time range in the designated area; Aligning the second grassland shrub area image with the first grassland shrub area image, removing grassland areas where shrubs have not occurred from the aligned second grassland shrub area image, marking areas in the remaining areas where pixel values ​​are greater than a set threshold, and obtaining the distribution range of the shrubs; The first set time range refers to mid-April to early May; the second time range refers to early May to early June.

2. A method for quickly identifying the distribution range of shrubbery in shrubby grassland as claimed in claim 1, characterized in that: The first grassland scrub region image refers to a MODIS remote sensing image of a grassland scrub region.

3. The method for quickly identifying the distribution range of shrubbery in shrubby grassland as claimed in claim 1, characterized in that: The designated area refers to a part or all of the area selected from the grassland.

4. The method for quickly identifying the distribution range of shrubbery in shrubby grassland as claimed in claim 1, characterized in that: The second grassland shrub area image is aligned with the first grassland shrub area image, and the image alignment is performed based on ORB features.

5. The method for quickly identifying the distribution range of shrubbery in shrubby grassland as claimed in claim 1, characterized in that: The removing of the grassland area where no bushing has occurred from the aligned second grassland bushing area image refers to: selecting the grassland area where no bushing has occurred from the aligned second grassland bushing area image, segmenting and removing the selected area or setting the pixel value of the selected area to zero.

6. A method for quickly identifying the distribution range of shrubbery in shrubby grassland as claimed in claim 1, characterized in that: The step of marking the areas in the remaining area where the pixel value is greater than a set threshold to obtain the distribution range of the bush specifically includes: performing area statistics on the marked areas to obtain the distribution range of the bush.

7. A system for quickly identifying the distribution range of shrubs in shrubby grasslands, characterized in that: include: A first acquisition module is configured to: acquire a first grassland shrubbery area image within a first set time range of a designated area; A screening module is configured to: from the first grassland shrub area image, consider an area with a pixel value greater than a set threshold as a grassland area where shrubs have not occurred; A second acquisition module is configured to: acquire an image of a second grassland shrubbery area within a second set time range of the designated area; The output module is configured to: align the second grassland shrub area image with the first grassland shrub area image, remove the grassland area without shrubs from the aligned second grassland shrub area image, mark the area with pixel point value greater than a set threshold in the remaining area, and obtain the distribution range of the shrubs; The first set time range refers to mid-April to early May; the second time range refers to early May to early June.

8. An electronic device, comprising: a memory for non-transitory storage of computer readable instructions; as well as a processor for executing the computer readable instructions, Wherein, when the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 6 is executed.

9. A storage medium, characterized in that: The computer-readable instructions are non-transitorily stored, wherein when the computer-readable instructions are executed by a computer, the instructions of the method according to any one of claims 1 to 6 are executed.

Images