Accurate assessment method for influence event of composite dry heat on vegetation

Abstract

The invention relates to a precise evaluation method for an influence event of composite dry heat on vegetation, and the method comprises the steps: obtaining a candidate drought index, a candidate heat wave index and an NPP index, carrying out the pairwise correlation calculation of the candidate drought index and the candidate heat wave index corresponding to each pixel and the NPP index, and determining the adaptation index of each pixel in a to-be-detected region; and constructing a brand new composite dry heat index according to the adaptation index of each pixel, and performing spatial run-length analysis on the pixel scale by using the brand new composite dry heat index to obtain an analysis result. According to the method, the precise screening and combination process of the drought index and the heat wave index is independently carried out innovatively for each pixel in the research area, and the precision of composite dry heat evaluation can be greatly improved by considering the significant difference between the geographical environment and the vegetation coverage type corresponding to different pixels through the operation refined to the pixel level.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment assessment, and in particular to a method for accurately assessing the impact of combined dry heat on vegetation. Background Art

[0002] With the intensification of global climate change, the frequency and intensity of compound dry and hot events are showing an upward trend, posing a serious threat to ecosystems, agricultural production, water resources, energy supply, and human health. However, existing environmental assessment methods have many limitations when facing compound dry and hot events. Traditional drought assessments mainly focus on single factors such as insufficient precipitation and soil moisture deficit, but do not adequately consider the complex situation under the synergistic effects of dryness and heat. At the same time, the assessment of dry and hot events is often based only on simple meteorological indicators such as temperature and humidity, and fails to fully integrate other relevant factors. This scattered and one-sided assessment method cannot accurately grasp the full picture and actual impact of compound dry and hot events on vegetation, making it difficult to formulate scientific and effective response strategies and resource allocation plans when responding to such disasters, and thus unable to timely and effectively mitigate the adverse effects and losses caused by compound dry and hot events. Summary of the Invention

[0003] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to propose a precise assessment method for the impact of combined dry and hot events on vegetation. It innovatively carries out a precise screening and combination process of drought index and heat wave index independently at the pixel scale within the study area. Taking into account the significant spatial differences in the geographical environment and vegetation cover types corresponding to different pixels, this operation refined to the pixel level can greatly improve the accuracy of the assessment of the impact of combined dry and hot events on vegetation.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A precise assessment method for the impact of combined dry and hot events on vegetation, including:

[0006] Obtaining a candidate drought index, a candidate heat wave index, and a NPP index, performing pairwise correlation calculations on the candidate drought index and the candidate heat wave index corresponding to each pixel and the NPP index, respectively, to determine a suitable index for each pixel in the test area;

[0007] According to the adaptation index of each pixel, a new composite dry heat index is constructed, and the spatial run analysis is performed on the pixel scale using the new composite dry heat index to obtain analysis results.

[0008] Optionally, the candidate drought indices include: Standardized Precipitation Index, Precipitation Anomaly Percentage and Palmer Drought Index; the candidate heat wave indices include: High Temperature Threshold Days Index, Continuous High Temperature Days Index and Heat Index.

[0009] Optionally, performing pairwise correlation calculations on the candidate drought index and the candidate heat wave index in each pixel and the NPP index, respectively, includes:

[0010]

[0011] Among them, x i 、y i are the i-th observation values of the two variables, are the means of the two variables, and n is the number of observations.

[0012] Optionally, determining the adaptation index of each pixel in the area to be measured includes:

[0013] Perform pairwise correlation calculations on the candidate drought index and the candidate heat wave index corresponding to each pixel and the NPP index to obtain correlation coefficients;

[0014] According to the correlation coefficient, the optimal drought index and candidate heat wave index combination with the largest correlation coefficient among all pixels is determined, that is, the adaptation index of each pixel in the area to be tested is determined.

[0015] Optionally, constructing the new composite dry heat index includes:

[0016]

[0017] in, represents the inverse function of the standard normal distribution, G represents the Gringorten empirical distribution, and P1 represents the composite dry heat index.

[0018] Optionally, the expression of the composite dry heat index is:

[0019] P1=P(SPEI≤spei,STI≥sti)=P(SPEI≤spei)-P(SPEI≤spei,STI≥sti)=F(spei)-C(F(spei),F(sti))

[0020] Where F is the marginal distribution of variables SPEI and STI, that is, the normal distribution, sti is the threshold of STI, spei is the threshold of SPEI, and C is the Copula joint function.

[0021] Optionally, obtaining the analysis result includes:

[0022] The new composite dry heat index was used to perform spatial run analysis at the pixel scale to analyze the spatial run characteristics of the new composite dry heat index, and the sensitivity of vegetation to composite dry heat stress was quantitatively evaluated in combination with the fitting function relationship.

[0023] Optionally, quantitatively evaluating the sensitivity of the vegetation to combined dry-heat stress includes:

[0024]

[0025] Among them, sensitivity represents the sensitivity of vegetation to drought events; SA 植被 refers to the standardized anomaly of vegetation parameters; SA CDHI is a standardized anomaly of CDHI.

[0026] The beneficial effects of the present invention are:

[0027] High accuracy: This invention can accurately quantify the various characteristics of complex dry-heat events, such as the degree of synergy between drought and high temperature, greatly improving the assessment accuracy in fields such as agriculture, and providing a reliable basis for response measures.

[0028] High accuracy: This invention is based on advanced algorithms and strict processes to reduce evaluation errors and uncertainties, and the predicted results are highly consistent with the actual situation, which enhances the credibility of the results and avoids decision-making errors.

[0029] Wide adaptability: The present invention utilizes multi-source data fusion and dynamic updating, making it applicable to the assessment of dry and hot events in a variety of geographical, climatic, and ecological environments, significantly expanding its scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] 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. 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.

[0031] Figure 1 This is a flow chart of a method for accurately assessing the impact of combined dry heat on vegetation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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 efforts are within the scope of protection of the present invention.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] like Figure 1As shown, this embodiment discloses a precise assessment method for composite dry-heat events affecting vegetation, including: obtaining candidate drought indices and candidate heat wave indices, performing pairwise correlation calculations on the candidate drought index and candidate heat wave index corresponding to each pixel and the NPP index (net primary productivity index), and determining the adaptation index of each pixel in the test area; constructing a new composite dry-heat index based on the adaptation index of each pixel, and performing spatial run analysis at the pixel scale using the new composite dry-heat index to obtain analysis results.

[0035] Specifically, this embodiment discloses a method for accurately assessing the impact of combined dry heat on vegetation, including:

[0036] S1. First, extract daily temperature, precipitation, and relative humidity data to construct drought and heat wave indices, and perform pixel-by-pixel pairing analysis on these indices.

[0037] S2. According to the Pearson correlation method, the linear correlation between the drought index and the heat wave index and the NPP index was analyzed. By calculating the Pearson correlation coefficient of each pixel, the pixel combination with the strong correlation between the drought index and the heat wave index and the NPP index was selected.

[0038] S3. For each pixel, the drought index and heat wave index are calculated using a couple function to generate a composite dry heat index (CDHI). This process is performed pixel by pixel, meaning that the drought index and heat wave index for each pixel are independently calculated using the couple function to generate a corresponding composite dry heat index value. Ultimately, each pixel will have a composite index value that represents the combined impact of drought and heat waves at that location.

[0039] S4. In the pixel run stage, based on the pixel-by-pixel analysis theory, the composite dry heat event of each pixel is first calculated. Then, different fitting methods such as linear regression, logarithmic function, exponential function, etc. are used to establish the response relationship model between CDHI and NDVI to reveal the nonlinear response mechanism of vegetation growth under the background of superposition of drought and heat wave. For areas with complex response morphology or unclear function form, the generalized additive model (GAM) is further used for non-parametric fitting. Finally, the slope and goodness of fit (R 2 ) and model residuals were used to quantitatively assess the sensitivity and adaptability of vegetation to combined dry-heat stress, and the response differences among different regions and vegetation types were analyzed at the spatial scale.

[0040] Furthermore, candidate drought indices include: Standardized Precipitation Index, Precipitation Anomaly Percentage and Palmer Drought Index; candidate heat wave indices include: High Temperature Threshold Days Index, Consecutive High Temperature Days Index and Heat Index.

[0041] Specifically, pixel-based index selection involves selecting the most representative drought index from a range of candidate drought indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI), the Percent Precipitation Anomaly (PA), and the Palmer Drought Index (PDSI). This approach considers spatial heterogeneity and adapts the index pixel by pixel to the most representative drought index. For example, in mountainous areas with poor soil water retention, high elevation, and large seasonal fluctuations in precipitation, a comprehensive analysis revealed that the SPI more sensitively reflects the rapidly changing soil moisture deficit in these areas. Therefore, the SPI was selected as the drought indicator for these pixels. In plain pixels with flat terrain and frequent agricultural irrigation, the PDSI, due to its advantage in considering the long-term water balance, is a more preferred option.

[0042] From multiple candidate heat wave indices, the most appropriate heat wave indicator is selected pixel by pixel, taking into account spatial heterogeneity and based on factors such as the pixel's regional climate, urbanization level, and vegetation heat tolerance. The most appropriate heat wave indicator is selected from a selection of alternative heat wave indices, including the 95th percentile-based High Temperature Threshold Days Index (HTI), the Consecutive High Temperature Days Index (CHTI), and the Heat Index (HI). For example, in densely populated urban pixels with a significant urban heat island effect, the HI comprehensively considers the dual effects of temperature and humidity on the perceived temperature of humans and vegetation, accurately capturing the intensity of heat wave stress on vegetation in urban environments and becoming the heat wave indicator for that pixel. In relatively remote pixels with less human interference, such as natural ecological reserves, the CHTI focuses on the duration of high temperatures alone, more accurately reflecting the actual conditions of vegetation subjected to prolonged high temperatures in pristine ecological environments and becoming the preferred option.

[0043] Furthermore, pairwise correlation calculations are performed on the candidate drought index and candidate heat wave index in each pixel with the NPP index, including:

[0044]

[0045] Among them, r is the correlation coefficient, x i 、y i are the i-th observation values of the two variables, are the means of the two variables, and n is the number of observations.

[0046] Furthermore, determining the adaptation index of each pixel in the area to be tested includes: performing pairwise correlation calculations on the candidate drought index and candidate heat wave index corresponding to each pixel and the NPP index to obtain correlation coefficients; and determining, based on the correlation coefficients, the candidate drought index and candidate heat wave index with the largest correlation coefficient among all pixels, that is, determining the adaptation index of each pixel in the area to be tested.

[0047] Specifically, a new composite dry heat index (CDHI) is constructed using a couple function: the optimal combination of drought index and heat wave index is selected for each pixel. For the composite dry heat event index (CDHIs), if two random variables X and Y represent the standardized precipitation evapotranspiration index (SPEI) and temperature (STI), respectively, a composite dry heat event can be described as one variable X being less than or equal to the threshold value x and the other variable Y being greater than or equal to the threshold value y. The composite dry heat index (CDHIs) is calculated as follows:

[0048] P1=P(SPEI≤spei,STI≥sti)=P(SPEI≤spei)-P(SPEI≤spei,STI≥sti)=F(spei)-C(F(spei),F(sti))#(1)

[0049] Where F is the marginal distribution of variables SPEI and STI, that is, the normal distribution, sti is the threshold of STI, spei is the threshold of SPEI, and C is the Copula joint function.

[0050] CDHIs is calculated as shown in formula (2):

[0051]

[0052] Where, represents the inverse function of the standard normal distribution, and G represents the Gringorten empirical distribution.

[0053] Gringorten's empirical distribution formula is as follows:

[0054]

[0055] Where m is the sorting number of the samples in ascending order, n is the total number of samples, and a is a constant parameter.

[0056] Furthermore, obtaining analysis results includes: using the new composite dry heat index to conduct spatial free analysis at the pixel scale, analyzing the spatial range characteristics of the new composite dry heat index, and simulating the relationship between the new composite dry heat index and vegetation changes by combining the fitting function relationship.

[0057] Specifically, a run analysis of the CDHI (composite drought and high temperature index) sequence was carried out at the pixel scale, and a functional relationship between CDHI and vegetation response variables was constructed to simulate the response changes of vegetation during dry and hot events. First, based on the monthly CDHI sequence, the run analysis method was used to identify the run of dry and hot events, and the time period in which CDHI continuously exceeded the set threshold (such as CDHI ≥ 0) was defined as a dry and hot event. The changes in vegetation response variables (such as NDVI) corresponding to the composite dry and hot events were extracted, and the functional relationship between CDHI and vegetation indicators was established to characterize the ecological effects of dry and hot stress on vegetation. The fitting function form is selected according to the response characteristics, including linear models, piecewise linear models, exponential models or generalized additive models (GAM). The slope and goodness of fit (R 2 ) and model residuals to quantitatively assess the sensitivity of vegetation to combined dry-heat stress, and further analyze the impact intensity of CDHI on vegetation in different regions and at different travel stages, providing a quantitative basis for identifying vulnerable areas of ecosystems and their adaptability to extreme climate events.

[0058] The normalized anomaly is calculated as follows:

[0059]

[0060] Among them, SA i is the standardized anomaly of vegetation on day i; x i is the variable value of the vegetation parameter on day i; μ(x) and δ(x) are the mean and standard deviation of the vegetation parameter on day i during the study period, respectively.

[0061] Then, the sensitivity index can be defined by equation (4):

[0062]

[0063] Among them, sensitivity represents the sensitivity of vegetation to drought events; SA 植被 refers to the standardized anomaly of vegetation parameters; SA CDHI is a standardized anomaly of CDHI.

[0064] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A precise assessment method for the impact of combined dry heat on vegetation, characterized in that: include: Obtaining a candidate drought index, a candidate heat wave index, and a NPP index, performing pairwise correlation calculations on the candidate drought index and the candidate heat wave index corresponding to each pixel and the NPP index, respectively, to determine a suitable index for each pixel in the test area; According to the adaptation index of each pixel, a new composite dry heat index is constructed, and the spatial run analysis is performed on the pixel scale using the new composite dry heat index to obtain analysis results.

2. The precise assessment method for the impact of combined dry heat on vegetation according to claim 1 is characterized in that: The candidate drought indices include: Standardized Precipitation Index, Precipitation Anomaly Percentage and Palmer Drought Index; the candidate heat wave indices include: High Temperature Threshold Days Index, Continuous High Temperature Days Index and Heat Index.

3. The precise assessment method for the impact of combined dry heat on vegetation according to claim 1 is characterized in that: Calculating pairwise correlations between the candidate drought index and the candidate heat wave index in each pixel and the NPP index includes: Among them, x i 、y i are the i-th observation values of the two variables, are the means of the two variables, and n is the number of observations.

4. The precise assessment method for the impact of combined dry heat on vegetation according to claim 1 is characterized in that: Determining the adaptation index of each pixel in the area to be measured includes: Perform pairwise correlation calculations on the candidate drought index and the candidate heat wave index corresponding to each pixel and the NPP index to obtain correlation coefficients; According to the correlation coefficient, the optimal drought index and candidate heat wave index combination with the largest correlation coefficient among all pixels is determined, that is, the adaptation index of each pixel in the area to be tested is determined.

5. The precise assessment method for the impact of combined dry heat on vegetation according to claim 1 is characterized in that: The construction of the new composite dry heat index includes: in, represents the inverse function of the standard normal distribution, G represents the Gringorten empirical distribution, and P1 represents the composite dry heat index.

6. The precise assessment method for the impact of combined dry heat on vegetation according to claim 5 is characterized in that: The expression of the composite dry heat index is: P1=P(SPEI≤spei,STI≥sti)=P(SPEI≤spei)-P(SPEI≤spei,STI≥sti) =F(spei)-C(F(spei),F(sti)) Where F is the marginal distribution of variables SPEI and STI, that is, the normal distribution, sti is the threshold of STI, spei is the threshold of SPEI, and C is the Copula joint function.

7. The precise assessment method for the impact of combined dry heat on vegetation according to claim 1 is characterized in that: Obtaining the analysis result includes: The new composite dry heat index was used to perform spatial run analysis at the pixel scale to analyze the spatial run characteristics of the new composite dry heat index, and the sensitivity of vegetation to composite dry heat stress was quantitatively evaluated in combination with the fitting function relationship.

8. The precise assessment method for the impact of combined dry heat on vegetation according to claim 7 is characterized in that: Quantitative assessment of the sensitivity of the vegetation to combined dry and heat stress includes: Among them, sensitivity represents the sensitivity of vegetation to drought events; SA 植被 refers to the standardized anomaly of vegetation parameters; SA CDHI is a standardized anomaly of CDHI.

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