Grassland ecological degradation recovery effect dynamic evaluation method, device, equipment and medium

Abstract

The present application relates to a method, device, equipment and medium for dynamically evaluating the effect of grassland ecological degradation recovery, the method comprising: obtaining annual remote sensing data for a target area, performing vegetation feature extraction on the annual remote sensing data based on a predetermined window sliding operation on the annual remote sensing data to obtain a baseline time series; calculating the relative change rate of the vegetation in the target area based on the baseline time series; and evaluating the degradation or recovery of the vegetation in the target area according to the relative change rate and a preset threshold. According to the method of the present application, the optimal spatial analysis scale (window) is determined according to the spatial heterogeneity characteristics (semi-variogram range), which can realize adaptive conversion from a pixel to a landscape scale, eliminate subjective interference, and thus accurately determine the baseline time series reflecting the vegetation characteristics based on the window, and further accurately evaluate the degradation or recovery of the target area based on the baseline time series.

Description

Technical Field

[0001] This invention relates to the field of remote sensing monitoring and dynamic assessment technology for the ecological environment. Specifically, this invention relates to methods, devices, equipment, and media for dynamic assessment of the effectiveness of grassland ecological degradation restoration. Background Technology

[0002] Constructing a scientifically sound assessment benchmark is a crucial prerequisite for accurately evaluating the effectiveness of ecosystem degradation restoration. Currently, commonly used benchmark construction methods mainly include the fixed benchmark method and the spatial homogenization method, but both have significant limitations. The fixed benchmark method (such as the historical state method) assumes that the benchmark state remains constant, ignoring the dynamic impacts of climate change and natural vegetation succession. Studies using this method show that the NDVI benchmark value of a typical grassland in Inner Mongolia has increased significantly over the past 20 years at a rate of 0.012 / 10a (P<0.01). Using the fixed benchmark method would lead to an overestimation of the degraded area by up to 18.3%. Therefore, this method introduces assessment errors because it cannot characterize the natural succession process of vegetation communities. The spatial homogenization method typically uses a single-pixel historical benchmark. However, if the pixel itself has been in a degraded state for a long time, it cannot effectively assess the restoration effectiveness. If a sliding window (such as 3×3km) is used, it is highly subjective due to the lack of quantitative evidence of spatial heterogeneity. Furthermore, if the global average is used directly, the results are easily affected by outliers. Simulation data shows that when the proportion of outliers exceeds 5%, the mean deviation will exceed 15%. Furthermore, in the benchmark validation phase, existing methods generally rely on point plot data to validate areal assessment results, which is clearly insufficient in terms of spatial representativeness. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for dynamic evaluation of the effectiveness of grassland ecological degradation restoration, and to solve at least one of the above-mentioned technical problems.

[0004] Firstly, the technical solution of the present invention to solve the above-mentioned technical problems is as follows: a method for dynamic evaluation of the effectiveness of grassland ecological degradation restoration, the method comprising:

[0005] Acquire interannual remote sensing data for a target area, which is an area including vegetation, and the interannual remote sensing data is selected from remote sensing data of at least two years corresponding to the target area;

[0006] Based on the sliding operation of a predetermined window on interannual remote sensing data, vegetation features are extracted from the interannual remote sensing data to obtain a baseline time series. The window is quantitatively determined based on the range of the semivariogram, and the baseline time series is data describing the changes in vegetation features within a year. Each sliding operation corresponds to a baseline value in the baseline time series.

[0007] Based on the baseline time series, the relative rate of change of vegetation in the target area is calculated;

[0008] The degradation or recovery of vegetation within the target area is assessed based on the relative rate of change and a preset threshold.

[0009] The beneficial effects of this invention are: by determining the optimal spatial analysis scale, i.e., the window, based on the spatial heterogeneity characteristics (semivariogram range), it is possible to achieve adaptive conversion from pixel to landscape scale, eliminate subjective interference, and by using a predetermined window to slide on interannual remote sensing data, the obtained benchmark time series can accurately reflect the natural succession process of vegetation communities, thereby accurately assessing the degradation or recovery of the target area based on the benchmark time series.

[0010] Based on the above technical solution, the present invention can be further improved as follows.

[0011] Furthermore, the aforementioned window is determined based on the following method:

[0012] Based on interannual remote sensing data and semivariograms, determine the estimated values ​​of the semivariograms corresponding to the first and second samples in the interannual remote sensing data.

[0013] The window is determined based on the estimated value.

[0014] Furthermore, the aforementioned sliding operation on the interannual remote sensing data based on a pre-determined window extracts vegetation features from the interannual remote sensing data to obtain a baseline time series, including:

[0015] Based on the sliding operation of the window in interannual remote sensing data, determine all pixels within the window corresponding to each sliding operation;

[0016] For each sliding operation, calculate the 85%-99% quantile based on all pixels within the window corresponding to the sliding operation;

[0017] For each sliding operation, a truncated dataset is constructed based on the 85%-99% quantiles and all pixels corresponding to the sliding operation; for each sliding operation, the baseline value corresponding to the sliding operation is determined based on the truncated dataset corresponding to the sliding operation.

[0018] Based on the baseline values ​​corresponding to all sliding operations, the baseline time series corresponding to the interannual remote sensing data is determined.

[0019] Furthermore, for each sliding operation, the baseline value corresponding to the sliding operation is determined based on the truncated dataset corresponding to the sliding operation, including:

[0020] For each sliding operation, a candidate baseline value is determined based on the truncated dataset corresponding to the sliding operation. The candidate baseline values ​​include the baseline value determined based on the mean of the truncated dataset and the baseline value determined based on the median of the truncated dataset. For each sliding operation, the truncated dataset is validated for normality based on the Shapiro-Wilk normality test, and the baseline value corresponding to the sliding operation is determined from the candidate baseline values ​​based on the validation results.

[0021] Furthermore, the method also includes:

[0022] Acquire new interannual remote sensing data and update the baseline time series based on the new interannual remote sensing data.

[0023] Furthermore, the method also includes:

[0024] Acquire verification data for the target area. The verification data can be ground-based measured data or UAV aerial survey data.

[0025] The consistency of the verification data and the interannual remote sensing data is verified, and the assessment results of the target area are calibrated based on the verification results. The assessment results are used to evaluate the degradation or restoration of vegetation in the target area.

[0026] Furthermore, the method also includes:

[0027] For each sliding operation, based on the baseline value corresponding to the sliding operation, the pre-determined minimum contiguous area threshold, and the mean and standard deviation of all baseline values ​​corresponding to the target region, it is determined whether the baseline value corresponding to the sliding operation is the top-level community threshold.

[0028] Secondly, to solve the above-mentioned technical problems, the present invention also provides a dynamic evaluation device for the effectiveness of grassland ecological degradation restoration, the device comprising:

[0029] The acquisition module is used to acquire interannual remote sensing data for a target area, which is an area including vegetation, and the interannual remote sensing data is selected from remote sensing data of at least two years corresponding to the target area.

[0030] The baseline time series determination module is used to extract vegetation features from interannual remote sensing data by sliding operations on interannual remote sensing data based on a pre-determined window, and obtain a baseline time series. The window is quantitatively determined based on the range of the semi-variogram function, and the baseline time series is data describing the changes in vegetation features within one year. Each sliding operation corresponds to a baseline value in the baseline time series.

[0031] The relative change rate determination module is used to calculate the relative change rate of vegetation within the target area based on the baseline time series.

[0032] The assessment module is used to evaluate the degradation or recovery of vegetation within a target area based on the relative rate of change and preset thresholds.

[0033] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the dynamic evaluation method for grassland ecological degradation restoration effectiveness of the present application.

[0034] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the dynamic evaluation method for grassland ecological degradation restoration effectiveness of the present application.

[0035] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below.

[0037] Figure 1 A flowchart illustrating a method for dynamically evaluating the effectiveness of grassland ecological degradation restoration, provided in one embodiment of the present invention;

[0038] Figure 2 A flowchart illustrating another method for dynamically evaluating the effectiveness of grassland ecological degradation restoration, provided in one embodiment of the present invention;

[0039] Figure 3 Figures (a) to (d) in the figure are distribution diagrams of NDVI semivariogram parameter results provided in an embodiment of the present invention;

[0040] Figure 4 (a) to (d) are schematic diagrams of the distribution of NDVI different quantiles and interval pixel values ​​provided in an embodiment of the present invention;

[0041] Figure 5 (a) to (e) in the figure are distribution diagrams of different quantiles and interval reference values ​​of NDVI provided in an embodiment of the present invention;

[0042] Figure 6 (a) to (d) in the figure are distribution diagrams of NDVI quantile differences provided in an embodiment of the present invention;

[0043] Figure 7 (a) to (c) in the figure are spatial distribution diagrams of the original and difference values ​​of NDVI provided in an embodiment of the present invention;

[0044] Figure 8 (a) to (c) in the figure are NDVI data distribution feature maps provided in one embodiment of the present invention;

[0045] Figure 9 This is a schematic diagram of the structure of a dynamic evaluation device for grassland ecological degradation restoration effectiveness provided in one embodiment of the present invention;

[0046] Figure 10 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0047] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0048] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0049] The solution provided in this invention can be applied to any application scenario that requires assessment of the degradation or restoration of areas containing vegetation. The solution provided in this invention can be executed by any electronic device, such as a user's terminal device, including at least one of the following: smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart TV, or smart in-vehicle device.

[0050] This invention provides a possible implementation, such as... Figure 1 As shown, a flowchart of a method for dynamically evaluating the effectiveness of grassland ecological degradation restoration is provided. This method can be executed by any electronic device, such as a terminal device, or jointly executed by a terminal device and a server. For ease of description, the method provided in this embodiment will be described below using a terminal device as the execution subject as an example. Figure 1 The flowchart shown indicates that the method may include the following steps:

[0051] S10, acquire interannual remote sensing data for the target area, the target area being an area including vegetation, and the interannual remote sensing data being remote sensing data selected from at least two years of remote sensing data corresponding to the target area;

[0052] S20, Based on the sliding operation of a pre-determined window on the interannual remote sensing data, vegetation features are extracted from the interannual remote sensing data to obtain a baseline time series. The window is quantitatively determined based on the range of the semi-variogram function, and the baseline time series is data describing the changes in vegetation features within a year. Each sliding operation corresponds to a baseline value in the baseline time series.

[0053] S30, based on the baseline time series, calculates the relative rate of change of vegetation within the target area;

[0054] S40 assesses the degradation or recovery of vegetation within the target area based on the relative rate of change and a preset threshold.

[0055] The method of this invention determines the optimal spatial analysis scale, i.e., the window, based on spatial heterogeneity characteristics (semivariogram range), enabling adaptive conversion from pixel to landscape scale, eliminating subjective interference, and using a pre-determined window to slide across interannual remote sensing data. The resulting benchmark time series can accurately reflect the natural succession process of vegetation communities, thereby accurately assessing the degradation or recovery of the target area based on this benchmark time series.

[0056] The following specific embodiments further illustrate the solution of this invention. The core innovation of this application lies in proposing and constructing a spatiotemporally adaptive dynamic benchmark system to overcome the aforementioned limitations. Its core technologies include: generating benchmark time series corresponding to interannual remote sensing data, determining an adaptive window based on the semi-variogram range, determining the 85%-99% quantile, extracting benchmark values, and constructing a multi-index coupled degradation recovery quantification model. By iteratively generating benchmark time series, vegetation succession dynamics are effectively captured, significantly reducing assessment errors caused by ignoring temporal variations. Determining the optimal spatial analysis scale based on spatial heterogeneity characteristics (semi-variogram range) enables adaptive conversion from pixel to landscape scale, eliminating subjective interference. Using the mean or median truncated by the 85%-99% quantile to calculate benchmark values ​​effectively suppresses extreme value interference, thereby improving the robustness of benchmark values. Establishing a multi-source verification framework coupling ground quadrats, UAV aerial surveys, and multi-temporal remote sensing inversion data can significantly improve the spatial representativeness of the verification results.

[0057] This application aims to address three core contradictions in existing technologies for constructing assessment benchmarks for grassland ecological degradation and restoration: first, the contradiction between spatiotemporal staticity and ecological dynamics, i.e., fixed benchmark values ​​cannot characterize the natural succession process of vegetation communities; second, the contradiction between the assumption of spatial homogenization and the reality of heterogeneity, i.e., fixed window analysis will ignore the spatial differentiation characteristics of landscape patterns; and third, the contradiction of the cumulative deviation between assessment results and the actual ground conditions, i.e., the lack of an effective dynamic calibration mechanism will lead to the propagation of errors.

[0058] Based on this, this application provides a method for dynamic evaluation of the effectiveness of grassland ecological degradation restoration, which may include the following steps, see below. Figures 2 to 8 :

[0059] S10, acquire interannual remote sensing data for the target area, the target area being an area including vegetation, and the interannual remote sensing data being remote sensing data selected from at least two years of remote sensing data corresponding to the target area;

[0060] Interannual remote sensing data can be selected from remote sensing data of at least two years corresponding to the target area, such as remote sensing data selected from remote sensing data corresponding to the target area from 2021 to 2022. Interannual remote sensing data can be image data.

[0061] The target area can be grassland, and the aforementioned interannual remote sensing data can be NDVI / EVI remote sensing data with a spatial resolution ρ∈{30m,250m}. This interannual remote sensing data can be processed to obtain an image for subsequent processing, which will reflect the vegetation conditions in the target area at different times of the year.

[0062] S20, based on a pre-determined window sliding operation on interannual remote sensing data, vegetation features are extracted from the interannual remote sensing data to obtain a baseline time series. The window is quantitatively determined based on the range of a semi-variogram, and the baseline time series describes the changes in vegetation features within a year. Each sliding operation corresponds to a baseline value in the baseline time series. Specifically, each sliding of the window on the interannual remote sensing data yields a baseline value, which reflects the vegetation features in the region corresponding to that window on the interannual remote sensing data. After multiple sliding operations, the resulting multiple baseline values ​​can form a baseline time series, better reflecting the temporal sequence of vegetation features.

[0063] S30, based on the baseline time series, calculates the relative rate of change of vegetation within the target area;

[0064] The relative rate of change can reflect the changes in vegetation within the target area, that is, the changes in vegetation within the target area are quantified by the relative rate of change.

[0065] Alternatively, one implementation of S30 above is as follows:

[0066] S301, Calculate the absolute change based on the baseline time series;

[0067] Specifically, the calculation formula is as follows:

[0068] ΔV(x,y,t)=V(x,y,t)-B t (x, y) (1)

[0069] Among them, B t (x, y) represents the baseline value at (x, y) in year t, V(x, y, t) represents the actual observed value at (x, y) in year t, and ΔV(x, y, t) represents the deviation between the actual observed value and the baseline value.

[0070] S302, the relative change is calculated based on the absolute change.

[0071] Specifically, the calculation formula is as follows:

[0072]

[0073] Where R(x, y, t) represents the relative change.

[0074] Before S301, the method further includes: S300, performing time consistency correction on the reference time series to obtain the corrected reference time series;

[0075] One implementation of S300 is as follows:

[0076] Based on interannual remote sensing data, the first average NDVI of the target area for the corresponding year in the interannual remote sensing data is calculated; the first average NDVI can be expressed as μ. GLOBAL (t), where t represents the year corresponding to the interannual remote sensing data;

[0077] Based on the first average NDVI and the predetermined second average NDVI, a time consistency correction is performed on the reference time series to obtain the corrected reference time series. The second average NDVI can be expressed as μ. GLOBAL (t0), where t0 is the baseline starting year (reference year), which is usually selected from years with high data quality or stable ecological conditions.

[0078] The above-mentioned time consistency correction of the reference time series based on the first average NDVI and the predetermined second average NDVI results in a corrected reference time series. Specifically, the correction of each reference value in the reference time series can be expressed as follows:

[0079]

[0080] Among them, B′ t (x, y) represents the corrected reference value, B t (x, y) represents the reference value before correction, and (x, y) represents the position point corresponding to a reference value.

[0081] Therefore, the reference time series in the formulas for obtaining the absolute change and relative rate of change can be replaced with the corrected reference time series.

[0082] S40 assesses the degradation or recovery of vegetation within the target area based on the relative rate of change and a preset threshold.

[0083] The preset thresholds include a first threshold and a second threshold. The first threshold corresponds to the degradation condition, and the second threshold corresponds to the recovery condition. Therefore, one possible implementation of the above S40 is as follows:

[0084] When the relative rate of change is less than the first threshold for n consecutive years, the target area is assessed as being in a state of degradation.

[0085] When the relative rate of change is greater than the second threshold for m consecutive years, the target area is evaluated as having recovered.

[0086] As an example, the first threshold could be -15%, and the second threshold could be 20%. n is 3, and m is 2.

[0087] The evaluation result of the target area can then be expressed as:

[0088] Table 1

[0089] state Judgment conditions Ecological explanation Degeneration R < -15% and persists for ≥ 3 years The vegetation condition remains significantly below the baseline level and requires long-term monitoring for confirmation. recover R > +20% and lasts for ≥ 2 years The vegetation condition has significantly improved and stabilized, and the recovery trend can be confirmed in the short term.

[0090] Optionally, the above window is determined based on the following method:

[0091] S1. Based on the interannual remote sensing data and the semivariogram, determine the estimated values ​​of the semivariograms corresponding to the first and second sample points in the interannual remote sensing data.

[0092] S2, determine the window based on the estimated value.

[0093] In S1, the first sample point and the second sample point are those in the target region that differ by h. k The distance between two sampling points; specifically, the range of the above semi-variogram can be expressed as:

[0094]

[0095] Wherein, γ(h k () represents the distance between them, which is h. k The estimated value of the semivariogram, h k N(h) is the distance between the first and second sample points. k To satisfy (x) i -x j )∈(h k -V h h k +V hThe number of pixel pairs, where one pixel pair consists of two samples, V h =0.5ρ, where ρ represents the spatial resolution, z(x i ) is the first point x i The average density, z(x) i +h k ) is the second sample point (x) i +h k () average density.

[0096] In S2 above, one way to determine the window based on the estimated value is as follows:

[0097] S21. Based on the estimated values, a theoretical model of the variogram is established. Based on this theoretical model, the range value is calculated. As an example, taking a spherical model, the theoretical model of the variogram can be expressed as:

[0098]

[0099] Where a is the range, c0 is the nugget value, and c is the structural variance.

[0100] S22, based on the value of the range and predetermined parameters, determine the window.

[0101] Specifically, the window can be determined based on the following formula:

[0102] W = min(2a, W) max (6)

[0103] Among them, W max These are predetermined parameters, specifically determined based on ecological zoning, such as W in a typical grassland region. max =5km.

[0104] Optionally, in step S20 above, vegetation features are extracted from the interannual remote sensing data based on a predetermined window sliding operation on the interannual remote sensing data to obtain a baseline time series, including:

[0105] S201, Based on the sliding operation of the window in the interannual remote sensing data, determine all the pixels in the window corresponding to each sliding operation;

[0106] S202, for each sliding operation, calculate the 85%-99% quantiles based on all pixels within the window corresponding to the sliding operation; in this scheme, the quantile corresponding to 85% can be expressed as Q. 85 The quantile corresponding to 99% can be represented as Q. 99 .

[0107] S203, For each sliding operation, construct a truncated dataset based on the 85%-99% quantiles and all pixels corresponding to the sliding operation;

[0108] Specifically, truncate the dataset Z ref It can be represented as:

[0109] Z ref ={z i |Q 85 ≤z i ≤Q 99} (7)

[0110] Among them, z i This represents the values ​​of all pixels within the window corresponding to each swipe operation.

[0111] S204, For each sliding operation, determine the baseline value corresponding to the sliding operation based on the truncated dataset corresponding to the sliding operation;

[0112] S205, based on the baseline values ​​corresponding to all sliding operations, determine the baseline time series corresponding to the interannual remote sensing data.

[0113] Optionally, in S204 above, for each sliding operation, the baseline value corresponding to the sliding operation is determined based on the truncated dataset corresponding to the sliding operation, including:

[0114] S2041, For each sliding operation, based on the truncated dataset corresponding to the sliding operation, determine the candidate benchmark value corresponding to the sliding operation. The candidate benchmark value includes the benchmark value determined based on the mean of the truncated dataset and the benchmark value determined based on the median of the truncated dataset.

[0115] The above processing procedure can be specifically represented as follows:

[0116]

[0117] in, and median(Z) ref ) is the candidate benchmark value.

[0118] S2042, For each sliding operation, the truncated dataset is validated for normality based on the Shapiro-Wilk normality test, and the baseline value corresponding to the sliding operation is determined from the candidate baseline values ​​based on the validation results.

[0119] The process of verifying the normal distribution can be represented as follows:

[0120]

[0121] Among them, a i The coefficient z represents the Shapiro-Wilk normality test coefficient. iSample values ​​are typically obtained by sorting the data in a truncated dataset (e.g., from smallest to largest or largest to smallest, usually in ascending order). This represents the mean of the sorted sample values, and n represents the total number of data points in the truncated dataset.

[0122] If W < W a (For example, if a = 0.05), then the null hypothesis is rejected, indicating that the truncated dataset does not follow a normal distribution, and the benchmark value corresponding to the median pattern is selected; if W ≥ W a If so, the null hypothesis is accepted, and the truncated dataset is assumed to follow a normal distribution. The benchmark value corresponding to the mean pattern is selected.

[0123] Optionally, the method further includes:

[0124] Acquire new interannual remote sensing data and update the baseline time series based on the new interannual remote sensing data.

[0125] Dynamic iteration of the baseline time series can capture vegetation succession dynamics in real time and eliminate errors caused by ignoring time variations.

[0126] Optionally, the method further includes:

[0127] For each sliding operation, based on the baseline value corresponding to the sliding operation, the pre-determined minimum contiguous area threshold, and the mean and standard deviation of all baseline values ​​corresponding to the target region, it is determined whether the baseline value corresponding to the sliding operation is the top-level community threshold.

[0128] After each sliding operation, it can be determined whether the baseline value corresponding to that operation is a top-level community threshold. If so, the subsequent baseline time series is determined based on this top-level community threshold. If not, it is determined to be a non-top-level community value and will not participate in the determination of the subsequent baseline time series.

[0129] The specific implementation process for determining whether the reference value corresponding to the sliding operation is the top-level community threshold for each sliding operation, based on the reference value corresponding to the sliding operation, the pre-determined minimum contiguous area threshold, and the mean and standard deviation of all reference values ​​corresponding to the target region, is as follows:

[0130] For each sliding operation, determine whether the reference value corresponding to the sliding operation satisfies the first condition and the second condition. If the reference value corresponding to the sliding operation satisfies both the first and second conditions, determine that the reference value corresponding to the sliding operation is the top-level community threshold; otherwise, determine that the reference value corresponding to the sliding operation is a non-top-level community value.

[0131] The first and second conditions are expressed as follows:

[0132]

[0133] Where, μ B σ represents the mean of all benchmark values ​​in the target region. B S represents the standard deviation of all benchmark values ​​in the target region. min This represents the minimum contiguous area threshold, which can be determined by the vegetation type (e.g., meadow-steppe is set to: S). min =1km 2 ).

[0134] Optionally, the method further includes:

[0135] Acquire verification data for the target area. The verification data can be ground-based measured data or UAV aerial survey data.

[0136] The consistency of the verification data and the interannual remote sensing data is verified, and the assessment results of the target area are calibrated based on the verification results. The assessment results are used to evaluate the degradation or restoration of vegetation in the target area.

[0137] As an example, to verify the consistency between remote sensing inversion values ​​(i.e., parameter values ​​obtained from inversion based on interannual remote sensing data) and ground-measured values ​​(parameter values ​​that express the same meaning as the inverted parameter values), the following pixel-sample matching model can be established:

[0138]

[0139] The verification condition is: |1-R remote | < 0.15;

[0140] Among them, V i field V represents the measured value of the i-th ground quadrat in the ground measurement data. i remote R represents the extracted value (remote sensing inversion value) of the remote sensing pixel corresponding to the i-th ground quadrat (the pixel in the interannual remote sensing data that corresponds to the same location as the i-th ground quadrat), k1 is the normalization coefficient, usually k1 = 1 / k, k is the number of matched quadrats (to cover different habitat types), and R remote This is an index for the matching degree between interannual remote sensing data and ground-measured data. The number of matching quadrats refers to the logarithm of the ground quadrats and remote sensing pixels when the extracted remote sensing pixel values ​​and the measured ground quadrats are identical.

[0141] If R remote If the verification conditions are met, it can reflect the accuracy of the proposed scheme in assessing the effectiveness of grassland ecological degradation restoration based on interannual remote sensing data.

[0142] Optionally, in this application, to quantify the contribution of different driving factors such as climate and anthropogenic factors to ecological degradation or restoration, partial least squares regression (PLSR) is used for driving force analysis, and the calculation formula is as follows:

[0143]

[0144] Where R represents the target variable, i.e., the relative rate of change R(x, y, t), and X... j The driving factors include temperature, precipitation, and carrying capacity, while β0 represents the intercept term. j Let represent the regression coefficient of the j-th driving factor, reflecting the direction and strength of its influence on the target variable. This represents the residual term, which is the unexplained random error.

[0145] To better illustrate and understand the principle of the method provided by this invention, the following description uses an optional specific embodiment to illustrate the solution of this invention. It should be noted that the specific implementation of each step in this specific embodiment should not be construed as a limitation of the solution of this invention. Other implementations that can be conceived by those skilled in the art based on the principle of the solution provided by this invention should also be considered within the scope of protection of this invention.

[0146] See Figure 2 The following Examples 1 and 2 further illustrate the dynamic evaluation method for grassland ecological degradation restoration provided in this application:

[0147] Example 1: This example uses the alpine meadow of the Three-River-Source region as the target area to further illustrate the method for dynamic evaluation of the effectiveness of grassland ecological degradation restoration. The method includes:

[0148] 1. Data Input and Preprocessing

[0149] Interannual remote sensing data were obtained by using MODIS NDVI data (spatial resolution 250m, synthesized from 8 days of remote sensing data selected from the corresponding remote sensing data between 2001 and 2020). After radiometric correction, atmospheric correction and cloud removal, the maximum NDVI composite image corresponding to the growing season (remote sensing data from May to September) was generated.

[0150] Ground data (measured ground data) came from 126 ground quadrats. All ground quadrats uniformly covered alpine meadow habitats. Vegetation cover was surveyed synchronously every August. The data accuracy was ±5%.

[0151] 2. Spatial Heterogeneity Analysis and Window Optimization

[0152] First, based on the maximum NDVI composite image and the semivariogram, the estimated value of the distance h is calculated. kThe range is 0-20km, with a step size ρ = 500m. A spherical model is then fitted, yielding the following parameters: nugget value c0 = 0.0, sill value c0 + c = 0.18, and range a = 3.2km. Finally, the window size is determined, and W is set... max =8km, and the final window W is calculated to be 6.4km.

[0153] Robust benchmark calculation

[0154] First, based on the sliding operation of the window in the sample remote sensing data, all pixels within the window corresponding to each sliding operation are determined. For each sliding operation, based on all pixels within the window corresponding to the sliding operation, a probability distribution model is performed, and the quantile Q of the NDVI data within the window is calculated. 85 =0.78 and Q 99 =0.92, outliers with NDVI < 0.2 or > 0.92 were removed. Then, the Shapiro-Wilk normality test was performed, and the test results were W = 0.92, P = 0.008 < 0.05, so the normality hypothesis was rejected, and the median model was used to calculate the baseline value B(x, y) corresponding to this sliding operation.

[0155] 4. Top-level community benchmark mapping

[0156] First, the relevant thresholds are set, with the global baseline mean μ. B =0.75, standard deviation σ B =0.11, the apex community threshold B(x,y)≥0.86. Then, contiguousness analysis was performed using an 8-neighborhood clustering algorithm, removing clusters with areas <1km². 2 The patch corresponds to 40 pixels.

[0157] 5. Dynamic benchmark generation

[0158] Based on the first average NDVI and the predetermined second average NDVI, the benchmark time series is subjected to time consistency correction. The benchmark year t0 = 2005 is selected. After correction, the interannual volatility is reduced from the original 4.9% to 2.7%-4.1%.

[0159] 6. Quantification of Degradation and Recovery

[0160] The threshold conditions for degradation and recovery are obtained by calculating the absolute change and relative change rate. The degradation condition is: R < -20% and lasts for ≥3 years; the recovery condition is: R > +20% and lasts for ≥2 years. The output results are: the degradation area accounts for 12.3%, and the recovery area accounts for 6.8%.

[0161] 7. Construction of a multi-source verification system

[0162] First, ground validation was conducted by selecting 30 independent ground quadrats and calculating the matching degree R.remote =0.98, satisfying |1-R remote |=0.02<0.15. Then, driving force analysis was performed. The PLSR model results showed that reduced precipitation (VIP=1.52, β=-0.67) and overgrazing (VIP=1.33, β=-0.55) were the main causes of degradation, with a combined contribution rate of 81.5%.

[0163] 8. Computational efficiency and verification accuracy

[0164] For spatial heterogeneity analysis, the processing time for a single image was 4.2 minutes (Intel Xeon Gold 6248R, 128GB RAM). Validation accuracy R... 2 =0.91, and the Kappa coefficient is 0.84, indicating that it is consistent with the ground survey.

[0165] Example 2, this example uses the Hulunbuir meadow grassland as the target area to further illustrate the method for dynamic evaluation of the effectiveness of grassland ecological degradation restoration. The method includes:

[0166] 1. Data Input and Preprocessing

[0167] The remote sensing data used was Landsat-8 OLI data (spatial resolution ρ = 30m, 2013-2020, cloud cover <10%, after radiometric calibration, topographic correction and NDVI calculation, a 16-day composite image was generated).

[0168] The drone observation data consisted of 50 1km units. 2 Orthophotos of the sample area (spatial resolution 5cm) were used to extract vegetation cover, with a data accuracy of ±2%.

[0169] Spatial Heterogeneity Analysis and Window Optimization

[0170] First, based on the synthetic image and semivariogram, estimated values ​​are determined. Then, an exponential model is fitted to obtain the following parameter results: nugget value c0 = 0.01, sill value c0 + c = 0.15, and range a = 0.8 km. Finally, the window size is determined, and W is set... max =1.2km, and the final window W = 1.2km is calculated.

[0171] 3. Robust benchmark calculation

[0172] First, based on the sliding operation of the window in the sample remote sensing data, all pixels within the window corresponding to each sliding operation are determined. For each sliding operation, based on all pixels within the window corresponding to the sliding operation, a probability distribution model is performed, and the quantile Q of the NDVI data within the window is calculated. 85 =0.82 and Q 99=0.95, outliers with NDVI < 0.2 or > 0.95 were removed. Then, the Shapiro-Wilk normality test was performed, and the test results were W = 0.96, P = 0.21 > 0.05, so the normality hypothesis was accepted, and the baseline value B(x, y) was calculated using the mean model.

[0173] Top-level community benchmark mapping

[0174] First, the relevant thresholds are set, with the global baseline mean μ. B =0.68, standard deviation σ B =0.09, the climax community threshold B(x,y)≥0.77. Then, contiguousness analysis was performed using a morphological erosion-dilation algorithm to remove areas <0.09km². 2 The patch corresponds to 10 pixels.

[0175] 5. Dynamic benchmark generation

[0176] Based on the first average NDVI and the predetermined second average NDVI, the benchmark time series is corrected for time consistency. The benchmark year t0 = 2015 is selected, and the interannual volatility is stabilized at 1.8%-3.2% after correction.

[0177] 6. Quantification of Degradation and Recovery

[0178] The threshold criteria for degradation and recovery were obtained by calculating the absolute change and relative change rate. The degradation criterion was: R < -25% and lasting for ≥3 years; the recovery criterion was: R ≥ +25% and lasting for ≥2 years. The output result was: the minimum recovered patch was 0.09 km. 2 The NDVI level in the recovery area increased by 27.4%.

[0179] 7. Construction of a multi-source verification system

[0180] First, verify the drone data and calculate the matching degree R. remote =1.03, satisfying |1-R remote |=0.03<0.15. Driving force analysis was then performed. The PLSR model results showed that increased precipitation (VIP=1.78, β=+0.62) and decreased grazing rate (VIP=1.45, β=+0.29) dominated the recovery, with precipitation contributing 62.3% and grazing pressure relief contributing 28.7%, for a combined contribution of 91.0%.

[0181] 8. Computational efficiency and spatial accuracy

[0182] For spatial heterogeneity analysis, the single-scene image processing time is 9.8 minutes (Intel Xeon Gold 6248R, 128GB RAM), and it can detect 0.09km. 2Level 1 degraded plaques, with spatial resolution advantages.

[0183] The solution of the present invention has the following advantages compared with the prior art:

[0184] 1. Spatial Heterogeneity-Driven Scale Adaptation: Based on the semi-variogram range, the optimal spatial analysis scale is quantitatively determined to overcome the subjectivity of the fixed window and thus accurately reflect the spatial differentiation of the landscape;

[0185] 2. Probabilistic truncation filtering improves robustness: Data is truncated using 85%-99% quantile constraints, and the mean or median of the truncated data is calculated as the benchmark value, which effectively suppresses extreme value interference and thus improves the robustness of the benchmark results;

[0186] 3. Interannual baseline series dynamic iteration: Establish a baseline time series that is updated annually to capture vegetation succession dynamics in real time and eliminate errors caused by ignoring time changes;

[0187] 4. Multi-source data fusion verification framework: Couple ground sample plot measurements, UAV aerial surveys, and multi-temporal remote sensing inversion data to construct a verification system with strong spatial representativeness, which can effectively calibrate and evaluate the results.

[0188] This approach significantly overcomes the spatiotemporal limitations of traditional fixed benchmarks, increasing assessment accuracy to over 85% and providing reliable technical support for precise management of grassland ecosystems. This method can be widely applied to assessing the effectiveness of grassland ecological protection projects, planning degraded grassland restoration, and dynamically monitoring the carbon sequestration capacity of grassland ecosystems. It can also provide a key quantitative tool for monitoring and achieving "zero net land degradation."

[0189] Based on and Figure 1 Based on the same principle as the method shown, this embodiment of the invention also provides a dynamic evaluation device 20 for the effectiveness of grassland ecological degradation restoration, such as... Figure 9 As shown, the dynamic evaluation device 20 for grassland ecological degradation restoration effectiveness may include an acquisition module 210, a baseline time series determination module 220, a relative change rate determination module 230, and an evaluation module 240, wherein: the acquisition module 210 is used to acquire interannual remote sensing data for a target area, the target area being an area including vegetation, and the interannual remote sensing data being remote sensing data selected from at least two years of remote sensing data corresponding to the target area;

[0190] The baseline time series determination module 220 is used to extract vegetation features from interannual remote sensing data based on a sliding operation of a pre-determined window on the interannual remote sensing data to obtain a baseline time series. The window is quantitatively determined based on the range of the semi-variogram function, and the baseline time series is data describing the changes in vegetation features within a year. Each sliding operation corresponds to a baseline value in the baseline time series.

[0191] The relative change rate determination module 230 is used to calculate the relative change rate of vegetation in the target area based on the baseline time series; the evaluation module 240 is used to evaluate the degradation or recovery of vegetation in the target area according to the relative change rate and a preset threshold.

[0192] Optionally, the above window is determined based on the following method:

[0193] Based on interannual remote sensing data and semivariograms, determine the estimated values ​​of the semivariograms corresponding to the first and second samples in the interannual remote sensing data.

[0194] The window is determined based on the estimated value.

[0195] Optionally, the reference time series determination module 220 is specifically used for:

[0196] Based on the sliding operation of the window in interannual remote sensing data, determine all pixels within the window corresponding to each sliding operation;

[0197] For each sliding operation, calculate the 85%-99% quantile based on all pixels within the window corresponding to the sliding operation;

[0198] For each sliding operation, a truncated dataset is constructed based on the 85%-99% quantiles and all pixels corresponding to the sliding operation; for each sliding operation, the baseline value corresponding to the sliding operation is determined based on the truncated dataset corresponding to the sliding operation.

[0199] Based on the baseline values ​​corresponding to all sliding operations, the baseline time series corresponding to the interannual remote sensing data is determined.

[0200] Optionally, for each sliding operation, when the reference time series determination module 220 determines the reference value corresponding to the sliding operation based on the truncated dataset corresponding to the sliding operation, it is specifically used for:

[0201] For each sliding operation, a candidate baseline value is determined based on the truncated dataset corresponding to the sliding operation. The candidate baseline values ​​include the baseline value determined based on the mean of the truncated dataset and the baseline value determined based on the median of the truncated dataset. For each sliding operation, the truncated dataset is validated for normality based on the Shapiro-Wilk normality test, and the baseline value corresponding to the sliding operation is determined from the candidate baseline values ​​based on the validation results.

[0202] Optionally, the device further includes:

[0203] The update module is used to acquire new interannual remote sensing data and update the baseline time series based on the new interannual remote sensing data.

[0204] Optionally, the device further includes:

[0205] The calibration module is used to acquire verification data for the target area, which can be ground-based measured data or UAV aerial survey data; it performs consistency verification between the verification data and interannual remote sensing data, and calibrates the assessment results of the target area based on the verification results. The assessment results evaluate the degradation or recovery of vegetation within the target area.

[0206] Optionally, the device further includes:

[0207] The judgment module is used to determine whether the benchmark value corresponding to the sliding operation is the top-level community threshold for each sliding operation, based on the benchmark value corresponding to the sliding operation, the pre-determined minimum contiguous area threshold, and the mean and standard deviation of all benchmark values ​​corresponding to the target area.

[0208] The dynamic evaluation device for grassland ecological degradation and restoration effectiveness in this embodiment of the invention can execute the dynamic evaluation method for grassland ecological degradation and restoration effectiveness provided in this embodiment of the invention. The implementation principle is similar. The actions performed by each module and unit in the dynamic evaluation device for grassland ecological degradation and restoration effectiveness in each embodiment of the invention correspond to the steps in the dynamic evaluation method for grassland ecological degradation and restoration effectiveness in each embodiment of the invention. For detailed functional descriptions of each module of the dynamic evaluation device for grassland ecological degradation and restoration effectiveness, please refer to the descriptions in the corresponding dynamic evaluation methods for grassland ecological degradation and restoration effectiveness shown above, which will not be repeated here.

[0209] The aforementioned dynamic evaluation device for grassland ecological degradation and restoration effectiveness can be a computer program (including program code) running on a computer device, such as an application software; the device can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0210] In some embodiments, the dynamic evaluation device for grassland ecological degradation restoration effectiveness provided in this invention can be implemented using a combination of hardware and software. As an example, the dynamic evaluation device for grassland ecological degradation restoration effectiveness provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the dynamic evaluation method for grassland ecological degradation restoration effectiveness provided in this invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0211] In other embodiments, the dynamic evaluation device for grassland ecological degradation restoration effectiveness provided in this invention can be implemented using software. Figure 9 A device for dynamically evaluating the effectiveness of grassland ecological degradation restoration, stored in a memory, is shown. It can be software in the form of programs and plug-ins, and includes a series of modules, including an acquisition module 210, a baseline time series determination module 220, a relative change rate determination module 230, and an evaluation module 240, for implementing the dynamic evaluation method for grassland ecological degradation restoration provided in the embodiments of the present invention.

[0212] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0213] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0214] In one alternative embodiment, an electronic device is provided, such as Figure 10 As shown, Figure 10The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0215] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0216] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0217] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0218] The memory 4003 stores the application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0219] Among these, electronic devices can also be terminal devices. Figure 10 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0220] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0221] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0222] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0223] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0224] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0225] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0226] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for dynamic evaluation of the effectiveness of grassland ecological degradation restoration, characterized in that, include: Acquire interannual remote sensing data for a target area, wherein the target area is a region including vegetation, and the interannual remote sensing data is remote sensing data selected from at least two years of remote sensing data corresponding to the target area; Based on a predetermined window sliding operation on the interannual remote sensing data, vegetation features are extracted from the interannual remote sensing data to obtain a baseline time series. The window is quantitatively determined based on the range of the semi-variogram function, and the baseline time series is data describing the changes in vegetation features within a year. Each sliding operation corresponds to a baseline value in the baseline time series. Based on the baseline time series, the relative rate of change of vegetation within the target area is calculated; The degradation or recovery of vegetation within the target area is assessed based on the relative rate of change and a preset threshold. The method further includes: Based on the interannual remote sensing data, the first average NDVI of the target area in the corresponding year of the interannual remote sensing data is calculated; The time consistency of the reference time series is corrected based on the first average NDVI and the predetermined second average NDVI to obtain the corrected reference time series. The second average NDVI is the average NDVI of the target area in the reference starting year, and the reference starting year is a year with high data quality or stable ecological status. The calculation of the relative rate of change of vegetation within the target area based on the baseline time series includes: Based on the corrected baseline time series, the relative rate of change of vegetation in the target area is calculated; The window is determined based on the following method: Based on the interannual remote sensing data and the semivariogram, determine the estimated values ​​of the semivariograms corresponding to the first and second sample points in the interannual remote sensing data. The window is determined based on the estimated value.

2. The method according to claim 1, characterized in that, The method of sliding on the interannual remote sensing data based on a pre-determined window to extract vegetation features from the interannual remote sensing data and obtain a baseline time series includes: Based on the sliding operation of the window in the interannual remote sensing data, determine all pixels within the window corresponding to each sliding operation; For each sliding operation, the 85%-99% quantile is calculated based on all pixels within the window corresponding to the sliding operation; For each sliding operation, a truncated dataset is constructed based on the 85%-99% quantiles and all pixels corresponding to the sliding operation; For each sliding operation, a baseline value corresponding to the sliding operation is determined based on the truncated dataset corresponding to the sliding operation. Based on the baseline values ​​corresponding to all sliding operations, the baseline time series corresponding to the interannual remote sensing data is determined.

3. The method according to claim 2, characterized in that, For each swipe operation, a baseline value corresponding to the swipe operation is determined based on the truncated dataset corresponding to the swipe operation, including: For each sliding operation, a candidate benchmark value corresponding to the sliding operation is determined based on the truncated dataset corresponding to the sliding operation. The candidate benchmark value includes a benchmark value determined based on the mean of the truncated dataset and a benchmark value determined based on the median of the truncated dataset. For each sliding operation, the truncated dataset is validated for normality based on the Shapiro-Wilk normality test, and the baseline value corresponding to the sliding operation is determined from the candidate baseline values ​​based on the validation results.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Acquire new interannual remote sensing data and update the baseline time series based on the new interannual remote sensing data.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain verification data for the target area, wherein the verification data is ground-based measured data or UAV aerial survey data; The consistency of the verification data and the interannual remote sensing data is verified, and the evaluation results of the target area are calibrated based on the verification results. The evaluation results are the results of assessing the degradation or restoration of vegetation in the target area.

6. The method according to claim 2 or 3, characterized in that, The method further includes: For each sliding operation, based on the baseline value corresponding to the sliding operation, the pre-determined minimum contiguous area threshold, and the mean and standard deviation of all baseline values ​​corresponding to the target region, it is determined whether the baseline value corresponding to the sliding operation is the top-level community threshold.

7. A dynamic evaluation device for the effectiveness of grassland ecological degradation restoration, characterized in that, The device for dynamically evaluating the effectiveness of grassland ecological degradation restoration according to claim 1 includes: The acquisition module is used to acquire interannual remote sensing data for a target area, wherein the target area is an area including vegetation, and the interannual remote sensing data is remote sensing data selected from at least two years of remote sensing data corresponding to the target area. The baseline time series determination module is used to extract vegetation features from the interannual remote sensing data based on a sliding operation on the interannual remote sensing data using a pre-determined window, thereby obtaining a baseline time series. The window is quantitatively determined based on the range of a semi-variogram function, and the baseline time series is data describing the changes in vegetation features within a year. Each sliding operation corresponds to a baseline value in the baseline time series. The relative change rate determination module is used to calculate the relative change rate of vegetation in the target area based on the benchmark time series. The evaluation module is used to evaluate the degradation or recovery of vegetation in the target area based on the relative rate of change and a preset threshold.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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