System and method for assessing resistance and resilience of vegetation under drought stress

Abstract

The present invention belongs to the field of global change ecology research. Disclosed are a system and method for assessing the resistance and resilience of vegetation under drought stress. The method comprises: using a data collection module to collect data related to vegetation resistance and resilience; inputting the data related to vegetation resistance and resilience into a data storage module for storage; invoking a vegetation physiological index processing sub-module, a vegetation structural index processing sub-module and a vegetation functional index processing sub-module in a data processing module to process the collected and stored data; then, on the basis of data output by the data processing module, invoking a lagged cumulative correlation coefficient calculation sub-module, a vegetation resistance index and resilience index assessment model sub-module and a vegetation resistance and resilience level classification sub-module in an analysis and assessment module; and finally, outputting vegetation resistance and resilience assessment levels. By means of the present invention, comprehensive, systematic and precise assessment and level classification can be performed on the resistance and resilience of vegetation under drought stress, vegetation that has difficulty in resisting drought or recovering from drought stress can be effectively identified from an assessment area, and the operation is simple and easily implemented.

Description

A system and method for assessing vegetation resistance and resilience under drought stress Technical Field

[0001] This invention relates to the field of global change ecology, specifically to an assessment system and method for vegetation resistance and resilience under drought stress based on hysteresis and cumulative effects. Background Technology

[0002] Against the backdrop of global change, a clear trend towards warming and drying is emerging. Warming accelerates evaporation, leading to an increase in the frequency, intensity, and duration of droughts. Climate change, especially drought, affects vegetation physiological and biochemical processes such as stomatal regulation, transpiration, and chlorophyll recovery, further impacting ecosystem structure, such as the appearance of withered leaves or death in tree canopies. When drought exceeds the maximum adaptive range of vegetation, growth is inhibited, resulting in impaired ecosystem function, reduced productivity, and reverse succession. Whether vegetation can maintain normal life activities after experiencing stress depends on its resistance and resilience. Resistance is the ability of vegetation to withstand stress and maintain its original state; resilience is the ability of vegetation to adjust its survival strategies and recover to its original state after stress has disrupted its original state. Therefore, assessing the resistance and resilience of vegetation to drought is of significant scientific importance for in-depth research into the physiological and biochemical mechanisms of vegetation response to drought and for predicting vegetation response patterns under future global warming. However, since the vegetation growth and development process is indirectly affected by drought stress at a previous point in time or period, the time lag effect and cumulative effect of this indirect effect, as well as the complex response process of vegetation to drought stress, make the accurate assessment of vegetation's resistance and resilience to drought stress a current research challenge and hot topic.

[0003] Existing models and methods for assessing vegetation resistance and resilience under drought stress (such as the "ball-and-cup" model, the CENTURY model, controlled experiments, and multi-index comprehensive evaluation methods) mostly focus on the resistance and resilience corresponding to a specific level of vegetation response to drought stress (e.g., physiological, structural, functional). They emphasize a single response level and lack a comprehensive assessment of vegetation resistance and resilience across all response levels. This makes it difficult to identify vegetation with low resistance and resilience in the assessment area from the source, resulting in weak scientific rigor and comprehensiveness in practical scenarios of regional ecological restoration and management. Furthermore, existing methods for assessing vegetation resistance and resilience have relatively poor accuracy, easily leading to unreasonable classifications of resistance and resilience levels, making it difficult to scientifically and accurately assess vegetation resistance and resilience. Moreover, the assessment of vegetation resistance and resilience mainly relies on manual statistics and calculations, resulting in low accuracy and a lack of intuitive understanding of the accurate assessment results. Summary of the Invention

[0004] Technical Problem: This invention aims to provide a system and method for assessing vegetation resistance and resilience under drought stress based on lag and cumulative effects. Using drought-stressed vegetation as the assessment object and based on the physiological, structural, and functional response levels of vegetation, it comprehensively, systematically, and accurately assesses vegetation resistance and resilience under drought stress, laying the foundation for future research on ecosystem stability assessment and dynamic prediction of ecosystem stability under future climate change. Therefore, the invention of a system and method for assessing vegetation resistance and resilience under drought stress based on lag and cumulative effects is of significant value.

[0005] This invention adopts the following technical solution: a method for assessing vegetation resistance and resilience under drought stress, comprising the following steps:

[0006] Using the vegetation under drought stress in the assessment area as the assessment object, data on the resistance and resilience of vegetation in the assessment area are obtained and stored.

[0007] To address different response levels of vegetation under drought stress, lagged cumulative correlation coefficients were established between vegetation indices and SPEI indices at different time scales. Then, based on the vegetation indices and lagged cumulative correlation coefficients corresponding to each response level of vegetation under drought stress, vegetation resistance index and resilience index assessment models were established, and vegetation resistance index and resilience index of all response levels were integrated.

[0008] Based on the classification method of vegetation resistance and resilience, the vegetation resistance index and resilience index are divided into several levels. Based on the classification level, the resistance and resilience of vegetation to drought stress are assessed.

[0009] The vegetation resistance and resilience data include: remote sensing image data, vegetation index data, and drought index data.

[0010] The lagged cumulative correlation coefficients between the vegetation index and the SPEI index at different time scales are as follows:

[0011] In the formula, E s Time series representing vegetation indices; t represents the time series of the drought index; i is the lag time of the vegetation index relative to the drought index, N is the longest lag time of the vegetation index relative to the drought index; f is the cumulative duration of the drought index's influence on the vegetation index, in months, f = 1, 2, 3…48; n For time; and These are the average values ​​of the vegetation index and the drought index, respectively; the vegetation index is one of the following: vegetation physiological index, vegetation structure index, and vegetation function index.

[0012] When the lagged cumulative correlation coefficient (LCCC) reaches its maximum value, record the lag time i of the vegetation index relative to the drought index and the cumulative influence duration f of the drought index on the vegetation index.

[0013] The vegetation resistance index and resilience index assessment model is established based on the vegetation index and lagged cumulative correlation coefficient corresponding to each response level of vegetation under drought stress, and is obtained by the following formula:

[0014] In the formula, V k This represents the vegetation index corresponding to the k-th response level; ε is the standardized precipitation evapotranspiration index at time ti, f is the time scale of the SPEI index, and i and f are obtained by establishing the lagged cumulative correlation coefficient (LCCC) between the vegetation index and the SPEI index at different time scales; t This represents the model residuals.

[0015] The vegetation resistance index and resilience index, which integrate all response levels, are as follows:

[0016] In the formula, VS represents the vegetation resistance index; α k VL represents the resistance index corresponding to the k-th response level of vegetation under drought stress; β represents the vegetation resilience index; k The resilience index represents the k-th level of vegetation response under drought stress; w k This represents the weight corresponding to the k-th response level of vegetation under drought stress; m represents the type of response level.

[0017] Where, α k β k This is obtained by evaluating the model fit.

[0018] The method for classifying vegetation resistance and resilience levels involves dividing the vegetation resistance index and resilience index into several levels, and assessing the vegetation's resistance and resilience to drought stress based on these levels. This includes the following steps:

[0019] The vegetation resistance index and resilience index are normalized and compared with a set threshold range, and divided into multiple levels; the vegetation resistance index and resilience index are visualized based on regional display.

[0020] The higher the resistance index and resilience index, the lower the resistance and resilience of the vegetation.

[0021] A system for assessing vegetation resistance and resilience under drought stress, comprising:

[0022] The data collection module is used to acquire data on vegetation resistance and resilience;

[0023] The data storage module is used to store the vegetation resistance and resilience data;

[0024] The data processing module is used to process the vegetation index corresponding to each response level according to the response level of vegetation to drought stress.

[0025] The analysis and evaluation module is used to analyze and evaluate the resistance and resilience of vegetation.

[0026] The data processing module includes:

[0027] The vegetation physiological index processing submodule is used to obtain vegetation physiological indices based on vegetation index data.

[0028] The vegetation structure index processing submodule is used to obtain the vegetation structure index based on remote sensing image data;

[0029] The vegetation function index processing submodule is used to obtain the vegetation function index based on the vegetation index data.

[0030] The analysis and evaluation module includes:

[0031] The lagged cumulative correlation coefficient calculation submodule is used to establish lagged cumulative correlation coefficients between vegetation indices and SPEI indices at different time scales for different response levels of vegetation under drought stress.

[0032] The vegetation resistance index and resilience index assessment model submodule is used to establish a vegetation resistance index and resilience index assessment model based on the vegetation index and lagged cumulative correlation coefficient corresponding to each response level of vegetation under drought stress, and to integrate the vegetation resistance index and resilience index of all response levels.

[0033] The vegetation resistance and resilience classification submodule is used to classify the vegetation resistance index and resilience index into several levels according to the vegetation resistance and resilience classification method, and to evaluate the vegetation's resistance and resilience to drought stress based on the classified levels.

[0034] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for assessing vegetation resistance and resilience under drought stress based on hysteresis and cumulative effects.

[0035] The present invention has the following beneficial effects and advantages:

[0036] 1. The vegetation resistance and resilience assessment system under drought stress based on lag and cumulative effects of the present invention includes a data collection module, a data storage module, a data processing module, and an analysis and assessment module. The system can complete data collection, data storage, and vegetation resistance and resilience assessment, automatically complete the entire assessment process of vegetation resistance and resilience, and visualize the data, thereby enabling environmental protection departments to more quickly and conveniently carry out scientific and systematic protection and restoration of vegetation under drought stress.

[0037] 2. This invention provides a system and method for assessing vegetation resistance and resilience under drought stress. Based on lag and cumulative effects, and taking vegetation under drought stress as the object, it constructs assessment models for vegetation resistance and resilience indices according to vegetation indices corresponding to each response level under drought stress and lag cumulative correlation coefficients. The vegetation index corresponding to each response level under drought stress is used to characterize the degree of impact of drought stress on different levels of vegetation. When calculating this index, different levels of vegetation response to drought stress are considered, rather than being limited to a single level, thereby making the method of this invention more accurate and comprehensive, and enabling a more scientific and systematic comprehensive assessment of vegetation resistance and resilience.

[0038] 3. The vegetation resistance index and resilience index assessment model constructed by the method of the present invention can individually assess the resistance and resilience of each response level of vegetation under drought stress, as well as the comprehensive resistance and resilience of all response levels. By assessing the resistance and resilience of vegetation, it is possible to identify vegetation that is difficult to resist and recover under drought stress in the assessment area from the source, thereby enabling relevant departments to scientifically and accurately prevent the adverse effects of drought stress on vegetation. Attached Figure Description

[0039] Figure 1 is a framework diagram of the vegetation resistance and resilience assessment system based on hysteresis and cumulative effects under drought stress according to the present invention.

[0040] Figure 2 is a flowchart of a method for assessing vegetation resistance and resilience under drought stress according to the present invention. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0042] This invention is a system and method for assessing vegetation resistance and resilience under drought stress based on lag and cumulative effects. Taking vegetation under drought stress as the object, it scientifically and accurately assesses and analyzes the resistance and resilience of vegetation to drought stress, determines the ability of vegetation to resist drought and recover to its original state from drought stress, thereby enabling the identification of vegetation that is difficult to resist and recover under drought stress from the source, and thus enabling scientific and effective management of regional vegetation.

[0043] As shown in Figure 1, the vegetation resistance and resilience assessment system under drought stress based on lag and cumulative effects of the present invention includes a data collection module, a data storage module, a data processing module, and an analysis and evaluation module. The data collection module is used to collect data related to vegetation resistance and resilience. The data storage module is used to store the data related to vegetation resistance and resilience. The data processing module is set up with several sub-data processing modules according to the response level of vegetation to drought stress, which are used to process the vegetation index corresponding to each response level.

[0044] In embodiments of the present invention, the vegetation response to drought stress includes three levels: physiological, structural, and functional. Therefore, the corresponding data processing module includes three sub-data processing modules: a vegetation physiological index processing sub-module, a vegetation structural index processing sub-module, and a vegetation functional index processing sub-module. The vegetation physiological index processing sub-module processes the vegetation physiological indices corresponding to the vegetation physiological response level; the vegetation structural index processing sub-module processes the vegetation structural index corresponding to the vegetation structural response level; and the vegetation functional index processing sub-module processes the vegetation functional index corresponding to the vegetation functional response level.

[0045] The vegetation resistance and resilience assessment system of the present invention can be used to assess vegetation resistance and resilience. With the help of the system, the entire process of vegetation resistance and resilience assessment, including data collection, data storage and grade assessment, can be completed, thereby conducting a scientific and comprehensive assessment of the resistance and resilience of vegetation under drought stress.

[0046] This invention discloses a method for assessing vegetation resistance and resilience under drought stress based on lag and cumulative effects. The method employs the vegetation resistance and resilience assessment system based on lag and cumulative effects under drought stress to evaluate vegetation resistance and resilience. As shown in Figure 2, the method first determines the assessment area. Through a survey and analysis of the basic vegetation conditions in the region, the vegetation under drought stress within the assessment area is identified. Typically, a specific type of vegetation or a certain range of vegetation is selected as the assessment object according to the assessment requirements. A data collection module collects relevant data on vegetation resistance and resilience within the assessment area. This data includes remote sensing image data, vegetation index data, and drought index data. The relevant data is stored in a data storage module for assessing vegetation resistance and resilience under drought stress based on this data.

[0047] After the preliminary work is completed, vegetation resistance and resilience assessment models are established for different response levels of vegetation under drought stress, based on the vegetation indices and lagged cumulative correlation coefficients corresponding to each response level. These assessment models include resistance and resilience indices for each response level under drought stress, as well as a comprehensive vegetation resistance and resilience index integrating all response levels. The constructed vegetation resistance and resilience assessment models are then placed in the analysis and evaluation module of a vegetation resistance and resilience assessment system based on lagged and cumulative effects under drought stress. This allows the system to more scientifically and accurately assess vegetation resistance and resilience.

[0048] Vegetation indices corresponding to each response level of vegetation under drought stress are used to characterize the degree of impact of drought stress on different vegetation levels, while lagged cumulative correlation coefficients are used to identify the time scale of drought stress that leads to vegetation resistance and recovery. The constructed vegetation resistance and resilience index assessment models, based on lag and cumulative effects, consider the physiological, structural, and functional response levels of vegetation to drought stress, making the assessment of vegetation resistance and resilience more accurate and comprehensive.

[0049] In embodiments of the present invention, the response levels of vegetation to drought stress include physiological, structural, and functional levels. Therefore, the corresponding vegetation indices include: vegetation physiological index, vegetation structural index, and vegetation functional index; the corresponding vegetation resistance indices include: vegetation physiological resistance index, vegetation structural resistance index, and vegetation functional resistance index; and the corresponding vegetation resilience indices include: vegetation physiological resilience index, vegetation structural resilience index, and vegetation functional resilience index. The calculation of these indices and the construction process of the evaluation model will be described in detail below with reference to specific implementation procedures.

[0050] 1. Calculation of vegetation indices: In this embodiment of the invention, three levels of response of vegetation under drought stress are included: physiological, structural and functional. Correspondingly, vegetation indices include vegetation physiological index, vegetation structural index and vegetation functional index.

[0051] For vegetation physiological indices, daylight-induced chlorophyll fluorescence (SIF) is a long-wavelength signal released by vegetation during photosynthesis, which can reflect the actual dynamic changes of vegetation from a physiological perspective. Therefore, SIF is used as a vegetation physiological index. SIF datasets were extracted from the database, and the SIF data were cropped using boundary vector data of the study area to obtain the vegetation physiological indices for the study area.

[0052] For vegetation structure indices, the Enhanced Vegetation Index (EVI), as an improvement on the Normalized Difference Vegetation Index (NDVI), has unique advantages in dynamic detection of vegetation structure by correcting for soil reflection and atmospheric scattering. Therefore, EVI was selected as the vegetation structure index. Landsat remote sensing images with cloud cover less than 10% during the growing season were extracted from the database. These images underwent radiometric calibration and atmospheric correction. The study area boundary was used as a mask for cropping to obtain the remote sensing image of the study area. Finally, batch calculations of the EVI were performed using the Google Earth Engine platform. The calculation formula is as follows:

[0053] In the formula, ρ NIR Near-infrared reflectance; ρ Red ρ is the reflectivity in the infrared band. Blue denoted as blue light reflectance; G is the adjustment coefficient, G = 2.5; C1 and C2 are atmospheric correction parameters, C1 = 6 and C2 = 7.5; L is the soil adjustment factor, L = 1. The calculated values ​​are then derived to obtain the vegetation structure index of the study area.

[0054] For vegetation function indices, Gross Primary Productivity (GPP) is the total amount of organic carbon fixed by vegetation per unit time through photosynthesis by absorbing atmospheric carbon dioxide. It characterizes the productive function of vegetation, therefore GPP is used as the vegetation function index. The MODIS MOD17A3HGF.v006 dataset was extracted from the database. The MODIS Reprojection Tool was used to extract, stitch, and mosaic the GPP data. Finally, the vegetation function index of the study area was obtained by cropping the boundary vector data of the study area.

[0055] 2. Construct assessment models for vegetation resistance index and vegetation resilience index.

[0056] 2.1 Establishing the Lag Cumulative Correlation Coefficient (LCCC): To clarify the timing and time scale of drought stress that leads to vegetation resistance and recovery, the lag cumulative correlation coefficient (LCCC) was constructed using the Pearson correlation coefficient method.

[0057] In the formula, E s Time series of vegetation indices; t represents the time series of the drought index; i represents the lag time of the vegetation index relative to drought stress; f represents the cumulative duration of the effect of drought stress on the vegetation index, f = 1, 2, 3…48; t n For time (month); and These are the average values ​​of the vegetation index and the drought index, respectively. When the LCCC reaches its maximum value, the corresponding i and f values ​​are recorded to determine the month and time scale of the SPEI that cause vegetation resistance and recovery.

[0058] 2.2 Calculation of Vegetation Resistance Index and Vegetation Resilience Index: The vegetation resistance index and vegetation resilience index are used to characterize the level of resistance and recovery of vegetation to drought stress. In the embodiments of this invention, the vegetation response to drought stress includes three levels: physiological, structural, and functional. Correspondingly, the vegetation resistance index includes: vegetation physiological resistance index, vegetation structural resistance index, and vegetation functional resistance index; the vegetation resilience index includes: vegetation physiological resilience index, vegetation structural resilience index, and vegetation functional resilience index. The vegetation physiological index, vegetation structural index, and vegetation functional index data obtained and calculated in step 1, and the SPEI data identified in step 2.1, are respectively input into the vegetation physiological resistance index and vegetation physiological resilience index evaluation model, the vegetation structural resistance index and vegetation structural resilience index evaluation model, and the vegetation functional resistance index and vegetation functional resilience index evaluation model.

[0059] The evaluation models for the vegetation physiological resistance index and the vegetation physiological resilience index are as follows:

[0060] In the formula, α SIF β is the physiological resistance index of vegetation. SIF f(SIF) represents the vegetation physiological resilience index. t Let be the vegetation physiological index at time t; ε is the standardized precipitation evapotranspiration index at time ti, f is the time scale of the SPEI index, and i and f are obtained by establishing the lagged cumulative correlation coefficient (LCCC) between vegetation physiological indices and SPEI indices at different time scales; t For model residuals;

[0061] The evaluation models for the vegetation structure resistance index and vegetation structure resilience index are as follows:

[0062] In the formula, α EVI β is the vegetation structure resistance index. EVI f(EVI) represents the resilience index of vegetation structure. t Let be the vegetation structure index at time t; ε is the standardized precipitation evapotranspiration index at time ti, f is the time scale of the SPEI index, and i and f are obtained by establishing the lagged cumulative correlation coefficient (LCCC) between the vegetation structure index and the SPEI index at different time scales; t For model residuals;

[0063] The evaluation models for the vegetation functional resistance index and the vegetation functional resilience index are as follows:

[0064] In the formula, α GPP β is the vegetation functional resistance index. GPP f(GPP) represents the vegetation functional resilience index. t Let be the vegetation function index at time t; ε is the standardized precipitation evapotranspiration index at time ti, f is the time scale of the SPEI index, and i and f are obtained by establishing the lagged cumulative correlation coefficient (LCCC) between the vegetation function index and the SPEI index at different time scales; t This represents the model residuals.

[0065] Where, α SIF and β SIF The α value was obtained by fitting the evaluation model based on the vegetation physiological resistance index and the vegetation physiological resilience index. EVI and β EVI The α value was obtained by fitting the vegetation structure resistance index and vegetation structure resilience index assessment model. GPP and β GPP The results were obtained by fitting the evaluation model of vegetation functional resistance index and vegetation functional resilience index.

[0066] 2.3 Calculate the vegetation resistance index and resilience index at each response level: Combine the α calculated in 2.2 SIF β SIF α EVI β EVI α GPP β GPP Substituting into the following formula: VS = w SIF α SIF +w EVI α EVI +w GPP α GPP VL = w SIF β SIF +w EVI β EVI +w GPP β GPP

[0067] In the formula, VS is the vegetation resistance index; α SIF α represents the physiological resistance index of vegetation. EVI α represents the resistance index of vegetation structure. GPP VL is the vegetation functional resistance index; β is the vegetation resilience index. SIF β is the vegetation physiological resilience index. EVI β is the vegetation structure resilience index. GPPThe vegetation functional resilience index; w SIF w EVI w GPP These are the weights for vegetation physiological resistance, vegetation structural resilience, and vegetation functional resilience, respectively. Since the physiological, structural, and functional aspects of vegetation are equally important during growth and development, therefore... SIF =w EVI =w GPP = 1 / 3. The larger the absolute values ​​of VS and VL, the smaller the resistance and resilience of the vegetation.

[0068] 3. Construct a classification system for vegetation resistance and resilience.

[0069] The vegetation resistance and resilience calculated in step 2 were normalized to a range of 0-1. The vegetation resistance and resilience were then classified into five levels as shown in Table 1, with the classification criteria as follows:

[0070] Table 1

[0071] Visualization of vegetation resistance and resilience indices based on regional display.

[0072] This invention takes vegetation under drought stress as the evaluation object and is based on the physiological, structural and functional response levels of vegetation. It can comprehensively, systematically and accurately evaluate and classify the resistance and resilience of vegetation under drought stress, effectively identify vegetation in the evaluation area that is difficult to resist drought or recover from drought stress, and is simple to operate and easy to implement.

Claims

1. A method for assessing vegetation resistance and resilience under drought stress, characterized in that, Includes the following steps: Using the vegetation under drought stress in the assessment area as the assessment object, data on the resistance and resilience of vegetation in the assessment area are obtained and stored. To address different response levels of vegetation under drought stress, lagged cumulative correlation coefficients were established between vegetation indices and SPEI indices at different time scales. Then, based on the vegetation indices and lagged cumulative correlation coefficients corresponding to each response level of vegetation under drought stress, vegetation resistance index and resilience index assessment models were established, and vegetation resistance index and resilience index of all response levels were integrated. Based on the classification method of vegetation resistance and resilience, the vegetation resistance index and resilience index are divided into several levels. Based on the classification level, the resistance and resilience of vegetation to drought stress are assessed.

2. The method for assessing vegetation resistance and resilience under drought stress according to claim 1, characterized in that, The vegetation resistance and resilience data include: remote sensing image data, vegetation index data, and drought index data.

3. The method for assessing vegetation resistance and resilience under drought stress according to claim 1, characterized in that, The lagged cumulative correlation coefficients between the vegetation index and the SPEI index at different time scales are as follows: In the formula, E s Time series representing vegetation indices; t represents the time series of the drought index; i is the lag time of the vegetation index relative to the drought index, N is the longest lag time of the vegetation index relative to the drought index; f is the cumulative duration of the drought index's influence on the vegetation index, in months, f = 1, 2, 3…48; n For time; and These are the average values ​​of the vegetation index and the drought index, respectively; the vegetation index is one of the following: vegetation physiological index, vegetation structure index, and vegetation function index. When the lagged cumulative correlation coefficient (LCCC) reaches its maximum value, record the lag time i of the vegetation index relative to the drought index and the cumulative influence duration f of the drought index on the vegetation index.

4. The method for assessing vegetation resistance and resilience under drought stress according to claim 1, characterized in that, The vegetation resistance index and resilience index assessment model is established based on the vegetation index and lagged cumulative correlation coefficient corresponding to each response level of vegetation under drought stress, and is obtained by the following formula: In the formula, V k This represents the vegetation index corresponding to the k-th response level; ti is the standardized precipitation evapotranspiration index at time ti, f is the time scale of the SPEI index, and i and f are obtained by establishing the lagged cumulative correlation coefficient (LCCC) between the vegetation index and the SPEI index at different time scales. ε t This represents the model residuals.

5. The method for assessing vegetation resistance and resilience under drought stress according to claim 1, characterized in that, The vegetation resistance index and resilience index, which integrate all response levels, are as follows: In the formula, VS represents the vegetation resistance index; α k VL represents the resistance index corresponding to the k-th response level of vegetation under drought stress; β represents the vegetation resilience index; k The resilience index represents the k-th level of vegetation response under drought stress; w k This represents the weight corresponding to the k-th response level of vegetation under drought stress; m represents the type of response level. Where, α k β k This was obtained by evaluating the model fit.

6. The method for assessing vegetation resistance and resilience under drought stress according to claim 1, characterized in that, The method for classifying vegetation resistance and resilience levels involves dividing the vegetation resistance index and resilience index into several levels, and assessing the vegetation's resistance and resilience to drought stress based on these levels. This includes the following steps: The vegetation resistance index and resilience index are normalized and compared with a set threshold range, and divided into multiple levels; the vegetation resistance index and resilience index are visualized based on regional display. The higher the resistance index and resilience index, the lower the resistance and resilience of the vegetation.

7. The vegetation resistance and resilience assessment system under drought stress according to claim 1, characterized in that, include: The data collection module is used to acquire data on vegetation resistance and resilience; The data storage module is used to store the vegetation resistance and resilience data; The data processing module is used to process the vegetation index corresponding to each response level according to the response level of vegetation to drought stress. The analysis and evaluation module is used to analyze and evaluate vegetation resistance and resilience.

8. The vegetation resistance and resilience assessment system under drought stress according to claim 7, characterized in that, The data processing module includes: The vegetation physiological index processing submodule is used to obtain vegetation physiological indices based on vegetation index data. The vegetation structure index processing submodule is used to obtain the vegetation structure index based on remote sensing image data; The vegetation function index processing submodule is used to obtain the vegetation function index based on the vegetation index data.

9. The vegetation resistance and resilience assessment system under drought stress according to claim 7, characterized in that, The analysis and evaluation module includes: The lagged cumulative correlation coefficient calculation submodule is used to establish lagged cumulative correlation coefficients between vegetation indices and SPEI indices at different time scales for different response levels of vegetation under drought stress. The vegetation resistance index and resilience index assessment model submodule is used to establish a vegetation resistance index and resilience index assessment model based on the vegetation index and lagged cumulative correlation coefficient corresponding to each response level of vegetation under drought stress, and to integrate the vegetation resistance index and resilience index of all response levels. The vegetation resistance and resilience classification submodule is used to classify the vegetation resistance index and resilience index into several levels according to the vegetation resistance and resilience classification method, and to evaluate the vegetation's resistance and resilience to drought stress based on the classified levels.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method for assessing vegetation resistance and resilience under drought stress based on hysteresis and cumulative effects as described in any one of claims 1-6.

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