A method and device for calculating vegetation phenological diversity based on time series images of phenological cameras

Abstract

The present invention discloses a method and device for calculating vegetation phenological diversity based on phenological camera time series images, which is used to solve the technical problem that most existing vegetation phenological diversity calculation methods adopt manual ground survey methods, resulting in low efficiency in calculating vegetation phenological diversity. The method includes obtaining phenological camera time series images of multiple vegetations, and preprocessing the phenological camera time series images of each vegetation based on the bicubic interpolation method, outputting multiple crown selection area maps of each vegetation; generating a green index annual change curve corresponding to each vegetation based on the multiple crown selection area maps of each vegetation; extracting data from the green index annual change curve corresponding to each vegetation, and determining the vegetation physical quantity corresponding to each vegetation; using a preset phenological diversity index function to perform calculations based on the vegetation physical quantity corresponding to each vegetation, and determining the 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 the periodic changes in temperature conditions, forming corresponding growth and development patterns. This phenomenon is called phenology, which mainly refers to the growth, development, and activity patterns of animals and plants and the response of non-living things to climatic changes.

[0003] Changes in plant phenology reflect their adaptability to environmental conditions, directly impacting the function and stability of ecosystems. Plant phenological diversity is an important indicator of the differences in phenological characteristics of individual plants within a specific habitat, revealing the adaptive strategies and ecological processes of different plant populations. Therefore, understanding phenological diversity is crucial for studying terrestrial ecological processes.

[0004] Most 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 time series images of a phenological camera, which is used to solve the technical problem that most existing vegetation phenological diversity calculation methods use manual ground surveys, 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 from 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 maps of each vegetation;

[0008] Generating an annual change curve of the greenness index corresponding to each vegetation according to the plurality of tree crown selection area maps of each vegetation;

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

[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 multiple crown selection area bitmaps of each vegetation includes:

[0012] Using a bicubic interpolation method to optimize the phenological camera time series images of each of the vegetation, and outputting the optimized phenological camera time series images of each of the vegetation;

[0013] The regions of interest are selected from the optimized time series images of the phenological cameras of the vegetation to generate multiple crown selection area maps of the vegetation.

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

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

[0016] Calculating 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] Using the greenness index pixel values ​​corresponding to each crown selection area bitmap of each vegetation respectively, constructing greenness index pixel time series data corresponding to each vegetation;

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

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

[0020] Optionally, the smoothing process is performed on the greenness index pixel time series data corresponding to each vegetation using a percentile method based on a preset time interval to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each vegetation, 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] sorting the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to the respective vegetation in descending order to determine sorting data of the multiple greenness index pixel time subsequences corresponding to the respective vegetation;

[0023] The 90th percentile value is selected for each of the plurality of greenness index pixel time subsequence sorting 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; and the using 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] Calculating the relative overlap of the greenness index annual change curve and the degree of coincidence with multiple greenness index annual change curves based on the greenness index time series values ​​in the greenness index annual change curve corresponding to each of the vegetation;

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

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

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

[0029] ;

[0030] Wherein, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual variation curves of the greenness index, which represents the relative overlap sum of the phenological curves; The overlap degree of the greenness index annual change curves between the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between 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 amount of vegetation.

[0031] A second aspect of the present invention provides a device for calculating vegetation phenological diversity based on time-series images from a phenological camera, 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 to output multiple crown selection area maps of each vegetation;

[0033] A generating module, configured to generate an annual change curve of the greenness index corresponding to each of the vegetation according to a plurality of tree crown selection area maps of each of the vegetation;

[0034] An extraction module is used to extract data from the annual change 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 vegetation to determine the vegetation phenological diversity index.

[0036] The third aspect of the present invention provides a computer device comprising 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 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 vegetation phenological diversity calculation method 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 solution 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 maps of each vegetation; then, according to the multiple crown selection area maps 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 based on the vegetation physical quantity corresponding to each vegetation to determine the vegetation phenological diversity index; based on the above solution, the multiple phenological camera time series images of the multiple vegetations obtained 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. 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[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 flow chart of a method for calculating vegetation phenological diversity based on time-series images from a phenological camera 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 the second embodiment 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 is used to solve the technical problem that most existing vegetation phenological diversity calculation methods use manual ground surveys, resulting in low efficiency in vegetation phenological diversity calculation.

[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 making creative work are within the scope of protection of the present invention.

[0047] Explanation of terms:

[0048] PhenoCam—Phenology Camera.

[0049] Region of interest—Region of interest.

[0050] See also Figure 1 , Figure 1 This is 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 from a phenological camera, comprising:

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

[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) continuously taken of the same observation area at different time points 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 pre-processing 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 optimized time series images of the phenological camera 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 image quality and reduce 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 crown selection bitmap in the labeled image file format, namely the crown selection 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, thereby obtaining the crown selection area map of each vegetation at different time points.

[0059] In this embodiment, phenological camera time series images of multiple vegetation 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 change curve of the greenness index corresponding to each vegetation type based on the multiple crown selection area maps of each vegetation type.

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

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

[0063] Step S22: Calculate the greenness index pixel value corresponding to each crown selection area bitmap of each vegetation based on the multiple color channel information data corresponding to each crown selection area bitmap of each vegetation;

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

[0065] Multiple color channel information data includes pixel values ​​of green channel, pixel values ​​of red channel, pixel values ​​of 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 represent 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 multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation are determined;

[0071] Furthermore, 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 to determine 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 ​​over three days are calculated to improve the accuracy and stability of the data. Specifically, a time window is defined: for the t-th 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 sorted data corresponding to each vegetation at time intervals of 3 days are obtained. The greenness index pixel time subseries sorted data includes the greenness index pixel values ​​at different time points within the corresponding three days, and the corresponding image set can be expressed as:

[0076] ;

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

[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 day t, that is, the set of greenness index pixel values ​​at different time points within three days; is the greenness index pixel value on day t-1; I(t,x,y) is the greenness index pixel value on day t; is the greenness index pixel value on day t+1.

[0081] Furthermore, all 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] Where I′(t,x,y) is the 90th percentile greenness index pixel value on day t; 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 smoothed Gcc time series for each day, and then the pixel time series within the crown of each ROI plant individual are 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 : using multiple 90th percentile greenness index pixel values ​​corresponding to each vegetation, respectively, to construct an annual greenness index variation curve corresponding to each vegetation.

[0086] In this embodiment, a 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 on it 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, a calculation is performed 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: Calculate the relative overlap of the greenness index annual variation curve and the degree of coincidence with multiple greenness index annual variation curves based on the greenness index time series values ​​in the greenness index annual variation curve corresponding to each vegetation;

[0094] Step S42: Calculate the vegetation relative intensity corresponding to each vegetation type based on the number of occurrences of each vegetation type;

[0095] Step S43: Substitute the relative overlap of the annual greenness index change curve, the coincidence of multiple annual greenness index change curves, and the relative vegetation intensity 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 overlap of annual change curves and the number of occurrences of individual vegetation are used to calculate the diversity of plant phenology (vegetation phenological diversity index). A 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 values ​​in the annual change curve of the greenness index corresponding to each vegetation, the relative overlap of the annual change curve of the greenness index and the overlap of multiple annual change curves of the greenness index are calculated; based on the number of occurrences of individual vegetation corresponding to each vegetation, the relative intensity of the 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 day t, that is, the greenness index time series value in the greenness index annual variation curve of the i-th plant individual on day t; is the Gcc time series value of the j-th plant individual on day t (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 day t; 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 j-th plant individual.

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

[0103] ;

[0104] Wherein, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual variation curves of the greenness index, which represents the relative overlap sum of the phenological curves; The overlap degree of the greenness index annual change curves between the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between 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 visually presents 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, existing technologies can be used as a reference. Changes in plant phenology reflect their adaptability to environmental conditions, which directly affects the function and stability of ecosystems. Plant phenological diversity is an important indicator for measuring the differences in phenological characteristics of individual plants in a specific habitat, and can reveal the adaptive 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 imagery and manual ground surveys) still have certain limitations. Current phenological diversity observation methods mostly rely on high-resolution satellite imagery, phenological cameras, and manual field surveys. At the same time, there is a lack of computational tools to quantify phenological diversity. In addition, there are technical accuracy, cost, and difficulties in ground surveys. As one of the ways to observe phenology, phenological cameras can effectively circumvent the technical shortcomings of field surveys and satellites.

[0108] To address the above issues, this paper proposes a method for calculating vegetation phenological diversity based on time series images from phenological cameras, aiming to provide more efficient and accurate data support for fields such as plant ecology and global change biology. Figure 2 This method uses high-quality PhenoCam image data, image interpolation, and region of interest selection to process the images. It then uses color band analysis (particularly the green band) to extract plant growth information, calculating the Greenness Index (GCC) value (pixel value). Based on long-term, fixed-point monitoring, it analyzes temporal trends in plant phenology and identifies plant phenological characteristics. By comparing the overlap of phenological characteristics across different plants, phenological diversity is quantified. This method not only provides a new tool for quantifying phenological diversity but also offers a means of validating phenological observation methods that rely on satellite imagery.

[0109] In summary, this study accurately quantifies the degree of phenological diversity by analyzing the overlap in the annual GCC (Greenness Index) curves of different plant species in PhenoCam image data. This provides ecological researchers with an effective tool to further understand the differences in the adaptability of different plant populations under different environmental conditions.

[0110] Compared to traditional ground surveys and high-resolution satellite imagery, this technical solution uses phenological cameras to capture high-frequency, high-quality images. Combined with image processing and color band analysis techniques, this approach reduces observation costs and the complexity of manual surveys, while improving observation efficiency and data reliability. This method is particularly suitable for phenological monitoring across large areas, providing a new approach for widespread ecological monitoring.

[0111] This method can effectively supplement and validate phenological observation techniques that rely on satellite imagery. By comparing and analyzing ground-based phenological camera data with satellite imagery, we can more accurately assess the effectiveness and limitations of satellite imagery in observing phenological diversity, providing data support for the application and improvement of related remote sensing technologies.

[0112] These highlights give this technical solution 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. First, 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; then, based on the multiple crown selection area maps 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.

[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 the second embodiment of the present invention.

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

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

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

[0118] Extraction module 303, for extracting data from the annual variation curve of the greenness index corresponding to each vegetation, and determining 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 based on the vegetation physical quantities corresponding to each vegetation to determine the vegetation phenological diversity index.

[0120] Furthermore, the acquisition module 301 is specifically configured to:

[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 camera 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 crown selection area bitmaps of each vegetation, and output multiple color channel information data corresponding to each 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 based on 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 value corresponding to each tree 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 an 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 configured to:

[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] 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;

[0132] The 90th percentile value of the sorted time subseries data of multiple greenness index pixels corresponding to each vegetation is selected respectively, and the 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 calculation module 304 is specifically used to:

[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 of multiple greenness index annual change curves are calculated;

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

[0136] The relative overlap of the annual change curves of the greenness index, the coincidence of multiple annual change curves of the greenness index and the relative vegetation intensity 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] Wherein, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual variation curves of the greenness index, which represents the relative overlap sum of the phenological curves; The overlap degree of the greenness index annual change curves between the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between 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 amount of vegetation.

[0140] Those skilled in the art will 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 vegetation phenological diversity calculation method 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 this 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 merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, 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 separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[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 above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can 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 maps of each vegetation; Generating an annual change curve of the greenness index corresponding to each vegetation according to the plurality of tree crown selection area maps of each vegetation; Extracting data from the annual variation curve of the greenness index corresponding to each of the vegetation types to determine the vegetation physical quantity corresponding to each of the vegetation types; Using a preset phenological diversity index function to perform calculations based on vegetation physical quantities corresponding to each of the vegetation, to determine a vegetation phenological diversity index; The 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; The method of using 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: Calculating the relative overlap of the greenness index annual change curve and the degree of coincidence with multiple greenness index annual change curves based on the greenness index time series values ​​in the greenness index annual change curve corresponding to each of the vegetation; Calculating the relative intensity of vegetation corresponding to each vegetation according to the number of occurrences of vegetation individuals corresponding to each vegetation; Substituting the relative overlap of the greenness index annual change curve, the coincidence degree of multiple greenness index annual change curves and the relative vegetation intensity corresponding to each vegetation into a preset phenological diversity index function to calculate the vegetation phenological diversity index; The preset phenological diversity index function is specifically: ； Wherein, PD is the vegetation phenological diversity index; Q is the relative overlap sum of the annual variation curves of the greenness index, which represents the relative overlap sum of the phenological curves; The overlap degree of the greenness index annual change curves between the i-th vegetation and the j-th vegetation, indicating the pairwise overlap distance between 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 amount of 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 multiple crown selection area bitmaps of each vegetation includes: The bicubic interpolation method is used to optimize the phenological camera time series images of each vegetation, and output the optimized phenological camera time series images of each vegetation; The regions of interest are selected from the optimized time series images of the phenological cameras of the vegetation to generate a plurality of crown selection area maps of the vegetation.

3. The method for calculating vegetation phenological diversity based on phenological camera time series images according to claim 1, characterized in that: Generating the greenness index annual change curve corresponding to each vegetation according to the plurality of crown selection area maps of each vegetation includes: Extracting color channel information from the plurality of tree crown selection area bitmaps of each of the vegetation, and outputting a plurality of color channel information data corresponding to each of the tree crown selection area bitmaps of each of the vegetation; Calculating 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; Using the greenness index pixel values ​​corresponding to each crown selection area bitmap of each vegetation respectively, constructing greenness index pixel time series data corresponding to each vegetation; Based on a preset time interval, smoothing the greenness index pixel time series data corresponding to each of the vegetation using a percentile method to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetation; A plurality of 90th percentile greenness index pixel values ​​corresponding to each of the vegetations are respectively used to construct an annual change 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 vegetation using the percentile method based on a preset time interval to determine a plurality of 90th percentile greenness index pixel values ​​corresponding to each vegetation includes: 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; sorting the greenness index pixel values ​​in the multiple greenness index pixel time subsequences corresponding to the respective vegetation in descending order to determine sorting data of the multiple greenness index pixel time subsequences corresponding to the respective vegetation; The 90th percentile value is selected for each of the plurality of greenness index pixel time subsequence sorting data corresponding to the vegetation, and the plurality of 90th percentile greenness index pixel values ​​corresponding to the vegetation are output.

5. A device for calculating vegetation phenological diversity based on time-series images of a phenological camera, applied to the method for calculating vegetation phenological diversity based on time-series images of a phenological camera according to claim 1, 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 to output multiple crown selection area maps of each vegetation; A generating module, configured to generate an annual change curve of the greenness index corresponding to each of the vegetation according to a plurality of tree crown selection area maps of each of the vegetation; An extraction module is used to extract data from the annual change 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 vegetation to determine the vegetation phenological diversity index.

6. 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 vegetation phenological diversity calculation method based on phenological camera time series images as described in any one of claims 1 to 4.

7. 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 4 is implemented.

8. 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 4.

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