Vegetation phenological diversity calculation method and device based on phenological camera time sequence image

Abstract

The invention discloses a vegetation phenological diversity calculation method and device based on phenological camera time sequence images, and is used for solving the technical problem that the vegetation phenological diversity calculation efficiency is low due to the fact that most of the existing vegetation phenological diversity calculation methods adopt an artificial ground survey mode. The method comprises the following steps: acquiring phenological camera time sequence images of a plurality of vegetations, preprocessing the phenological camera time sequence images of the vegetations based on a bicubic interpolation method, and outputting a plurality of crown selection area bitmaps of the vegetations; generating a green degree index annual change curve corresponding to each vegetation according to the plurality of crown selection area bitmaps of each vegetation; performing data extraction on the green degree index annual change curve corresponding to each vegetation, and determining a vegetation physical quantity corresponding to each vegetation; and calculating according to the vegetation physical quantity corresponding to each vegetation by adopting a preset phenological diversity exponential function, and determining a vegetation phenological diversity index.

Description

Technical Field

[0001] The present invention relates to the technical field of phenological observation, and in particular to a method and device for calculating vegetation phenological diversity based on time series images of a phenological camera. Background Art

[0002] Phenology refers to the long-term adaptation of organisms to periodic changes in temperature conditions, forming corresponding growth and development patterns. This phenomenon is called phenology, which mainly refers to the growth, development, activity patterns of animals and plants and the response of non-biological changes to climate.

[0003] Changes in plant phenology reflect their adaptability to environmental conditions, which directly affects the function and stability of the ecosystem. Plant phenological diversity is an important indicator to measure the differences in phenological characteristics of individual plants in a specific habitat, and can reveal the adaptation strategies and ecological processes of different plant populations. Therefore, clarifying phenological diversity plays a key role in studying terrestrial ecological processes.

[0004] Most of the existing methods for calculating vegetation phenological diversity use manual ground surveys. Investigators need to arrive at sample points one by one according to the predetermined survey route and time schedule, use professional survey tools to record the basic information of the sample points, and then use the basic information of the sample points to calculate vegetation phenological diversity. The above process is too dependent on manual labor and requires a lot of manpower and material resources. The degree of automation is low, resulting in low efficiency in calculating vegetation phenological diversity. Summary of the invention

[0005] The present invention provides a method and device for calculating vegetation phenological diversity based on phenological camera time series images, which are used to solve the technical problem that most existing vegetation phenological diversity calculation methods adopt artificial ground survey methods, resulting in low efficiency in vegetation phenological diversity calculation.

[0006] The first aspect of the present invention provides a method for calculating vegetation phenological diversity based on time series images of a phenological camera, comprising:

[0007] Acquire phenological camera time series images of multiple vegetations, pre-process the phenological camera time series images of each vegetation based on the bicubic interpolation method, and output multiple crown selection area bitmaps of each vegetation;

[0008] Generate a greenness index annual variation curve corresponding to each of the vegetation according to a plurality of tree crown selection area maps of each of the vegetation;

[0009] Extracting data from the annual variation curve of the greenness index corresponding to each of the vegetations to determine the vegetation physical quantity corresponding to each of the vegetations;

[0010] The vegetation phenology diversity index is determined by using a preset phenology diversity index function to perform calculations based on the vegetation physical quantities corresponding to each of the vegetation.

[0011] Optionally, the preprocessing of the phenological camera time series images of each vegetation based on the bicubic interpolation method to output a plurality of crown selection area bitmaps of each vegetation includes:

[0012] The bicubic interpolation method is used to optimize the phenological camera time series images of each of the vegetation, and the optimized phenological camera time series images of each of the vegetation are output;

[0013] The regions of interest are selected from the time series optimized images of the phenological cameras of the vegetation, and a plurality of crown selection area bitmaps of the vegetation are generated.

[0014] Optionally, generating a greenness index annual variation curve corresponding to each of the vegetations according to a plurality of tree crown selection area maps of each of the vegetations includes:

[0015] Extracting color channel information from the multiple tree crown selection area bitmaps of each of the vegetation, and outputting multiple color channel information data corresponding to each tree crown selection area bitmap of each of the vegetation;

[0016] Calculate the greenness index pixel value corresponding to each of the tree crown selection area bitmaps of each of the vegetation according to the multiple color channel information data corresponding to each of the tree crown selection area bitmaps of each of the vegetation;

[0017] The greenness index pixel values ​​corresponding to the crown selection area bitmaps of the vegetation are respectively used to construct the greenness index pixel time series data corresponding to the vegetation;

[0018] Based on a preset time interval, the greenness index pixel time series data corresponding to each of the vegetations is smoothed using the percentile method to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations;

[0019] A plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations are respectively used to construct an annual variation curve of the greenness index corresponding to each of the vegetations.

[0020] Optionally, based on a preset time interval, the percentile method is used to smooth the greenness index pixel time series data corresponding to each of the vegetations to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations, including:

[0021] Based on a preset time interval, the greenness index pixel time series data corresponding to each of the vegetations is divided to determine a plurality of greenness index pixel time subsequences corresponding to each of the vegetations;

[0022] respectively sorting the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to the respective vegetations in descending order to determine sorting data of the multiple greenness index pixel time subsequences corresponding to the respective vegetations;

[0023] The 90th percentile value is selected for each of the plurality of greenness index pixel time subseries sorted data corresponding to the vegetation, and the plurality of 90th percentile greenness index pixel values ​​corresponding to the vegetation are output.

[0024] Optionally, the vegetation physical quantity includes a greenness index time series value in a greenness index annual variation curve and the number of occurrences of individual vegetation; the use of a preset phenological diversity index function to calculate the vegetation physical quantity corresponding to each of the vegetation to determine the vegetation phenological diversity index includes:

[0025] Based on the greenness index time series value in the greenness index annual change curve corresponding to each of the vegetation, calculating the relative overlap of the greenness index annual change curve and the coincidence degree with multiple greenness index annual change curves;

[0026] Calculating the vegetation relative intensity corresponding to each of the vegetation according to the number of occurrences of the vegetation individuals corresponding to each of the vegetation;

[0027] The relative overlap of the annual variation curve of the greenness index, the coincidence degree of a plurality of annual variation curves of the greenness index and the relative intensity of vegetation corresponding to each of the vegetation are substituted into a preset phenological diversity index function to calculate the vegetation phenological diversity index.

[0028] Optionally, the preset phenological diversity index function is specifically:

[0029] ;

[0030] Among them, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual change curves of the greenness index, which represents the relative overlap sum of the phenological curves; is the overlap of the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation; is the relative intensity of vegetation corresponding to the i-th vegetation; is the relative intensity of vegetation corresponding to the jth vegetation; s is the total number of vegetation.

[0031] The second aspect of the present invention provides a vegetation phenological diversity calculation device based on phenological camera time series images, comprising:

[0032] An acquisition module is used to acquire phenological camera time series images of multiple vegetations, and pre-process the phenological camera time series images of each vegetation based on the bicubic interpolation method, and output multiple crown selection area bitmaps of each vegetation;

[0033] A generating module, for generating an annual variation curve of the greenness index corresponding to each of the vegetations according to a plurality of tree crown selection area maps of each of the vegetations;

[0034] An extraction module is used to extract data from the annual variation curve of the greenness index corresponding to each of the vegetations to determine the vegetation physical quantity corresponding to each of the vegetations;

[0035] The calculation module is used to use a preset phenological diversity index function to perform calculations according to the vegetation physical quantities corresponding to each of the vegetations to determine the vegetation phenological diversity index.

[0036] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in any one of the above items.

[0037] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in any one of the above items.

[0038] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in any one of the above items.

[0039] It can be seen from the above technical solutions that the present invention has the following advantages:

[0040] The above technical scheme of the present invention provides a method for calculating vegetation phenological diversity based on phenological camera time series images. First, phenological camera time series images of multiple vegetations are obtained, and the phenological camera time series images of each of the vegetations are preprocessed based on the bicubic interpolation method to output multiple crown selection area bitmaps of each of the vegetations; then, according to the multiple crown selection area bitmaps of each vegetation, an annual change curve of the greenness index corresponding to each vegetation is generated; data is extracted from the annual change curve of the greenness index corresponding to each vegetation to determine the vegetation physical quantity corresponding to each vegetation; finally, a preset phenological diversity index function is used to perform calculations based on the vegetation physical quantity corresponding to each vegetation to determine the vegetation phenological diversity index; based on the above scheme, the multiple phenological camera time series images of the multiple vegetations obtained are processed to obtain the annual change curve of the greenness index corresponding to each vegetation, and then the vegetation physical quantity corresponding to each greenness index annual change curve is calculated to determine the vegetation phenological diversity index. This process can replace the manual ground survey method, reduce the dependence on investigators, and thus improve the efficiency of vegetation phenological diversity calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0042] Figure 1 A flowchart of the steps of a method for calculating vegetation phenological diversity based on time series images of a phenological camera provided in the first embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the flow of a method for calculating vegetation phenological diversity based on phenological camera time series images provided in the first embodiment of the present invention;

[0044] Figure 3 This is a structural block diagram of a vegetation phenological diversity calculation device based on phenological camera time series images provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0045] The embodiments of the present invention provide a method and device for calculating vegetation phenological diversity based on time series images of a phenological camera, which are used to solve the technical problem that most existing methods for calculating vegetation phenological diversity use artificial ground surveys, resulting in low efficiency in calculating vegetation phenological diversity.

[0046] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Terminology explanation:

[0048] PhenoCam—Phenology Camera.

[0049] Region of interest—Region of interest.

[0050] See also Figure 1 , Figure 1 A flowchart of the steps of a method for calculating vegetation phenological diversity based on phenological camera time series images provided in Example 1 of the present invention.

[0051] The present invention provides a method for calculating vegetation phenological diversity based on time series images of a phenological camera, comprising:

[0052] Step 101: Acquire time series images of phenological cameras of multiple vegetations, pre-process the time series images of the phenological cameras of each vegetation based on the bicubic interpolation method, and output multiple crown selection area maps of each vegetation.

[0053] It should be noted that by collecting vegetation images taken regularly at specific locations and angles through phenological cameras, a series of vegetation images (JPEG images) taken continuously at different time points in the same observation area are obtained, that is, phenological camera time series images of multiple vegetations; among them, multiple vegetations include single trees or shrubs, etc.

[0054] Furthermore, the process of preprocessing the phenological camera time series images of each vegetation based on the bicubic interpolation method and outputting multiple crown selection area bitmaps of each vegetation can be performed through sub-steps S11-S12:

[0055] Step S11, optimizing the phenological camera time series images of each vegetation using a bicubic interpolation method, and outputting the optimized phenological camera time series images of each vegetation;

[0056] Step S12: selecting regions of interest from the phenological camera time series optimized images of each vegetation, and generating multiple crown selection area maps of each vegetation.

[0057] It should be noted that the obtained JPEG images were imported into MATLAB for processing, and the image data (phenological camera time series images) were optimized using the bicubic interpolation method to improve the image quality and reduce the image interference caused by weather and other factors. Then, the crown part of the plant was calibrated and extracted in the image, and a representative area (such as the crown of a single tree or shrub) was selected to generate a bitmap of the crown selection area in the labeled image file format, namely, the crown selection area bitmap.

[0058] It is worth mentioning that, based on the above content, the phenological camera time series optimized image of each vegetation is composed of the phenological camera optimized images at different time points. Therefore, when performing the region of interest selection operation on the image data, the phenological camera optimized images at different time points will be calibrated and the crown part of the plant will be extracted, so as to obtain the crown selection area map of each vegetation at different time points.

[0059] In this embodiment, phenological camera time series images of multiple vegetations are obtained, and the phenological camera time series images of each vegetation are preprocessed based on the bicubic interpolation method to output multiple crown selection area maps of each vegetation.

[0060] Step 102: Generate an annual variation curve of the greenness index corresponding to each vegetation based on multiple tree crown selection area maps of each vegetation.

[0061] Specifically, step 102 may include the following sub-steps S21-S25:

[0062] Step S21, extracting color channel information from a plurality of tree crown selection area bitmaps of each vegetation, and outputting a plurality of color channel information data corresponding to each tree crown selection area bitmap of each vegetation;

[0063] Step S22, calculating the greenness index pixel value corresponding to each crown selection area bitmap of each vegetation according to the multiple color channel information data corresponding to each crown selection area bitmap of each vegetation;

[0064] Step S23, respectively using the greenness index pixel values ​​corresponding to each crown selection area bitmap of each vegetation to construct greenness index pixel time series data corresponding to each vegetation;

[0065] The multiple color channel information data includes the pixel value of the green channel, the pixel value of the red channel, and the pixel value of the blue channel.

[0066] It should be noted that the RGB color channel information of the image (crown selection area bitmap) is extracted and the greenness index (G CC ), to quantitatively describe the growth status of the plant. The formula is:

[0067] G CC =G DN / (R DN +GDN +B DN );

[0068] Among them, G CC is the greenness index pixel value; G DN is the pixel value of the green channel; R DN is the pixel value of the red channel; B DN is the pixel value of the blue channel.

[0069] Furthermore, based on the above steps, the G of each plant at each time node in each year is calculated. CC The values ​​are used to form time series data that characterize the changes in plant canopy greenness, namely, greenness index pixel time series data.

[0070] Step S24: Based on a preset time interval, the percentile method is used to smooth the greenness index pixel time series data corresponding to each vegetation, and a plurality of 90th percentile greenness index pixel values ​​corresponding to each vegetation are determined;

[0071] Further, step S24 may include the following sub-steps:

[0072] Step S241: dividing the greenness index pixel time series data corresponding to each vegetation based on a preset time interval to determine a plurality of greenness index pixel time subsequences corresponding to each vegetation;

[0073] Step S242, sorting the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to each vegetation in descending order, and determining sorting data of the multiple greenness index pixel time subsequences corresponding to each vegetation;

[0074] Step S243: Select the 90th percentile value of the multiple greenness index pixel time subsequence sorting data corresponding to each vegetation, and output the multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation.

[0075] It should be noted that the present invention uses a time interval of 3 days (preset time interval) and uses the percentile method to calculate the G CC The values ​​are smoothed and the 90th percentile GCC values ​​in three days are calculated to improve the accuracy and stability of the data. Specifically, the time window is defined: for the tth day in the time series, a three-day window centered on tt is considered, that is, the greenness index pixel time series data corresponding to each vegetation is divided into time intervals of 3 days, and multiple greenness index pixel time subseries sorting data corresponding to each vegetation at time intervals of 3 days are obtained. The greenness index pixel time subseries sorting data includes the greenness index pixel values ​​at different time points in the corresponding three days, and the corresponding image set can be expressed as:

[0076] ;

[0077] Among them, I(t) represents the canopy selection area map on the tth day, (To ensure the integrity of the window, it is usually from the second day to the second to last day); Wt represents the image set of the crown selection area bitmap on the tth day, the crown selection area bitmap on the t-1th day, and the crown selection area bitmap on the t+1th day.

[0078] Furthermore, for each pixel position (greenness index pixel value (x, y)), the pixel value at the corresponding position is extracted from the window Wt:

[0079]

[0080] Among them, Vt,(x,y) is the set of values ​​of the pixel position at three time points in the window of the tth day, that is, the set of greenness index pixel values ​​at different time points within three days; is the greenness index pixel value on the t-1th day; I(t,x,y) is the greenness index pixel value on the tth day; is the greenness index pixel value on day t+1.

[0081] Furthermore, all the greenness index pixel values ​​in the set Vt,(x,y) are sorted in descending order, and the 90th percentile value is calculated, which is used as the smoothed value of the pixel position on the tth day, that is, the 90th percentile greenness index pixel value:

[0082] I′(t,x,y)=sorted(Vt,(x,y))

[0083] Among them, I′(t,x,y) is the 90th percentile greenness index pixel value on the tth day; sorted is a sorting function used to sort the greenness index pixel values.

[0084] Furthermore, for each t and each pixel position (x, y), the above steps are performed to obtain the Gcc time series after smoothing for each day, and then the pixel time series within the crown of each ROI plant individual is averaged to obtain the time series of the crown of each ROI plant individual; that is, after sorting the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to each vegetation in descending order, the 90th percentile value of the sorted multiple greenness index pixel values ​​corresponding to each vegetation is selected, and the multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation are output, and then the multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation are used to construct the greenness index annual change curve corresponding to each vegetation.

[0085] Step S25: Use multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation to construct an annual change curve of the greenness index corresponding to each vegetation.

[0086] In this embodiment, the greenness index annual variation curve corresponding to each vegetation is generated based on a plurality of tree crown selection area maps of each vegetation.

[0087] Step 103: extract data from the annual variation curve of the greenness index corresponding to each vegetation to determine the vegetation physical quantity corresponding to each vegetation.

[0088] Vegetation physical quantities include the greenness index time series value in the greenness index annual change curve and the number of occurrences of vegetation individuals.

[0089] It should be noted that after obtaining the greenness index annual variation curve corresponding to each vegetation based on the above steps, data extraction is performed thereon to obtain the vegetation physical quantity corresponding to each vegetation.

[0090] In this embodiment, data is extracted from the annual variation curve of the greenness index corresponding to each vegetation to determine the vegetation physical quantity corresponding to each vegetation.

[0091] Step 104: using a preset phenological diversity index function to perform calculations based on the vegetation physical quantities corresponding to each vegetation, to determine the vegetation phenological diversity index.

[0092] Specifically, step 104 may include the following sub-steps S41-S43:

[0093] Step S41, based on the greenness index time series value in the greenness index annual change curve corresponding to each vegetation, calculating the relative overlap of the greenness index annual change curve and the coincidence degree with multiple greenness index annual change curves;

[0094] Step S42, calculating the vegetation relative intensity corresponding to each vegetation according to the number of occurrences of the vegetation individuals corresponding to each vegetation;

[0095] Step S43: Substitute the relative overlap of the annual variation curve of the greenness index, the coincidence degree of multiple annual variation curves of the greenness index, and the relative intensity of vegetation corresponding to each vegetation into the preset phenological diversity index function to calculate the vegetation phenological diversity index.

[0096] It should be noted that according to the G CC The diversity of plant phenology (vegetation phenological diversity index) is calculated based on the overlap of annual change curves and the number of occurrences of vegetation individuals. Low overlap indicates high phenological diversity, and vice versa. The phenological diversity index (PD) is obtained by calculating the proportion of non-overlapping parts of the curves of each plant. Specifically, based on the greenness index time series value in the greenness index annual change curve corresponding to each vegetation, the relative overlap of the greenness index annual change curve and the overlap of multiple greenness index annual change curves are calculated; according to the number of occurrences of vegetation individuals corresponding to each vegetation, the relative intensity of vegetation corresponding to each vegetation is calculated; the above process can be expressed as:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] in, is the Gcc time series value of the i-th plant individual on the t-th day, that is, the greenness index time series value in the greenness index annual variation curve of the i-th plant individual on the t-th day; is the Gcc time series value of the j-th plant individual on the t-th day (starting from January 1 of the year), that is, the greenness index time series value in the greenness index annual variation curve of the j-th plant individual on the t-th day; N is the total number of plant samples; is the number of occurrences of the i-th plant individual; is the number of occurrences of the jth plant individual.

[0102] Furthermore, a phenological diversity index function is preset, specifically:

[0103] ;

[0104] Among them, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual change curves of the greenness index, which represents the relative overlap sum of the phenological curves; is the overlap of the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation; is the relative intensity or abundance of vegetation corresponding to the i-th vegetation; is the relative intensity or abundance of vegetation corresponding to the jth vegetation; s is the total number of vegetation.

[0105] It is worth mentioning that MATLAB is used to draw an annual curve chart of phenological changes, which visualizes the seasonal changes and phenological diversity of plant phenology.

[0106] In this embodiment, a preset phenological diversity index function is used to perform calculations based on vegetation physical quantities corresponding to each vegetation to determine the vegetation phenological diversity index.

[0107] As a comparison of technical effects, it can be combined with existing technologies for reference. The changes in plant phenology reflect its adaptability to environmental conditions, which directly affects the function and stability of the ecosystem. Plant phenological diversity is an important indicator to measure the differences in phenological characteristics of individual plants in a specific habitat, and can reveal the adaptation strategies and ecological processes of different plant populations. Therefore, clarifying phenological diversity plays a key role in studying terrestrial ecological processes. However, common phenological observation methods (such as high-resolution satellite images, artificial ground surveys, etc.) still have certain limitations. The current phenological diversity observation methods mostly rely on high-resolution satellite images, phenological cameras and artificial field surveys. At the same time, there is a lack of computational tools to quantify phenological diversity. In addition, there are difficulties in technical accuracy, cost and ground surveys. As one of the ways to observe phenology, phenological cameras can effectively avoid the technical shortcomings of field surveys and satellites.

[0108] In view of the above problems, this paper proposes a method for calculating vegetation phenological diversity based on time series images of phenological cameras, aiming to provide more efficient and accurate data support for fields such as plant ecology and global change biology. Figure 2 The present invention uses high-quality PhenoCam image data, image difference method and region of interest selection to process the image, and then uses color band analysis technology (especially green band) to extract plant growth status information, that is, calculate the GCC value (greenness index pixel value), and based on long-term fixed-point monitoring, analyze the change trend of plants on a time scale, and then identify the phenological characteristics of plants. By comparing the overlap of phenological characteristics of different plants, phenological diversity can be quantified. This method not only provides a new phenological diversity quantification tool, but also provides a verification method for phenological observation methods that rely on satellite image data.

[0109] In summary, this paper accurately quantifies the diversity of phenology by analyzing the overlap of the annual change curves of GCC (greenness index) of different plants in PhenoCam image data. This provides an effective tool for ecological researchers to deeply understand the differences in adaptability of different plant populations under different environmental conditions.

[0110] Compared with traditional ground surveys and high-resolution satellite image observation methods, this technical solution uses phenological cameras to capture high-frequency, high-quality images, and combines image processing and color band analysis technology to reduce observation costs and the complexity of manual surveys, while improving observation efficiency and data reliability. This method is particularly suitable for phenological monitoring in large areas and provides a new way to carry out extensive ecological monitoring.

[0111] This invention can effectively supplement and verify phenological observation technology that relies on satellite images. By comparing and analyzing ground phenological camera data with satellite images, the application effect and limitations of satellite images in phenological diversity observation can be more accurately evaluated, providing data support for the application and improvement of related remote sensing technologies.

[0112] These highlights make this technical solution have important application prospects and practical value in plant phenology research, global change monitoring and ecology.

[0113] In an embodiment of the present invention, the present invention provides a method for calculating vegetation phenological diversity based on phenological camera time series images, firstly, phenological camera time series images of multiple vegetations are obtained, and the phenological camera time series images of each vegetation are preprocessed based on the bicubic interpolation method, and multiple crown selection area bitmaps of each vegetation are output; then, according to the multiple crown selection area bitmaps of each vegetation, the greenness index annual change curve corresponding to each vegetation is generated; data is extracted from the greenness index annual change curve corresponding to each vegetation to determine the vegetation physical quantity corresponding to each vegetation; finally, a preset phenological diversity index function is used to perform calculations according to the vegetation physical quantity corresponding to each vegetation to determine the vegetation phenological diversity index; based on the above scheme, the multiple phenological camera time series images of the obtained multiple vegetations are processed to obtain the greenness index annual change curve corresponding to each vegetation, and then the vegetation physical quantity corresponding to each greenness index annual change curve is calculated to determine the vegetation phenological diversity index. The process can replace the manual ground survey method, reduce the dependence on investigators, and thus improve the efficiency of vegetation phenological diversity calculation.

[0114] See also Figure 3 , Figure 3 This is a structural block diagram of a vegetation phenological diversity calculation device based on phenological camera time series images provided in Example 2 of the present invention.

[0115] The present invention provides a vegetation phenological diversity calculation device based on phenological camera time series images, comprising:

[0116] The acquisition module 301 is used to acquire the phenological camera time series images of multiple vegetations, and pre-process the phenological camera time series images of each vegetation based on the bicubic interpolation method, and output multiple crown selection area bitmaps of each vegetation;

[0117] A generating module 302 is used to generate an annual change curve of the greenness index corresponding to each vegetation according to a plurality of tree crown selection area maps of each vegetation;

[0118] The extraction module 303 is used to extract data from the annual variation curve of the greenness index corresponding to each vegetation, and determine the vegetation physical quantity corresponding to each vegetation;

[0119] The calculation module 304 is used to use a preset phenological diversity index function to perform calculations according to the vegetation physical quantities corresponding to each vegetation to determine the vegetation phenological diversity index.

[0120] Furthermore, the acquisition module 301 is specifically used for:

[0121] The bicubic interpolation method is used to optimize the phenological camera time series images of each vegetation, and the optimized phenological camera time series images of each vegetation are output;

[0122] The regions of interest were selected from the optimized time series images of the phenological cameras of each vegetation, and multiple crown selection area maps of each vegetation were generated.

[0123] Furthermore, the generating module 302 includes:

[0124] The first submodule is used to extract color channel information from multiple tree crown selection area bitmaps of each vegetation, and output multiple color channel information data corresponding to each tree crown selection area bitmap of each vegetation;

[0125] The second submodule is used to calculate the greenness index pixel value corresponding to each crown selection area bitmap of each vegetation according to multiple color channel information data corresponding to each crown selection area bitmap of each vegetation;

[0126] The third submodule is used to construct the greenness index pixel time series data corresponding to each vegetation by using the greenness index pixel values ​​corresponding to each crown selection area bitmap of each vegetation;

[0127] The fourth submodule is used to smooth the greenness index pixel time series data corresponding to each vegetation using the percentile method based on a preset time interval, and determine multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation;

[0128] The fifth submodule is used to construct the annual change curve of the greenness index corresponding to each vegetation by using multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation.

[0129] Furthermore, the fourth submodule is specifically used for:

[0130] Based on a preset time interval, the greenness index pixel time series data corresponding to each vegetation is divided to determine a plurality of greenness index pixel time subsequences corresponding to each vegetation;

[0131] Sort the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to each vegetation in descending order respectively, and determine the sorting data of the multiple greenness index pixel time subsequences corresponding to each vegetation;

[0132] The 90th percentile value is selected for the multiple greenness index pixel time subseries sorting data corresponding to each vegetation, and the multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation are output.

[0133] Furthermore, the vegetation physical quantity includes the greenness index time series value in the greenness index annual variation curve and the number of occurrences of individual vegetation; the operation module 304 is specifically used for:

[0134] Based on the greenness index time series values ​​in the greenness index annual change curve corresponding to each vegetation, the relative overlap of the greenness index annual change curve and the coincidence degree with multiple greenness index annual change curves are calculated;

[0135] According to the number of occurrences of vegetation individuals corresponding to each vegetation, the relative intensity of vegetation corresponding to each vegetation is calculated;

[0136] The relative overlap of the annual change curve of the greenness index, the coincidence of multiple annual change curves of the greenness index and the relative intensity of vegetation corresponding to each vegetation are substituted into the preset phenological diversity index function to calculate the vegetation phenological diversity index.

[0137] Furthermore, a phenological diversity index function is preset, specifically:

[0138] ;

[0139] Among them, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual change curves of the greenness index, which represents the relative overlap sum of the phenological curves; is the overlap of the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation; is the relative intensity of vegetation corresponding to the i-th vegetation; is the relative intensity of vegetation corresponding to the jth vegetation; s is the total number of vegetation.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, modules and sub-modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] An embodiment of the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in the first embodiment above.

[0142] An embodiment of the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in the first embodiment above are implemented.

[0143] An embodiment of the present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in the first embodiment above.

[0144] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating vegetation phenological diversity based on time series images of phenological cameras, characterized in that: include: Acquire phenological camera time series images of multiple vegetations, pre-process the phenological camera time series images of each vegetation based on the bicubic interpolation method, and output multiple crown selection area bitmaps of each vegetation; Generate a greenness index annual variation curve corresponding to each of the vegetation according to a plurality of tree crown selection area maps of each of the vegetation; Extracting data from the annual variation curve of the greenness index corresponding to each of the vegetations to determine the vegetation physical quantity corresponding to each of the vegetations; The vegetation phenology diversity index is determined by using a preset phenology diversity index function to perform calculations based on the vegetation physical quantities corresponding to each of the vegetation.

2. The method for calculating vegetation phenological diversity based on phenological camera time series images according to claim 1 is characterized in that: The method of preprocessing the phenological camera time series images of each vegetation based on the bicubic interpolation method and outputting a plurality of crown selection area bitmaps of each vegetation includes: The bicubic interpolation method is used to optimize the phenological camera time series images of each of the vegetation, and the optimized phenological camera time series images of each of the vegetation are output; The regions of interest are selected from the time series optimized images of the phenological cameras of the vegetation, and a plurality of crown selection area bitmaps of the vegetation are generated.

3. The method for calculating vegetation phenological diversity based on phenological camera time series images according to claim 1 is characterized in that: The step of generating the greenness index annual variation curve corresponding to each of the vegetations according to the plurality of tree crown selection area maps of each of the vegetations comprises: Extracting color channel information from the multiple tree crown selection area bitmaps of each of the vegetation, and outputting multiple color channel information data corresponding to each tree crown selection area bitmap of each of the vegetation; Calculate the greenness index pixel value corresponding to each of the tree crown selection area bitmaps of each of the vegetation according to the multiple color channel information data corresponding to each of the tree crown selection area bitmaps of each of the vegetation; The greenness index pixel values ​​corresponding to the crown selection area bitmaps of the vegetation are respectively used to construct the greenness index pixel time series data corresponding to the vegetation; Based on a preset time interval, the greenness index pixel time series data corresponding to each of the vegetations is smoothed using the percentile method to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations; A plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations are respectively used to construct an annual variation curve of the greenness index corresponding to each of the vegetations.

4. The method for calculating vegetation phenological diversity based on phenological camera time series images according to claim 3 is characterized in that: The method of smoothing the greenness index pixel time series data corresponding to each of the vegetations by using the percentile method based on the preset time interval to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations comprises: Based on a preset time interval, the greenness index pixel time series data corresponding to each of the vegetations is divided to determine a plurality of greenness index pixel time subsequences corresponding to each of the vegetations; respectively sorting the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to the respective vegetations in descending order to determine sorting data of the multiple greenness index pixel time subsequences corresponding to the respective vegetations; The 90th percentile value is selected for each of the multiple greenness index pixel time subsequence sorting data corresponding to the vegetation, and the multiple 90th percentile greenness index pixel values ​​corresponding to the vegetation are output.

5. The method for calculating vegetation phenological diversity based on phenological camera time series images according to claim 1, characterized in that: The vegetation physical quantity includes the greenness index time series value in the greenness index annual change curve and the number of occurrences of individual vegetation; the preset phenological diversity index function is used to calculate the vegetation physical quantity corresponding to each of the vegetation to determine the vegetation phenological diversity index, including: Based on the greenness index time series value in the greenness index annual change curve corresponding to each of the vegetation, calculating the relative overlap of the greenness index annual change curve and the coincidence degree with multiple greenness index annual change curves; Calculating the vegetation relative intensity corresponding to each of the vegetation according to the number of occurrences of the vegetation individuals corresponding to each of the vegetation; The relative overlap of the annual variation curve of the greenness index, the coincidence degree of a plurality of annual variation curves of the greenness index and the relative intensity of vegetation corresponding to each of the vegetation are substituted into a preset phenological diversity index function to calculate the vegetation phenological diversity index.

6. The method for calculating vegetation phenological diversity based on phenological camera time series images according to claim 1, characterized in that: The preset phenological diversity index function is specifically: ； Among them, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual change curves of the greenness index, which represents the relative overlap sum of the phenological curves; is the overlap of the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between the annual greenness index change curves corresponding to the i-th vegetation and the j-th vegetation; is the relative intensity of vegetation corresponding to the i-th vegetation; is the relative intensity of vegetation corresponding to the jth vegetation; s is the total number of vegetation.

7. A device for calculating vegetation phenological diversity based on time series images of a phenological camera, characterized in that: include: An acquisition module is used to acquire phenological camera time series images of multiple vegetations, and pre-process the phenological camera time series images of each vegetation based on the bicubic interpolation method, and output multiple crown selection area bitmaps of each vegetation; A generating module, for generating an annual variation curve of the greenness index corresponding to each of the vegetations according to a plurality of tree crown selection area maps of each of the vegetations; An extraction module is used to extract data from the annual variation curve of the greenness index corresponding to each of the vegetations to determine the vegetation physical quantity corresponding to each of the vegetations; The calculation module is used to use a preset phenological diversity index function to perform calculations according to the vegetation physical quantities corresponding to each of the vegetations to determine the vegetation phenological diversity index.

8. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for calculating vegetation phenological diversity based on phenological camera time series images as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for calculating vegetation phenological diversity based on phenological camera time series images as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the vegetation phenological diversity calculation method based on phenological camera time series images as described in any one of claims 1 to 6.

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