Method for determining the number of vegetation growth seasons
By fitting and filtering the vegetation GPP time series, and combining the time distance between peaks and adjacent troughs with GPP values, the problem of inaccurate growing season numbers in existing technologies has been solved, and a more scientific determination of growing season numbers has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- KUNMING UNIV OF SCI & TECH
- Filing Date
- 2026-03-13
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies do not fully consider the comprehensive correlation between peaks and adjacent troughs when determining the number of growing seasons, resulting in the retention of false peaks or troughs and affecting the accuracy of the number of growing seasons.
By fitting the vegetation GPP time series, peaks and troughs are identified, and adjacent peaks and troughs are filtered multiple times based on preset filtering rules. By combining the time distance between the peak and the adjacent troughs and the GPP value, unreasonable peaks and troughs are removed to ensure that the remaining data conforms to the actual situation.
It improves the accuracy of determining the number of growing seasons by retaining the true peak and trough information through multi-dimensional filtering, making the determination of the number of growing seasons more reasonable and scientific.
Smart Images
Figure CN121834249B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vegetation phenology prediction technology, and more specifically, to a method for determining the number of vegetation growing seasons. Background Technology
[0002] The vegetation growing season refers to the period within a year during which vegetation engages in active photosynthesis. Accurately determining its quantity, start and end times, and duration is crucial for understanding ecosystem carbon cycling, agricultural production planning, and climate change responses. Gross primary productivity (GPP), as a key indicator of vegetation's photosynthetic carbon sequestration capacity, provides a dynamic time series that directly reflects the cyclical changes in vegetation growth, decline, and dormancy. Therefore, analyzing vegetation growing season quantities based on GPP time series has become an important tool in current ecological remote sensing and phenological research.
[0003] In existing technologies, determining the number of vegetation growing seasons mainly relies on simple statistics and intuitive judgment of GPP time series. The common practice is to first perform preliminary smoothing on the GPP data of the target area over a period of time to reduce the interference of random noise, and then set a fixed GPP value threshold. Periods above this threshold are considered vegetation growing seasons, while periods below the threshold are considered non-growing seasons. Alternatively, a sliding window method can be used, calculating the average GPP data within the window. When the average exceeds a preset average threshold, it is determined to be a growing season; otherwise, it is considered a non-growing season. Additionally, some methods involve manually observing the fluctuations of the GPP time series curve, relying on experience to roughly determine the start and end times of the growing season, and then counting the number of growing seasons.
[0004] However, the shortcomings of existing technologies lie in their overly simplistic filtering logic for extreme points, relying solely on time intervals or simple GPP difference constraints. This fails to adequately consider the comprehensive correlation between peaks and adjacent troughs, potentially retaining false secondary peaks or troughs, thus increasing the error rate in growing season quantity determination. Furthermore, trough filtering focuses only on GPP thresholds or single time conditions, neglecting their synergistic relationship with adjacent peaks. This leads to the incorrect removal of genuine primary troughs, or secondary troughs interfering with the effective identification of peaks, ultimately affecting the accuracy of growing season quantity determination. Summary of the Invention
[0005] To address at least one deficiency or improvement need in the prior art, the present invention provides a method, apparatus, device, and storage medium for determining the number of growing seasons, which solves the problems in the prior art that do not fully consider the comprehensive correlation between peaks and adjacent troughs, resulting in the retention of false secondary peaks or troughs, thus erroneously increasing the determination of the number of growing seasons, and that the filtering of troughs does not take into account their synergistic relationship with adjacent peaks, resulting in the erroneous removal of true major troughs, ultimately affecting the accuracy of the number of growing seasons.
[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for determining the number of vegetation growing seasons is provided, comprising:
[0007] Fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset composed of GPP values of peaks and troughs;
[0008] Based on preset filtering rules, adjacent peaks and adjacent troughs are filtered separately, and troughs with GPP values greater than the trough threshold are removed until troughs and peaks alternate in the GPP time series. Peak data that does not meet the preset requirements are removed from the dataset, and the corresponding peaks are also removed.
[0009] Adjacent peaks and valleys are filtered according to preset filtering rules until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value.
[0010] Based on preset filtering rules, adjacent peaks and troughs are filtered separately until each remaining peak and trough in the GPP time series appears alternately. The number of vegetation growing seasons is determined based on the time distance between a peak and its two adjacent troughs.
[0011] In one possible implementation, the vegetation GPP time series of the target area is fitted, and all peaks and troughs are identified to obtain a dataset consisting of GPP values of peaks and troughs. This also includes:
[0012] The vegetation GPP time series of the target area within a preset time period is obtained, and the GPP time series is fitted using the cubic spline method;
[0013] Using a moving window of preset duration, all peaks and troughs are identified on the fitted GPP time series, resulting in a dataset composed of GPP values of peaks and troughs.
[0014] In one possible implementation, filtering adjacent peaks and adjacent troughs based on preset filtering rules also includes:
[0015] Obtain the GPP values of two adjacent peaks and compare them, then remove the peak with the smaller GPP value;
[0016] Obtain the GPP values of two adjacent valleys and compare them, then remove the valley with the larger GPP value.
[0017] In one possible implementation, removing peak data that does not meet the preset requirements from the dataset, and removing the corresponding peaks, also includes:
[0018] Determine whether the GPP value of each peak is reasonable, remove unreasonable peak GPP values from the dataset, and remove the corresponding peaks.
[0019] Determine if the first data point in the dataset is the GPP value of the peak. If the first data point is the GPP value of the peak, remove the first data point and the corresponding peak.
[0020] In one possible implementation, determining whether the GPP value of each peak is reasonable also includes:
[0021] Get the time distance |Δt between the current peak and its adjacent peak. _peak |Δt|, the time distance between a wave crest and the two nearest troughs _peak_trough (left) |and|Δt _peak_trough (right) | and the GPP difference between the peak and the two nearest troughs |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) |;
[0022] If the time distance is |Δt _peak |Greater than the first preset time and time distance|Δt _peak_trough (left) |and|Δt _peak_trough (right) Both are greater than the second preset time and the GPP difference |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) If all values are greater than the preset threshold, then the GPP value of the current peak is reasonable.
[0023] In one possible implementation, filtering the valley GPP value based on the time distance between the peak and the two adjacent valleys and the valley GPP value also includes:
[0024] If the time distance between two adjacent troughs to the left and right of a peak is less than the length of the moving window, then the trough with the larger GPP value is removed from the dataset.
[0025] In one possible implementation, determining the number of vegetation growing seasons based on the time distance between a wave crest and two adjacent troughs also includes:
[0026] The time distance between two adjacent troughs of the same peak is defined as a vegetation growing season. Determining all vegetation growing seasons yields the number of vegetation growing seasons.
[0027] According to a second aspect of the invention, an apparatus for determining the number of vegetation growing seasons is also provided, comprising:
[0028] The data processing module is configured to fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset consisting of GPP values of peaks and troughs.
[0029] The peak filtering module is configured to filter adjacent peaks and adjacent valleys based on preset filtering rules, and remove valleys with GPP values greater than the valley threshold, until valleys and peaks alternate in the GPP time series, remove peak data that do not meet the preset requirements from the dataset, and remove the corresponding peaks.
[0030] The valley filtering module is configured to filter adjacent peaks and valleys based on preset filtering rules until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value.
[0031] The quantity determination module is configured to filter adjacent peaks and adjacent troughs based on preset filtering rules until each remaining peak and trough in the GPP time series alternates, and determine the number of vegetation growing seasons based on the time distance between the peak and the two adjacent troughs.
[0032] According to a third aspect of the invention, an apparatus for determining the number of vegetation growing seasons is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the above-described methods for determining the number of vegetation growing seasons.
[0033] According to a fourth aspect of the invention, a storage medium is also provided, which stores a computer program executable by a device for determining the number of vegetation growing seasons, which, when run on the device for determining the number of vegetation growing seasons, causes the device for determining the number of vegetation growing seasons to perform the steps of any of the above-described methods for determining the number of vegetation growing seasons.
[0034] In summary, compared with the prior art, the above technical solutions of the present invention can achieve the following beneficial effects:
[0035] This invention provides a method for determining the number of growing seasons. When filtering peaks and troughs, it considers the synergistic relationship between troughs and adjacent peaks, fully taking into account the comprehensive correlation between peaks and adjacent troughs, rather than viewing each peak or trough in isolation. This better preserves the true information about peaks and troughs, making the determination of the number of growing seasons more accurate. Through multiple rounds of filtering adjacent peaks and troughs based on preset filtering rules, data that does not meet the preset requirements is gradually removed from the dataset, continuously optimizing the data and making the remaining peak and trough data more consistent with reality, thus facilitating accurate determination of the number of growing seasons. When filtering troughs, in addition to preliminary processing based on preset filtering rules, the method further filters the trough GPP value based on the time distance between the peak and its two adjacent troughs and the trough GPP value. This multi-dimensional and refined filtering approach more scientifically judges the effectiveness of troughs, improving the accuracy of filtering and thus enhancing the accuracy of determining the number of growing seasons. The number of growing seasons is determined by the time distance between the peak and the two adjacent troughs. This fully considers the time relationship between the peak and the trough, making the determination of the number of growing seasons more reasonable, scientific, and in line with the actual situation of vegetation growth. Attached Figure Description
[0036] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0037] Figure 1 A flowchart illustrating an embodiment of the method for determining the number of vegetation growing seasons provided by the present invention;
[0038] Figure 2 A schematic diagram of a curve representing an embodiment of the growing season time series of farmland provided by the present invention;
[0039] Figure 3 A schematic diagram of an embodiment of the device for determining the number of vegetation growing seasons provided by the present invention;
[0040] Figure 4 A schematic diagram of the structure of the device for determining the number of vegetation growing seasons provided in an embodiment of the present invention. Detailed Implementation
[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.
[0042] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.
[0043] This invention provides a method, apparatus, device, and storage medium for determining the number of vegetation during the growing season, which will be described below.
[0044] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the method for determining the number of vegetation growing seasons provided by the present invention. In a specific embodiment of the present invention, a method for determining the number of vegetation growing seasons is disclosed, including:
[0045] S101. Fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset composed of GPP values of peaks and troughs.
[0046] S102. Based on the preset filtering rules, filter adjacent peaks and adjacent valleys respectively, and remove valleys with GPP values greater than the valley threshold until valleys and peaks alternate in the GPP time series. Remove peak data that does not meet the preset requirements from the dataset and remove the corresponding peaks.
[0047] S103. Filter adjacent peaks and adjacent valleys based on preset filtering rules until each remaining valley and peak in the GPP time series appears alternately. Filter the valley GPP value according to the time distance between the peak and the two adjacent valleys and the valley GPP value.
[0048] S104. Based on the preset filtering rules, filter adjacent peaks and adjacent valleys respectively until each remaining peak and valley in the GPP time series appears alternately. Determine the number of vegetation growing seasons based on the time distance between the peak and the two adjacent valleys.
[0049] In the above embodiments, firstly, time-series data of total primary productivity (GPP) of vegetation in the target area over a period of time (e.g., one year) are obtained from flux tower networks or remote sensing data sources. This data may contain noise and disturbances, therefore preprocessing is required. The GPP time series is then fitted using the cubic spline (CS) method to smooth the data, eliminate short-term fluctuations, and highlight long-term trends. The fitted curve should effectively reflect the periodic changes in vegetation GPP.
[0050] A peak is defined as the point where the GPP value reaches its maximum value within a local region, and a trough is defined as the point where the GPP value reaches its minimum value within a local region. On the fitted GPP time series, all peaks and troughs are identified using a fixed-duration moving window (e.g., 30 days), and the identified peaks and troughs, along with their corresponding GPP values, constitute a dataset.
[0051] Adjacent peaks and troughs are filtered based on preset filtering rules. A trough threshold can then be set as needed to remove troughs with GPP values exceeding this threshold, thus filtering out troughs that may be caused by abnormal weather conditions or data errors. This step is repeated until troughs and peaks alternate in the GPP time series, meaning each peak is immediately adjacent to a trough. Finally, based on preset peak validity rules, peak data that do not meet the requirements, along with their corresponding peaks, are removed from the dataset. The retained peaks accurately reflect the vegetation growth status.
[0052] Again, based on the preset filtering rules, adjacent peaks and troughs are filtered to ensure that each remaining trough and peak in the GPP time series alternates, eliminating potential false peaks and troughs. For each peak, the time distance (|Δt) between it and its two adjacent troughs is calculated. _ trough|) and GPP difference (|ΔGPP) _ (peak_trough|), further removing unreasonable trough GPP values.
[0053] Repeat the filtering steps again to ensure that each remaining peak and trough in the GPP time series alternates. For each peak, identify its two adjacent troughs. Determine the number of growing seasons for the vegetation based on the time distance between these two troughs (i.e., the length of the growing season). If the time distance between the two troughs meets the preset growing season length range (e.g., more than one month), then the peak is considered to represent a complete growing season. Count all peaks that meet the criteria; this is the number of growing seasons for the vegetation.
[0054] Compared with existing technologies, the method for determining the number of growing seasons provided in this embodiment combines the synergistic relationship between troughs and adjacent peaks when filtering peaks and troughs. It fully considers the comprehensive correlation between peaks and adjacent troughs, rather than viewing each peak or trough in isolation. This better preserves the true information about peaks and troughs, making the determination of the number of growing seasons more accurate. Through multiple rounds of filtering adjacent peaks and troughs based on preset filtering rules, data that does not meet the preset requirements is gradually removed from the dataset, continuously optimizing the data and making the remaining peak and trough data more consistent with reality, thus making it easier to accurately determine the number of growing seasons. When filtering troughs, in addition to preliminary processing based on preset filtering rules, the GPP value of the trough is further filtered based on the time distance between the peak and the two adjacent troughs and the trough GPP value. Through multi-dimensional and refined filtering, the effectiveness of troughs is judged more scientifically, improving the accuracy of filtering and thus enhancing the accuracy of determining the number of growing seasons. The number of growing seasons is determined by the time distance between the peak and the two adjacent troughs. This fully considers the time relationship between the peak and the trough, making the determination of the number of growing seasons more reasonable, scientific, and in line with the actual situation of vegetation growth.
[0055] In some embodiments of the present invention, the vegetation GPP time series of the target area is fitted, and all peaks and troughs are identified to obtain a dataset composed of GPP values of peaks and troughs, further comprising:
[0056] The vegetation GPP time series of the target area within a preset time period is obtained, and the GPP time series is fitted using the cubic spline method;
[0057] Using a moving window of preset duration, all peaks and troughs are identified on the fitted GPP time series, resulting in a dataset composed of GPP values of peaks and troughs.
[0058] In the above embodiments, time-series data of total primary productivity (GPP) of vegetation in the target area within a preset time range are obtained from reliable observation stations such as flux tower networks and satellite remote sensing data sources. This data should have sufficient temporal resolution (e.g., daily or weekly) to ensure accurate capture of seasonal variations in vegetation growth. It should be noted that the preset time range should be reasonably set according to the research objectives and vegetation type. For example, for annual crops, a complete growing year can be selected as the time range; for perennial vegetation, multiple growing years may need to be selected to capture interannual variations.
[0059] Cubic spline is a commonly used curve fitting method that approximates the original data points by constructing piecewise cubic polynomials, while ensuring the continuity and smoothness of the curve at the nodes. It can effectively handle time series data with nonlinear characteristics. The smooth curve generated by cubic spline can reflect the long-term trend of vegetation gross page percentage (GPP) while eliminating the influence of short-term fluctuations and noise.
[0060] The duration of the moving window should be set reasonably according to the vegetation growth cycle and data characteristics. For vegetation with obvious seasonal changes, a window of one month or longer can be selected; for vegetation with a shorter growth cycle, a shorter window may be required.
[0061] On the fitted GPP time series, a moving window of preset duration is used to slide point by point, and the maximum and minimum values of GPP within the window are calculated. When the window slides to a certain position, if the GPP value at that position is greater than the GPP values at all other positions within the window, then that position is considered a peak; conversely, if the GPP value at that position is less than the GPP values at all other positions within the window, then that position is considered a trough.
[0062] The GPP values of all identified peaks and troughs, along with their location information in the time series, are integrated into a single dataset for subsequent filtering and determination of the number of growing seasons for vegetation. After constructing the dataset, its accuracy can be verified through visualization or comparison with the original data to ensure that no peaks or troughs have been missed or incorrectly identified.
[0063] In some embodiments of the present invention, filtering of adjacent peaks and adjacent troughs based on preset filtering rules further includes:
[0064] Obtain the GPP values of two adjacent peaks and compare them, then remove the peak with the smaller GPP value;
[0065] Obtain the GPP values of two adjacent valleys and compare them, then remove the valley with the larger GPP value.
[0066] In the above embodiments, the GPP values of two adjacent peaks are obtained, and peaks with relatively small GPP values are identified and removed. In practical applications, peaks with small GPP values often represent short-term fluctuations in productivity or measurement errors, rather than reflecting significant changes in the true level of ecosystem productivity. Therefore, removing these smaller peaks can effectively reduce data noise, allowing subsequent analysis to focus more on truly ecologically significant changes in productivity.
[0067] Similarly, for two adjacent troughs, their GPP values are compared, and the larger trough is removed. The GPP values of these two troughs are then compared. Unlike the crest processing, in the trough comparison, only the trough with the larger GPP value is removed. This is because, in most cases, troughs with relatively large GPP values may not represent true vegetation low points, but rather outliers caused by data noise or measurement errors. By removing these larger troughs, it is ensured that the retained data more accurately reflects the true productivity level of the ecosystem.
[0068] In some embodiments of the present invention, removing peak data that does not meet preset requirements from the dataset and removing the corresponding peaks further includes:
[0069] Determine whether the GPP value of each peak is reasonable, remove unreasonable peak GPP values from the dataset, and remove the corresponding peaks; this operation avoids isolated peaks without left-side trough support at the beginning of the time series from interfering with the determination of the growing season.
[0070] Determine if the first data point in the dataset is the GPP value of the peak. If the first data point is the GPP value of the peak, remove the first data point and the corresponding peak.
[0071] In some embodiments of the present invention, determining whether the GPP value of each peak is reasonable further includes:
[0072] Get the time distance |Δt between the current peak and its adjacent peak. _peak |Δt|, the time distance between a wave crest and the two nearest troughs _peak_trough (left) |and|Δt _peak_trough (right) | and the GPP difference between the peak and the two nearest troughs |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) |;
[0073] If the time distance is |Δt _peak |Greater than the first preset time and time distance|Δt _peak_trough (left) |and|Δt _peak_trough (right) Both are greater than the second preset time and the GPP difference |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) If all values are greater than the preset threshold, then the GPP value of the current peak is reasonable.
[0074] In a preferred embodiment, the first preset time, the second preset time, and the preset threshold need to be preset according to the vegetation type, climate characteristics, and data time resolution of the target area. For example, for vegetation with an annual growth cycle, the first preset time is 30 days, the second preset time is 15 days, and the preset threshold is 0.13 of the maximum GPP value. It is understood that the specific parameters can be set according to actual needs, and the present invention does not impose further limitations on this.
[0075] In some embodiments of the present invention, filtering the valley GPP value based on the time distance between the peak and the two adjacent valleys and the valley GPP value further includes:
[0076] If the time distance between two adjacent troughs to the left and right of a peak is less than the length of the moving window, then the trough with the larger GPP value is removed from the dataset.
[0077] In the above embodiment, the time distance reflects the distribution of peaks and troughs over time, demonstrating the periodicity and trend of data changes. The GPP value of a trough directly reflects the productivity level of the ecosystem at that moment. Comparing the time distance between two adjacent troughs to the left and right of a peak with the length of the moving window, if the time distance between two adjacent troughs is less than the length of the moving window, it means that these two troughs are relatively close in time, potentially indicating data anomalies or noise interference. To simplify the data structure, reduce redundant information, and improve data quality, the trough with the larger GPP value is removed from the dataset.
[0078] In some embodiments of the present invention, determining the number of vegetation growing seasons based on the time distance between a wave crest and two adjacent wave troughs further includes:
[0079] The time distance between two adjacent troughs of the same peak is defined as a vegetation growing season. Determining all vegetation growing seasons yields the number of vegetation growing seasons.
[0080] In the above embodiments, vegetation exhibits distinct periodic changes during its growth process, typically manifested as alternating peaks and troughs on the vegetation time series data curves. Peaks often represent periods of vigorous vegetation growth and high productivity, such as the summer stage when vegetation is lush and photosynthesis is active; while troughs correspond to periods of relatively slow vegetation growth and lower productivity, such as the winter stage when vegetation is dormant and photosynthesis is reduced. Therefore, the time distance between two adjacent troughs of the same peak can reasonably define a complete vegetation growth cycle, or a vegetation growing season. By statistically analyzing the number of such growing seasons, a clear understanding of the cyclical changes in vegetation over a period of time can be obtained.
[0081] Please seeFigure 2 , Figure 2 This is a schematic diagram of a curve representing an embodiment of the growing season time series of a farmland site provided by the present invention. A specific embodiment of the present invention provides the growing season time series of CH-Oe2 (a farmland site) in 2007, 2008, and 2009 obtained after processing using the method for determining the number of vegetation growing seasons provided by the present invention. Figure 2 As shown, using the vegetation growth season determination method provided by this invention, red and blue dots indicate reasonable peaks and troughs.
[0082] To better implement the method for determining the number of vegetation growing seasons in the embodiments of the present invention, based on the method for determining the number of vegetation growing seasons, please refer to the corresponding documentation. Figure 3 , Figure 3 This is a schematic diagram of an embodiment of the device for determining the number of vegetation growing seasons provided by the present invention. The embodiment of the present invention provides a device 300 for determining the number of vegetation growing seasons, comprising:
[0083] The data processing module 310 is configured to fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset consisting of GPP values of peaks and troughs.
[0084] The peak filtering module 320 is configured to filter adjacent peaks and adjacent valleys based on preset filtering rules, and remove valleys with GPP values greater than the valley threshold, until valleys and peaks alternate in the GPP time series, remove peak data that do not meet the preset requirements from the dataset, and remove the corresponding peaks.
[0085] The valley filtering module 330 is configured to filter adjacent peaks and adjacent valleys based on preset filtering rules until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value.
[0086] The quantity determination module 340 is configured to filter adjacent peaks and adjacent troughs based on preset filtering rules until each remaining peak and trough in the GPP time series alternates, and determine the number of vegetation growing seasons based on the time distance between the peak and the two adjacent troughs.
[0087] Please see Figure 4 , Figure 4This is a schematic diagram of the structure of the vegetation growing season quantity determination device 400 provided in an embodiment of the present invention. Based on the above-described method for determining the vegetation growing season quantity, the present invention also provides a corresponding vegetation growing season quantity determination device 400, which can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The vegetation growing season quantity determination device 400 includes a processor 410, a memory 420, and a display 430. Figure 4 Only some components of the equipment for determining the number of vegetation growing seasons are shown; however, it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead.
[0088] In some embodiments, memory 420 may be an internal storage unit of the vegetation growing season quantity determination device 400, such as a hard disk or memory of the device 400. In other embodiments, memory 420 may be an external storage device of the device 400, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, or flash card equipped on the device 400. Furthermore, memory 420 may include both internal and external storage units of the device 400. Memory 420 is used to store application software and various types of data installed on the device 400, such as the program code of the device 400. Memory 420 may also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 420 stores a program 440 for determining the number of vegetation growing seasons. This program 440 can be executed by the processor 410 to implement the method for determining the number of vegetation growing seasons in the embodiments of this application.
[0089] In some embodiments, processor 410 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 420 or process data, such as executing a method for determining the number of vegetation growing seasons.
[0090] In some embodiments, display 430 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 430 is used to display information from the vegetation growing season quantity determination device 400 and to display a user interface for visualization. The components of the vegetation growing season quantity determination device 400—processor 410, memory 420, and display 430—communicate with each other via a system bus.
[0091] In one embodiment, when the processor 410 executes the procedure 440 for determining the number of vegetation growing seasons in the memory 420, it implements the steps in the above-described method for determining the number of vegetation growing seasons.
[0092] This embodiment also provides a computer-readable storage medium storing a program for determining the number of vegetation growing seasons, which, when executed by a processor, performs the following steps:
[0093] Fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset composed of GPP values of peaks and troughs;
[0094] Based on preset filtering rules, adjacent peaks and adjacent troughs are filtered separately, and troughs with GPP values greater than the trough threshold are removed until troughs and peaks alternate in the GPP time series. Peak data that does not meet the preset requirements are removed from the dataset, and the corresponding peaks are also removed.
[0095] Adjacent peaks and valleys are filtered according to preset filtering rules until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value.
[0096] Based on preset filtering rules, adjacent peaks and troughs are filtered separately until each remaining peak and trough in the GPP time series appears alternately. The number of vegetation growing seasons is determined based on the time distance between a peak and its two adjacent troughs.
[0097] In summary, the method for determining the number of growing seasons provided by this invention, when filtering peaks and troughs, combines the synergistic relationship between troughs and adjacent peaks, fully considering the comprehensive correlation between peaks and adjacent troughs, rather than viewing each peak or trough in isolation. This better preserves the true information about peaks and troughs, making the determination of the number of growing seasons more accurate. Through multiple rounds of filtering adjacent peaks and troughs based on preset filtering rules, data that does not meet the preset requirements is gradually removed from the dataset, continuously optimizing the data and making the remaining peak and trough data more consistent with the actual situation, thus making it easier to accurately determine the number of growing seasons. When filtering troughs, in addition to preliminary processing based on preset filtering rules, the GPP value of troughs is further filtered based on the time distance between a peak and its two adjacent troughs and the trough GPP value. Through multi-dimensional and refined filtering methods, the effectiveness of troughs is judged more scientifically, improving the accuracy of filtering and thus enhancing the accuracy of determining the number of growing seasons. The number of growing seasons is determined by the time distance between the peak and the two adjacent troughs. This fully considers the time relationship between the peak and the trough, making the determination of the number of growing seasons more reasonable, scientific, and in line with the actual situation of vegetation growth.
[0098] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs (read-only optical disks), microdrives, magneto-optical disks, ROMs (Read-Only Memory), RAMs (Random Access Memory), EPROMs (Erasable Programmable Read-Only Memory), EEPROMs (Electrically Erasable Programmable Read-Only Memory), DRAMs (Dynamic Random Access Memory), VRAMs (Video Random Access Memory), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.
[0099] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0100] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0101] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.
[0102] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0103] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0104] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0105] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0106] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.
[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0108] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for determining the quantity of vegetation during the growing season, characterized in that, include: Fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset composed of GPP values of peaks and troughs; Based on the preset filtering rules, adjacent peaks and adjacent troughs are filtered for the first time, and troughs with GPP values greater than the trough threshold are removed, until troughs and peaks alternate in the GPP time series. Remove peak data that does not meet the preset requirements from the dataset, and remove the corresponding peaks: obtain the time distance |Δt between the current peak and its adjacent peaks. _peak |Δt|, the time distance between a wave crest and the two nearest troughs _peak_trough (left) |and|Δt _peak_trough (right) | and the GPP difference between the peak and the two nearest troughs |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) |; If the time distance is |Δt _peak |Greater than the first preset time and time distance|Δt _peak_trough (left) |and|Δt _peak_trough (right) Both are greater than the second preset time and the GPP difference |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) If all values are greater than the preset threshold, then the GPP value of the current peak is reasonable. Otherwise, remove the GPP value of the current peak and the corresponding peak from the dataset; Perform a second filtering: filter the valley GPP value based on the time distance between the peak and the two adjacent valleys on the left and right and the valley GPP value; A third filtering step is performed: the number of vegetation growing seasons is determined based on the time distance between the peak and the two adjacent troughs.
2. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The process of fitting the vegetation GPP time series of the target area and identifying all peaks and troughs to obtain a dataset composed of GPP values of peaks and troughs also includes: Obtain the vegetation GPP time series of the target area within a preset time period, and fit the GPP time series using the cubic spline method; Using a moving window of preset duration, all peaks and troughs are identified on the fitted GPP time series, resulting in a dataset composed of GPP values of peaks and troughs.
3. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The first filtering based on preset filtering rules for adjacent peaks and adjacent troughs also includes: Obtain the GPP values of two adjacent peaks and compare them, then remove the peak with the smaller GPP value; Obtain the GPP values of two adjacent valleys and compare them, then remove the valley with the larger GPP value.
4. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The step of removing peak data that does not meet the preset requirements from the dataset, and removing the corresponding peaks, further includes: Determine whether the GPP value of each peak is reasonable, remove unreasonable peak GPP values from the dataset, and remove the corresponding peaks. Determine whether the first data in the dataset is the GPP value of the peak. If the first data is the GPP value of the peak, remove the first data and the corresponding peak.
5. The method for determining the number of vegetation growing seasons as described in claim 2, characterized in that, The second filtering of the valley GPP value based on the time distance between the peak and the two adjacent valleys and the valley GPP value also includes: If the time distance between two adjacent troughs to the left and right of a peak is less than the length of the moving window, then the trough with the larger GPP value is removed from the dataset.
6. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The method of determining the number of vegetation growing seasons based on the time distance between the peak and the two adjacent troughs also includes: The time distance between two adjacent troughs of the same peak is defined as a vegetation growing season. Determining all vegetation growing seasons yields the number of vegetation growing seasons.
7. A device for determining the quantity of vegetation during the growing season, characterized in that, include: The data processing module is configured to fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset consisting of GPP values of peaks and troughs. The peak filtering module is configured to perform a first filtering on adjacent peaks and adjacent valleys based on preset filtering rules, and remove valleys with GPP values greater than the valley threshold, until valleys and peaks alternate in the GPP time series. Remove peak data that does not meet the preset requirements from the dataset, and remove the corresponding peaks: obtain the time distance |Δt between the current peak and its adjacent peaks. _peak |Δt|, the time distance between a wave crest and the two nearest troughs _peak_trough (left) |and|Δt _peak_trough (right) | and the GPP difference between the peak and the two nearest troughs |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) |; If the time distance is |Δt _peak |Greater than the first preset time and time distance|Δt _peak_trough (left) |and|Δt _peak_trough (right) Both are greater than the second preset time and the GPP difference |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) If all values are greater than the preset threshold, then the GPP value of the current peak is reasonable. Otherwise, remove the GPP value of the current peak and the corresponding peak from the dataset; The valley filtering module is configured to perform a second filtering on adjacent peaks and valleys based on preset filtering rules, until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value. The quantity determination module is configured to perform a third filtering on adjacent peaks and adjacent troughs based on preset filtering rules, until each remaining peak and trough in the GPP time series appears alternately, and determine the number of vegetation growing seasons based on the time distance between the peak and the two adjacent troughs.
8. A device for determining the quantity of vegetation during the growing season, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method for determining the number of vegetation growing seasons according to any one of claims 1 to 6.
9. A storage medium, characterized in that, It stores a computer program executable by a device for determining the number of vegetation growing seasons, which, when run on the device for determining the number of vegetation growing seasons, causes the device for determining the number of vegetation growing seasons to perform the steps of the method for determining the number of vegetation growing seasons according to any one of claims 1 to 6.