Methods, apparatus, equipment and media for assessing vegetation ecological resilience

Abstract

This application discloses a method, apparatus, equipment, and medium for assessing the ecological resilience of vegetation. The method includes: determining the LAI, NPP, WUE, and NDII of a target vegetation within a target time series; determining the extreme drought years for the target vegetation within the target time series; calculating stability, resistance, and resilience indices of the target vegetation during the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages based on the extreme drought years, LAI, NPP, WUE, and NDII; and determining the ecological resilience assessment results of the target vegetation based on the stability, resistance, and resilience indices for each regulation stage. According to the scheme provided in the embodiments of this application, multidimensional data of the target vegetation can be comprehensively considered to deeply characterize the multi-regulation processes of the ecosystem resilience of the target vegetation, improving the accuracy of the resilience assessment results, and thus providing an effective data foundation for ecological restoration strategies of the target vegetation.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of ecological resilience analysis technology, and in particular to a method, apparatus, equipment and medium for assessing the ecological resilience of vegetation. Background Technology

[0002] Assessing the ecological resilience of vegetation provides a solid data foundation for determining ecological restoration strategies for vegetation when it faces subsequent disturbances. Existing methods for assessing vegetation ecological resilience rely on a single vegetation index (such as the Normalized Difference Vegetation Index (NDVI)) to represent the state and function of the corresponding ecosystem. However, NDVI only reflects the overall greenness of the vegetation and cannot reveal the deep mechanisms of resilience formation, thus failing to guarantee the accuracy of ecological resilience assessment results and consequently failing to provide a solid data foundation for ecological restoration strategies for that vegetation. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and medium for assessing vegetation ecological resilience, which can effectively improve the accuracy of ecological resilience assessment results.

[0004] In a first aspect, embodiments of this application provide a method for assessing vegetation ecological resilience, including:

[0005] Determine the leaf area index (LAI), net primary productivity (NPP), water use efficiency (WUE), and normalized infrared index (NDII) of the target vegetation within the target time series, and determine the extreme drought years of the target vegetation within the target time series.

[0006] Based on the extreme drought year, LAI, NPP, WUE, and NDII, the stability, resistance, and resilience indices of the target vegetation were calculated in the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages, respectively.

[0007] The ecological resilience assessment results of the target vegetation are determined based on the stability index, resistance index, and resilience index at each adjustment stage.

[0008] In some embodiments, the steps for determining the WUE and the NDII include:

[0009] Acquire the total primary productivity (GPP), evapotranspiration (ET), near-infrared (NIR), and short-wave infrared (SWIR) data of the target vegetation.

[0010] Divide GPP by ET to obtain the WUE;

[0011] The difference between NIR and SWIR is determined as a first value. The sum of NIR and SWIR is used to obtain a second value. The first value is divided by the second value to obtain the NDII.

[0012] In some embodiments, based on the extreme drought year, LAI, NPP, WUE, and NDII, stability, resistance, and resilience indices of the target vegetation are calculated during the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages, respectively, including:

[0013] The first stability index of the target vegetation in the evapotranspiration regulation stage is calculated based on the first algorithm and the WUE. The second stability index of the target vegetation in the canopy water content regulation stage is calculated based on the first algorithm and the NDII. The third stability index of the target vegetation in the carbon regulation stage is calculated based on the first algorithm, the LAI and the NPP. The input parameters of the first algorithm include the LAI, the NPP, the WUE and the NDII corresponding to the extreme drought year.

[0014] The first resistance index of the target vegetation in the evapotranspiration regulation stage is calculated based on the second algorithm and the WUE. The second resistance index of the target vegetation in the canopy water content regulation stage is calculated based on the second algorithm and the NDII. The third resistance index of the target vegetation in the carbon regulation stage is calculated based on the second algorithm, the LAI and the NPP. The input parameters of the second algorithm include the LAI, the NPP, the WUE and the NDII corresponding to the extreme drought year.

[0015] The first resilience index of the target vegetation in the evapotranspiration regulation stage is calculated based on the third algorithm and the WUE. The second resilience index of the target vegetation in the canopy water content regulation stage is calculated based on the third algorithm and the NDII. The third resilience index of the target vegetation in the carbon regulation stage is calculated based on the third algorithm, the LAI, and the NPP. The input parameters of the third algorithm include the LAI, the NPP, the WUE, and the NDII corresponding to the extreme drought year.

[0016] In some embodiments, the first stability index, the second stability index, and the third stability index are calculated using the first algorithm, and obtained according to the following formula:

[0017] ;

[0018] ;

[0019] in, Standard deviationt This is the adjustment phase. i For any time node within the target time series. n The total number of the aforementioned time points. As a stability indicator, As the primary stability indicator, As the second stability index, As the third stability index, for and The average value, As a basic stability index based on LAI calculation, As a fundamental stability metric based on NPP calculations, The standard deviation of the evapotranspiration regulation stage is given. The standard deviation of the canopy water content adjustment stage is given. and The standard deviation of the carbon regulation phase is given. In The WUE is the value of the extreme drought year within the target time series. In The average value of all WUEs within the target time series. In The NDII for the extreme drought years within the target time series. In The average of all NDII values ​​within the target time series. In The LAI corresponding to the extremely dry years within the target time series. In The average value of all LAIs within the target time series. In The NPP corresponding to the extreme drought year within the target time series. In The average value of all NPPs within the target time series. In The WUE is the value of the extreme drought year within the target time series. In The NDII for the extreme drought years within the target time series. In The LAI corresponding to the extremely dry years within the target time series. In The NPP is the NPP corresponding to the extremely dry year within the target time series.

[0020] In some embodiments, the first resistance index, the second resistance index, and the third resistance index are calculated using the second algorithm, and obtained according to the following formula:

[0021] ;

[0022] in, As an indicator of resistance, t This is the adjustment phase. As the primary indicator of resistance, As the second indicator of resistance, As the third indicator of resistance, In The WUE is the value of the extreme drought year within the target time series. In It is the average WUE for the remaining time points within the target time series, excluding the extremely dry years. In The NDII for the extreme drought years within the target time series. In It is the average NDII for the remaining time points within the target time series, excluding the extremely dry years. for and The average value, This is a basic resistance index calculated based on LAI. As a basic resistance index calculated based on NPP, In The LAI corresponding to the extremely dry years within the target time series. In It is the average LAI (Lower Amount Indicator) for the remaining time points within the target time series, excluding the extremely dry years. In The NPP corresponding to the extreme drought year within the target time series. In It is the average NPP for the remaining time points within the target time series, excluding the extremely dry years.

[0023] In some embodiments, the first resilience index, the second resilience index, and the third resilience index are calculated using the third algorithm, and obtained according to the following formula:

[0024] ;

[0025] in, As an indicator of resilience, t This is the adjustment phase. As the primary indicator of resilience, As the second resilience indicator, As the third resilience indicator, In The WUE is the value of the extreme drought year within the target time series. In The WUE is the value two years after the extremely dry year. In It is the average WUE for the remaining time points within the target time series, excluding the extremely dry years. In The NDII for the extreme drought years within the target time series. In The NDII is the value two years after the extremely dry year. In It is the average NDII for the remaining time points within the target time series, excluding the extremely dry years. for and The average value, As a baseline resilience index calculated based on LAI, As a basic resilience index calculated based on NPP, In The LAI corresponding to the extremely dry years within the target time series. In The LAI is the corresponding value two years after the extremely dry year. In It is the average LAI (Lower Amount Indicator) for the remaining time points within the target time series, excluding the extremely dry years. In The NPP corresponding to the extreme drought year within the target time series. In The NPP is the value two years after the extremely dry year. In It is the average NPP for the remaining time points within the target time series, excluding the extremely dry years.

[0026] In some embodiments, the ecological resilience assessment results of the target vegetation are determined based on the stability index, the resistance index, and the resilience index at each adjustment stage, including:

[0027] The stability index, resistance index, and resilience index for each regulation stage are standardized and calculated to obtain corresponding reference stability index, reference resistance index, and reference resilience index. The reference stability index includes a first reference stability index corresponding to the evapotranspiration regulation stage, a second reference stability index corresponding to the canopy water content regulation stage, and a third reference stability index corresponding to the carbon regulation stage. The reference resistance index includes a first reference resistance index corresponding to the evapotranspiration regulation stage, a second reference resistance index corresponding to the canopy water content regulation stage, and a third reference resistance index corresponding to the carbon regulation stage. The reference resilience index includes a first reference resilience index corresponding to the evapotranspiration regulation stage, a second reference resilience index corresponding to the canopy water content regulation stage, and a third reference resilience index corresponding to the carbon regulation stage.

[0028] The first reference stability index, the first reference resistance index, and the first reference resilience index are input into the Euclidean distance model to calculate the first toughness index of the evapotranspiration regulation stage.

[0029] The second reference stability index, the second reference resistance index, and the second reference resilience index are input into the Euclidean distance model to calculate the second toughness index of the canopy water content adjustment stage.

[0030] The third reference stability index, the third reference resistance index, and the third reference resilience index are input into the Euclidean distance model to calculate the third resilience index of the canopy water content adjustment stage.

[0031] The first resilience index, the second resilience index, and the third resilience index are fused to obtain the ecological resilience assessment results of the target vegetation.

[0032] Secondly, embodiments of this application provide a control device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the vegetation ecological resilience assessment method as described in the first aspect.

[0033] Thirdly, embodiments of this application also provide an electronic device, including the control device of the second aspect.

[0034] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions for performing the vegetation ecological resilience assessment method as described in the first aspect.

[0035] This application provides a method, apparatus, equipment, and medium for assessing vegetation ecological resilience. The method includes: determining the leaf area index (LAI), net primary productivity (NPP), water use efficiency (WUE), and normalized infrared index (NDII) of a target vegetation within a target time series, and determining the extreme drought years for the target vegetation within the target time series; calculating stability, resistance, and resilience indices for the target vegetation in the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages based on the extreme drought years, LAI, NPP, WUE, and NDII; and determining the ecological resilience assessment result of the target vegetation based on the stability, resistance, and resilience indices for each regulation stage. According to the scheme provided by this application, multidimensional data of the target vegetation can be comprehensively considered to deeply characterize the multi-regulation processes of the ecosystem resilience of the target vegetation, improving the accuracy of the resilience assessment results and thus providing an effective data foundation for ecological restoration strategies of the target vegetation. Attached Figure Description

[0036] Figure 1 This is a flowchart of the steps of a vegetation ecological resilience assessment method provided in one embodiment of this application;

[0037] Figure 2 This is a structural diagram of a control device provided in another embodiment of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0040] Assessing the ecological resilience of vegetation provides a solid data foundation for determining ecological restoration strategies for vegetation when it faces subsequent disturbances. Existing methods for assessing vegetation ecological resilience rely on a single vegetation index (such as the Normalized Difference Vegetation Index (NDVI)) to represent the state and function of the corresponding ecosystem. However, NDVI only reflects the overall greenness of the vegetation and cannot reveal the deep mechanisms of resilience formation, thus failing to guarantee the accuracy of ecological resilience assessment results and consequently failing to provide a solid data foundation for ecological restoration strategies for that vegetation.

[0041] To address the aforementioned problems, this application provides a method, apparatus, equipment, and medium for assessing vegetation ecological resilience. The method includes: determining the leaf area index (LAI), net primary productivity (NPP), water use efficiency (WUE), and normalized difference infrared index (NDII) of a target vegetation within a target time series, and determining the extreme drought years for the target vegetation within the target time series; calculating stability, resistance, and resilience indices for the target vegetation during the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages, respectively, based on the extreme drought years, LAI, NPP, WUE, and NDII; and determining the ecological resilience assessment results of the target vegetation based on the stability, resistance, and resilience indices for each regulation stage. According to the solution provided by this application, multidimensional data of the target vegetation can be comprehensively considered to deeply characterize the multi-regulation processes of the ecosystem resilience of the target vegetation, improving the accuracy of the resilience assessment results and thus providing an effective data foundation for ecological restoration strategies of the target vegetation.

[0042] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0043] refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a vegetation ecological resilience assessment method according to an embodiment of this application. This application provides a vegetation ecological resilience assessment method, which includes, but is not limited to, the following steps:

[0044] Step S10: Determine the leaf area index (LAI), net primary productivity (NPP), water use efficiency (WUE), and normalized infrared index (NDII) of the target vegetation within the target time series, and determine the extreme drought years for the target vegetation within the target time series.

[0045] It is understood that, before assessing the ecological resilience of the target vegetation, this embodiment first determines the multidimensional data of the target vegetation within the target time series, including leaf area index (LAI), net primary productivity (NPP), water use efficiency (WUE), normalized difference infrared index (NDII), and extreme drought years. This data is used to determine the stability, resistance, and resilience indicators of the target vegetation ecosystem in the three stages of evapotranspiration regulation, canopy water content regulation, and carbon regulation. Only after obtaining these multidimensional indicators can the resilience of the vegetation ecosystem be more comprehensively and deeply characterized. Compared with existing resilience assessment methods that rely solely on a single vegetation index (such as NDVI or EVI) to represent the state and function of the entire vegetation ecosystem, this embodiment can effectively improve the accuracy of the resilience assessment results of the target vegetation and provide an effective data foundation for subsequent ecological restoration strategies for the target vegetation.

[0046] It should be noted that the extreme drought year in this embodiment corresponds to the extreme drought year of each pixel in the target vegetation. Here, a pixel is the smallest basic unit in the target vegetation, and the target vegetation is composed of multiple pixels.

[0047] It should be noted that in this embodiment, the method for determining the extreme drought years of the target vegetation within the target time series is to first obtain drought index data SPEI-12 from the Science Data Bank, and then use Python code to identify the extreme drought years corresponding to each pixel in the target vegetation based on SPEI-12. Specifically, assuming that SPEI-12 includes monthly drought index data from 1984 to 2024 (assumed to be the target time series), this embodiment first obtains the drought index (SPEI value) of each pixel for each month of each year within the target time series from SPEI-12, determines the target threshold based on the percentile of the SPEI value (in this embodiment, the target threshold is 10%), and defines the year corresponding to any pixel whose SPEI value is less than or equal to 10% within the target time series as an extreme drought year, meaning that only 10% of the years within the entire target time series (41 years) are drier than this year. Understandably, after determining the extreme drought year for each pixel in the target vegetation, this extreme drought year can serve as a reference time anchor, marking the point at which the ecosystem of the corresponding pixel begins to be disturbed. Only by comparing the multidimensional data (LAI, NPP, WUE, etc.) corresponding to the extreme drought year with the multidimensional data before and after the extreme drought year can the extent of the impact of the extreme drought year on each pixel of the target vegetation, as well as the pixel's resistance and resilience, be determined, thus quantifying the pixel's ecological resilience. Otherwise, data such as GPP, ET, and LAI are merely a collection of numbers that change over time and cannot be interpreted as a "response" to extreme climate, making it impossible to calculate ecological resilience indicators.

[0048] Specifically, in some embodiments, the determination steps of WUE and NDII in step S10 include, but are not limited to, the following steps:

[0049] Step S11: Obtain the total primary productivity (GPP), evapotranspiration (ET), near-infrared (NIR), and short-wave infrared (SWIR) data of the target vegetation.

[0050] Step S12: Divide GPP by ET to obtain WUE;

[0051] Step S13: Determine the difference between NIR and SWIR as the first value, sum NIR and SWIR to obtain the second value, and divide the first value by the second value to obtain NDII.

[0052] Specifically, in this embodiment, GPP and NPP are obtained from the MOD17A3HGF dataset of the GEE platform, ET is obtained from the MOD16A2GF dataset, LAI is obtained from the MOD15A2H dataset, and NIR and SWIR are obtained from the MOD09A1 dataset.

[0053] Understandably, determining WUE and NDII can provide an effective data basis for subsequently identifying the stability, resistance, and resilience indicators of the target vegetation ecosystem in the three stages of evapotranspiration regulation, canopy water content regulation, and carbon regulation.

[0054] Step S20: Based on the extreme drought year, LAI, NPP, WUE, and NDII, calculate the stability, resistance, and resilience indices of the target vegetation in the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages, respectively.

[0055] It is understood that this embodiment calculates the stability, resistance, and resilience indices of the target vegetation in the evapotranspiration regulation, canopy water content regulation, and carbon regulation stages based on extreme drought years, LAI, NPP, WUE, and NDII. Thus, this embodiment sets nine resilience indices for each pixel of the target vegetation, which can provide an effective data foundation for calculating more accurate ecological resilience assessment results that provide a deeper characterization of the target vegetation.

[0056] It should be noted that the method in this embodiment also includes mosaicking and cropping the data of LAI, NPP, WUE and NDII by study area before calculating stability, resistance and resilience indices. The data is then resampled to 1km resolution, so that the preprocessed data are spatially aligned, scale-consistent and range-accurate datasets, providing an effective data foundation for subsequent calculation of accurate ecological resilience.

[0057] Specifically, in this embodiment, step S20 includes, but is not limited to, the following steps:

[0058] Step S21: Calculate the first stability index of the target vegetation in the evapotranspiration regulation stage based on the first algorithm and WUE; calculate the second stability index of the target vegetation in the canopy water content regulation stage based on the first algorithm and NDII; calculate the third stability index of the target vegetation in the carbon regulation stage based on the first algorithm, LAI, and NPP. The input parameters of the first algorithm include LAI, NPP, WUE, and NDII corresponding to the extreme drought year.

[0059] Step S22: Calculate the first resistance index of the target vegetation in the evapotranspiration regulation stage based on the second algorithm and WUE; calculate the second resistance index of the target vegetation in the canopy water content regulation stage based on the second algorithm and NDII; calculate the third resistance index of the target vegetation in the carbon regulation stage based on the second algorithm, LAI, and NPP. The input parameters of the second algorithm include LAI, NPP, WUE, and NDII corresponding to the extreme drought year.

[0060] Step S23: Calculate the first resilience index of the target vegetation in the evapotranspiration regulation stage based on the third algorithm and WUE; calculate the second resilience index of the target vegetation in the canopy water content regulation stage based on the third algorithm and NDII; calculate the third resilience index of the target vegetation in the carbon regulation stage based on the third algorithm, LAI, and NPP. The input parameters of the third algorithm include LAI, NPP, WUE, and NDII corresponding to the extreme drought year.

[0061] It should be noted that the first stability index, the second stability index, and the third stability index are calculated using the first algorithm, and are obtained according to the following formula:

[0062] ;

[0063] ;

[0064] in, Standard deviation t This is the adjustment phase. i For any time node within the target time series. nThe total number of time points. As a stability indicator, As the primary stability indicator, As the second stability index, As the third stability index, for and The average value, As a basic stability index based on LAI calculation, As a fundamental stability metric based on NPP calculations, The standard deviation of the evapotranspiration regulation phase. The standard deviation of the canopy water content adjustment stage. and The standard deviation of the carbon regulation phase. In The WUE is the value of the extreme drought year within the target time series. In The average value of WUE across all values ​​within the target time series. In The NDII for the most extreme drought years within the target time series. In The average of all NDII values ​​within the target time series. In The LAI corresponding to the most extreme drought years within the target time series. In The average of all LAIs within the target time series. In The NPP corresponding to the extremely dry years within the target time series. In The average of all NPPs within the target time series. In The WUE is the value of the extreme drought year within the target time series. In The NDII for the most extreme drought years within the target time series. In The LAI corresponding to the most extreme drought years within the target time series. In The NPP is the NPP corresponding to the extremely dry years within the target time series.

[0065] In other words, referring to the description of the above embodiments, the first stability index of each pixel of the target vegetation during the evapotranspiration regulation stage is calculated using the calculation formula corresponding to the first algorithm described above. Second stability index of canopy water content regulation stage The third stability index in the carbon regulation phase .

[0066] It should be noted that the first resistance index, the second resistance index, and the third resistance index are calculated using the second algorithm, and are obtained according to the following formula:

[0067] ;

[0068] in, As an indicator of resistance, t This is the adjustment phase. As the primary indicator of resistance, As the second indicator of resistance, As the third indicator of resistance, In The WUE is the value of the extreme drought year within the target time series. In This represents the average WUE for the remaining time points within the target time series, excluding years of extreme drought. In The NDII for the most extreme drought years within the target time series. In This represents the average NDII for the remaining time points within the target time series, excluding years of extreme drought. for and The average value, This is a basic resistance index calculated based on LAI. As a basic resistance index calculated based on NPP, In The LAI corresponding to the most extreme drought years within the target time series. In This represents the average LAI (Labour Area Index) for the remaining time points within the target time series, excluding years of extreme drought. In The NPP corresponding to the extremely dry years within the target time series. In This represents the average NPP for the remaining time points within the target time series, excluding years of extreme drought.

[0069] In other words, referring to the description of the above embodiments, the first resistance index of each pixel of the target vegetation during the evapotranspiration regulation stage is calculated using the calculation formula corresponding to the second algorithm described above. Second resistance index during the canopy water content regulation stage The third resistance index in the carbon regulation phase .

[0070] It should be noted that the first, second, and third resilience indices are calculated using the third algorithm, and are obtained according to the following formula:

[0071] ;

[0072] in, As an indicator of resilience, t This is the adjustment phase. As the primary indicator of resilience, As the second resilience indicator, As the third resilience indicator, In The WUE is the value of the extreme drought year within the target time series. In This represents the WUE two years after an extremely dry year. In This represents the average WUE for the remaining time points within the target time series, excluding years of extreme drought. In The NDII for the most extreme drought years within the target time series. In This refers to the NDII corresponding to an extremely dry year two years later. In This represents the average NDII for the remaining time points within the target time series, excluding years of extreme drought. for and The average value, As a baseline resilience index calculated based on LAI, As a basic resilience index calculated based on NPP, In The LAI corresponding to the most extreme drought years within the target time series. In This refers to the LAI (Location Area) two years after an extremely dry year. In This represents the average LAI (Labour Area Index) for the remaining time points within the target time series, excluding years of extreme drought. In The NPP corresponding to the extremely dry years within the target time series. In This represents the NPP two years after an extremely dry year. In This represents the average NPP for the remaining time points within the target time series, excluding years of extreme drought.

[0073] In other words, referring to the description of the above embodiments, the first resilience index of each pixel of the target vegetation during the evapotranspiration regulation stage is calculated using the calculation formula corresponding to the third algorithm described above. Second resilience index during the canopy water content regulation phase The third resilience index in the carbon regulation phase .

[0074] Step S30: Determine the ecological resilience assessment results of the target vegetation based on the stability index, resistance index and resilience index of each regulation stage.

[0075] It is understandable that the evapotranspiration regulation stage refers to the early stage of drought. The stability, resistance, and resilience indicators corresponding to this period can indicate the vegetation's ability to reduce water loss by regulating stomata. The canopy water content regulation stage corresponds to the middle stage of drought. At this time, the vegetation begins to consume the water in its own tissues, and the leaf water potential decreases. The stability, resistance, and resilience indicators corresponding to this period are related to the physiological functions and tissue vitality of the vegetation. The carbon regulation stage corresponds to the middle and late stages of drought to the recovery period. Long-term or severe drought begins to affect the core physiological process of photosynthesis in vegetation. The stability, resistance, and resilience indicators corresponding to this period are related to the vegetation's ability to survive and grow. The target vegetation exhibits different values ​​for its multidimensional index parameters (including NPP, LAI, etc.) at different stages. As described in the above embodiments, the specific values ​​of different types of parameters can reflect the survival ability of the target vegetation at different stages. It is one-sided to determine the ecological resilience of the target vegetation by using only a single type of index parameter. For example, the vegetation may be less resilient in the evapotranspiration regulation stage and the canopy water content regulation stage, but may be more resilient in the carbon regulation stage. Overall, the resilience is moderate. It is necessary to use a combination of multidimensional indicators to evaluate the ecological resilience of the target vegetation in order to make the evaluation results more mechanistic and interpretable.

[0076] Specifically, in some embodiments, step S30 includes, but is not limited to, the following steps:

[0077] Step S31: Perform data standardization calculations on the stability index, resistance index, and resilience index of each regulation stage to obtain the corresponding reference stability index, reference resistance index, and reference resilience index. The reference stability index includes the first reference stability index corresponding to the evapotranspiration regulation stage, the second reference stability index corresponding to the canopy water content regulation stage, and the third reference stability index corresponding to the carbon regulation stage. The reference resistance index includes the first reference resistance index corresponding to the evapotranspiration regulation stage, the second reference resistance index corresponding to the canopy water content regulation stage, and the third reference resistance index corresponding to the carbon regulation stage. The reference resilience index includes the first reference resilience index corresponding to the evapotranspiration regulation stage, the second reference resilience index corresponding to the canopy water content regulation stage, and the third reference resilience index corresponding to the carbon regulation stage.

[0078] Step S32: Input the first reference stability index, the first reference resistance index, and the first reference resilience index into the Euclidean distance model to calculate the first toughness index of the evapotranspiration regulation stage.

[0079] Step S33: Input the second reference stability index, the second reference resistance index, and the second reference resilience index into the Euclidean distance model to calculate the second toughness index during the canopy water content adjustment stage.

[0080] Step S34: Input the third reference stability index, the third reference resistance index, and the third reference resilience index into the Euclidean distance model to calculate the third resilience index during the canopy water content adjustment stage.

[0081] Step S35: Data fusion of the first resilience index, the second resilience index and the third resilience index is performed to obtain the ecological resilience assessment results of the target vegetation.

[0082] Furthermore, it should be noted that after calculating the stability, resistance, and resilience indices for each regulation stage, directly weighting and fusing these indices to obtain the final ecological resilience assessment result means that the weights associated with this assessment result depend entirely on the statistical characteristics (such as the dispersion of index values) of the indicator data used (stability, resistance, and resilience indices for each regulation stage). Thus, the assessment result lacks ecological mechanistic support. Changing the vegetation or time frame will alter the weights, meaning that resilience values ​​obtained from different vegetation types cannot be directly compared in absolute terms. Instead, they can only be used to relatively rank the resilience of individual pixels within a single vegetation type, resulting in weak universality.

[0083] Based on this, this embodiment first standardizes the data of each index to obtain corresponding reference indices, providing a unified standard for subsequent calculation of resilience indices for the corresponding adjustment stages. Next, using a Euclidean distance model, the first reference stability index, the first reference resistance index, and the first reference resilience index are input into the Euclidean distance model to calculate the first resilience index for the evapotranspiration adjustment stage. The second reference stability index, the second reference resistance index, and the second reference resilience index are then input into the Euclidean distance model to calculate the second resilience index for the canopy water content adjustment stage. Finally, the third reference stability index, the third reference resistance index, and the second reference resilience index are input into the Euclidean distance model to calculate the second resilience index for the canopy water content adjustment stage. The third reference resilience index is input into the Euclidean distance model to calculate the third resilience index of the canopy water content regulation stage. That is, by calculating the geometric distance between various vegetation state indicators (stability index, resistance index, resilience index) and the preset ideal state, the resilience value of each regulation process and the final comprehensive ecological resilience value are quantified and integrated. This makes the assessment results have clearer physical meaning and better cross-regional comparability. The assessment results are stable and less affected by data distribution. It overcomes the volatility and incomparability of results caused by the weight dependence of sample statistical characteristics in weighted fusion and other methods, and significantly improves the mechanism and universality of the method.

[0084] Specifically, in this embodiment, the ecological resilience assessment result of the target vegetation is obtained by fusing the data of the first resilience index, the second resilience index, and the third resilience index.

[0085] Specifically, in this embodiment, the resilience index for any adjustment stage is calculated using the Euclidean distance model according to the following formula:

[0086] ;

[0087] ;

[0088] in, R si This represents the initial resilience index for any pixel at any adjustment stage. S t , R t and H t These are the normalized values ​​of the stability, resistance, and resilience indices of the target vegetation in the corresponding pixels. S tmin , R tmin and H tmin These represent the worst-case states of the target vegetation in terms of stability, resistance, and resilience in the corresponding pixels;R si Scale to the range [0,1] to get R s This makes subsequent comparison and classification easier, when R s The closer to 1, the better the corresponding resilience index; when R s The closer it is to 0, the worse its resilience index is.

[0089] like Figure 2 As shown, Figure 2 This is a structural diagram of a control device provided in one embodiment of this application. The present invention also provides a control device 200, comprising:

[0090] The processor 210 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0091] The memory 220 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 220 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 220 and called and executed by the processor 210 using the vegetation ecological resilience assessment method of the embodiments of this application.

[0092] Input / output interface 230 is used to implement information input and output;

[0093] The communication interface 240 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0094] Bus 250 transmits information between various components of the device (e.g., processor 210, memory 220, input / output interface 230, and communication interface 240);

[0095] The processor 210, memory 220, input / output interface 230 and communication interface 240 are connected to each other within the device via bus 250.

[0096] In addition, this application also provides an electronic device, including the control device 200 described in the above embodiments.

[0097] In addition, this application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described vegetation ecological resilience assessment method.

[0098] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0100] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method of assessing ecological resilience of vegetation, characterized by, The method comprises the following steps: determining leaf area index data LAI, vegetation net primary productivity data NPP, water use efficiency WUE, normalized difference infrared index data NDII of target vegetation in a target time series, and determining extreme drought years of the target vegetation in the target time series; based on the extreme drought years, LAI, NPP, WUE and NDII, calculating the stability index, resistance index and recovery index of the target vegetation in the evapotranspiration adjustment stage, canopy water content adjustment stage and carbon adjustment stage, respectively; determining the ecological resilience evaluation result of the target vegetation based on the stability index, resistance index and recovery index of each adjustment stage; based on the extreme drought years, LAI, NPP, WUE and NDII, calculating the stability index, resistance index and recovery index of the target vegetation in the evapotranspiration adjustment stage, canopy water content adjustment stage and carbon adjustment stage, respectively, comprising: based on a first algorithm and the WUE, calculating a first stability index of the target vegetation in the evapotranspiration adjustment stage, based on the first algorithm and the NDII, calculating a second stability index of the target vegetation in the canopy water content adjustment stage, and based on the first algorithm, the LAI and the NPP, calculating a third stability index of the target vegetation in the carbon adjustment stage, wherein the input parameters of the first algorithm include the LAI, the NPP, the WUE and the NDII corresponding to the extreme drought years; based on a second algorithm and the WUE, calculating a first resistance index of the target vegetation in the evapotranspiration adjustment stage, based on the second algorithm and the NDII, calculating a second resistance index of the target vegetation in the canopy water content adjustment stage, and based on the second algorithm, the LAI and the NPP, calculating a third resistance index of the target vegetation in the carbon adjustment stage, wherein the input parameters of the second algorithm include the LAI, the NPP, the WUE and the NDII corresponding to the extreme drought years; based on a third algorithm and the WUE, calculating a first recovery index of the target vegetation in the evapotranspiration adjustment stage, based on the third algorithm and the NDII, calculating a second recovery index of the target vegetation in the canopy water content adjustment stage, and based on the third algorithm, the LAI and the NPP, calculating a third recovery index of the target vegetation in the carbon adjustment stage, wherein the input parameters of the third algorithm include the LAI, the NPP, the WUE and the NDII corresponding to the extreme drought years.

2. The method of assessing ecological resilience of vegetation of claim 1, wherein, The determination of the WUE and the NDII comprises: obtaining vegetation gross primary productivity data GPP, evapotranspiration data ET, near-infrared band data NIR and short-wave infrared band data SWIR of the target vegetation; dividing GPP by ET to obtain the WUE; determining the difference between NIR and SWIR as a first value, summing the NIR and the SWIR to obtain a second value, and dividing the first value by the second value to obtain the NDII.

3. The method of assessing ecological resilience of vegetation of claim 1, wherein, The first stability index, the second stability index and the third stability index are calculated by using the first algorithm, and are obtained according to the following formula: ； ； wherein, is the standard deviation, t is the adjustment phase, i is any time node within the target time series, n is the total number of time nodes, is the stability index, is the first stability index, is the second stability index, is the third stability index, is is the average of is the base stability index calculated based on LAI, is the base stability index calculated based on NPP, is the standard deviation of the evapotranspiration adjustment phase, is the standard deviation of the canopy water content adjustment phase, and is the standard deviation of the carbon adjustment phase, is is the WUE corresponding to the extreme drought year within the target time series, is is the average of all WUEs within the target time series, is is the NDII corresponding to the extreme drought year within the target time series, is is the average of all NDII within the target time series, is is the LAI corresponding to the extreme drought year within the target time series, is is the average of all LAI within the target time series, is is the NPP corresponding to the extreme drought year within the target time series, is is the average of all NPP within the target time series, is is the WUE corresponding to the extreme drought year within the target time series, is is the NDII corresponding to the extreme drought year within the target time series, is is the LAI corresponding to the extreme drought year within the target time series, is ​NPP of the extreme drought year corresponding to the target time series.

4. The method of assessing ecological resilience of vegetation of claim 1, wherein, The first resistance index, the second resistance index and the third resistance index are calculated by using the second algorithm, and are obtained according to the following formula: ； wherein, is a resistance index, t is an adjustment phase, is a first resistance index, is a second resistance index, is a third resistance index, in is the WUE corresponding to the extreme drought year within the target time series, in is the average of the WUE corresponding to the time nodes remaining within the target time series except for the extreme drought year, in is the NDII corresponding to the extreme drought year within the target time series, in is the average of the NDII corresponding to the time nodes remaining within the target time series except for the extreme drought year, is and is the average of, is a basic resistance index calculated based on LAI, is a basic resistance index calculated based on NPP, in is the LAI corresponding to the extreme drought year within the target time series, in is the average of the LAI corresponding to the time nodes remaining within the target time series except for the extreme drought year, in is the NPP corresponding to the extreme drought year within the target time series, in is the average of the NPP corresponding to the time nodes remaining within the target time series except for the extreme drought year.

5. The method of assessing ecological resilience of vegetation of claim 1, wherein, The first resilience index, the second resilience index and the third resilience index are calculated by using the third algorithm, and are obtained according to the following formula: ； wherein, is a recovery force indicator, t is an adjustment phase, is a first recovery force indicator, is a second recovery force indicator, is a third recovery force indicator, in is the WUE corresponding to the extreme drought year within the target time series, in is the WUE corresponding to two years after the extreme drought year, in is the average of the WUE corresponding to the time nodes remaining within the target time series except for the extreme drought year, in is the NDII corresponding to the extreme drought year within the target time series, in is the NDII corresponding to two years after the extreme drought year, in is the average of the NDII corresponding to the time nodes remaining within the target time series except for the extreme drought year, is and is the average of, is a basic recovery force indicator calculated based on LAI, is a basic recovery force indicator calculated based on NPP, in is the LAI corresponding to the extreme drought year within the target time series, in is the LAI corresponding to two years after the extreme drought year, in is the average of the LAI corresponding to the time nodes remaining within the target time series except for the extreme drought year, in is the NPP corresponding to the extreme drought year within the target time series, in is the NPP corresponding to two years after the extreme drought year, in is the average of the NPP corresponding to the time nodes remaining within the target time series except for the extreme drought year.

6. The method of assessing ecological resilience of vegetation of claim 1, wherein, The ecological resilience evaluation result of the target vegetation is determined based on the stability index, the resistance index and the resilience index of each adjustment stage, and includes: The data standardization calculation is performed on the stability index, the resistance index and the resilience index of each adjustment stage to obtain corresponding reference stability index, reference resistance index and reference resilience index, wherein the reference stability index includes a first reference stability index corresponding to the evapotranspiration adjustment stage, a second reference stability index corresponding to the canopy water content adjustment stage and a third reference stability index corresponding to the carbon adjustment stage, the reference resistance index includes a first reference resistance index corresponding to the evapotranspiration adjustment stage, a second reference resistance index corresponding to the canopy water content adjustment stage and a third reference resistance index corresponding to the carbon adjustment stage, and the reference resilience index includes a first reference resilience index corresponding to the evapotranspiration adjustment stage, a second reference resilience index corresponding to the canopy water content adjustment stage and a third reference resilience index corresponding to the carbon adjustment stage; The first reference stability index, the first reference resistance index and the first reference resilience index are input into the Euclidean distance model to calculate a first resilience index of the evapotranspiration adjustment stage; The second reference stability index, the second reference resistance index and the second reference resilience index are input into the Euclidean distance model to calculate a second resilience index of the canopy water content adjustment stage; The third reference stability index, the third reference resistance index and the third reference resilience index are input into the Euclidean distance model to calculate a third resilience index of the carbon adjustment stage; The data fusion is performed on the first resilience index, the second resilience index and the third resilience index to obtain the ecological resilience evaluation result of the target vegetation.

7. A control device characterized by comprising: The control device includes at least one control processor and a memory connected in communication with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to perform the vegetation ecological resilience evaluation method according to any one of claims 1 to 6.

8. An electronic device, comprising: The control device includes the control device according to claim 7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions for causing a computer to perform the vegetation ecological resilience evaluation method according to any one of claims 1 to 6.

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