Method for evaluating climate impact factors of vegetation photosynthesis considering time effect

Abstract

The application provides a vegetation photosynthesis climate influence factor evaluation method considering time effect, and the method comprises the following steps: acquiring sunlight-induced chlorophyll fluorescence data and a plurality of climate factor data, performing data preprocessing to obtain preprocessed data; obtaining climate variables according to the time effect of the preprocessed data; obtaining a partial correlation coefficient and a significance test value according to the climate variables; obtaining an optimal time effect according to the partial correlation coefficient, extracting an optimal partial correlation coefficient according to the optimal time effect; obtaining a percentage of significant positive and negative correlations under different time effects in a total area according to the partial correlation coefficient and the significance test value, comparing the percentage to obtain a comparison result; and evaluating the influence of the climate factor on the vegetation photosynthesis after considering the time effect according to the comparison result. The method adopts a partial correlation analysis method, evaluates the time effect of the climate factor on the vegetation photosynthesis, and increases the accuracy of evaluating the influence of the climate factor on the vegetation photosynthesis.

Description

Technical Field

[0001] This invention relates to the fields of ecology and vegetation photosynthesis, and in particular to a method for assessing the climatic impact factors of vegetation photosynthesis that takes into account time effects. Background Technology

[0002] Against the backdrop of increasingly severe global climate change, terrestrial ecosystems, as one of the Earth's major carbon sinks, absorb more than 30% of global carbon dioxide emissions, making a significant contribution to mitigating climate change. Vegetation, as a monitoring indicator reflecting the ecological environment under climate change, directly reflects the environment's response to climate change. Therefore, studying the driving factors of vegetation photosynthesis and fully exploring the potential of vegetation carbon sinks has become a key strategy for achieving global climate goals. Existing research shows that climate change has a significant impact on vegetation distribution, growing season, productivity, photosynthetic rate, and carbon absorption capacity.

[0003] However, previous studies have often overlooked the impact of multiple climatic factors on the time effects (lag effects and cumulative effects) of vegetation photosynthesis, thus underestimating the influence of climatic factors on vegetation photosynthesis. Therefore, it is essential to design a method for assessing the climatic influences of vegetation photosynthesis that considers time effects. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing the climatic impacts of vegetation photosynthesis that takes into account the time effect, so as to increase the accuracy of assessing the impact of climatic factors on vegetation photosynthesis.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for assessing the climatic impacts of vegetation photosynthesis that considers time effects includes the following steps:

[0007] Data on sunlight-induced chlorophyll fluorescence and various climatic factors were acquired and preprocessed to obtain preprocessed data.

[0008] Climate variables are derived from the time effects of preprocessed data;

[0009] Partial correlation coefficients and significance test values ​​were obtained based on climate variables;

[0010] The optimal time effect is obtained based on the partial correlation coefficient, and the optimal partial correlation coefficient is extracted based on the optimal time effect;

[0011] Based on the partial correlation coefficient and significance test value, the percentages of significant positive and negative correlations in the total area under different time effects were obtained, and the comparison results were obtained by comparing the percentages.

[0012] Based on the comparison results, the impact of climate factors on vegetation photosynthesis after taking into account the time effect was assessed.

[0013] Optionally, data preprocessing includes: projection transformation, outlier removal, resampling, and image cropping.

[0014] Optionally, climate variables can be obtained based on the time effect of the preprocessed data, and the specific steps are as follows:

[0015] Determine the time effects of climate factors; time effects include: no time effect, lag effect, cumulative effect, and combined lag-cumulative effect;

[0016] Time scales are derived based on different time effects; time scales include: lag scales, cumulative scales, and lag-cumulative scales.

[0017] Climate variables at different time scales under different time effects are obtained based on time effects and time scales.

[0018] Alternatively, the formula for calculating climate variables is:

[0019]

[0020] Among them, VPD t(m,n) Let m be the number of lagged months, n be the number of cumulative months, and VPD be the climate variable for t months. t-m-i Let be the climate variable for the (m+i)th month prior to the tth month.

[0021] Optionally, the formula for calculating the partial correlation coefficient is as follows:

[0022]

[0023] Where x is the first variable, y is the second variable, and r xy.z r is the partial correlation coefficient between x and y, and z is the control variable; xy r xz and r yz These are the correlation coefficients between x and y, x and z, and y and z, respectively.

[0024] Optionally, the formula for calculating the significance test value is:

[0025]

[0026] Where r is the partial correlation coefficient, n is the sample size, q is the order of partial correlation, and t is the significance test value.

[0027] Optionally, the optimal time effect is obtained based on the partial correlation coefficient, and the optimal partial correlation coefficient is extracted based on the optimal time effect. The specific steps are as follows:

[0028] By comparing the partial correlation coefficients under different time effects, the largest number of lag months and the largest number of cumulative lag months among the partial correlation coefficients are taken as the optimal lag time and the optimal cumulative time.

[0029] Extract the partial correlation coefficient with the largest absolute value based on the optimal lag time and optimal cumulative time.

[0030] Optionally, when the significance test value is <0.05, if the partial correlation coefficient is positive, it is a significant positive correlation; if the partial correlation coefficient is negative, it is a significant negative correlation.

[0031] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides a method for assessing the climatic influence factors of vegetation photosynthesis considering the time effect. The method includes: acquiring sunlight-induced chlorophyll fluorescence data and multiple climatic factor data, and performing data preprocessing to obtain preprocessed data; obtaining climatic variables based on the time effect of the preprocessed data; obtaining partial correlation coefficients and significance test values ​​based on the climatic variables; obtaining the optimal time effect based on the partial correlation coefficients, and extracting the optimal partial correlation coefficient based on the optimal time effect; obtaining the percentages of significant positive and negative correlations under different time effects in the total area based on the partial correlation coefficients and significance test values, and comparing the percentages to obtain comparison results; and evaluating the impact of climatic factors on vegetation photosynthesis after considering the time effect based on the comparison results. This method employs partial correlation analysis to assess the time effect of climatic factors on vegetation photosynthesis, increasing the accuracy of assessing the impact of climatic factors on vegetation photosynthesis. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the method for assessing the climate impact factors of vegetation photosynthesis considering the time effect, according to an embodiment of the present invention.

[0034] Figure 2 This is a detailed flowchart of the method for assessing the climate impact factors of vegetation photosynthesis that takes into account the time effect, according to an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, this embodiment of the invention provides a method for assessing the climatic impact factors of vegetation photosynthesis considering time effects, including the following steps:

[0038] Step 100: Acquire sunlight-induced chlorophyll fluorescence (SIF) data and data on various climatic factors, and perform data preprocessing to obtain preprocessed data;

[0039] Specifically, such as Figure 2 As shown, data preprocessing includes: projection transformation, outlier removal, resampling, and image cropping.

[0040] Step 200: Obtain climate variables based on the time effect of the preprocessed data; the specific steps are as follows:

[0041] The time-dependent effects of pretreated climate factors on vegetation SIF were determined, including: no time-dependent effect, lag effect, cumulative effect, and combined lag-cumulative effect.

[0042] The time scale is determined based on different time effect types, such as 1 month lag, 1 month accumulation, or 1 month lag and 1 month accumulation.

[0043] Climate variables at different time scales under different time effects are obtained based on the type of time effect and the time scale.

[0044] Specifically, in this embodiment, the climate variable is taken as the vapor pressure difference (VPD). The formula for calculating VPD is as follows:

[0045]

[0046] Among them, VPD t(m,n) Let VPD be the value for t months, m be the number of lagging months, and n be the cumulative number of months. t-m-i This represents the VPD for the (m+i)th month prior to the tth month.

[0047] Step 300: Obtain the partial correlation coefficient and significance test value based on the climate variables;

[0048] Specifically, the formula for calculating the partial correlation coefficient is:

[0049]

[0050] Where x is the first variable, y is the second variable, and r xy.z Let r be the partial correlation coefficient between x and y, z be the control variable, and r be the partial correlation coefficient between x and y. xy r xz and r yz These are the correlation coefficients between x and y, x and z, and y and z, respectively, where x, y, and z represent different climate variables; the partial correlation coefficients range from -1 to 1, and the higher the absolute value, the stronger the correlation.

[0051] Specifically, the steps to obtain the significance test value are as follows:

[0052] Establish the hypothesis test: assume that the partial correlation coefficient is not significantly different from zero, that is, there is no correlation between the two variables;

[0053] Calculate the test statistic t based on the test hypothesis, and determine the corresponding probability significance value based on the t-value; the formula for calculating the test statistic is:

[0054]

[0055] Where r is the partial correlation coefficient, n is the sample size, q is the partial correlation order, and t follows a t-distribution with nq-2 degrees of freedom.

[0056] Step 400: Obtain the optimal time effect based on the partial correlation coefficient, and extract the optimal partial correlation coefficient based on the optimal time effect; the specific steps are as follows:

[0057] By comparing the partial correlation coefficients of a pixel under different time effects, the parameters m and n with the largest absolute partial correlation coefficients are regarded as the optimal lag time and optimal cumulative time affecting the SIF of that pixel. The same applies to other pixels.

[0058] The partial correlation coefficient under the optimal time effect of a pixel is extracted based on the optimal lag time and optimal cumulative time, i.e., the partial correlation coefficient with the largest absolute value.

[0059] Step 500: Based on the partial correlation coefficient and significance test value, obtain the percentage of the total area of ​​significant positive correlation and significant negative correlation under different time effects, and compare the percentages to obtain the comparison results;

[0060] Specifically, when the significance test value is <0.05, it is considered a significant correlation. If the partial correlation coefficient is positive, it is considered a significant positive correlation. If the partial correlation coefficient is negative, it is considered a significant negative correlation.

[0061] Specifically, the different time effects are the optimal time effect and the neglected time effect.

[0062] Step 600: Based on the comparison results, assess the impact of climate factors on vegetation photosynthesis after considering the time effect.

[0063] Corresponding to the above method, this embodiment also provides an assessment system for climate impact factors of vegetation photosynthesis that considers time effects. The system includes a data collection and preprocessing unit and a unit for analyzing the time lag and cumulative effects of climate factors on SIF.

[0064] The data collection and preprocessing unit is used to acquire climate variable data from the SIF and TerraClimate datasets for a certain region over 20 years (2002-2021), including multi-source remote sensing data such as temperature (TEM), precipitation (PPT), solar radiation (SRAD), soil moisture (SM), and saturated vapor pressure difference (VPD); it is also used to perform preprocessing operations on image data.

[0065] The unit for analyzing the time lag and cumulative effects of climate factors on SIF is used to redefine lagged and cumulative climate variables; to calculate the optimal lag time and optimal cumulative time corresponding to the partial correlation coefficient and the maximum absolute partial correlation coefficient between climate variables and SIF under different combinations of time lag and cumulative effects; and to compare the partial correlation coefficients between SIF with optimal time effect and SIF with neglect of time effect and climate factors, and to quantitatively assess the impact of time effect on vegetation SIF.

[0066] The beneficial effects of this invention are as follows:

[0067] 1) By redefining lagged and cumulative climate variables, and fully considering the temporal impact of climate change on vegetation photosynthesis, comprehensive monitoring of the spatiotemporal evolution of regional photosynthesis can be achieved.

[0068] 2) The multi-dimensional analysis and evaluation provides multiple perspectives for the analysis of vegetation adaptation to climate change;

[0069] 3) Partial correlation analysis and time effect analysis increased the accuracy of assessing the impact of climate factors on vegetation photosynthesis.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0071] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for assessing the climatic impacts of vegetation photosynthesis considering time effects, characterized in that, Includes the following steps: Data on sunlight-induced chlorophyll fluorescence and various climatic factors were acquired and preprocessed to obtain preprocessed data. Climate variables are obtained based on the time effects of preprocessed data; the specific steps are: determining the time effects of the climate factors; the time effects include: no time effect, lag effect, cumulative effect, and lagged cumulative combined effect; The time scales are derived based on the different time effects described above; the time scales include: lag scales, cumulative scales, and lag-cumulative scales. Based on the time effect and the time scale, climate variables at different time scales under different time effects are obtained; The partial correlation coefficients and significance test values ​​were obtained based on the aforementioned climate variables; The optimal time effect is obtained based on the partial correlation coefficient, and the optimal partial correlation coefficient is extracted based on the optimal time effect. The specific steps are as follows: compare the partial correlation coefficients under different time effects, and take the largest number of lag months and the largest number of cumulative months among the partial correlation coefficients as the optimal lag time and the optimal cumulative time. Extract the partial correlation coefficient with the largest absolute value based on the optimal lag time and the optimal cumulative time; Based on the partial correlation coefficient and the significance test value, the percentages of significant positive correlation and significant negative correlation in the total area under different time effects are obtained, and the comparison results are obtained by comparing the percentages. Based on the comparison results, the impact of climate factors on vegetation photosynthesis after taking into account the time effect was assessed.

2. The method for assessing the climatic impact factors of vegetation photosynthesis considering time effects according to claim 1, characterized in that, The data preprocessing includes: projection transformation, outlier removal, resampling, and image cropping.

3. The method for assessing the climatic impact factors of vegetation photosynthesis considering time effects according to claim 1, characterized in that, The formula for calculating the climate variable is: ； in, Let m be the climate variable for t months, m be the number of lagged months, and n be the cumulative number of months. Let be the climate variable for the (m+i)th month prior to the tth month.

4. The method for assessing the climatic impact factors of vegetation photosynthesis considering time effects according to claim 1, characterized in that, The formula for calculating the partial correlation coefficient is as follows: ； in, x As the first variable, y As the second variable, r xy.z for x and y The partial correlation coefficient between them z For control variables; r xy , r xz and r yz They are respectively x and y , x and z and y and z The correlation coefficient between them.

5. The method for assessing the climatic impact factors of vegetation photosynthesis considering time effects according to claim 1, characterized in that, The formula for calculating the significance test value is as follows: ； in, r The partial correlation coefficient, n For sample size, q For the order of partial correlation, t This is the significance test value.

6. The method for assessing the climatic impact factors of vegetation photosynthesis considering time effects according to claim 1, characterized in that, When the significance test value is <0.05, if the partial correlation coefficient is positive, it is a significant positive correlation; if the partial correlation coefficient is negative, it is a significant negative correlation.

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