A quantitative analysis method for the impact of environmental factors on vegetation resistance and resilience

Abstract

The present invention discloses a quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience. By collecting tree ring samples and meteorological data, a composite drought assessment index is established, and a mixed linear effect model is constructed to quantitatively analyze the influence of environmental factors on vegetation resistance and resilience. The method first obtains standardized data of tree ring width, calculates monthly vapor pressure deficit and potential evapotranspiration, determines the composite drought year, and then calculates vegetation resistance and resilience index, and finally analyzes the influence of environmental factors through a mixed linear effect model; the method of the present invention improves the assessment accuracy through high-resolution tree ring data, and adopts a mixed effect model to improve the robustness of the analysis, which can effectively identify key environmental factors and provide a scientific basis for ecosystem management.

Description

Technical Field

[0001] The invention belongs to the technical field of ecosystem monitoring and evaluation, and specifically relates to a quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience. Background Art

[0002] Compound drought events, which are the combined effects of atmospheric drought (water deficiency in the air) and soil drought (lack of available water in the soil), occur frequently. Compared with single drought, compound drought has more serious impacts on the ecosystem, not only restricting vegetation growth, but also significantly exacerbating the vulnerability of the ecosystem, and having a profound impact on both the short-term and long-term dynamics of vegetation growth.

[0003] In the existing technology, drought assessment usually relies on a single indicator, such as the Standard Precipitation Index (SPI) or the Standard Temperature Index (STI). These methods mainly focus on a single type of drought, ignoring the combined effects of atmospheric drought and soil drought, and cannot fully reflect the actual impact of compound drought on vegetation. This single-dimensional assessment method may lead to a one-sided understanding of drought stress, thereby limiting the in-depth study of vegetation resilience and recovery. In addition, due to the lack of high-resolution data over a long time scale, the response of vegetation to drought stress usually has a significant time lag effect. Although remote sensing data provides large-scale ecological monitoring capabilities, its temporal resolution and accuracy are still insufficient. In the existing technology, the assessment of environmental factors mainly relies on linear regression models to assess the impact of environmental factors on tree growth. This method assumes that the impact of climate factors on tree growth is fixed and independent. Although it is simple and intuitive, it does not take into account the differences between individual trees and the hierarchical structure of the impact of environmental factors on tree growth. Especially in the case of compound drought, the growth responses of different tree individuals in different locations may be significantly different. A single linear model cannot fully capture such individual differences or geographical differences, which may lead to deviations in the assessment results. In contrast, although the mixed linear model is still based on linear relationships, it can effectively handle the hierarchical structure in the data by introducing fixed effects and random effects. The fixed effects are used to capture the impact of climatic factors on tree growth, while the random effects take into account the differences brought about by factors such as individual trees and locations.

[0004] Therefore, there is an urgent need for an analytical method that can quickly and accurately study environmental factors to reflect their impact on vegetation resistance and resilience. Summary of the invention

[0005] The purpose of the present invention is to provide a quantitative analysis method of the impact of environmental factors on vegetation resistance and resilience, so as to achieve the purpose of quickly and accurately identifying the impact of different environmental factors;

[0006] The specific technical solutions are as follows:

[0007] The present invention provides a quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience, the method comprising the following steps:

[0008] Step S1, collecting tree ring samples in the target study area and preprocessing them to obtain standardized tree ring width data;

[0009] The calculation formula for standardizing the annual ring width data is: ,in, is the standardized tree ring width index, is the original annual ring width, is the sample mean, is the sample standard deviation.

[0010] Step S2, collecting historical meteorological data of the target study area, and calculating monthly vapor pressure deficit and monthly potential evapotranspiration based on the historical meteorological data.

[0011] The historical meteorological data at least include: monthly average temperature T, monthly total precipitation P, and monthly average relative humidity RH.

[0012] The calculation formula for the monthly steam pressure deficit is: ; The calculation formula for the monthly potential evapotranspiration is: ,in, is the monthly steam pressure deficit, is the monthly potential evapotranspiration, is the monthly net radiation, MJ / m 2 ; is the monthly soil heat flux, MJ / m 2 ; is the monthly average wind speed at 2m height, m / s, is the saturated water vapor pressure, kPa, is the actual monthly water vapor pressure, kPa; is the slope of the saturated water vapor pressure curve, kPa / ℃; is the psychrometric constant.

[0013] Step S3, based on the annual vapor pressure deficit and the annual potential evapotranspiration index, the annual cumulative water surplus and deficit is calculated to obtain a composite drought index and determine the drought year.

[0014] Annual steam pressure deficit The value of is taken as the average value of the total vapor pressure deficit of the trees in the target study area during the growth month. ;in, For the Monthly steam pressure deficit for the month, and are the starting and ending months of tree growth in the target study area, respectively.

[0015] The monthly water surplus and deficit is obtained based on the monthly precipitation and potential evapotranspiration. The standardized water surplus and deficit is obtained by calculating the cumulative probability according to the fitted distribution function. The annual potential evapotranspiration is obtained according to the standardized water surplus and deficit. .

[0016] The standardization process transforms the annual vapor pressure deficit and annual potential evapotranspiration into a normal distribution with a mean of 0 and a standard deviation of 1:

[0017] , ,in, for The multi-year average of for The standard deviation of for The multi-year average of for The standard deviation of .

[0018] Defining the Composite Drought Severity Index : , then the determination index of compound drought year is: and ,or When the drought is greater than 10%, the year is judged to be a compound drought year.

[0019] Step S4, constructing a mixed linear effect model, and providing quantitative calculation results of the effects of environmental factors on vegetation resistance and resilience in the drought year based on the mixed linear effect model.

[0020] Construct a mixed linear effects model: ;

[0021] in, is the response variable, indicating the vegetation resistance index or vegetation resilience index; For the environmental factors, including at least: , , , , is the number of environmental factors; is the intercept, is the fixed effect coefficient of the environmental factor; For the and The interaction effect coefficients among the environmental factors; is a random effect term; is a random error term.

[0022] Vegetation resistance index The calculation method is: ;in, is the tree ring width index in drought years; It is the average annual ring width index in the three years before the drought.

[0023] Vegetation resilience index The calculation method is: ;in, It is the average annual ring width index three years after the drought.

[0024] For the calculated and After standardization, various environmental factors are introduced as the response variables of the model, and the main effect coefficients of each environmental factor and the interaction effect coefficients between environmental factors are estimated through the model.

[0025] Furthermore, the annual potential evapotranspiration The calculation method includes the following steps:

[0026] Step S1.1, calculate the monthly water surplus and deficit based on the monthly precipitation and potential evapotranspiration.

[0027] , where D is the monthly water surplus or deficit.

[0028] Step S1.2: Calculate the annual cumulative water surplus and deficit. For each natural year in the time series, calculate the annual cumulative water surplus and deficit. .

[0029] ,in, For the Monthly water surplus or deficit for the month.

[0030] Step S1.3, calculate the probability distribution fitting, calculate the cumulative probability based on the fitted distribution function to obtain the standardized water surplus and deficit .

[0031] Use the three-parameter log-logistic distribution: ,in, is the scale parameter, is the shape parameter, is the origin parameter; is the cumulative probability distribution function.

[0032] when When , ;

[0033] when hour, , .

[0034] Step S1.4, based on the standardized water surplus and deficit Get annual potential evapotranspiration .

[0035] ;in, , , and , , are the constant coefficients of the fitting formula, , , ; , , .

[0036] Furthermore, after constructing the mixed linear effect model, the explanatory power of the model is also evaluated, specifically:

[0037] Calculating Margin , assess the explanatory power of fixed effects: , calculation conditions , evaluate the overall explanatory power of the model: ; represents the total variance of the response variable, represents the variance explained by the fixed effects; Indicate the variance explained by random effects; analyze the residual distribution and test the model assumptions; calculate the marginal means of the fixed effects in the model and determine the extent to which different environmental factors affect vegetation resistance and resilience.

[0038] Furthermore, the time lag effect analysis is performed on the collected meteorological data, the optimal lag period is selected, and the meteorological variables in the model are updated;

[0039] The mathematical expression of the time lag effect analysis model is: ;in, for The response variable at time Lag Environmental factors during the period, is the maximum lag order, Lag The effect coefficient of the period, is a random error term.

[0040] Furthermore, the selection criterion of the optimal lag period is that the Akaike information criterion AIC is minimized, and the specific calculation formula of the Akaike information criterion AIC for the linear mixed effect model is: ;in, is the sample size, is the residual sum of squares, is the number of parameters in the model including the lag terms.

[0041] Furthermore, the starting and ending months of tree growth in the target study area, that is, the tree growing months are determined according to the climate zone, among which the temperate zone is April to September, the subtropical zone is March to October, and the tropical zone is all year round.

[0042] Furthermore, the steps of collecting tree ring samples in the target research area and preprocessing them specifically include: using a growth cone to drill samples from trees 1.3 m above the ground, and drilling one sample from each tree in the east-west and north-south directions;

[0043] The drilled cores were stored in paper straws and air-dried, fixed and polished;

[0044] The tree growth time series was obtained through cross-dating, and the original width data of each tree ring was measured and recorded.

[0045] Furthermore, the collected tree ring samples are uniformly numbered and classified, and a sample information database is established to record the sampling location, sampling time, and sample characteristic information.

[0046] Furthermore, after obtaining the main effect coefficient of each environmental factor and the interaction effect coefficient between environmental factors through model estimation, the relative importance index of each environmental factor is calculated: ,in, For the The relative importance index of an environmental factor indicates the relative contribution of the environmental factor to the resistance or resilience of vegetation; Indicates The standard deviation of the environmental factor, is the sum of the standardized effects of all environmental factors, The larger the value, the higher the relative importance of the environmental factor.

[0047] Furthermore, the study area was geographically zoned and the sample plots were divided into different geographical units based on topographic factors such as altitude, aspect, and slope.

[0048] A mixed linear effect model was constructed in each geographical unit to compare the effects of environmental factors on vegetation resistance and resilience in different geographical units.

[0049] Based on the analysis results of geographical units, the spatial distribution map of the impact of environmental factors is drawn to identify the spatial heterogeneity characteristics of the impact of environmental factors.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a method for comprehensively evaluating the impact of compound drought on vegetation ecology, which can identify the key role of different environmental factors; combined with high-resolution tree ring data, the spatiotemporal accuracy of drought impact assessment is improved; the introduction of mixed effect models improves the robustness of the analysis and can effectively control the interference of random effects; through quantitative analysis of marginal effect coefficients, the key influencing factors under compound drought are clarified, providing a scientific basis for ecosystem management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention is a flow chart of a quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described implementation mode is a part of the present invention, not all implementation modes. Based on the implementation modes of the present invention, all other implementation modes obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that tree rings, as an important tool for ecological and climate research, are an ideal data source for analyzing vegetation response and resilience because they can provide high-resolution records over long time scales. Tree rings record the growth changes of vegetation on an interannual scale and can accurately reflect its response characteristics to drought stress, especially in terms of temporal resolution and sampling convenience. In addition, tree rings can quantify the resistance of vegetation in drought years and its recovery after drought through changes in the width of tree rings, providing a scientific basis for a comprehensive assessment of the dynamic impact of drought on ecosystems. In this context, there is an urgent need for a method that combines tree ring data and composite drought indicators to provide a scientific basis for specifying ecological protection and restoration strategies by comprehensively analyzing the effects of multiple environmental factors on vegetation growth resistance and resilience.

[0055] The method proposed in the present invention is particularly suitable for studying the impact of compound drought on forest ecosystems. In the specific implementation process, it is first necessary to clarify the climate characteristics and vegetation types of the study area. For example, in temperate forest areas, local dominant tree species should be selected as research objects. These tree species are usually more sensitive to environmental changes and can better reflect the impact of climate change.

[0056] like Figure 1 As shown, it is a flow chart of a quantitative analysis method of the influence of environmental factors on vegetation resistance and resilience of the present invention, and the method comprises the following steps:

[0057] Step S1, collect tree ring samples in the target study area and perform preprocessing to obtain standardized tree ring width data.

[0058] Use growth cones to sample tree ring samples. The diameter of the drill hole should be suitable for the diameter of the selected tree to avoid damaging the tree. Select the location where the tree is 1.3m above the ground for drilling. Make sure the drill bit is perpendicular to the trunk during drilling to avoid poor sample quality due to angle deviation. Drill one sample from each tree in the east-west-north-south direction. Multiple sampling can be performed if necessary. The sample cores obtained by drilling are stored in paper straws. After pre-treatment by air drying, fixation, polishing and other processes, cross dating is performed. The growth time of the tree and the width of the annual rings are obtained according to the cross dating results. The specific steps are: use growth cones to drill samples at 1.3m above the ground, and drill one sample from each tree in the east-west-north-south direction; store the drilled sample cores in paper straws, air dry, fix and polish; obtain the tree growth time series through cross dating, and measure and record the original width data of each annual ring.

[0059] The collected annual ring samples are uniformly numbered and classified, and a sample information database is established to record the sampling location, sampling time, and sample characteristic information.

[0060] The calculation formula for standardizing the annual ring width data is: ,in, is the standardized tree ring width index, is the original annual ring width, is the sample mean, is the sample standard deviation.

[0061] Step S2, collecting historical meteorological data of the target study area, and calculating monthly vapor pressure deficit and monthly potential evapotranspiration based on the historical meteorological data.

[0062] Collect meteorological data of the corresponding area, including temperature, precipitation, vapor pressure deficit (VPD) and standardized precipitation evapotranspiration index (SPEI); and time match the tree ring data with the meteorological data; the selection and processing of meteorological data is one of the key links of this method; taking a certain study area as an example, monthly meteorological data from 1960 to 2020 were collected, including temperature, precipitation, relative humidity, etc.; the data sources include observation records of nearby meteorological stations and gridded data obtained by interpolation, and the temporal resolution and spatial representativeness of the data need to be carefully evaluated.

[0063] The historical meteorological data at least include: monthly average temperature T, monthly total precipitation P, and monthly average relative humidity RH.

[0064] The calculation formula for the monthly steam pressure deficit is: ; The calculation formula for the monthly potential evapotranspiration is: ,in, is the monthly steam pressure deficit, is the monthly potential evapotranspiration, is the monthly net radiation, MJ / m 2 ; is the monthly soil heat flux, MJ / m 2 ; is the monthly average wind speed at 2m height, m / s, is the saturated water vapor pressure, kPa, is the actual monthly water vapor pressure, kPa; is the slope of the saturated water vapor pressure curve, kPa / ℃; is the psychrometric constant.

[0065] Step S3, based on the annual vapor pressure deficit and the annual potential evapotranspiration index, the annual cumulative water surplus and deficit is calculated to obtain a composite drought index and determine the drought year.

[0066] Annual steam pressure deficit The value of is taken as the average value of the total vapor pressure deficit of the trees in the target study area during the growth month. ;in, For the Monthly steam pressure deficit for the month, and are the starting and ending months of tree growth in the target study area, respectively.

[0067] The calculation of VPD needs to take into account regional climate characteristics. For example, in a study in a semi-arid area, it was found that the VPD values ​​in spring and summer were significantly higher than those in other seasons. This is consistent with the local climate characteristics. High VPD values ​​usually occur in periods of high temperature and low relative humidity. Under such conditions, plant water stress is most obvious.

[0068] The starting and ending months of tree growth in the target study area, that is, the tree growth months are determined according to the climate zone, among which the temperate zone is April to September, the subtropical zone is March to October, and the tropical zone is all year round.

[0069] The monthly water surplus and deficit is obtained based on the monthly precipitation and potential evapotranspiration. The standardized water surplus and deficit is obtained by calculating the cumulative probability according to the fitted distribution function. The annual potential evapotranspiration is obtained according to the standardized water surplus and deficit. ; Through independent standardization, baseline differences between different sites are eliminated, facilitating comparison across sites and time.

[0070] annual potential evapotranspiration The calculation method includes the following steps:

[0071] Step S1.1, calculating the monthly water surplus and deficit based on monthly precipitation and potential evapotranspiration;

[0072] , where D is the monthly water surplus or deficit;

[0073] Step S1.2: Calculate the annual cumulative water surplus and deficit. For each natural year in the time series, calculate the annual cumulative water surplus and deficit. ;

[0074] ,in, For the Monthly water surplus or deficit for the month;

[0075] Step S1.3, calculate the probability distribution fitting, calculate the cumulative probability based on the fitted distribution function to obtain the standardized water surplus and deficit ;

[0076] Use the three-parameter log-logistic distribution: , where α is the scale parameter, β is the shape parameter, and γ is the origin parameter; F(x) is the cumulative probability distribution function;

[0077] when When , ;

[0078] when hour, , ;

[0079] Step S1.4, based on the standardized water surplus and deficit Get annual potential evapotranspiration ;

[0080] ;in, , , and , , are the constant coefficients of the fitting formula, , , ; , , .

[0081] In the calculation process of SPEI, the selection of parameters needs to be adjusted according to the characteristics of the study area. In a specific case, by comparing the SPEI values ​​at different time scales, the researchers found that the SPEI at the 12-month scale can better reflect the soil drought conditions in the study area; this is because this time scale can reflect the long-term water accumulation effect and match the annual growth cycle of vegetation.

[0082] The standardization process transforms the annual vapor pressure deficit and annual potential evapotranspiration into a normal distribution with a mean of 0 and a standard deviation of 1:

[0083] , ,in, for The multi-year average of for The standard deviation of for The multi-year average of for The standard deviation of

[0084] Defining the Composite Drought Severity Index : , then the determination index of compound drought year is: and ,or When the drought is greater than 10%, the year is judged to be a compound drought year.

[0085] For example, in a temperate study area, when the average VPD in the spring and summer (April-September) of a certain year is greater than 1.5 (corresponding to a normal distribution probability of 7%), it is defined as an atmospheric drought year; when the scaled SPEI in December of a certain year is less than -1.5 (corresponding to a normal distribution probability of 7%), it is defined as a soil drought year.

[0086] Compound drought is defined as two situations:

[0087] 1) If two types of drought occur simultaneously and the conditions of VPD>1.5 and SPEI<-1.5 are met at the same time, then this year is defined as a composite drought year.

[0088] 2) The trade-off contribution model calculates the value of VPD-SPEI. When VPD-SPEI>3, the year is a compound drought year. In this case, one type of drought is allowed to significantly exceed the threshold to make up for the deficiency of another type of drought, thereby capturing the interaction between drought types.

[0089] Taking a certain study area as an example, the analysis shows that when VPD and SPEI reach the critical value at the same time, tree growth is most significantly inhibited. Statistical results show that between 1960 and 2020, a total of 15 complex drought events occurred in the region, and the frequency of occurrence increased significantly after 2000.

[0090] Step S4, constructing a mixed linear effect model, and providing quantitative calculation results of the effects of environmental factors on vegetation resistance and resilience in the drought year based on the mixed linear effect model.

[0091] Construct a mixed linear effects model: ;

[0092] in, is the response variable, indicating the vegetation resistance index or vegetation resilience index; For the environmental factors, including at least: , , , , is the number of environmental factors; is the intercept, is the fixed effect coefficient of the environmental factor; For the and The interaction effect coefficients among the environmental factors; is a random effect term; is the random error term;

[0093] Vegetation resistance index The calculation method is: ;in, is the tree ring width index in drought years; is the average annual ring width index in the three years before the drought; in addition, in further refined calculations, the calculation of vegetation resistance needs to take into account the age effect of tree growth. For example, in a certain study, it was found that adult trees (50-100 years old) are generally more resistant than young trees (<50 years old) and overmature trees (>100 years old). This difference may be related to the physiological characteristics of trees and the degree of root development.

[0094] Vegetation resilience index The calculation method is: ;in, It is the average annual ring width index three years after drought. There are significant differences in the resilience of different tree species. For example, in a mixed forest study, the average resilience index of broad-leaved tree species (1.15) was significantly higher than that of coniferous tree species (0.92), which may be related to the physiological characteristics and adaptation strategies of the tree species.

[0095] For the calculated and After standardization, various environmental factors are introduced as the response variables of the model, and the main effect coefficients of each environmental factor and the interaction effect coefficients between environmental factors are estimated through the model.

[0096] The construction of a mixed linear effects model requires careful consideration of the selection of fixed effects and random effects. Preliminary analysis has shown that in addition to traditional meteorological factors, the influence of soil type and topographic factors also need to be considered; these factors are added to the model as random effects, which significantly improves the explanatory power of the model.

[0097] After constructing the mixed linear effect model, the explanatory power of the model is also evaluated, specifically:

[0098] Calculating Margin , assess the explanatory power of fixed effects: , calculation conditions , evaluate the overall explanatory power of the model: ; represents the total variance of the response variable, represents the variance explained by the fixed effects; Indicate the variance explained by random effects; analyze the residual distribution and test the model assumptions; calculate the marginal means of the fixed effects in the model and determine the extent to which different environmental factors affect vegetation resistance and resilience.

[0099] By analyzing the model results, the environmental factors that have the greatest impact on vegetation resistance and resilience can be identified; for example, in a temperate forest study, the relative importance index of VPD reached 35%, significantly higher than other environmental factors, indicating that atmospheric drought is a key factor affecting the drought resistance of vegetation in the region.

[0100] Conduct time lag effect analysis on the collected meteorological data, select the optimal lag period, and update the meteorological variables in the model;

[0101] The mathematical expression of the time lag effect analysis model is: ;in, for The response variable at time Lag Environmental factors during the period, is the maximum lag order, Lag The effect coefficient of the period, is a random error term.

[0102] The selection criterion of the optimal lag period is that the Akaike information criterion AIC is minimized. The specific calculation formula of the Akaike information criterion AIC for the linear mixed effect model is: ;in, is the sample size, is the residual sum of squares, is the number of parameters in the model including the lag terms.

[0103] After obtaining the main effect coefficient of each environmental factor and the interaction effect coefficient between environmental factors through model estimation, the relative importance index of each environmental factor is calculated: ,in, For the The relative importance index of an environmental factor indicates the relative contribution of the environmental factor to the resistance or resilience of vegetation; Indicates The standard deviation of the environmental factor, is the sum of the standardized effects of all environmental factors, The larger the value, the higher the relative importance of the environmental factor.

[0104] The study area was geographically divided, and the sample plots were divided into different geographical units based on topographic factors such as altitude, aspect and slope. A mixed linear effect model was constructed in each geographical unit to compare the effects of environmental factors on vegetation resistance and resilience in different geographical units. Based on the analysis results of the geographical units, a spatial distribution map of the impact of environmental factors was drawn to identify the spatial heterogeneity of the impact of environmental factors.

[0105] In summary, the embodiments of the present invention distinguish and quantify different types of drought (atmospheric drought and soil drought), quantitatively determine the thresholds for the occurrence of single drought and compound drought; measure the resilience of vegetation by comparing the changes in vegetation resistance and resilience under different types of drought, and clarify the degree of vegetation response to different types of drought. And determine the sensitivity of vegetation resilience to environmental factors under different types of drought, which helps to measure the changes in vegetation response to the environment under compound drought and determine the dominant environmental factors of vegetation resilience; help predict the impact of future drought on vegetation and provide new solutions for vegetation planting and management.

[0106] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quantitative analysis method for the impact of environmental factors on vegetation resistance and resilience, characterized in that: The method comprises the following steps: Step S1, collecting tree ring samples in the target study area and preprocessing them to obtain standardized tree ring width data; The calculation formula for standardizing the annual ring width data is: ,in, is the standardized tree ring width index, is the original annual ring width, is the sample mean, is the sample standard deviation; Step S2, collecting historical meteorological data of the target study area, and calculating monthly vapor pressure deficit and monthly potential evapotranspiration based on the historical meteorological data; The historical meteorological data at least include: monthly average temperature T, monthly total precipitation P, monthly average relative humidity RH; The calculation formula for the monthly steam pressure deficit is: ; The calculation formula for the monthly potential evapotranspiration is: ,in, is the monthly steam pressure deficit, is the monthly potential evapotranspiration, is the monthly net radiation, MJ / m 2 ; is the monthly soil heat flux, MJ / m 2 ; is the monthly average wind speed at 2m height, m / s, is the saturated water vapor pressure, kPa, is the actual monthly water vapor pressure, kPa; is the slope of the saturated water vapor pressure curve, kPa / ℃; is the psychrometric constant; Step S3, based on the annual vapor pressure deficit and the annual potential evapotranspiration index, the annual cumulative water surplus and deficit is calculated to obtain a composite drought index and determine the drought year; Annual steam pressure deficit The value of is taken as the average value of the total vapor pressure deficit of the trees in the target study area during the growth month. ;in, For the Monthly steam pressure deficit for the month, and are the starting and ending months of tree growth in the target study area, respectively; The monthly water surplus and deficit is obtained based on the monthly precipitation and potential evapotranspiration. The standardized water surplus and deficit is obtained by calculating the cumulative probability based on the fitted distribution function. The annual potential evapotranspiration is obtained based on the standardized water surplus and deficit. ; The standardization process transforms the annual vapor pressure deficit and annual potential evapotranspiration into a normal distribution with a mean of 0 and a standard deviation of 1: , ,in, for The multi-year average of for The standard deviation of for The multi-year average of for The standard deviation of Defining the Composite Drought Severity Index : , then the determination index of compound drought year is: and ,or When the year is 1, it is determined to be a compound drought year; Step S4, constructing a mixed linear effect model, and providing quantitative calculation results of the effects of environmental factors on vegetation resistance and resilience in the drought year based on the mixed linear effect model; Construct a mixed linear effects model: ; in, is the response variable, indicating the vegetation resistance index or vegetation resilience index; For the environmental factors, including at least: , , , , is the number of environmental factors; is the intercept, is the fixed effect coefficient of the environmental factor; For the and The interaction effect coefficients among the environmental factors; is a random effect term; is the random error term; Vegetation resistance index The calculation method is: ;in, is the tree ring width index in drought years; is the average annual ring width index in the three years before the drought; Vegetation resilience index The calculation method is: ;in, is the average annual ring width index 3 years after the drought; For the calculated and After standardization, various environmental factors are introduced as the response variables of the model, and the main effect coefficients of each environmental factor and the interaction effect coefficients between environmental factors are obtained through model estimation.

2. The quantitative analysis method of the influence of environmental factors on vegetation resistance and resilience according to claim 1, characterized in that: annual potential evapotranspiration The calculation method includes the following steps: Step S1.1, calculating the monthly water surplus and deficit based on monthly precipitation and potential evapotranspiration; , where D is the monthly water surplus or deficit; Step S1.2: Calculate the annual cumulative water surplus and deficit. For each natural year in the time series, calculate the annual cumulative water surplus and deficit. ; ,in, For the Monthly water surplus or deficit for the month; Step S1.3, calculate the probability distribution fitting, calculate the cumulative probability based on the fitted distribution function to obtain the standardized water surplus and deficit ; Use the three-parameter log-logistic distribution: ,in, is the scale parameter, is the shape parameter, is the origin parameter; is the cumulative probability distribution function; when When , ; when hour, , ; Step S1.4, based on the standardized water surplus and deficit Get annual potential evapotranspiration ; ;in, , , and , , are the constant coefficients of the fitting formula, , , ; , , .

3. The quantitative analysis method of the influence of environmental factors on vegetation resistance and resilience according to claim 2, characterized in that: After constructing the mixed linear effect model, the explanatory power of the model is also evaluated, specifically: Calculating Margin , assess the explanatory power of fixed effects: , calculation conditions , evaluate the overall explanatory power of the model: ; represents the total variance of the response variable, represents the variance explained by the fixed effects; Indicate the variance explained by random effects; analyze the residual distribution and test the model assumptions; calculate the marginal means of the fixed effects in the model and determine the extent to which different environmental factors affect vegetation resistance and resilience.

4. The quantitative analysis method of the influence of environmental factors on vegetation resistance and resilience according to claim 3 is characterized in that: Conduct time lag effect analysis on the collected meteorological data, select the optimal lag period, and update the meteorological variables in the model; The mathematical expression of the time lag effect analysis model is: ;in, for The response variable at time Lag Environmental factors during the period, is the maximum lag order, Lag The effect coefficient of the period, is a random error term.

5. The quantitative analysis method of the influence of environmental factors on vegetation resistance and resilience according to claim 4, characterized in that: The selection criterion of the optimal lag period is that the Akaike information criterion AIC is minimized. The specific calculation formula of the Akaike information criterion AIC for the linear mixed effect model is: ;in, is the sample size, is the residual sum of squares, is the number of parameters in the model including the lag terms.

6. A quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience according to claim 5, characterized in that: The starting and ending months of tree growth in the target study area, that is, the tree growth months are determined according to the climate zone, among which the temperate zone is April to September, the subtropical zone is March to October, and the tropical zone is all year round.

7. A quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience according to any one of claims 1 to 6, characterized in that: The steps of collecting tree ring samples in the target research area and pre-processing them specifically include: using a growth cone to drill samples from trees 1.3 m above the ground, and drilling one sample from each tree in the east-west and north-south directions; The drilled cores were stored in paper straws and air-dried, fixed and polished; The tree growth time series was obtained through cross-dating, and the original width data of each tree ring was measured and recorded.

8. A quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience according to claim 7, characterized in that: The collected annual ring samples are uniformly numbered and classified, and a sample information database is established to record the sampling location, sampling time, and sample characteristic information.

9. A quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience according to claim 8, characterized in that: After obtaining the main effect coefficient of each environmental factor and the interaction effect coefficient between environmental factors through model estimation, the relative importance index of each environmental factor is calculated: ,in, For the The relative importance index of an environmental factor indicates the relative contribution of the environmental factor to the resistance or resilience of vegetation; Indicates The standard deviation of the environmental factor, is the sum of the standardized effects of all environmental factors, The larger the value, the higher the relative importance of the environmental factor.

10. A quantitative analysis method for the influence of environmental factors on vegetation resistance and resilience according to claim 9, characterized in that: The study area was geographically zoned and the sample plots were divided into different geographical units based on topographic factors such as altitude, aspect, and slope; A mixed linear effect model was constructed in each geographical unit to compare the differences in the impact of environmental factors on vegetation resistance and resilience in different geographical units. Based on the analysis results of geographical units, the spatial distribution map of the impact of environmental factors is drawn to identify the spatial heterogeneity characteristics of the impact of environmental factors.

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